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How to cite this thesis

Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/ M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved from: https://ujdigispace.uj.ac.za (Accessed: Date).

Choice factors and the perceived value that influence prospective university students’ intention to enrol - a choice model

Isolde Lubbe

201048840

Thesis Submitted in fulfilment of the requirements for the degree PhD in Marketing Management in the Faculty of Management at the University of Johannesburg

Johannesburg Supervisor: Prof Chris Jooste Co-supervisor: Prof Danie Petzer

January 2013 DECLARATION

I declare that the Doctoral thesis, which I hereby submit for the degree PhD (Marketing Management) at the University of Johannesburg, is my own independent work and has not previously been submitted by me for a degree at another university.

ISOLDE LUBBE January 2013

Language editing declaration: I hereby declare that I have performed the language editing of the thesis entitled: “Choice factors and the perceived value that influence prospective university students’ intention to enrol - a choice model”.

E. Marnitz

i | ACKNOWLEDGEMENTS

A sincere thank you to all the people who contributed to the completion of this study. My special appreciation is extended to:  My Heavenly Father who gave me the courage and perseverance to successfully complete this study. Through His power and grace, He gave me the ability and the special people around me to complete this task that seemed at times overwhelming.  My husband, Jan, for his support and encouragement and for always believing in me. Thank you Jan, for keeping the kids busy when I had to work. Thank you for still loving me, even if I had really neglected you the last few months to finish this project. I absolutely love and adore you!  My children. Although you are still very young, you really tried to be patient and your laughter and funny stories carried me through when I was tired or stuck. I had loads of comments about working on my pink laptop!  My supervisors and mentors, Prof Chris Jooste and Prof Danie Petzer, who provided continuous support, advice and assistance. Thank you for your unbelievable patience and leadership. Thank you, Prof Jooste for not giving up on me, even when it took months to finalise my research proposal. Your encouragement was crucial to my success and much is appreciated. Thank you, Prof Petzer for your assistance and perseverance, thank you for believing in me and in my ability. When I doubted my ‘statistical abilities’, especially with structural equation modelling, you encouraged me.  Elsabeth Marnitz for your hard work with the language editing and for being a friend. Your support, coffee and laughter were music to my soul.  The University of Johannesburg’s Marketing and Recruitment team, in distributing the questionnaires and for dealing with the targeted schools. I couldn’t have done this without your guidance, support and assistance.  All the Gauteng public schools that participated in the study, your cooperation is much appreciated.  The Department of Education (Gauteng) for believing in my project and for providing written approval to visit the schools. Your letter of approval has provided legitimacy to the study and made school visits easier. Thank you for your

ii | assistance with the top public schools list and for your patience, support and assistance and for explaining how the top schools list is compiled. Thank you for listening to my sampling requirements and for your advice in this regard.  STATKON, for conducting all the statistical analysis and for explaining structural equation modelling (SEM) in particular. Thank you to Richard Devey for your support and assistance, especially with the questionnaire development and pre- testing. Thank you to Jacklyn Smith for all your patience and all your explanations.  Colleagues, friends and staff of the Department of Marketing Management at the University of Johannesburg. Thanks to all of you for your encouragement and support. Thank you for all the cups of tea and coffee and moral support (Susan Schmidt and Dr Christine de Meyer), and jokes (Prof Mornay Roberts-Lombard)!  Petro Beukes who assisted me with drawing tables and figures, you are so friendly and helpful and your Word and PowerPoint knowledge is extraordinary!  To all my friends and family who accepted my ‘disappearing act from the social scene’ for a while. Thank you for your support, friendship and interest.

iii | ABSTRACT

Despite the extensive research undertaken in the subject area of prospective students’ university decision-making processes when selecting a university, much is still unknown about the interrelationships between the choice factors they consider when choosing a university, the perceived value they expect to derive from their choice, and their intention to enrol at their chosen university. This study attempted to address this gap by developing a theoretical model to test the possible interrelationships of three main constructs namely, choice factors, perceived value and intention to enrol.

Higher Education Institutions (HEIs) such as universities, realise the need to embrace marketing related ideas and practices to attract the ‘right’ students in an increasingly competitive Higher Education (HE) landscape. The ‘right’ students are those prospective students with potential to succeed, and the top performing students. Competition is evident as universities need to compete with a number and variety of universities and HEIs and prospective students have choice. Prospective students are also more mobile, and better informed to make judgements about a range of potential universities at home and abroad.

It is therefore important for universities to understand what students desire and expect from them, and to engage in consumer behaviour research to grasp prospective students’ decision-making processes and the choice factors that are most influential in selecting a university. Choice is further a function of prospective students’ perceived value they believe they will derive from their chosen university and understanding the concept of value is important as it drives consumer decision-making. Perceived value is furthermore an accurate indicator of the student’s intent to enrol.

In order to test the theoretical model that can possibly guide universities in determining choice factors, perceived value and the intention of prospective students to enrol, an empirical investigation was conducted. A cross-sectional descriptive research design was followed where the researcher made use of the survey research technique. A drop-off self-administered questionnaire was designed and distributed. For the purpose of this study, a non-probability sampling technique was employed where the researcher used the judgement of an experienced individual to select the sample

iv | units. A letter of approval was obtained from the Department of Education to approach these schools and fieldworkers were used to deliver questionnaires. Only those grade 12 scholars who were considering studying at a university/university of technology were targeted. Of the 1 733 questionnaires received, 1 476 could be included for analysis and interpretation purposes.

Multivariate statistical techniques were used to analyse the data; including an exploratory factor analysis (EFA) to reduce the data, a second-order exploratory factor analysis (2nd order EFA) to verify the data, a confirmatory factor analysis (CFA) to refine the data, and structural equation modelling (SEM) to determine and measure the interrelationships between the main constructs of the study. A number of inferential statistical techniques were further employed to test hypotheses formulated for the study.

The results indicate that six of the initial seven choice factors as confirmed through CFA, influence prospective students’ university choice. The seventh choice factor namely accessibility-price was removed during SEM because of multicollinearity. The six remaining choice factors include reputation, cultural acceptance, accessibility- location, physical evidence, prestige/prominence and future employability influencing prospective students’ university choice.

The results furthermore reveal that there are interrelationships between the choice factors prospective university students consider when choosing a university, the perceived value they expect to derive from their chosen university, and their intention to enrol at their chosen university.

The study indicate that universities should implement marketing related strategies with equal effort into understanding the choice factors influencing prospective students’ university choice, and the perceived value prospective students expect their chosen university offers, as interrelationships exist.

v | TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION AND ORIENTATION ------1

1.1 Introduction ------1

1.2 Background ------1

1.2.1 The global Higher Education (HE) landscape ------1

1.2.2 Factors influencing university choice ------4

1.2.3 Perceived value and intention to buy/enrol ------6

1.3 The problem statement ------7

1.4 Research objectives ------9

1.4.1 Primary objective ------9

1.4.2 Secondary objectives ------9

1.5 The proposed theoretical model for the study ------10

1.6 Hypotheses formulated for the study ------11

Choice factors ------11

Perceived value ------12

The Structural model ------13

1.7 Research methodology------14

1.7.1 Research design ------14

1.7.1.1 Descriptive research ------14

1.7.1.2 Measurement and scaling ------15

1.7.1.3 The sampling process ------17

The target population ------17

The sampling technique ------18

The sample size ------19

1.7.1.4 Data collection ------19

1.7.1.5 Data analysis ------19

vi | Multivariate statistical techniques ------20

1.8 Chapter outline ------22

1.9 Conclusion ------23

CHAPTER 2: THE HIGHER EDUCATION LANDSCAPE ------25

2.1 Introduction ------25

2.2 The global Higher Education (HE) landscape ------28

2.2.1 Globalisation in HE ------28

2.2.1.1 The impact of globalisation on the HE landscape ------29

The increasing number of students studying outside their own countries ------29

English as the dominant and sometimes default language ------31

The emergence of the Internet ------33

2.2.1.2 Internationalisation in HE ------34

2.2.2 Funding in HE ------36

2.2.3 Massification of HE ------38

2.2.4 The student as the customer ------41

2.3 HE trends in developing countries ------44

2.3.1 Globalisation------44

2.3.1.1 The increasing number of students studying outside their own countries ------45

2.3.1.2 English as the dominant and sometimes default language ------45

2.3.1.3 The emergence of the Internet ------46

2.3.2 Funding ------46

2.3.3 Massification------47

2.3.4 The student as the customer ------49

2.4 The South African Higher Education landscape ------50

2.4.1 Globalisation------54

vii | 2.4.1.1 The increasing number of students studying outside their own countries ------55

South African students studying abroad ------56

South Africa as an exporter of educational services ------56

South Africa receiving international students ------57

2.4.1.2 English as the dominant and sometimes default language ------59

2.4.1.3 The emergence of the Internet ------60

2.4.2 Funding in South African HE ------62

2.4.3 Massification in South African HE ------66

2.4.4 The student as the customer ------71

2.5 Marketing Higher Education Institutions (HEIs) ------72

2.5.1 Marketing Higher Education Institutions (HEIs) in South Africa ------75

2.6 Marketing universities in the service industry ------77

Intangibillity ------78

Inseparability of production and consumption ------79

Heterogeneity ------79

Perishability ------79

2.7 Conclusion ------81

CHAPTER 3: DECISION-MAKING PROCESS OF PROSPECTIVE UNIVERSITY STUDENTS ------84

3.1 Introduction ------84

3.2 Consumer behaviour ------85

3.2.1 An overview of consumer behaviour ------86

3.2.2 Levels of consumer decision-making ------87

3.2.2.1 Extensive problem-solving ------87

3.2.2.2 Limited problem-solving ------88

3.2.2.3 Routinised response behaviour ------89

viii | 3.2.3 University students’ consumer decision-making process ------90

3.3 Consumer behaviour decision-making models------94

3.3.1 The ‘simple’ consumer behaviour decision-making model ------95

3.3.1.1 Need recognition ------96

3.3.1.2 Information search ------96

3.3.1.3 Pre-purchase evaluation of alternatives ------98

Identifying alternatives ------98

Determining evaluation criteria ------99

Applying the criteria to the alternatives ------101

3.3.1.4 Purchase and consumption ------102

3.3.1.5 Post-consumption evaluation ------102

3.3.2 Educational consumer behaviour decision-making models ------103

3.3.2.1 Economical, sociological and information processing models ------104

Economic models of student choice ------105

Sociological models of student choice ------106

Information processing models of student choice ------106

(i) Chapman's model of student college choice ------107

(ii) The Jackson model ------108

(iii) The Hanson and Litten model ------109

(iv) The Hossler and Gallagher model ------111

(v) The Hodkinson, Sparkes and Hodkinson model ------113

(vi) The Ball, Maguire and MacRae model ------114

(vii) The Foskett and Hemsley-Brown model ------114

(viii) The Cubillo, Sánchez and Cerviño model ------115

(ix) The Vrontis, Thrassou and Melanthiou model ------116

(x) The Maringe and Carter model ------117

3.3.2.2 Comparing student university choice models ------117

ix | 3.3 South African educational consumer behaviour decision- making models ------121

3.4 Choice defined ------122

3.4.1 Choice in the educational marketplace ------125

3.4.2 Key choice factors influencing university choice (decision-making variables) ------127

3.4.2.1 Course or programme offered ------128

3.4.2.2 Employment opportunities ------131

3.4.2.3 Image ------132

3.4.2.4 Quality ------135

3.4.2.5 Facilities ------137

3.4.2.6 Location ------139

3.4.2.7 Safety ------140

3.4.2.8 Price/Cost ------141

3.4.2.9 Reputation ------142

3.4.3 Key choice factors influencing university choice ------145

3.5 Conclusion ------147

CHAPTER 4: PERCEIVED VALUE AND WILLINGNESS/INTENTION TO BUY ------150

4.1 Introduction ------150

4.2 Value defined ------151

4.3 Perceived value ------156

4.3.1 The factors of perceived value ------159

4.3.1.1 The ‘get’ benefit factors of perceived value ------160

Quality/Functional value as benefit ------164

Social value ------167

Emotional value ------168

x | Epistemic value ------170

Conditional value ------170

Reputational value ------171

Image value------173

Market value ------175

4.3.1.2 The ‘give’ or sacrifice factors of perceived value ------176

Perceived monetary sacrifice ------179

Perceived non-monetary sacrifice ------182

4.4 Purchase intention and/or willingness to buy------185

4.4.1 The role of satisfaction, perceived value and intention to buy ------188

4.5 Value-intention frameworks and models ------190

4.5.1 Dodds and Monroe’s Value-intention framework ------192

4.5.2 Zeithaml’s Means-end model relating price, quality and value ------193

4.5.3 Sweeney and Soutar’s PERVAL model to assess customers’ perceptions of the value of a consumer durable good at a brand level ------194

4.5.4 Chu and Lu’s Research model of online music purchase ------197

4.5.5 Ledden, Kalafatis and Samouel’s Research model of personal values, consumer value and satisfaction ------198

4.5.6 Brown and Mazzarol applying PERVAL to the university context ------201

4.5.7 Ledden and Kalafatis’ Research model on the impact of time on perceptions of educational value ------203

4.6 A summary of the chosen value-intention frameworks and models ------205

4.7 A proposed model for South African universities to explain prospective students’ university choice ------209

4.8 Conclusion ------211

xi | CHAPTER 5: RESEARCH METHODOLOGY ------213

5.1 Introduction ------213

5.2 Marketing research defined ------213

5.3 Importance of marketing research ------215

5.4 The marketing research process ------216

5.5 Formulating a research design ------217

5.5.1 Classification of the research design ------219

5.5.1.1 Exploratory research design ------219

5.5.1.2 Conclusive research ------220

Causal research design ------220

Descriptive research design ------220

5.5.2 Type of information needed ------222

5.5.3 Measurement and scaling procedures ------225

5.5.3.1 Nominal scale of measurement ------226

5.5.3.2 Ordinal scale of measurement ------227

5.5.3.3 Interval scale of measurement ------227

5.5.3.4 Ratio scale of measurement ------228

5.5.4 Constructing and pre-testing a questionnaire ------231

5.5.4.1 Step 1: Specify the information needed ------231

5.5.4.2 Step 2: Specify the type of interviewing method ------232

5.5.4.3 Step 3: Determine the question response format ------232

5.5.4.4 Step 4: Decide on the question wording ------237

5.5.4.5 Step 5: Establish the questionnaire flow and layout ------237

5.5.4.6 Step 6: Evaluate the questionnaire ------240

5.5.4.7 Step 7: Obtain approval from all relevant parties ------241

5.5.4.8 Step 8: Eliminate errors by pre-testing the questionnaire ------241

5.5.4.9 Step 9: Prepare final questionnaire copy ------247

xii | 5.5.4.10 Step 10: Implement survey ------247

5.5.5 Specify the sampling process and sample size ------247

5.5.5.1 Step 1: Define the target population ------249

5.5.5.2 Step 2: Determine the sampling frame ------250

5.5.5.3 Step 3: Select a sampling technique ------250

5.5.5.4 Step 4: Determine the sample size ------252

5.5.5.5 Step 5: Execute the sampling process ------252

5.5.6 Develop a plan for data analysis ------253

5.5.6.1 Fieldwork and the data collection process ------253

5.5.6.2 Data preparation and analysis ------254

Editing questionnaires, coding, transcription and data cleaning ------254

5.5.6.3 Selecting a data analysis strategy ------256

Presenting the descriptive statistics ------257

Determining the distribution of the results ------257

Determining the validity and reliability of the scales used ------258

Deciding whether to use parametric vs. non-parametric tests to test hypotheses ------260

Testing the stated hypotheses ------262

Conducting an Exploratory Factor Analysis (EFA) ------266

Conducting a second order Exploratory Factor Analysis (2nd order EFA) ------269

Performing Confirmatory Factor Analysis (CFA) ------269

Performing Structural Equation Modelling (SEM) ------271

5.6 Report preparation and presentation ------278

5.7 Conclusion ------278

xiii | CHAPTER 6: DISCUSSION AND INTERPRETATION OF RESULTS ------279

6.1 Introduction ------279

6.2 Sample realisation rate ------279

6.3 Planned university attendance ------281

6.4 Schools respondents are attending ------281

6.5 Demographic profile of respondents ------282

6.6 Respondents’ most preferred university/university of technology ------283

6.7 The choice construct ------285

6.7.1 Descriptive results ------285

6.7.2 Distribution of results ------288

6.7.3 Exploratory Factor Analysis (EFA) ------291

6.7.3.1 Second order Exploratory Factor Analysis (2nd order EFA) ------296

6.7.4 Confirmatory Factor Analysis (CFA) ------300

6.7.5 Reliability of choice factors ------302

6.7.6 Overall mean score for factors ------303

6.7.7 Testing for significant differences between groups ------304

6.7.7.1 Hypothesis 1------304

6.7.7.2 Hypothesis 2------306

6.7.7.3 Hypothesis 3------309

6.7.7.4 Hypothesis 4------311

6.7.7.5 Hypothesis 5------316

6.8 Perceived value construct ------318

6.8.1 Descriptive results ------319

6.8.2 Distribution of results ------320

6.8.3 Confirmatory Factor Analysis (CFA) for the perceived value construct ------321

xiv | 6.8.4 Reliability of perceived value factors ------322

6.8.5 Overall mean score for factors ------323

6.8.6 Testing for significant differences between groups ------324

6.8.6.1 Hypothesis 6------324

6.8.6.2 Hypothesis 7------325

6.8.6.3 Hypothesis 8------327

6.8.6.4 Hypothesis 9------328

6.8.6.5 Hypothesis 10 ------330

6.9 Intention to enrol ------332

6.9.1 Descriptive results ------332

6.9.2 Distribution of results ------333

6.9.3 Confirmatory Factor Analysis (CFA) for the intention to enrol construct ------334

6.9.4 Reliability of intention to enrol factor ------335

6.9.5 Overall mean score for factor ------336

6.9.6 Testing for significant differences between groups ------336

6.9.6.1 Hypothesis 11 ------336

6.9.6.2 Hypothesis 12 ------337

6.9.6.3 Hypothesis 13 ------338

6.9.6.4 Hypothesis 14 ------339

6.9.6.5 Hypothesis 15 ------341

6.10 Testing the theoretical model ------342

6.10.1 The measurement model ------342

6.10.2 The structural model ------344

6.10.3 Hypothesis 16 ------349

6.11 Summary of findings ------349

6.11.1 Main findings pertaining to the descriptive results ------349

xv | 6.11.1.1 Choice factor ------350

6.11.1.2 Perceived value factor ------350

6.11.1.3 Intention to enrol factor ------350

6.11.1.4 The structural model ------351

6.11.2 Main findings pertaining to hypotheses testing------351

6.11.2.1 Choice factor ------352

6.11.2.2 Perceived value factor ------354

6.11.2.3 Intention to enrol factor ------356

6.11.2.4 The structural model ------357

6.12 Conclusion ------358

CHAPTER 7: OVERVIEW, CONCLUSIONS AND RECOMMENDATIONS ------359

7.1 Introduction ------359

7.2 Overview of the study------359

7.2.1 Literature overview ------360

7.2.1.1 The Higher Education (HE) landscape ------360

7.2.1.2 Decision-making process of prospective university students ------362

7.2.1.3 Perceived value and willingness/intention to enrol ------363

7.2.2 Objectives of the study ------364

7.2.3 Methodology overview ------365

7.3 Conclusions and recommendations for secondary objectives ------366

7.3.1 Secondary objective 1 ------366

7.3.2 Secondary objective 2 ------369

7.3.3 Secondary objective 3 ------372

7.3.4 Secondary objective 4 ------378

7.3.5 Secondary objective 5 ------382

xvi | 7.3.6 Secondary objective 6 ------385

7.4 Linking the objectives, hypotheses, questions in the questionnaire, the findings, conclusions and recommendations ------389

7.5 Limitations ------391

7.5.1 Limitations of the literature review ------391

7.5.2 Limitations of the empirical research------392

7.6 Recommendations for future research ------393

7.7 Conclusion ------394

xvii | LIST OF REFERENCES

Bibliography ------395

APPENDICES ------419

A List of public Higher Education Institutions (HEIs) in South Africa ------419

B Top Gauteng schools list for 2010-2011 ------421

C A summary of value-intention frameworks and models ------430

D A summary of perceived value measurement scales and their statements ------450

E A summary of behavioural intention/willingness to buy measurement scales and their statements ------477

F Choice factor statements and corresponding choice construct as identified in the literature ------482

G Coding of questionnaire: ------484

G1 Coding of the HEIs in South Africa ------484

G2 Coding of Gauteng High Schools that form part of the sample ------485

H Questionnaire: ------486

H1 Final questionnaire used in this study ------487

H2 Pilot questionnaire sent for pre-testing ------491

H3 Initial pilot questionnaire (distributed in 2008)------492

H4 Refined questionnaire (instrument) ------494

I Letter of approval from the Department of Education ------496

J Top 250 public schools in Gauteng list, indicating which schools have been included in this study ------497

K Results of the first Exploratory Factor Analysis (EFA) (14 factors) ------CD

L Detailed results with respect to subsequent Mann-Whitney U Tests performed ------CD

M Results of Structural Equation Modelling (SEM) output on CD ------CD

xviii | LIST OF FIGURES

Figure 1.1 Proposed theoretical model ------10

Figure 1.2 Flow diagram depicting the multivariate statistical techniques and goodness-of-fit indices employed in this study ------21

Figure 2.1 The Higher Education (HE) landscape ------26

Figure 2.2 Map of the SADC (South African Development Community) countries ------51

Figure 2.3 Three distinct product levels of universities’ offering ------78

Figure 3.1 The ‘simple’ consumer behaviour decision-making model ------93

Figure 3.2 Successive sets in decision-making ------98

Figure 3.3 South Africa’s unemployment rate ------132

Figure 3.4 Serious crime in the Republic of South Africa (2010/2011) ------141

Figure 4.1 Dodds and Monroe’s (1985) Value-intention framework ------192

Figure 4.2 Zeithaml’s (1988) Means-end model relating price, quality and value ------194

Figure 4.3 Sweeney and Soutar’s (2001) PERVAL scale as illustrated by Brown and Mazzarol (2009) and willingness to buy added by the author ------196

Figure 4.4 Cu and Lu’s (2007) Research model of online music purchase behaviour ------198

Figure 4.5 Ledden Kalafatis & Samouel’s (2007) Research model of personal value, consumer value and satisfaction in education ------199

Figure 4.6 Brown and Mazzarol’s (2009) Model on constructs in an Australian Higher Education (HE) setting ------202

Figure 4.7 Ledden and Kalafatis’ (2010) Research model of the impact of time on perceptions of educational value ------203

Figure 4.8 Depicting the basic elements of the proposed theoretical model ------209

Figure 5.1 The steps in the marketing research process ------217

Figure 5.2 The components of the research design ------218

Figure 5.3 The steps in the sampling design process ------249

xix | Figure 6.1 The theoretical model ------342

Figure 6.2 The structural model ------348

Figure 7.1 The structural model ------388

Figure 7.2 A Summary of the primary and secondary objectives, questions in the questionnaire, hypotheses, findings, conclusions and recommendations ------390

Figure C.1 Dodds, Monroe and Grewal’s (1991) Product evaluation model ------438

Figure C.2 Sheth, Newman and Gross’ (1991) Five consumption values. influencing consumer choice ------439

Figure C.3 Cronin, Brady, Brand, Hightower and Shemwell’s (1997) Value added model ------440

Figure C.4 Teas and Agarwal’s (2000) Conceptual model of extrinsic product-cues effects on consumers’ perceived quality, perceived sacrifice and perceived value ------442

Figure C.5 Cronin, Brady and Hult’s (2000) Model on the effects of quality, value, and customer satisfaction on consumer behavioural intentions in service environments ------443

Figure C 6 Petrick’s (2002) Hypothetical model portraying post- experience perceived value in the service choice process ------445

Figure C 7 Petrick’s (2004) Model on the variables (quality, value and satisfaction) related to cruise passengers’ behavioural intentions ------447

Figure C.8 Sánchez, Callarisa, Rodríguez and Moliner (2006) GLOVAL scale of measurement of the overall perceived value ------449

xx | LIST OF TABLES

Table 2.1 Trends in the number of foreign students enrolled outside their country of origin (2002 to 2008) ------30

Table 2.2 The type and number of HEIs (including universities) in SADC countries ------52

Table 2.3 The South African Higher Education Landscape - Student Population in 2010 ------54

Table 2.4 Modes of South African HE Services Supply under GATS ------57

Table 2.5 Increase in HE student numbers in South Africa ------58

Table 2.6 A summary of the world’s Internet usage statistics ------60

Table 2.7 A summary of Africa’s Internet usage statistics ------61

Table 2.8 Education spending of SADC countries ------64

Table 2.9 Participation rate and anticipated/projected growth of HE enrolment, 2008 – 2016 ------68

Table 3.1 Comparing student university choice models ------118

Table 3.2 Choice factors considered by prospective university students ------146

Table 4.1 A summary of the ‘get’ or benefit factors of perceived value ------162

Table 4.2 A summary of the ‘give’ or sacrifice factors------177

Table 4.3 A summary of the chosen value-intention frameworks and models’ different elements ------207

Table 5.1 Types of question response formats and type of primary scale used in the questionnaire ------233

Table 5.2 Perceived value statements used in the final questionnaire ------235

Table 5.3 Behavioural intention statements used in the final questionnaire ------236

Table 5.4 Secondary objectives and corresponding sections of the questionnaire ------239

Table 5.5 Changes made to the wording of question B2 ------243

Table 5.6 Changes made to question B2 statements ------244

Table 5.7 Changes made to the wording of question C1 ------245

xxi | Table 5.8 Additional perceived value statements added ------245

Table 5.9 Changes made to the wording of section C ------246

Table 5.10 Changes made to the wording of question D1 ------246

Table 5.11: The sample plan for this study ------248

Table 5.12 The final sample realised ------252

Table 5.13 The differences between non-parametric tests and parametric tests ------262

Table 5.14 A summary of the secondary research objectives, hypotheses, related questions in questionnaire and statistical techniques used in this study ------274

Table 6.1 Sample realisation rate ------280

Table 6.2 Respondents’ planned university/university of technology attendance ------281

Table 6.3 Schools in Gauteng from where respondents originate ------281

Table 6.4 Demographic profile of respondents ------282

Table 6.5 The most preferred university/university of technology of respondents ------283

Table 6.6 The respondents’ choice statements influencing their university decision ------286

Table 6.7 Skewness and kurtosis of choice statement results------289

Table 6.8 KMO and Bartlett’s test results for five factor extraction ------291

Table 6.9 Total Variance explained Table ------292

Table 6.10 Rotated Factor Matrix for the EFA including all the statements ------292

Table 6.11 Rotated Factor Matrix for the EFA including retained statements ------294

Table 6.12 KMO and Bartlett’s test results for 2nd order factor analysis ------296

Table 6.13 Three sub-factors for the accessibility factor (Factor 2) ------297

Table 6.14 Statements retained after 2nd order EFA ------299

Table 6.15 Fit indices for CFA models of the choice factors ------300

Table 6.16 Final set of factors and corresponding statements retained after CFA ------301

xxii | Table 6.17 Cronbach’s alpha coefficients for the seven choice factors ------303

Table 6.18 Overall mean score for each of the seven choice factors ------303

Table 6.19 Gender-based differences with respect to the choice factors ------304

Table 6.20 Home language differences with respect to the choice factors ------306

Table 6.21 Subject-choice (mathematics vs mathematics literacy) differences with respect to the choice factors ------309

Table 6.22 Expected average grades for grade 12 differ with respect to the choice factors ------312

Table 6.23 Prospective students with parents who attended and those with parents who did not attend university differ with respect to the choice factors ------316

Table 6.24 The respondents’ perceived value influencing their university decision ------319

Table 6.25 A summary of perceived value factors’ skewness and kurtosis results ------320

Table 6.26 Fit indices for CFA models of the perceived value factor ------321

Table 6.27 Perceived value statements retained after CFA ------322

Table 6.28 A summary of the results of the Cronbach’s alpha coefficient for the perceived value factors ------323

Table 6.29 A summary of the overall mean score for each of the perceived value factors ------323

Table 6.30 Gender-based differences with respect to the perceived value factors ------324

Table 6.31 Home language differences with respect to the perceived value factors ------326

Table 6.32 Subject-choice (mathematics vs mathematics literacy) differences with respect to the perceived value factors ------327

Table 6.33 Expected average grades for grade 12 differ with respect to the perceived value factors ------329

Table 6.34 Prospective students with parents who attended and those with parents who did not attend university differ with respect to the perceived value factors ------331

Table 6.35 The respondents’ intention to enrol ------333

xxiii | Table 6.36 A summary of intention to enrol factors’ skewness and kurtosis results ------334

Table 6.37 Fit indices for CFA models of the intention to enrol factor ------334

Table 6.38 CFA results indicating the intention to enrol statements retained ------335

Table 6.39 A summary of the results of the Cronbach’s alpha coefficient for the intention to enrol factor ------335

Table 6.40 A summary of the overall mean score for the intention to enrol factor ------336

Table 6.41 Gender-based differences with respect to the intention to enrol factors ------337

Table 6.42 Home language difference with respect to the intention to enrol factor ------338

Table 6.43 Subject-choice (mathematics vs mathematics literacy) differences with respect to the intention to enrol factor ------339

Table 6.44 Expected average grades for grade 12 differences with respect to the intention to enrol factors ------340

Table 6.45 Prospective students with parents who attended and those with parents who did not attend university differ with respect to the perceived value factors ------341

Table 6.46 Fit indices for the measurement model ------343

Table 6.47 Fit indices for the structural SEM ------345

Table 6.48 Significant direct paths evident in the structural model ------346

Table 6.49 Standardised paths coefficient ------347

xxiv |

CHAPTER 1

Introduction and Orientation

1.1 Introduction

The purpose of this chapter is to introduce all the elements of this study. The chapter commences with a background discussion, specifically focusing on the main constructs under investigation. The background to the study is followed by the presentation of the identified research problem and the subsequent research objectives. The proposed theoretical model for the study is presented as well as the hypotheses formulated for the study. This is followed by a brief overview of the research methodology used in this study and the chapter concludes with the chapter outline of the study.

1.2 Background to the study

This brief background to the study is introduced with an investigation of the global Higher Education (HE) landscape and its challenges to set the scene. The South African HE landscape is not exempt from the global challenges that universities globally experience and is also facing issues of globalisation, changes in government funding, massification and the emergence of the student as the customer. Thus, a brief South African view is interwoven in the discussion. The term ‘university’ is used throughout this chapter to describe any Higher Education Institutions (HEIs), including universities and universities of technology. This section is followed by a brief overview of the main constructs under investigations, namely choice factors, perceived value and intention to enrol. Since it is not an extensive literature review, it only serves to present a better understanding of the problem at hand with the emphasis on the constructs that form the basis of the proposed study.

1.2.1 The global Higher Education (HE) landscape

There is a realisation that student recruitment for universities has become much

1 |

1The term ‘universities’ will be used in this chapter, although it is recognised as a type of HEI. The term ‘universities’ was chosen, as the focus of this study is primarily on universities or universities of technology in South Africa. Some countries refer to a university as a college or as a HEI. In these cases the term ‘universities’ is also used. more competitive (Read, Higgs & Taylor, 2005:31) for various reasons. Globalisation has led to the ‘borderless-ness’ of the educational marketplace, which realised in the increasing mobility of students (Hemsley-Brown & Oplatka, 2010:204; Opoku, Hultman & Salehi-Sangari, 2008:125). Further, greater mobility for a growing segment of the population, the emergence of the Internet, and the rise in English as the dominant language of scientific communication have resulted in the massification of HE (Altbach, Reisberg & Rumbley, 2009; Dickson, 2009:170; Akoojee & Nkomo, 2007:385). Massification in the context of HE describes the rapid increase in student enrolment in the latter part of the twentieth century (Mohamedbhai, 2008:5). This mass demand and increasing mass access for HE have also put great pressure on universities to obtain the relevant funding and subsidies from government and to look ‘elsewhere’ for funding to deal with increasing costs associated with increasing student numbers (Susanti, 2011:209; Navehebrahim, 2009:290).

Prospective students act like customers with specific needs and wants who usually have a wide variety of universities and programmes to choose from (Hemsley-Brown & Oplatka, 2010:208; Mohamedbhai, 2008:9; Vrontis, Thrassou & Melanthiou, 2007:979; Freeman & Thomas, 2005:163; Gater, 2001:2-3). For these reasons, the main challenges of globalisation, funding issues, massification and the notion of the student as customer are further addressed next to gain a better understanding of the HE landscape.

Globalisation has led to a changing HE landscape with universities having to compete with new competitors (Cubillo, Sánchez & Cerviño, 2006:101) such as the private institutions, and non-university competitors in the form of industry and non- university educators (Harrison-Walker, 2009:103; Freeman & Thomas, 2005:169 Veloutsou, Lewis & Paton, 2004:160) and new entrants such as universities providing distance-learning and Internet-based courses (Niculescu, 2006:725). An increasing number of universities nationally and internationally encourage prospective students to ‘shop the world’ and choose from a variety of universities (Ivy, 2008:288; Drummond, 2004:319,321; Veloutsou et al., 2004:160). Universities realise they need to increase their attractiveness to reach these prospective students operating in the borderless world (Robertson & Keeling, 2008:224).

2 | Increased national and international competition has also resulted in universities experiencing immense pressure to find new ways to generate income and to seek funding (Yamamoto, 2006:559). The level of government support and the distribution of that support have also become a raising concern for universities (Maringe, Foskett & Roberts, 2009:147; Gordon, 1995:25). In South Africa specifically, universities are facing increasing competition for funding, such as subsidies from government, as 23 public universities (Appendix A) all want a bigger portion of the ‘funding cake’ and various factors contribute to who is getting which portion (Fish, 2011:9, 11; Wiese, Van Heerden & Jordaan, 2010:151). Furthermore, as more students have to pay, or have to pay more for their university education, the pressure increases for universities to communicate the value students will receive.

Massification resulting in the massive expansion of universities and ‘other’ providers of HE, coupled with the expansion in the number of students, also raise concern for universities. With more players in the market and new players in the all-becoming ‘open-market’ (Baldwin & James, 2000), many universities are finding that they need to compete for students, especially for the ‘right’ students (Yamamoto, 2006:560; Naude & Ivy, 1999:126). Widening educational access (Bathmaker & Thomas, 2009; Drummond, 2004:319,321) in some countries including South Africa (Badsha & Cloete, 2011) has also opened new markets and provided students with new and more alternative choices (Prugsamatz, Pentecost & Ofstad, 2006; Veloutsou et al., 2004:161).

Universities need to change their view of prospective students by adopting the concept of prospective students as customers as this can be of value to universities. Glaser-Segura, Mudge, Bratianu, Jianu and Valcea (2007:124) define the customer in the university context as: “the knowledgeable student, as an external customer that pays the university system for customer-defined instructional services.” Redding (2005:409) argues that there is a semantic distinction between customers and consumers and that the term customers is more associated with those who pay. More so as in most countries students are expected to pay a share of the costs of their tuition, thus there is an increasing tendency for universities to become more market-orientated and student-focused and therefore to refer to students as customers (Hemsley-Brown & Oplatka, 2010:208; Eagle & Brennan, 2007:44,51).

3 |

There is also the notion that students who pay for their education demand more from that particular university. The power is shifting towards the customer. Thus, universities that compete for the revenue derived from the students will have to be more responsive to students’ demands (Scott, 1999:194; Woodruff, 1997:139). Vrontis et al. (2007:988) argue that the prospective student is the customer and define universities’ products and their role in modern societies. What is left to universities is to influence their perceptions.

In South Africa, the university sector is facing many new challenges such as the transformation of Technikons into universities of technology, as well as the mergers of other universities (Wiese, 2008:1; Ivy, 2001). These changes increase competition and complicate choice for prospective students, as they do not always understand what a university of technology or newly merged university stands for. Choice is further complicated as South African prospective students can choose from 23 public institutions, not to mention the ‘other’ private education service providers, of which there have been a noticeable increase over the last few years (Wiese, 2008:1). South African prospective students also have the choice of international universities, as prospective students in general are no longer restricted by national boundaries (Freeman & Thomas, 2005:153). It can be concluded that universities compete for the ‘right’ students, who can be described as the prospective student with greater ability (better academic performance) (Gater, 2001:2-3) and the prospective student with potential (Palmer & James, 2011:36), and that prospective students have a choice. To understand these choice processes, university marketers need to understand the decision-making behaviour and thought processes of students, specifically the choice factors influencing the decision of which university to attend (Durvasula, Lysonski & Madhavi, 2011:35; Cubillo et al., 2006:102).

1.2.2 Factors influencing university choice

Choice is an iterative, complex and multi-dimensional concept (Petruzzellis & Romanazzi, 2010:141; Brown, Varley & Pal, 2009) that is an integral part of consumer behaviour (Hoyer & MacInnis, 2008:3-5). It is therefore imperative for university marketers to gain an understanding of the consumer behaviour decision-

4 | making process, specifically the evaluate alternatives stage where prospective students compile a choice set that is used as the final list of alternative universities from which to choose (Kotler & Fox, 1995:251).

Most existing choice models (Chapter 3, Section 3.3.2.2) portray that choice factors are considered and weighed by consumers (prospective students) in the evaluation of alternatives stage of the decision-making process. The challenge is for universities to understand these choice factors, but specifically those choice factors that are most important when prospective students select a university (Moogan, 2011:572; Vrontis et al. 2007:979; Kotler & Fox, 1995:251). Choice further involves a wide range of variables and is influenced by many factors (Briggs & Wilson, 2007:58).

The choice of which university to attend, is influenced by the background and current characteristics of the prospective student, such as academic achievement at school, and his/her life and school experience (Briggs & Wilson, 2007:58; Chapman, 1981:503). Significant persons, such as friends, parents or teachers can sway decisions, as can career officers (Moogan & Baron, 2003:273). The fixed characteristics of the university, and the university’s own efforts to communicate with prospective students can also affect prospective students’ university choice (Chapman, 1981:503).

The literature review on choice factors further revealed that the key choice factors considered by prospective students when selecting a university are the course and programme offered, employment opportunities, image, quality, facilities, location, safety, price/cost and the reputation (Chapter 3, Section 3.4.2). There is general agreement that reputation of the university (Petruzzellis & Romanazzi, 2010:141; Brown et al., 2009: 314; Russell, 2005:66; Moogan & Baron, 2003:273; Mazzarol & Soutar, 2008) is of prime importance when choosing a university. A South African study (Wiese, 2008) conducted with first-year students, indicated that the quality of teaching, employment prospects and campus safety and security of universities were the three most important factors influencing a student’s selection process.

5 | 1.2.3 Perceived value and intention to buy/enrol

Martin and Dixon’s (1991) study revealed that universities should not only consider the choice factors for marketing purposes, but also have to anticipate prospective students’ perceived value. Knowledge about prospective students’ current perceived value can be applied by university marketers to anticipate what prospective students value, and to building and maintaining a sustainable advantage (Blocker & Flint, 2007:249), achieving competitive advantage (Stodnick & Rogers, 2008:115; De Chernatony, Harris & Dall’Olmo Riley, 2000:39; Woodruff, 1997) and superior value can lead to student loyalty and prospective student recruitment (Khalifa, 2004:645).

Universities should understand value from the customers’ perspective, because it is ‘something that is perceived’ by the customer (Whittaker, Ledden & Kalafatis, 2007:346), being the prospective student. The prospective student defines value in terms of customer needs and what is desirable (De Chernatony et al., 2000:40-41) and perceives value in terms of what will be gained when purchasing the service (Khalifa, 2004:647), or in this context, when enrolling at the chosen university.

Although value is a complex and an abstract construct (Whittaker et al., 2007:346; LeBlanc & Nguyen, 1999; Patterson & Spreng, 1997:416; Sheth, Newman & Gross, 1991), there is consensus that customer value involves a trade-off between benefits (quality, worth, utilities, prestige, happiness, convenience) and sacrifices (price, cost, money, time, effort, risk) within various use situations (Blocker & Flint, 2007:250; Ledden, Kalafatis & Samouel, 2007:966; Khalifa, 2004:647; Teas & Agarwal, 2000:278; Patterson & Spreng, 1997:415; Woodruff, 1997:141; Zeithaml, 1988:14). Thus, value is perceived as the sum of benefits received, minus the costs incurred by the customer in acquiring a product or service (Tracey & Wiersima, 1995).

Ledden et al. (2007) explain the ‘get’/benefit factor of value to include the following factors: image, functional value, social value, epistemic value, emotional value and conditional value. Kantamneni and Coulson (1996:5) added market value, and Petrick (2002:125) included reputation (or prestige) to the ‘get’/benefit value factor mix. The ‘give’/sacrifice factor of value includes both monetary sacrifice and non- monetary sacrifice (Ledden et al., 2007).

6 | Perceived value is further a key determining force guiding the prospective student’s decision-making process (Ledden & Kalafatis, 2010:142; Blocker & Flint, 2007:249; Cronin, 2003:333). Patterson and Spreng’s (1997:416) study revealed that a number of authors linked willingness to buy or purchase intentions as a key consequence of value perceptions. In the HE context a prospective student’s desired perceived value is an accurate predictor of course enrolment decision, thus perceived value determines a prospective student’s choice of course and which university to attend (Ledden et al., 2007:966). It can be concluded from these arguments that perceived value is an accurate indicator of the prospective student’s intention to enrol (Ledden et al., 2007:966; Patterson & Spreng, 1997:416).

1.3 The problem statement

Universities are competing for students (Maringe, 2006:466; Freeman & Thomas, 2005:154), specifically the ‘right’ students and prospective students have the freedom and luxury of choice (Ivy, 2008:288; Veloutsou et al., 2004:160). Prospective students can choose from a variety of universities including new degree- awarding institutions (Brown et al., 2009:311) and from the already existing number of universities in the marketplace (Ivy, 2008:288; Veloutsou et al., 2004:160).

Prospective students are not passive choosers (Petruzzellis & Romanazzi, 2010:149). They are rigorous in the information search and evaluation of alternatives stages of the decision-making process (Greenbank, 2009:260) and are thus better informed (Briggs, 2006:707) about the choices that are available to them. They are also more mobile and able to make judgements about a range of potential preferred university suppliers at home and abroad. They decide (Lorange, 2005:786) and exercise their preferences by choosing their own universities (Briggs, 2006:707; Redding, 2005:411).

It is therefore important for universities to understand what students desire and expect from the university they choose to better market to them (Petruzzellis & Romanazzi, 2010:141). A comprehensive set of choice factors might provide a more accurate picture of the criteria students perceive important in university selection (Briggs, 2006:709). These choice factors can be used to develop marketing-related

7 | strategies to enhance competitiveness and to reach the chosen student market (Cubillo et al., 2006:101). Understanding choice factors can further be used for informed marketing that would not just influence the decision-making process, but might positively influence the choices prospective students make (Briggs, 2006:707).

Although knowing the importance of understanding prospective students’ choice factors (Harrison-Walker, 2009; Weise, 2008; Briggs, 2006; Maringe, 2006; Chapman, 1985), it could be argued that the perceived value should also be investigated. Prospective students’ decision to enrol (buying decision) at a chosen university derives from their evaluation of those products’ (or services) attributes and their performances that could be negative or positive. This evaluation process develops perceived preferences for a specific attribute (or choice factor) and facilitates value that will ultimately determine if enrolling at a chosen university is enhanced or blocked (Ledden & Kalafatis, 2010:145-146; Ledden et al., 2007:972; Woodruff, 1997:142). Customers such as prospective students’ evaluation of value is further described as an exchange process between the monetary and non-monetary costs (sacrifices) associated with the service and the benefits received (LeBlanc & Nguyen, 1999:188). In turn, the perceived value will directly influence the intention to buy (Chu & Lu, 2007; Dodds & Monroe, 1985), or in this case, the decision of prospective students to enrol at a university.

It is therefore necessary to develop a model to assist universities to explain prospective students’ university choice. Universities can then use such a model as a guide to understand the important choice factors influencing prospective students choice and determining the perceived value prospective students believe they will derive from their chosen university. This model could furthermore assist universities in gauging the intention of prospective students to enrol at their chosen university. It is envisaged that this model will guide universities to direct their marketing strategies towards the choice factors that their chosen target market values, and that will eventually lead to a stronger intention to enrol at the particular university. Thus, the interrelationships between choice factors, perceived value and intention to enrol might be determined.

8 | 1.4 Research objectives

In order to address the identified problem at hand, the following research objectives are formulated:

1.4.1 Primary objective

The primary objective of the study is to propose a model to explain prospective students’ university choice.

1.4.2 Secondary objectives

In order to achieve the primary objective of the study, the following secondary objectives have been formulated. o To gain insight into the South African higher education (HE) environment. o To provide an overview of the extant literature related to the main constructs of the study, namely the choice factors influencing university choice, the perceived value universities offer, and prospective students’ intention to enrol at their chosen university. o To measure the extent to which the different choice factors, identified through the literature review, influences prospective students’ university choice. o To assess the value prospective students perceive they will derive from their chosen university. o To gauge the intention of prospective students to enrol at their chosen university. o To determine whether groups of prospective students who exhibit different demographic characteristics differ significantly from each other in terms of the influence of different choice factors on their university choice, the perceived value that their chosen university offers, and their level of agreement regarding their intention to enrol at their chosen university. o To determine the interrelationships between the main constructs of the study in order to propose a model to explain prospective students’ university choice.

Before addressing the proposed research design, it is necessary to obtain a brief understanding of the proposed model for the study and the related hypotheses set.

9 | 1.5 The proposed theoretical model for the study

It is suggested that the main constructs (choice, perceived value and intention to enrol) discussed in the background to the study section, be put together in a model that should assist universities to better understand prospective students’ university choice. Figure 1.1 depicts this proposed theoretical model.

Figure 1.1 Proposed theoretical model

This proposed theoretical model depicts that choice factors have a relationship with perceived value, and that perceived value has a relationship with intention to enrol. Thus, the hypotheses are set to prove that there are significant and positive interrelationships between the choice factors prospective university students consider when choosing a university, the perceived value they believe to derive from their chosen university, and their intention to enrol at their chosen university.

It was determined in the literature review that understanding university choice and choice factors is important (Aldous, 2009a:1; Maringe, 2006; Maringe & Foskett, 2002:36). A comprehensive set of choice factors could result in an improved predication of student university choice and provide university marketers with a more accurate picture of those university factors students believe are important in the university selection process (Hoyt & Brown, 2003:5).

The existing models evaluated during the literature review revealed that most authors acknowledge that choice and choice factors are an integral part of the decision-making process. However, what prospective students value when deciding on which university to attend, also plays a role in university choice (Moogan,

10 | 2011:583). Ledden and Kalafatis’ (2010) study within the HE context proposed that prospective students believe they derive perceived value from their chosen university by evaluating the perceived benefits (‘get’ components) and perceived sacrifices (‘give’ components). The difference between what they perceive they need to sacrifice (give up) and the benefits they perceive to receive, forms their perceived value. Thus, the prospective student arrives at value by weighing specific choice factors’ perceived benefits, such as a positive reputation, possible employment, or the associated image, to name but a few. Also, these perceived benefits are then weighed against the perceived sacrifices such as their effort, time and financial sacrifices they need to incur if enrolling (Ledden & Kalafatis, 2010; Blocker & Flint, 2007).

It is further evident from the literature review, specifically when investigating existing value-intention frameworks and models, that there is a strong or positive link from perceived value to an actual behaviour intention (to purchase, to recommend, to repurchase, to satisfaction, and to positive word-of-mouth) (Chapter 4, Section 4.6). Thus, the assumption can be made that prospective students’ intention and willingness to enrol can be positively linked to the perceived value they believe they derive from their chosen university (Ledden & Kalafatis, 2010:142; Blocker & Flint, 2007:249; Chu & Lu, 2007; Cronin, 2003:333; Patterson & Spreng, 1997; Dodds & Monroe, 1985).

1.6 Hypotheses formulated for the study

The purpose of this section is to present all the hypotheses pertaining to this study. This section is directed at addressing the hypotheses relating to the main constructs of the study, i.e. choice factors, perceived value and the intention to enrol, as well as the hypotheses formulated for the model.

 Choice factors

H1. Female and male prospective university students differ significantly in terms of their level of agreement regarding the influence of different choice factors on their university choice.

11 | H2. Prospective university students with different home languages differ significantly in terms of their level of agreement regarding the influence of different choice factors on their university choice.

H5. Prospective university students with parents who went to university differ significantly from prospective university students with parents who did not go to university in terms of their level of agreement regarding the influence of different choice factors on their university choice.

 Perceived value

H6. Female and male prospective university students differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers.

H7. Prospective university students with different home languages differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers.

12 | H10. Prospective university students with parents who went to university differ significantly from prospective university students with parents who did not go to university in terms of their level of agreement regarding perceived value that their chosen university offers.

Intention to enrol

H11. Female and male prospective university students differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university.

H12. Prospective university students with different home languages differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university.

 The structural model

13 | 1.7 Research methodology

This section explains the research design followed in this study. The research design specifically covers the descriptive research design employed, the measurement and scaling techniques implemented, the sampling process followed, the data collection method used, and the data analysis procedures applied in this study and these aspects are to be briefly discussed in the following sections.

1.7.1 Research design

A research design is the blueprint or framework that guides a researcher in fulfilling the research objectives and answers the proposed research questions (Cooper & Schindler, 2006:193; Malhotra & Birks, 2005:58). It is the basic plan that guides the data collection and analysis phases of the research project. The research design defines the structure and specifies the type of information collected, the sources of data and the data collection procedure(s) (Kinnear & Taylor, 1996:129).

Research designs may be classified into two types of research, exploratory and conclusive research designs. Conclusive research can further be categorised into descriptive or causal research designs (Zikmund & Babin, 2010b:51-53; Malhotra, 2009:96; McDaniels & Gates, 2006:33-34). For the purpose of the research study, the researcher utilises descriptive research. Descriptive research is a more formalised study that aims to answer the questions who, what, when, where or how much and typically includes clearly stated hypotheses or investigative questions (Malhotra, 2009:97; Shiu, Hair, Bush & Ortinau, 2009:61; Wiid & Diggines, 2009:55- 56; Burns & Bush, 2006: 121; Cooper & Schindler, 2006:194, 202).

1.7.1.1 Descriptive research

The objective of descriptive research is to describe the research domain accurately and thoroughly (Wiid & Diggines, 2009:55-56; Malhotra, 2007:181). This is possible by applying a set of scientific methods and procedures to collect data and create data structures that describe the existing characteristics (e.g. attitudes, intentions, preferences, purchase behaviours, evaluations of current marketing mix strategies and the market potential of a product) of a defined target population (Shiu et al., 2009:62; Burns & Bush, 2006:122). The emphasis is on an in-depth description of a

14 | specific individual, situation, group, organisation, tribe, subculture and interaction or social object (Wiid & Diggines, 2009:55-56; Hair, Bush & Ortinau, 2006:63).

There are two basic types of descriptive research studies available: cross-sectional and longitudinal studies. Cross-sectional studies measure units from a sample of the population at one point in time. Longitudinal studies repeatedly measure the same sample units of a population over a period of time. Because longitudinal studies involve multiple measurements, they are often described as “movies” of the population while cross-sectional studies are one-time measurements often described as “snapshots” of the population (Malhotra 2009:101; Wiid & Diggines, 2009:55-56; Burns & Bush, 2006: 122-123). For the purpose of this study, a descriptive research design, specifically a cross-sectional descriptive research design, is followed.

In terms of the descriptive analysis, the research study makes use of the survey technique, whereby a drop-off self-administered questionnaire is designed and given to a sample of the population to complete with the intention to obtain specific information from them (Malhotra, 2007:183). Information is obtained from grade 12 scholars in Gauteng and the same scholar is only approached once.

1.7.1.2 Measurement and scaling

Measurement is the process of obtaining meaning by describing some element of a phenomenon, usually by assigning numbers in a reliable and valid way (Zikmund & Babin, 2010b:322). In this instance prospective students are not measured, but their perceptions, attitudes, preferences, or other relevant characteristics are evaluated. In obtaining information about these prospective students a measuring instrument is used, in this study particularly by means of a drop-off self-administered questionnaire.

A questionnaire as measuring instrument, is a formalised set of questions and scales for obtaining information from respondents (Shiu et al., 2009:329; Malhotra, 2007:299). It allows the researcher to standardise the data collection process, and thereby enables her to analyse the data in a consistent and uniform manner (Shiu et al., 2009:329). Scales are measurement tools used in the questionnaire to determine

15 | quantitative measures of subjective and sometimes abstract concepts (McDaniel & Gates, 2006:228). Scaled-response questions are closed-ended questions that permit the measurement of the intensity of prospective students’ answers (McDaniel & Gates, 2006:269).

A researcher can choose between two types of scaling techniques: comparative scales and non-comparative scales (Aaker, Kumar, Day & Leone, 2011:254; Malhotra, 2009:284-288). Comparative scales ask respondents to express their attitudes about something (an object or its attributes) on the basis of comparing a statement against a standard (Malhotra, 2007:257). Non-comparative scales require a judgement without reference to another object or another statement (Aaker et al., 2011:432). For the purpose of this study, non- comparative 7-point Likert-type scales are used that are anchored at 1 indicating ‘strongly disagree’ and 7 indicating ‘strongly agree’. This scale-type is used to measure prospective students’ attitudes and preferences on choice factors, perceived value and intention to enrol.

The questionnaire fielded in this study consists of four main sections: o Section A: This section serves as an introduction and explains the intent of the survey. A screening question or qualifying question is asked to identify appropriate respondents (McDaniel & Gates, 2010:389). Only respondents indicating that they are intending to study at a university or university of technology are included for further analyses. Also, general background information questions are asked to obtain demographic characteristics. o Section B (choice factors), Section C (perceived value) and Section D (willingness/intention to enrol) of the questionnaire, cover the specific information requirement sections. These sections of the questionnaire employ different question response formats and mostly 7-point Likert-type scales of measurement.

The questionnaire is also pre-tested before distributed to the sample. Pre-testing refers to the testing of a questionnaire on a small group of people who are representative of the sample and who are capable of highlighting possible design errors (Malhotra, 2007:319). Based on the feedback received, a number of changes are made before the questionnaire is implemented.

16 | After the questionnaire has been finalised, it is necessary to identify a representative sample of the population to take part in the study.

1.7.1.3 The sampling process

Sampling refers to the steps that are employed in obtaining information from a subset (sample) of a larger group of people (McDaniel & Gates, 2006:296). As it is unpractical and expensive to reach all grade 12 public school scholars in South Africa, a sample is drawn from a population.

The sampling process includes various steps to guide the researcher in the way individuals are selected for the sample. These steps include defining the target population, including determining the sampling units, sampling elements and sampling frame. The sampling technique is further decided upon and the sample size is determined before executing the sampling process (Malhotra, 2007:336).

 The target population

The target population comprises the collection of people (elements) who possess the information needed by the researcher. The target populating is further defined in terms of elements, sampling units, extent and time (Wiid & Diggines, 2009:193; Malhotra, 2007:336). For the purpose of this study, the target population include all the grade 12 scholars attending a public school in Gauteng and who have the ability (high enough marks to be accepted at a university and passing with matric exemption) and are considering entering a university/university of technology to further their education. Thus, the sampling elements are the grade 12 scholars who are intending to study at a university or university of technology, the sampling units are the identified public schools, the extent refers to the geographical area, in this case the Gauteng Province, and the time refers to the period when questionnaires are collected. Questionnaires were collected during August and September 2011.

The sampling frame refers to a list or set of guidelines that represents the elements (grade 12 scholars) and the units (the public schools) of the population used to identify the target population under study (Malhotra, 2007:337; Cooper & Schindler, 2006:443). A list containing the top 250 public schools in Gauteng, is used as

17 | sampling frame. This list is compiled every year by the Department of Education by using algorithms on a combination of factors including the percentage of scholars passing grade 12, the number of distinctions obtained by the group of grade 12s for a particular exam, and the number of scholars passing with HE entrance (matric exemption) to a possible Bachelor’s degree (Department of Education, 2011).

 The sampling technique

The two broad sampling techniques available to researchers are probability sampling and non-probability sampling (McDaniel & Gates, 2010:423; Malhotra, 2007:337- 339; Kotler & Fox, 1995:85). When probability sampling is employed, everyone in the population has a known probability of selection and the likelihood of being chosen is known beforehand. Non-probability sampling is more subjective and does not make use of chance. The sample is selected on the basis of personal judgement or convenience (Zikmund & Babin, 2010b:423; Cooper & Schindler, 2006:440).

For the purpose of this study, a non-probability sampling technique is employed where the researcher uses judgement sampling techniques. In judgement sampling, an experienced individual selects the sample based on his or her judgement about some appropriate characteristics required of the sample element (Zikmund & Babin, 2010b:424). In this study, the researcher uses the judgement of the head of the UJ Marketing and Recruitment team. The head of the UJ Marketing and Recruitment team and his team members have regular contact with a large number of public schools in Gauteng. The head of this team uses his experience to judge the top 250 Gauteng public schools list (the sampling frame) obtained from the DOE, and he chooses the public schools that should be included in the sample. Judgement is also used to ensure all population groups take part in the study by considering the medium (language) of instruction at these schools when selecting a school from the sampling frame. Schools are selected and contacted until the required number of respondents is reached. A census of the sampling elements present in each sampling unit is undertaken.

18 |  The sample size

With sample size, the researcher needs to decide on how many elements to include in the research study (Malhotra, 2007:338). For the purpose of this study, a sample of 100 sampling elements (grade 12 scholars attending public schools in Gauteng) at each of the 15 chosen public schools in Gauteng is proposed. Thus, a total sample size of 1 500 respondents is proposed.

The next step in the sampling process is the implementation of the sampling process This step comprises collecting the data and the plan (Shiu et al., 2009:486; Malhotra, 2007:336).

1.7.1.4 Data collection

In most situations the person who designs the research seldom collects the data, but involves a number of fieldworkers (Malhotra, 2007:413). For the purpose of this study, the UJ Marketing and Recruitment team consisting of six individuals will visit the chosen public schools in Gauteng and drop the questionnaires off with the relevant teacher at the schools. They already have access to these schools and they are only visiting the schools with which they already have relationships.

The fieldworkers are also sent with a letter of approval obtained from the Department of Education to help to provide legitimacy to the project (Appendix I).

1.7.1.5 Data analysis

Data is checked for accuracy, completeness and validity (Malhotra, 2007:436), before entering data into a software programme, SPSS (Statistical Package for the Social Sciences) Family version 20. The data file is then sent to the UJ’s Statistical Consultation Service (STATKON) to commence data cleaning. An inferential analysis process is followed as statistical procedures are used to generalise the quantitative results obtained from the sample to make judgements about the whole population (Burns & Bush, 2006:426). For the purpose of this study, multivariate statistical techniques are suitable for analysing data, as factors are analysed simultaneously.

19 | In the first instance the various descriptive statistics calculated for this study are presented, including the count, mean, standard deviation and top-box and low-box scores (Shiu et al., 2009:529-534; Eiselen, Uys & Potgieter, 2007:44, 50; Malhotra, 2007:460-461). This is followed by an examination of the distribution of the results (Eiselen et al., 2007:79), and interpreting skewness and kurtosis to determine whether the results for each scale are normally distributed (Pallant, 2010:57).

The validity and reliability of scales used are also investigated by interpreting the Cronbach’s alpha coefficient. The researcher then decides whether to use parametric or non-parametric tests to test hypotheses.

Parametric tests are employed when group sizes under investigation are to a certain extent equal in size and relatively large, and the results are normally distributed (Pallant, 2010:208; Erceg-Hurn & Mirosevich, 2008:594; Eiselen et al., 2007:79-80). At the other end of the spectrum, non-parametric tests are also employed when group sizes under investigation are unequal in size and relatively small, and the results are not normally distributed (Pallant, 2010:208; Erceg-Hurn & Mirosevich, 2008:594; Eiselen et al., 2007:79-80).

Only three statistical techniques are needed to test the stated hypotheses for this study. These tests include independent-samples t-tests (parametric test), Kruskal- Wallis and Mann-Whitney U Tests (non-parametric tests).

 Multivariate statistical techniques

For the purpose of this study, an exploratory factor analysis (EFA), a second-order exploratory factor analysis (2nd order EFA), confirmatory factor analysis (CFA) and structural equation modelling (SEM) are employed. Data has to be reduced and simplified (EFA), verified (2nd order EFA), refined (CFA) and by employing SEM, the proposed model is tested (Pallant, 2010:105; Malhotra, 2007:609:4; Chen, Sousa & West, 2005:487; Tisak & Tisak, 2005).

Figure 1.2 portrays these multivariate statistical techniques employed, as well as the fit indices used specifically with CFA and SEM.

20 | Figure 1.2 Flow diagram depicting the multivariate statistical techniques and goodness-of-fit indices employed in this study

Multivariate Second-order Confirmatory Exploratory Structural statistical exploratory factor analysis factor analysis equation techniques factor analysis (CFA) (EFA) modelling (SEM) employed (2nd Order EFA)

To verify the set To refine the Main goal of common To reduce data factor structure To examine the of the factors, ensuring structure of to a of a set of multivariate they are entirely interrelationships manageable observed statistical responsible for among a diverse number variables technique the measured set of variables

variables

The relative chi- square ratio (X2/ Statistical The root mean df), the root technique square error mean square to approximation N/A N/A error determine (RMSEA), and approximation goodness- the comparative (RMSEA), and of-fit fit index the comparative fit index (CFI)

Source: Information used in this flow diagram is adapted from: Pallant, 2010:105; Hoe, 2008:78; Hooper, Coughlan and Mullen, 2008:54-55; Malhotra, 2007:609:4; Meyers, Gamst and Guarino, 2006:558; Suhr, 2006a:2 Tisak and Tisak, 2005; Chen, Sousa and West, 2005:487)

The flow diagram portrays the sequential process that started with an EFA to reduce and simplify data, followed by a 2nd order EFA to verify the initial (or first-order) factor means, variances and covariances (Pallant, 2010:105; Malhotra, 2007:609; Chen et al., 2005:487; Tisak & Tisak, 2005). The 2nd order EFA assures that the set of common factors are entirely responsible for the measured variables (Chen et al., 2005:487). The next step was to employ a CFA to examine the relationship between a set of measured variables and a smaller set of factors that might account for the variables (Ary, Jacobs, Razavieh & Sorensen, 2006:393). The last step was to employ SEM that examines a series of dependence relationships simultaneously, thus the structure of interrelationships among a diverse set of variables can be examined (Pallant, 2010:105; Shiu et al., 2009:649-650). EQS software was used to employ a CFA and SEM.

Figure 1.2 also portrays the fit measures employed, or the goodness-of-fit to determine the adequacy of model fit to the data. For both the CFA and SEM the root mean square error of approximation (RMSEA) and the comparative fit index (CFI)

21 | measures were used, however with structural equation modelling an additional fit index namely the relative chi-square ratio (X2/df) was also employed (Hoe, 2008:78; Hooper, Coughlan & Mullen, 2008:54-55; Meyers, Gamst & Guarino, 2006:558; Suhr, 2006a:2).

1.8 Chapter outline

This study comprises of 7 chapters to support the primary objective of developing a model to explain prospective students’ university choice.

Chapter 1 comprises of the Introduction and Orientation. The research background to the study, the problem statement, the research objectives, the proposed theoretical model for the study, the hypotheses formulated for the study and the methodology applied are discussed in this chapter. Chapter 2 follows with an introduction into the Higher Education (HE) landscape to unearth an understanding of the marketplace within which universities are operating. The Global HE landscape, HE in developing countries, and the South African HE landscape are investigated in terms of globalisation, raising concern regarding increasing costs and obtaining funding, the impact of massification, and the notion of the student as customer in the HE context. Marketing as concept in the HE arena and marketing universities in the service industry are also addressed. Chapter 3 focuses on the decision-making process of prospective university students, with the aim to determine why the prospective university student chooses to consider a certain university for possible enrolment, and not another. Choice factors are explored, specifically those choice factors considered most influential in the university selection process. As choice is an integral part of consumer behaviour, consumer behaviour theory is also briefly addressed with a specific focus on the levels of the consumer decision-making process where alternatives (universities) are evaluated, before educational consumer behaviour decision-making models, or existing models are investigated. Chapter 4 follows by discussing the conception of perceived customer value and argues that the basic building blocks of perceived customer value are the difference between the benefits gained and the sacrifices incurred. The next section in this chapter discusses purchase intention or willingness to buy, as perceived value has both a direct and indirect effect on behavioural intentions. The conclusion is thus

22 | drawn that perceived value has both a direct and indirect effect on prospective students’ intention to enrol as proposed in the theoretical model. The various value- intention frameworks or models are also unearthed and the researcher explores those specific models or frameworks that bring value, perceived value and intention to buy (or willingness to buy) together. The proposed model is also explained in this chapter. Chapter 5 explains the research methodology employed, thus how the empirical research component for the study is conducted. This chapter commences with a brief discussion on marketing research as concept, and the focus of the chapter is on the discussion of the marketing research process and on the application of this process in the study. Specifically, the descriptive research design used is explained. The type of information required, the measurement and scaling procedures, the questionnaire design and pre-testing, the sampling design and the data analysis process are discussed. Chapter 6 reveals the results obtained from the analysis while the researcher presents the hypotheses and their relative findings. Conclusions and recommendations are made, as well as limitations discussed in Chapter 7. The final model is tested by employing SEM and is also presented in Chapter 7.

1.9 Conclusion

This chapter serves as an introduction to the study at hand. It was determined in the background of the study as well as in the problem statement discussion that in the context of increasing competition for home-based and overseas students, universities now recognise that they need to market themselves in a climate of international competition (Harrison-Walker, 2009:103; Hemsley-Brown & Oplatka, 2006:316, Freeman & Thomas, 2005:153; Nguyen & LeBlanc, 2001). Universities realise the importance of student recruitment (Ivy, 2008:288) and the need to use marketing techniques to market themselves more explicitly (Hemsley-Brown & Oplatka, 2010:204). Lomas (2007:32) and Russell (2005:73) suggest that universities should commit to a marketing orientation by putting the prospective students’ interests first in order to gain a competitive edge.

Being familiar with the choice factors prospective students take into account when choosing a university to enrol at as well as being familiar with their perception of the

23 | value offered by a university, is seen as central in the development of appropriate marketing-related strategies. When marketing strategies are developed correctly, they can assist universities in attracting the ‘right’ prospective students, to improve the image of the university, to segmenting and targeting their chosen market, as well as developing a distinctive positioning to gain a competitive advantage (Moogan, 2011; Harrison-Walker, 2009:104; Mazzarol & Soutar, 2008; Vrontis et al., 2007:979; Briggs, 2006:708; Maringe, 2006:469: Lowry & Owens, 2001:28; Nguyen & LeBlanc, 2001; Sweeney & Soutar, 2001:217). It is furthermore beneficial for universities to understand what prospective students perceive as value and the value offered by their chosen university, as knowing and understanding this perceived value can predict the intention to enrol (Lu & Shiu, 2011:1184; Zeithaml, 1988; Dodds & Monroe, 1985).

The primary objective and secondary objectives are formulated to address the problem at hand, and it is suggested that a model should enable university marketers to determine choice factors that have a relationship with perceived value and that perceived value predicts the intention to enrol.

The hypotheses are formulated for the study and the research methodology described as well as the chapter outline are discussed to guide the reader to the chapters to follow.

The next chapter addresses the Higher Education (HE) landscape.

24 |

CHAPTER 2

The Higher Education Landscape

2.1. Introduction

Higher Education (HE) plays a major role in the economic development of countries. It is one of the foundations to build success in an increasingly competitive marketplace where organisations need highly skilled employees to remain competitive (Navehebrahim, 2009:290; Freeman & Thomas, 2005: 154).

The World Trade Organisation (WTO) recognises education, which includes HE, as a global commodity based on the need to prepare tomorrow’s workers for a life that demands global literacy (Maringe & Gibbs, 2009:15; Freeman & Thomas, 2005:155). There is a surge in the demand for a highly skilled and technologically competent workforce. Worldwide, 84 million students attended regular higher education institutions (HEIs) in 2003 and it is estimated that this demand for HE is constantly rising and likely to reach 160 million by 2025 (Gupta, 2008:565; Glakas, 2003).

The increasing demand and estimated growth in demand for HE (Gupta, 2008) already brings forth challenges and changes for HEIs such as universities, to accommodate the changing world we live in. Universities1 realise that they need to understand these changes and challenges as the student recruitment market has become much more competitive and it becomes challenging to universities to be sustainable and to survive in this marketplace (Altbach, Reisberg & Rumbley, 2009; Read, Higgs & Taylor, 2005:31; Comm & Mathaisel, 2003).

Understanding the HE landscape has become vital, especially at the backdrop of the challenges and changes that universities are facing. Universities will find it difficult to plan for the future if they do not know what the state and trends of their playing field are. A comprehensive picture of the HE landscape will also enable the researcher to fulfil the purpose of this study, which is to develop a choice model for prospective students. 25 |

The first part of Chapter 2 will investigate the HE landscape to unearth an understanding of the marketplace within which universities are operating in. The second part of this chapter addresses marketing as concept in the HE arena and marketing universities in the service industry.

For the purpose of unearthing the HE landscape, this chapter presents a discussion on the HE landscape organised in three main sections, namely: The Global HE landscape (2.2), HE in developing countries (2.3), and the South African HE landscape (2.4) (Figure 2.1).

Figure 2.1 The Higher Education (HE) landscape

Each of these three main streams of the HE landscape is further investigated in terms of the main challenges and changes, and the focus is on aspects such as globalisation, funding, massification, and the increasing trend to view the student as the customer.

26 | Globalisation in HE is discussed in its sense of ‘borderless-ness’ and the increasing mobility of students. Universities now compete on a national and international level and new competitors such as private institution and non-university competitors as well as distance learning and internet-based courses make this playing field fierce (Hemsley-Brown & Oplatka, 2010:204; Opoku, Hultman & Salehi-Sangari, 2008:125; Cubillo, Sánchez & Cerviño, 2006:101).

Funding is an increasing concern as universities compete not only with other similar universities for students and top performing students, but they are facing higher costs and compete for funding from governmental and/or private sources (Comm & Mathaisel, 2003; Gordon, 1995). Universities are actively encouraged by some governments to seek an increasing proportion of their funding from industry as opposed to traditional government funding (O’Brien & Deans, 1995). Seeking funding puts increasing pressure on and competition between universities to attract the best candidates ‘because of the perception that the more high-profile the alumni, the greater likelihood of increased private funding’ (Freeman & Thomas, 2005:154, Ahola & Kokko, 2001:192).

Massification in its context of ‘mass demand for HE’ in general and the widening access to HE is discussed. The increasing number of institutions as well as newer ways of study (such as distance learning, e-learning and internet courses) comprise but some of the repercussions of the increasing demand for higher education and is providing today’s prospective student with more choice (Wiese, 2008:33-34; Drummond, 2004:317). Although student numbers have grown and the trend of massification (Gupta, 2008; Gordon, 1995) exists, a rise in consumerism among students has resulted (Freeman & Thomas, 2005). Especially where more students have to pay for their university education the value of money spent on education, is seen as a prevalent in the choice of which university will be chosen, and students consider themselves as customers ‘buying a service’ (Lomas, 2007:32; Freeman & Thomas, 2005:158). Students are now viewed as customers and Naude and Ivy (1999) argue that students are HEIs primary customers.

It is against the backdrop of this increased competition caused by various factors (competing with different numbers and types of universities, competing for students,

27 | and competing for funds) together with the challenges such as globalisation, internationalisation, massification, consumerism among students and the students’ “right” as a customer, that universities now recognise that they need to market themselves more aggressively (Harrison-Walker, 2009:103; Hemsley-Brown & Oplatka, 2006:316; Freeman & Tomas, 2005:153; Nguyen & LeBlanc, 2001).

As marketing of universities is viewed to be essential in promoting and benefiting a university’s competitiveness, and to retain or expand a university’s share of the market (Edirisooriya, 2009), the second part of this chapter will focus on marketing of HEIs (2.5). This discussion is not complete without an investigation into the type of market that universities are finding themselves in, namely the service environment (Hemsley-Brown & Oplatka, 2006:318).

A discussion will follow on ‘why’ it is argued that universities exist in a service environment, and the important role that services marketing plays in the university’s quest to stay competitive and to survive will also be argued. The nature of services as well as marketing universities as a service in this environment (2.6) is discussed.

2.2 The global Higher Education (HE) landscape

The global HE landscape is faced with trends such as globalisation, a decrease in state funding, massification and widening of access to universities, and the demands and needs that the student as the customer hold. Each of these trends and challenges will be discussed in more detail.

2.2.1 Globalisation in HE

The concept globalisation means the ‘spanning’ of national borders and refers to ‘all processes by which the people of the world are incorporated into a single global socio-economic unit (Dickson, 2009:174; Maringe, 2009:554). Globalisation refers to the interconnectedness of global and local forces and often the term is used to describe a transition to an “unbounded” era, such as that of the 21st century (Mählck & Thaver, 2010:24). It is an increasing recognition of the world as one marketplace (Freeman & Thomas, 2005:161).

28 | Cohen and Kennedy as cited in Maringe (2009:554), identified six component strands of globalisation, which can be used to define the concept: o Changing concept of time and space – mutual interaction of societies, a compression of the world based on the notion of the global village; o Increasing cultural interactions and flows – the increasing human migration for political, economic, religious, social, educational and natural reasons that is causing “confluence” of cultures; o Communality of world problems – people share living in a “risk society”; o The dominance of transnational actors and organisations – The World Bank and the International Monetary Fund (IMF) are examples of organisations that have influenced world politics and economics; o Interconnectedness and interdependence of societies – the World Wide Web is a force behind the development of a global social interdependence among people and nations across the world; and o Synchronisation of all dimensions – A move to greater concern with global issues such as climate change, poverty, crime, terrorism, migrations and drug trafficking.

2.2.1.1 The impact of globalisation on the HE landscape

Hemsley-Brown and Oplatka (2006:316) argue that the HE market is now well established as a global phenomenon, and Dickson (2009:175) highlights the increasing impact of globalisation on the HE sector as: o The increasing number of students studying outside their own countries; o English as the dominant and sometimes default language; and o The emergence of the Internet.

 The increasing number of students studying outside their own countries

There are similar HE opportunities across borders (Bateson & Taylor, 2004) and this encourages students to investigate and sometimes opt for HE opportunities outside of their home countries (Dickson, 2009:175; Hemsley-Brown & Oplatka, 2006:318). The Organisation for Economic Co-operation and Development (OECD) reported that the number of international students has grown substantially over the past years. In 1975, 0.8 million students worldwide studied outside their home countries, and this number has increased to 1.9 million in 2000 and to 3.3 million in 2008, more

29 | than a four-fold increase (OECD, 2010:32) (Table.2.1). The growth has accelerated since the late 1990s, mirroring the globalisation of economies and societies. Students realise that they must develop some key global cultural skills and this has increased students’ search for HE around the world (Cubillo et al., 2006:101).

Table 2.1 Trends in the number of foreign students enrolled outside their country of origin (2002 to 2008)

Number of foreign students

2008 2007 2006 2005 2004 2003 2002 Foreign students 3 343 092 3 082 420 2 957 364 2 852 296 2 738 507 2 546 223 2 337 886 enrolled worldwide Foreign students enrolled in 2 646 046 2 522 757 2 440 657 2 370 897 2 270 346 2 090 474 1 902 749 OECD countries

Source: Adapted from OECD (2010:33). Available at: http://dx.doi.org/10.1787/888932310434. (Accessed 5 January 2012)

Universities are responding to this student mobility by expanding their reach beyond the borders of their traditional students as defined by financial and/or social classes and their country of origin (Freeman & Thomas, 2005:161). Universities are also beginning to establish significant physical presences in other countries through investing in branch campuses, e.g. Monash now in China (and South Africa), Liverpool University in China and Malaysia’s LimKokWing University in London and Botswana (Dickson, 2009:175).

Universities realise that they need to increase their attractiveness to reach these ‘mobile’ students. One such an initiative is the Bologna Process that was created to establish a European Higher Education Area (EHEA) based on international cooperation and academic exchange that is attractive to European students and staff as well as to students and staff from other parts of the world. The vision of the European Higher Education Area is to (1) facilitate mobility of students, graduates and higher education staff, (2) prepare students for their future careers and for life as active citizens in democratic societies and support their personal development, and (3) offer broad access to high-quality higher education, based on democratic principles and academic freedom (www.ond.vlaanderen.be). In essence, the

30 | Bologna Process was created to make European HE intelligible as a single system and to improve its attractiveness and global profile as a destination for international students (Robertson & Keeling, 2008:224).

In Canada this trend of mobile students, has resulted in the export of university programmes to other countries into the global marketplace. This is also true for universities in the United States of America (USA), the United Kingdom (UK), and Australia (Freeman & Thomas, 2005:165). The USA is the leading exporter of international education, followed by the UK and Australia (Binsardi & Ekwulugo, 2003:318).

Attracting the students, in whom a specific university is interested, is a competitive business. It is realised by many universities that trading in knowledge is organised, commercial and part of the global capitalist market (Maringe & Gibbs, 2009:14). This is evident in the rapid growth in the quantity of HE newcomers (Navehebrahim, 2009:290), such as the newly established universities and colleges in the Middle East, North Africa region and South-East Asia. These institutions are providing specific courses, mostly vocational related such as English language, management, paramedical services, media and information technology (Maringe & Gibbs, 2009:15).

Newer institutions are more able to innovate quickly and develop curricula that are directly relevant to their customers who are focused on career outcomes (Dickson, 2009:178). These newer institutions’ attractiveness lures students from all around the world to study at them, increasing competition to the more traditional and public universities (Newman & Couturier, 2001:10-11).

 English as the dominant and sometimes default language

English has become the dominant global language of communication in business, aviation, entertainment, diplomacy and the Internet. It is the first choice language of nearly 400 million people in the UK, the USA and the Commonwealth countries and over 1.4 billion people speak English as their second or foreign language. In China and India alone, it is estimated that over 533 million people use English. It is also the

31 | official language of 53 countries in the world and is used in the United Nations, European Union and NATO (Millward & Hayes, 2012:342; Guo & Beckett, 2007:118).

Although English is growing as a dominant language, there is still an awareness that medium-of-instruction policies in education have considerable impact not only on the performance of students and the daily work of lecturers, but also on various forms of social and economic (in)equality. Moreover, for the reason that educational institutions, such as universities, play such a crucial role in determining social hierarchies, political power, and economic opportunity, medium-of-instruction policies play an important role in organising social and political systems (Tollefson & Tsul, 2008:vii).

The debate will go on to whether universities should also use the native languages of minority students as medium-of-instruction for reasons of seeing multilingualism and multiculturalism as an asset (Tollefson & Tsul, 2008:vii). There is however also a recognition of English as the default language of international business and of international higher education that has also lead to the increasing “thirst” for international education (Dickson, 2009:175; Altbach, 2008:58-59; Guo & Beckett, 2007).

Countries such as China have responded to the increasing use of English by making English compulsory in primary schools from Grade 3 upwards in 2001. Chinese universities have responded to this phenomenon by also introducing English as a compulsory subject in university entrance examinations because it was realised that English has become a gateway to education, global employment and economic and social prestige (Guo & Beckett, 2007:118).

In Norway, universities pay their academics who publish in English fees for the accomplishment, while their colleagues who publish in Norwegian are paid less or not at all. In Korea, great pressure is placed on academics to publish in recognised international journals in English. Universities further developed more academic programmes that are offered in English in many English non-speaking countries, and

32 | there is a worldwide branch campus movement that for the most part uses English as the medium of instruction (Altbach, 2008:58).

 The emergence of the Internet

Already in 1995, Gordon (1995:25) reported that many leading business schools used computer technology to support their international and in-house company distance learning programmes. Modern technologies at that stage already supported and enhanced many facets of continuing professional development.

Due to the mobility of students in the wake of globalisation, an inverse in online institutions is also recognised (Gupta, 2008). The emergence of the Internet makes it easier for distance learning through online study (Dickson, 2009:17). Because of technology, the entry barriers into the HE market have lowered and as a result the cost of entering the market has now also decreased (for students), although it has financial implications for universities to implement this technology. Technology has improved the range of teaching and the learning tools available to lecturers and it has led to an increase in both distance-learning and Internet-based courses (DeShields, Kara & Kaynak, 2005:130; Naude & Ivy, 1999:126).

One of the reasons for the accelerating growth in the number of universities in developing countries is the explosion in online learning (Bradshaw, 2011:8; Li, Wilson & Doran, 2009:228). Some researchers predict that the result of the rapid increase in the online delivery of education by traditional and non-traditional providers, will be that education will become a distanced activity (Wood, Tapsall & Soutar, 2005:431).

It can thus be concluded that the Global HE landscape has definitely seen (1) the increase in the number of students choosing to pursue their studies outside their own home countries, that (2) English is the dominant business language and default language of international business and of international HE; and lastly that (3) the emergence of the Internet has forced many HEIs to develop e-programmes and use the Internet for distance learning. Dickson’s (2009) three indicators of globalisation

33 | are true and applicable to the Global HE landscape and globalisation indeed affects the way that HEIs do business.

Dickson’s (2009) three indicators of the globalisation’s evident presence will also be argued for the HE landscape of developing countries (2.3) as well as for the South African HE landscape (2.4). Before these issues will be addressed, it is important for the purpose of this study to discuss internationalisation as another element that is also sometimes mentioned as part of globalisation.

2.2.1.2 Internationalisation in HE

There is a school of thought that suggests that internationalisation preceded globalisation. Another school argues that it is the consequence of globalisation. The term internationalisation assumes that universities have at some stage NOT been internationalised and that something is driving them towards becoming internationalised (Maringe, 2009:554-555). Maringe (2009) argues that universities have always been international in character, because even the first medieval university lecturers were known for their travels between nations to disseminate knowledge. Globalisation has however sped up the internationalisation process.

Globalisation in education focuses on competition between nations, while internationalisation tends to seek the strengthening of international cooperation (Maringe, 2009:557). The term internationalisation can be defined as “the process of integrating an international perspective in the teaching/learning, research and service functions of higher education institutions” (Knight, 2001: 229).

The “international perspective” in the USA HE is evident as there is a growing acceptance in the USA for the need for American HEIs to encourage awareness and understanding of the international environment in order to produce globally competent citizens. It is being argued by the USA HE community that “to be fully educated, is to be educated internationally”. With less than 1.5% of all American students ever studying abroad, the emphasis in the USA is being placed on the need to provide ‘internationalism at home’. The USA HE is trying hard to attract international students and researchers (Robertson & Keeling, 2008:227).

34 | The receiving countries for international students are mostly the USA, the UK, France, Germany and Australia (Dickson, 2009:175). These mentioned countries receive over 50 per cent of all students who study abroad between them. Many students also opt to take an international degree offered by a foreign university through a franchise programme, but still remain in their home country (Dickson, 2009:175). A trend can also be seen in the growth of university cities where foreign HEIs are offered favourable conditions, such as low or zero taxation, to establish a campus presence in a particular country. The aim is to produce a cluster of HEIs to attract international students and highly skilled labour (Dickson, 2009:176).

It is not only attracting international students or providing current students with international learning experiences that are important, but international collaboration between universities, especially in research, has become a major feature of university development. It is reflected in a whole series of collaborative alliances between groups of universities because there is an increased importance in most countries in internationally recognised research (Dickson, 2009:177).

Internationally recognised research is vital to any university’s survival. Especially if a university would want to classify itself as a world-class university, excellence in research becomes a very important determinant. World-class refers to international recognition, based on reputation as well as academic and research performance, and performance is evaluated on the number of publications, citations and exclusive international awards (Salmi, 2009: x, 4-5).

Not only is research performance an indication of possible world-class status, another benefit is the ability to attract talent that will include attracting top students. Attracting top students to ‘unknown’ universities is especially difficult and a very expensive exercise (Salmi, 2009:9). With so many existing educational ‘players’ in the market, and most competing for top students, it is also becoming increasingly more difficult for universities to maintain their competitive advantage (Cubillo et al., 2006:101).

Universities’ competitive advantage challenges do not stop at competing successfully for students and for international recognition in its research to survive,

35 | universities are also facing decreasing government subsidies and are now competing for funds as well (Susanti, 2011; Navehebrahim, 2009:290).

2.2.2 Funding in HE

Yamamoto (2006:559) argues that “as a result of increased national and international competition more and more research institutions and universities are under pressure to find new ways to generate income”. Also, as more students have to pay for their university education, the pressure increases for universities to communicate the value students will receive (Susanti, 2011; Lomas, 2007:32; Freeman & Thomas, 2005:158).

Three issues dominate the funding debate for universities, namely the level of government support, the distribution of that support, and the means of rewarding successful performance (Gordon, 1995:25). The focus of this section will be on government support as there is a trend of decreasing government subsidies and decreasing financial resources that has lead to increasing costs for universities (Susanti, 2011:209, 211; Navehebrahim, 2009:290; Kaye, Bickel & Birtwistle, 2006:14). The shift in ‘who pays for education’ is mostly a result of gradual state disinvestment in public higher education. In the USA for example, HE funding has moved from states to students, due to insufficient and reduced state appropriations for HE (Alexander, Harnisch, Hurley & Moran, 2010:76).

Some argue that students who benefit from HE should make a contribution to the cost of that HE. In the UK, this has resulted that the majority of students will have to pay at least a proportion of their studies (Lomas, 2007; Brookes, 2003:140).

In the UK the HE Act of 2004 with subsequent legislation led to the introduction of student fees and created a consumer environment that left the HE sector in a difficult position (Moogan, 2011:2). Suddenly, ‘top-up fees’ were introduced which took effect in September 2006. This Graduate Contribution Scheme allowed universities to charge up to £3000 per annum per course (Maringe, Foskett & Roberts, 2009:150, 153). It is estimated that the tuition fees (the amount universities charge) from September 2012 will increase to new full-time students paying up to £9,000 a year, and new part-time students paying up to £6,750 a year

36 | (www.direct.gov.uk). Although prospective students would not have to pay their fees up front and they will be able to get loans from the government to cover the cost, the overall feeling is that if you pay more you need to get more (Maringe et al., 2009:150, 153).

This view will impact universities’ provision of benefits, as the students want better student services, better accommodation, more ICT facilities and they are expecting better qualified university staff (Maringe et al., 2009:152). Unfortunately, money has something to do with the range of services available at universities’ campuses. Improved offerings are only possible with additional infusion of funds (Michael, 2005: 366).

Those universities affected by decreased national government funding, have been attempting to recruit more international students whose fees are often much higher than those charged to their own students (Dickson, 2009:178). In Australia successive federal governments have encouraged universities to generate external revenue by promoting the recruitment of full-time, foreign fee-paying students (Robertson & Keeling, 2008:229).

American universities have also experienced a decline in public dollars for higher education relative to the number of students served. Mellow and Woolis (2010:315) predict that the future might be that public institutions funding will have been slashed to such an extent that tuition costs will be astronomical for American universities.

This decline in HE funding has a big impact on economic development and for HE as a business in its own right. This trend has in turn driven a much more rapid internationalist outlook in universities. Looking elsewhere for funding has major implications for universities. It has in the USA resulted in an income received in 2000 from international students to the extent of over $10 billion. This income was more than the amount of public spending on HE in all of Latin America (Dickson, 2009:175-178).

The decline in state funding to universities has led universities looking for innovative ways to fill the funding gaps. For example, universities in Canada, the USA, the UK

37 | and Australia have responded to the issue of limited funding and resources by taking university programmes to other countries into the global HE marketplace (Freeman & Thomas, 2005:165). In some countries (such as the UK), ‘top-up fees’ and increasing student contributions towards their studies have been introduced (Maringe et al., 2009:147).

The resultant implication for the student is the consideration of financial aid and the availability of scholarships, bursaries and loans. Financial information and assistance are now becoming important factors and act as guidance when students make choices of where to study. Grants and loans will thus influence prospective students’ decision to participate in HE (Bell, Rowan-Kenyon & Perna, 2009:663; Maringe et al., 2009:147).

The criticism from low or lower-income families is their likely exclusion from HE. However, with increased tuition fees and the lack of grants or scholarships the possible exclusion to HE becomes even more of a reality. The problem is that access to higher education for these prospective students is denied (Susanti, 2011:212) and the outlook from most governments is that their youth should have equal opportunity to access HE. For this reason the concept of massification of HE will be discussed in more detail.

2.2.3 Massification of HE

The term ‘massification’ was already used in 1995 to describe the development of mass higher education during the latter part of the 20th century (Lomas, 2002:71). Massification refers to the mass demand for HE in general and for widening access to HE. Another view of academic massification is a system where all the youth in the nation should be equally educated. It means integrating multiple capabilities of students into a similar and standard system, regardless of distinctive characteristics (Parhizgar, 2010:78).

The first country to achieve mass HE was the USA with 40% to the age cohort attending post-secondary education in 1960 (Altbach et al., 2009:6). In 2001 in the

38 | UK, participation in HE was 32% and this number increased to 36% in 2009 of those in the 18-21 age group (Corver, 2010:5; Lomas, 2002:71).

Globally, the percentage enrolled in post-secondary education between 1999 and 2006 has grown by roughly 50 per cent from 94.7 million to 142.1million (UNESCO, 2009). The biggest growth was in upper middle and higher income countries, with Western European countries and Japan experiencing rapid growth. Developed countries of East Asia and Latin America followed, and China and India are currently the world’s largest and third largest academic systems respectively (Altbach et al., 2009:6).

Massification of HE is the result of greater social mobility for a growing segment of the population, the rise in English as the dominant language of scientific communication, new HE funding patterns, increasingly diversified HE systems in most countries, and in general an overall lowering of academic standards (Altbach et al., 2009; Akoojee & Nkomo, 2007:385).

The massive expansion in the number of students in the UK HE has resulted in concern for academic standards (Lomas, 2002; Jackson, 2001:219). The question has been raised to whether massification has led to the end of quality HE provision, however Lomas (2002:77) argues that the answer depends on how quality is defined. Li et al. (2009:228) argue that there is a relaxation of the criteria to gain university status and pose the question: “Isn’t this in itself evidence of lowering standards to widen participation to achieve a better mass education rate and growth”?

To fight educational inequity (Susanti, 2011), a trend is seen towards widening access to HE, which could also be referred to as mass education (Hemsley-Brown & Oplatka, 2010; Bennett, 2004; Jackson, 2001:219). There is an increasing proportion of school leavers entering HE and this is evident in the enrolment rates that have increased (Bell et al., 2009:663; Lomas, 2007). Especially established education institutions are expanding enrolments, increasing the variety of programmes, and increasing the variety of courses offered in the established programmes (Freeman & Thomas, 2005:159).

39 | The American HE market has another problem; excess supply of institutions. To a varying degree, universities are dependent upon the students’ demand for education (Freeman & Thomas, 2005:167). American HEIs are creating demand by introducing inventive strategies such as scholarship programmes, certification and accreditation of high school curricula, high school-university partnerships, unified university entrance examination, and relaxation of admission standards (Edirisooriya, 2009:115). In this wake of widening access to all, sustaining and improving quality will be some of the main challenges of HE (Navehebrahim, 2009:293).

Although there is an increase in the demand for HE globally driven by the shift to post-industrial economies, the rise of service industries and the knowledge economy, other countries like Canada and the UK are also experiencing excess supply of HEIs (Altbach et al., 2009:3; Freeman & Thomas, 2005:169).

In Canada there were 75 higher education institutions in 2003 for a population of 31.6 million, thus an oversupplied market. In the UK there were 195 higher education institutions for a population in 2003 of 59.5 million. Branding becomes paramount to attract each university’s share of targeted students while competing with many numbers of institutions nationally, internationally and globally (Freeman & Thomas, 2005:169).

There is an increased global competition among institutions offering HE (Helgesen, 2008; Ahola & Kokko, 2001). With more players in the market and new players in the all-becoming ‘open market’ (Baldwin & James, 2000), many universities are finding that they have to compete for students (Naude & Ivy, 1999:126).

One such technique for attracting the ‘right’ student in an oversupplied market of institutions, has forced universities to become more marketing focused. This is not a new phenomenon as universities have started with marketing already since the 1990s. The expansion and commercialisation of HE has seen the wide scale adoption of marketing techniques within the sector (Yamamoto, 2006:560).

For universities to remain viable in the longer term, they need to recognise the “sovereignty of their customers” (Naude & Ivy, 1999:127). Marketing to whom?

40 | Naude and Ivy (1999) argue that students are universities’ primary customers that need to be understood and targeted.

2.2.4 The student as the customer

According to Kotler and Fox (1995:23), the term consumer refers to the person who uses and benefits from the product or service. The term customer means the person who selects a particular source for this product or service. Students can thus be viewed as both consumers and customers. They ‘use’ the universities’ services and receive benefits from studying a degree. They can also be considered as customers with the view of selecting courses (which degree to study), but the source of this ‘product’ (the course/degree) is the university, so ultimately the student has to select the university - making students customers (Hemsley-Brown & Oplatka, 2006:319).

Conway, Mackay and Yorke (1994) argue that students can be seen as both customers and products simultaneously. Students are changed by the experience of attending the university and become themselves a product of that particular university.

Universities have many customers, such as students, staff, faculty, alumni, donars, families, society and other customers, however the term customer in university context is most often used in talking about attracting and serving students (Freeman & Thomas, 2005:160; Navarro, Iglesias & Torres, 2005:506; Kotler & Fox, 1995:23 &32). For the purpose of this study, the student will be referred to as the customer, as the author mainly focuses on the ‘pre-purchase’ phase when students still weigh benefits and sacrifices before ultimately selecting the university at which to enrol.

The person who engages in the educational experience and has the opportunity to determine the extent of the benefit of this experience, is the student and this makes the student an important customer. It is the student, perhaps with the help of their funder(s), who ultimately makes the choice of which university to attend (Freeman & Thomas, 2005:163).

41 | Naude and Ivy (1999) argue that students are universities’ primary customers. Vrontis et al. (2007:980), also argue that the customer is the student in HE and that universities have a primary purpose to satisfy their customers (Vrontis et al., 2007:980). DeShields et al. (2005:128-12) argue that a greater emphasis is placed on universities in meeting the expectations and needs of participating customers, and that these customers are the students. Thus, customers are satisfied when the offered products and services meet their needs, desires and requests, or create customer value (Petruzzellis & Romanazzi, 2010:140; Helgesen, 2008:51).

But, is it important for organisations, such as universities to consider their customers? Freeman and Thomas (2005:16) argue that it is indeed important and they found that there is increased recognition of customers as a priority around the world because the best organisation in the world will be ineffective if the focus on customers is lost.

The focus on customers (the students) can be gained by following a market orientation - by having responsiveness to information about customers and competitors to better serve their markets (Slater, 2001:231). A marketing orientation puts customers’, i.e. students’ interests first, in order to gain a competitive edge in a competitive market (Hemlsey-Brown, 2010:205).

Universities must keep students at the centre of their purpose (Navehebrahim, 2009:291; Sahney, Banwet and Karunes, 2004). Maringe and Gibbs (2009:36) agree with Navehebrahim (2009) and Sahney et al. (2005) by arguing that universities should adopt a fundamental customer perspective principle from the commercial world: “the interests and needs of students should be central to the organisation”. Some authors however warn readers that this does not imply that students as customers are always right. In fact, they may often be wrong (Maringe & Gibbs, 2009:36; McMillan & Cheney, 1996), but it is important to understand where they are coming from. Maringe and Gibbs (2009:36/7) argue that universities should recognise the following about their customers:

42 | o Who these customers are (demography, geographical distribution and psychographic qualities); o What they like and dislike about the institution; o The knowledge and skills they expect to acquire through studying with the institution; o What they expect to learn in the programme; o Their motives for studying with the institution; o Their progression and post-qualification needs and expectations.

Changes in the HE landscape, such as the rapid expansion of universities, increases in education costs and demographic shifts in the populations, force universities to think differently about the role of the student and student satisfaction for their survival (DeShields et al., 2005; Kotler & Fox, 1995). To implement successful strategies for universities, depends on an understanding of the needs and wants of customers in the market in order to deliver the right goods and services effectively and efficiently (Conway et al., 1994:35).

Intense competition in today’s competitive educational market will force universities to try and differentiate their offerings from those of their competitors. They need to assess the target market needs, modify their offerings to meet those needs, and deliver superior quality services (DeShields et al., 2005:129). Universities need to focus on their strengths or position themselves around aspects in which they are excellent, or in which they can become excellent (Petruzzellis & Romanazzi, 2010:142). Thus, marketing universities are essential as students are becoming more discerning customers (Maringe & Gibbs, 2009:35).

Glaser-Segura, Mudge, Bratianu, Jianu and Valcea (2007:124) define the student as customer as follows: “the knowledgeable student, as an external customer, pays the university system for customer-defined instructional services”. Because of the trends of students having to pay towards the cost of their education (as discussed earlier in this chapter) and the emergence of the increasingly ‘knowledgeable’ student, students are becoming more discerning customers and they place more emphasis and concern on receiving quality education and value for money (Maringe & Gibbs, 2009:35; Lomas, 2007; Redding, 2005:409). Quality is in ‘the eye of the customer’,

43 | and the components of the university’s value affect the students’ choice (Petruzzelis & Romanazzi, 2010:140; Barrett, 1996:70). It is the customer (student) who defines the value and quality of the service (Glaser-Segura et al., 2007:121).

As customers, students expect quality of educational services and they know they should have the right to receive quality education. As customers they have become more discriminating and investigative in their selection and more demanding with the universities they choose (Petruzzellis & Romanazzi, 2010:140-141; Freeman & Thomas, 2005:164). Students are customers with specific needs and wants (Pitman, 2000:170; Scott, 1999:194).

2.3 HE trends in developing countries

The nineteenth century has seen Europe dominating the university landscape, while the US universities were generally seen as superior in the twentieth century. The developing countries should not be underestimated as the result of globalisation, thus the global movement of human resources and capital, will pose new realities and challenges to China, India, the Middle East, Malaysia, Indonesia, South America and all parts of Africa (Mellow & Woolis, 2010:309). Funding is also an issue in the developing world, if not more so than the developed world. Massification still plays a role and the students from developing countries have greater mobility and access to education than previously. Firstly, the effect of globalisation on HE in developing countries will be discussed (2.3.1), followed by a discussion on funding issues (2.3.2), massification trends (2.3.3) and the student as the customer ‘phenomenon’ (2.3.4).

2.3.1 Globalisation

As discussed previously, Dickson (2009) mentioned three reasons that are an indication of the increasing impact of globalisation on the HE sector, and in this section the author aims to adopt these reasons to the developing world’s situation to prove that globalisation indeed also has an impact on the developing world’s education. Dickson (2009) mentioned the following reasons: (1) the increasing number of students studying outside their own countries, (2) English is the dominant and sometimes default language; and (3) the emergence of the Internet.

44 | 2.3.1.1 The increasing number of students studying outside their own countries

There is a general concern that the ‘borderless education’ phenomenon could harm developing countries and that they may be flooded with foreign and private providers delivering essentially profitable subjects, and loyal universities are left with the non- profitable subjects in arts, humanities, science and technology that are still vital for a country’s development (Knight, 2003:11). The other concern is that the USA and European universities will even try harder to capitalise on the shortage of home grown HEI and try to lure students out of their home countries, such as from China (Mellow & Woolis, 2010:310).

The truth is that the demand for HE from developing countries like China, has increased the trend that USA and European universities will grasp at the opportunity to ‘host’ these students to study outside of China. India also poses as an opportunity as it has almost one-third of the world’s population, yet only enrols slightly more than 10 per cent of traditional age students (Mellow & Woolis, 2010:310).

An estimated 63 500 students left Africa between 1960 and 1989 to study abroad. Most of these students left Africa because they believed that to study in developed countries could help them to enhance their future employment prospects as there is high status associated with overseas study (Mpinganjira, 2011a:169, 171).

For African countries and their respective universities, the challenge is (and will be) to attract students wanting to study abroad. This can only be done if these universities have the ability to understand the needs and wants of the market (Mpinganjira, 2011a:169).

2.3.1.2 English as the dominant and sometimes default language

In China and India, the English language proficiency is growing fast and opening up the door to studying at international universities outside their own country (Dickson, 2009:178).

45 | In Africa, most universities use English and French as media of instruction. The exception is Afrikaans in South Africa and Arabic in Egypt. Most South African universities offer their courses in English, with some offering core programmes in another language (such as Afrikaans) as commitment to the particular community (Wiese, Van Heerden & Jordaan, 2010:153).

2.3.1.3 The emergence of the Internet

Navehebrahim (2009:290) argues that HE is where knowledge can be gained and that HE plays a significant role in the economic development both in countries with advanced technology, as well as those still in a stage of technology development or newly industrialised (such as some developing countries). Universities in developing countries will have to try harder to keep abreast with the fast growing nature of technology as universities that have fallen behind will be unable to cope with the needs of customers and will struggle to remain competitive in the world market.

The Internet has the ability to deliver courses globally and creates an opportunity for organisations to enter the market without the risk of “bricks and mortar” campuses (Mazzarol, Soutar & Seng, 2003:96). Information technology is becoming a key part of the commercial and economic cultural capital of both developed and developing countries. Students expect to utilise it as an information search tool. However, in many parts of Africa, students either do not have adequate or total access, or the technology is outdated (Maringe & Carter, 2007:461). Maringe’s (2004) study conducted in Zimbabwean’s HE (a developing country) also raised concern for technology and the infrastructure and use of technology at universities.

China and India each have a growing middle class prepared to pay for higher education. They have access to TV and the Internet and know what HE international opportunities exist (Dickson, 2009:178).

2.3.2 Funding

In most developing countries (as is the case in some developed countries), the governments cannot fund provisional excess demand and this results in the growth of private HEIs (Dickson, 2009:178). During 2010 in Argentina (an upper middle-

46 | income country like South Africa), there were 398 876 students at private universities vs 1 556 205 students at public universities (Wolhuter, Higgs (P.P), Higgs (L.G) & Ntshoe, 2010:210).

Maringe’s (2004) study in Zimbabwe identified eight challenges that universities were facing. Funding was one of these challenges. It was noted in this particular study that the neo-liberal universities considered funding as the primary challenge, which is consistent with their product-focused philosophy where quality and excellence are a direct result of external inputs such as finance.

A trend in many developed and developing countries is that the growth in public funding of HE is not keeping pace with the high levels of private investment in the sector. This funding issue, brings forth a more receptive environment for private and commercial providers of post-secondary education (Knight, 2003:15). This lack of funding calls for more competition for public universities and more choice for our students in developing countries.

Dwindling state funding (Ramachandran, 2010:545) will even have a greater effect on developing countries’ education systems, as more funding will be required if the need is there for more students to gain access to HE.

2.3.3 Massification

Countries with the largest and fastest growing populations are still facing a huge unmet demand for HE. China has doubled its undergraduate university population in the last five years and, now exceeds the USA in the number of students in its higher education system. Even though China only enrols about 15 per cent of traditional age-eligible students at universities, some believe it to be closer to 20 per cent. It is still far less students compared to the USAs almost 50 per cent and an average for the Organisation for Economic Co-operation and Development (OECD) countries of over 40 per cent (Mellow & Woolis, 2010:309; Dickson, 2009:177; www.oecd.org).

The immense growth potential for HE in some of these countries is exponential, both from increasing demand from students and weight of the size of the population. India only has 11 per cent of school leavers gaining a university place and Vietnam with a

47 | population of over 70 per cent of its people under 30, also faces huge challenges to increase widening participation in HE. China had an increase of 8.3 million enrolments and India had an increase of 5.6 million (Dickson, 2009:177).

Sub-Saharan Africa has the lowest gross enrolment ratio (GER) at primary, secondary and tertiary levels of all the major regions of the world (Mpinganjira, 2011a:168). In Africa, the economics of developing elite institutions is not an effective way to move millions of very undereducated individuals towards a skill set that would promote a viable, long-term economic development strategy (Mellow & Woolis, 2010:310). Thus, Mellow and Woolis (2010) predict that establishment of community colleges or similar institutions of HE will be seen as a viable response to educate these students. This trend for ‘community-type’ HE creates an opportunity for the private or for profit HE sectors in China, India and other developing economies (Mellow & Woolis, 2010:310). Traditional or public universities would most probably experience an increase in competition.

With the General Agreement on Trade in Services (GATS) supporting the increasing international trade that will help countries satisfy the growing demand for education, these traditional or state-funded universities even in developing economies, will experience more threat of new ‘educational players’ entering their markets (www.wto.org). GATS supporters believe that increased student access to education and training is one of the strong rationales and articulated benefits linked to trade liberalisation (Knight, 2003:13).

On a global level there was already in 1998 an international call for greater access to HE (evident in the United Nations Educational, Scientific and Cultural Organisation’s (UNESCO) World Conference on Higher Education at that time). For developed and developing countries the call was there for ‘equality of access’ (Akoojee & Nkomo, 2007:385). Widening access is not just a case of increasing the number of university places. The reason behind this theme of widening access, comprises access for economic, social justice and manpower development reasons. It is also more a case of “developing programmes that customers of universities will be willing to access” (Maringe, 2004:58).

48 | In the past, Africa’s HE sectors have generally struggled to expand their HE access and to widen HE participation. Following independence in many parts of Africa, lower levels of pre-university learning were expanded phenomenally as a result of the “education for all” policy many countries adopted. Africa’s HE sectors have generally not expanded as well as the rest of the world. One of the reasons for this is the World Bank and IMF’s recommendations for Africa to focus mainly on pre-university education, as these institutions argue that primary level education delivers the greatest rate of returns to the economy (Maringe & Carter, 2007:466).

On the other hand, although access to HE hasn’t been improved in African countries, the argument by some academics and politicians is to be cautious to grant access to all because of the possible lowering of standards to achieve this. In Maringe and Foskett’s 2002 Southern African countries’ study, educational marketing practitioners revealed that the second biggest challenge, after image and reputation, was the overwhelming perception that their universities were experiencing a decline in standards (Maringe & Foskett, 2002:42).

2.3.4 The student as the customer

Not much is published around the phenomena of “the student as customer” in developing countries. One can just assume that arguments that were made in general (previously discussed in this chapter) that the customer and focus on the student as customer are just as important for HE in developing countries as well. It was noted by Maringe (2004) that even in a developing country such as Zimbabwe, students would be looking for individual universities to provide products and services that meet their needs in a more discerning way (Maringe, 2004:66).

Maringe and Foskett’s (2002:43) study illustrated that most South African universities view marketing from the perspective of the customer, while those in other countries, like Zimbabwe, consider it from the view of selling or the product.

49 | 2.4. The South African Higher Education landscape

South Africa is a member of the South African Regional Universities Association (SARUA), which was founded in 2005 to assist in the revitalisation and development of the leadership of universities in the southern African region. The aim of SARUA is to consolidate a southern African agenda for HE, enabling HE to make a significant contribution to national and regional development, thus enabling universities in this sector to meaningfully respond to the developmental challenges facing the region (www.sarua.org).

SARUA is a membership-based organisation, which is open to all the public universities of the 14 countries that make up the Southern African Development Community (SADC). These SADC countries (Figure 2.2) include Angola, Botswana, Democratic Republic of Congo, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe (www.sarua.org).

50 | Figure 2.2 Map of the SADC (South African Development Community) countries

Source: The Safari company (2011). (www.thesafaricompany.co.za)

The SADC countries share a common history of political struggle before their respective independence. The economies of these countries are highly unstable and depend largely on agriculture and mining (Maringe & Foskett, 2002:36). Although South Africa shares a similar history with these neighbouring countries, South Africa was one of the last countries on the African continent to attain sovereignty, but it has the longest history of university education and managed to obtain SARUA membership for more universities (Table 2.2) than any other SADC country (www.sarua.org; Maringe & Foskett, 2002:36). At the beginning of 2012, there were 57 universities in the SADC that were SARUA members of which 22 were in South Africa (www.sarua.org). It is thus not surprising that nearly half of SADC mobile students are choosing to study in South Africa (Chien & Chiteng, 2011:4).

51 | Table 2.2 The type and number of HEIs (including universities) in SADC countries

How many Public universities Public SADC funded HE Privately in each funded Total HEIs countries institutions funded HEIs country are a universities and colleges member of SARUA? Angola 1 2 11 14 1 Botswana 1 21 5 27 2 DRC 4 149 227 380 3 Lesotho 1 1 7 9 1 Madagascar 6 23 1 30 1 Malawi 2 7 4 13 2 Mauritius 2 37 - 39 2 Mozambique 3 3 12 18 3 Namibia 1 2 2 5 1 South Africa 17 6 80 103 22 Swaziland 1 1 4 6 1 Tanzania 8 13 12 33 6 Zambia 3 43 32 78 3 Zimbabwe 9 22 3 34 9 Total: 59 360 400 789 57

Source: Adapted from SARUA (Accessed on 6 December 2011 and 12 January 2012 via www.sarua.org)

The history of HE in South Africa does not end with the number of institutions and the longest history of established universities in the SADC region. South African education and HE was characterised by racial segregation, fragmentation and inequalities. Change was inevitable when the new government came into power in 1994 (Matoti, 2010:568).

Since the end of apartheid in 1994, South Africa has been transforming its HE to rid itself of its apartheid past (Mpinganjira, 2011a:170). Post-apartheid, the new government implemented a restructuring of HE aimed at strengthening the sector and changing it to be more focused and efficient, within a framework of policies and regulations that included: the 1997 Higher Education Act and the 2001 National Plan for Higher Education (Fish, 2011:13).

The restructuring resulted in many changes being implemented, such as the National Qualifications Framework (NQF) and mergers between universities were introduced during 2000- 2004 (Bonnema & Van der Waldt, 2008:314). In 2010 the Department of HE produced the Higher Education and Training Laws Amendment Bill and a

52 | Strategic Plan 2010-2015 in order to clarify the roles and responsibilities of all parties within this new departmental structure (Fish, 2011:13).

The NQF was introduced to regulate programmes (Bonnema & Van der Waldt, 2008:314) and mergers and incorporations were introduced to make the system more efficient – to reduce wastage that were caused by duplication of programmes (Le Grange, 2011:5).

The total number of universities was reduced from 36 to 23 institutions in 2004 (Ferreira, 2010:3). This reduction in the number of universities was initiated firstly by the National Plan for Higher Education that stipulated that funding should be directed to a more “equitable framework in accordance with the democratic ethos”. To achieve this, a restructuring took place (Mählck & Thaver, 2010:27). Technikons were re-designated as Universities of Technology and some other HEIs were merged or re-designed. Universities have been reorganised to meet the needs of different target markets in South Africa and with the intention of reaching government’s objectives for equity, efficiency and development (Bonnema & Van der Waldt, 2008:315). This new HE system comprises three institution types namely 11 universities, 6 universities of technology and 6 comprehensive universities (providing theoretical and vocational training) (Table 2.3) (Council of Higher Education, 2011; Fish, 2011:13; Mählck & Thaver, 2010:27; Du Plessis, 2005:1379).

In 2011, 893 024 students enrolled at the 23 public higher education institutions in South Africa (Fish, 2011:14)(Table 2.3), but these public universities are experiencing increasing threat from ‘other’ and private HEIs. These public institutions compete with just over 100 private higher education institutions, not all of which are officially accredited (Mählck & Thaver, 2010:27). There are 87 registered and 27 provisionally registered private HEIs (Council of Higher Education, 2011).

53 | Table 2.3 The South African Higher Education Landscape - Student Population in 2010.

Student Type Name population

1 Universities University of Cape Town 24 674 2 Rhodes University 7 222 3 University of Pretoria 57 115 4 University of the Free State 29 902 5 University of Fort Hare 10 735 6 North-West University 55 732 7 University of KwaZulu-Natal 41 244 8 University of Limpopo 18 177 9 University of the Western Cape 18 009 10 Stellenbosch University 27 372 11 University of the Witwatersrand 29 745 12 Universities of Technology Cape Peninsula University of Technology 32 167 13 Central University of Technology 12 581 14 Durban University of Technology 25 184 15 Tshwane University of Technology 51 797 16 Mangosuthu University of Technology 10 033 17 Vaal University of Technology 21 423 18 Comprehensive Universities Nelson Mandela Metropolitan University 26 123 19 UNISA 293 238 20 University of Johannesburg 48 373 21 University of Venda 10 679 22 University of Zululand 14 727 23 Walter Sisulu University 26 772 TOTAL 893 024

Source: Adapted from Fish (2011:14). (www.ieasa.studysa.org)

The South African HE sector is not exempt from the trends and challenges that universities globally experience. South African universities opened up to the world with the end of apartheid and sanctions (MacGregor, 2007) and are also faced with issues of globalisation (2.4.1), changes in government funding (2.4.2), broadening access to HE (2.4.3), changing student profiles, the emergence of the student as the customer (2.4.4), as well as mergers and increased competition (Wiese et al., 2010:151; Akoojee & Nkomo, 2007).

2.4.1. Globalisation

As noted in the previous two sections, Dickson’s (2009) ‘measures’ or three reasons that are an indication of the increasing impact of globalisation on the HE sector, will also be applied to the South African situation.

54 | 2.4.1.1 The increasing number of students studying outside their own countries

The trend of an increasing number of students entering ‘new’ and other markets outside of their own countries is also as a result of the introduction of GATS (General Agreement on Trade in Services) in 1995. The whole purpose of GATS, as stated by the World Trade Organisation (WTO), is to reduce or eliminate barriers to trade, also in education. Traditionally, South Africa was, and still is, cautious and apprehensive about the impact of trade. The view is that South Africa should remain vigilant to ensure that increased trade in education does not undermine their own national efforts to transform HE. The South African public sector should also be strengthening so that it can effectively participate in an increasingly globalising environment (Knight, 2003:8,10).

If GATS is the sole reason for an increase in international students entering South Africa, which is debateable, it seems however as if South African HE has also benefited from the introduction of GATS. One of these benefits emanating from a vibrant HE sector is the global partnerships that are forged and enable universities in South Africa to gain access to the vast resources available in first-world countries through co-operation agreements with universities situated in these countries (Malaza, 2011:7). In addition, the International Education Association of South Africa (IEASA), a non-profit organisation, was established as a result of the need for universities and universities of technology in South Africa to respond to the introduction of GATS (www.ieasa.studysa.org).

Established in 1997, The International Education Association of South Africa (IEASA) was specifically formed to respond to international educational trends. The IEASA’s viewpoint is that if South Africa is to remain competitive within the global economic environment, it is important that its HE provides opportunities for students to obtain a global perspective to their studies. It also means that South Africa can open up international contacts which in turn will benefit students, its universities and will assist South Africa to be a competitor and participant in world markets (www.ieasa.studysa.org).

55 | IEASA’s goals are to: o Create opportunities for South African students to take advantage of exchange agreements and study abroad; o Liaise with organisations which are involved in promoting international education cooperation; and o Help institutional members to assist their South African students returning from a study period abroad to re-assimilate into South Africa (International Education Association of South Africa) (www.ieasa.studysa.org).

Globalisation affects the mobility of South African HE by providing opportunities for South African students to study abroad or at least to consider studying abroad, to export educational services, and for receiving international students.

 South African students studying abroad

Exact data on how many students are leaving South Africa to study outside its borders is scarce. In a study conducted by the University of Johannesburg (Lubbe, 2009) 2% of the grade 12 scholars (in Gauteng) who intended to further their education at a university or university of technology, revealed that intended to further their education outside South Africa. The reason given by these students for opting outside South Africa’s borders was in most instances to study music.

 South Africa as an exporter of educational services

South Africa is playing a leading role in the exporting of HE service in Africa (Mpinganjira, 2011a:170). Kwaramba (2012:1) investigated the extent to which South Africa has strategically marketed its educational services and he found that South African universities’ export of HE services has been modelled mostly in line with three of the four modes of supply identified in the World Trade Organisation’s (WTO) General Agreement on Trade in Services (GATS). The three modes of supply are (i) cross-border supply, (ii) consumption abroad, (iii) commercial presence, and (iv) presence of natural persons. Table 2.4 provides South African HE examples for each of these four modes of supply (Kwaramba, 2012:1-4).

56 | Table 2.4 Modes of South African HE Services Supply under GATS

Examples for Examples of SA trade in higher Universities exporting GATS mode of supply Description education education services under this Mode Distance Delivery of education, tele- University of South Africa education services education, (UNISA), Stellenbosch Mode 1 – from South Africa to education testing University, Rhodes Cross-border supply an importing SADC services and University, University of country education via Cape Town Internet University of Cape Town (UCT); University of Movement of Pretoria (UP); students from SADC students Witwatersrand University; Mode 2 – importing SADC studying at South Stellenbosch University, Consumption abroad countries to South African universities UNISA, Africa to obtain in South Africa University of Western education services Cape, North-West University, University of KwaZulu Natal South Africa’s course offerings Establishment of through branch local unit of campuses or Mode 3 – institution from UNISA subsidiaries of Commercial presence South Africa in institutions, importing SADC franchising, countries twinning/articulated and arrangements Temporary movement of teachers, lecturers, Mode 4 – and education Teacher exchange UNISA Presence of natural personnel from programmes persons South Africa to SADC country to provide education services

Source: Kwaramba (2012: 4). (Note: although more universities cater for SADC students, these are the examples that Kwaramba (2012:4) highlight).

 South Africa receiving international students

The Protocol on Education and Training was produced in 1997 from the Southern Africa Development Community (SADC) that suggests “Member States agree to recommend to universities and other tertiary institutions in their countries to reserve at least 5% of admission for students from SADC nations, other than their own”. Although not enforceable, South Africa reached that target in 2003 and has maintained it to date with 5% of our student cohort coming from the SADC region (Fish, 2011:9).

57 | The number of international students at South Africa’s 23 public universities has increased significantly since 1994 from 12 557 (Fish, 2011:9; MacGregor, 2007) to 66 137 in 2010 (Fish, 2011:9) (Table 2.5). During 2010, the total number of non- South African students contributed to 7.25% of the grand total (893 024) of South Africa’s HE student population (Fish, 2011:9).

Table 2.5 Increase in HE student numbers in South Africa

Year 1994 1997 2000 2003 2006 2008 2009 2010

SADC 6209 7822 21 318 36 207 35 917 45 851 41 906 46 496 NON-SADC 1521 2079 4263 6664 8569 9554 10 663 10 986 Africa total Rest of the 4827 5268 5568 7108 7673 6619 7011 7302 World No 14 228 1447 1574 1928 1276 1353 Information Grand total 591 161 717 793 741 383 799 490 837 779 893 024 % Non-South 7.68 7.16 7.25 7.76 7.31 7.25 African

Source: Department of Higher Education and Training (2011). Provisional figures for 2010 (Fish, 2011:9)

South Africa is steadily growing to be a place of choice for international students. Although most of the country’s international students are mostly from SADC countries and secondly from the rest of Africa excluding SADC (Table 2.5), an Open Doors Report (2011:2) reported South Africa as the 13th most popular destination to study for US students. In the 2010 OECD (Organisation for Economic Co-operation and Development) report, South Africa ranked 11th in the world as a preferred destination for international students and was the only African country that features (Fish, 2011:10).

Seventy-two per cent of South Africa’s international students are from SADC countries and seventeen per cent from the rest of Africa because it is close geographically, uses English as the primary language of instruction, charges lower fees than many developed nations, and has a lower cost of living (Fish, 2011:10). South African universities also offer internationally recognised academic qualifications, are highly rated in terms of local availability of research and training institutions, quality of scientific research institutions, quality of overall infrastructure and institutions (Kwaramba, 2012:1).

58 | Although South Africa tries hard to market the country as an international study destination and in particular as an ‘African International Students Alternative’, competition in the international student market is fierce (Mpinganjira, 2011a:170). Even on South African soil, international institutions such as St. Augustine and Monash South Africa are offering a range of degree and postgraduate qualifications and compete directly with South Africa’s 23 public HEIs. The Independent Institute of Education, Damelin and Midrand Graduate Institute, as well as major providers of advanced certificates and diplomas such as Lyceum College and City Varsity are but some of the private HEIs that are alternatives to South African as well as international students (Fish, 2011:19). To attract more and suitable students will only happen if South African HEIs know how to meet the educational needs and wants of the market (Mpinganjira, 2011a:170).

One of the primary effects of globalisation on HE is that HE systems are compared with and measured against each other. It brings to the fore the question as to how ready South African HE is, and how strong the South African academic profession is compared to the international context (Wolhuter et al., 2010: 197). According to Jooste (2011:33), South African HE is ready to compete for international students and he argues that the world recognises the quality and attractiveness of South African HEIs. The IEASA purposefully put a marketing strategy in place to market South African HE globally, and formally introduced IEASA with it.

2.4.1.2 English as the dominant and sometimes default language

In the past, much of the inequality and inaccessibility of HEIs in South Africa originated from class distinctions and discrimination based on language. Linguistic abilities have been seen as a barrier to access and success in HE, mainly because African and other languages have not been developed as academic languages. For the majority of students, entering HE at that stage was problematic as they were not fully proficient in English or Afrikaans (Mählck & Thaver, 2010:33; Wiese et al., 2010:153).

Today though, English is the communication medium in the South African Higher Educational setting, although not the majority’s mother tongue. As mentioned before,

59 | most South African universities offer their courses in English. Some offer core programmes in another language (such as Afrikaans) as commitment to the particular community (Wiese et al., 2010:153).

English as dominant language of instruction thus lifts barriers to entry to international students planning to study in South Africa.

2.4.1.3 The emergence of the Internet

On average, Africa and South Africa do not compare at the same high levels as the rest of the world with Internet usage. In 2010, the number of South African Internet users passed the five-million mark for the first time, finally breaking through the 10% mark in Internet penetration for the country (www.gcis.gov.za, World Wide Worx, 2011). This Internet penetration stood at 13.9% for South Africa in 2011 compared to the entire Africa (including South Africa) at 11.4%, North America’s 78.3%, Oceania/Australia with 60.1% and Europe with 58.3% (Internet World Stats, 2011) (Table 2.6).

Table 2.6 A summary of the world’s Internet usage statistics

Penetra- Internet Internet Growth Users World tion % Population Users Dec Users Jun 2000- % of Regions Popula- 2000 2011 2011 % Table tion Africa 1037 524 058 4 514 400 118 609 620 11.4 2 527.4 5.7 Asia 3 879 740 877 114 304 000 922 329 554 23.8 706.9 44.0 Europe 816 426 346 105 096 093 476 213 935 58.3 353.1 22.7 Middle East 216 258 843 3 284 800 68 553 666 31.7 1 987 3.3 North 347 394 870 108 096 800 272 066 000 78.3 151.7 13.0 America Latin America/ 597 283 165 18 068 919 215 939 400 36.2 1037.4 10.3 Caribbean Oceania/ 35 426 995 7 620 480 21 293 830 60.1 179.4 1.0 Australia World 6 930 055 154 360 985 492 2 095 006 005 30.2 480.4 100 Total

Source: Adapted from: Internet usage statistics (2011). Available at: http://www.internetworldstats.com/stats1.htm (Accessed on 24 January 2012).

Compared to the rest of Africa, South Africa still lags behind to Egypt’s 24.5% and Nigeria’s 28.35% Internet penetration as a percentage of the population’s usage at the same time (Internet World Stats, 2011) (Table 2.7). It is a bit surprising that

60 | South Africa lags behind two other African countries, when it has the most developed telecommunications network in Africa, with a network that is 99% digital and includes the latest in fixed-line, wireless and satellite communications (www.gcis.gov.za, 2010/2011).

Table 2.7 A summary of Africa’s Internet usage statistics

Penetra- Internet Internet Facebook tion % Users % Country Population Users Users Jun 31 Dec Popula- Africa Dec 2000 2011 2011 tion Egypt 82 079 636 450 000 20 136 000 24.5 16.9 9 391 580 Nigeria 155 215 573 200 000 43 982 000 28.35 37.0% 4 369 740 South Africa 49 004 031 2 400 000 6 800 000 13.9 5.7% 4 822 820 Total Africa 1 037 524 058 4 514 400 118 848 060 11.5 100 37 739 380

Source: Adapted from: Internet usage statistics (2011). Available at: http://www.internetworldstats.com/stats1.htm. (Accessed on 24 January 2012)

In South Africa, a mere 0.98 per 100 people were broadband subscribers (www.tradingeconomics.com) in 2009, an increase compared to 0.35 per 100 people in 2008 (Wolhuter et al., 2010:205). The country’s fixed line broadband penetration rate of 1.4% is however far behind the OECD’s 24.9%, a problem which the Department of Communications plans to solve through Local Loop Unbundling (LLU). South Africa’s wireless broadband penetration is better, but at a rate of just over 5.5% it is still lagging behind when compared with the OECD’s average of 36.7% (OECD, 2010).

According Blignaut (2009:581), there is an awareness of the need for “access to technology,” however, the digital divide is not only a matter of access; the challenge is also to empower people to become proficient computer users, even those with general literacy backlogs. Demographic aspects such as age, gender, education and socio-economic status affect Internet usage patterns. The long-term solution to solve the problem of the digital divide is to uplift the socio-economic standard of a community (Blignaut, 2009: 581).

The Internet is often seen as a value-neutral tool, meaning that it potentially allows individuals to overcome the constraints of traditional ‘elitist’ spaces and gain unhindered access to learning. It is suggested that online technologies can help

61 | address issues of educational equity and social exclusion, and open up democratic and accessible educational opportunities. Internet access and usage can reduce the cost of reaching and educating large numbers of students who are currently missing out on education (Gulati, 2008:1).

Distance education is an example of how the Internet can be used in reaching potential HE students. Distance education was driven originally mainly by the private sector (after the mid-nineteenth century), which applied printing and postal service technologies to create correspondence education (Walsh, 2009:13). eDegree is an example of a South African public company that provides e-learning and online learning support by establishing partnerships with existing reputable HEIs in South African and abroad (www.edegree.co.za). The extremely successful University of South Africa (UNISA) is another example, and the oldest of the world’s distance education universities (Walsh, 2009:13). UNISA uses flexible and accessible distance education by applying computer-aided instruction and learning and web- based learning in the milieu of e-learning. It is still though problematic for UNISA that many of their students still lack broadband Internet access that hinders that type of learning (De Villiers, 2007:455).

Although universities find it difficult to ‘access their students’ because of broadband and/or Internet access in general, South Africans will reach a crucial moment in the evolution of the Internet as it is becoming more affordable and more accessible as people from a variety of economic backgrounds are increasingly accessing the Internet using their cellphones (Goldstuck, 2011).

2.4.2. Funding in South African HE

During 2011, the Department of Higher Education and Training was in the process to review the funding of universities with the aim of streamlining the funding formula to achieve greater efficiency and parity for all universities. For many years funding was declined in terms of the proportion of total state finance committed to HE. This forced universities to raise tuition fees and at the same time as student numbers grew and staff numbers remained the same, it put a lot of pressure on universities to source their own funding by means of third-stream income (Fish, 2011:17).

62 | Universities have three primary sources of funding: government, student fees and donations and entrepreneurial activities providing for ‘third-stream’ income. Direct funding to universities is based on research graduates and publication outputs, teaching outputs weighted by qualification level, student numbers weighted by study fields, and course levels. The number of poor students also plays a role, where government allocates earmarked grants of infrastructural funding for institutions that have high numbers of poor students. A large part of the “earmarked funding” is directed towards supporting National Student Financial Aid Scheme (NSFAS) funding (Fish, 2011:17).

The government-funded NSFAS was formed in 1999 and has played a critical role in enabling financially disadvantaged students to access higher education. It is estimated that study bursaries and loans worth R4.7 billion are expected to be awarded to students in 2011/2012, which should benefit over 150 000 students (Fish, 2011:15). The government also introduced occupation-specific bursaries (e.g. the Fundza Lushaka bursaries for initial teacher education), and some South African universities also provide recruitment bursaries - all examples of attempts to provide wider access to, and participation in, HE (Le Grange, 2011:5-6; Maringe & Foskett, 2002:36).

During the financial year 2010/2011 the Department of Higher Education and Training (DHET) appropriated R23 776.202 million to Higher Education and Training of which R23 752.243 million was spent. Subsidies to HEIs during 2010/2011 amounted to R17 516.740 million and R 1 909.359 million to the NSFAS fund (DHET, 2010/2011:31, 123).

During 2011, education in totality took up the largest share of government spending – 21% of non-interest allocations – and received the largest share of the additional allocations (The Budget Speech, 2011). The National Treasury commented that “education and skills development are the first priority in government expenditure allocations”. For Higher Education specifically, it was possible for the Government to increase this budget substantially because of an unprecedented agreement within government that all departments would accept a 0,3% cut off their budgets (Sapa, 2011).

63 | This cut amounted to about R6 million that was allocated to the Minister of HE for FET (Further Education and Training) colleges and for the student financial fund (Sapa, 2011).

The total amount of spending on education as a percentage of GDP for South Africa is 5.4% (UNESCO, 2011) (Table 2.8). On average this compares to OECD countries that spend 6.2% of their collective GDP on education, but levels vary greatly between countries. Denmark, Iceland and the USA, Israel and the Russian Federation spend 7% of their collective GDP on education, whereas 4.5% is allocated in Italy and the Slovak Republic (OECD, 2010:56).

Of South Africa’s total education budget, 13% is spent on tertiary or HE compared to 40.5% on primary education and 31.4% of the total education budget on secondary education (UNESCO, 2011) (Table 2.8). Although not all the SADC countries’ information was available in the UNESCO (2011) report, it seems from the data that Botswana (43.5%), Lesotho (36.8%), Madagascar (17.1%), Mozambique (14.3%) and Swaziland (22.7%) all spend more on their HE as a percentage of their respective education budgets than South Africa (Table 2.8).

Table 2.8 Education spending of SADC countries

Ratio of Spending on Spending on Spending on Spending on spending per SADC education primary secondary tertiary (HE) student countries (as % of education (% education (% (% of total) tertiary GDP) of total) of total) (HE)/Primary Angola 2.6 27.6 42.7 8.2 20.6 Botswana 7.9 19.2 31.3 43.5 34.1 DRC No information available Lesotho 12.4 35.7 20.8 36.8 49.1 Madagascar 3 47.2 20.1 17.1 23.7 Malawi No information available Mauritius 3.2 27.9 42.6 11.3 2.2 Mozambique 5 56.2 28.5 14.3 5.8 Namibia 6.4 No information available South Africa 5.4 40.5 31.4 13 1.2 Swaziland 7.8 35 30.2 22.7 2.4 Tanzania 6.8 No information available Zambia 1.3 No information available Zimbabwe No information available . Source: adapted from UNESCO (2011). Available at The Guardian (UK) at: http://www.guardian.co.uk/news/datablog/2011/apr/27/africa-education-spending-aid-data. (Accessed on 29 January 2012)

64 | With 23 public universities in South Africa, universities are facing increasing competition for funding and as a result they are responding by trying to make their programmes more relevant for the South African market. Because universities in South Africa are experiencing increased pressure from government on broader access that implies that universities have to ensure that they recruit and attract students from different ethnic orientations and gender groups, they are eliminating activities that are not commercially profitable (Wiese et al., 2010:151).

Widening access to South African universities will pressurise universities even more for funding. On the one hand, South African universities have to reserve an allocation for admitting SADC students who receive the same government subsidy as local students and are charged the same fees (plus a modest international levy), while subsidy from the government is also heavily linked to pass rates (Fish, 2011:9, 11).

Low pass rates (also called throughput rates in university jargon) means less money in the pockets of universities. It also means undesirable outcomes such as dropping courses from the academic offering, closing departments, less money to spend on sports facilities and fewer rugby bursaries. It is therefore in South African universities’ best interest to make sure as many students as possible pass, as the university funding formula is based on the number of students who graduate. Universities are somewhat incentivised to push as many undergraduate students through the system as soon as possible, as the ‘reward’ for universities is subsidies from the state (Boshoff, 2012).

The NPC’s (National Planning Commission) National Development Plan for HE stated a continuous effort to make wider access and participation in HE possible by providing financial assistance. In this National Development Plan for HE, the vision for 2030 HE in South Africa was presented and included that every student in public HE will have access to an appropriate financial assistance package of bursaries, loans and service-linked scholarships depending on personal circumstances (Badsha & Cloete, 2011:3).

65 | 2.4.3. Massification in South African HE

Massification was earlier explained as the general mass demand for HE, widening access to HE and the ‘right’ to be equally educated, thus integrating students with various capabilities into a similar standard (Parhizgar, 2010:78).

In recent years, South African HE has seen increased student access to universities and the demise of apartheid has increased the urgency of greater participation into South African universities (Akoojee & Nkomo, 2007:385). It is for this reason of increasing students’ access to universities that the Department of Education split into Basic Education and the Department of Higher Education and Training in 2009 (Fish, 2011:12).

Since 2000, South African HE has experienced student enrolment growth of an average of about 4.2% per year. This figure takes into account a negative growth, which took place between 2004 and 2005 during the merger of higher education institutions. Between 2005 and 2009, student enrolment growth slowed to 2.3% per annum, however between 2010 and 2011, the figure shot up to 6.2% or 55 000 additional students in the system during the last year. The increase of 6.2% is not due to more international students as the number of international students present in the system has remained stable at 7% (Fish, 2011:12).

South Africa had a public HE participation* rate (* is the enrolment as a proportion of the 20 to 24-year-old cohort) of 17% in 2010 (Table 2.9). This is much higher than the average of 6% for sub-Saharan African countries, but significantly lower than that for comparable middle-income countries. (National Development Plan, 2011:273). The top 40 countries with high participation rates range from South Korea at 98% to Chile, Slovak Republic and Lebanon at around 50%. A new recalculation of the South African Tertiary Education enrolment ratio that includes post-secondary public and private education, shows an enrolment ratio closer to 20% (Badsha & Cloete, 2011:8; Wiese et al., 2010:151).

The main goal previously behind the expansion of student numbers and improving access to HE, was initially for disadvantaged black people to overcome apartheid

66 | inequalities, creating a stable society and producing the skills needed to drive economic growth. Universities were required to enrol many more students of all race groups. Student numbers have nearly doubled in the past 17 years, from 473 000 in 1993 to some 893 024 in 2010 according to the Department of Higher Education (Fish, 2011:14, 15) (Table 2.5).

The arguably ‘low’ HE enrolment ratio in South Africa will result in pressure to expand HE in South Africa to continue (Wolhuter et al., 2010:206). The National Plan for Public Higher Education is to increase participation in HE to 20% by 2014-2016. The school system would have to produce an additional 173 000 matriculants qualified to enter HE between 2010 and 2016 to reach this participation target, an increase of 19% (Badsha & Cloete, 2011:9; National Development Plan, 2011:276- 278).

Historically, expansion of HE in South Africa and the Southern African regions took place mainly because of the following reasons: o as a response to post-independence and a generally limited educational access for the majority groups in the population; o an ever-increasing demand for more and more education as people saw it as a way to escape from poverty; o universities attempted to make place and space for a broader base of students (Maringe & Foskett, 2002:36) o Through expansion, the higher education arena has seen the ‘massification’ of the sector, including students who wouldn’t have been necessarily included in previous decades (Mählck & Thaver, 2010:25). Most of these reasons still apply as catalysts for HE expansion today.

To accommodate the projected possible expansion and thus increase to 1 067 776 enrolments by 2016, the Department of Higher Education and Training (DHET) has identified the need to increase learning opportunities after school. The Green Paper on post-schooling proposes creating different types of institutions to meet the high demand for education and training. There are also new planned universities in Mpumalanga and the Northern Cape, and the new medical school in Limpopo that

67 | will contribute to the expansion of capacity in the HE sector (Badsha & Cloete, 2011:9; National Development Plan, 2011:276-278) (Table 2.9).

Table 2.9 Participation rate and anticipated/projected growth of HE enrolment, 2008 – 2016

Actual Actual Actual Actual Actual Actual Actual Year 2008 2009 2010 2011 2012* 2013* 2016* Public HE headcount 799 893 838 250 894 074 908 684 944 643 983 471 1 067 776 enrolment Gross participation rate as 16.6% 17% 17.8% 17.9% 18.4% 19.0% 20% defined by UNESCO

*Assuming a cohort growth rate of 1% pa Source: Adapted from Badsha and Cloete (2011:9)

There are three terms that the American HE researcher Trow (2005) introduced that can be used in characterising the HE expansion trends. These terms are: (1) the elite HE system, (2) the mass HE system, and (3) Universal HE system. These three HE systems are categorised according to the enrolment ratio in a country (Wolhuter et al., 2010:202-203; Teichler, 2004:4). o The Elite HE system, where gross tertiary HE enrolment ratios are 15% and less. This system is shaping the mind and character of a ruling class, in preparation for elite roles. In an elite system, education is seen as a privilege of the few to join an exclusive club (Wolhuter et al., 2010:202-203; Trow, 2005; Teichler, 2004:4). ‘Factor driven’ countries that compete based on their factor endowments; primary unskilled labour and natural resources, are countries that with low gross tertiary education enrolment rates are designated as ‘elite’ HE systems. These are countries like Ghana (6.2%), Kenya (4.1%), Mozambique (1.5%), Tanzania (1.5%) and Uganda (3.7%)(Badsha & Cloete, 2011:7-8). o The Mass HE system, where gross HE enrolment ratios are between 15% and 50%. This system sees the transmission of skills and preparation for a broader range of technical and economic elite roles. Education is seen by students in the mass HE system as a right among many classes of society (Wolhuter et al.,

68 | 2010:202-203; Trow, 2005; Teichler, 2004:4) A country like Botswana, with 26% tertiary education enrolment rate will fall in this category, as well as South Africa with 17%. These countries are efficiency-driven and have to increase efficient production processes and product quality (Badsha & Cloete, 2011:7-8). o The Universal HE system where gross HE enrolment ratios exceed 50% they (typically belong to the world’s high income countries). This system describes the adaptation of the “whole population” to rapid social and technological change. With universal higher education, many groups begin to see it as an obligation to attend; non-attendance is perceived as a weakness or a problem, which also impacts hiring practices (Wolhuter et al., 2010:202/3; Trow, 2005; Teichler, 2004:4). Innovation-driven countries that compete through innovation, producing new and different goods by combining sophisticated production processes with a high skill work force, fall into this classification. The following countries with above 50% enrolment ratios are but a few examples of the universal HE system: Finland (94.4%), South Korea (98.1%) and the United States of America (82.9%) (Badsha & Cloete, 2011:7-8).

These three stages were first introduced by Trow in 1973, and are not presented as empirical descriptions of real higher education systems, but rather as models or ideal types. They can be seen as sequential stages, but it is not inevitable that later stages will completely replace earlier ones. Trow (2005), argues that “there are definite possibilities of examples of elite forms surviving in the mass and universal stages.” Trow (Teichler, 2004:4) further explains that mass higher education developed different characteristics alongside the persisting ‘elite education’ in order to protect the elite sector from the pressures and consequences of mass higher education.

Some of the consequences of mass HE in South Africa is the influx of ill-prepared students for university study, with negative effects on efficiency and student throughput that remain a major concern (Fish, 2011:15; Wolhuter et al., 2010:206), even when increased investments in the HE system have been seen. Students that South African universities enrol are more than likely to be underprepared for the ‘rigors of HE’ and will require institution-wide measures to ensure their success (Akoojee & Nkomo, 2007:396). Johnson (2010:5) stated that “there is a flow of

69 | uneducable students being poured into the universities, lowering standards”. High school teachers also raised their concern about the declining quality of education in South Africa that includes the issue of underprepared learners resulting from the pressure on schools to achieve high pass rates. Learners are promoted to higher classes even if they are not intellectually ready (Matoti, 2010:577). Some of these unprepared, not ready students then enter South Africa’s HE system, a factor that further raises questions about the quality of programmes and degrees that are awarded.

The greater access (and investment) has not produced better outcomes in the level of academic performance or graduation rates. The quality of education has remained poor although enrolment and attainment gaps have narrowed across different race groups (National Development Plan, 2011:273).

Poor performance is evident in the statistics showing that only 1 in 5 first-time entering students graduated in regulation time. Well under one‐third of the intake completed in regulation time, even when UNISA was excluded from the figures. This picture of ‘poor graduates’ is further compounded by reports of increasing numbers of students with marginal passes of 50- 55%. These low pass rates at university level have implications in particular for the pipeline into postgraduate studies (Badsha & Cloete, 2011:10).

In 2009, 316 320 (43%) students who had borrowed money from NSFAS had dropped out without completing their studies. Overall, HE in South Africa has a worrying 45% drop-out rate among students, undermining the access gains of universities. It is mostly ‘first generation’ students from low-income, less educated families who are most likely to drop out. To counteract drop-out rates, students entering university for the first time were during 2007 and 2009 enrolled in foundation programmes. In 2010, foundation programmes were provided for 15 863 students (Fish, 2011:15).

Akoojee and Nkomo (2007:385) argue that access to HE and quality should not exist in contradiction to one another. These authors argue that quality in South African HE should be understood within a context of redress, equity and access, which has as

70 | its objective the transformation of society. Quality and access issues cannot be separated if committed to transformation.

Prof Jonathan Jansen, Vice Chancellor of the University of the Free State, wrote in the Weekender of 17 October 2009: “A university is not a welfare organisation. It is not a FET (further education and training) college. It is not a giant compensatory programme for students who have crawled over the matric finishing line, demanding to study for a degree. A university is an institution of higher learning, serving the best available talent in the nation and beyond” (Boshoff, 2012).

2.4.4. The student as the customer

Ramachandran (2010:547) argues that knowledge of the customer is fundamental to successful marketing of universities. He continues that it is important to recognise the student as a customer so that issues can be addressed, instead of getting polarised in debates on the correctness of applying this type of marketing metaphors to the university context. Potential students must be viewed as valuable customers (Moogan, 2011).

The customer concept has emerged from the business world and concern is that this concept in education minimises the nature of relationships between universities and those it relates with. In the business world, relationships with customers are often transactional based, but in education, the relationships are thought to go beyond mere transaction (Maringe, 2004:57). The customers are thus important in the HE context, but do South African universities view their students as customers?

The paucity of information on the view of the student as customer in South African HE perhaps tells the story that students are not readily viewed as customers yet, or studies on this particular subject have not yet been conducted/ are very limited. Saunders (2005:146) has though commented in the South African Journal of Higher Education that the students should not be treated as passive customers or consumers, but rather as active participants. The author argues that segmentation in the HE is needed, but it should be based on the student market needs to ensure that

71 | HEIs offer tailored services that will address the perceptions and expectations of that particular market segment.

One of the possible solutions to reach recruiting strategies that will breach over the social-economic circumstances of different South Africans is to implement marketing strategies that are both informed and heterogeneous (Bonnema & Van der Waldt, 2008:315). Informed strategies can only take place if the student’s needs are studied. Then only can marketing strategies be applied to address the needs that matter.

2.5. Marketing Higher Education Institutions (HEIs)

Kotler and Fox (1995) argue that marketing was often viewed as an alien concept to the education enterprise. This was mostly so, because it originated from business and industry. It was believed at this stage that marketing usually attempted to “mislead, deceive and create false impressions” (Maringe & Foskett, 2002:37). Today, however, many universities with a view to gaining a competitive edge are now gradually applying marketing theories and concepts that have been effective in the business world. University managers should not view marketing as an alien concept imported from the business world, but as a “viable philosophy and strategy for developing a HE sector which meets the needs of home-based and international customers” (Hemsley-Brown & Oplatka, 2006:317, 334).

But what is marketing? Cheung, Yuen (T.W.W), Yuen (C.Y.M.) and Cheng (2010:428) explain that marketing is a process that comprises defining markets, quantifying the needs of the customer segments within these markets, and determining the value proposition to meet these needs. Kotler and Armstrong (2008:5) explain that marketing deals with the customer and defines it as “the process by which companies create value for customers and build strong customer relationships in order to capture value from customers in return”. In a sense it is about satisfying customer needs.

For universities marketing means the same, understanding your customers’ needs, because universities that understand marketing principles often achieve their

72 | objectives more effectively. Universities must also attract resources, motivate employees, and find customers. Marketing can help universities to achieve four principal benefits: o Greater success in fulfilling the universities’ mission; o Improved satisfaction of the universities’ publics and markets; o Improved attraction of marketing resources in striving to satisfy their customers; and o Improved efficiency in marketing activities (Kotler & Fox, 1995:26)

It can be argued that efficiency in marketing has become important to universities because of increased competition that is a reality for universities. Education has become a global industry and universities have become involved in international education (as an example) for financial and other reasons (as discussed earlier). University managers will need to act strategically to secure a competitive advantage and it requires marketing commitment to achieve this (Mazzarol & Soutar, 2008).

There is a larger number of universities for students to choose from and a greater freedom to choose among universities. Traditional universities experience the threat of virtual programmes, distance learning, ‘site-less’ universities and related entrepreneurial approaches to study and training which exist. Universities compete for students and for funding, and this all is happening in a changing environment with demographic shifts and the impact that internationalisation and globalisation has on the HE environment. All these factors necessitate that universities develop strategies for increased market share (i.e. more students or/and quality of enrolments), growth and survival. In the past, the role of universities was to serve the community; to develop a knowledge society, but all these environmental trends and changes have now forced educators to take a hard look at marketing (Ramachandran, 2010; Akonkwa, 2009:315; Helgesen, 2008:50; Hemsley-Brown & Oplatka, 2006:317; Edgerton, Holm, Daspit & Farber, 2005:118; Naude & Ivy, 1999:126).

Survival of universities depends on retaining customers (students) and maintaining a high number of enrolments (Helgesen, 2008:50; Navarro et al., 2005:506). If universities do not recognise the new demands and adapt their strategies to overcome challenges, student numbers will dwindle (Akonkwa, 2009:315).

73 | Attracting students and the ‘right’ students can only happen if universities better understand their markets’ needs and wants (Vrontis et al., 2007:979). According to Gater (2001:2-3), the right student is the top quality student or the top-performing student. Business and service organisations, such as universities, seek to achieve a competitive advantage, and this can in part be achieved by being market-driven by anticipating, understanding and responding to the preferences and behaviours of customers, thus determining their needs (Oplatka & Hemsley-Brown, 2007:293).

Universities need to become aware of their students’ needs (Hemsley-Brown & Oplatka, 2006:325) as a student centric marketing approach will focus more on addressing students’ needs than on identifying strategies to sell universities’ own ‘products’ (Ramachandran, 2010:552). This awareness of students’ needs can only happen if universities study their customers. Akonkwa (2009:313) argues that universities will understand their customers better if they follow a marketing orientation and define it as: “the organisation-wide generation of market intelligence pertaining to current and future needs of customers, dissemination of intelligence within the organisation, and responsiveness to it.”

Oplatka and Hemsley-Brown (2006:293) define marketing orientation as the degree to which an organisation generates and uses intelligence about the current and future needs of customers; develops a strategy to satisfy these needs. The strategy should also be implemented to meet those needs and wants.

Identifying, understanding and determining university customers’ needs will enable universities to plan accordingly and to adapt to them (Navarro et al., 2005:506). Understanding of customers (students) is also necessary for segmentation and targeting of universities’ markets (Vrontis et al., 2007:979). When marketing strategies are developed correctly, it can help to attract the most suitable students (Moogan, 2011:3), and it is essential for the in-depth investigation into the causes and effects of customer behaviour within individual segments (Vrontis et al., 2007:979).

Marketing strategies designed to attract sufficient and desired students can be achieved by applying information that has been gathered regarding the student

74 | selection process (Wiese et al., 2010:151). The decision-making factors that are affecting the applicants’ university selections are a priority and need to be investigated. It is the only way to make informed student recruitment decisions (Moogan, 2011:3). Wiese et al. (2010:151) agree and continue that universities that have knowledge about the factors that influence students’ application and enrolment decisions can increase the fit between the students and the university.

Marketing strategies do not stop at just determining potential students’ needs. Marketing should also be used to improve the image of a university, since a university’s image will impact upon potential applicants as well as sponsors and community attitudes (Moogan, 2011). Image and reputation appear to be the most widely perceived marketing challenges to universities (Maringe & Foskett, 2002:41), however, Cubillo et al. (2006:101) argue that it cannot be ignored as, for universities to maintain their competitive advantage they need to develop a distinctive image and positioning. Integrating a university’s marketing goals with its strategic and academic goals is important and it has implications to the university’s visibility and reputation (Ramachandran, 2010:545).

Marketing has become a philosophy and strategy for universities to develop and deliver programmes that meet customers’ needs (Maringe, 2004.58).

2.5.1. Marketing Higher Education Institutions (HEIs) in South Africa

According to Maringe (2004:54), there is evidence that the same forces that have led to the marketisation of HE in the developed world are replicating themselves in universities of developing countries. Developing a marketing orientation in the university sector requires a full appreciation of who the potential customers of university education are (Maringe, 2004:59).

Apart from China and Japan, Africa has more students studying abroad on undergraduate and postgraduate courses than any other country, and the most popular destinations are England, Australia, then the USA, Canada, France, Germany and New Zealand (Maringe & Carter, 2007:460). This ‘outward migration’ from Africa to other countries has serious ramification for Africa and South Africa to compete successfully with these countries’ HEIs, and South Africa should endeavour

75 | rather to attract students to come and study in South Africa. This is a good example of why South African universities will have to become more market-orientated as they increasingly compete for students (and funding) (Wiese et al., 2010:151).

The older and more established universities in South Africa use the term marketing regularly and this may reflect institutionalisation of the marketing function in these universities. Although marketing of universities is present in South Africa, it is still in sharp contrast with the marketing visibility indicators in the developed world. There could be several reasons for this trend, of which one could be a lack of funding or lack of marketing budget (Maringe & Foskett, 2002:40).

The issues of quality and standards appear to be the major preoccupation of the more established or ‘mature’ South African universities. Public relations is the dominant marketing activity, but it includes branding campaigns and customer focused research. This is in direct contradiction to what is seen in the developed world. Marketing at these more established universities is perceived as having the role of image building and reputation (Maringe & Foskett, 2002:48). It has already been established that the image of a university is definitely important since this image will impact upon potential students’ application decisions (Moogan, 2011). The question that arises is however, what information regarding how and why South African students select a certain university?

Wiese et al. (2010) argue that the process of transformation of HE in South Africa expects institutions to deliver the much-needed graduates for social and economic development, while at the same time addressing equity and diversity. To better understand the South African student market, is one way of possibly achieving these transformation demands. Universities should better understand the student market in terms of the choice factors they consider when deciding on enrolling with a chosen university (Wiese et al., 2010:150; Hemsley-Brown & Oplatka, 2006:331).

Choosing at which university to study is further complicated by the intangible nature of the HE services and its associated benefits. The essence of marketing a university is to communicate and to deliver on the service promise and the customer value offered to the student (Moogan, 2011; Maringe & Carter, 2007:46).

76 | 2.6. Marketing universities in the Service Industry

Services marketing literature appeared for the first time in the early 1980s in the form of anecdotal service research that either extended or replicated existing marketing concepts (Boksberger & Melsen, 2011:229). Literature on education marketing originated in the UK and US at the same time, also in the 1980s. It was mostly theoretical-normative in nature and was based on business sector models. In the 1990s marketing of HE institutions was interpreted within the narrower definition of marketing communication. Later it was recognised that HE was a service with all the ‘hallmarks’ of a service industry, and that marketing services were different from marketing products (Durvasula, Lysonski & Madhavi, 2011:33; Hemsley-Brown & Oplatka, 2006:319; Mazzarol, 1998; Nicholls, Harris, Morgan, Clarke & Sims, 1995).

Naude and Ivy (1999:127) argue that universities deliver product offerings on three levels which include the core, the tangible element, and lastly the intangible element. It is evident from the literature that the third level of intangibility also forms part of one of the most recognised characteristics of HE marketing (Ramachandran, 2010:554; Cubillo et al., 2006:103). Information on the first three levels is provided next.

The universities’ offering is made up of different products that can be regarded as existing on three separate and distinct levels (Figure 2.3): The core: the most basic level; students are not buying degrees, they are buying the benefits that a degree can provide in terms of employment, status, and lifestyle. The second: tangible attributes might include the physical layout of the campus, the library, laboratories, and other facilities. The final level: the augmented level that is made up of intangible attributes such as library membership for graduates, students loans, finance, and employment placement (Naude & Ivy, 1999:127).

Services show special characteristics and marketing the HE sector is quite different from marketing in the commercial sector. Services marketing requires a particular marketing strategy application (Ramachandran, 2010:554; Cubillo et al., 2006:103). Zeithaml, Bitner and Gremler (2006) identified four primary characteristics of services: intangibility, inseparability of production and consumption, heterogeneity

77 | and perishability. Mazzarol (1998:164) argues that all these characteristics can be found in education, and that it produces problems that must be addressed and overcome by deliberate marketing strategies.

Figure 2.3 Three distinct product levels of universities’ offering

 Intangibility

Services cannot be touched, tasted, stored or possessed. The intangibility of a service is usually associated with high levels of risk and it hinders the communication of services to the customer (Ramachandran, 2010:554; Cubillo et al., 2006:103; Mazzarol, 1998:164; Dibb & Simkin, 1993:26). To overcome the intangibility of the service, the customers (students) analyse aspects such as the image of the brand, the institution and the country of destination (Cubillo et al., 2006:103).

78 |  Inseparability of production and consumption

The ‘purchase’ of education usually takes place only once in a lifetime, and it calls for an extreme level of involvement from its customers (the students) (Conway et al., 1994:32). The core service of education is co-created by the ‘supplier’ (the university) with the ‘customer’ (the student). In the HE service, the output is created cooperatively by faculty and staff, and by the students and they ‘create value together’ (Schultz, 2006:24).

It is difficult to separate production from consumption in services. One of the outcomes of this aspect of services is the need to involve the customer in the production of the service, there has to be consumer and producer interaction. Education is the breeding ground where the students participate in their learning process (Mazzarol et al., 2003:92; Mazzarol, 1998:165). For example, students failing to do the necessary advance preparation for tutorials, directly contribute to the quality of service that will be delivered. They form part of the production and delivery processes (Hill, 1995:11).

Universities are part of a services business where the output is created cooperatively by faculty and staff, and by the students and they all create value together (Schultz, 2006:24). Thus, students are at the centre of the production and consumption process that occurs simultaneously (Durvasula et al., 2011:33).

 Heterogeneity

Heterogeneity poses problems in the area of quality control and standardisation (Mazzarol, 1998:165). Human interaction and labour intensity are involved in the delivery of most services, making services heterogeneous, as each service act is unique. This means that service quality can vary considerably from one situation to the next within the same university (Hill, 1995:11).

 Perishability

The perishability of services means that they cannot be placed into an inventory. This causes problems for under or over-supply (Mazzarol, 1998:165). Services

79 | cannot be stored the way physical goods can, and their utility is short-lived and mass production, which can be possible with certain goods, is impossible when compared to services (Hill, 1995:11).

Increasingly, universities are being recognised as operating in a service industry. This recognition resulted in universities placing greater emphasis on meeting the expectations and needs of their participating customers - that is the students (DeShields et al., 2005:129). Emphasis should be placed on the students as customers, as students are viewed as the primary customers of the HE service (Hill, 1995:13). In the paradigm of consumer sovereignty, the focus is on the needs of the recipient of the service, the customer (Naude & Ivy, 1999:127). Education is a people-based performance (Durvasula et al., 2011:33; Mazzarol, 1998) where relationships with customers are becoming increasingly more important (Mazzarol, 1998).

Recent research has put the focus on building strong relationship with students so as to increase satisfaction, loyalty, positive word-of-mouth and retention (Durvasula et al., 2011:34).

The ‘purchase’ of a HE service equates to the promise of future benefit, but the exact rewards are not known at the start of this extended decision-making process with perceived risk being very high for all those parties concerned (Moogan, 2011:3). The challenge for marketers is to determine how the service is being perceived, consumed, or enacted (Durvasula et al., 2011:34).

HE is a pure service and is characterised by a greater amount of interpersonal contact, complexity, divergence and customisation than other service businesses. Most of the quality attributes in HE cannot be perceived, felt, or tested in advance (Cubillo et al., 2006:103). The challenge is further to communicate quality, because as education is a service, recipients of this service are not usually in a position to determine the quality even at the point of experience (Michael, 2005:267).

With intense competition knocking on universities’ doors, differentiating the institution and the service offering is becoming important and to better anticipate the needs of

80 | students is becoming essential issues in the marketing of HE services (Durvasula et al., 2011:35).

Students act more like ‘service customers’ and they seek value (Cheung et al., 2010:429). If today’s South African university fails to meet the desired students’ expectations and needs and fails to recognise that students seek value, they will lose these customers to other competing universities.

2.7. Conclusion

It is argued that HE plays a critical role in enhancing standards of living and increasing longevity. It encourages individuals’ willingness to engage in critical thought and forms the foundation upon which individual countries’ economies are based (Freeman & Thomas, 2005:155). It was brought to light that there is also a surge in the demand for highly skilled and technologically competent individuals, a factor that in exchange sees the rise of private higher education institutions in the developed and the developing countries. The speed with which this unprecedented and unwarranted growth has taken place brought forward privatisation of HE and new types of institutions that provide HE. There is an inverse in online and for-profit private institutions in the wake of massification of HE - all due to the mobility in the wake of globalisation (Gupta, 2008).

Investigating public universities can still be warranted, as in general it still plays a large and important role in the higher education landscape. 80% of America’s 14 million undergraduate students are still being educated at America’s public institutions (Symonds, 2004). In South Africa, just over 800 000 undergraduate students enrolled in 2010 at the 23 public higher education institutions and the National Development Plan forecast that 1 067 776 students will enrol in 2016 (Badsha & Cloete, 2011:9; Council of Higher Education, 2011).

Universities are operating in very different markets than they did even a decade ago. Their overall success or failure is likely to be determined by how well they make the transition from local to regional to global players, while not losing sight of their

81 | educational objectives and their roles as developers and disseminators of knowledge and wisdom (Wood, Tapsall & Soutar, 2005:435).

The effects of globalisation, funding issues and increased scarcity of funds, massification and the trend of widening access coupled with students as customers with specific needs and wants, all impact on the future of South African universities. It cannot be ignored and that is why marketing plays such an important role in universities.

In the face of the increasing global competition for students, a key challenge for universities is to determine how to attract and retain students using marketing methods. In particular, identifying and then satisfying students’ needs. Knowing what students need, helps with the developing of messages to attract prospective students or in tuning and adapting their offering(s) to make them more appealing (Durvasula et al., 2011:35). A good understanding of students’ decision-making processes creates a sound basis for developing curriculum programmes that address the real, rather than the perceived needs (Maringe & Carter, 2007:460).

To market universities effectively, marketers have to assist universities in understanding the decision-making behaviour and thought process of students (Durvasula et al., 2011:35). It is very important for marketers to know the factors influencing the purchase intention of prospective students and to understand the nature of the relationships among those factors. University marketers need to be more aware of the underlying factors considered by customers when evaluating services, especially if they want to survive in this competitive environment (Cubillo et al., 2006:102).

If universities do not actively respond to their potential customer base, others will. This will cause great losses for universities in their reputation as well (Freeman & Thomas, 2005:172).

The quest for best possible students (Ahola & Kokko, 2001) is on, but it should be remembered that the best student with greater ability moves early in the decision- making process when choosing a university (Litten, 1982:398), and this places

82 | greater emphasis on universities to understand the factors that influence university decision BEFORE enrolment, thus in the pre-purchase phase.

It is at the backdrop of this closing argument that Chapter 3 introduces consumer behaviour theory with specific focus on the decision-making process of customers (students) in the pre-purchase stage. A detailed investigation is portrayed on which factors or variables play a role in determining which university to attend (Freeman & Thomas, 2005:167).

83 |

CHAPTER 3

Decision-making process of prospective university students

3.1 Introduction

The main focus of this chapter is to determine why prospective university students choose to consider a certain university for possible enrolment, and not another. Specifically, the literature will be explored to determine what choice factors influence prospective students’ university choice. Determining the choice factors is a key element of this study, as these factors will form the first step and the foundation of the proposed choice model that will be developed and empirically tested.

As choice (and decision) is an integral part of consumer behaviour (Hoyer & MacInnis, 2008:3-5), it is imperative to introduce this chapter briefly with consumer behaviour theory and the levels of consumer decision-making before delving deeper into the actual university students’ consumer decision-making process. This study’s goal is not to investigate and elaborate on consumer behaviour and all its elements. The goal is to understand the theory’s essence and to grasp the ‘simple’ five-step consumer behaviour decision-making model that will be discussed as background information. However, as choice factors are predominantly weighed and considered in the third stage of this ‘simple’ five-step model, the focus will mainly be on this ‘evaluating alternatives’ stage (Maringe & Carter, 2007:460) before moving to the ‘educational’ consumer behaviour decision-making models.

The consumer behaviour decision-making process’ simple model, specifically the ‘evaluate alternative’ stage of this model, is not the only model that will be discussed. As the study focuses on prospective university students’ choice and how prospective students make these decisions, other educational consumer behaviour decision- making models or educational choice models will also be investigated. A new model cannot be proposed before familiarisation with existing choice models. Some elements of these existing ‘educational’ choice models will also form the basis of the researcher’s proposed choice model. 84 |

The last section of this chapter will explore the complex and dynamic dimension of choice (Foskett & Hemsley-Brown, 2001:202). What is meant with the term ‘choice’? The key choice factors that are considered by prospective students and South African students when considering a university to further their education will also be discussed and these factors will form the basis of the proposed choice model. The chapter will conclude with the first stage of the proposed choice model, suggesting which factors should be tested as an integral part of a university choice model in the South African education market.

3.2 Consumer behaviour

The increasing demand for educational services coupled with competition in the HE marketplace has caused an intense struggle to attract new students (Cubillo, Sánchez & Cerviňo, 2006:101). To attract these new as well as ‘right’, ‘better’ and ‘best’ students (Freeman & Thomas, 2005:154; Ahola & Kokko, 2001:199; Baldwin & James, 2000:141), it has become important for universities to engage in consumer behaviour research, understanding students’ decision-making processes and the factors that are most influential in selecting a university (Brown, Varley & Pal, 2009:311; Briggs & Wilson, 2007:59; Chen & Zimitat, 2006:97; Veloutsou, Lewis & Paton, 2004:160).

Consumer behaviour forms the foundation of modern marketing (Vrontis, Thrassou & Melanthiou, 2007:982), and it was determined in Chapter 2 that marketing is essential to gain or maintain a university’s competitive edge in the quest for students. An investigation into the causes and effects of customer behaviour within individual universities’ segments should enable marketers to better understand their markets’ needs and wants (Vrontis et al., 2007:979). When universities understand their students’ needs and wants, they become customer focused and can adapt their marketing strategies to attract and entice the more suitable students (Moogan, 2011:572).

The following section will start with a brief discussion on consumer behaviour. The adoption of consumer behaviour enables researchers to attain knowledge from the

85 | consumer that can be used to achieve organisational, business and marketing objectives. In this scenario, knowledge will be attained by focusing on how the customer (the prospective student) or group of customers generally make their university decisions, which should in turn assist universities to attract the ‘right’ students (Vrontis et al., 2007:982).

The theoretical consumer behaviour overview will be followed with a brief discussion on the three levels of consumer decision-making that consumers normally follow. The focus will fall on the extensive problem solving level, as prospective students follow this type of decision-making (Moogan, Baron & Harris, 1999:211).

3.2.1 An overview of consumer behaviour

The term consumer behaviour is defined as the “behaviour that consumers display in searching for, purchasing, using, evaluating and disposing of products and services that they expect will satisfy their needs” (Schiffman, Kanuk & Hansen, 2008:4). It involves the acquisition behaviour, the using and disposing behaviour (Hoyer & MacInnis, 2008:4). In broader terms this means that consumer behaviour focuses on how individuals make decisions on what they buy, why they buy it, when they buy it, where they buy it, how often they buy it, how often they use it, how they evaluate it after the purchase, the impact of such evaluations on future purchases, and how they eventually dispose of it (Schiffman et al., 2008:4).

The philosophy of consumer behaviour recognises the consumer as being the focus of marketing activity (Vrontis et al., 2007:982; Binsardi & Ekwulugo, 2003:319). Although consumer behaviour evolved in the late 1950s, it was already then realised that if products were produced when consumers want them, they would buy them. Thus, the key assumption underlying the marketing concept is that to be successful and competitive, a company must determine the needs and wants of specific target markets. It was the widespread adoption of the marketing concept that provided the driving force for the study of consumer behaviour (Schiffman et al., 2008:4-6).

In essence, consumer behaviour studies analyse the behaviour behind purchases to find potential market opportunities and to determine the appropriate marketing-mix.

86 | This will be followed by the development of practical and effective marketing strategies (Chen & Zimitat, 2006:92).

The consumer’s behaviour behind purchases can be studied by researching the consumer decision-making process (Vrontis et al., 2007:982). Although the consumer behaviour decision-making process is key to this chapter, specifically the evaluate alternatives stage, the different levels of consumer decision-making has to be understood. While prospective students are making these decisions about what to study and where to go, they will not be able to experience university life until they obtain a definite place and then enrol at that particular institution. They will only experience university life once they have arrived. Because of the intangible nature of this educational service on offer, information has to be gathered and weighed in advance to make the choice. It is a complex decision that requires prospective students to apply an extensive problem-solving investigation during the decision- making process (Moogan et al., 1999:212).

3.2.2 Levels of consumer decision-making

Schiffman et al. (2008:71) define three specific levels of consumer decision-making: extensive problem-solving, limited problem-solving, and routinished response behaviour. Not all consumer decision-making situations require extensive effort or the same degree of information search.

3.2.2.1 Extensive problem-solving

When consumers have not established specific criteria for evaluating a product category it can be classified as extensive problem-solving. Further, if consumers have not narrowed the number of brands they will consider to a small, manageable subset, their decision-making efforts will be extensive and time consuming (Schiffman et al., 2008:71).

Given the time spent and the complexity and variety of choices involved, one would classify prospective students’ university decision-making as extensive problem solving (Drummond, 2004:317; Moogan et al., 1999:211; Litten, 1982:386). Prospective students know that choosing a university is an important choice to make

87 | and there is a lot of risk involved with choosing the ‘right’ institution (Vrontis et al., 2007:980; Yamamoto, 2006; Moogan et al., 1999:222,225). These students will be highly involved in the complex decision-making process when selecting a university and motivated to seek out information in order to reduce levels of perceived risk (Brown et al., 2009:312). Information will be gathered in the decision-making process on many factors that need to be considered when choosing a university; the location and reputation, the perceived quality of the degrees and the teaching facilities to name but a few. The increased tuition fees further complicate the search and evaluation process, as students now consider value for money as well (Ivy, 2010:394; Moogan et al., 1999:225).

The complex and intangible nature of the university service that has not been experienced before by prospective students, coupled with the many factors that need to be considered in university choice, will steer prospective students to apply all the phases of the consumer buying decision-making process (Section 3.3) (Maringe & Carter, 2007:461; Cubillo et al., 2006:103; Moogan et al., 1999:213). According to Gabbott and Hogg (1994) as cited in Moogan et al. (1999:214), the simplified decision-making approach permits prospective student to break complex behaviour down into meaningful ‘chunks’ as the prospective student progresses logically through the sequence of events, in order to solve their problem.

Due to their initial lack of knowledge of the courses and of the university in general, they will be active in searching information about alternative universities and may spend a long time obtaining the relevant information and choosing where to take their custom. The prospective students will spend a lot of time appraising the alternative university brands available. Initially, they will form a list or set of alternatives from which a choice is made, and a number of decision rules will be applied to make a selection from the choice alternatives (Moogan et al., 1999:213).

3.2.2.2 Limited problem-solving

Consumers have already established the basic criteria for evaluating the product category at the limited problem-solving level. They know the various brands in the category; however, they have not fully established preferences concerning a select

88 | group of brands. Consumers gather additional brand information to distinguish between the various brands, but the search for additional information is not extensive. It is more like ‘fine-tuning’ the search (Schiffman et al., 2008:71).

From the extensive problem-solving discussion, it can be concluded that it is unlikely that prospective students will make a university decision at the limited problem- solving level. University life and the expected service and quality of degrees have not yet been experienced when a university has to be chosen. The entire choice equates to the promise of future benefit, but exact rewards are not known at the start of this extended decision-making process. The prospective students need a lot of information to weigh and consider the options available (Moogan, 2011:572; Moogan et al., 1999).

3.2.2.3 Routinished response behaviour

At this level, consumers have experience with the product category. They have a well established set of criteria with which to evaluate the brands they are considering. Sometimes, they may search for a small amount of additional information; other times they simply review what they already know (Schiffman et al., 2008:71). Undergraduate prospective students will unlikely get involved in routinished response behaviour, unless they know the specific university very due to the fact of a parent who is working there or a sibling who has attended the specific university. In some instances, this type of response behaviour could also be expected of a student who is considering post-graduate education at the same institution that they have just graduated.

Although three levels of consumer decision-making can occur when choices are made (Schiffman et al., 2008:71), it is evident from the literature that most prospective students will be highly involved in the decision-making process when deciding which university to attend. They will employ extensive problem-solving in their university search (Brown et al., 2009; Moogan et al., 1999).

In the South African HE context it can be argued that prospective students also undergo an extensive decision-making process when applying at a university. In Grade 9 this process commences when school learners have to make a choice of

89 | subjects, which will influence their career paths three years down the line to Grade 12. These subjects chosen form part of the entrance requirement for HE and the prerequisites for certain programmes. In South Africa, school learners (or scholars) usually only start to enquire about different universities and types of universities and programmes when they reach Grade 11 or in some cases Grade 12. The decision- making process is therefore lengthy. It is a high-involvement decision because it has high personal relevance and a variety of different alternatives are available to choose from (Chapter 2) (Wiese, 2008:152).

Just understanding the level of decision-making that occurs when prospective students make their university choices, is not enough for university marketers to build a marketing strategy to attract the suitable prospective students. It is imperative to further understand how customers (potential students) are attracted to a university and why they are encouraged to continue through the decision-making process and in the end enrol. This is the crucial decision-making process information to any university (Moogan, 2011:572).

3.2.3 University students’ consumer decision-making process

In Chapter 2 it has been established that prospective students should be viewed as customers, and a solution for universities to attracting the suitable students is to employ marketing concepts and to develop strategies that focus on its customers, i.e. the prospective students (Moogan, 2011:572; Binsardi & Ekwulugo, 2003). There should be responsiveness to customers’ needs and one way is to study and understand the prospective students’ decision-making process (Brown et al., 2009:311; Baldwin & James, 2000:147).

Prospective students’ decision-making is a complex process. The outcome involves the filtering of many layers of information that even for students with access to quality information, is challenging. Universities should reduce the complexity of decision-making by understanding what their customers need and want. Knowing what your customers need and want will help universities to communicate the information that is truly important to the applicant, thereby simplifying the decision- making process for the customer (Briggs & Wilson, 2007:58).

90 | As prospective students (as customers) are becoming extremely critical and analytical when choosing their university (Bisardi & Ekwulugo, 2003:319-320), it becomes necessary to communicate information to these students whom they want and value. It is argued that they will become even better informed customers, making rational choices of which university they would want to attend (Baldwin & James, 2000), and it is at this backdrop of challenges that universities are forced to equip themselves with the necessary marketing intelligence and information that would enable them to understand their customers’ needs (Hemsley-Brown & Oplatka, 2006:318). Prospective students are no longer standing at the border of decision- making, they have views and opinions about what they prefer (Maringe & Gibbs, 2009:163). It is the university’s job to understand the prospective students’ needs and to facilitate their decision-making when selecting the university at which they intend to study (Veloutsou et al., 2004:160). Facilitation and communication can only happen if the university has the information on what these students want and value. Then only can a university communicate the right message to attract the students they want (Wiese, Van Heerden & Jordaan, 2010:151).

Moogan (2011:572) argues that information including, the decision-making factors that are affecting the applicants’ university selections, is a priority and needs to be recognised for the prospective students’ sake, but also to assist universities to understand and explore their markets.

In a competitive market place, universities must research their potential students’ requirements, finding out the motivation of why prospective students choose to enrol at a certain university (Maringe, 2006; Moogan et al., 1999:225). Prospective students have preferences, and these are exercised by considering what is important for them in their university choice, and then they make a conscious/unconscious trade-off among the attributes (Cubillo et al., 2006:104). These trade-offs will take place throughout the decision-making process and will be further discussed in Chapter 4.

Prospective students will move through the consumer behaviour decision-making process that includes the mental and physical steps taken by these students. From the point of realising that they want to attend a university to further their education,

91 | they will collect information to quantify (ask peers/teachers/parents, attend open- days) the possible benefits linked with the alternatives available and then make a well balanced decision up to their divestment of it (Vrontis et al., 2007:982; Moogan et al., 1999:214).

Although a general and ‘global’ discussion on prospective students’ university decision-making has been displayed and the importance of understanding universities’ customer needs for tailored university marketing strategies to attract suitable students have been argued thus far (Durvasula, Lysonski & Madhavi, 2011:35), how relevant is this to the South African HE context? The answer is definitely most relevant! HE in South Africa expects HEIs, such as universities, to deliver the much needed graduates for the country’s social and economic development, while simultaneously addressing equity and diversity. Wiese et al. (2010:150) suggest that a solution to achieve this suggested outcome of HE transformation, is to better understand the student market in terms of the choice factors they consider when deciding on enrolling at a university.

It has already been established in Chapter 2 that university marketers in South Africa claim that they view marketing from the perspective of the customer (Maringe & Foskett, 2002:43). South African universities realised that they have to become more market-oriented as they also increasingly compete for students and government funding (Wiese et al., 2010:151). Coupled with the increased pressure from government on broader access implies that universities must ensure that they recruit and attract the suitable students from different ethnic orientations. Understanding the decision-making process of students in terms of how they select a university, will assist South African universities to target the student market better. This knowledge obtained regarding the prospective students’ decision-making process can provide universities with insight into their market and the development of a differentiated marketing strategy. The fit between the student and a particular university can also be increased if a university has knowledge about the factors that influence students’ university choice decisions (Wiese et al., 2010:151/2).

But what is this decision-making process? What are the steps in the decision-making process model?

92 | The decision-making process is a theoretical model used to understand prospective students’ behaviour and the parameters that affect it (Vrontis et al., 2007:982). The process is neither instantaneous nor constant, but rather a dynamic process. It is the final outcome of a series of small decisions that progressively commit the person to a particular course of action. Each small decision may be perceived in isolation, but in real life decisions occur in sequences and in unison, and information available for later decisions is likely to be based on the nature and consequences of earlier ones (Foskett & Hemsley-Brown, 2001:28). The influence of others may change preferences over time as well. Marketing can affect the influence of others and may have a part to play in providing information, which could have a bearing on outcomes of the individual’s university choice (Gupta & Ogden, 2009:376-379; Foskett & Hemsley-Brown, 2001:28). One such an outcome of understanding the choice and decision-making of prospective students is that universities will be in a position to better qualify the university’s positioning strategy (Petruzzellis & Romanazzi, 2010:142). Simplifying the positioning through a clear identification of what will be ‘bought’, will make the choice for prospective students easier as they will know what they are choosing.

Although it is recognised that prospective students will most likely go through all the five suggested consumer buying decision-making steps (Figure 3.1) (Moogan et al., 1999:214) of problem recognition, information search, evaluation of alternatives, purchase (enrol at university) and post-purchase (enrolment) evaluation, the focus of this study is mainly on the evaluation of alternative stage of the process. These steps do overlap, however all prospective students will at some point in time progress through the phases of deciding which university and course to consider, followed by which one to apply to with the intention of enrolling (Moogan et al., 1999:222).

Figure 3.1 The ‘simple’ consumer behaviour decision-making model

93 | There are a number of choice models suggested by authors, however the basic five- stage process (Figure 3.1) has formed the basis for studies of consumer buying behaviour (Moogan et al., 1999; Chapman, 1981). Most of these models are based on Kotler‘s Consumer Buying Decision-Making Process (Moogan et al., 1999:214). For this reason the foundation of most of these models has to be discussed first in order, to understand how the other models were formulated by using the elements of Kotler’s model. One of the uses for decision-making models would be to assist universities to market themselves more effectively to their prospective students (Vrontis et al., 2007:981)

3.3 Consumer behaviour decision-making models

The decision to attend a university is generally a long-term process and in a funnel- like manner. Prospective students start with a broad concept of HE opportunities open to them, and through a series of steps, refine their perceptions into the choice of a single university (Martin & Dixon, 1991:253; Litten, 1982:386).

The decision-making process, which students follow when selecting a university is generally a lengthy and complex process (Wiese et al., 2010:152; Moogan et al., 1999). Students usually progress through all five steps (Wiese et al., 2010:152) and for this reason the ‘simple’ consumer behaviour decision making model’s five steps will be discussed first before an overview of the basic choice models for university choice available will be briefly discussed. It is important to understand what is happening in the decision-making process as areas in which university marketers can get involved in and influence students’ behaviour can be identified (Wiese et al., 2010:152; Litten, 1982:383).

Although all five steps will be briefly discussed, the main focus will be on the third step, evaluation of alternative stage. It is in this stage that students will weigh factors and criteria regarding universities to help them decide at which university to apply and finally enrol.

94 | 3.3.1 The ‘simple’ consumer behaviour decision-making model

The simple model (Figure 3.1) of consumer decision-making is designed to combine many of the ideas on consumer decision-making, but at the same time it does not provide an exhaustive picture of the complexities of consumer decision-making. It is designed to “coordinate and synthesise relevant concepts into a significant whole” (Schiffman et al., 2008:75).

In HE in particular, a student making a decision about post-school education would be considered as engaging with the following processes: pre-search; active search; the application stage; making the choice; decision-making acceptance or declining of the offer, and lastly on whether the decision was the right or wrong one (Maringe & Carter, 2007:461). It is during the ‘active search stage’ where choices are prioritised and short listed, and as prospective students have little or no experience of HE, they will research the educational-market by whatever means available in order to make the best possible choice (Maringe & Carter, 2007:461; Moogan, Baron & Bainbridge, 2001:180).

When services are considered for possible purchasing, alternatives available are often evaluated without the benefits of any direct experience of the ‘product’. Education is a service and prospective students do not have the opportunity to ‘try’ their future university or course. It is in most cases problematic for students to gain the correct quantity and quality of information prior to the purchase, since the intangibility of education is great (Durvasula et al., 2011; Michael, 2005:367; Moogan et al., 1999:213). If students could gain more knowledge in the relevant areas and increase the amount of searching, they should feel more confident about making a decision. That is why prospective students will ask friends, other students, parents, people they think have experience, and word-of-mouth information acts as a risk- reducing strategy for those embarking on furthering their education in HE (Moogan et al., 1999:213).

Although there are variations in the consumer behaviour models, decision- making everywhere is generally conceptualised as a five-stage process (Maringe & Carter,

95 | 2007:460; Moogan et al., 1999:214). As mentioned earlier, the five-stage process includes the identification of a problem or (3.3.1.1) need recognition, (3.3.1.2) the search for information, (3.3.1.3) evaluation of alternatives, (3.3.1.4) selection and making the purchase decision, and finally, (3.3.1.5) evaluating the purchase decision.

3.3.1.1 Need recognition

There are many reasons why prospective students will identify the need to go to university. Although the purpose of the study is not to dwell and elaborate on this step as it is not the main goal of the study to understand the detail of this step, it is important to briefly introduce this step that prospective students will go through.

Briefly, Ivy (2010) identified five main motivators for why prospective student will consider applying to go to university: (1) career or economic motives that include to earn more money, and further career prospects, (2) academic motives that include interest in the subject or good exam results, (3) social motives that include factors like being with one’s friends, (4) family motives that include parents’ influence on a child to further his/her education or to apply for university admission, and (5) personal motives that include degrees giving status, becoming more independent, and getting away from home.

Ivy’s (2010) study found that most of his respondents felt very strongly that their careers were a most important motivating factor for the need to go to university. For example, if the particular individual decides that he/she wants to be a doctor, he/she needs to go to university and prospective students realise that the career they choose will influence the need to go or not to go to university.

3.3.1.2 Information search

As soon as an individual has identified the need to attend a university, the next step would be to seek information regarding which universities are available and what courses there are to study.

96 | Prospective students considering university will undertake varying degrees of information gathering, depending on their own level of need for information. From the marketer’s perspective it is important to know the following: o How much information are students likely to gather before making a decision about what course to study and at which university (information neediness)? o What information sources will prospective students use, and what will their relative influence be (information sources)? (Adapted from Kotler & Fox, 1995:252).

For the prospective student, information gathering is an important step in the decision process. When faced with a purchase decision, students first examine memory for information, which may be relevant to the decision (Gabbott & Hogg, 1994:314), but in most instances they do not have previous experience with the university or relevant university’s services. The characteristics of services (as discussed in Chapter 2) place an additional information burden on prospective students. As services are produced as they are consumed, pre-purchase trial will not be an option and the reliance on information so much greater.

Moogan et al. (1999:212) commented that the pre-purchase information acquisition process in university decision-making is being carried out with greater involvement by the prospective students and their parents. Maybe for some reason the increased fees play a role, but the investigation and the comprehension of the decision-making process as undertaken by these students are becoming increasingly important.

University marketers must understand the information gathering and information evaluation activities of prospective students, as their task will be to help inform students about the key attributes of university, the importance of university, how important they are as customers, and the particular university’s standing should be communicated. Marketers can also determine the usefulness of messages and media used to communicate the right and relevant information to prospective students (Wiese, 2008:146; Kotler & Fox, 1995:253).

97 | 3.3.1.3 Pre-purchase evaluation of alternatives

At the heart of the decision-making process is the involvement of prospective students in narrowing down the range of choices by (1) identifying alternatives, (2) determining evaluation criteria, and (3) applying the criteria to the alternatives in order to come to a choice (Kotler & Fox, 1995:251).

 Identifying alternatives

Prospective students will compile a choice set first that will be used as the final list of alternative universities from which to choose (Kotler & Fox, 1995:251; Hossler & Gallagher, 1987; Jackson, 1982 as cited in Vrontis et al., 2007:981). This choice set will be made up out of firstly, the total set of universities which prospective students will not necessarily be aware of, then the awareness set followed by the consideration set, to finally arrive at the choice set (Figure 3.2) (Kotler & Fox, 1995:252).

Figure 3.2 Successive sets in decision-making

The awareness set of alternatives will be the universities known to or those universities heard of by the prospective student. There is also the unawareness set that will not be considered unless somehow they make their way into the awareness set. Of all the universities the prospective students are aware of, the list will be narrowed down to a list that will form the consideration set. It is at this level that a lot of information will be gathered and people will be spoken to in order to examine the remaining universities in detail that are in this consideration set. The prospective student will only arrive at the choice set after further reducing the existing consideration set by eliminating the ones that are not considered anymore (Kotler & Fox, 1995:251-252). The choice set may include one university or several

98 | universities. The prospective students will take their own preferences, the university attributes and the ‘courtship procedures’ into account when arriving at the choice set (Hossler & Gallagher, 1987:216). A final decision will be made from the few remaining universities (or alternatives) in the choice set (Kotler & Fox, 1995:251- 252).

The choice between different alternatives, would it be during the awareness set, consideration set or choice set, is affected by prior knowledge of the alternatives, attitudes towards these alternatives, and whether or not the alternatives seem pleasing (Keskinen, Tiuraniemi & Liimola, 2008:640). Whether the university seems pleasant is extremely difficult to determine, because the university and product (or course) under evaluation are not tangible (Chapter 2), and have not yet been experienced in advance. Further pressure is placed on the prospective students because they realise that the consumption process may last for several years, so the element of risk of choosing the wrong university is vitally important (Moogan et al., 1999:212).

According to Gabbott and Hogg (1994:317), the evaluation stage is the critical stage in the consumption process (and of the product after consumption) as a means of building experience and knowledge as well as learning about the product class, or in this case learning about universities.

To narrow alternative universities down from the awareness set to the consideration set to arrive at the choice set, requires prospective students to evaluate and weigh all the alternatives according some evaluation criteria. These criteria will become more explicit as the prospective student goes along in the forming of a choice set.

 Determining evaluation criteria

Before evaluating the evaluation criteria stated in literature, it should be noted that the evaluation of alternatives would also be influenced by discussions with trustworthy personnel such as subject teachers, parents and friends. Parents in most cases have a great impact in influencing their offspring (Moogan et al., 1999:222). How a prospective student will form his or her evaluation criteria will further be

99 | determined by individual determinants such as attitude and values, as well as environmental determinants such as economic and demographic factors. Environmental determinants could be influenced from family, peers and media (Vrontis et al., 2007:985).

However, as the subject matter of who influences the prospective student’ warrants a study of its own, this will not be further discussed in detail. The objective of this study is to identify the choice factors as the first step of the proposed choice model, followed by the next level of the model that will determine the perceived value received when choosing the selected university that will be discussed in Chapter 4. The goal is NOT to determine the main influencers on prospective students, but it is noted that they do play an important role in the prospective university student’s decision process.

Prospective students’ needs will also play a role in determining which criteria are more important than others when weighing and evaluating alternatives in the choice set (Kotler & Fox, 1995:253). On an individual level, as an example there are prospective students who will be very worried about the fees and if they can afford university, and at another group level there can be groups of prospective students who will be more concerned about safety than others.

Although the choice factors or evaluation criteria used to help prospective students to select a university will be discussed fully at the end of this chapter, the main factors that are used as evaluation criteria were found to be those of course content, the reputation, as well as location of the university, and social considerations. From prospective students’ consideration set, students who engage in rational decision- making will make comparisons between the varying attributes (e.g. geographical location, distance from home, content/structure of the intended programme of study, required entry grades and accommodation facilities) in selecting the course and university which have the best value on the greatest number of benefits (Moogan et al., 1999:222).

100 |  Applying the criteria to the alternatives

After the selection criteria (choice factors) have been identified, it will be applied to the choice set at which the prospective student has arrived.

It should be remembered that comparing service alternatives presents problems mainly because of the intangibility and heterogeneity, which makes effective assessment difficult. Identifying or generating attributes upon which to base a choice and applying the criteria to evaluate alternative universities are problematic and difficult. What is being assessed in the case of a service (such as education) is the perceived benefit possibly derived from the service rather than the service itself (Gabbott & Hogg, 1994:319) (Perceived value will be discussed in Chapter 4).

Due to the nature of services, evaluation criteria play an even much bigger role in choosing a university than when choosing products. What the prospective student believes regarding reputation, academic quality or safety to name but a few factors, will in the end determine his/her choice. He/she cannot experience the university prior to the selection process and admission, they cannot touch or feel it and have to trust what they know and learn about a combination of attributes they perceive to be important. Exactly which attributes or choice factors play a crucial role in this decision process will be discussed in more detail in Section 3.4.2.

To summarise the ‘analyse alternative stage’ of the decision process, Kotler and Fox (1995:255) suggest a practical set of six basic concepts that should be applied at this stage when analysing how prospective students evaluate the alternative universities available. These authors’ advice for university marketers is that it is necessary to analyse the prospective student evaluation process by: o Understanding the choice set, which has already been described as consisting of a few universities. o Determining the university attributes. It is assumed that each prospective student sees a given university as containing one or more attributes, the most important choice factors used can be academic quality, social life, location and cost.

101 | o Gathering information regarding the prospective students’ set of perceptions about where each specific university stands regarding each attribute. This set of perceptions about a particular university is the university’s image. o Understanding the prospective students’ utility function for each attribute. This is necessary, as prospective students often believe that satisfaction will rise with higher levels of academic quality and social life. If the preferred attribute levels are combined, it will make up the ideal university. o Obtaining knowledge regarding why prospective students value some universities’ attributes more than others, thus why they are attaching different importance weights to the various attributes. o Understanding that the prospective students arrive at preferences about the university alternatives through some evaluation procedure (adapted from Kotler & Fox, 1995:255).

Following the final choice set of universities, prospective students will make a choice of which university they will apply at and when successful with the application, they will enrol. This is also called the purchasing stage or consumption stage.

3.3.1.4 Purchase and consumption

This stage refers to five basic questions, whether to buy, when to buy, what to buy, where to buy, and how to pay (Vrontis et al., 2007:983).

In the context of choosing a university, the evaluation stage leads the prospective students to form a ranked set of preferences among the alternative universities in the choice set. In most instances, the next step will be to enrol at the most preferred university (Kotler & Fox, 1995:262).

3.3.1.5 Post-consumption evaluation

Once the service performance has been completed, it is conceivable that satisfaction about how the service was delivered, will be reviewed. Post-purchase or - consumption evaluation takes place when the prospective student reflects back on his/her experiences and decides if the good or service was satisfactory or unsatisfactory (Vrontis et al., 2007:984; Gabbott & Hogg, 1994:317).

102 | There is a fundamental expectation of a service, which implies that it provides what it promises. The problem of evaluating educational service performance, i.e. determining satisfaction or dissatisfaction, is much more complex than evaluating products. The cause of this problem is that it is difficult to evaluate the unknown. When consumers, like students, do not have the knowledge or experience to evaluate what they have received, what do they apply to compare their experiences? Sometimes the perceptions of what students wanted from the service are also not clear (Gabbott & Hogg, 1994:317).

If a prospective student cannot or does not clearly articulate or understand his/her own requirements, or has formed unrealistic expectations of the service, then he/she may feel that some cause for the failure was their own instrumental decisions. Therefore the process of evaluating services in terms of satisfaction and dissatisfaction is a shared responsibility between provider and consumer (Gabbott & Hogg, 1994:318).

This section has created the platform on which most other authors have built their educational decision-making models. There are several suggested international models and variations of models, however only the models that are mentioned several times and cross referenced by different authors will be discussed in the following section.

3.3.2 Educational consumer behaviour decision-making models

In this section, various educational consumer behaviour decision-making models within the classification of (3.3.2.1) economical, sociological and information process models will be briefly explained. It is important to have a brief understanding of these educational consumer behaviour models, as they indicate the structure of consumer behaviour and buying behaviour and how it is represented by the decision-making process (Wiese, 2008:126). This section, will serve as background information for the proposed educational choice model.

It will be evident from the following brief overview of these existing educational models, that choice is a key element of each model. Quite a few of the ‘student college choice’ or university choice models describe university choice as a

103 | developmental process (Hossler & Gallagher, 1987:207-208). This is true as the decision to enter HE involves a series of successive decisions finally resulting in enrolment. It is thus a multistage process and generally three broad stages can be distinguished in the process: (1) deciding to enter HE, (2) selecting a particular university (or HEI) and programme of study, and (3) persisting in HE (Cosser, 2010:44). Each broad stage involves choices, the choice to enter HE, the choices that need to be made between different institutions and programmes, and the choice of staying in HE.

Section 3.3.2.2 will portray in Table format how each of the discussed ten student university choice models suggests that choice take place. The different elements of each model will be compared with each other (Table 3.1) as there are some similarities and differences emerging between these models.

Intertwining these choices, are the choices of taking out a loan to study or financing by other means, where to stay during studies. For these reasons ‘choice’ will be discussed in its general context in Section 3.4.

Before addressing the various educational choice models the broader classification of economic, sociological and information processing consumer behaviour models will be discussed. The focus will mainly fall on the information processing models of student choice. Many of the issues raised in both the economic and sociological models are combined in the information processing models.

3.3.2.1 Economical, sociological and information processing models

The prospective students’ university choice process is captured and portrayed in various developed educational consumer behaviour decision-making models. Historically, the university choice process was underlined by three perspectives namely the sociological, psychological, and economic perspectives (Aldous, 2009a:3). Ivy (2010) on the other hand, identified three themes of student choice models: the economic models, the sociological models and the information processing models of student choice.

104 | It seems from the literature (Ivy, 2010; Aldous, 2009a; Wiese, 2008) that the sociological perspective and the sociological student choice model, as well as the economic perspective and economic student choice models are actually the same thing and will briefly be introduced. It is necessary to have an understanding of these two models as they form the basis for the information processing model which in turn forms the platform of most of the ‘educational’ or university student choice models.

Aldous’ (2009:3) psychological perspective is not reflected as a model in its own right, however, this perspective is intertwined and applied by the information processing models without stating so explicitly. The psychological perspective views the climate of the HE environment and how perceptions of that climate influence students’ university choices. HEIs’ characteristics including cost of tuition, room and board, location, curriculum, and financial aid availability are instrumental in the psychological aspect of a university decision (Aldous, 2009a:3).

Most university student choice models at some stage introduce the factors (or HEIs characteristics) influencing students’ choice during the prospective student’s university choice process, thus applying some aspects of the psychological perspective such as cost of tuition, location and other factors into their choice processing models.

 Economic models of student choice

The economic perspective constructed the university choice process as an investment decision. During this decision, prospective students will conduct a cost- benefit analysis. They will weigh the costs and benefits of attending university and make choices based on their evaluation of the economic benefits of post-secondary education (Aldous, 2009a:4).

Prospective students take into account aspects of costs related to their studies, be they real costs (e.g. real financial cost of attending, the amount of financial aid available) and/or opportunity costs (the forgone earnings from a decision to attend university) related to studying or not studying. The benefits of expected earnings on graduation are taken into account to determine whether or not it is to the student’s

105 | financial benefit to study for a degree (Ivy, 2010:395; Aldous, 2009a:4; Wiese, 2008:127).

 Sociological models of student choice

The sociological perspective focused on the university choice process and was historically seen as part of the status attainment process with emphasis on individual background factors that influence the decision of whether and where to go to university (Aldous, 2009a:3). These models include aspects such as the student’s personal background in making a decision to study further (Kotler & Fox, 1995; Chapman, 1981). The background factors include race and ethnicity, family income, parent education, peer groups, school contexts, parental expectations, parental and student educational aspirations, academic achievement, academic ability, school counsellors, self-fulfilment, motivation and personal goals (Ivy, 2010:395; Aldous, 2009a:3; Wiese, 2008:127). All the above background factors impact student choices (Ivy, 2010:395).

 Information processing models of student choice

These models combine many of the issues raised in the economic and sociological models. The goal behind the information processing model is to try and determine how individual students go through both the decision to go to university, and the processes that they use to select the university to which they apply (Ivy, 2010:395; Wiese, 2008:127). The aim of this section is to review the university selection process models proposed by different authors and to summarise the differences and similarities in Table 3.1. Reviewing existing university selection models will provide the researcher with a good understanding of common themes that can be applied to the South African situation.

There are a number of choice models, however the models most commonly mentioned in literature (Sia, 2011:178) are: (i) the Chapman (1981) model, (ii) the Jackson (1982) model, (iii) the Hanson and Litten (1982) model, and (iv) the Hossler and Gallagher (1987) model. The more recently explored models that will be discussed are (v) the Hodkinson, Sparkes and Hodkinson (1996) model, (vi) the Ball,

106 | Maguire and MacRae (2000) model, (vii) the Foskett and Hemsley-Brown model (2001), (viii) the Cubillo, Sánchez and Cerviño (2006) model, (ix) the Vrontis, Thrassou and Melanthiou (2007) model, and (x) the Maringe and Carter (2007) model.

(i) The Chapman model of student college choice (1981)

This model presents the influences affecting prospective students’ choice of which university to attend. The intention of this model was to (1) assist university administrators responsible for setting their recruitment policy to identify the pressures and influences they need to consider in developing university recruitment policy, and (2) add to the continued research in the student university choice arena.

Chapman’s model (1981:492) is longitudinal and suggests that to understand a student’s choice of which university to attend, it is necessary to take into account both the prospective student’s background (family and family income, socio- economic status, aptitude) and current characteristics (level of education, high school performance), and the characteristics of the university (location, availability of programme, communication efforts) (Sia, 2011:178).

Chapman (1981) discussed the importance of printed material as a direct influence on prospective students’ choice. However, it should be remembered that in 1981 there were no cellphones, no Internet and websites or social networking pages such as facebook, and therefore this argument in isolation is outdated.

Although Chapman’s (1981) model does not explicitly mention or portray all of Kotler’s (Kotler et al., 1996:291) simple decision-making model’s steps, it can be argued that some of these steps are represented without realising it. Chapman suggests that the student’s characteristics like the level of educational aspiration and high school performance influence the choice of going to university. This represents similar influences described in Kotler’s first step in his model of need recognition. Similarly, external influences like significant persons, the fixed university characteristics (cost and location) and the university’s efforts to communicate with students are mentioned as factors that will influence the decision of which university

107 | to attend. What Chapman (1981) describes here, is the evaluation of alternatives stage, but specifically the influences during this third step of Kotler’s simple decision- making model.

All Chapman’s (1981) influences on the decision-making process arrive at the choice of which institution to attend, which is similar to Kotler’s fourth step, purchase or entry to university. However, Chapman (1981) does not mention the evaluation of post-purchasing (Table 3.1).

Chapman’s (1981) model is a conceptual model which describes the interaction and influences on the university selection process, however, the model does not have defined phases or stages. This model has served as a catalyst for later models of student university choice (Sia, 2011:179).

(ii) The Jackson model (1982) as cited in Vrontis, Thrassou and Melanthiou (2007:981) and Gallagher and Hossler (1987:208/9)

It was the first model to address the idea that different factors were more or less important at different stages of the process (Sia, 2011:180). This model suggests that the student goes through three stages prior to making a choice of which university to attend. Jackson (1982) broke the three-stage model into sections of preference, exclusion and evaluation. o The first stage is the preference stage, determining whether the student is interested in going to university or not. In this phase a student’s educational aspiration will be strongly linked to the student’s academic achievement. Jackson argues that academic achievement is the strongest predictor of university aspirations and the better the student, the more likely the chances that he/she is going to attend university (Sia, 2011:180). He also suggests that the family background and the social context of the students will influence these aspirations (Vrontis et al., 2007:981).

108 | It can be argued that this is similar to Kotler’s simple decision-making model’s first step of need recognition. The preference of going to university is the same as the recognition of a need to go to university. o The second stage is an exclusion stage where some universities will be excluded from the prospective list. Prospective students form a choice set and identify the institutions about which they want to learn more. Factors such as tuition fees, location or academic quality may be the reasons for excluding some universities from the list (Vrontis et al., 2007:981). The difficulty in this stage lies in obtaining accurate information as misinformation during this stage may result in the exclusion of positive ‘university’ alternatives (Sia, 2011:180/1). It can be argued that this is similar to Kotler’s simple decision-making model’s second step of information gathering. A choice set cannot be determined without an investigation of what institutions are available to the prospective student. o The third and final stage is called the evaluation stage, which is made up of the rating scheme leading to the final choice. Prospective students evaluate their choice set and select an institution (university) to apply to (Vrontis et al., 2007:981). The key factors that play a role in the selection of the chosen university at this stage, are job attributes/prospects, university attributes and the costs associated with each. The family background and academic experience play significant roles in the evaluation process (Sia, 2011:181).

Jackson (1982) found that parental influence, family background and academic achievement were important in all his suggested three phases and will influence the student’s choice (Sia, 2011:181).

It can be argued that this is similar to Kotler’s simple decision-making model’s third step of evaluating the alternatives, as Jackson (1982) indeed also calls this the evaluation stage (Table 3.1).

(iii) The Hanson and Litten model (Litten, 1982)

This model also suggests a three-stage model namely predisposition, exploratory and application. Their model was influenced by Lewis and Morrison (1975) who

109 | attempted to better understand how students acquire information, how they combine information, how they form overall evaluations of universities, and what strategies students employ in applying to universities (Sia, 2011:179; Kinzie, Palmer, Hayek, Hossler, Jacob & Cummings, 2004:21). o They argue that the first phase (predisposition) consists of a two-step process. First a student begins with the desire to attend a university, and then he/she decides to participate in HE (Litten, 1982).

It can be argued that this is similar to Kotler’s simple decision-making model’s first step of need recognition. The identification of the desire to attend university is very similar to the recognition of a need to go to university. o The second stage (exploratory) comprises of the student’s investigating potential universities by seeking information about these institutions, and they then create a set of possible candidates (thus formulating a choice set of alternatives) (Sia, 2011:179; Litten, 1982).

It can be argued that this is similar to Kotler’s simple decision-making model’s second step of information gathering. The creation of possible alternatives to choose from can only happen if the prospective student actively searches for information on the particular universities. o The last stage (application) is the process of applying to a university/HEI, actual admission and enrolment. The parallel application for financial aid (if needed) also occurs during this stage (Sia, 2011:179; Hossler & Gallagher, 1987:208; Litten, 1982).

It can be argued that this is similar to Kotler’s simple decision-making model’s fourth step of making the purchase decision. There isn’t evidence in the literature that Hanson and Litten’s (1982) model included an explicit step of evaluation, however, their model does mention that discussions between friends, family and peers will happen before choosing the university at which to apply. It could be argued that these negotiations comprise a form of evaluation. Their third step is

110 | though similar to Kotler’s fourth step where the choice has been made and the student ‘purchases’ the service and enrols at the university.

However, Hanson and Litten’s (Litten, 1982) model doesn’t stop after the decision has been made. Within these three stages they propose that five distinct processes exist that a student passes through: having college (university) aspirations, starting the search process, gathering information, sending applications and enrolling.

This five-step process includes multiple variables, which affect college choice (race and family culture, quality and social composition of high school, parents and counsellors, self-image and personality, economic conditions of the environment, financial aid available, recruitment activities of colleges and the size and programmes of universities).

The Hanson and Litten model (Vrontis et al., 2007:981; Litten, 1982) is a cross between the Jackson’s student-based model and the more institution-based Chapman (1981) model (Table 3.1).

(iv) The Hossler and Gallagher (1987) model as cited in Martin and Dixon (1991)

These authors proposed a three-phase model of university choice decision-making by students. The three stages interact with one another, each affecting the other in complex ways. Students progress as they move from educational aspirations to university enrolment. Like Chapman (1981) and Hanson and Litten’s (1982) models, they are considered developmental in that each stage is associated with particular “cognitive and affective outcomes” that will ultimately lead to enrolment (Aldous, 2009b:22; Wiese, 2008:128). o The first phase is the predisposition phase, also called the developmental phase. Prospective students’ attitude and influences contribute to their decision of whether or not to attend a university. Attending high-quality high schools, positive attitudes towards education, and early information on financial aid may be

111 | important factors in stimulating the demand for places in universities (Martin & Dixon, 1991).

The interactions between environment variables such as socio-economic status, student ability, achievement, race and gender and the student’s characteristics have an effect upon the student’s aspirations (Sia, 2011:182).

It can be argued that this is similar to Kotler’s simple decision-making model’s first step of need recognition for attending a university. o The second phase is the information search and the formation of a choice set comprising of a list of universities to consider. Gallagher and Hossler (1987) argue that universities can exert a modest influence on the student choice process during this phase.

It can be argued that this is similar to Kotler’s simple decision-making model’s second step of information gathering. o The third phase is the evaluation stage where students narrow the viable choices and make the final university choice. It is at this stage, that the choice set is evaluated. Aldous (2009b:27) also refers to this stage as the ‘choice’ stage where students use information to select a university and complete the enrolment process.

Kotler’s simple decision-making model’s third step is also the evaluation stage, and therefore comparisons can be drawn between the two models’ third steps.

At each phase of the student university choice process, individual and organisational factors interact to produce outcomes (Hossler & Gallagher, 1987:208). It can be concluded from Hossler and Gallagher’s (1987) three-phase model and from previous discussions, that this model is mostly based on the Jackson (1982) model and to a limited extent on the Hanson and Litten (1982) model. Therefore, the same argument can be drawn that these three phases reflect in some way or another, the simple decision-making model of Kotler’s first three stages (Table 3.1).

112 | The slight difference in Hossler and Gallagher’s (1987) model from Jackson’s (1982) and Hanson and Litten’s (1982) model, is that it does not exclusively focus upon the attributes of students. It is an interactive model, which takes the nature of HE into account at both the pre-university and the university level. It thus draws on Chapman’s (1981) model, which proposes that many characteristics of educational organisations such as high schools and the university, actually influence the prospective student university choice process.

(v) The Hodkinson, Sparkes and Hodkinson model (1996) as cited in Foskett and Hemsley-Brown (2001:214)

These authors perceive choice as emerging from the interaction of young people with their habitus, key experiences in their personal life histories, and stakeholders in the post-16 market place (Foskett & Hemsley-Brown, 2001:214). The Hodkinson et al. (2006) model does not follow a linear sequence and suggests that choice does have some elements of rationality within it, and that decisions will be made by considering the information that is available (Paton, 2007:7).

This model identified three key elements of pragmatic rational decision-making: o The decision-making is part of a wider choice of lifestyle (influenced by social context and culture); o Decision-making is part of an ongoing life course; and o Decision-making evolves through interactions with others, so decisions are in fact the outcome of negotiations between people’s social networks of friends, family members and acquaintances (Paton, 2007:8).

Hodkinson et al.’s model (1996), like Chapman’s (1981), addresses more the influences and interaction that prospective students will experience when making the choice of which university to attend. Thus, also drawing a little bit on Kotler’s simple decision-making model’s second step of information search and how this search influences the decision (Table 3.1).

113 | (vi) The Ball, Maguire and MacRae model (2000) as cited in Foskett and Hemsley-Brown (2001:214)

This model is an extension of the Hodkinson et al. (2000) model in two ways. They suggest that there are three critical arenas of action and centres of choice making, which interact in the shaping of choice – (1) the arena of family, home and domesticity, (2) the arena of work, education and training, and (3) the arena of leisure and social life. Secondly, they suggest that the choice model cannot ignore that prospective students’ choice operates within a social-cultural sphere and that personality, expectations, aspirations and history all play a role (Paton, 2007:10; Foskett & Hemsley-Brown, 2001:214).

According to Foskett and Hemsley-Brown (2000:215), the Hodkinson et al. (1996) and Maguire et al. (2000) models are valuable in demonstrating the broad social- cultural spheres which interact to shape a young person’s decision or choice. However, they provide little by way of understanding the processes of choice within the individual’s mind.

Ball et al.’s (2000) model describes the critical arenas of action and centres of choice making, which could be argued is similar to Kotler’s simple decision-making model’s third step of evaluation of alternatives (Table 3.1).

(vii) The Foskett and Hemsley-Brown (2001) model as cited in Paton (2007:12- 14)

These authors distinguish between two different stages in the post-16 decision- making process: (1) a preliminary search stage, and (2) a refined search stage. o The preliminary search stage is dominated by parental and peer group pressures and brings together young people’s pre-conceptions of careers and institutions with the pursuit of a choice that will secure social approval. It is important to this group to choose a university and programmes which attract ‘people like me’ or ‘people like I aspire to be’ (Paton, 2007:14; Foskett & Hemsley-Brown, 2001:121).

114 | o During the refined stage evidence will be collected through visiting universities, attending career days/open days, reading promotional materials.

Foskett and Hemsley-Brown’s (2001) model describes a search for information stage (although in two stages) and it can be argued that this particular step is similar to Kotler’s simple decision-making model’s second step of information search. Although Foskett and Hemsley-Brown’s (2001) model does not specifically mention an evaluation of alternative stage, evaluation does happen during its search stage when possible choices of universities are compared (evaluated) to those of peers and ‘people like me’. It can thus be argued that there are similarities with Kotler’s simple decision-making model’s third step of evaluate alternatives. Foskett and Hemsley- Brown (2001) do not particularly mention the choice phase, however they do mention that prospective students will choose a university that is similar to Kotler’s simple decision-making model’s fourth step of purchase decision (Table 3.1).

(viii) The Cubillo, Sánchez and Cerviño (2006) model

These authors aim to explain with their model the factors influencing the purchase intention of international students. The purchase intention is used as a predictor for the preferential choices of consumers, and is defined as the intention of the student regarding the destination country as provider of the education service. This model comprises the purchase intention, as a dependent and not observable variable, and four factors with a total of 19 independent variables identified in existing literature.

The four main factors which Cubillo et al. (2006) focus on are: personal reasons, country image, institution image and programme evaluation. Underpinning the choice and influencing these main factors is advice in the form of recommendation from family, friends, or acquaintances who have already selected the service before. Cubillo et al. (2006) argue that recommendation is one of the most important factors determining choice. These authors (Cubillo et al., 2006:107) further argue that the creation of service expectation is important to attract potential customers. They expand by explaining that service expectation comprises personal needs, previous experience, and institution image.

115 | Cubillo et al. (2006) discuss three stages of the international prospective student’s decision-making process, although not explicitly laid out in such a structured manner. Their recommendation mirrors elements of Kotler et al.’s (1996:291) simple decision- making model’s second step of information search where influences from others play a role. Likewise it can be argued that Cubillo et al.’s (2006) choice factors represent Kotler’s third phase where his evaluation stage also discusses the choice factors and Cubillo et al.’s (2006) purchase intent is similar to Kotler‘s fourth step of purchase or consumption (Table 3.1).

(ix) The Vrontis, Thrassou and Melanthiou (2007) model

This model is a combination of the Hanson and Litten model (Litten, 1982), the Chapman (1981) and the Jackson (1982 as cited in Vrontis et al., 2007) model. These authors name their model “A preliminary integrated generic higher education student-choice model”. Vrontis et al. (2007) use the Hanson and Litten model (Litten, 1982) as a base and maps onto it the other two models. The authors’ aim is to provide a model that is a descriptive contemporary model for the developed countries and that will assist university administrators in their marketing efforts.

Vrontis et al.’s (2007) model is based on the ‘simple’ consumer behaviour decision model’s steps (need recognition, information search, alternatives evaluation, purchase and consumption, and post-consumption evaluation). Surrounding these five steps are the environmental, individual and institutional determinants as derived from the previously developed generic HE student-choice models (Table 3.1). Affecting all these presented factors and processes are the characteristics and forces relating to the wider business environment of the developed countries. Vrontis et al. (2007) also separately listed the various effects on the business of higher education.

The limitation of this model is that it is a conceptual model that still needs to be tested. Vrontis et al. (2007:987) suggest that the different elements it encompasses require separate studies.

116 | (x) The Maringe and Carter (2007) model for African students’ overseas study decision-making

This model was developed based on an exploratory study on a small sample of students from Africa in two universities in the South of England. Although not built on a robust basis for generalising about African students as a whole, there is sufficient data to generate hypotheses about African students’ overseas decision making.

Their model suggests that there are six elements that shape decision-making for overseas study. (1) Push factors (political, economic and home country HE capacity), (2) Key influencers (friends, family), (3) Pull factors country level (high quality HE experience, safe environment, easy application process), (4) Pull factors institutional level (course availability, accommodation costs and availability), (5) Risk and anxieties (financial risk, legal administrative risks) and (6) Experiential dissatisfiers (such as information inadequacies, financial uncertainties) (Maringe & Carter, 2007:471/2).

Maringe and Carter’s (2007) model was specifically developed for African prospective students who would consider overseas study. Some similarities between their model and Kotler’s model can be drawn, for example: information inadequacies can only be experienced if there is a search for information which thus reflects the second step of information search and push-and-pull factors are also the factors discussed in Kotler et al. model’s third stage of evaluating the alternatives (Table 3.1).

3.3.2.2 Comparing student university choice models

It is evident from the above discussion and comparison of existing educational models that there are some similarities and differences emerging among these models. These key themes and features of the discussed ten student university choice models are summarised in Table 3.1.

117 | Table 3.1 Comparing student university choice models

Similar to Similar to Similar to Similar to Similar to Three - Formulate Individual and Kotler’s step Kotler’s step Kotler’s step Kotler’s step Kotler’s step phase a choice institutional (2) (1) Identify (3) Evaluation (4) Purchase (5) Post- model set factors interact Information Need of alternatives decision purchase search Not √ √ √ √ (i) explicitly, Not explicitly, Influences the External Arrive at choice Chapman’s X but √ but how univ. x decision to influences play of which univ. model (1981) mention communication attend univ. role here to attend influences influences √ x √ √ Evaluate their Choice (ii) Jackson’s Search phase √ √ √ Preference choice set and described as x model (1982) where choice stage select an part of 3-phase set is formed institution model √ Not explicitly √ (iii) Hanson √ √ mentioned, but Yes, & five √ and Litten’s √ √ Desire to Investigate model based on x distinct Apply and enrol model (1982) attend universities Jackson’s and processes Chapman model (iv) Hossler √ √ √ and √ √ √ Predisposition Formation of a Evaluation x x Gallagher’s phase choice set stage, choice model (1987) √ (v) √ √ Negotiations Hodkinson et Life histories and Not explicitly, between X X x x x al.’s model stakeholders but describes people’s social (1996) interact influences network of friends

118 | Similar to Similar to Similar to Similar to Similar to Three - Formulate Individual and Kotler’s step Kotler’s step Kotler’s step Kotler’s step Kotler’s step phase a choice institutional (2) (1) Identify (3) Evaluation (4) Purchase (5) Post- model set factors interact Information Need of alternatives decision purchase search √ √ √ √ Choice Ball et al.’s X X Social-cultural x Similar to Centres of operates within x model (2000) spheres Hodkinson’s choice making social-cultural sphere √ √ Foskett & √ Individual gets in Evaluates how Hemsley- Two stages (1) √ X X contact with univ. x searches Brown’s search and (2) Choose a univ. material and visits compare to model (2001) refined search univ. peers’ √ Cubillo et √ √ √ Recommenda- al.’s model X X Personal reasons x Describes Purchase x tion from (2006) and univ. image choice factors intention others √ Based on √ Chapman; Vrontis et Environmental, Hanson √ √ √ √ √ al.’s model X individual and and Litten; (2007) institutional and determinants Jackson models √ √ √ Not explicitly Push-and-pull Country and Maringe and called search, factors, key univ. pull Carter’s X X x but talks about x x influencers, risk factors model (2007) influencers, and experiential influence information dissatisfiers decision inadequacies Source: Adapted from: Maringe and Carter (2007); Vrontis, Thrassou and Melanthiou (2007); Cubillo, Sánchez and Cervinõ (2006); Foskett and Hemsley- Brown (2001) as cited in Paton (2007); Ball, Maguire and MacRae (2000) as cited in Foskett and Hemsley-Brown (2001); Hodkinson, Sparkes and Hodkinson model (1996) as cited in Foskett and Hemsley-Brown (2001); Hossler and Gallagher (1987) as cited in Martin and Dixon (1991); Litten (1982); Jackson (1982) as cited in Vrontis, Thrassou and Melanthiou (2007); Chapman (1981).

119 | Firstly, Table 3.1 portrays the ten student university choice models in the left hand column and compares each model to the key themes that emerged from the literature. These themes act as questions to test each of the ten models against each other and they are: o Does the mentioned model propose a three-phase model? o Does the mentioned model propose that prospective students formulate a choice set? o Does the mentioned model argue that individual and institutional (or university) factors interact with each other during this decision-making process?

If the answer to a particular question is ‘no’, it is indicated with an ‘X’, and if the answer is YES, then it is indicated with a ‘√’ in the appropriate column. From the abovementioned three questions, it is evident from Table 3.1 that only three out of the ten student university choice models propose a three- phase model. These are the Jackson model (1982), the Hanson and Litten model (1982) and the Hossler and Gallagher (1987) model. These three models were all developed during the 1980s, and it can be argued that future student university choice models most probably do not have to consist out of only three phases, as since then, other more recent models have evolved and considered an even more holistic approach.

Five out of ten of the reviewed student university choice models mention that prospective students formulate a choice set from which a choice will be made. Further, all the models in Table 3.1 mentioned that individual and institutional factors do interact in the decision-making process. It can beargued that this is an important point that needs to be included in future proposed university choice models.

Secondly, Table 3.1 compares the ten student university choice models’ steps or lack of steps with Kotler’s simple decision-making processes’ five steps: o Identify needs o Information search o Evaluation of alternatives

120 | o Purchase decision o Post-purchase

All the listed models in Table 3.1 mention at least two of the possible five of Kotler’s simple decision-making process steps. Not all the models recognised that students will first determine the need to further his/her education, however all the models mentioned that prospective students will go through the information search step.

Apart from Hanson and Litten’s model (1982), the remaining nine models mention that prospective students will evaluate their alternatives and it is during this phase or step that factors will be compared and analysed, such as social-cultural factors, the universities’ attributes or benefits, and where negotiations and discussions will take place with friends, family and peers. Although Hanson and Litten’s model does not explicitly mention this third step of evaluation, it can be assumed that evaluation will also happen in their model as it is based on Jackson’s (1982) student-based model and on Chapman’s (1981) institution-based model that mentions an evaluation stage.

It can be concluded from this discussion that the decision to go to a specific university is a complex process and a multistage process (Cosser, 2010:44). Each of the ten models portrayed in this section, suggests that choices need to be made between different universities and programmes, that individuals and institutional factors interact with one another, and that all prospective students will enter some sort of information search process followed by evaluation before a choice will be made.

3.3 South African educational consumer behaviour decision-making models

To date, no specific educational consumer behaviour decision-making models for South Africa could be detected. However, a few studies like those of Wiese (2008), Cosser and Du Toit (2002:95) and Bonnema and van der Waldt (2008) have focused on consumer behaviour, and the choice factors and sources of information in the decision-making process of the South African student in the HE context. The paucity

121 | of this South African-related information and the lack of a choice model support the argument that the South African HE market could benefit from a choice model.

Before addressing a proposed choice model, it is essential to address the concept ‘choice’ and the meaning of choice as it appears to be central so far in all the discussions on consumer behaviour decision-making and decision-making models.

3.4 Choice defined

According to the Oxford Concise English Dictionary (Fowler & Fowler, 1996:231), choice is defined as “the act or an instance of choosing/a range from which to choose/the power or opportunity to choose.” Synonymous words that also describe and help to define the word choice, are: selection, picking, option, preference, election or adoption (Kirkpatrick, 1996:112).

With this definition in mind, understanding university choice or university selection and the multiple influences on prospective students’ university choice can help university administrators to chart recruitment strategies, implicate practice and policy, and influence research (Aldous, 2009a:1; Chapman, 1981:503). Knowing and understanding student university choice and its implication could further result in more effective use of resources and enhance the goals of both access and choice for traditional-age students (Hossler & Gallagher, 1987:220).

This section will first introduce choice in its broad sense and context, then address choice in the educational market place in Section 4.1 before arriving at the key choice factors in Section 4.2. The key choice factors will be portrayed as found in existing literature, and these findings will act as the foundation on which the proposed choice factors to be used in the researcher’s choice model will be portrayed in Section 4.3.

Choice is a dynamic, emergent and multi-dimensional concept that involves a wide range of variables. It is also an interactive process and a complex phenomenon (Moogan, 2011:2; Petruzzellis & Romanazzi, 2010:141; Foskett & Hemsley-Brown, 2001:1, 202, Hossler & Gallagher, 1987:218).

122 |

Choice is not an instantaneous or even short-term period of decisions. It is dynamic and emergent because it originates in the attitudes, values, perceptions, images and beliefs of a young child’s family. It is also influenced by the child’s family’s circumstances, before evolving in the child’s own emerging values and perceptions as he/she grows up. Choice in the end is expressed as an external expression of the balance between a wide range of internal and external social, cultural and economic perceptions (Foskett & Hemsley-Brown, 2001:201-203).

Choice is an interactive process. This is evident in that every choice impacts on other people and vice versa (Foskett & Hemsley-Brown, 2001:1). A father’s choice to move from Johannesburg to Stellenbosch may influence his son’s choice of universities, as suddenly he is closer to a university that he never really considered before. In turn, the son’s choice to consider Stellenbosch as a real alternative to further his studies may influence the father to rethink exactly where they want to live in Stellenbosch.

The choices an individual, a group or an organisation make result in changes to the world they occupy. This changes the environment of choice for every other individual, group or organisation (Foskett & Hemsley-Brown, 2001:1). If more students with higher marks chose a certain university to enrol at, the university is perceived as the institution with the better reputation and more students would also like to apply at this university. Thus, the choice of a group of ‘better’ students can influence the possible choice of other students (Baldwin & James, 2000:140).

Baldwin and James (2000:140) further explain that prospective students’ choice ensures that good quality is ‘rewarded’ and poor quality ‘punished’, but these authors warn that this is a difficult concept to apply directly to HE. They explain that students with higher marks have a better chance to choose from a variety of universities, thus it is easier for these students to ‘reward’ the universities with the better perceived reputation by enrolling at them and ‘punish’ the ones with weaker brands (i.e. average reputations) by not enrolling at them. The ‘weaker’ students will have to accept any place that is allocated to them and they are limited by choice. This is of course another reason why universities seek the ‘better’ students as it influences

123 | their reputations positively, it influences other prospective students’ choice, and these students have a better chance of passing.

Choice boosts efficiency by stimulating competition amongst providers (Baldwin & James, 2000:140). Now more universities need to ‘raise their game’ to attract the ‘right’ students. Already in 1981 Chapman raised a concern that universities should gain or maintain their competitive edge in the scramble for students. He warned that universities then persisted in the belief that they can affect students’ choice of university merely by modifying their institutional descriptions or the targeting of their recruiting. Chapman suggested that university marketers (or admission officers) should look at all the influences on student university choice, which include understanding choice factors (Chapman, 1981:490).

As student recruitment is most important in today’s competitive environment, it is necessary to identify the factors that influence student decision-making and their impact so that university marketers can benefit (Moogan, 2011:574). Problems however encountered are that prospective students find it hard to identify benefits from education as they are often intangible and hard to quantify. The intangible, non- observable qualities in HE makes it much harder to assess and compare, and prospective students are often influenced by perceptions and values held by not only them, but those significant others who constitute a network of life influences on choosers (Maringe & Carter, 2007:462; Foskett & Hemsley-Brown, 2001:203; Baldwin & James, 2000:142).

By knowing and understanding choice factors, marketers can benefit by using this knowledge to develop marketing strategies to differentiate and position universities accordingly. The competitive nature of the educational marketplace encourages universities to be responsive and proactive in communicating the institution’s identity and values to the potential student (Moogan, 2011:571). In turn, prospective students will benefit from this clearer communication as they will understand the difference between institutions and it could make the choice more tangible.

124 |

Students choose carefully when they make the important choice of which university to attend and which programme to follow (Vrontis et al., 2007:980), however, what does choice in the educational marketplace really mean?

3.4.1 Choice in the educational marketplace

The term ‘choice’ in relation to education can be misleading, for the term has an implication of irrevocable commitment to a line of actions or decisions. It suggests that choice is the ultimate culmination of a rational, reasoned process, but in some cases consumer behaviour is irrational and happens as a result of the impression and image they have of a university (or HEIs) (Baldwin & James 2000:142; Foskett & Hemsley-Brown, 2001:213).

Prospective students who are rational, aspire to high-status universities, in that they seem to have an implicit understanding of the screening function of the university selection process (Maringe, 2006:467; Baldwin & James 2000:142).

For choice to occur, there must be in existence some form of marketplace within which that choice process can operate. In understanding the nature of markets in general and of public sector markets and education markets in particular, it is important to understand both the context of choice, and the impact of market processes on choice outcomes (Foskett & Hemsley-Brown, 2001:15).

Bowe, Ball and Gold (1992) as cited in Foskett and Hemsley-Brown (2000:16) identified the characteristics of markets as the existence of: o Competition between more than one ‘provider’ of a product of service; o Choice for consumers; o Free and full information for consumers on which to base their choice; o Rational choice processes by consumers. This is based on the idea that consumers make objective, rational, fully informed choice on the basis of comprehensive, vigilant decision-making; o Some form of ‘exchange mechanism’, e.g. money;

125 | o Diversity and active differentiation between providers, demonstrating differences between products or services that enable a choice to be made by the consumer; and o Some form of ‘marketing’ organisation and activity by the providers.

In Foskett and Hemsley-Brown’s (2000:16) view, the above summary reflects a view of free markets and perfect competition in classical economic terms. These authors warn though that perfect competition rarely, if ever exists, and the characteristics of real markets will differ from the model presented by Bowe et al. (1992) as cited in Foskett and Hemsley-Brown (2001).

In the context of the public sector, the phrase quasi markets is rather used to emphasise their distinctiveness from free markets. Public sector markets are quasi markets because (Foskett & Hemsley-Brown, 2001:16-17): o Producers or suppliers are mostly ‘not-for-profit’ organisations; o The state, through national government, tightly defines and constrains the ‘product’ and hence choice, by defining curricula (e.g. the National Curriculum in the UK), or by imposing quality assurance systems and quality standards; o Exchange mechanisms are not direct between the consumer and provider. The exchange takes place via a third party (central funding organisation); and o Public sector organisations are statutorily constrained from acting as ‘true’ commercial organisations – for example, most cannot raise independent finance from private financial institutions.

In Foskett and Hemsley-Brown’s (2001:17) opinion it is clear, even if there are concerns about the precise definition, that a market exists wherever customers can make choices between different services or products whether the differences are small or large, and whether the choice is free or highly constrained.

It is argued that choice occurs at a variety of scales in the educational environment. There are choices between attending state and independent school pathways, or between academic or vocational post-compulsory educational pathways. There are choices of individual subjects for study and choices about where to study these subjects/courses (Foskett & Hemsley-Brown, 2001:17).

126 |

Expansion of HE participation accompanied by the substantial reductions in funding for HE (as discussed in Chapter 2), resulted in strong competition and increased empowerment of young people as choosers in the HE market place, even for South African prospective students. The international market is more accessible and there are 23 public HEIs to choose from in South Africa alone.

A specific South African university in a fiercely competitive environment would want prospective choosers (students) to choose their university. The likelihood of universities to survive and grow and attract the ‘right’ students would be enhanced by up-to-date knowledge and information regarding the HE environment. More specifically, by using this knowledge and information to devise marketing strategies that might influence students’ decisions on which university to enrol at (Wiese, Van Heerden, Jordaan & North, 2009:39).

Information and knowledge that should be gathered and that are very important for marketers to know are the factors influencing the purchase intention of prospective students, and to understand the nature of the relationship among those factors. Due to the increasing demand for educational services, marketers need to be more aware of the underlying factors considered by consumes when evaluating services if they want to survive in this competitive environment (Wiese et al., 2009; Cubillo et al., 2006:101; Maringe, 2006).

3.4.2 Key choice factors influencing university choice (decision-making variables)

The choice factors are prevalent in the “choice” stage of the university decision- making process. According to many authors this choice stage could also be referred to as the evaluation stage (Section 3.1). It is at this stage where students make final decisions on the options from the choice set developed by them (Pérez, 2010:21).

Prospective students associate the intangibility of the educational service with a high level of risk. Consequently, the decision process of prospective students is influenced by indirect mechanisms of service evaluation such as the image of the university, and the country of destination. Most of the quality attributes in HE cannot

127 | be perceived, felt, or tested in advance, and brings difficulties to the evaluation of a programme (Wiese et al., 2009:41; Cubillo et al., 2006:103).

Literature not only reports on the choice factors that students consider, but also suggests that some choice factors may be more important than others. This section will discuss the literature findings of which choice factors prospective students consider when choosing a HEI to further their education.

It should though be noted, that some prospective students find it difficult to make a distinction between what the most important factors are that influence their decision to attend a university. However, most of them find the availability of a desired programme, academic reputation and quality of teaching as the main reasons influencing their decision to enrol (Afful-Broni & Noi-Okwei, 2011:5).

There are different views on what the exact rank order of the most important factors that influence university choice of prospective students is. However, there is a lot of agreement on what these factors are, although not in rank importance, and the aim of the next section is to briefly introduce the factors mostly written about that were identified in playing an important role when making a university decision before attempting to develop a set of proposed choice factors (Section 4.3) that should be researched with grade 12 scholars on the brink of entering university life.

The following choice factors and their underlying determining factors will be briefly introduced and will serve as background information to the proposed choice model’s statements that will be portrayed in Section 4.3 (Table 3.2): course and programme offered, employment opportunities, image, quality, facilities, location, safety, price/cost and reputation.

3.4.2.1 Course or programme offered

The choice of which course to study at university is determined by a complex mixture of considerations relating to the broad field of study, the particular course and the university which offers it (Baldwin & James, 2000:145). Course choice is also very strongly linked to career choice and the decision on which course to study tends to be closely related to institutional choice decisions as well (Maringe, 2006:470).

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On the one hand, universities are chosen because they offer the courses that prospective students are interested in, but on the other hand certain courses are chosen, because of the perceived belief that they will provide the best opportunity of employment. A range of factors influences the course preference, including: belief that the individual’s school results will allow entry to the course; the reputation of the course among employers; graduate satisfaction from the course; graduate employment rates from the course; the quality of teaching in the course; and approaches to teaching (Moogan, 2011:581; Wong & Liu, 2010:83; Maringe, 2006:470).

How prospective students choose their career is complex and three types of factors determine people’s course of action when choosing a career: chance factors, the laws of supply and demand, and the ‘folkways or institutions of society’. The choice of career is strongly influenced and interlinked with their parents’ socio-economic status, their parents’ cultural background and occupational choice that definitely warrants a study on its own (Wong & Liu, 2010:83; Salami, 2007:651).

According to Moogan’s study (2011:573), course content was identified as one of the most important decision-making variables, or choice factors. Choosing ‘what to study’ is normally a decision that is made earlier in the decision-making period. It was also found that as soon as prospective students have chosen their two universities they most probably would like to study at, the importance of course content and their desire for information relating to this become important (Moogan, 2011:581).

The availability of courses and the benefits students will derive from those courses are the most important characteristics students look for in choosing a university. This is especially true in professional and other specialised areas of training such as medicine and architecture, and least true in content areas that are widely available such as liberal arts (Chapman, 1981:497).

A study conducted in Finland, revealed that prospective students ranked course suitability as one of the top four most important determinants of university preference (Keskinen, Tiuraniemi & Liimola, 2008:64). Lange’s (2009:362-365) study revealed

129 | that programme (or course) choice was selected as the most important reason to choose a university and that courses that are relevant to their career interests and of high quality determine their choice. Interestingly, programme choice came out stronger than institutional choice with students wanting to move from a college to a university (Lange, 2009:362-365).

Brown et al. (2009:315) had similar findings regarding the deciding factors when narrowing the choice to just two universities as course content, location, grade requirements and to a lesser extent, facilities. Hoyt and Brown’s (2003:5) study revealed that availability of programmes was consistently across several studies, one of the most frequently listed factors and in several studies in the ‘Top nine factors’ mentioned as influencing university choice.

How courses are structured (i.e. evening classes, special courses, seminars) will also have an influence on students’ choice. There is the emergence of ‘new’ customers in the HE market, where some students are older and want to study part- time, and others who want to explore different educational offers like evening classes, special courses and seminars. More students nowadays work while attending university, even if just on a part-time basis (Petruzzellis, D’Uggento & Romanazzi, 2006:360; Navarro, Iglesias & Torres, 2005:505; Hoyt & Brown, 2003:6).

The reputation of a course is also important and according to Petruzzellis et al. (2006:360) this is seen, together with the location of an institution, as the most important factors in students’ decision making.

According to a South African study conducted by Wiese et al. (2009), availability of the course or programme was not identified as one of the top 5 factors influencing first-year respondents in selecting a specific university. Although it was not explicitly mentioned as a ‘top’ influencing choice factor, quality of teaching, employment prospects (possible job opportunities) and international links (study and job opportunities) were listed as some of the top factors influencing choice. It could thus be argued when drawing on conclusions from the above literature review, that these top factors mentioned by Wiese et al. (2009:49) play a role in course choice and have an influence on ‘institution’ choice. 130 |

3.4.2.2 Employment opportunities

Prospective students have great expectations regarding the role of the university as a link with the job market, through internships and in general, the possibility of the employability of the particular university’s graduates (Ali-Choudhury, Bennett & Savani, 2009:11; Petruzzellis, et al., 2006:358). It is also argued by Petruzzellis and Romanazzi (2010:144) that the academic preparation of the prospective student’s chosen career at university should be based on the real needs expressed by the local job market.

Because prospective students put more emphasis now on the university as preparation for careers, programmes and price-related information are considered as being critical for decision-making (Petruzzellis & Romanazzi, 2010:149).

Especially at the backdrop of increased university fees as a global and local phenomenon (Chapter 2), prospective students consider more carefully consider economic factors such as job opportunities while studying to supplement their incomes and contribute to accommodation cost (Maringe, 2006:470), as well as job opportunities in their chosen careers after graduating.

Wiese et al.’s (2009:49) study indicated that employment prospects is a very important deciding factor for South African students when considering a university. This factor ranked second and international study and job opportunities ranked fifth, indicating that students are concerned about their future career opportunities. Current high unemployment in South Africa reported at 25.7 per cent in the second quarter of 2011 (Figure 3.3) can be a contributing factor to prospective students’ concern. From 2000 until 2008, South Africa's unemployment rate averaged 26.38 per cent, reaching an historical high of 31.20 per cent in March of 2003 and a record low of 23.0 percent in September of 2007 (www.tradingeconomics.com).

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Figure 3.3 South Africa’s unemployment rate

Source: Trading Economics (2011). (www.tradingeconomics.com/south-africa/unemployment-rate)

3.4.2.3 Image

Within any decision-making process an important element is an individual’s belief and understanding of the benefits and disadvantages of choosing each particular option. It is a form of cost-benefit analysis (that will be more discussed in more detail in Chapter 4). This analysis is based on conscious and subconscious processes of feeling, ideas and image, and central to this process is the role of perception. For the prospective students, it will be their perception of the world that is their objective reality (Pasternak, 2005:200; Foskett & Hemsley-Brown, 2001:208).

Unfortunately, education choices are mostly concerned with decisions about an adult world that young prospective students have not yet directly experienced. It is very important to understand that almost all their perceptions will have been passed through the filter of other people’s perceptual models before reaching them. Friends and family members’ evaluations of a particular university’s image rating may affect

132 | the image the prospective student will have. Universities cannot forsake this issue when developing marketing strategies to attract their suitable students (Arpan, Raney & Zivnuska, 2003:110; Foskett & Hemsley-Brown, 2001:208).

Marketers have to realise that the image and reputation of the university plays a crucial role in the development of marketing strategies such as a defined market positioning (Hemsley-Brown & Oplatka, 2006:316; Ivy, 2001; Nguyen & LeBlanc, 2001). A clear market position is important to be competitive in the education market place as it is a means to differentiate and distinguish a university from another. Ivy’s (2001) study focused on how universities use their marketing to differentiate their images in the South African HE market (old universities and technikons at that stage). He based his arguments on theory developed by Kotler and Fox (1985) and confirmed that it was important for universities to conduct a market analysis to establish their market position and to present the institutional image effectively (Hemsley-Brown & Goonawardana, 2007:946).

Developing and maintaining a distinct image is important to universities to create a competitive advantage in an increasingly competitive market (Petruzzellis & Romanazzi, 2010:152). It is further argued that universities have to develop and communicate the relevant messages to increase their organisational image and offering status to attract prospective students (Chapleo, 2010; Hemsley-Brown & Oplatka, 2006:326; Veloutsou et al., 2004). Not only will this distinct image help to attract prospective students, but the interaction between institutional image and institutional reputation contributes to improved customer (student) loyalty (Nguyen & LeBlanc, 2001).

It is not just the potential applicants who positively respond to a favourable image, but a university’s image will impact upon sponsors and community attitudes as well (Moogan, 2011:581). Sponsors or ‘federal agencies’ awarding grants, are guided by the university’s prestige or reputation for quality, because it is the university’s perceived excellence that is often more important than the actual quality (Ivy, 2001:277).

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Prospective students will respond positively by enrolling at a university with a positive image, as the image can strongly influence the decision to attend an educational institution. The particular characteristics of service make the prospective student analyse indirect elements when evaluating the service such as image (Cubillo et al., 2006:110; Conway, Mackay & Yorke, 1994). In the end, the university’s image is the sum of opinions, ideas and impressions that prospective students have of the institution (Cubillo et al., 2006:110; Kotler & Fox, 1995).

Prospective students’ opinion about the image of this institution is formed from word- of-mouth, past experience, and marketing activities of the institution (Cubillo et al., 2006:110). Other factors that influence the university’s image are through auxiliary services such as: library facilities, availability of computers, availability of quiet areas, availability of areas for self-study (Ali-Choudhury et al., 2009:15; Cubillo et al., 2006:111). Arpan et al. (2003:99) found that factors controlled by the university itself such as: existence of particular programmes, strength of academic programmes, sports programmes, libraries and technical facilities, were stronger predictors of overall image rating than were demographic characteristics of prospective students’ environmental factors (e.g. location, expense, and admission standards).

It is more than just the auxiliary services that influence a university’s image. Other factors such as the combination of academic offerings, student experience, the university’s prestige and other known intangibles comprise a HE brand promise. Prospective students are “hyper aware” of the numerous brand images in HE because online tools, publications, and other resources make today’s prospective student better informed and more aware of available options than ever before (Lockwood & Hadd, 2007).

It seems thus that image and reputation of some universities have been argued to be even more important factors than actual teaching quality. Concluding from this statement, it can be argued that communicating the image would be very important, which in turn suggests the necessary role for branding (Chapleo, 2010:172; Mazzarol, 1998). Although it appears that branding seems to play a vital role in prospective students’ university decision-making (Chapleo, 2010; Hemsley-Brown & Goonawardana, 2007; Lockwood & Hadd, 2007; Lowrie, 2007; Vrontis et al.,

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2007:985), it is a complex subject matter that should be investigated on its own in more detail in a separate study.

In South Africa, Wiese et al.’s (2009:49) study found that first-year students consider image as a deciding factor when choosing HEIs to further their education, and this factor ranked as the seventh most important factor.

3.4.2.4 Quality

Tsinidou, Gerogiannis and Fitsilis (2010:227) argue that universities have realised that their long-term survival depends on how good their services are and suggest that quality sets one university apart from the rest. Sustaining and improving quality is one of the main challenges of HE, because of the nature of educational services (such as the intangibility) that makes it so difficult to measure. The outcome of a quality HEI is reflected in the transformation of individuals in their knowledge, their characteristics and their behaviour. In other words, quality, as defined by students, is the result of the status of the graduates’ competencies in knowledge, skills and abilities gained (Tsinidou et al., 2010:227-228; Navehebrahim, 2009:293).

Other authors have attempted to define quality in the HE sector as excellence, as exceptional, as transformative, as fitness of purpose, multicustomer focus or as value for money. Even perfection or striving for perfection consistently (Saunders, 2005:146; Lagrosen, Seyyed-Hashemi & Leitner, 2004:63; Lomas, 2002:71)! The interpretation of quality as excellence though, is the interpretation that best matches the student’s view of quality (Lagrosen et al., 2004:67).

The construct of quality as further conceptualised in the services literature centres on perceived quality, and Zeithaml (1988) defines perceived quality as a customer’s judgement about an entity’s overall excellence or superiority. Perceived quality of a service is the result of an evaluation process of perceptions, rather than by the comparisons of expectations and perceptions (Saunders, 2005:152).

Prospective students’ perceptions of quality appear to be important in establishing a prospective student’s first and second choice of universities to choose from (Hossler & Gallagher, 1987:216). Perceptions can be formed by evaluating various factors 135 | that influence service quality (Saunders, 2005) and HE quality in general (Tsinidou et al., 2010).

The factors that influence service quality in HE are: quality of lecturers, lecturing arrangements, support systems and support facilities, manageability of programmes, physical logistics and intellectual value. In the end, the students’ perceived satisfaction will depend on the delivery of these seven factors (Saunders, 2005:152). Hill (1995:11) argues that service quality can be broken down into two sub- components, namely technical quality and functional quality. Technical quality relates to what is provided during the service process (knowledge, tangible, technical solutions), while functional quality refers to the interpersonal behaviours contributed by the service employee during the service encounter (friendly, respectful, co- operative).

Tsinidou et al.’s (2010:238-241) study revealed that the evaluation of HE quality is determined by the following factors: academic staff, administration service, library service, curriculum structure, location, facilities and career prospects. Lagrosen et al.’s (2004:67) study indicated that there are also seven dimensions that were found to be important when evaluating quality from the student’s view, namely corporate collaboration, information and responsiveness, courses offered, internal evaluations, computer facilities, collaboration and comparisons, and library resources. Each of Tsinidou et al.’s (2010) seven factors has sub-constructs (or dimensions) too that could be further discussed in a detailed research paper that should solely focus on quality in HE. It is a complex construct (dimension) and contains too much detail for the purpose of this particular study.

Mazzarol (1998:165-168) identified 17 factors as critical to the success of education institutions operating in international markets, and the following critical success factors included the quality of reputation and level of market recognition/profile, and the quality and expertise of staff. A factor analysis of the critical success factors revealed that a highly successful recruiting strategy, retaining quality and experienced staff were very important aspects to be successful and ranked as the second most important critical success factor for a high performance university. A high-quality image ranked as the third most important factor of a high performance 136 | university. Although Mazzarol (1998) conducted an international study, it could be argued that some of these factors also apply to universities nationally, as they are increasingly competing with international universities and need to know what they are up against (Chapter 2).

Quality and how prospective students perceive quality are important to universities as the satisfaction of the customer (student or prospective student) is affected by expectations and perceived quality. For any university quality perception is a core and strategic element (Eagle & Brennan, 2007:45; Cubillo et al., 2006:110) and universities should manage all service encounters to enhance student satisfaction, thus enhancing perceived quality (Petruzzellis & Romanazzi, 2010:145).

Perceived quality is such an important deciding factor for prospective students as many universities are eliminated before even reaching their choice set. The impressions of quality may have already determined the actual enrolment decision for many students (Hossler & Gallagher, 1987:218).

Quality is also an important choice factor for South African students. Wiese’s (2008:5) study indicated that quality of teaching was the most important choice factor when selecting a university.

3.4.2.5 Facilities

The physical environment, in which the service production constitutes, is an important element in the decision-making process. When provided with a high standard, facilities are considered as a relevant factor in influencing the prospective students’ selection of the university where they will pursue their studies (Cubillo et al., 2006:110).

Baldwin and James (2000:145) also argue that facilities play a relevant role in prospective student’ decision-making process, but that it is less important than other factors. They argue that prospective students in most cases have considerate information about the campus surroundings, size and facilities, but they are considered less important in their decision-making (Baldwin & James, 2000:145).

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Hoyt and Brown (2003:5) had similar findings than Baldwin and James (2000) where campus facilities were not ranked as one of the top ten deciding factors when choosing a university, but in the next set of importance, thus confirming that it plays a relevant role in the decision-making process.

Nguyen and LeBlanc (2001) have a different view and commented that facilities on campus were critical factors that helped determine students’ perceptions of the image or reputation of a particular university. These authors are of the view that facilities play an important role in students’ decision-making and agree with the ‘Centre for Facilities Research’ (Cain & Reynolds, 2007), which posit that facilities affect student recruitment.

In 2007, the Center for Facilities Research (CFaR) that represents facilities directors in HE, conducted a study of 16 153 students from 46 HEIs in the U.S. and Canada to determine what physical factors affect student recruitment and retention. The key findings were: o 73% found “facilities related to my major” to be extremely or very important; o the standard and quality of library facilities, sophisticated academic technology, classroom buildings and residence halls could sway students’ choice; o 64% of students strongly agreed or agreed that the condition of facilities on campus was important in choosing a college; o only 32% said that the recreation facilities were either ‘important’ or very important in their decision to attend and institution; and o 26% agreed that an inadequate facility led them to eliminate that university off the choosing list (Cain & Reynolds, 2007:9).

Academic facilities (libraries and laboratories) were ranked as the fourth most important choice factor in Wiese et al.’s (2009:49) South African study, and Coetzee and Liebenberg’s (2004:71) South African study revealed that sporting facilities were among the top five most important choice factors mentioned in their study conducted among Gauteng Province secondary school learners.

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3.4.2.6 Location

Students do evaluate aspects such as the university’s location which includes the distance from home, when deciding which university to attend (Ivy, 2010:394; Drewes & Michael, 2006:799). In the 1970s, location and the size of the university were the criteria most frequently used in deciding which university to attend, followed by reputation for academic quality (Moogan et al., 1999:214).

Today, location still plays a vital role in decision-making, however, prospective students choose a specific course mainly because of its reputation as well as the location where it will be studied (Moogan, 2011), while at least half of university applicants choose the university closest to their home towns or home (Keskinen et al., 2008; Chapman, 1981:497).

Proximity to home is in turn influenced by the number of educational alternatives in the geographical area. Thus, prospective students in an area with many universities are less apt to travel as far to university as prospective students in a rural area with a limited choice of universities in their area (Chapman, 1981:497). A note of caution though, is the fact that this study by Chapman was conducted in 1981, before distance learning via the internet was introduced.

Although Briggs’ (2006) study ranked location (3rd) and distance from home (2nd) as highly influential factors when deciding which university to attend, nonetheless, prospective students indicated that they will travel and incur costs to access reputation (Briggs, 2006:720). This argument is further supported by Chapleo (2010:177) when he suggests that location has a very important part to play in the success of the brand, and that a strong university brand says something about the reputation of the university. He explains that in the UK, a university in Bournemouth is considered desirable from a lifestyle perspective, a university like Manchester has some ‘renaissance of image’, and a London university on the other hand has a reputation of a global centre and these different locations influence the brand reputation, overall reputation and eventually the choice.

In South Africa, location also plays a role in the university decision-making process. Location was the fifteenth most important choice factor in Wiese et al.’s (2009:49) 139 |

South African study. It could possibly be argued that ‘where’ the university campus is located, can have an effect on how students perceive the safety of South African universities. This is an area of possible investigation for universities, especially if a particular South African university has several different campuses. If lectures for a specific course are taking place in the middle of Johannesburg’s City Centre, prospective students who do not know Johannesburg well, could be afraid that their personal safety will be in jeopardy, and therefore not select the particular university because of the location of that one particular campus.

3.4.2.7 Safety

According to Hoyt and Brown’s (2003:7) and Price, Matzdorf, Smith and Agahi’s (2003:215) studies, campus safety failed to make the top ten important factors when considering a university to attend. This is in stark contrast with South Africa’s situation. Wiese et al.’s (2009:49) South African study revealed that campus safety and security ranked as the third most important choice factor which first-year students consider when they select a university.

Wiese et al. (2009:50) commented that the high crime rate in South Africa may be a contributing factor to the high importance of safety. Figure 3.4 illustrates the high crime rate and illustrates numbers of cases registered and the proportional contribution of each of the broad crime types to the total crime picture in South Africa for the period 2010/2011. Of the approximately 2,1 million cases, almost a third (30,8% or 638 468 cases) were contact crimes, about a quarter (25,8% or 534 866 cases) were other serious crimes, another quarter (25,8% or 534 451 cases) were property-related crimes, 11,2% (231 842 cases) were crimes detected as a result of police action, and 6,4% (131 860 cases) were contact-related crimes (Crime Report 2010/2011, South African Police Force).

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Figure 3.4 Serious crime in the Republic of South Africa (2010/2011)

Source: Crime Report (2010/2011), South African Police Service (Accessed via http://www.saps.gov.za/statistics/reports/crimestats/2011/crime_situation_sa.pdf on 1 November 2011)

3.4.2.8 Price/Cost

For universities, funding issues will influence widening participation (Chapter 2), and for the prospective students it will influence to some degree choice, as most will recognise the possibility of leaving university with high debt levels (Moogan, 2011:574).

How prospective students will fund their studies is a concern, however a good quality programme at a competitive price is a fitting description of what students are looking for when choosing a university (Hoyt & Brown, 2003:6). Unfortunately, a competitive price will still have an influence on some prospective students’ decision to study at all, and for others it will limit options by rather choosing the university closer to home. The choice factor of cost or price is probably more of an influence on whether or not a student goes to a university rather than on which particular university he or she attends (Chapman, 1981:496).

Research has shown that a lack of knowledge about university, but specifically financial aid, is prevalent in society today, although some prospective students are

141 | aware of financial costs and will be searching for financial aid information and the availability thereof. Prospective students are attracted by scholarships (Bell, Rowan- Kenyon & Perna, 2009:664; Lange, 2009:362-365; Drewes & Michael, 2006:799) and financial aid is supposed to increase students’ university choices if cost was the constraining factor (Lauer, 2002:184; Chapman, 1981:497). Universities can use this knowledge to inform prospective students about the possibility of scholarships and financial aid and it should be communicated to parents to. Especially parents who have no direct personal experience with university lack knowledge of financial options and it is most pronounced with low-income parents (Bell et al., 2009:664).

The cost of studying is important to a potential student, but perhaps not the most important deciding factor (Moogan, 2011:574). Initially price (or cost) is a concern, but as students move through their studies, they are becoming increasingly involved in calculating anticipated rates of return on the investment they made. This is a clear signal of how students’ choice behaviour is changing, highlighting the growing consumerism in HE choice (Petruzzellis & Romanazzi, 2010:149).

Students seldom know the actual net price of individual institutions from their choice set on the basis of list price rather than net price (Hossler & Gallagher, 1987:214). If individual institutions hope to expand their pool of potential applicants, then efforts to communicate the net cost of attendance to various market segments should be enhanced (Hossler & Gallagher, 1987:214/5).

Cosser and Du Toit’s (2002:94) South African study revealed that prospective students predict that they will not be studying at the HEIs of their first choice because of financial constraints as the key reason. Wiese et al.’s (2009:49-50) South African study indicated that fees or cost, was still an influencing factor when deciding which university to attend and ranked in the top 15 important choice factors at number 13.

3.4.2.9 Reputation

Hemsley-Brown and Oplatka (2006:327) warn educational marketers that it is important to realise the difference between how the concept of institutional image and reputation might be interpreted in HE compared with other services organisations. A company’s high reputation is usually connected to high sales and

142 | high demand from customers. In contrast, a university’s high reputation is often linked to minimal “sales”, meaning that the more prestigious the university is, the better its position to select top students onto its educational programmes. But is reputation really that important, even if prospective students are aware that places at prestigious universities are limited?

The answer is still yes. Canadian authors Nguyen and LeBlanc (2001) claimed that there is consensus on the essence of the concept of reputation in that it is an important deciding factor when selecting a university. They also argue that the reputation of a university is the result of the past actions of an organisation, thus a perception of the reputation is formed. The more favourable the prospective students’ perception of the reputation, the higher the student’s loyalty will be and the better the likelihood of applying at that particular university (Petruzzellis & Romanazzi, 2010:151).

Reputation is important, probably the most important choice factor as Hoyt and Brown’s (2003:5) study indicated academic reputation as the most frequently listed, and thus the number one factor, influencing prospective students’ university choice. Reputable programmes also ranked as the seventh most important factor playing a role in university decision-making. Briggs (2006:713) had similar findings and agrees that academic reputation is the top choice factor influencing undergraduate students’ university choice. Briggs (2006:713) also mentioned teaching reputation (ranked 8th) and research reputation (ranked 11th) as important choice factors.

Briggs (2006:716-717) conducted a factor analysis of 22 ‘choice’ attributes to determine underpinning relationships. The factor analysis portrayed the reputation construct with the strongest loadings, and thus the number one factor influencing choice. The sub-constructs for reputation were: teaching reputation, quality of faculty, academic reputation and research reputation. The factor analysis also reduced the original 22 attributes to five constructs of student choice, made up of: reputation, institution features, information, demographics and employment.

Although other studies do not necessarily repeat the exact same wording for reputation-related sub-constructs, there are definitely similarities found in literature to 143 |

Brigg’s (2006) findings. The university’s reputation for quality is explained by some others as quality of faculty, service quality, teaching quality, academic quality, recognition of the qualifications, and international quality, while the presence of well- known lecturers is summarised by others as teaching reputation and staff reputation that are all important when prospective students choose a university (Petruzzellis & Romanazzi, 2010:151; Keskinen et al., 2008:649; Maringe & Carter, 2007; Maringe, 2006:474-475; Pasternak, 2005:196).

There are various constructs (dimensions) and sub-constructs (sub-dimensions) that influence the perception of reputation of a university. Aspects not included in the above discussion but also evident in the literature are: availability and quality of information, availability and quality of facilities (Petruzzellis & Romanazzi, 2010:151), prominence factors that include sub-dimensions such as the institution’s reputation, staff reputation, press reviews and league tables (Maringe, 2006:474-475) as well as academic reputation that also play a role in prospective students’ university choice.

Recommendations by family, friends or acquaintances who attended the university or who are familiar with the university also contribute to the reputation of the university (Pasternak, 2005:196). If prospective students were familiar with a specific department’s teaching and research, it added to their willingness to apply and, alternatively, if the applicants had no information on the characteristics of the department, it had no or little bearing on the decision-making (Keskinen et al., 2008:649).

Although the word reputation did not rank as the most important deciding factor in Wiese et al.’s (2009:49) South African study, quality of teaching did rank as the most important, and academic reputation (prestige) ranked 9th in the top 15 choice factors list. Both these constructs (or dimensions) were mentioned several times as a sub- construct (or sub-dimensions) of reputation (Petruzzellis & Romanazzi, 2010:151; Keskinen et al., 2008:649; Maringe & Carter, 2007; Maringe, 2006:474-475; Pasternak, 2005:196), and therefore the conclusion can be made that reputation must also be a very important choice factor in the South African situation, as already revealed by Cosser and Du Toit’s (2002:95) South African study.

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It is evident from the above discussion on key choice factors found in literature that course or programme offered, employment opportunities, quality, facilities, location, safety, price/cost (or fees) and reputation can be viewed as the top eight construct (or dimension) themes relating to decision-making. Briggs (2006:715-716) added to these initial main constructs (after conducting a factor analysis) the following three main constructs of (1) institution features (reputation with disabled, competition for place, programme flexibility, cost of package, academic support, facilities, first university to offer place), (2) information (guidance from parents, accommodation for first-years, information supplied by the university, entry requirements) and (3) demographics (own perception, student placements, graduate employment). It is though evident from these additional three main constructs that their sub-constructs (or sub-dimensions) overlap with constructs (or dimensions) already mentioned in the discussion.

The researcher suggests for reasons explained in Chapter 1, that these key choice factors should be tested and explored in the South African market but with the grade 12 scholars and not with first-years as Wiese’s (2008) study.

3.4.3 Key choice factors influencing university choice

The importance of key choice factors identified in this chapter will be tested by portraying it as statements (Table 3.2) in a self-administered paper-based questionnaire to grade 12 scholars who are intending to further their education at a university or a university of technology in South Africa. These statements were compiled by merging all the factors found in the literature to arrive at an exhaustive list that will be reduced by conducting factor analysis. The factor analysis will also be employed to determine any possible underpinning relationships between statements (Briggs, 2006:715).

Key factors or key issues that influence prospective students’ university choice that were identified in the literature were written down and then expanded to arrive at statements. The detail of these statements will be discussed in Chapter 5.

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Table 3.2 Choice factors considered by prospective university students

Factors influencing your decision 1 It has excellent lecturers 2 Its lecturers are knowledgeable experts 3 It is known that only the very intelligent study there 4 Only good students get in 5 Academic programmes are nationally known 6 It offers many good cultural experiences (fine arts, music, theatre, etc.) 7 It offers a variety of courses 8 It offers the courses that I am interested in 9 It offers courses with a good reputation 10 It offers courses that the job market is interested in 11 It is committed to social service (involved with local community) 12 Sport teams have a good reputation 13 There are good sporting opportunities at the university 14 It is committed to academic excellence 15 It offers a world-class education 16 Its qualifications are internationally recognised 17 It is a reputable institution (in South Africa) 18 Its qualifications are reputable 19 It has a positive image with possible employers 20 Its admission requirements are high (ie students must do well in grade 12 to get in) 21 Hostel/residential facilities are attractive 22 The campus looks attractive 23 The buildings look attractive 24 The campus looks prestigious 25 Its buildings and grounds are well maintained 26 The sports facilities are up to date 27 It has good resources for students (computers, library, etc) 28 It offers a safe environment 29 The recreation facilities (e.g. student centre) look attractive 30 I will find a job after completing my qualification 31 Studying at this university will make it possible to find a job after qualifying 32 Studying at this university will enhance chances of employment opportunities 33 Studying at this university will increase career prospects 34 Studying at this university will provide better salary prospects 35 Current students’ perception at this university is positive 36 My friends’ perception of this university is positive 37 My friends also consider to study at this university 38 My parents’ perception of this university is positive 39 My school teachers’ perception of this university is positive 40 The particular university’s representatives are positive about the university 41 Others from my cultural group are present on campus 42 My culture will be respected 43 I will feel at home at this university 44 I will be able to express my culture at the university

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Factors influencing your decision 45 All population groups are represented on the campus 46 It is known that there is NO racism 47 I will be taught in English 48 The distance of the university from home is not too far 49 The university’s campus is easily accessible (transport) 50 The university’s campus is located near shops/malls 51 The university’s campus is close to health services (hospitals, dentists, etc) 52 Accommodation (other than residence) is near the campus 53 The cost of tuition at the university is fairly priced 54 Financial aid and scholarships are available at the university 55 Studying at the university is value for money 56 My parents/guardians are able to afford the university 57 There will be the opportunity for part-time jobs (nearby campus)

3.5 Conclusion

This chapter’s main goal is to introduce a proposed set of statements or choice factors that will be used as the first stage of the proposed prospective student university choice model and that will be tested and discussed in future chapters (Table 3.2). It was determined that at the backdrop of increasing competition for students, understanding university choice and choice factors are important as they have potential implications for universities’ future practice, policy, and research (Aldous, 2009a:1; Maringe, 2006; Maringe & Foskett, 2002:36). A comprehensive set of choice factors could result in an improved prediction of student university choice and provide university marketers with a more accurate picture of those university characteristics students believe are important in the university selection process (Hoyt & Brown, 2003:5).

As choice and choice factors form an integral part of the entire consumer behaviour decision-making process (Hoyer & MacInnis, 2008:3-5), this chapter was introduced with a discussion on consumer behaviour as background information. Studying and understanding consumer behaviour and decision-making even in the educational context will help university marketers to better segment and target their market(s) (Moogan, 2011:583; Ivy, 2010; Hoyt & Brown, 2003).

Although the decision-making process which students follow is complex (Moogan et al., 1999), they normally progress through a five-step (Wiese et al., 2009:152) 147 | process. For this reason, the ‘simple’ five-step consumer behaviour decision-making model is briefly discussed as it is designed to coordinate and explain relevant concepts in the decision-making process into a significant whole (Schiffman et al., 2008:75) and many educational choice models use this model or parts of this model as a foundation for their own models (Table 3.1).

The classification of the educational consumer behaviour decision-making models is portrayed with the main focus and discussion on the information processing models. The literature review on these information processing models reveals that most authors acknowledge that choice and choice factors are an integral part of the ‘educational’ decision-making process (Table 3.1).

The literature review reveals that at the time of writing, no educational or choice model for South African prospective university students existed, and therefore the need to develop one has been identified (Section 3.3). Applying the knowledge gathered in this literature review, a proposed set of statements or choice factors has been developed (Table 3.2) by using constructs such as course and programme offered, employment opportunities, image, quality, facilities, location, safety, price/cost (or fees) and reputation. Prospective students consider many more factors in much more complex ways than only these mentioned nine factors; however, the sub-constructs (or sub-dimensions) found in literature are also portrayed in Table 3.2. It should though be noted that the choice decision becomes more complex, because universities differ in reputation, focus, location, infrastructure and physical environment, and therefore, evaluating these choices, even for students with access to quality information, is problematic (Briggs, 2006:708).

Choice factors alone will not be enough to understand prospective students’ complex evaluation of possible universities, but value also becomes important and will be discussed in Chapter 4. What prospective students value when deciding on which university to attend, also plays a role in university choice (Moogan, 2011:583). Value is measured as preferences of benefits that influence purchase, or benefits could also be explained as relative rates of returns that students make when evaluating choice factors (Maringe & Carter, 2007:462; Woodruff, 1997:141). Schiffman et al. (2008:9) define customer value as the ratio between the customer’s perceived

148 | benefits (economic, functional and psychological) and resources (monetary, time, effort, psychological) used to obtain those benefits.

Knowing what prospective students value can help universities with more tailor-made communication strategies (Moogan, 2011:583) and effective and informed marketing would not just influence the decision-making process but might positively influence the choices made (Briggs, 2006:707). If universities can predict where applicants will come from and what they value, scarce resources can be focused on marketing areas that will give the highest return (Briggs, 2006:708). Consequently, marketing strategies can be developed in order to attract the most suitable students, whereby communication practices are matched to the information needs of the potential student (Bonnema & Van der Waldt, 2008) thus reducing barriers to entry as well as increasing retention rates (Moogan, 2011:572).

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CHAPTER 4

Perceived value and willingness/intention to buy

4.1 Introduction

It was indicated in Chapter 3 that insights in choice factors alone will not be sufficient to understand prospective students’ ‘complex’ evaluation of possible universities (Moogan, 2011:583). Choice is a function of multiple independent consumption values (LeBlanc & Nguyen, 1999:188) and therefore value also becomes an important determinant of choice (Moogan, 2011:583). The main focus of this chapter is to determine what value is and specifically what perceived value is. In reviewing the literature, the goal is to understand the essence of the theory and to obtain knowledge and an understanding of the terms, specifically in a Higher Education (HE) environment. Understanding the concept of value, or value as perceived by the prospective student, is important as it drives consumer decision-making (Cronin, 2003:333), and it is an accurate indicator of the students’ intention to enrol (Ledden, Kalafatis & Samouel, 2007:966; Patterson & Spreng, 1997:416). Knowledge of prospective students’ embedded values permits a university to position itself better (Ledden & Kalafatis, 2010:42), and to be more strategic in how they evaluate, develop and tailor their offerings to students that can optimise students’ learning experience (Lai, To, Lung & Lai, 2012:273; Durvasula, Lysonski & Madhavi & Madhavi, 2011:42; Boksberger & Melsen, 2011:229).

This chapter firstly defines value and secondly perceived value before elaborating on perceived value’s two key factors, the ‘get’ factor (or benefits received factor), and ‘give’ factor (or sacrifices incurred factor). Determining the underlying factors of both the ‘get’ and ‘give’ factors further forms a key element of this study, as perceived value as a construct forms an integral part of the proposed theoretical model (Figure 4.9).

Once perceived value and all its underlying factors have been determined and discussed, the next section of the chapter addresses purchase intention and/or willingness to buy. Perceived value has both a direct and indirect effect on behavioural intentions (Tam, 2000:38), however, before the effect of this construct 150 | on behavioural intention can be explored, the concept of behavioural intention or willingness to buy needs to be grasped. Figure 4.9 portrays the link /influence that perceived value has on prospective students’ intention to enrol in the proposed model that will be tested in Chapter 6.

Following the discussion on behavioural intention and/or willingness to buy, the various elements of value-intention frameworks or models have been unearthed and the researcher explores the various existing value-intention frameworks or models that bring value, perceived value and intention to buy and/or willingness to buy together. It is important to understand how perceived value is modelled, with the likely outcome of a purchase, intention to purchase or willingness to buy, before the researcher can portray, test and analyse the proposed model (Figure 4.9).

This chapter will conclude with a portrayal and discussion of the proposed choice model and its various elements that will be empirically tested in Chapter 6.

4.2 Value defined

Customer value provides the foundation for all marketing activity (Holbrook, 1996:138). It should be considered as the cornerstone of marketing and this is reflected in the prominent position that value occupies in the definition of marketing of the American Marketing Association: “Marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large (Ledden & Kalafatis, 2010:141).” Exchange is thus a value-creating process (Alford, 2001:4) and it explains why the product interests the buyer (Khalifa, 2004:648).

Jordaan and Wiese (2010:539) argue that universities can only survive in their dynamic environment if they offer more value to their target market than competitors. The exchange of value to potential students will result in these students in return ‘giving’ their ‘choice’ to the university offering with the highest value (Simões & Soares, 2010:373). Taking these authors’ arguments into consideration (Jordaan & Wiese, 2010; Simões & Soares, 2010), it can therefore be argued that superior value

151 | will result in students choosing that particular university. But, what exactly is value and what does it mean?

According to the Oxford Dictionary, value is described as “the worth, desirability, or utility of a thing’’ (Fowler & Fowler, 1996:1549) and ‘perceive’ is described as “to apprehend with the mind; understand, regard mentally in a specified manner” (Fowler & Fowler, 1996: 1013). Woodruff (1997:142) proposed the following definition for value: “Customer value is a customer’s perceived preference for and evaluation of those product attributes, attribute performances, and consequences arising from use that facilitate (or block) achieving the customer’s goals and purposes in use situations”. Boksberger and Melsen (2011:230) described value to refer to a preferential judgement of either a single transaction or an ultimate end- state, while values refer to social behaviour including attitude, ideology, beliefs and justification (Boksberger & Melsen, 2011:230). Holbrook (1996:139) agrees that customer value refers to preference and defined customer value as “an interactive relativistic preference experience”. Holbrook (1996:139) clarifies his definition by explaining that preference includes: predisposition, attitude, opinion, directional behaviour, valence, judgement, or evaluation. He argues that all these words refer to value in the singular form as opposed to values in the plural form. Value can shape consumers’ motivations and product choices (Chung, Fam & Holdsworth, 2009). Value is also regarded as ‘enduring beliefs’ that a particular mode of behaviour or end-state of existence is preferable to opposite models of behaviour (Durvasula et al., 2011).

It is this “preferential judgement” of the “worth, desirability or utility of a thing” of either a single transaction or an ultimate end-state from the customer’s (or prospective student’s) point of view that will be the main focus of this study. Khalifa (2004:646) categorised value in three groups of value: financial economists’ shareholder value, marketers’ customer value, and stakeholders’ stakeholder value. However, customer value is seen as the source of all other values. The importance of the customer is also evident from De Chernatony, Harris and Dall’Olmo Riley’s (2000: 40-41) study which found that consumer behaviour literature defined value in terms of customer needs and perceived desires. Khalifa (2004:653) defines value as cognitive representations underlying customers’ needs and goals.

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The importance of the student as customer has already been argued in Chapter 2 and will not be repeated. How the prospective customer (or student) arrives at these “preferential judgements” of the perceived value offered by a particular university, is the focus of this chapter.

Although it is evident from the above discussion that value can be determined as perceived customer needs and desires (De Chernatony et al., 2000) and the preferential judgements from the customer’s point of view (Chung et al., 2009), a single ‘agreed’ definition is not evident in the literature. Value has a number of different concepts, explanations and theories that make up its foundation (Boksberger & Melsen, 2011:233). The literature on defining value is fragmented (De Chernatony et al., 2000; Woodruff, 1997) and the complexity (Patterson & Spreng, 1997) of arriving at a single definition is further enhanced from the subjectivity of value, as it is highly personal (Khalifa, 2004:645; Zeithaml, 1988:14), it is perceived by the customer (Whittaker, Ledden & Kalafatis, 2007) and researchers often depend on uni-dimensional self-report measures to capture the concept (Zeithaml, 1988:2). Value is an abstract concept (Patterson & Spreng, 1997; Dodds, Monroe & Grewal, 1991:307) that is dynamic and evolves over time (Khalifa, 2004:645; De Chernatony et al., 2000). It can be considered at different times, during or after use, or when making a purchase decision (Eggert & Ulaga, 2002:110; Woodruff, 1997:141), it varies across different situations (LeBlanc & Nguyen, 1999:188; Zeithaml, 1988) and it involves difficult trade-offs (Bettman, Luce & Payne, 1998).

De Chernatony et al. (2000) found in their literature review of ‘value’ that this concept is definitely dynamic and varies in meaning with authors having different opinions. For example, the pricing literature defines value as the trade-off between customers’ perceptions of benefits received (and sacrifices incurred). As already mentioned earlier, their study revealed that the consumer behaviour literature defines value in terms of customer needs and desires and that some define value in terms of the mental images or cognitive representations underlying customers’ needs and goals. De Chernatony et al. (2000:40) also mention Sheth, Newman & Gross’ (1991) five consumption values (functional, social, emotional, epistemic and conditional), located within the consumer behaviour literature, which could influence consumer purchase behaviour. Consumption values can thus guide consumers when they 153 | make the choices they do (LeBlanc & Nguyen, 1999:188; Sheth et al., 1991). De Chernatony et al. (2000:41) mentions that the strategy literature defines value as that amount of money buyers are willing to pay, or by meeting or exceeding customers’ expectations in product quality, service quality and value-based prices.

Dodds, Monroe and Grewal (1991:307) argue that value is frequently confused with the concepts of quality, benefits and price. The strategy literature defines value as the customers’ expectations in quality (product and service) and prices. De Chernatony et al. (2000:41) and Zeithaml (1988:2) agree that although there are different opinions about what expresses value, customers agree on cues that signal quality. Sánchez-Fernandez and Iniesta-Bonillo (2009:426) also agree that quality is a factor of value as they posit that the economical components of value justify the adoption of a bi-dimensional structure of the construct thus including the factors of efficiency and quality. Although in agreement, Zeithaml (1988:2) warns that value and quality comprise an indistinct and elusive construct that often gets mistaken for imprecise adjectives like goodness, or luxury or shininess or ‘weight’. Zeithaml (1988:14) also argues that value is more individualistic and personal than quality and is therefore a higher-level concept than quality. Although there is agreement from customers on cues that signalled quality in Zeithaml’s (1988) study, respondents differed considerably in their expressions of value, and the author grouped respondents’ answers into four consumer definitions of value.

These four definitions of value were portrayed as follows (Zeithaml, 1988:13): o Value is low price o Value is whatever I want in a product o Value is the quality I get for the price I pay o Value is what I get for what I give

Each definition involves a different set of linkages among the elements in the model, and each consumer definition has its counterpart in the academic or trade literature on the subject. It is the fourth definition though that is consistent with Sawyer and Dickson’s (1984), as cited in Zeithaml (1988), conceptualisation of value as a ratio of “attributes weighted by their evaluations divided by price weighted by its evaluation”

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(Zeithaml, 1988:13). In simpler terms, this fourth definition states that value is, what you are paying for, is what you are getting, thus concluding that value involves a trade-off of ‘give’ and ‘get’ components. It is these four consumer expressions of value that can be captured in one overall definition: “Perceived value is the consumer’s overall assessment of the utility of a product based on perceptions of what is received and what is given. Though what is received varies across consumers and what is given varies, value represents a trade-off of the salient ‘give’ and ‘get’ components” (Zeithaml, 1988:14). Khalifa (2004:649) concluded in simpler terms that value is defined as the difference between customers’ perceptions of benefits received and sacrifices incurred. These benefits include tangible and intangible attributes of the product/ service offering.

The ‘trade-off’ that Zeithaml (1988:14) addresses, is also referred to as the customer’s comparison of value on the same attributes. This comparison takes place by customers assigning important weights and scale values (utilities) to product attributes for which information is available at the time of decision-making, and they combine these weights and values according to some rules to come up with an overall evaluation (Jiang, 2004:74). It is this overall evaluation which helps customers to arrive at perceived value for a product or service.

For the purposes of this study, the following conclusion can be made about the definition of value: Value can be defined as students’ “preferential judgement” (Boksberger & Melsen, 2011:230) and “overall assessment” (Zeithaml, 1988:14) of how a specific university will meet their needs and desires (De Chernatony et al., 2000). Value is thus an evaluation of possibly meeting a student’s needs and desires that are based on the perception of student as customer (Jiang, 2004:74; De Chernatony et al., 2000; Zeithaml, 1988:14). Although value has a number of different concepts, explanations and theories, there is agreement in the literature that value represents a trade-off of the ‘give’ (sacrifice) and ‘get’ (benefits) components (Boksberger & Melsen, 2011:230; Blocker & Flint, 2007; Ledden et al., 2007; Khalifa, 2004; Teas & Agarwal, 2000; LeBlanc & Nguyen, 1999:186; Woodruff, 1997; Zeithaml, 1988) or weighing costs against benefits (Pasternak, 2005:189). Once value as concept has been defined, the challenge lies in the definition of perceived value. 155 |

4.3 Perceived value

Perceived value refers to the comparison and evaluation of some concept by some subject (usually the consumer). Perceived value can be viewed as relational exchanges where transactions between two parties take place in which each party gives up ‘something of value’ in return for ‘something of greater value’ received. Thus, in line with this definition, the process of arriving at perceived value is interactive and preferential (Boksberger & Melsen, 2011:229, 230, 232). Woodruff’s (1997:142) proposed definition for value states that “Customer value is a customer’s perceived preference…” It could thus be argued that value and perceived value are about customers’ preference.

It will be argued in the following section that the terms ‘value’ and ‘perceived value’ are interrelated and sometimes used interchangeably, and that it is difficult to comprehend and to arrive at a definite distinction between these two concepts.

As example, in the literature already portrayed, perceived value is described as a subjective construct, which is similar to Khalifa (2004:645) and Zeithaml’s (1988) description of value. Perceived value is described as a dynamic construct (Petrick, 2002:121), similar to De Chernatony et al. (2000) and Khalifa’s (2004:645) descriptions of value. Woodruff (1997) defines value as perceived preference, while Boksberger and Melsen (2011) describe perceived value as interactive and also as preferential.

It can be concluded from the few examples mentioned, that some authors use ‘value’ and ‘perceived value’ interchangeably. The Oxford Dictionary state that the word “perceive” only describes how value is “understood”, how the term is “apprehended with the mind; and how it is regarded mentally in a specified manner” (Fowler & Fowler, 1996: 1013). The word “perceive” in front of the word “value” will not change the meaning of value, it describes how value is interpreted as it will differ from customer to customer and from situation to situation.

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Boksberger and Melsen (2011:233) argue that perceived value has a number of different concepts and theories that make up its foundation. There are the cognitive and affective components, where Dodds (1996:1) describes the cognitive components of perceived value as the link between the cognitive attitudes of perceived quality and monetary sacrifice, and the behavioural intention to buy. There is also the relationship with concepts (Sánchez, Callarisa, Rodríguez & Moliner, 2006:395) including; value, service quality, price, benefits, sacrifices and customer satisfaction that highlights the diversity and complexity of the theory. This complexity makes it difficult to arrive at a single definition of perceived value (Boksberger & Melsen, 2011:233).

For the purpose of this study the following ‘perceived value’ definition will be adopted: “Perceived value is a trade-off between what customers receive such as quality, benefits, and utilities, and what they sacrifice such as price, opportunity cost, time and efforts (Kuo, Wu & Deng, 2009:890; Sánchez et al., 2006:395; Tam, 2000:32; Zeithaml, 1988) or a trade-off between perceived benefits and perceived costs (Chen, 2008:710). In short, “perceived value is a trade-off based on perceptions of what is received and what is given” (Lai & Chen, 2011:319; Chen, 2008:710; Zeithaml, 1988).

Perceived value can also result in three possible value positions (Bojanic, 1996) as cited in Petrick (2002:121, 2004:398): o Offering comparable quality at a comparable price, o Offering superior quality at a premium price, or o Offering inferior quality at a discounted price.

Bojanic (1996) as cited in Petrick (2002, 2004), concludes that service providers’ relative value will change if they modify what they are doing, if competitors modify what they are doing, or if a customer’s needs and/or preferences change.

Cronin, Brady, Brand, Hightower and Shemwell (1997:375) also touched on the concept of quality, specifically service quality. They argue that explanations of consumers’ services purchases have focussed on the relationship between service quality and purchase intention. Although consumers do not always buy the highest

157 | quality service or the lowest cost service, the importance of service quality and price in predicting purchase behaviour is according to these authors, so important that it should be investigated to provide insight into the decision-making process of consumers. Not only has service value (or perceived service value) been identified as an antecedent to behavioural intentions, but to satisfaction as well (Chen, 2008; Cronin, Brady & Hult, 2000).

Perceived value can easily be confused with satisfaction (e.g. meeting customers’ needs). These constructs are however related, but distinct (Sweeney & Soutar, 2001:206). Sánchez-Fernández and Iniesta-Bonillo (2009:427) argue that perceived value and satisfaction are related (although different), but they have certain characteristics in common: both are relative judgements, both are dependent on the consumption context, and both involve aspects of costs and benefits, especially with respect to the use situation.

The distinction that has been noted, is that satisfaction is a post-purchase evaluation and an emotional state of mind created by the exposure to a service experience, while perceived value can occur at various stages of the purchase process, including the pre-purchase stage. Satisfaction depends on the experience of having used the product or service, while value perceptions can be generated without the product or service being bought or used. Satisfaction serves as a tactical purpose in measuring how well a market offering is perceived by existing customers. It thus provides guidelines of action for improving current products and services. In contrast, perceived value serves as a strategic purpose in indicating future directions for creating value for customers and best meeting their requirements (Sánchez- Fernández & Iniesta-Bonillo, 2009:427; Petrick, 2004:399; Sweeney & Soutar, 2001:206; Woodruff, 1997).

In order to investigate this relationship between service quality and purchase intention, Cronin et al. (1997:376) argue that the ‘value’ construct is the crucial unifying construct in consumer decision-making models. It is a multi-dimensional construct and it is a construct configured by two parts, one of benefits received by the customer (economic, social and relationship), and another of sacrifices made (price, time effort, risk and convenience) (Moliner, Sánchez, Rodríguez & Callarisa, 158 |

2007:1398). Benefits have been identified as the salient ‘gets’ characteristics, while the sacrifices made to acquire or consume these ‘gets’ has been identified as the ‘gives’ components (Boksberger & Melsen, 2011:232).

4.3.1 The factors of perceived value

To investigate the ‘value’ construct further, the different factors will be investigated under the headings of ‘get’ factors and ‘give’ factors as the adopted definition of perceived value defines it as the trade-off based on perceptions of what is received and what is given (Lai & Chen, 2011:319; Kuo, Wu & Deng, 2009:890; Chen, 2008:710; Sánchez et al., 2006:395; Tam, 2000:32; Zeithaml, 1988).

These ‘get’ and ‘give’ components are mostly considered to be distinct and independent, but opinions vary upon the weighting of these components. Consumer behaviour research assumes that individuals tend to weigh losses such as price significantly more heavily than gains such as quality (Boksberger & Melsen, 2011:232).

Generally, measuring perceived value as a trade-off between price as the sacrifice and quality as the benefit is too simplistic, since consumers may be able to identify fifty or more different attributes that shape their perceptions of value prior, during and after consumption (Boksberger & Melsen, 2011:233). Although simplistic, Moliner et al. (2007:1398) also argued that the factors of the construct have to include functional factors such as quality and price.

Moliner et al. (2007:1398) further argued that perceived value is a cognitive variable, since it is necessary to incorporate the affective component (refers to specific emotions or feelings of fear, anger, envy and the social impact), and it is a dynamic variable that is also experienced after consumption, thus necessary to include subjective or emotional reactions that are generated in the consumer buying decision process (Boksberger & Melsen, 2011:231; Moliner et al., 2007:1398). It is for this reason of complexity that perceived value has been dominantly operationalised using multiple item scales for better measurement (Boksberger & Melsen, 2011:233), thus involving a number of factors.

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4.3.1.1 The ‘get’ or benefit factors of perceived value

Determining the ‘get’ or benefit construct of perceived value is important, as prospective students will consider and evaluate a certain benefit from the product/service when evaluating alternatives (Simões & Soares, 2010).

There are different opinions about which ‘get’ factors should be included as influencing or having a direct effect on perceived value. Table 4.1, depicts a summary of the ‘get’ or benefit factors, and reflects these factors as proposed by several authors in previous studies, and each factor will be discussed in the following section.

The following ‘get’ factors are proposed in the literature: o perceived quality (Brown & Mazzarol, 2009; Sánchez-Fernández & Iniesta- Bonillo, 2009:426; Petrick, 2002, 2004; Sweeney & Soutar, 2001; Teas & Agarwal, 2000; Cronin et al., 1997; Dodds et al., 1991; Zeithaml, 1988; Dodds & Monroe, 1985) o functional value (Lai et al., 2012:277; Ledden & Kalafatis, 2010; Brown & Mazzarol, 2009; Ledden et al., 2007; Sánchez et al., 2006; Sweeney & Soutar, 2001) o social value (Lai et al., 2012:277; Ledden & Kalafatis, 2010; Brown & Mazzarol, 2009; Ledden et al., 2007; Sweeney & Soutar, 2001; Sheth et al., 1991) o emotional value (Lai et al., 2012:277; Ledden & Kalafatis, 2010; Brown & Mazzarol, 2009; Ledden et al., 2007; Sánchez et al., 2006; Petrick, 2002, 2004; Sweeney & Soutar, 2001; Sheth et al., 1991) o epistemic value (Lai et al., 2012:277; Ledden & Kalafatis, 2010; Ledden et al., 2007; Sheth et al., 1991) o conditional value (Lai et al., 2012:277; Ledden & Kalafatis, 2010; Ledden et al., 2007) o reputation (Petrick, 2002, 2004) o image (Ledden & Kalafatis, 2010; Brown & Mazzarol, 2009; Ledden, Kalafatis & Samouel, 2007; LeBlanc & Nguyen, 1999) o market value (Kantamneni & Coulson, 1996; Petrick, 2002:122)

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Sheth et al. (1991) proposed five consumption values (Boksberger & Melsen, 2011:232; Moliner et al., 2007:1399) influencing consumer choice behaviour that were also used in other author’s models. According to Sheth et al. (1991:160) and Lai et al. (2012:277), functional value is presumed to be the primary driver of consumer choice, while other authors (Petrick, 2002, 2004; Dodds & Monroe, 1985) argue that quality is a key player. It also seems that in certain cases, the term functional value is used to capture elements of quality and will for this reason be discussed under the same heading before expanding on the meaning of the other factors mentioned above.

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Table 4.1 A summary of the ‘get’ or benefit factors of perceived value

Perceived value

image the factors – value value Image aspect Quality the usefulness

Conditional – experiential (ofdegree) a Social value Reputational Market value –

Emotionalvalue Functionalvalue Functionalvalue Functionalvalue Functionalvalue Dodds & Monroe, 1985 X Zeithaml, 1988 X Dodds, Monroe & X Grewal, 1991 Cronin, Brady, Brand, Hightower & Shemwell, X 1997 Teas & Agarwal, 2000 X Sweeney & Soutar, X X X X 2001 Petrick, 2002 X X X X Petrick, 2004 X X X Brown & Mazzarol, X X X X X 2009 Sánchez-Fernández & X Iniesta-Bonillo, 2009 Sánchez, Callarisa, Rodríguez & Moliner, X X X 2006 Ledden, Kalafatis & X X X X X X X Samouel, 2007 Sheth, Newman & X X X X X Gross, 1991 Ledden & Kalafatis, X X X X X X 2010 Lai, To, Lung, & Lai, X X X X X X X X 2012

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Perceived value

image the

factors – value value Image aspect Quality the usefulness

Conditional – experiential (ofdegree) a Social value Reputational Market value –

Emotionalvalue Functionalvalue Functionalvalue Functionalvalue Functionalvalue Boksberger & Melsen, X X X X X 2011 Moliner, Sánchez, Rodríguez , & Callarisa, X X X X X 2007 LeBlanc & Nguyen, X X X X X X 1999 Kantamneni & Coulson, X X X X 1996 Chu & Lu, 2007 X X

Source: Adapted from various frameworks and models evident in existing literature (Dodds and Monroe, 1985; Zeithaml, 1988; Dodds, Monroe and Grewal, 1991; Sheth, Newman and Gross, 1991; Kantamneni and Coulson, 1996; Cronin, Brady, Brand, Hightower and Shemwell, 1997; LeBlanc and Nguyen, 1999; Teas and Agarwal, 2000; Sweeney and Soutar, 2001; Petrick, 2002; Petrick, 2004; Sánchez, Callarisa, Rodríguez and Moliner, 2006; Ledden, Kalafatis and Samouel, 2007; Moliner, Sánchez, Rodríguez , and Callarisa, 2007; Brown and Mazzarol, 2009; Sánchez-Fernández and Iniesta-Bonillo, 2009; Ledden and Kalafatis, 2010; Boksberger and Melsen, 2011; Lai, To, Lung, and Lai, 2012)

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The following section will elaborate on the ‘get’ factors as listed earlier on in Table 4.1. Each of these ‘get’ factors will be incorporated and adapted for the perceived value scale that will be used in the questionnaire (Chapter 5), and forms a key step in the proposed choice model.

The first ‘get’ factor to be addressed is functional value and the strong link it has with quality. This will be followed with a discussion of social value, emotional value, epistemic value, conditional value, reputational value, image value and market value. In each instance, these ‘get’ or benefit factors of perceived value will be linked to the Higher Education (HE) landscape. Table 4.1 provides a summary of the ‘get’ or benefit factors of perceived value and it portrays which of these ‘get’ factors are the more popular factors. It depicts the evolvement of perceived value since 1985 where Dodds and Monroe (1985) argued that perceived value’s ‘get’ factor was quality and quality alone, however, Boksberger and Melsen (2011) and Lai et al.’s (2012) expanded on Dodds and Monroe’s (1985) model to include functional value, emotional value, social value, epistemic value, conditional value and image.

 Quality / Functional Value as a benefit

Quality is an antecedent of both satisfaction and perceived value and is a good predictor of consumer behaviour (Petrick, 2004:399). According to Dodds and Monroe (1985:86), price (as seen in their diagram later in this chapter, Section 4.5.1) plays a dual role in the trade-off between perceived quality and sacrifice, and that this trade-off will lead to perceived value. Perceived quality is thus seen as the ‘get’ factor. Zeithaml (1988:14) also argues that perceived quality is a benefit, but expands on Dodds and Monroe’s (1985) model, arguing that perceived value’s benefit factor includes salient intrinsic attributes, extrinsic attributes, perceived quality, and other relevant high-level abstractions. Dodds et al.’s (1991) model conceptualised quality as a benefit factor of perceived value, however they argued that there are other indicators of quality. These quality indicators are price, brand, store (name) or intrinsic product information. Teas and Agarwal (2000) added country name as an additional quality indicator to their adaption of Dodds et al.’s (1991) model.

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The important role of quality as a ‘get’ or benefit factor of perceived value, is further evident by Cronin et al.’s (1997) value added model, Teas and Agarwal’s (2000) conceptual model of extrinsic product cues’ effects on consumers’ perceived quality, perceived sacrifice and perceived value. Cronin et al.’s (2000) model of the effects of quality, satisfaction and value on consumers’ behavioural intentions and Petrick’s (2002) SERV-PERVAL scale also impacts this role. The difference is that Cronin et al. (1997) and Cronin et al. (2000) specifically labelled their quality factor as ‘service’ quality, while Teas and Agarwal’s (2000) study focussed on products (calculators and wrist watches). On the other end of the argument, Petrick’s (2004:399) definition of quality is portrayed as “a consumer’s judgement about a product or service’s overall excellence or superiority…”

Sánchez-Fernández and Iniesta-Bonillo (2009:427, 430) argued that the notion of excellence has similar connotations to the concept of quality, and they considered excellence and quality’ as similar constructs in their study. They investigated the economic components of perceived value, which they argued are essential determinants of the constructs. They developed a bi-dimensional representation of economic value that included efficiency and excellence. According to these authors, the factor of excellence involves a relative appreciation of the possible ability of an object or experience to accomplish a goal or to perform a function.

It was Sheth et al. (1991) in their identification of five consumption values that first used the ‘functional value’ factor as more than just quality, however, and included perceived quality and performance as part of the positive component of functional value. Sweeney and Soutar (2001) built on Sheth et al.’s (1991) consumption values to arrive at their PERVAL scale that included functional value as well. Sweeney and Soutar’s (2001) perceived importance of quality is evident in their measurement of this factor, as they included 17 statements on perceived quality (and 15 for price) as their components of the functional value factor. Brown and Mazzarol’s (2009) application of the PERVAL scale in the HE environment found that performance and quality could be grouped together as the functional value factor.

Sánchez et al.’s (2006) GLOVAL scale also used the term functional value to capture quality as a benefit factor of perceived value. Although they identified four types of 165 | functional values, it is the functional value of the establishment, personnel and the product that captures the quality element as functional value on price can be argued as playing a ‘sacrifice’ role, more than a benefit role.

Ledden et al. (2007) and Ledden and Kalafatis (2010) included the functional value factor in the HE context, but did not include the word ‘quality’. However, it focuses more on the possible outcome of obtaining a degree that will in turn: (1) allow the student to earn a good salary, (2) achieve his/her career goals, (3) lead to promotion in future jobs, (4) help the individual to do his/her job better. It can be assumed, that a good quality degree should enable all these positive outcomes, and although the word ‘quality’ isn’t mentioned as such, these authors’ functional value factor does capture quality as well.

Lai et al. (2012:277) agree with Sheth et al. (1991) on the importance of functional value as their study conducted in the HE context revealed that functional value, specifically in terms of the experiential aspects and the usefulness of a degree, was rated by students as the most important factor that explains students’ satisfaction.

Lai et al.’s (2012:277) study resulted in seven principal perceived value ‘factors’ or factors of which three factors represent functional value: (1) functional value – the usefulness of a degree, (2) functional value – the experiential aspect, and (3) functional value – the image (the other factors included in their study were social value, emotional value, epistemic value, and conditional value). The functional value representing the usefulness of a degree, was measured by asking questions relating to the possible future employment the degree will enable, the possible promotion, the possible good salary, the positive things employers will say about the chosen university, and the belief that the particular degree will help in achieving career goals (Lai et al., 2012:278). Chu and Lu (2007) also included ‘perceived usefulness’ as a benefit in their model of online music purchase behaviour. In Chu and Lu’s (2007:142) explanation, perceived usefulness is defined by focusing on functional and convenience benefits. In their particular study it refers to the degree to which the consumer believes that listening to music online would fulfil the certain purpose. According to Ledden et al. (2007), functional value in the higher education (HE) context tries to encapsulate the usefulness of a degree. 166 |

Functional value – the experiential aspect captures quality and the belief by students that the particular department offers quality services. Students were also asked to consider what they pay for the tuition and evaluate if it reflects the department’s sufficient services. The size of the particular department was also considered in relation to the value it adds to the particular students’ education. The third functional value – the image, the researchers asked students’ opinions about their perceptions of the reputation of their particular department and the image projected by their chosen university as an influence on the value of their degree (Lai et al., 2012:279).

It can be concluded from the above discussion that the literature conveys ‘quality’ as a ‘get’ or benefit factor of perceived value, and that the term ‘functional value’ also captures the factor of quality. Quality is an important factor to include in determining perceived value, especially in the HE context as university students have indicated that they use quality as a means of defining ‘added-value’ (Navehebrahim, 2009:293). Cronin (2003:335) further argues that exceptional service quality is suggested to result in consumer delight (benefit).

 Social value

Social value describes the “perceived utility acquired from the association with one or more specific social groups” (Sheth et al., 1991:161). Perceived “utility” can be defined as “the satisfaction received from consuming a good or service” (www.investopedia.com). It can thus be argued that social value is the perceived satisfaction received from the association with positively or negatively stereotyped demographic, socio-economic and cultural-ethic groups (Ledden & Kalafatis, 2010; Sheth et al., 1991:161). It is the value generated from the social image transmitted by the use of the product or service (Moliner et al., 2007:1399), or in short, the enhancement of social self-concept (Sweeney & Soutar, 2001:211).

Sheth at al. (1991:161) explain that the choices consumers make involve highly visible products such as clothing or jewellery and goods or services to be shared with others like gifts and products used in entertaining, and they are often driven by social value. These authors continue to explain that even products generally thought

167 | to be functional (like kitchen appliances), are selected on the basis of their social value.

Sweeney and Soutar’s (2001:216) study indicated that customers assess products, not just in functional terms of expected performance of value for money and versatility, but also in terms of the social consequences of what the product communicates to others (social value). Items that were included to measure social value were based on respondents’ possible feelings of acceptance when buying the product, the possible improvement in the way he/she will be perceived, and if by buying the particular product would make a good impression on other people or give them social approval (Sweeney & Soutar, 2001:212).

Sánchez et al.’s (2006:406) study in the tourism industry revealed that tourism in general and tourism packages in particular, follow social motivations linked to the need to belong to groups. Individuals would like to be recognised and to have status in the social milieu and thus their perceived social value received for going on a particular tour, influences the choices they make.

In the HE context, social value captures the association with specific social groups such as the friends made in classes and social activities at the university. It is the approval and recognition that a student seeks from people who are important to them, wanting family and friends to think that taking this course is a good idea, and hoping that these family and friends will see the individual in a better light when he/she has finished the degree (Ledden et al., 2007:968,973). Lai et al.’s, 2012:278) study in the HE landscape defined social value by focussing on how Chinese HE students perceive the importance of friends to be in classes, the effect that working in groups has on the value of their education, and if social activities at their particular university make their studies more interesting.

 Emotional value

Emotional value is defined as the “perceived utility acquired from an alternative’s capacity to arouse feelings or affective states” (Sheth et al., 1991:161). It is the creation or perpetuation of feelings or affective states (Ledden & Kalafatis, 2010).

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Moliner et al. (2007:1399) argue that emotional value refers to the value associated with the feelings or affective states generated by the products and services, as well as the human relationships developed between the parties.

Sheth et al. (1991:161) explain that emotional value constitutes the feelings that are associated with emotional responses, for example, the romance that an individual feels if eating by candlelight dinner, or the fear that is felt while viewing a horror movie. It is also associated with aesthetic alternatives such as religion and social causes. Some more tangible products also have emotional value like some foods that arouse feelings of comfort through their association with childhood experiences.

Sweeney and Soutar’s (2001:212) emotional value factor included items such as enjoyment, feelings of relaxation, pleasure, and feelings of ‘make me feel good’ when purchasing the product. Their study’s findings indicated that the quality and emotional value factors were more important in explaining value perceptions when purchasing a product. However, each value factor in their study (Quality, Emotional, Price and Social) plays an important and separate role in forming attitudes and behaviours in the purchase process (Sweeney & Soutar, 2001:214). Petrick (2004:402) adapted Sweeney and Soutar’s (2001) emotional value factor in their study by adding a sense of joy, feelings of delight and happiness, while Sánchez et al. (2006) included the feelings of being comfortable with the choice and the service provider’s personnel’s friendliness and positive reactions to the emotional value factor.

In the HE context, emotional value describes the ‘happy’ feelings students have about how glad they are that they have chosen the specific course in that particular specialisation and whether they find it interesting. It is the feelings of being proud, boosted self-confidence and self-achievement for studying (Ledden et al., 2007:968,973) and the feelings that students ‘like’ the courses they are taking (Lai et al., 2012:278).

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 Epistemic value

Sheth et al. (1991:162) describe epistemic value as “the perceived utility acquired from an alternative’s capacity to arouse curiosity, provide novelty, and/or satisfy a desire for knowledge.” It is also the capacity of the product to surprise (Moliner et al., 2007:1399).

New experiences can provide epistemic value, and an alternative that provides some change of pace can also be experienced as epistemic value. The consumer can choose the alternative because he/she is bored with the current brand, is curious about the new one, or has a desire to learn. Sheth et al. (1991:162) further provide examples to clarify this explanation by adding that if a customer tries a new type of coffee or a new flavour of coffee because he/she is bored, when a new night club is visited because he/she is curious or when there is a desire to learn and new places are visited, he/she experiences a new culture.

Ledden et al. (2007:968) explain that the primary benefit of attending university is the acquisition of knowledge. They describe epistemic value in the HE context as the desire for knowledge and that it is the students’ judgements on the quality of education, and course contents they receive. Students measure epistemic value by evaluating the courses’ ability to keep them interested, to learn new things from the course, to experience that the course contributes to the high value of their education and the belief that the academic guidance they receive from their lecturers has enhanced the value of their degree. This factor is particularly relevant to the HE context, in which the primary benefit is the acquisition of knowledge (Ledden et al., 2007:968, 973). Lai et al.’s (2012:277) study indicated that the third most important factor that explains students’ satisfaction is the epistemic value, followed in fourth and fifth place with social value and emotional value respectively.

 Conditional value

Conditional value is defined as the perceived utility or satisfaction acquired by an alternative product or service as a result of the specific situation or set of circumstances that the decision maker is in when faced with the choice of which product/service to buy. Conditional value takes place in the “presence of antecedent

170 | physical or social contingencies” in a specific situation (Ledden & Kalafatis, 2010; Sheth et al., 1991:162). It refers to a series of circumstantial factors or situations, such as illnesses, or specific social situations that may condition perceived value (Moliner et al., 2007:1399).

The possible satisfaction of the customer’s needs will often depend on the situation. Sheth et al. (1991:162) explain that some products only have seasonal value like Christmas cards, and that some are associated with a “once in a lifetime” such as, buying a wedding dress. Certain services are only used in emergency situations. An ambulance service and some other products would have more subtle conditional associations like buying popcorn at the movies.

In the HE context, conditional value refers to how students are influenced in a class situation by situational variables that can influence the value of the educational experience (Lai et al., 2012:273). Students can make certain negative or positive assumptions of the perceived value received by for example evaluating the teaching materials prescribed or received. They will evaluate support materials supplied, study-group work’s benefits received (or not), the campus’s facilities can contribute to the value of their course and the campus’s location also contribute to the perceived value received (Ledden et al., 2007:968, 973).

 Reputational value

Reputation is defined as the “prestige or status of a product or service as perceived by the purchaser, based on the image of the supplier” (Petrick, 2002:125). Petrick (2002) developed a valid and reliable five-dimensional scale for measuring perceived value, of which one factor is reputation. He captured this factor’s value by asking respondents questions relating to their understanding of the provided service in terms of respect, on how well it is thought of in terms of status, of a good reputation, and if it is regarded as a reputable service.

Dodds et al.’s (1991:317) study indicated that customers who particularly lack knowledge about a certain product may rather use store and brand name information, if available, to make the quality assessment and rely less on price as the

171 | cue. In certain circumstances, customers are less likely to rely on the presence of a price-quality relationship for a particular product class in order to rely more on the familiar information cues of brand and store name to assess the product’s worth. It could be argued that this information relating to store and brand name, could also be interpreted as reputational cues that are evaluated.

Although seemingly an important factor of the perceived value construct (Petrick, 2002; Dodds et al., 1991:317), Petrick (2004) applied his perceived value construct, i.e. SERV-PERVAL, that was published in 2002, to a different context and came to a different conclusion about the possible inclusion of reputation. His aim was to examine the relationships between satisfaction, perceived value, and quality in respondents’ prediction of intentions to repurchase and positive word-of-mouth publicity. However, when the hypotheses were analysed with the use of structural equation modelling that is related to behavioural intentions, reputation of perceived value in this context indicated that reputation had to be removed in the overall model. Thus the path from reputation to perceived value was not significant. Examination of the five items used to measure reputation revealed that the items had the lowest standard deviation. This result is an indication that as a whole, the visitors on the cruise felt that the cruise line had a very good reputation, with little variance between responses. As items did not differ among respondents, it couldn’t be used in prediction and reputation was therefore dropped from the model.

Although Petrick (2004) decided to drop reputation from their SERV-PERVAL (service perceived value) model, it should not implicate that reputation is not important in the HE environment. HEIs such as universities operate in a complex HE environment because they offer an intangible service (discussed in Chapter 2) that is highly reputational (Moogan, 2011:575). Moogan’s (2011:581) study indicated that students select a specific academic programme because of its reputation, and that the university’s reputation (or perception of its reputation) assisted prospective students when limiting the final choice set of universities (Brown, Varley & Pal, 2009:318). Although reputation is discussed as choice factor by these authors, Petruzzellis and Romanazzi (2010:140, 146) remind university marketers that the components of the university’s value affect the students’ choice, and that evaluation in general is based on the reputation of the university. A favourable perception of 172 | reputation is positively related to loyalty, and reputation management is very important for attracting and retaining students. Reputation is also closely related to image (Petrusellis & Romanazzi, 2010:150) and the following discussion on ‘image value’ will highlight the difference/close similarities between reputational and image value.

 Image value

Image assesses the overall reputation, while image of the university includes the value of the degree, the perception of learning that occurs, and the comparison with other similar universities (Matherly, 2012: 44). LeBlanc and Nguyen (1999) argue that there should be a separate factor called ‘image benefits’ to represent the ‘get’ component of perceived value. These authors argue that image is important, as it has been acknowledged as being a key element in the positioning of an organisation in its competitive environment. Institutional image, specifically for HEIs, is important, as it is a key antecedent for consumer value perception, satisfaction and loyalty (Brown & Mazzarol, 2009:90). The image of the HEI represents the benefits derived from studying at that particular HEI, and this image thus adds to the students’ perceived value received (Ledden et al., 2007:968). It is for this reason that LeBlanc and Nguyen (1999:193) suggest that HEIs should invest in promotional activities aimed at promoting its image to students and the various stakeholder groups with whom it interacts.

LeBlanc and Nguyen (1999) specifically examined perceived service value among business college students and their results revealed that service value is influenced by perceived image. They further suggested that HEIs’ management should focus on the building of image with the various stakeholder groups. Elements that contribute to the building of image should be considered, such as management style and leadership, corporate identity, level and quality of service, contact personnel, and the tangible cues that are part of the environment where the service is produced and consumed. Further, by positioning the HEI as innovative, up-to-date, involved in current issues, and capable of providing prospective employers with quality people, management should be able to build a strong reputation (LeBlanc & Nguyen, 1999:194).

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Although ‘reputation’ was suggested to be included as a separate factor by Petrick’s (2002) first study when he developed the SERV-PERVAL scale, Ledden and Kalafatis (2010:156), Ledden et al. (2007:973) and LeBlanc and Nguyen (1999:191), include ‘reputation’ as a deciding item for determining image in their image factor. These authors (Ledden & Kalafatis, 2010:156; Ledden et al., 2007:973; LeBlanc & Nguyen, 1999:191) have all examined perceived value in the HE context and determined image by asking questions about the following: if students have heard positive things about the particular institution, if students believe that employers would have positive things to say about the particular institution, if the students believe the particular institution has a good reputation, if the image projected by the particular institution has an influence on the value of their particular degree, and if the reputation of the particular institution influences the value of their particular degree.

Brown and Mazzarol’s (2009) study conducted in the Australian HE environment, developed a model that suggested that student loyalty is predicted by student satisfaction, which is in turn predicted by the perceived image of the particular university. Of most importance, according to these authors, was the impact of the HEIs institutional image, which strongly predicted perceived value, and to a lesser extent student satisfaction. Their image factor comprised of three components, which were named: (1) study environment with ten items, (2) practicality with three items, and (3) conservativeness with three items. Study environment measured such things as whether the institution was viewed as friendly, supportive, innovative, student focussed and offering a good range of courses. Practicality measured how practically focussed the courses were, whether entry requirements were flexible, and how ‘job oriented’ the study programmes were. The third factor of conservativeness was a measure to determine whether the HEI was long-established, or perceived as traditional or prestigious. Their findings indicated that brand image within universities and other educational institutions is likely to be just as important as for other types of service organisations. They argue that despite the trend towards a stronger market orientation and commercial focus within universities, the process of brand building within such HEIs is likely to be difficult and challenging (Brown & Mazzarol, 2009:90).

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 Market value

Kantamneni and Coulson (1996) used a student focus group to identify ‘value indicators’. Their study indicated that the concept of perceived value is multi- dimensional and they identified 21 indicators of value. After conducting a factor analysis, four factors were identified, namely: societal value, experiential value, functional value and market value.

Market value was identified as a product with a good brand name and bought in a good or upscale store, as well as a product with a high price that will have more value (Kantamneni & Coulson, 1996:5). This definition has similarities with Dodds et al.’s (1991) model that also mentioned brand or store name as factors of perceived value. Brand name and how it is positioned is also important to the image as stated by Brown and Mazzarol (2009:91), and the brand name says something about the image. It was already established by LeBlanc and Nguyen (1999:190) that image influences perceived value. It could therefore be argued that market value as described by Kantamneni and Coulson (1996) actually refers to the image of the product.

The mention of ‘high price’ by Kantamneni and Coulson (1996), will for the purpose of this study be included as a ‘give’ factor.

It is evident from the literature review on the ‘get’ factors of perceived value, that except for market value that can also be viewed as including a strong image element, all the remaining ‘get’ factors should be included in the proposed benefit section of the perceived value scale (Chapter 5) adapted from existing scales and literature for this study. Thus, to conclude, functional value, social value, emotional value, epistemic value, conditional value and image value that includes reputational value, should be included in the proposed scale (Appendix D). All these ‘get’ or benefit factors play a key role in the HE student’s view of the perceived value they will ‘get’ for choosing a specific university to further his/her education.

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4.3.1.2 The ‘give’ or sacrifice factors of perceived value

It has already been determined that perceived value is a multi-dimensional construct that covers benefits and also covers both financial (or monetary) and non-monetary sacrifice and risk (Dodds, 1996:7). Sacrifice is defined as “what is given up or sacrificed” to acquire a service (Cronin et al., 2000:201). Perceived sacrifice includes monetary and non-monetary prices or costs of a product or service experience (Boksberger & Melsen, 2011:231; Ledden & Kalafatis, 2010; LeBlanc & Nguyen, 1999:186; Cronin et al., 1997:380; Dodds, 1996; Zeithaml, 1988:14-15). Cronin et al. (1997:376) argue that a particular purchase can be associated with risk assumption, and Dodds (1996:7) also refers to ‘risk’ by adding monetary risk and non-monetary risk such as social risk to the ‘sacrifice’ mix. Khalifa (2004:649) adds to the sacrifice factor the costs of purchasing that consumers may consider, which include monetary costs, time costs, search costs, learning costs, emotional costs, and cognitive and physical effort coupled with financial, social and psychological risks.

The next section will introduce two broad categories of sacrifice (or ‘give’ factors) namely perceived monetary sacrifice and perceived non-monetary sacrifice. Perceived non-monetary sacrifice further includes non-monetary risk such as functional risk, social risk, psychological risk, time risk, physical risk and overall risk that will be discussed in more detail in the following section. Table 4.2 summarises different authors’ inclusion or omission of monetary and non-monetary factors in their perceived value models. Table 4.2 depicts those sacrifice factors mostly applied, thus those that are more popular, as well as how the concept of sacrifice has evolved since the introduction of Dodds and Monroe’s (1985) value intention framework in 1985. It is evident from Table 4.2 that the most popular factor of sacrifice, being monetary sacrifice, includes price paid or perceived value received for the price paid. In terms of risk factor, effort and time risk seems to be mostly included when risk as factor has been included.

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Table 4.2 A Summary of the ‘give’ or sacrifice factors

Perceived value factors risk risk risk Non price e risk hysical al riskal (price) sacrifice P Time risk Monetary Monetary monetary Social risk Functional Overall risk Performanc Psychologic X X Cronin, Brady, Brand, X X (Financial X X X (Personal X (Effort) X Hightower & Shemwell, 1997 risk) risk) LeBlanc & Nguyen, 1999 X** Cronin, Brady & Hult, 2000 X X X (Effort) X (Budget Teas & Agarwal, 2000 constraint) Sweeney & Soutar, 2001 X Lomas, 2002 X* Petrick, 2002 X* Petrick, 2004 X* Lagrosen, Seyyed-Hyashemi X* & Leitner, 2004 Sánchez, Callarisa, X** Rodríguez & Moliner, 2006 Moliner, Sánchesz,

Rodríguez & Callarisa, 2007 Ledden, Kalafatis & X (Give up X Samouel, 2007 interests) X (Ease Chu & Lu, 2007 of use) Brown & Mazzarol, 2009 Sánchez-Fernández &

Iniesta-Bonillo, 2009 Ledden & Kalafatis, 2010 X Lai, To, Lung & Lai, 2012 Boksberger & Melsen, 2011

Notes:* These authors included monetary sacrifice that can also be evaluated in terms of ‘value for money’ or as part of the perceived value construct.

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Notes on Table 4.2 (Continues…) **These authors argued that the ‘price factor’ should be presented as ‘functional value’.

Source: Adapted from various frameworks and models evident in existing literature (Lai, To, Lung, and Lai, 2012; Boksberger and Melsen, 2011; Ledden and Kalafatis, 2010; Sánchez-Fernández and Iniesta-Bonillo, 2009; Brown and Mazzarol, 2009; Moliner, Sánchez, Rodríguez , and Callarisa, 2007; Ledden, Kalafatis and Samouel, 2007; Sánchez, Callarisa, Rodríguez and Moliner, 2006; Petrick, 2004 Petrick, 2002; Sweeney and Soutar, 2001; Teas and Agarwal, 2000; LeBlanc and Nguyen, 1999; Cronin, Brady, Brand, Hightower and Shemwell, 1997; Kantamneni and Coulson, 1996; Sheth, Newman and Gross, 1991; Dodds, Monroe and Grewal, 1991; Zeithaml, 1988; Dodds and Monroe, 1985)

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 Perceived monetary sacrifice

From the customer’s perspective, price is what is given up or sacrificed to obtain a product (Chu & Lu, 2007:143; Zeithaml, 1988:10) and it is the most obvious ‘give’ factor of most service offerings (Cronin et al., 1997:380). Price is the key measure representing what customers have to pay in ‘money terms’ to obtain a product (Chu & Lu, 2007:143) or service offering (Cronin et al., 1997:380). Price can also both be an indicator of the amount of sacrifice needed to purchase a product and of the level of quality and/or value received. Price can be both an indicator of the amount of sacrifice needed to purchase a product, such as the price one pays expressed in dollars and cents (or Rand and cents)(Cronin et al., 2000:201), and of the level of quality (Kantamneni & Coulson, 1996:3; Dodds et al., 1991:308).

As early as 1985, Dodds and Monroe (1985:85) conducted an experiment on the effect of price on brand information on perceptions of quality and value and on willingness to buy. In their proposed model they conceptualised perceived value as a trade-off between perceived quality (i.e. benefit) and sacrifice. Their study confirmed that price plays a dual role in this trade-off, so if price increases subjects’ perception of product quality will also increase and consequently it will lead to greater willingness to purchase (Dodds et al., 1991:308; Dodds & Monroe, 1985:86, 89). Price and quality do not match absolutely, however, customers will make a judgement as to what combination of cost and quality they are willing to accept. Customers know that it doesn’t necessarily mean that the most expensive item has the best quality (Cronin et al., 1997:390).

If price as an external cue, is perceived differently than its ‘objective’ characteristic, buyers are likely to use similar perceptual processes for both brand and store names. Thus it is suggested that the external cues of price, brand name, and store name are three cues that influence perceptions of product quality and value, and hence willingness to buy (Dodds et al., 1991:308; Zeithaml, 1988).

Price can further be distinguished between objective price, which is the actual price of a product and perceived price, which describes the price as encoded by the customer (Chu & Lu, 2007:143; Zeithaml, 1988:10). This means that some

179 | customers will see the actual price of $xxx (or Rxxx), while other customers will only remember the price as ‘expensive’ or ‘cheap’. It has also been argued that some customers will not encode price at all (Zeithaml, 1988:10). Actual price is an objective external characteristic of a product that consumers perceive as a stimulus. Thus price, has both objective external properties and subjective internal representation that are derived from the perceptions of price and result in some meaning to customers (Dodds et al., 1991:308).

Teas and Agarwal (2000:281-282) argued that customers have different perceptions of the degree to which a particular price represents a sacrifice. They have measured the concept of sacrifice from a budget constraint perspective rather than just ‘price’ alone. The perception of sacrifice will vary depending on an individual’s financial situation. The same price may involve a higher level of sacrifice for a financially constrained individual when compared with a financially endowed individual. Therefore, to determine the monetary sacrifice, respondents (they were undergraduate students attending a major Midwestern university in the USA) were asked about the amount of money they will have left to spend on other things and the ‘other things’ that they would not be able to buy because of the particular price they have paid for a particular product.

In certain studies, the ‘price factor’ was included as part of the perceived value construct (Brown & Mazzarol, 2009; Sánchez et al., 2006; Petrick, 2002, 2004; Sweeney & Soutar, 2001; Kantamneni & Coulson, 1996) and in others it represented functional value (Brown & Mazzarol, 2009; Sánchez et al., 2006; Sweeney & Soutar, 2001; LeBlanc & Nguyen, 1999). Functional value is seen as price/value for money and can be described as “the utility derived from the product due to the reduction of its perceived short-term and longer-term costs” (Sweeney & Soutar, 2001:211). In functional value terms, price was determined by measuring value for money and perceptions about if a particular product was reasonably priced, would the product be ‘good’ for the price, and would it be perceived as an economical buy? (Sweeney & Soutar, 2001: 212). Petrick (2002:128) added items of fairness, worth and whether it would be seen as a bargain (i.e. the product is fairly priced, the product is a good buy, the product is worth the money, and it appears to be a good bargain).

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In Sánchez et al.’s (2006:405) study conducted in the tourism industry, price was seen as the most important of all the cognitive components. However, they have studied post-purchase perceived value, so the importance of the price should not be interpreted at the time of the decision to purchase, but as the tourist’s memory of the price paid is recalled. Price thus plays an important role after consumption in the valuation of the overall experience.

In the HE context, monetary sacrifice includes the price paid for the students’ tuition, thus, course fees, accommodation costs, the purchase of textbooks and any other additional costs associated with going to university (e.g. food and transport) (Ledden & Kalafatis, 2010:146; Ledden et al., 2007:968; Lomas, 2002:71). The monetary sacrifice can also be evaluated in terms of ‘value for money’ received compared to the price that needs to be paid (Brown & Mazzarol, 2009; Lomas, 2002:71; Sweeney & Soutar, 2001). Quality in HE is linked with value for money and many times equated with levels of specifications and is also directly related to costs (Lagrosen, Seyyed-Hashemi & Leitner, 2004:63).

Brown and Mazzarol (2009:86) selected Sweeney and Soutar’s (2001) PERVAL scale as well suited to a service-related context in a university environment. Price was thus seen as part of the ‘perceived value construct’ reflecting the same four items as mentioned in Sweeney and Soutar’s (2001) study (value for money, if the product is ‘good’, reasonably priced and economical). Their study revealed that universities can only be successful as long as their student-customers are being offered something that they wish to buy (something of value), at a quality they feel acceptable (value for money). As more and more students see a rise in the student’s own contribution to fees (as argued in Chapter 3) and the emergence of a trend towards more full-fee paying for places which ensure that many students would view themselves as customers. Customers evaluate their purchases and consider value and value for money (Brown & Mazzarol, 2009:91, Lagrosen et al., 2004:63; LeBlanc & Nguyen, 1999:193). Functional value (price) in HE terms involves what the student believes he/she is getting for what he/she is paying, and it relates to the economic person theory and to the relationship that exists between price and quality when value is considered (Alves, 2011:9; Ledden et al., 2007:973; LeBlanc & Nguyen, 1999:190). 181 |

In order to survive, HEIs need to add value to their services if they are to meet the challenges posed by funding cuts, rising tuition fees and students’ expectations with regard to their evaluation of service value and perceptions of price in the form of the price/quality (LeBlanc & Nguyen, 1999:190, 193). HEIs should seek to reduce the perception of costs among students (Alves, 2010:12) by convincing students that what they get, in the form of quality education, is greater than what they give (LeBlanc & Nguyen, 1999:194).

Zeithaml’s (1988) study indicated that monetary price is not the only sacrifice perceived by consumers. Direct monetary cost, expressed as price, is only one component of what consumers give up or sacrifice to obtain a service (Cronin et al., 1997:376). Time costs, search costs, and psychological costs all enter either explicitly or implicitly into the consumer’s perception of sacrifice. For example, when a customer has to travel distances to find a product, a sacrifice has been made (Zeithaml, 1988). Sacrifice is a broader construct and Cronin et al. (1997:376) add additional items of effort and risk assumptions associated with a particular purchase. Service value can be defined as SV =f(SQ, SAC) (SV = Service Value, SQ = Service Quality, and SAC= Sacrifice) (Cronin et al., 1997:376).

 Perceived non-monetary sacrifice

It has been established in the previous discussion that perceived sacrifice components of perceived value include monetary prices and non-monetary prices. On the non-monetary side, as and example, some supermarket shoppers will invest hours clipping coupons (time and effort), reading food advertising and travelling to different stores to obtain the best bargains (time and effort). To them, anything that reduces the monetary sacrifice will increase the perceived value of the product. The ‘less price-conscious’ customers will find value in benefits such as home delivery (even at the expense of higher costs), ready-to-serve food products and store proximity because time and effort are perceived as more costly (Zeithaml, 1988:14- 15).

It can be seen from the above example, that non-monetary sacrifice includes the time spent, the search made, the distance travelled to get the product (Kantamneni &

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Coulson, 1996:3) and convenience cost (Boksberger & Melsen, 2011:231; Sánchez et al., 2006:395; Zeithaml, 1988:17). Boksberger and Melsen (2011:231) added brand image and psychological costs and argued that these sacrifices influence perceived value indirectly. Petrick (2002:128, 2004:402) added behavioural price as a ‘non-monetary’ sacrifice to their SERV-PERVAL scale. Behavioural price was determined by asking respondents to evaluate if the product was easy to buy, if it required little energy to purchase, if it was easy to shop for, and if it required little effort to buy. It can thus be argued that any physical or psychological efforts are considered as perceptions of sacrifice, including ‘perceived ease of use’, and capture the non-monetary cost (Chu & Lu, 2007:143). Cronin et al. (1997:376; 2000:201) and Dodds (1996) added risk assumptions associated with a particular purchase of a service to the non-monetary sacrifice mix.

Perceived risk can be defined as a customer’s “perception that there is some probability that an undesirable outcome may result from buying the product” (Dodds, 1996:1), it is the “subjective expectation of a loss” (Lu & Shiu, 2011:1185). Cronin et al. (1997:380) argue that risk should be included as it is an inherent part of the cost of the acquisition and use of any physical good or service. Sweeney, Soutar & Johnson (1999) and Lu and Shiu (2011:1185) argue that perceived risks should be included in a value model as it may help to explain how perceived value is evaluated. Dodds (1996) identified major types of monetary and non-monetary risk that consumers encounter in making a purchase decision as: o Monetary risk – The consumers may lose money; pay too much; miss buying somewhere else (Dodds, 1996). It can also be referred to as financial risk (Cronin et al., 1997:390). o Functional risk – The product may not perform its function well, it may not work, it may break (Dodds, 1996). Cronin et al. (1997:390) call this service performance risk. o Social risk – Friends, relatives or significant others may not approve of the purchase (Dodds, 1996). Social risk can also refer to the ‘embarrassment’ that may be caused due to the use of a particular facility (Cronin et al., 1997:390).

183 | o Psychological risk – A poor purchase decision may bruise the consumer’s ego (Dodds, 1996), or it can be the purchase decision that makes the buyer feel uncomfortable (Cronin et al., 1997:390). o Time risk – Time is a precious commodity (Zeithaml, 1988:18) and should not be wasted. However, time will be wasted if the product does not perform as expected (Dodds, 1996). o Physical risk – The product may be harmful or unhealthy, it may cause injury (Dodds, 1996). This risk is the association with personal risk (Cronin et al., 1997:390). o Cronin et al. (1997:390) add the overall risk associated with the use of a particular facility.

Quality offering, positive word-of-mouth and brand image, as well as perceived value may all reduce perceived risk (Lu & Shiu, 2011:1186). Although true, it should though be remembered that value perceptions, even sacrifice factors, are situational and depend on the context within which an evaluative judgement occurs (Zeithaml, 1988:15). When experience of the particular product or service is insufficient, the risk is high, and the search costs are low. A customer might undertake an external search that may include personal sources, relatives and friends, public sources and marketer-dominated sources (such as advertising or salespeople). This is particularly true for many high involvement products or purchases such as education (Chapter 3). Choosing which university to enrol at, the prospective student typically needs to gather an immense amount of information to evaluate the value and benefit of the service (Brown et al., 2009:312; Dodds, 1996:2). One would expect to find more rational evaluation in more high involvement purchase situations and where high information availability and processing ability are evident (Zeithaml, 1988:15).

In the HE context, non-monetary sacrifice also includes sacrifices such as time, energy and effort, but specific examples may include students’ loss of opportunity to participate in family and social events (Ledden et al., 2007:968) and loss of leisure time such as socialising with friends (Ledden & Kalafatis, 2010:146). It also includes students having to give up some other interest in order to study (Ledden et al., 2007:973).

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Marketers should consider the non-monetary prices that consumers perceive to ‘pay’ by trying to reduce time, effort, and search costs that in turn should reduce perceived sacrifice and thereby increase perceptions of value (Zeithaml, 1988:18).

It can thus be concluded from the discussion that perceived value can be described as customers’ evaluation of the trade-off of ‘benefits’ received compared to ‘sacrifices’ made (LeBlanc & Nguyen, 1999:186; Zeithaml, 1988). Perceived value, especially in services, can be described as the transactions between two parties in which each party gives up ‘something of value’ in return for ‘something of greater value’ (Boksberger & Melsen, 2011:229). It is the perceived value of the total package of products and services that influences consumer behaviour and perceived value is the best predictor of competitive success (Petrick, 2004:398). It is further argued that understanding the impact of perceived value on consumer behaviour is directly relevant to outcomes such as sales and revenue (Swait & Sweeney, 2000:77). Consumers’ perception of price, quality and value are considered key determinants of shopping behaviour and product choice (Zeithaml, 1988:2), behaviour that should lead to purchase intention (Dodds et al., 1991).

Perceived value is a multi-dimensional construct and a key moderator between product character (quality and price) and consumers’ willingness to buy (Dodds, 1996:7). In the next section the terms purchase intention and/or willingness to buy will be explored. The dynamics of purchase intention will be discussed as well as the role that attitude, quality, price and satisfaction play. Purchase intention in the HE context will also be discussed before the next section of discussion, the different value-intention frameworks and models, will be approached in Sections 4.4 and 4.5.

4.4 Purchase intention and/or willingness to buy

Willingness to buy is defined as “the likelihood that the buyer intends to purchase the product” (Grewal, Monroe & Krishnan, 1998:48; Dodds et al., 1991). There is the probability that the customer will buy the product or at least consider buying the product (Dodds et al., 1991:318). Buying or purchase intention is defined as a “future projection of consumer behaviour” (Espejel, Fandos & Flavián, 2008:869). Purchase intention also tries to establish if the customer is probably going to use the service or

185 | the likelihood that he/she might recommend the service (Cronin et al., 1997:389). Purchase intention is thus a predictor of subsequent purchase and a commonly used measure to anticipate response behaviour, a behaviour tendency that the consumer will purchase the product (Itsarintr, 2010:38).

It is evident from the above explanation that both outcomes of “willingness to buy” and “purchase intention” describe the ‘probability’ of buying, the ‘intention of buying’ and ‘considering’ the ‘likelihood’ of buying (Espejel et al., 2008:869; Grewal et al., 1998:48; Cronin et al., 1997:389; Dodds et al., 1991). It could therefore be concluded that willingness to buy and purchase intention can be used interchangeably, meaning the same thing. For the purpose of this study, the term “purchase intention” will be used, as the author would like to prove in the following chapters that prospective students will have the intention to enrol at a university of their choice IF they perceive that they will receive value from the particular university. Thus, the focus is mainly on the effect that perceived value has on the intention to buy.

Purchase intention can further be clarified as including a customer’s attitude and it also reflects the predictable consumer behaviour in the more immediate future buying decision, such as what product or brand the individual is going to buy. The attitudes are developed throughout time and are affected by familiar influences such as the social group in which the customer is involved, the information received, the experience of the product/service and the individual’s personality (Espejel et al., 2008:869). Attitude is an individual’s perception of how desirable it would be for him/her to perform the behaviour (Mathieson, 2004:368). In this particular instance, attitude is a good predictor of ‘intention’ and in certain instances attitude towards behaviour is measured by comparing the perceived benefits and costs (Shinasharkey & Praditbatuga, 2010:157, 160). It has already been established earlier in this chapter that comparing benefits and cost ultimately leads to perceived value. Shinasharkey and Praditbatuga’s (2010) statement can therefore be interpreted that perceived value is a good predictor of ‘intention’.

Perceived value leads to willingness/intention to buy (Lu & Shiu, 2011:1184; Zeithaml, 1988; Dodds & Monroe, 1985). Customers’ willingness or intention to buy is positively linked to their perceptions of value (Grewal et al., 1998:48; Rajendran & 186 |

Hariharan, 1996:13). Perceived value can thus be regarded as being antecedent to a person’s willingness/intention to buy (Lu & Shiu, 2011:1185). Perceived value further has both a direct and indirect effect on behavioural intentions (Cronin et al., 2000; Tam, 2000:38), such as the intention to repurchase (Petrick, 2004:398), or to ‘return’ to the product or service at the post-purchase level (Chen, 2008:711; Al-Sabbahy, Ekinci & Riley, 2004:226) and perceived value influences customers’ choice behaviour at the pre-purchase phase but also affects their intention to recommend the product or service (Ledden & Kalafatis, 2010; Petruzzelis & Romanazzi, 2010:140; Al-Sabbahy et al., 2004:226).

The direct and indirect effect of perceived value on behavioural intention was further studied by Cronin et al. (2000:210). These authors argued for the consideration of the indirect effects that service quality and service value have on consumers’ behavioural intentions (i.e., service quality through service value and customer satisfaction and service value through customer satisfaction). These indirect pathways are consistently significant across industries. Thus, service quality (which is a ‘benefit’ factor of perceived value) has a statistically significant effect on purchase intention (specifically in banking and dry cleaning) (Petruzzelis & Romanazzi, 2010:140; Laroche, Teng, Michon & Chebat, 2005:163; Cronin & Taylor, 1994:129), and it increases customer loyalty, repurchase and the willingness to offer positive word-of mouth recommendations (Petruzzelis & Romanazzi, 2010:140; Brady & Cronin, 2001:248). Sweeney et al. (1999:98) add ‘price’ to the mix, and argue that perceived value is an important mediator between quality, price and willingness/intention to buy. Cronin et al.’s (2000) study conclude that all three variables; quality, (perceived) value and satisfaction directly influence behavioural intentions, even when the effects of all three constructs are considered simultaneously.

Product quality perceptions enhance perceived value and willingness/intention to buy (Grewal et al., 1998:56). These include all of the antecedents of perceived value as described as quality (functional service quality, technical service quality, product quality) and relative price separately significantly effect willingness/intention to buy (Sweeney et al., 1999:98). The higher the perceived quality and the lower the perceived price, the greater the perceived value and willingness/intention to buy. 187 |

Customers consider perceived quality and perceived price, the immediate situational factors of benefits and sacrifices as separate and distinct factors when purchasing services (Lu & Shiu, 2011:1189).

Brown and Mazzarol (2009:86) argue that in the HE context, a university’s image has a stronger influence on purchase intentions than quality, specifically service quality. Service quality is strongly linked to value creation in HE, however, students will only believe that a university has good service quality if the image portrays this. The students’ perception of the reputation (or image is closely related to reputation) of the HE will increase the perceived value (Petruzzellis & Romanazzi, 2010:151-152). The importance of ‘image’ and its relation to purchase intention in HE, is further reiterated by a study conducted by Cubillo, Sánchez and Cerviño (2006). These authors argue that purchase intention is an independent variable dependent on five factors: personal reasons, the effect of country image, influenced by city image, institution image, and the evaluation of the programme of study (Cubillo, Sánchez & Cerviño, 2006).

Not only has service quality an effect on customers’ behaviour intentions, and on the customer’s consideration to buy or not to buy (Cronin et al., 2000:210), satisfaction also plays a role in both perceived value and intention to buy (Lai & Chen, 2011; Brown & Mazzarol, 2009:86; Chen, 2008:711; Alves & Raposo, 2007; Cronin et al., 2000; Tam, 2000:40). There are different arguments on the consequential order and effect of the ‘satisfaction’ and ‘perceived value’ constructs on purchase intention.

4.4.1 The role of satisfaction, perceived value and intention to buy

Perceived value is an important factor in customers’ evaluations of satisfaction and post-purchase behaviour (Alves & Raposo, 2007; Tam, 2000:40). Some authors argue that both satisfaction and perceived value are direct antecedents of behavioural intentions (Lai & Chen, 2011; Brown & Mazzarol, 2009:86; Chen, 2008:711; Cronin et al., 2000), while others argue that perceived value has been identified as an antecedent to both satisfaction and behavioural intentions (Lai & Chen, 2011:319). There is also the argument that higher satisfaction will lead to purchase intention (Lai & Chen, 2011:320; Espejel et al., 2008:873) or in contrast,

188 | that perceived value will lead to satisfaction (Malik, 2012:76; Ledden & Kalafatis, 2010; Ledden et al., 2007) and that satisfaction in turn will lead to positive word-of- mouth, loyalty and the intention to recommend (Ledden & Kalafatis, 2010; Alves & Raposo, 2007:800).

In the HE context, the purchase decision about which university to attend, derives from the customer ranking alternatives to form a purchase intention. It is very important for marketers to know the factors influencing the purchase intention of prospective students and to understand the nature of the relationship among those factors (Cubillo et al., 2006:102). Deciding which university to choose (‘to buy’) is a complex process and many factors are considered and evaluated. It is not only choice factors that need to be considered, but what students value, as then universities can be focussed on marketing areas that will give the highest return (Briggs, 2006:708). Cubillo et al.’s (2006:112) model presented purchase intention as an independent variable, dependent on five factors already mentioned earlier in this section (personal reasons, the effect of country image, influence by city image, institution image, and the evaluation of the programme of study). These authors argue that it will be the consideration of the different elements (conscious or unconscious) making up the factors included in their study, that will determine the final choice (the intent to enrol) made by that student. Purchase intention is used as the predictor for the preferential choice of students regarding the destination country and university they will choose (Chung et al., 2009). The perceived preference match will lead to the willingness/intention to enrol (Jiang, 2004:76).

Before a proposed perceived value ‘model’ with its different factors that will lead to intention to enrol at a university of choice can be proposed, it is important to evaluate existing value intention frameworks and models. The focus will mainly be on the most popular (thus most cited by various authors) value-intention frameworks that will ultimately lead to willingness/intention to buy. The researcher’s aim is to propose a choice model and its perceived value that influence prospective university students’ intention to enrol.

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4.5 Value-intention frameworks and models

The following section will briefly review the value-intention frameworks and models that are most relevant to this study. Only those models where the outcome of perceived value has some sort of ‘intention’ (willingness to buy, purchase, intention to purchase, repurchase intention, intention to recommend/word-of-mouth and intended satisfaction) will be discuss in order to guide the researcher with the development of her own model. Although fifteen value-intention frameworks or models have been identified from the literature study, not all fifteen will be discussed in this chapter. Only those models that can be considered as providing the foundation for this study, together with all the value-intention frameworks or models applied to the Higher Education landscape, will be discussed. The remaining frameworks or models are summarised in Appendix C. Although these models are included in Appendix C, they have all been considered and information obtained from all fifteen value-intention frameworks/models have been included in the earlier ‘give’ and ‘get’ factors, and intention to purchase discussions. These models in Appendix C, together with the models discussed next were thoroughly reviewed when the perceived value and intention to enrol scales have been developed for the questionnaire (Appendix D and E). They are also summarised in Table 4.3.

The discussion of the different models will follow a sequential approach to portray how frameworks or models have evolved. The section is introduced with a discussion of Dodds and Monroe’s (1985) value-intention framework, as it seems from literature that these authors were the first to address the perceived value construct consisting of a perceived quality (benefit) and a sacrifice factor that will ultimately lead to willingness to buy. This model will be followed by a brief overview of Zeithaml’s (1988) means-end model, as this author elaborated on Dodds and Monroe’s (1985) model, by describing perceived sacrifice as consisting of two factors, perceived monetary sacrifice and perceived non-monetary sacrifice. Zeithaml’s (1988) proposed model also distinguished perceived value as a trade-off of perceived benefits received, and perceived sacrifices made where sacrifices can be further classified as monetary and non-monetary sacrifices.

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Zeithaml’s (1988) model is followed by a discussion of Sweeney and Soutar’s (2001) PERVAL model that was developed to assess customers’ perceptions of the value of a consumer durable good at a brand level. Sweeney and Soutar’s (2001) PERVAL (Perceived Value) model needs to be included, as this model formed a fundamental part of Brown and Mazzarol’s (2009) study that developed a model to test customer satisfaction by investigating the drivers of student satisfaction and loyalty in a HE setting.

Sweeney and Soutar’s (2001) model is followed by a discussion of Chu and Lu’s (2007) research model of online music purchase behaviour. Although this model explains factors influencing online music purchase intention, its structure and components warrant a discussion, as it is similar to the model proposed by the researcher for the purposes of this study. This model consists of two central constructs: perceived customer value and purchase intention. Perceived customer value further consists of two factors: perceived benefits and perceived sacrifice, which are the two factors that the researcher also proposes for the perceived value construct that will be applied to the Higher Education (HE) context.

The next section provides a brief overview of the value-intention frameworks or models that were applied to the HE context. Ledden et al.,’s (2007), Brown and Mazzarol’s (2009), as well as Ledden and Kalafatis’ (2010) research models will be briefly reviewed. Ledden et al’s. (2007) research model of personal value, consumer value and satisfaction will be briefly discussed as it seems to be the first value- intention framework or model that has been adopted to the Higher Education environment. A brief overview will be provided of Brown and Mazzarol’s (2009) model that was developed to test customer satisfaction in a HE setting, followed by a discussion of Ledden and Kalafatis’ (2010) research model of the impact of time on perceptions of educational value.

Once these models have been discussed, the chapter will conclude with a summary of the chosen value-intention frameworks and models that are summarised in table format in Table 4.3.

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4.5.1. Dodds and Monroe’s (1985) Value-intention framework

Dodds and Monroe (1985) conducted an experiment on the effect of price and brand information on perceptions of quality and value, and on willingness to buy. These authors’ aim was to examine the effect of brand and price information on how individuals subjectively evaluate products. They used multiple constructs; perceived quality, perceived value, and willingness to buy that were measured in a 2x3x2 factorial design that varied brand information, price levels and even presentations for a given price level (Dodds & Monroe, 1985:86).

Their study further investigated whether perceptions differed when prices were odd or even. There were no differences due to odd vs. even prices. They argued that price (Figure 4.1) plays a dual role in the trade-off between perceived quality and sacrifice, and that this trade-off will lead to perceived value. Higher prices for instance will lead to greater perceived quality and consequently, to a greater willingness to purchase based on perceived quality. At the same time, these authors argue that higher prices represent a measure of what must be sacrificed to purchase the good and leads to a lesser willingness to buy. Thus, they conclude that willingness to buy is positively related to perceived value, and that perceived value as the core construct, represents a trade-off between the two variables, perceived quality and sacrifice (Dodds & Monroe, 1985:86).

They concluded that price positively influences the perceptions of quality and influences the perception of value and willingness to buy too, while brand information enhances the price effect (Dodds & Monroe, 1985).

Figure 4.1 Dodds and Monroe’s (1985) Value-intention framework

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4.5.2 Zeithaml’s (1988) Means-end model relating to price, quality and value

Zeithaml’s (1988) aim was to define the concepts of price, quality and value from the consumer’s perspective and to relate the concepts in a model. To accomplish these objectives, an investigation of quality and value in the product category of beverages was launched and company interviews, a focus group interview and 30 in-depth consumer interviews were conducted.

By adapting Dodds and Monroe’s (1985) value-intention framework (Figure 4.1), Zeithaml (1988:3-4) provided an overview of the relationships among the concepts of price, perceived quality and perceived value. The author used relevant literature and evidence from her exploratory investigation to define and describe each concept in the model. She firstly developed propositions on the basis of the qualitative data from the exploratory study and other conceptual work and secondly for each proposition, empirical evidence that supported and refuted the proposition was reviewed.

This model (Figure 4.2) delineates several strategies for adding value in products and services. Each of the boxes contributing to perceived value provides an avenue for increasing value perceptions. Reducing monetary and non-monetary costs, decreasing perceptions of sacrifice, adding salient intrinsic attributes, evoking perceptions of relevant high level abstractions, and using extrinsic cues to signal value are all possible strategies that companies can use to affect value perceptions.

Zeithaml (1988:17) proposed that understanding what quality and value mean to customers, offers the promise of improving brand positions through more precise market analysis and segmentation, product planning, promotion and pricing. The end result of perceived value is the decision the customer then makes as to which store to shop from or which product to buy (Zeithaml, 1988:13). Thus, perceived value, ultimately leads to the decision to buy (Figure 4.2).

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Figure 4.2 Zeithaml’s (1988) Means-end model relating price, quality and value

4.5.3 Sweeney and Soutar’s (2001) PERVAL, to assess customers’ perceptions of the value of a consumer durable good at a brand level

Sweeney and Soutar’s (2001) research project presented a 19-item measure, called PERVAL (or Perceived Value) that can be used to assess customers’ perceptions of the value of a consumer durable good at a brand level. They developed this measure for use in a retail purchase situation to determine what consumption value drives purchase attitude and behaviour. Their study resulted in four distinct value factors that were termed emotional, social, quality/performance and price/value for money. All four value factors were found to help in explaining attitudes and behaviour and they were tested in a pre-purchased and in a post-purchase situation and found to be both reliable and valid in this context.

Sweeney and Soutar (2001) designed a scale of measurement of perceived value through applying different empirical studies to arrive at their PERVAL scale. These authors argue that Sheth et al.’s (1991) model provides a strong foundation from which to build a perceived value scale. Although Sheth et al. (1991) argued that

194 | functional value was created by attributes such as reliability, durability and price, Sweeney and Soutar (2001:206) argue that the reliability and durability attribute have often been seen as aspects of quality and that quality and price can have separate influences on perceived value. It could be argued that price and quality, are functional sub-factors that contribute separately to perceived value and that they should be measured separately.

Value was examined in an in-store pre-purchase situation where university students of three Australian universities were asked to recall a situation in a shop in the previous three months when they had looked at a particular durable product which they could identify by brand and price, but which they had not bought. It could not have been an item that they had no intention of buying either, because they couldn’t afford it or didn’t need it or disliked it. The variation in behavioural intentions regarding the product was important as it was expected that a similar variation in perceived value would result in making it difficult to properly test the scale. Four weeks later the above ‘test’ was repeated and students were now asked to re- evaluate the same product (Sweeney & Soutar, 2001:208).

The same study was repeated by conducting a telephone survey and respondents were asked to think of a situation in a shop in the previous three months when they had looked at a particular durable good such as clothing, footwear, furniture, cars, computers, sports goods and household appliances. The main objective at this stage was to evaluate the robustness of the scale (Sweeney & Soutar, 2001:210-211).

These authors used a scale starting from an initial scale of 85 items, grouped into 34 functional items (17 for perceived quality, 15 for price and 2 general functional items), 29 social items and 22 emotional items. Following a process of refinement, they reduced the factors proposed by Sheth et al. (1991) to reach a final scale consisting of 19 items, grouped into four factors: emotional value, social value, functional value I (price/value for money) and functional value II (result of the product/perceived quality) (Moliner et al., 2007:1399; Sánchez et al., 2006:396; Sweeney & Soutar, 2001:208).

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Sweeney and Soutar’s (2001:214) study revealed that by using the four factors in combination, explained considerably more of the variance in possible outcomes than did a single item. It also showed that the factors had differential effects on various outcomes. It was evident that the “emotional value factor” played an important part in every purchase decision and is of great importance in predicting willingness to buy, in particular in the durable product category, while perceptions of quality had a particular influence on people’s expectations of problems (Figure 4.3 as adapted by the author). Although it was evident that the “emotions factor” plays an important part in every purchase decision, very few purchase decisions are entirely emotional and their study shows that multiple value factors explain consumer choice better (Sweeney & Soutar, 2001:216).

Figure 4.3 Sweeney and Soutar’s (2001) PERVAL scale as illustrated by Brown and Mazzarol (2009) and willingness to buy added by the author

Sweeney and Soutar (2001) followed a rigorous process of preparing their scale, and it permits empirical testing of multi-dimensional characters of the construct.

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However, according to Sánchez et al. (2006:397), it captures mostly the post- purchase evaluation of a product and not the perceived overall value of a purchase.

4.5.4 Chu and Lu’s (2007) research model of online music purchase behaviour

The purpose of Chu and Lu’s (2007) study was to provide an explanation of factors influencing online music purchase intention of Taiwanese early adopters of online music. These authors incorporated the value-intention framework, originally developed by Dodds and Monroe (1985) into a technology acceptance model to investigate the purchase behaviour of early adopters of online music in Taiwan (explanation of value-intention framework was done earlier in section 4.5.1).

Perceived value is the core variable in their research model and since perceived value is based on an overall assessment of the costs and benefits of a given market offering it reflects the ‘net gain obtained’ by customers. Based on the findings of other studies (Sweeney et al., 1997; Dodds et al., 1991; Zeithaml, 1988), Chu and Lu (2007) argue that there is sufficient evidence for the positive influence of perceived value on consumer willingness to buy (intention to purchase) and they propose that perceived value would provide a useful indicator of intention to purchase online music as well (Chu & Lu, 2007:142). Chu and Lu’s (2007) study defined perceived value as the consumer perception of ‘net benefit’ obtained in exchange for the sacrifices, while listening to online music and purchase intention were defined as the degree to which the consumer would like to purchase online music in the future.

Chu and Lu’s (2007:142) model comprises of two benefit factors; functional (perceived usefulness construct) and recreational benefits (perceived playfulness construct) for predicting the benefits perceived by online consumers. Perceived usefulness is the degree to which the consumer believes that listening to music online would fulfil a certain purpose and thus focuses on the functional and convenience benefits. On the other end of the benefit spectrum is Chu and Lu’s (2007:143) perceived playfulness contrast that focuses on recreational benefits and the degree to which the consumer believes that enjoyment could be derived when listening to online music (Figure 4.4).

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In this model, perceived sacrifice is described as individuals’ feelings regarding giving something up. That could be monetary and non-monetary costs. Price is used as the key measure, however, consumers’ monetary costs should be used to measure perceived price as seen by consumers instead of using actual product prices. Non-monetary costs include physical or psychological efforts. Perceived ease of use was acknowledged by Chu and Lu (2007:144) as a non-monetary sacrifice.

Figure 4.4 Chu and Lu’s (2007) Research model of online music purchase behaviour

4.5.5 Ledden, Kalafatis and Samouel’s (2007) Research model of personal values, consumer value and satisfaction

Ledden et al.’s (2007) research attempted to provide an insight into the impact of personal values in the development of perceived value in an educational environment. The model (Figure 4.5) consists of three central constructs: Personal Values (PV), Consumer Value (VAL) and Satisfaction (SF) and was tested among students enrolled for an MBA degree in a UK business school. Self-completion questionnaires were distributed of which 118 were usable (Ledden et al., 2007:967- 968).

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Figure 4.5 Ledden Kalafatis and Samouel’s (2007) Research model of personal value, consumer value and satisfaction in education

The purpose of their study was to test the impact of personal values on consumers’ perceptions of received value, and to test the Personal Value (PV) to the Consumer Value (VAL) relationship, although they acknowledged the existence of a number of Consumer Value (e.g. quality) antecedents (Ledden et al., 2007:967-968, 970).

Their starting position was that Consumer Value (VAL) is a higher order construct that comprises ‘get’ and ‘give’ factors and their study used the Sheth et al. (1991) ‘get’ component consisting of the Functional value (FV), Social value (SV), Epistemic value (EPV), Emotional value (EMV) and Conditional value (CV) (Sheth et al.’s (1991) model is discussed in Appendix C). They also added an ‘image of the institution’ (IM) component representing the benefits that a student will receive from studying at a high-status institution (Ledden et al., 2007:968). The ‘give’ component

199 | comprised both monetary (MS) and non-monetary sacrifice (NMS) considerations and it was built on Cronin et al.’s (1997) work (Ledden et al., 2007:968).

As all these components have already been discussed earlier in 4.3.1.1 (The ‘get’ or benefits factors of perceived value) and 4.3.1.2 (The ‘give” or sacrifice factors of perceived value), the detail of each factor will not be elaborated on again in this section.

The Personal Values (PV) component of Ledden et al.’s (2007) model was based on the Rokeach Value System. Personal values can be conceptualised as cognitive representations of potential students’ requirements, which include social interaction requirements and social ‘institutional demands’ experienced by the individual (Durvasula et al., 2011). Ledden et al. (2007) conceptualised PV comprising out of terminal values (TV) and instrumental values (IV), which together provide a blueprint for how to behave in life and providing guidelines on choices and on resolving conflicts: o IV describes the individual’s way of behaviour, such as his/her ambition, responsibility and honesty; and o TV describes an individual’s desired end-states of existence or goals in life, such as freedom and security (Ledden et al., 2007:968).

Ladhari, Pons, Bressolles and Zins (2011:953) argue that marketing studies acknowledge the importance of personal values and state that attitudes and behaviours are the major consequences of personal values, and that they influence perceived value. Ledden et al. (2007) agree to some extent, but state that although IV and TV are included in their model, these authors argue that IV and TV do not uniformly impact on the formation of value and/or ‘get’ and ‘give’ components. Personal values have however been found to have only moderate explanatory powers in terms of forming students’ perceptions of received educational value (Ledden et al., 2007:968, 972).

Personal values were of definite importance to students when associated with satisfaction and behavioural outcomes in higher education (Ledden et al., 2007). Durvasula et al., (2011) agree that by examining personal values facilitates a richer

200 | understanding of how such values may affect expectations about university services and concomitant behavioural outcomes such as satisfaction and loyalty.

Ledden et al.’s (2007) research findings suggested that marketing should operate at two levels where at a general level, the TV need to be emphasised such as how the course can help individuals to achieve their goals in life, and at a specific level, emphasis should be placed on how the course is designed to provide students with the skills and competencies (i.e. instrumental values) necessary to achieve their goals. It was further established that only terminal values have a significant impact on the ‘give’ component and it is suggested by the authors that this reflects students’ appreciation that their decision to enrol on a course is reliant on their willingness to pay the fees and make other forms of sacrifice such as less time with family and friends (Ledden et al., 2007:972).

Their study confirmed the proposition that perceived value is a significant determinant of satisfaction They argue that this result provides strong support for the claim that value gained through the educational experience is a significant determinant of satisfaction (Ledden et al., 2007:971).

4.5.6 Brown and Mazzarol (2009) applying PERVAL to the university context

Brown and Mazzarol (2009) developed a model to test customer satisfaction by investigating the drivers of student satisfaction and loyalty in a HE setting. They tested their model on students enrolled in four types of Australian universities where the findings suggested that student loyalty is predicted by student satisfaction, which in turn is predicted by the perceived image of the host university. The university’s image also strongly predicted perceived value and to a lesser extent student satisfaction. It was also found that perceived quality of “humanware” and “hardware” has an impact on perceived value, although this was found to be low (Figure 4.6).

Brown and Mazzarol’s (2009:86) study provided evidence of the importance of perceived value and customer satisfaction to student loyalty. They applied the nineteen-item PERVAL scale, which is a perceived value scale, of Sweeney and Soutar (2001) to a service-related context in a university environment. Two of the items associated with perceived quality did not seem to translate to the new 201 | environment, the remaining 17 items fitted into four components, the same as Sweeney and Soutar’s (2001) factors in their original study namely: Emotional, Social, Price/Value; and Quality/Performance.

Brown and Mazzarol’s (2009:88) image factor consisted of items such as the study environment, practicality and conservativeness, and displayed a strong relationship across several factors of the perceived value construct. The evidence suggested that brand image within universities and other HEIs is likely to be just as important as for other types of service organisations.

These authors (Brown & Mazzarol, 2009:88) evaluated their measurement of service quality by using a combination of the SERVQUAL scale and items from service quality measures used within university settings. They arrived at the “humanware” construct that included people and process, while the “hardware” construct represented the SERVQUAL’s tangible elements and infrastructure (Figure 4.6).

Figure 4.6 Brown and Mazzarol’s (2009) Model on constructs in an Australian Higher Education (HE) setting

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4.5.7 Ledden and Kalafatis’ (2010) Research model on the impact of time on perceptions of educational value

Ledden and Kalafatis’ (2010) study attempted to address the changes in perceptions of value factors, extended the analysis from a business-to business to a consumer domain, and endeavoured to adopt a true longitudinal approach to value.

The model, or research framework (Figure 4.7) involved decisions regarding value determinants, the structure of value and relevant outcomes. Although these authors acknowledged the importance of external factors possibly changing perceptions of value, the focus of their study was on the internal factors, specifically on cognitive mechanisms and affective states. The affective determinants of value that were included in this study were knowledge and emotions (Ledden & Kalafatis, 2010:144- 145).

Figure 4.7 Ledden and Kalafatis’ (2010) Research model of the impact of time on perceptions of Educational Value

In this model, knowledge represents the accumulated information/learning acquired by the student about his/her chosen course of study over the duration of its consumption. Knowledge is also related to the level of knowledge a consumer has

203 | about a particular offering. Emotions reflect the affective states experienced by the student during the consumption experience (Ledden & Kalafatis, 2010:145).

The authors argue that value should be treated as a multi-dimensional construct that comprises the ‘get’ and ‘give’ components, which in turn consist of a number of factors. Ledden and Kalafatis (2010:146) used the following five factors of the ‘get’ component of value in their research framework/model: functional value, emotional value, epistemic value, conditional value, social value and image value. The ‘give’ components of their study comprised both monetary and non-monetary considerations (Ledden & Kalafatis, 2010:145). For nomological validity, ‘satisfaction’ and ‘intention to recommend’ were also included.

In Ledden and Kalafatis’ (2010) study, the multi-dimensional approach to the impact of time on perceptions of value was tested with students enrolled for a full-time Master’s in marketing programme in a UK business school. The three most “distinct” points of the programme were, (1) time – induction that was associated with anticipation and excitement, (2) time – mid-point through the programme when formal examinations take place, and (3) time – notification of final results. Sixty-six responses could be used to address the first two points of time as students hadn’t received their final results at the time of the study yet.

Their study (Ledden & Kalafatis, 2010) revealed that overall, affective states are significant determinants of perceptions of value throughout the consumption process. Image of the service provider, which is the university in this case, played an important role in the decision-making process, and the decision to enrol to the preferred course was associated with a positive emotional state and at the same time with a high image level.

Knowledge played a significant role at setting expectations, and knowledge about a product or service specifically, shapes expectations about consumption outcomes against which received performance is evaluated. It was also determined that the ‘knowledge factor’ had been found to be a significant determinant of image and the two given factors.

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Ledden and Kalafatis’ (2010:149) study also indicated a stable pattern between the value factors and satisfaction. This finding correlates with Sánchez-Fernández et al.’s (2010) study in the Portuguese HE environment that also indicated that perceived value leads to satisfaction. A pattern of the impact of the value factors on satisfaction and in turn the intention to recommend, were also evident in Ledden and Kalafatis’ (2010) study. Petruzzellis and Romanazzi (2010:140) and Alves and Raposo (2007) also found that satisfaction in HE is an antecedent of the intention of continuing the service or positively recommend the service.

4.6 A summary of the chosen value-intention frameworks and models

It is evident from the literature review that although authors haven’t always agreed on how they arrived at perceived value, nine of the authors of the mentioned models agreed that perceived value is a trade-off between benefits received and sacrifices made (Table 4.3). It is for this reason that the research’s proposed model will also present perceived value as this trade-off.

Although the remaining six models discussed in this chapter as well as in the Appendix C (Brown & Mazzarol, 2009; Sánchez et al., 2006; Petrick 2002, 2004; Sweeney & Soutar, 2001; Sheth et al., 1991) haven’t specifically portrayed perceived value as a “trade-off” between benefits received and sacrifices made, a definite trend could be seen in the similar perceived value factors chosen for perceived value. Quality (or functional value) was the one factor that has been included by each and every author and emotional and social value was also seen as very important (Table 4.1). It should be noted though, that Sweeney and Soutar’s PERVAL scale has been adopted by Petrick (2002, 2004) and Brown and Mazzarol (2009), thus explaining the reason for the ‘many’ models not explicitly portraying perceived value as a ‘trade- off’ between benefits received and sacrifices made.

Most of the discussed value-intention frameworks/models in Section 4.5 showed a strong or positive link from perceived value to an actual outcome. The only models that haven’t explicitly mentioned perceived value leading to an actual outcome were Teas and Agarwal (2000), Sweeney and Soutar (2001) and Sánchez et al.’s (2006) models (Table 4.3). However, the reason for including these frameworks/models in

205 | the ‘value-intention framework/model’ of this study was already explained. Although the authors of these three models’ haven’t explicitly mentioned perceived value leading to some kind of outcome, they were all built on other models (Sheth et al., 1991) that indicated the link between perceived value and behavioural intentions (Table 4.3).

As discussed in this section, there are similarities and differences that are portrayed in Table 4.3 (A summary of the chosen value-intention frameworks and models’ different elements). This Table is depicting what was discussed in section 4.5 and in Appendix C. It summarises the most popular (most cited) value-intention frameworks/models found in literature.

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Table 4.3 A summary of the chosen value-intention frameworks and models’ different elements

off Elements/ existing -

models was to buy Actual to buy) value =value trade to actual outcome intention purchase sacrifices Perceived Perceived buynot or conducted Industryin value leadsvalue Outcome is Outcome is Outcome is Outcome is Outcome is Willingness Satisfaction Behavioural Repurchase which study intention (to benefits and X Products, stereo Dodds & Monroe’s (1985) X X (Quality and headset (4.5.1)* Sacrifice) (Walkman) X Products, Zeithaml (1988) (4.5.2) X X (Quality and beverages Sacrifice) Products, Dodds, Monroe & Grewal X calculators and (1991) X X (Quality and stereo headset (Appndx C)** Sacrifice) (Walkman) 200 application Sheth, Newman & Gross X X NO situations and (1991) (Appndx C) cigarettes Cronin, Brady, Brand, Hightower & Shemwell X X X Service industry (1997) (Appndx C) NO, but extended Products, Teas & Agarwal (2000) Dodds et al.’s handheld (Appndx C) (1991) study that X business

had outcome calculators and

(willingness to buy) wristwatches Cronin, Brady & Hult X (2000) X X (Quality and Service industry (Appndx C) Sacrifice) Only Not depicted or Sweeney & Soutar (2001) emotional Retail marketing illustrated, but (4.5.3) factor lead to NO – furniture and mentioned in their PERVAL scale willingness to car stereo store article of 2001. buy

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off Elements/ existing -

models was to buy Actual to buy) value =value trade to actual outcome intention purchase sacrifices Perceived Perceived buynot or conducted Industryin value leadsvalue Outcome is Outcome is Outcome is Outcome is Outcome is Willingness Satisfaction Behavioural Repurchase which study intention (to benefits and X Petrick (2002) Service industry, (and (Appndx C) X NO fast food word-of- SERV-PERVAL restaurant mouth) X Petrick (2004) Service industry, (and (Appndx C) X NO cruise word-of- SERV-PERVAL passengers mouth) Not depicted, but Sánchez, Callarisa, model based on Rodríguez & Moliner Sheth et al. (1991) Service, a (2006) NO that leads to tourist (Appndx C) behavioural GLOVAL intention Chu & Lu (2007) X X X “e-product”, (4.5.4) (purchase acceptance of intention) online music Ledden, Kalafatis & X X X Service, Higher Samouel (2007) Education (4.5.5) Brown & Mazzarol (2009) X X NO Service, Higher (4.5.6) (and loyalty) Education Ledden & Kalafatis X X NO Service, Higher (2010) (and Education (4.5.7) intention to recommend)

Source: Adapted from Ledden and Kalafatis, 2010; Brown and Mazzarol, 2009; Ledden, Kalafatis and Samouel, 2007; Chu and Lu, 2007; Sánchez, Callarisa, Rodríguez and Moliner, 2006; Petrick, 2004; Petrick, 2002; Sweeney and Soutar, 2001; Cronin, Brady and Hult, 2000; Teas and Agarwal, 2000; Cronin, Brady, Brand, Hightower and Shemwell, 1997; Sheth, Newman and Gross, 1991; Dodds, Monroe and Grewal, 1991; Zeithaml, 1988; Dodds and Monroe, 1985. * Refer to the relevant section in Chapter 4 where the particular model is discussed in more detail. ** Refer to Appendix C that discusses that particular model in more detail

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4.7 A proposed university choice model for South Africa

The researcher’s proposed model that will include choice factors (as discussed in Chapter 3) and the perceived value that influence prospective university students’ intention to enrol will be explained in this section.

The researcher’s proposed choice model will consist of three main constructs, choice factors (Chapter 3), perceived value and intention to enrol (Figure 4.8). Choice factors have already been discussed in Chapter 3 and perceived value literature has been discussed earlier in this chapter.

Figure 4.8 Depicting the basic elements of the proposed theoretical model

In order to depict perceived value, it is proposed to follow a multi-dimensional approach. The problem with a single-item scale is that it assumes that consumers have a shared meaning of value and according to Zeithaml (1988:471), the constructs quality and value are not well differentiated from each other and from similar constructs such as perceived worth and utility. It has therefore been argued that a single-item measure of perceived value lacks validity (Petrick, 2004:399).

As discussed in Section 4.6, perceived value will for the purpose of this study be evaluated as a trade-off between benefits (‘get’ components) received and sacrifices (‘give’ components) made. Ledden et al.’s (2007) benefit components and sacrifice components will be used in this study as their study was conducted in the HE environment and their measure of perceived value has already been found as reliable and valid.

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The proposed theoretical model (Figure 4.8) will investigate the possible positive relation between perceived value and intention to enrol. The researcher argues that such a relation can be proposed as several authors investigated and proved that perceived value leads to: o Willingness to buy (Dodds & Monroe, 1985; Dodds et al., 1991) and Sweeney and Soutar’s (2001) emotional factor leading to willingness to buy. o Actual purchase (Zeithaml, 1988). o Behavioural intentions (Sheth et al., 1991; Cronin et al., 1997; Cronin et al., 2000; Chu & Lu, 2007; Ledden & Kalafatis, 2010 (and to recommend to other customers). o Re-purchase intention and positive word-of-mouth (Petrick, 2002; Petrick, 2004). o Satisfaction (Ledden et al., 2007; Brown & Mazzarol, 2009; Ledden & Kalafatis, 2010)(Table 4.2).

Although Ledden et al.’s (2007) perceived value construct with mentioned factors will be adapted for this study, it will be argued that perceived value will lead to the intention to enrol. This argument is founded on several models, as discussed in the above section, that perceived value has a positive relation to willingness to buy and behavioural intentions, such as purchase intention which can be argued is very similar to intention to enrol. The argument that perceived value will lead to intention to enrol, will mostly be based on Chu and Lu’s (2007) model that argued that their study has provided sufficient evidence for the positive influence of perceived value on intention to purchase. Chu and Lu’s (2007) model was based on Dodds and Monroe’s (1985) model that founded the value-intention framework.

This proposed model that will be tested in the following chapters, will NOT entirely be based on Ledden et al.’s (2007) entire model, which proposed that perceived value leads to satisfaction. Satisfaction as a construct predicting purchase intention was NOT included, as it was evident in Petrick’s (2004:404) study that perceived value is a better predictor of re-purchase intentions than satisfaction. Also, although perceived value and satisfaction both are relative judgements, both are dependent on the consumption context, and both involve aspects of costs and benefits, satisfaction is more a post-purchase evaluation (Sánchez-Fernández & Iniesta- Bonillo, 2009:427). Satisfaction depends on experience of having used the product 210 | or service (Sánchez-Fernández & Iniesta-Bonillo, 2009:427, Petrick, 2004:399). It is for these reasons, that this proposed model will portray perceived value as having a strong relationship to intention to enrol, as the prospective student will not have had any experience with a university when responding in the conducting of the study. The focus is on the prospective undergraduate student who is in the position of choosing a preferred university to attend for the first time his/her life.

4.8 Conclusion

Universities are challenged to attract good students (Johnson, 2010:15), but this requires universities to understand the population they wish to attract by conducting an honest analysis of their strengths. This information should be used to define a genuinely distinctive mission and target marketing strategies, with the goal becoming one of ‘fit’ between student and university (Baldwin & James, 2000). One such way of achieving this ‘fit’, is by understanding prospective students’ needs and what they value by applying an appropriate ‘choice model’ that should provide some insight into these issues.

If universities can predict where applicants will come from and what they will value, scarce resources can be focussed on marketing areas that will give the highest return (Briggs, 2006:708). Marketers should make an effort to create value if they want to get sustainable competitive advantage (Espejel et al., 2008:874) as the ‘bidder’ (university) with the highest perceived value that matches consumer wants, will win the customers (students) (Rajendran & Hariharan, 1996:17).

It is for these mentioned reasons that this chapter determins that perceived value is defined by customers (Lu & Shiu, 2011:1185) as the “preferential judgement” (Boksberger & Melsen, 2011:230) and “overall assessment” (Zeithaml, 1988:14) of how a specific university will meet their needs and desires. This evaluation of perceived value takes place as a trade-off between “give’ and ‘get’ components (Zeithaml, 1988). These ‘give’ and ‘get’ factors are unravelled to unearth an understanding of the construct and to assist the researcher in knowing which factors to choose for the proposed choice model. As Ledden et al.’s (2007) perceived value

211 | factors have already been tested in a HE context, it was decided to adapt their portrayal of perceived value in the researcher’s own model.

Existing value-intention frameworks and models are investigated to enable the researcher to use the information obtained as foundation for her proposed model. It was decided that Chu and Lu’s (2007) findings that perceived value leads to purchase intention, will be used and adapted as ‘intention to enrol’, as Chu and Lu’s (2007) model was formed by investigating and adopting already existing models which proved that perceived value can have a strong and positive relationship to purchase intention.

Chapter 5 will discuss the methodology that will be used to test all the elements of the proposed model with the prospective student population. All the construct scales will be discussed in detail, and it will be argued why certain scales were used and which measurement scales have been adapted from already existing scales.

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CHAPTER 5

Research methodology

5.1 Introduction

The previous chapters offer an explanation of the Higher Education (HE) landscape, of choice factors and existing choice models, as well as of perceived value, existing value-intention models and the proposed theoretical model. This chapter elaborates on the research methodology described in Chapter 1.

The purpose of this chapter is to explain how the empirical research component for the study was conducted. First, marketing research as concept is introduced by offering a definition and by briefly discussing its importance. However, the focus of this chapter is on the discussion of the marketing research process and on this process’ application to the present study. The six steps of the marketing research process as suggested by Malhotra (2009:35) are used in this chapter to guide the discussion.

5.2 Marketing research defined

It can be argued that marketing research is concerned with “research from the perspective of marketing”. While marketing is about the planning and execution of the pricing, promotion, and distribution of products and services for mutual beneficial exchange between the firm and its customers, marketing research is the mechanism for generating information to shed light on uncertainty that is embedded within consumer behaviour that is unpredictable (Shiu, Hair, Bush & Ortinau, 2009:6).

Many authors present definitions of the term marketing research that includes elements of problem-identification research (Malhotra, 2009:53) and searching for the truth (Zikmund & Babin, 2010b:5), which help managers to solve problems and make decisions (Cant, Gerber-Nel, Nel & Kotzé, 2003:1; Shiu et al., 2009:6). Searching for the truth involves: the identification of marketing opportunities and problems (Zikmund & Babin, 2010b:5), the generation, refinement and evaluating of marketing actions (Cant et al., 2003:1), the planning, gathering and analysing of data

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(Cant et al., 2003:1), the acquiring of dependable and useful information (Ary, Jacobs, Razavieh & Sorensen, 2006:18), the identification, collection, dissemination and use of information (Malhotra, 2009:30), and the interpretation of needed information (Aaker, Kumar, Day & Leone, 2011:10).

Three definitions of marketing research are presented next that encapsulate the essence of what marketing research seems to be all about.

The European Society for Opinion and Marketing Research (ESOMAR) defines Marketing Research as follows:

“Marketing Research is a key element within the total field of marketing information. It links the consumer, customer and public to the marketer through information which is used to identify and define marketing opportunities and problems; generate, refine and evaluate marketing actions; improve understanding of marketing as a process and of the ways in which specific marketing activities can be made more effective. Marketing research specifies the information required to address these issues, designs the method for collecting information; manages and implements the data collection process; analyses the results; and communicates the findings and their implications” (Shiu et al., 2009:5).

The American Marketing Association’s official definition of Marketing Research states that:

“Marketing Research is the function that links the consumer, customer, and public to the marketer through information – information used to identify and define marketing opportunities and problems; generate, refine, and evaluate marketing actions; monitor marketing performance; and improve understanding of marketing as a process. Marketing research specifies the information required to address these issues, designs the method for collecting information, manages and implements the data collection process, analyses, and communicates the findings and their implications” (Aaker et al., 2011:10; McDaniel & Gates, 2010:7; Malhotra, 2007:7; McDaniel & Gates, 2006:5).

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McDaniel and Gates (2006:6) shortened the marketing definition provided by The American Marketing Association to: “Marketing research is the planning, collection, and analysis of data relevant to marketing decision making and the communication of the results of this analysis to management”.

The American Marketing Association’s definition is the mostly cited in marketing research handbooks, thus the most commonly used and best known definition to capture the essence and the elements of it. From the various definitions mentioned, the researcher adopts the following marketing research definition for the purpose of this study:

Marketing research’s aim is to identify, plan, collect, analyse, disseminate, and use information for the purpose of problem-identification and problem-solving to improve decision-making. It is a marketing tool that links consumers to the marketer through information (Aaker et al., 2011:10; McDaniel & Gates, 2010:7; Zikmund & Babin, 2010b:5; Shiu et al., 2009:5; Malhotra, 2007:7; Ary et al., 2006:18; McDaniel & Gates, 2006:6; Cant et al., 2003:3).

5.3 Importance of marketing research

The prime value of marketing research is the reduction of uncertainty as it aims to solve problems and to improve understanding of the market place (Shiu et al., 2009:5-6; Malhotra, 2009:53; Ary et al., 2006:11). It cuts decision risk because of the input obtained from information that facilitates decision-making about marketing strategies and tactics to achieve goals (Zikmund & Babin, 2010a:13). Decision risk is further minimised as marketing actions can be generated, refined and evaluated before the product or service reaches the market (Burns & Bush, 2006:10).

McDaniel and Gates (2010:7) suggest that marketing research is important to management as it enables businesses to acquire an understanding of what customers deem to be important – an understanding that is essential for the purposes of customer satisfaction and retaining existing customers. An understanding of the specific characteristics of the market is possible as marketing research makes a major contribution to clarifying and resolving issues and then choosing among decision alternatives to develop effective marketing campaigns for 215 | current and potential customers (Aaker et al., 2011:11; Shiu et al., 2009:5). Moreover, marketing research helps management to understand the changes that are occurring in the marketplace and to take timely action to avoid potential threats. Marketing research allows businesses to stay competitive, adding to the survival of the business (Aaker et al., 2011:14; McDaniel & Gates, 2010:7).

It can be concluded that marketing research plays an important role in marketing, and the various definitions highlighted that marketing research involves various actions and steps.

In this study the researcher specifically aims to determine which factors prospective students consider and value when choosing a university/university of technology, the perceived value that influences prospective students’ university choice, and the likelihood of the prospective students’ intention to enrol at his/her chosen university. In turn this information will assist universities in better segmenting and targeting their market.

5.4 The marketing research process

As mentioned in the introduction section of this chapter, this study followed the steps as presented by Malhotra (2009:35). The steps in the marketing research process are presented in Figure 5.1.

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Figure 5.1 The steps in the marketing research process

Not all six steps as presented in Figure 5.1 are discussed in detail, as steps one and two of the research process have already been addressed in Chapter 1. The problem has been defined (Chapter 1, Section 1.3) and the approach to the problem has also been discussed (Chapter 1, Section 1.7). The focus of this section falls on the formulation of a research design and its various components as illustrated in Figure 5.2.

5.5 Formulating a research design

A research design is a framework or blueprint for conducting a marketing research project. It is the basic plan that guides the data collection and analysis phases of the research project (Bickman & Rog, 1998: 10-11; Kinnear & Taylor, 1996:129). It serves as a master plan of the methods used to collect and analyse the data. Although every research problem is different and unique, most research objectives

217 | can be met by using one of three types or classifications of research designs: exploratory, causal and descriptive (Shiu et al., 2009:61).

Research design includes a number of components, which serve as the outline of the next section of this chapter. These components are illustrated in Figure 5.2 (Malhotra, 2007:78) and will be discussed to explain the chosen research design used: (5.5.1) classification of the research design, (5.5.2) type of information needed, (5.5.3) specify the measurement and scaling procedures, (5.5.4) construct and pre- test a questionnaire, (5.5.5) specify the sampling process and sample size which will include discussions around the target population, sampling frame, sample unit and sample element and the sampling technique used, and lastly (5.5.6), develop a plan of data analysis (adapted from McDaniel & Gates, 2010:78-80; Shiu et al., 2009:54- 65; Malhotra, 2007:11).

Figure 5.2 The components of the research design

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5.5.1 Classification of the research design

There are two broad types of research designs: exploratory and conclusive research designs (Malhotra, 2009:96). Exploratory research is a loosely structured type of research that is used when a researcher seeks insights into the general nature of a problem. Its aim is to obtain a better understanding of a problem or concept (Aaker et al., 2011:72; Malhotra, 2009:96; McDaniels & Gates, 2006:27). Conclusive research is more structured in nature and attempts to verify the information obtained during exploratory research. Also, conclusive research is either descriptive or causal (Zikmund & Babin, 2010b:51-53; Malhotra, 2009:96; McDaniels & Gates, 2006: 33- 34).

5.5.1.1 Exploratory research design

The aim of exploratory research is to collect secondary and/or primary data and use an unstructured format to interpret them. It is mostly used to classify the problems or opportunities and is not intended to provide conclusive information to determine a course of action. It can be used to help identify the decisions that need to be made and it can narrow the scope of the research topic (Zikmund & Babin, 2010b:60; Shiu et al., 2009:61).

Malhotra (2007:80) provides the following reasons for using exploratory research: o Defining the problem more accurately; o Identifying alternative options for action; o Formulating hypotheses to be tested; o Separating core factors and relationships for further investigation; o Obtaining an understanding of how the problem could be approached; and o Identifying future research possibilities.

This type of research is mostly used when there is little prior knowledge on which to build and it is conducted on a small and non-representative sample. The findings should be regarded as tentative and be used as building blocks for further research. It should be noted that exploratory research can be valuable when researchers are

219 | faced with a problem that is not fully understood (Aaker et al., 2011:72-73; Malhotra, 2009:97).

5.5.1.2 Conclusive research

Conclusive research is designed to assist the researcher in determining, evaluating, and selecting the best course of action in a given situation. This type of research is based on the assumption that the researcher has an accurate understanding of the problem at hand, and that the information needed for addressing the problem has been clearly specified. Thus, the objective of conclusive research is to test specific hypotheses and examine specific relationships. Large, representative samples are used to collect data that is then analysed with statistical techniques (Malhotra, 2009:97).

 Causal research design

The aim of causal research is to obtain evidence of cause-and-effect (causal) relationships. This is achieved by collecting data and by creating data structures that enable the decision maker to determine cause-and effect relationships. Decisions are made based on assumed causal relationships. These assumptions may not always be justifiable, and the validity of the causal relationships should be examined via formal research. By applying this type of research the researcher, can determine if assumed decrease in price will lead to increased sales (Shiu et al., 2009:62; Malhotra, 2007:89).

 Descriptive research design

Shiu et al. (2009:62) define descriptive research as research that employs scientific methods and procedures to collect data to create data structures that describe the existing characteristics of a defined target population. Descriptive research describes something and is conducted for the following reasons. To: o Describe the marketing problem or opportunity in detail; o Describe the characteristics of relevant groups, such as consumers, salespeople, organisations or market areas; o Estimate how many people in a specified population exhibit a certain behaviour;

220 | o Determine the marketing variables and the degree to which marketing variables are associated; o Determine the market potential of a product, demographics, and the attitudes of consumers; and o Make specific predictions (Malhotra, 2007:82; Cant et al., 2003:31).

A descriptive research design requires a clear specification of the who, what, when, where, why, and way of the research. Although researchers may have a general understanding of the research problem or opportunity, they still require conclusive evidence that provides answers to the questions. It provides information about customers, competitors, target markets, environmental factors, or other phenomena of concern (Shiu et al., 2009:62; Malhotra, 2007:82). In brief, it can be used to describe characteristics of certain groups (Cant at al., 2003:35), to “paint a picture”, or to provide a “snapshot” of a given situation (Aaker et al., 2011:73; Zikmund & Babin, 2010a:45).

Shiu et al. (2009:225) and Hair, Bush and Ortinau (2006:221) argue that to determine if a research design should be descriptive, it should be based on three factors: (1) the nature of the initial decision problem/opportunity at hand, (2) the set of research question, and (3) the research objectives. If the nature of the research problem/opportunity is to describe the characteristics of existing market situations or to evaluate current marketing mix strategies, then the descriptive research design is an appropriate choice. In the second place, if the management’s research questions focus on issues such as the who, what, where, when and how elements of target populations, then a descriptive research design may be appropriate. In the last instance, if the task is to identify meaningful relationships, determine whether true differences exist, or verify the validity of relationships between the marketing phenomena, then descriptive research designs should be considered.

There are furthermore two basic types of descriptive research studies available: cross-sectional and longitudinal studies. In cross-sectional designs, only one sample of respondents is drawn from the target population. Information is obtained from this sample only once. These designs are also called sample survey research designs and can be thought of as a snapshot of the marketplace taken at a specific point in time. In contrast, in longitudinal designs, a fixed sample of population elements is 221 | measured repeatedly on the same variables. The sample (or samples) in a longitudinal design remains the same over time, so the same people are studied over time (Malhotra, 2009:101).

Descriptive research allowed the researcher to collect data from grade 12 scholars considering attending a university, and created data structures that described the existing characteristics of this defined target population (Shiu et al., 2009:62). In this case, the existing characteristics of the prospective student were described.

For the purpose of this study, a descriptive research design, more specifically, a cross-sectional descriptive research design was followed. Information was obtained from grade 12 scholars in Gauteng and the same scholar was only approached once.

5.5.2 Type of information needed

To specify the type of information that is needed, Malhotra (2009:79) advises that the researcher should focus on each component of the problem, the analytical framework and models, and the research questions and hypotheses. It is for this reason that Chapters 2, 3 and 4 focused on the main components that are essential building blocks of the proposed choice model. These three chapters present all the elements of the research problem, research objectives and collected and presented information relating to the hypotheses formulated for the study. As no evidence in the literature could be found of an existing choice model for prospective university students in South Africa, it is necessary to collect new information in the South African context to achieve research objectives set earlier in this study (Chapter 1, Section 1.4).

To obtain new (and necessary) information, a researcher is able to select between a number of different descriptive research techniques, including (Aaker et al., 2011:144-149; Malhotra, 2009:101): o Secondary data techniques, done on a quantitative basis; o Primary data techniques, such as surveys;

222 | o Observational and other data collection techniques; and o Panels.

Secondary data is already available or exists in some type of recognisable format. This data was typically collected for some purpose other than solving the present problem and typically includes existing company information systems, databanks of other organisations, government sources such as the Census Bureau to trade association studies and reports, and syndicated data sources, such as consumer purchase panels. Public libraries, universities, and Internet websites also have this type of data available (Aaker et al., 2011:76; Shiu et al., 2009:63). For the purpose of this study, secondary data was obtained from speaking to other people, searching the Internet and reading relevant books and articles.

Primary data is first-hand data and is collected especially to address a specific research objective. This type of data is the result of conducting some type of exploratory, descriptive, or causal research projects. A variety of methods exist, ranging from qualitative research to surveys to experiments that may be employed. Surveys, focus groups, depth interviews or observation are also methods of primary data collection (Aaker et al., 2011:77; Shiu et al., 2009:63).

Observation data collection techniques monitor respondents’ actions without the researcher being physically present. This technique is a systematic procedure that involves activities of observing and recording the behavioural patterns of objects, people, events and other phenomena as opposed to asking consumers why they do what they do. Researchers watch people and situations and sometimes a machine fulfils the observation work (McDaniel & Gates, 2010:78; Shiu et al., 2009:311; Cant et al., 2003:46). Experiments comprise another method of gathering data. An experiment holds the greatest potential for establishing cause-and-effect relationships. This type of research is distinguished by the researcher’s changing of one or more independent variables such as price, package, design, shelf space, advertising theme, or advertising expenditures, and observing the effects of those changes on a dependent variable (usually sales) (Zikmund & Babin, 2010b:55; Malhotra, 2009:248; McDaniel & Gates, 2006:35).

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Consumer surveys are the mainstay of marketing research and are mostly associated with descriptive and causal research situations (Shiu et al., 2009:226). Its aim is to obtain information on beliefs, values, attitudes, preferences and intentions (Malhotra, 2009:154, 212-213). The survey method is based on questioning respondents in some form, or the behaviour of respondents is observed and described in some way. Survey data does more than just to report behaviour. It can provide insights into who the consumers are, how they behave, and why they behave in certain ways. It is mostly used when the research involves sampling a large number of people (Zikmund & Babin, 2010b:55; Malhotra, 2009:213). Some examples of survey methods include person-administered, telephone-administered, self-administered and online methods (Shiu et al., 2009:237).

There are different types of self-administered surveys, namely mail, mail panel, fax and drop-off surveys (Shiu et al., 2009: 237): o Mail surveys involve the distribution of questionnaires to respondents via the postal service. The researcher has no contact with the respondent and it is considered the least expensive form of data collection (Aaker et al., 2011:205; Shiu et al., 2009:237). o Panel surveys measure the same group of respondents over time, however not necessarily on the same variables (Malhotra, 2009:155). A mail panel consists of a large and representative sample of individuals who have agreed to participate in a limited number of mail surveys each year. Once an individual joins the panel, detailed demographic and lifestyle data is collected on the individual’s household (Aaker et al., 2011: 228; Malhotra, 2009:221). o Fax surveys are distributed to and returned from respondents via fax by hand or computer. Fax provides quicker responses than mail and it can provide higher response rates without reducing data quality (Aaker et al., 2011:228; Shiu et al., 2009:237). o Drop-off surveys are questionnaires that are left with the respondent to be completed at a later time. It has a higher response rate than mail surveys, but is more expensive too (Aaker et al., 2011:232; Shiu et al., 2009:237; Malhotra, 2009:224)

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For the purposes of this study, a drop-off self-administered questionnaire as survey technique was used to collect data. A self-administered drop-off questionnaire was chosen for the following reasons: (i) only lightly trained interviewers or fieldworkers are required to gain the co-operation of the respondents (in this case the teachers’ co-operation), and they deliver the questionnaires and arrange a return visit, (ii) response rates are high, about 70 and 80 per cent, in part because of the initial commitment to co-operate, and the realisation that the person who dropped off the survey will return to pick up the completed questionnaires, (iii) it a very cost-effective method, (iv) lengthy questionnaires can be used without affecting the response rate, (v) several questionnaires can be left at each school, and (vi) it is a useful tool for local-market surveys (visiting schools in Gauteng) (Aaker et al., 2011:232; Malhotra, 2009:224).

5.5.3 Measurement and scaling procedures

In business and marketing, concepts can often be measured in more than one way. When a concept is measured poorly, the outcome can be inaccurate data that will result in inaccurate conclusions and business advice. Before the measurement process can be defined, the researcher will have to decide exactly what needs to be measured and what scales need to be used. The research objectives, research questions and research hypotheses can be used to decide what concepts need to be measured (Zikmund & Babin, 2010b:322).

Measurement can be described as a process of obtaining meaning by describing some property of a phenomenon, usually by assigning numbers in a reliable and valid way (Zikmund & Babin, 2010b:322). Thus, consumers or prospective students are not measured, but only their perceptions, attitudes, preferences, or other relevant characteristics. It is important that the assignment of numbers must be isomorphic, meaning that there must be one-to-one correspondence between the numbers and the characteristics being measured (Malhotra, 2007:252). The four basic measurement levels are nominal level, ordinal level, interval level and ratio level. However, before these four basic measurement levels are discussed, the term scaling needs to be explained to put measurement in perspective.

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Scaling refers to procedures that attempt to determine quantitative measures of subjective and sometimes abstract concepts (McDaniel & Gates, 2010:333; McDaniel & Gates, 2006:228;). It is an extension of measurement that involves the creation of a continuum upon which measured objects are located. Thus, respondents are placed on a continuum with respect to their attitude towards a situation, a person or object such as an institution (Malhotra, 2009:274).

A scale is a measurement tool that can be either uni-dimensional or multi- dimensional. Uni-dimensional scales are designed to measure only one attribute of a concept, respondent, or object. For example, a uni-dimensional scale can measure a consumer’s price sensitivity. The uni-dimension price scale can then include several items to measure this sensitivity, but combined into a single measure. All interviewees’ attitudes are then placed along a linear continuum, called degree of price sensitivity. On the other hand, multi-dimensional scales are based on the premise that a concept, respondent, or object might be better described using several factors. Thus, not only price sensitivity, but also factors such as level of wealth and appreciation of a certain product can be added (McDaniel & Gates, 2010:333).

When differentiating between the four measurement levels, it is important to know the different properties of each, as each allows for the use of different statistical techniques. The following section briefly differentiates between the four measurement levels available to the researcher and they are summarised in Table 5.1. They are nominal, ordinal, interval, and ratio basic levels of measurement (McDaniel & Gates, 2010:307-311; Malhotra, 2009:277-282; Shiu et al., 2009:388- 400; Malhotra, 2007:252-260):

5.5.3.1 Nominal scale of measurement

Nominal scales are among those most commonly used in marketing research. The researcher uses numbers as labels or tags for identifying and classifying objects. This scale partitions data into categories that are mutually exclusive and collectively exhaustive, hereby implying that every bit of data will fit into one, and only one category, and that all data will fit somewhere on the scale. The term nominal means “name-like”, meaning that numbers are assigned to objects.

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The instances where nominal scales of measurement are used in the questionnaire are presented in Table 5.1

5.5.3.2 Ordinal scale of measurement

These scales have the labelling characteristics of nominal scales and have the ability to order data. Thus, it is a ranking scale. When using this scale, numbers are assigned to objects, which allows researchers to determine whether an object has more or less of a characteristic than some other object. However, the researcher cannot determine how much more or less from this type of scale. Objects ranked first have more of the characteristic being measured than objects ranked second. However, it is not possible to determine whether the object ranked second, is a close second or a distant second.

The instances where ordinal scales of measurement are used in the questionnaire are presented in Table 5.1.

5.5.3.3 Interval scale of measurement

This scale contains all the information of an ordinal scale, but it also allows for the comparison of the differences between objects. Thus, the numbers are used to rate objects. Comparing is possible, as interval measurement scales incorporate equality of interval, meaning that numerically equal distances exist between the values which an object is given. The differences between any two adjacent scale values are identical to the difference between any other two adjacent values of an interval scale. For example, the difference between 1 and 2 is the same as the difference between 2 and 3. With this type of scale, the position of the zero point is not fixed. An everyday example is a temperature scale. In marketing research, attitudinal data obtained from rating scales is often treated as interval data. Marketing researchers often prefer interval scales because they can measure how much more of a trait one consumer has than another.

The instances where interval scales of measurement are used in the questionnaire are presented in Table 5.1.

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5.5.3.4 Ratio scale of measurement

The ratio scale possesses all the elements of the nominal, ordinal, and interval scales and, in addition, an absolute zero point. As there is an agreement as to the location of the zero point, this makes comparisons among the magnitudes of ratio- scaled values possible. This means that a ratio scale reflects the actual amount of a variable. Physical characteristics of a respondent such as age, weight and height are examples of ratio-scaled variables.

Ratio scales of measurement have not been used in the questionnaire.

The researcher needs to select scaling techniques that will assist in addressing the stated objectives. A researcher can choose between two types of scaling techniques to measure a construct: comparative scales, and non-comparative scales (Aaker et al., 2011:254; Malhotra, 2009:284-288).

A comparative rating scale is a scale format that requires a judgement comparing one object against another on the scale. The objective is to have a respondent express his/her attitude about an object or its attributes on the basis of some other object or its attributes (Aaker et al., 2011:254). Thus, with comparative scales, respondents are asked to directly compare a statement against a standard. These are mostly used when marketers are interested in benchmarking (Malhotra, 2007:257). These scales have ordinal and rank properties and enable the researcher to discover small differences between the different statements. Some of the more popular comparative scales are paired comparison, rank order, constant sum and Q- sort scales (Aaker et al., 2011:435-436; Malhotra, 2007:257-258).

A non-comparative rating scale is sometimes referred to as a monadic scale because only one object is evaluated at a time (Malhotra, 2009:284, 302). It is a scale format that requires a judgement without reference to another object. It is typically used when the objective is to have a respondent express his or her attitudes about a specific object (which can be a product, a person, a phenomenon, etc.)(Aaker et al., 2011:432). These scales allow researchers to measure each statement independently of the other statements in a measured set, and no

228 | comparison with other statements or a standard, is required. Data obtained through such scales is usually interval or ratio in nature (Malhotra, 2007:258).

The non-comparative ratings scale technique is the most widely used scaling technique in marketing research (Malhotra, 2007:258). This technique can further be classified into continuous rating scales and itemised rating scales (Malhotra, 2009:303). With continuous rating scales respondents are asked to rate a statement by placing a mark on a line that runs from one extreme to the other. However, itemised rating scales provide respondents with numbers or brief descriptions linked to a category, and respondents are asked to indicate the category that they feel best describes the statement being measured (Zikmund & Babin, 2010b:331; Malhotra, 2007: 258, 272-274). Some of the more popular multiple-item rating scales that marketers can use are the Likert, semantic differential, and staple scales. Marketers often make use of multiple-item rating scales that possess the characteristics of description, order, and distance to measure attitude (Aaker et al., 2011:259-262; Malhotra, 2009:305; Cant et al., 2003:3-4).

Measurement of attitudes relies on less precise scales than those found in the physical sciences and is therefore more difficult (McDaniel & Gates, 2010:334). It is more difficult to measure, because attitude is a social-psychological concept that can be defined as a “relationship enduring predisposition to respond consistently to various things including, people, activities, events and objects (Zikmund & Babin, 2010b:343). The true structure of an attitude lies in the mind of the individual holding that attitude. To accurately capture consumers’ attitudes, researchers must be able to understand the factors of the relevant attitude construct, which include the cognitive component (beliefs, perceptions and knowledge), affective component (emotions or feelings) and behavioural component (persons’ intended or actual behavioural response) (Shiu et al., 2009:420).

Attitude scaling can be defined as the process by which an attitudinal characteristic is evaluated along a continuum stretching from ‘highly favourable’ to ‘highly unfavourable’ (Cooper & Schindler, 2006:364). An attitude scale therefore consists of a series of statements relating to a specific topic, and respondents are asked to indicate their feeling towards each specific statement. There are several types of

229 | attitudinal scaling formats that can be used in many different situations. Three attitude scale formats can be identified namely, Likert scales, semantic differential scales and direct and indirect approach (sometimes referred to as behaviour intention scales) (Shiu et al., 2009:421/2; Malhotra, 2007:272,276; Cooper & Schindler, 2006:370,374; Shiffman & Kanuk, 2004:37-38): o The Likert scale presents a set of attitude statements, and respondents are asked to indicate whether they agree or disagree with the statement. In this study a 7-point scale was used where respondents indicated the extent to which they agreed or disagreed with a series of belief statements about a given object. o The semantic differential scale captures a person’s thoughts or feelings about a given object. A set of bipolar rating scales is provided (good/bad, like/dislike, expensive/inexpensive) – usually, with seven points on the scale respondents are asked to rate each statement. This type of scale has not been employed in this study as there was no objective to obtain both cognitive and affective data for any given factor. It is more appropriate for identifying a ‘perceptual image profile’. o A direct and indirect approach can be used to measure a respondent’s actions or intended actions. In the direct approach, respondents are directly asked whether they will act in a certain way in the near future (i.e. buy a product again, or recommend it). Indirect questioning is more appropriate for sensitive topics: a respondent is asked to predict the behaviours of his/her peer group (i.e. neighbours, family or friends, people in a similar profession) with the intention to find out how the respondent feels about himself/herself. A purchase intention scale has been used in this study to determine if prospective university students will enrol at the university at which they would most like to study.

For the purpose of this study, a non-comparative 7-point Likert-type scale was used that was anchored where 1 indicated ‘strongly disagree’ and 7 indicated ‘strongly agree’. This scale was used to measure choice factors, perceived value and intention to enrol. The 7-point scale was chosen since it is evident from the work of Ledden, Kalafatis and Samouel (2007); Ledden and Kalafatis (2010); Sweeney and Soutar (2001); and Dodds, Monroe and Grewal (1991) amongst others to be most suitable for the purposes of this study.

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The direct approach has also been applied. All questions in this questionnaire asked respondents to think about the university/university of technology they preferred, and to think about this institution when answering all the questions. All the questions related directly to the individual respondents and they were asked what they rate as important choice factors (Section B2 of the questionnaire), how they perceived value offered at this preferred institution (Section C of the questionnaire), and their view of possibly enrolling at their chosen institution (Section D of the questionnaire).

5.5.4 Constructing and pre-testing a questionnaire

Almost every form of survey research relies on the use of a questionnaire (Appendix H2). The use of a questionnaire is the common thread in almost all data collection methods (McDaniel & Gates, 2006:258). It is one of the most basic instruments that a researcher can use to gather quantitative data during a descriptive study (Malhotra, 2007:299).

A questionnaire can be defined as a formal framework that consists of a set of questions and scales designed to generate primary data (Shiu et al., 2009:329). It is furthermore designed to obtain information from respondents and allows researchers to standardise the data collection process, and thereby enables them to analyse the data in a consistent and uniform manner (Malhotra, 2007:299).

As mentioned earlier (Section 5.5.2), a self-administered drop-off questionnaire was used in this study, and it was constructed by applying Malhotra’s (2007:300) and McDaniel and Gates’ (2006:263) ten guiding steps as discussed next:

5.5.4.1 Step 1: Specify the information needed

The first step in designing the questionnaire is to determine the need for decision- making information that is not currently available. The study or survey’s objectives should be spelled out as clearly as possible to ensure the rest of the process will follow efficiently (Malhotra, 2007:300; McDaniel & Gates, 2006: 262-263).

The information needed from the questionnaire was determined by analysing the research objectives set in Chapter 1 (Section 1.4) and to transform these objectives into relevant questions. Furthermore, a thorough literature review was conducted on

231 | the Higher Education (HE) landscape (Chapter 2), choice factors and existing choice models (Chapter 3), and perceived value and intention to buy and all their relevant factors and existing scales (Chapter 4) and information obtained from this literature review was translated into relevant questions.

5.5.4.2 Step 2: Specify the type of interviewing method

McDaniel and Gates (2006:263) also refer to this step as the step when the data collection method has to be determined. There are various of ways in which survey data can be gathered, such as via the Internet, telephone, mail, or self- administration. Considering how the data will be collected, will have an impact on the questionnaire design (Malhotra, 2007:301).

As already indicated above, for the purpose of this study, a self-administered drop- off questionnaire was used that had to be explicit and short, as no interviewer was present to clarify a question. Most questionnaires were delivered to school teachers who are responsible for career guidance/advice. The researcher’s telephone number was given in case clarity had to be obtained.

5.5.4.3 Step 3: Determine the question response format

Once the data collection method was determined, a decision was made about the types of questions that should be used in the survey. There are three major types of questions that are mostly used in marketing research: open-ended, closed-ended, and scale-response questions (McDaniel & Gates, 2006:264).

Open-ended questions are questions to which the respondent replies in her/his own words. The researcher does not limit the response choice. Each respondent’s answer is somewhat unique and it could be difficult to categorise and summarise the answers. The researcher explored a sample of questions to develop a classification scheme or code, that was used to organise answers (Zikmund & Babin, 2010a:273; McDaniel & Gates, 2006:264).

A closed-ended question asks a respondent to make a selection from a list of responses. The greatest advantage of these types of questions is that coding and data entry can be done automatically with questionnaire software programmes.

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Traditionally, marketing researchers have divided closed-ended questions into two types, dichotomous questions and multiple-choice questions. Dichotomous questions provide two-item response options, such as between “yes” and “no”, or “agree” and “disagree”. Multiple-choice questions portray a number of alternatives and the respondent is asked to give one alternative that correctly expresses his/her opinion (Aaker et al., 2011:279; McDaniel & Gates, 2006:264).

Scaled-response questions are closed-ended questions where the response choices are designed to capture intensity of feeling. An advantage of using these types of questions is that scaling permits measurement of the intensity of respondents’ answers (McDaniel & Gates, 2006:269).

For the purpose of this study, all three types of question response formats have been used in the questionnaire. Table 5.1 provides a summary of the question response formats used. The Table also indicates the screening question (Question A1(3) of the questionnaire) that was asked to allow the researcher to omit respondents from the study who indicated that they were not intending to further their education after school (Section 5.5.4, Step 5).

Table 5.1 Types of question response formats and type of primary scale used in the questionnaire

Type of primary Type of Question Comments scale used question

A1 (1), (2), and (3) were possible answers for Closed-ended the multiple-choice question, with A1 (2) A1 Nominal multiple choice providing an open-ended format, IF chosen question (see below) Only IF the respondent’s choice was that he/she would attend a university but NOT in Open-ended A1 (2) Nominal South Africa, the opportunity was given in question open-ended format to indicate why their intention was to study outside South Africa Respondents had to write the name of their Open-ended school A2 Nominal question Providing a list of schools would have been to lengthy Closed-ended A3 Nominal dichotomous Yes or No question question

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Type of primary Type of Question Comments scale used question

Closed-ended A4 Nominal multiple choice Six options for home language were provided question Closed-ended Mathematics or mathematics literacy were the A5 Nominal dichotomous only two options question Closed-ended Six options of overall expected grade for A6 Ordinal multiple choice grade 12 were provided question Closed-ended A7 Nominal dichotomous Yes or No question question Respondents were required to write down the name of the university/ university of Open-ended B1 Nominal technology they would most like to study at. question Providing a list of the possible HEIs in South Africa would have been lengthy Interval (Likert- Scale-response The extent to what the respondents agreed B2 type scale) question with a list of statements was asked Interval (Likert- Scale-response The extent to what the respondents agreed C1 type scale) Question with a list of statements was asked Interval (Likert- Scale-response The extent to what the respondents agreed D1 type scale) question with a list of statements was asked

Source: Adapted from Zikmund and Babin (2010b:273); Malhotra (2007:300); and McDaniel and Gates (2006:264).

Section B2 (choice factors), Section C (perceived value) and Section D (intention to enrol) consist of scaled-response questions: o Section B consists of 57 statements compiled from the literature review to measure the extent to which the respondents agreed with the listed choice factors when considering a university (Appendix H2). o Section C includes 17 statements measuring perceived value (Table 5.2). The statements were adapted from the work of various authors focusing on monetary sacrifice, non-monetary sacrifice, functional value, conditional value, epistemic value, social value, reputational value, emotional value and service value (Appendix D and Chapter 4, Section 4.3.1).

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Table 5.2 Perceived value statements used in the final questionnaire

Aspect Perceived value statements Author measured 1 The benefit of attending this Non- Ledden, Kalafatis and Samouel (2007) university will outweigh the financial monetary (education specific), Ledden and cost sacrifice Kalafatis (2010) 2 I am happy to make financial Monetary Ledden, Kalafatis and Samouel (2007) sacrifices to attend this university sacrifice (education specific), Ledden and Kalafatis (2010) 3 The price paid for studying at this Monetary Sweeney and Soutar (2001), Petrick’s university is reasonable sacrifice (2002) SERV-PERVAL scale and Petrick (2004) 4 I am happy that the price of the Monetary Ledden, Kalafatis and Samouel (2007) university is an indication of good sacrifice (education specific) quality Ledden and Kalafatis (2010), Petrick’s (2002) SERV-PERVAL scale and Petrick (2004) 5 I am happy to give up some of my Non- Ledden, Kalafatis and Samouel (2007) interests to attend this university monetary (education specific), Ledden and sacrifice Kalafatis (2010), Cronin, Brady, Brand, Hightower and Shemwell (1997) 6 The benefit of attending the Non- Zeithaml (1988), Cronin, Brady and Hult university will outweigh the time monetary (2000) sacrifices (less time with friends and sacrifice family) 7 I will achieve my career goals Functional Ledden, Kalafatis and Samouel (2007) (because I study at this university) value/ (education specific) conditional Ledden & Kalafatis (2010), Sheth, value Newman and Gross (1991) (used their definition), 8 The university will perform to my Functional Sánchez, Callarisa, Rodríguez and expectations value Moliner’s (2006) GLOVAL 9 The staff at the university will Functional Sweeney and Soutar (2001), Petrick’s provide service as I expect Value (2002) SERV-PERVAL scale and Petrick (2004) 10 I will gain the knowledge that I need Epistemic Sheth, Newman and Gross (1991) (used value their definition), Ledden, Kalafatis and Samouel (2007) (education specific) Ledden & Kalafatis (2010) (learn things) 11 I will feel a sense of ‘belonging’ Social value Sheth, Newman and Gross (1991) (used when attending the university their definition) 12 The reputation of the university will Reputational Ledden, Kalafatis and Samouel (2007) influence the value of my degree value (education specific) Ledden and Kalafatis (2010), Petrick’s (2002) SERV-PERVAL scale and Petrick (2004) 13 I believe that employers have good Reputational Ledden, Kalafatis and Samouel (2007) things to say about the university value (education specific) Ledden and Kalafatis (2010), Petrick’s (2002) SERV-PERVAL scale and Petrick (2004) 14 The university will give me a good Emotional Sheth, Newman and Gross (1991) (used experience (enjoyment, feel good, value their definition), Cronin, Brady and Hult pleasure, relaxed) (2000), Sweeney and Soutar (2001), Petrick’s (2002) SERV-PERVAL scale and Petrick (2004)

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Aspect Perceived value statements Author measured 15 The price I have to pay for the Monetary Sweeney and Soutar (2001), Petrick’s university is worth the money sacrifice (2002) SERV-PERVAL scale and Petrick (2004), Ledden, Kalafatis and Samouel (2007) (education specific) Ledden and Kalafatis (2010), Sánchez, Callarisa, Rodríguez and Moliner’s (2006) GLOVAL 16 The facilities (library, computer labs Service Cronin, Brady and Hult (2000) etc.) will meet my expectations value/ functional value 17 Compared to what I have to give Service Cronin, Brady and Hult (2000) up, the overall ability of the value/ university to satisfy my wants and functional needs is very high value

Source: Adapted from Ledden and Kalafatis (2010); Ledden, Kalafatis and Samouel (2007); Sánchez, Callarisa, Rodríguez and Moliner (2006); Petrick (2004), Petrick (2002); Sweeney and Soutar (2001); Cronin, Brady and Hult (2000); Cronin, Brady, Brand, Hightower and Shemwell (1997); Sheth, Newman and Gross (1991); and Zeithaml (1988).

o Sections D consists of a ‘behavioural intention’ scale with five statements adapted from the work of several authors. Table 5.3 portrays the five behavioural intention statements that were used in the final questionnaire. (Refer to Appendix E, and Chapter 4, Section 4.4)

Table 5.3 Behavioural intention statements used in the final questionnaire

Behavioural Intention Statements Author I would feel guilty if I go to another 1 Bruner II, Hensel and James (2005) university Bruner II, Hensel and James, (2005), Espejel, 2 I would never go to another university Fandos and Flavián (2008) Bruner II, Hensel and James (2005), Espejel, Fandos and Flavián (2008), Dodds, Monroe and Whenever possible, I would avoid going to Grewal (1991), Dodds, Brady, Brand, Hightower 3 another university and Shemwell (1997), Grewal, Monroe and Krishnan (1998), Cronin, Brady and Hult (2000) (it is about the probability of going…) Bruner II, Hensel and James (2005), Espejel, Fandos and Flavián (2008), Cronin, Brady and If a place is available at this university, I 4 Hult (2000), Chu and Lu (2007), Sweeney, would prefer to go to it Soutar and Johnson (1999), Grewal, Monroe and Krishnan (1998) Bruner II, Hensel and James (2005), Espejel, I do not like the idea of going to another 5 Fandos and Flavián (2008), Cronin, Brady and university Hult (2000)

Source: Adapted from: Espejel, Fandos and Flavián (2008), Chu and Lu (2007), Bruner II, Hensel and James (2005), Cronin, Brady and Hult (2000), Sweeney, Soutar and Johnson (1999), Grewal, Monroe and Krishnan (1998), Dodds, Brady, Brand, Hightower and Shemwell (1997), Dodds, Monroe and Grewal (1991).

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5.5.4.4 Step 4: Decide on the question wording

Following the decision to what type(s) of questions and response formats should be used the next task, is the actual writing of the questions. Every question in a questionnaire should contribute to the information needed or serve some specific purpose (Malhotra, 2007:302/3; McDaniel & Gates, 2006:270). The literature review assisted the researcher in selecting wording that can be used in the questionnaire’s different questions. Assistance from the Statistical Consultation Service at the University of Johannesburg (STATKON), as well reviewing relevant sections of the Marketing Scales Handbook (Bruner II, Hensel & James, 2005) provided valuable input and information in formulating the content of the questions (Appendix H2).

General guidelines about the wording of questions that were useful to keep in mind, were: o The wording must be clear; o Everyday terms/ordinary words should be used; o Unambiguous words should be used; o Implicit assumptions should be avoided; o Both positive and negative statements should be used; o The wording must not bias the respondent; o The respondents must be able to answer the question; o The respondent must be willing to answer the question; and o Keep away from generalisations and approximations (Malhotra, 2007:311-324; McDaniel & Gates, 2006:270-272;).

5.5.4.5 Step 5: Establish questionnaire flow and layout

Sequencing the questions is the next step when deciding on the questionnaire flow and layout (McDaniel & Gates, 2006:273). This step involves the drafting and refinement of the questionnaire, and incorporates various elements such as writing an introduction to the questionnaire, including participant-qualifying questions, considering the sequence of questions, inserting instructions for either the interviewer or the participant, and ending off the questionnaire with a closing remark, thanking the respondent (Cooper & Schindler, 2006:408-409).

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Shiu et al. (2009:337) suggest that a ‘flowerpot’ approach should be followed to assist with the flow and sequence of questions. It should start with an introduction section, followed with general information requirements, specific information requirements and if needed, general opinion information and a ‘thank you’ statement.

The questionnaire developed for this study (Appendix H2) started with a title, in this case it was entitled “Factors Influencing Prospective Students’ University Choice”. The respondent knew from first glance what the questionnaire was about. After the title, the questionnaire started with an introductory section, which included the purpose of the study, the targeted groups, a brief overview of the topics to be covered, the amount of time it would take to complete the questionnaire, and the researcher’s contact details. Following the introductory section, the body of the questionnaire consisted of four main sections. These four main sections (sections A, B, C and D) are discussed next by following Shiu et al.’s (2009) ‘flowerpot’ approach.

Section A was developed to collect background information of respondents. As the primary objective of the study is to develop a choice model that will assist universities to predict prospective students’ intention to enrol at a particular institution (Chapter 1, Section 1.4.1), only respondents who were interested at studying at a university or university of technology’s responses were of importance. For this reason the questionnaire’s first question in Section A was a screening question to identify target respondents.

A screening question (Question A1) is also called a qualifying question to identify appropriate respondents (McDaniel & Gates, 2010:389). If respondents indicated that they were not intending to study at a university, students were asked NOT to complete the rest of the questionnaire. These questionnaires were not used for any further analyses.

Following the screening question, general background information questions were asked that included questions on the present high school (A2) the respondent was attending, gender (A3), home language (A4), specific subjects taken at school (A5), academic achievement (A6) and parents’/guardians’ post school educational background (A7). These questions were asked to achieve one of the secondary

238 | objectives set earlier in this study (Chapter 1, Section 1.4.2) that should enable the researcher to predict a targeted group of prospective students’ intention to enrol at a specific university. Demographic characteristics are obtained by these questions, which were used to create a descriptive profile, identifying respondents who were potential university/university of technology students (Shiu et al., 2009:342).

Section B (choice factors), Section C (perceived value) and Section D (willingness/intention to enrol) of the questionnaire, covered the specific information requirement sections. These sections of the questionnaire used different question response formats (Table 5.1) and Likert-type interval scales of measurement (Table 5.1) to achieve the research objectives set in Chapter 1 (Section 1.4).

It can be argued that the questionnaire is structured in such a way, and questions formalised to gather information to achieve the secondary objectives formulated for the study. Table 5.4 provides some insight.

Table 5.4 Secondary objectives and corresponding sections of the questionnaire

Secondary objective Section of the questionnaire To gain insight into the South African higher Information obtained from the literature review education (HE) environment To provide an overview of the extant literature related to the main constructs of the study, namely the choice factors influencing university choice, the Information obtained from the literature review perceived value universities offer, and prospective students’ intention to enrol at their chosen university Section B – Factors influencing prospective To measure the extent to which the different choice students’ preferences of a factors, identified through the literature review, university/university of technology (choice influences prospective students’ university choice factors) Section C – The value offered by prospective To assess the value prospective students perceive students’ chosen university/university of they will derive from their chosen university technology Section D – Willingness/intention to enrol at To gauge the intention of prospective students to the prospective students’ preferred university enrol at their chosen university of technology Section A3, A4, A5, A6 and A7 Section B – Factors influencing prospective To determine whether groups of prospective students’ preferences of a students who exhibit different demographic university/university of technology (choice characteristics differ significantly from each other in factors) terms of the influence of different choice factors on Section C – The value offered by prospective their university choice the perceived value that their students’ chosen university/university of chosen university offers, and their level of technology agreement regarding their intention to enrol at their Section D – Willingness/intention to enrol at chosen university the prospective students’ preferred university/university of technology

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Secondary objective Section of the questionnaire Section B – Factors influencing prospective students’ preferences of a university/university of technology (choice To determine the interrelationships between the factors) main constructs of the study in order to propose a Section C – The value offered by prospective model to explain prospective students’ university students’ chosen university/university of choice technology Section D – Willingness/intention to enrol at the prospective students’ preferred university/university of technology

 General opinion information and thank you statement

No general opinion information was gathered, however, the questionnaire ended with a ‘thank you for your participation’ statement.

5.5.4.6 Step 6: Evaluate the questionnaire

The following questions should be considered when evaluating the questionnaire (McDaniel & Gates, 2006:278): (1) Is the question necessary? (2) Is the questionnaire too long? (3) Will the questions provide the information needed to accomplish the research objectives?

Each question must be directly and explicitly related to the stated objectives of the particular survey (McDaniel & Gates, 2006:278). Table 5.4 indicated how the secondary objectives were linked to the questions in the questionnaires. The questionnaire was developed to achieve the primary objective to build a choice model by obtaining choice factors (Section B), and determining the perceived value that will influence (Section C) prospective students’ intention to enrol (Section D).

The Likert-type scale used in Sections B, C and D, improved the ease with which the questions could be answered. Open-ended questions were also limited, with respondents only having to write down their school’s name, and the name of the university/university of technology at which they would most like to study. Only those respondents indicating that they are planning to study at a university outside of South Africa were asked to write down why they were intending to do so. The reason behind this question was to determine if there were any ‘choice factors’ that the researcher could have missed and if they were different from choosing a university/university of technology in South Africa.

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5.5.4.7 Step 7: Obtain approval from all relevant parties

The first draft of the questionnaire was distributed to the University of Johannesburg’s Marketing and Recruitment team for approval before sending it to schools. A copy of the final questionnaire was also sent to the Gauteng Department of Education and they gave written approval for the questionnaire and the project as a whole (Appendix I).

The relevant academic supervisors as well as STATKON proofread and approved the questionnaire. (Appendix H1).

5.5.4.8 Step 8: Eliminate errors by pre-testing the questionnaire

In a pre-test, researchers look for misinterpretations by respondents, lack of continuity, poor skip patterns, additional alternatives for pre-coded and closed-ended questions, and general respondent reaction to the interview if applicable (McDaniel & Gates, 2006:279). Pre-testing refers to the testing of a questionnaire on a small group of people who are representative of the sample and who are capable of highlighting possible design error (Malhotra, 2007:319). Cooper and Schindler (2006:417) provide the following reasons to why a researcher should pre-test a questionnaire: o To increase the respondents’ interest; o To re-structure questions if necessary to ensure respondents are engaged and encouraged to complete the entire questionnaire; o To determine whether any problems exist in terms of question content, wording and sequencing; o To highlight questions where interviewer training is needed; and o To look for ways in which the overall quality of the questionnaire can be improved.

The questionnaire was developed over a number of years and tested twice before the final questionnaire was distributed to the final sample of grade 12 Gauteng scholars. The initial questionnaire sent out for pre-testing purposes is included in Appendix H3. This questionnaire was distributed during August and September 2009

241 | to 38 Gauteng schools (20 Afrikaans and 18 English schools). The aim of this questionnaire was to understand why prospective students, but specifically Afrikaans speaking scholars, choose a specific university/university of technology to further his/her education and the focus was on determining the choice factors influencing these choices. A total of 2 700 respondents were included in this initial sample and a summary of main findings is included in Appendix H3.

The second pre-test of the questionnaire took place during February 2011. The initial questionnaire (Appendix H3) was refined and additional choice factor statements as well as perceived value and intention to enrol statements were included. This refined pilot questionnaire with a summary of main findings is included in Appendix H2. To pre-test this questionnaire, it was distributed to a University of Johannesburg’s first- year Business Management class of 680 students at the beginning of the year when they were still able to recall how their university choice decision was made and which factors influenced them when they made this decision.

Of the 680 pilot study questionnaires distributed, 605 could be included for analysis. The genders of respondents were fairly represented in the sample, with 56.3% male and 44.7% female. The majority of respondents were black (80.6%), with Nguni (32%), and Sotho (28.9%) as the home language the majority of respondents speak at home, followed by16.6% respondents speaking English at home, 13.1% indicated TshiVenda/XiTsonga and 5.8% indicating Afrikaans as their home languages.

The first-year Business Management respondents indicated that the availability of courses, the quality of degrees and the reputation of the institution were the top three factors that influenced these students to apply at UJ. Other top factors that influenced these students to choose UJ, were sufficient information that were provided by UJ and they commented that they liked the location of UJ. Regarding perceived value, the attainment of knowledge and achieving career goals as a result of studying at UJ were valued most. Regarding intention to enrol, these students indicated that they agree strongly that the availability of a place at UJ influenced their intention to enrol.

It was decided not to test the refined questionnaire with the schools again, as the initial questionnaire taken to the schools indicated that prospective students

242 | understood the questions. Secondly, the refined questionnaire tested with first-years verified the initial questionnaire and only minor word changes were evident. Lastly, it is very difficult to obtain approval/permission with the Department of Education to visit schools. Wiese (2009:190) encountered the same difficulty in obtaining permission from the Department of Education, and for this reason her study determined choice factors amongst a target population of first year Economic and Management Sciences students at six higher education institutions in five provinces. Although Wiese (2009:190) commented on this difficulty, the researcher highlights that not testing the refined pilot questionnaire with the intended sample (grade 12 Gauteng scholars) again, as a limitation of the study (Chapter 7, Section 7.5.2).

Based on the feedback received, a number of changes to the pilot questionnaire were made, including the following: o Introduction

The original questionnaire (pre-test questionnaire) explained in the introduction that the ‘questionnaire consists of four short sections’ and then listed these four sections. In the final questionnaire, these words were removed as the questionnaire was structured well and it was obvious that four sections followed.

Also, the timing of the questionnaire was changed. Initially the introduction stated that the questionnaire should not take longer than 10 minutes to complete, however this was changed to …”not take longer than 15 minutes….”. o Section A – Background information

No changes were made to this section. Respondents’ feedback indicated that this was easy to answer and they understood the questions. o Section B – Factors influencing your preferences of a university / university of technology

Section B originally consisted of two main questions. Question 1 (B1) had the objective to determine which university was the most preferred university, and to put this first choice or preferred university at the forefront of respondents’ minds when answering the rest of the questionnaire. Secondly, question 2 (B2) listed statements

243 | regarding possible aspects that influenced the perception of respondents’ preferred university/university of technology.

The format of question B1 was changed from the original closed-ended question with options, to an open-ended question where respondents had to write down the name of the university/university of technology at which they would most like to study. It was found that the list of options was too long (20 listed HEIs in South Africa), the list took up space. The new wording is presented in Table 5.5 and Appendix G1 contains the list of universities with their relevant codes used for capturing data.

Question B2 presented a list of factors, which could have influenced respondents’ decision to choose their first choice (or university that they would most like to study at) university/university of technology. This list was compiled from the literature review (Chapter 3). A few changes were made after the pre-test:

 The final questionnaire’s question wording was changed. As the pilot questionnaire was designed for first-year students and the final questionnaire’s target group was grade 12 scholars, the wording had to change to suit the different circumstances. The final questionnaire’s aim was also to have shorter, but succinct sentences. The wording changes are presented in the Table 5.5.

 The 7-point response scale also changed from 1 indicating ‘not important’ and 7 ‘very important’, to using the scale where 1 indicates ‘strongly disagree’ and 7 ‘strongly agree’. This change was made as ‘strongly agree’ and ‘strongly disagree’ -labels were considered a better fit with the statements (Table 5.5).

Table 5.5 Changes made to the wording of question B2

Pre-test questionnaire Final questionnaire

B2 Below is a list of factors, which could have influenced your decision to B2 To what extent do you agree with the choose your first choice university following statements regarding the (the university your really would like to university/university of technology at study at). How important would you which you would most like to study ? rate the following factors in affecting Please indicate your answers using the 7- your decision when choosing your point response sale provided where 1 first choice university/university of indicates ‘strongly disagree’ and 7 technology to further your education? ‘strongly agree’ Please indicate your answer using the 7-point response scale provided

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where 1 indicates ‘NOT important’ and 7 ‘VERY important’

 Originally, this question listed 58 statements (in the pilot questionnaire, Appendix H1) or factors that may or may influenced respondents’ decision in their preferred university/university of technology’s choice. This list was reduced to 57 statements. The statement that was removed from the original questionnaire was question B2.6 “It is active in social issues”. The reasons for the removal and other changes to the statements are presented in Table 5.6.

Table 5.6 Changes made to question B2 statements

Pre-test questionnaire Final questionnaire B2.3 It is known that only the very intelligent B2.3 It is known that only the very intelligent study study at the university there B2.4 It is tough to get into the particular B2.4 Only good students get in university B2.5 It has nationally known academic B2.5 Academic programmes are nationally known programmes This question was deleted in final the questionnaire as the term ‘social issues’ were too B2.6 It is active in social issues vague. (Numbering thus changed of the following questions) B2.32 The institution should have the ability to B2.31 Studying at this university will make it place me in a job after qualifying possible to find a job after qualifying B2.33 There should be available employment B2.32 Studying at this university will enhance opportunities after graduation chances of employment opportunities B2.34 I should have better career prospects B2.33 Studying at this university will increase after studying at that university career prospects B2.35 I expect a better salary after completing B2.34 Studying at this university will provide better my qualification at that university salary prospects B2.36 to B2.39 The previous word ‘institution’ was B2.37 to B2.40 included the word ‘institution’ replaced with ‘university’ B2.48 I should be taught in English B2.47 I will be taught in English B2.53 The cost of tuition at the university is fairly B2.54 The cost of tuition at the university priced B2.55 Studying at the university is value for B2.56 Perceived value for money money B2.57 My parents’/guardian’s financial ‘health’ B2.56 My parents/guardians are able to afford the influence the university decision university B3.58 Part-time jobs near chosen university to B2.57 There will be the opportunity for part-time help me earn money for studies jobs (nearby campus)

245 | o Section C – Perceived value

The statement ‘title’ has changed from the pre-test questionnaire: ‘What do you value when evaluating which university to attend?’ to ‘The value offered by your chosen university/university of technology’ in the final questionnaire. The C1 question’s wording was also changed as seen in Table 5.7. The wording was changed to keep it consistent with B1’s question format and style.

Table 5.7 Changes made to the wording of question C1

Pre-test questionnaire Final questionnaire C1 To what extent do you agree with the C1 Keep your ‘first choice’ university in mind following statements regarding the when answering the following questions. university/university of technology that you On a scale of 1 to 7 where, 1 is ‘strongly would most like to study at? Please indicate disagree’ and 7 is ‘strongly agree’, your answers using the 7-point response indicate the extent to which you agree scale provided where 1 indicates ‘strongly with each of the following statements disagree’ and 7 ‘strongly agree’

Section C further originally consisted of 10 ‘perceived value’ statements that were obtained from studying the literature as discussed in Chapter 4. A summary of all the perceived value measurement statements with their relevant authors is presented in Table format in Appendix D. It is evident from this summary which are the ‘repeated’ or most applied statements (and tested), and the correlation between these statements found in literature and the statements the author has chosen are also portrayed.

The following additional 7 statements were added to the final questionnaire, as it was evident that it would allow for the execution of a factor analysis to determine possible underlying perceived value factors with more statements. It was also evident that some constructs were not presented well enough, like for example the ‘service value construct’ and ‘non-monetary value construct’ and therefore these statements were added. Table 5.8 provides a summary of the new statements added.

Table 5.8 Additional perceived value statements added

Final questionnaire’s new statements

I am happy to make financial sacrifices to attend this university

The price paid for studying at this university is reasonable

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I am happy that the price of the university is an indication of good quality

I am happy to give up some of my interests to attend this university

I will achieve my career goals (because I study at this university)

I will gain the knowledge that I need

I believe that employers have good things to say about the university

Of the remaining 10 statements that were used in the final questionnaire, words from only four statements were adapted for better clarification or to capture the capture the construct under investigation. These changes are portrayed in Table 5.9.

Table 5.9 Changes made to the wording of section C

Pre-test questionnaire Final Questionnaire Perceived value statements Perceived value statements The university will give me a good experience I will enjoy attending the university (enjoyment, feel good, pleasure, relaxed) The reputation of the university will influence the The university has a good reputation value of my degree The price I have to pay for the university is worth The university offers value for money the money The benefit of attending the university will The benefit of attending the university will outweigh the social sacrifices (less time with outweigh the time sacrifices (less time with friends and family) friends ad family)

 Section D – Willingness/intention to enrol at your preferred university

The instruction question of this section numbered D1 was changed as illustrated in Table 5.10. This change was made to ensure that the instruction questions for question B2, C1 and D1 were in the same style and format.

Table 5.10 Changes made to the wording of question D1

Pre-test questionnaire Final questionnaire

D2 To what extent do you agree with the D1 Keep your ‘first choice’ university in mind following statements regarding the when answering the following questions. university/university of technology at On a scale of 1 to 7 where 1 is ‘strongly which you would most like to study at? disagree’ and 7 is ‘strongly agree’, indicate Please indicate your answers using the 7- the extent to which you agree with each of point response scale provided where 1 the following statements indicates ‘strongly disagree’ and 7 is ‘strongly agree’

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The only statement’s wording that was adapted in Section D was that of the fourth statement where “…I would prefer to go to it” in the pre-test questionnaire was adapted to: “…I will attend it” in the final questionnaire.

5.5.4.9 Step 9: Prepare final questionnaire copy

During this step, precise instructions for the skip question, numbering, and pre- coding was set up. Every question had to be proofread again (McDaniel & Gates, 2006:281).

5.5.4.10 Step 10: Implement survey

A number of forms and procedures must be issued with the questionnaire to ensure that the fieldworkers gather the data correctly, efficiently, and at a reasonable cost (McDaniel & Gates, 2006:281). As this questionnaire was a self-administered paper- based drop-off questionnaire (Section 5.5.2), instructions had to be kept to a minimum as the questionnaire had to be self-explanatory. However, the researcher had organised a meeting with the UJ Marketing and Recruitment team who have regular access to the Gauteng schools in question, and they were briefed on the purpose of the questionnaire, of the study as a whole, and each question was discussed to ensure clarity, should a teacher or scholar have any questions regarding the questionnaire.

After the questionnaire had been finalised, it was necessary to identify a representative sample of the population to take part in the study. This is discussed in the following section.

5.5.5 Sampling process and sample size

Sampling refers to the steps that are employed in obtaining information from a subset (a sample) of a larger group (the universe or population). The results from the sample are then used to make estimates of the characteristics of the larger group. The reasons for sampling, comprise the ability to make these estimates more quickly and at a lower cost than what would be possible by any other means (McDaniel & Gates, 2006:296).

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It is unpractical and expensive to reach all grade 12 public school scholars who have the ability and are considering entering a university to further their education. It is for this reason that a sample is drawn from a population.

The key, though, to making accurate predictions about the characteristics or behaviour of a large population (on the basis of a small sample) lies in the way in which individuals are selected for the sample (McDaniel & Gates, 2006:296). According to Malhotra (2007:336), the sampling design process includes five steps that guide the researcher in the way individuals are selected for the sample. The sample plan for this study is presented in Table 5.11 and the steps in the sampling design process are shown in Figure 5.3.

Table 5.11 The sample plan for this study

Define the target population

All grade 12 scholars who are considering to study at a university/university of technology and 1. Elements who have the ability to enter a university, thus high enough grades to be accepted at a university (passing with matric exemption)

2. Sampling units Public schools

3. Extent Gauteng Province, South Africa

4.Time August and September 2011

Determine the sampling frame A list of the top 250 Gauteng public schools

Non-probability, judgmental sampling technique Select sampling technique(s) to select sampling units and a census of selected sampling units was conducted

Determine the sample size 1 500 scholars

Execute the sampling process Follow the plan

Source: Adapted from Malhotra (2004:226)

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Figure 5.3 The steps in the sampling design process

5.5.5.1 Step 1: Define the target population

The term population describes everyone of interest who could possibly be included in a research study (Kolb, 2008:178), and target population describes the collection of elements/objects who possess the information sought by the researcher (Malhotra, 2007:336; Hair et al., 2006:310).

Defining the target population constitutes the first step of the sampling design process (Malhotra, 2007:336). It is an important step, as information needs to be gathered to solve the research problem and the target population determines which elements (could be people) can and cannot be included in the sample (Cant et al., 2003:125).

The target population is thus further defined in terms of elements, sampling units, extent and time (Malhotra, 2007:336). While elements can be defined as the objects from which the researcher would like to obtain information, the sampling unit refers to the entity that holds the various elements of the populations, such as public schools (Malhotra, 2007:336). Extent refers to the geographical area under study

250 | and explains the time period during which the study takes place (Malhotra, 2007:337). Table 5.11 depicts how the target population was defined by portraying the elements, the sampling units, the extent and time. In summary, the elements were defined as all grade 12 school scholars who are considering studying at a university/university of technology. The sampling units were identified as public schools, the extent as Gauteng Province, and the time as August and September 2011.

5.5.5.2 Step 2: Determine the sampling frame

A sampling frame is also called the ‘working population’ and is a list or set of guidelines that represents the elements of the population and that is used to identify the target population under study (Zikmund & Babin, 2010b:417; Malhotra, 2007:337; Cooper & Schindler, 2006:443). Examples of sample frames from which samples can be drawn include telephone directories, directories that list organisations within an industry, mailing lists, employee rosters, listings of students attending a university and maps (Cant et al., 2003:125).

The sampling frame for this study (Table 5.11) constituted a list of the top 250 Gauteng public schools obtained directly from the Department of Education. This list of schools can be seen in Appendix B. Appendix J portrays the list of schools that were included in the sample.

The Department of Education compiles a “top school” list by applying statistical algorithms on a combination of factors including: (1) percentage of scholars passing grade 12, (2) the number of distinctions obtained by the group of grade 12s for a particular exam, and (3) the number of scholars passing with HE entrance to a possible Bachelor’s degree (Department of Education, 2011).

5.5.5.3 Step 3: Select a sampling technique

Malhotra (2007:337-339) argues that choosing a sampling technique is one of the most important decisions. McDaniel and Gates (2010:423) also refer to this step as selecting a sampling method. The major alternative sampling techniques (or methods) available to the researcher can be grouped under two headings: probability

251 | sampling and non-probability sampling (McDaniel & Gates, 2010:423; Malhotra, 2007:337-339; Kotler & Fox, 1995:85).

Probability sampling (also known as random sampling) is a sampling technique in which every member of the population has a known, non-zero probability of selection. The likelihood of being chosen is known beforehand. Non-probability sampling is more subjective and does not make use of chance. It involves a sampling technique in which units of the sample are selected on the basis of personal judgement or convenience (Zikmund & Babin, 2010b: 423; Cooper & Schindler, 2006:440). These two broad categories are then further sub-divided into more specific sampling techniques:

Probability sampling is sub-divided into simple random sampling, systematic random sampling, stratified random sampling and cluster sampling (Shiu et al., 2009:471- 480). The more frequently used sub-sections of non-probability sampling include convenience sampling, judgement sampling, quota sampling and snowball sampling (Shiu et al., 2009:480-483; Malhotra, 2007:340).

For the purpose of this study, a non-probability sampling technique was chosen. More specifically, the researcher used judgement techniques. In judgement sampling, an experienced individual selects the sample based on his or her judgement about some appropriate characteristics required of the sample member (Zikmund & Babin, 2010b:424). In this study, the researcher presented the top 250 Gauteng public schools’ list (the sampling frame) to the Head of the UJ Marketing and Recruitment team to apply his judgement to the list of which schools should be included in the sample. Judgement was also used to ensure all population groups take part in the study by considering the medium (language) of instruction at the schools when selecting a school from the sampling frame. Schools were selected and contacted until the required number of respondents was reached.

A census of the sampling elements present in each sampling unit was undertaken.

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5.5.5.4 Step 4: Determine the sample size

According to Malhotra (2007:336), this step is probably one of the most complex decisions in the sampling process. Decisions had to be made concerning (1) the variability of the population characteristic under investigation, (2) the level of confidence desired in the estimates, and (3) the precision required. It is during this step that the decision had to be taken on how many completed surveys were needed for data analysis, however at the same time recognising that the initial sample size is often not the equal sample size (Shiu et al., 2009:486).

It was decided to select a sample of 1 500 sampling elements for the study. The sampling elements were finally obtained from 14 sampling units (schools). Table 5.12 provides an exposition of how the sampling plan finally materialised. This sample size is in line with other studies, specifically in South Africa. Wiese (2008:217), conducted a study to determine the HE marketing perspective on choice factors considered by South African students with a sample size that realised at 1 241 respondents.

5.5.5.5 Step 5: Execute the sampling process

The last step in the sampling design process is the implementation of the sampling process (Malhotra, 2007:336). In essence, this step is about collecting the data (Shiu et al., 2009:486).

Table 5.12 The final sample realised

Number of sampling Number of sampling Type of school units (schools) elements (scholars) realised realised

Afrikaans schools (Afrikaans is the language 6 993 of instruction) English schools (English is the language of instruction and schools in the ‘wealthier’ 4 363 neighbourhoods) African schools (attended mostly by scholars speaking an African-home language. 4 377 Instruction language can be in English or in an African language) Total 14 1733

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5.5.6 Develop a plan for data analysis

With the questionnaire finalised and the sample defined, the next step is to detail how the necessary data is going to be collected. In many cases and situations, the person who designed the research seldom collects the data, but it involves a number of fieldworkers. These fieldworkers often need to be selected, trained, supervised, and the data that has been gathered needs to be validated (Malhotra, 2007:413).

In the following section, the issues of fieldwork and the data collection process will be discussed. This process includes the recruiting of fieldworkers, training and supervising them, and validating the fieldwork in the study (Cant et al., 2003:137).

5.5.6.1 Fieldwork and the data collection process

The most appropriate fieldworkers need to be selected. Several factors need to be considered such as the fieldworkers’ backgrounds, opinions, perceptions, expectations and attitudes. These characteristics may influence the data collection process. Good fieldworkers need integrity, honesty, patience and tactfulness (Malhotra, 2009:432; Cant et al., 2003:138).

For the purposes of this study, the University of Johannesburg’s (UJ) Marketing and Recruitment team was used to distribute questionnaires. This team of people (consisting of six individuals) have access to most of the Gauteng schools, they know the schools, and they have relationships with the teachers responsible for career guidance and counselling.

Training of field workers is critical to the quality of data collected (Malhotra, 2007:414). Training should include aspects such as how to make initial contact with the respondent, how to ask questions, how to record the respondents’ responses, and how to terminate the interview (Cant et al., 2003:138).

For training purposes, a meeting was held with the six individual fieldworkers, explaining to them the purpose of the questionnaire, all the topic areas and how questionnaires should be completed. It was also reiterated that only grade 12 scholars who intended to apply at a university/university of technology should complete these questionnaires. Another topic of discussion was on how to convince

254 | teachers to distribute questionnaires. Fieldworkers had the responsibility of explaining the questionnaire to the relevant teachers at the specific school. An approval letter obtained from the Department of Education helped to provide legitimacy to the project (Appendix I).

Once fieldworkers had been trained, they were ready to enter the field and start the data collection process. The fieldworkers were given six weeks to distribute and collect the completed questionnaires. After three weeks, the researcher made follow- up calls to each of the fieldworkers to ensure that distribution and collection were going according to plan.

Once the completed questionnaires had been returned to the researcher, the collected data was validated for accuracy, completeness and authenticity (Malhotra, 2007:418) Validating fieldwork takes place when the researcher verifies the responses of respondents (Cant et al., 2003:139). Validation checks were conducted that included, checking whether the fieldworkers had returned all the questionnaires, whether the respondents were in grade 12 and intended to attend a university/university of technology, whether the questionnaires were completed in full, and whether all the pages of the questionnaire were returned.

5.5.6.2 Data preparation and analysis

Data preparation refers to the process of checking whether the quality of the data gathered is correct, before converting it into an electronic format so that it can be read and manipulated by computer software (Cant et al., 2003:149). The first step was to check for acceptable questionnaires, followed by editing, coding and transcribing the data. The data was cleaned and treated for missing responses (Malhotra, 2009:452).

 Editing questionnaires, coding, transcription and data cleaning

Editing involves reviewing questionnaires. This reviewing process increases accuracy and precision (Malhotra, 2009:452). The researcher checked for completeness and all the questionnaires with missing pages, or sections that had not been completed, were omitted from the study.

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Coding refers to the process of grouping and assigning numeric codes to the respondents’ answers to a particular question (McDaniel & Gates, 2010:466). The closed-ended questions were pre-coded (Appendix H2), meaning that numeric codes were assigned to the various responses on the questionnaire. Open-ended questions were coded as soon as questionnaires had been edited. The coding lists were compiled by the researcher and included the following (Appendix G): o Question A1(b) – A coded list of responses to the question of why a respondent would study at a university, but NOT in South Africa. o Question A2 – A coded list of the schools from which questionnaires were received. o Question B1 – A coded list of all public universities in South Africa.

Transcribing data involves transferring the coded data from the questionnaire directly into computers (Malhotra, 2009:458). An external supplier namely, CMC was used to enter data into a computerised data file and this file was sent to STATKON, the Consultation Service of the University of Johannesburg. STATKON transferred this data file into a statistical computer programme called SPSS (Statistical Package for the Social Sciences) version 20 to commence data cleaning.

According to Malhotra (2009:459), data cleaning includes consistency checks and treatment of missing responses. It is thus important to do a final computerised error check of the data which includes checking for consistency and missing responses (McDaniel & Gates, 2010:492). Consistency checks identify data that is out of range or logically inconsistent. It can also include data that have extreme values (Malhotra, 2009:460). SPSS can be programmed to identify out-of-range values for each variable and print out the respondent code, variable code, variable name, record number, column number, and out-of-range value (Malhotra, 2009:40).

Missing responses imply that the values of a variable are unknown, either because respondents provided ambiguous answers or their answers were not properly recorder. This happens because the respondent either refuses, or is unable to answer specific questions. The following options are available for the treatment of missing responses (Malhotra, 2009:460):

256 | o Substitute the missing response with a neutral value that is usually based on the mean response to the question; o Case-wise deletion, whereby a respondent with any missing values is rejected; and o Pair-wise deletion. This is where the researcher only uses the respondents who completed a specific question (Malhotra, 2009:460-461; Cant et al., 2003:164).

For the purpose of this study, pair-wise deletion was used, thereby only incorporating the responses of respondents who had answered a particular question. Once the data has been cleaned, the next step in the data analysis process is to adjust the data in cases where this is required (Malhotra, 2009:461). This can be done by means of weighting, or variable re-specification can be used when a new value is assigned to an existing variable, or scale transformation may be used, reverse coding, or standardisation of selected variables (Malhotra, 2009:461; Cant et al., 2003:165). For the purpose of this study, none of the above was applied to this study, as adjusting was not necessary.

5.5.6.3 Selecting a data analysis strategy

Selecting a data analysis strategy involves the consideration of the earlier steps in the process namely, problem definition, development of an approach and the research design. The next step is to consider the known characteristic of the data. For example, the measurement scales used in the questionnaire influence the choice of statistical techniques. The research design might also favour certain statistical techniques more than others (Malhotra, 2009:462; Cant et al., 2003:165).

For the purposes of the study, the following aspects are discussed to shed light on the data analysis strategy followed in this study: o Presenting the descriptive statistics; o Determining the distribution of the results; o Determining the validity and reliability of the scales used; o Deciding whether to use parametric vs. non-parametric tests to test hypotheses; o Testing the stated hypotheses; o Conducting an exploratory factor analysis (EFA);

257 | o Conducting second order exploratory factor analysis (2nd order EFA); o Performing a confirmatory factor analysis (CFA); and o Performing structural equation modelling (SEM).

 Presenting the descriptive statistics

It is necessary to present the various descriptive statistics that were calculated for this study. The different descriptive statistics include the count, mean, standard deviation and top-box and low-box scores (Shiu et al., 2009:529-534; Eiselen, Uys & Potgieter, 2007:44, 50; Malhotra, 2007:460-461): o The count is determined for nominal and ordinal values and refers to the number, as well as the percentage, of each response in a specific category. o The mean is a measure for central tendency and refers to the average value for a set of statements. All the statements in this calculation are put in a set and are added together, and this figure is divided by the total number of statements in the set. o The standard deviation refers to the square root of the variance, with the variance indicating how the different values are spread around the mean. o The top-box score and the low-box score refer to the number (or percentage) of respondents in the sample who selected the lowest value in the scale (low-box score) or the highest value in the scale (top-box score) with respect to a specific question or statement (www.measuringusability.com).

 Determining the distribution of results

According to Eiselen et al. (2007:79-80), it is necessary to determine whether the results for the scale statements used in this study are normally distributed, as this will determine whether parametric or non-parametric tests are used. Determining the distribution of the results needs to be done before the hypotheses can be tested. It is not imperative to measure the distribution of the results if the sample size is larger than 30 (Eiselen et al., 2007:79). Measuring the skewness and kurtosis of the distribution of results is one way the researcher is able to determine whether the results for the scale are normally distributed.

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The skewness provides an indication of the symmetry of the distribution, while kurtosis provides information about the ‘peakedness’ of the distribution. If the distribution is perfectly normal, a skewness and kurtosis value of null are realised (Pallant, 2010:57). According to West, Finch and Curran (1995:74), normal distribution is characterised by a skewness of less than two and a kurtosis of less than seven.

If the results are normally distributed, parametric tests can be used to test the hypotheses, while non-parametric tests are used when the results are not normally distributed (Eiselen et al., 2007:80).

 Determining the validity and reliability of the scales used

Before the hypotheses formulated for a study can be tested, a researcher also needs to examine the validity and reliability of the scales used.

The validity of a scale refers to the degree to which it measures what it is supposed to measure (Pallant, 2010:7; Malhotra, 2007:286). According to Pallant (2010:7), there is no one clear-cut indicator of a scale’s validity. The validation of a scale involves the collection of empirical evidence concerning its use. A researcher can differentiate between three different types of validity, including the following (Pallant, 2010:7; Shiu et al., 2009:282; Malhotra, 2007:286-287) o Content validity is also known as ‘face validity’ and determines the adequacy with which a measure or scale has sampled from the intended universe or domain of content. o Criterion validity verifies the relationship between scale scores and some specified, measurable criterion. Criterion validity attempts to estimate or predict whether the current behaviour of a respondent will be repeated. o Construct validity is the extent to which the variables under investigation are completely and accurately identified prior to hypothesising any functional relationships. It involves testing a scale not against a single criterion, but in terms of theoretically derived hypotheses concerning the nature of the underlying variable or construct.

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For the purpose of this study, both content and construct validity were used. As for content validity, the questionnaire was sent to the UJ Marketing and Recruitment team who are constantly involved in marketing projects relating to factors that influence prospective students’ university choice – before it was fielded. In addition, the questionnaire was pretested as discussed earlier (Section 5.5.4).

In terms of construct validity, a confirmatory factor analysis (CFA) was conducted to confirm the underlying structure of constructs and an exploratory factor analysis (EFA) to uncover the underlying structure of constructs. This is discussed in more detail later on in this chapter (Section 5.5.6.3).

The reliability of a scale indicates “how free it is from random error” (Pallant, 2010:6). Thus, reliability is the degree to which measures are free from random error and, therefore, provide consistent data. The less error there is, the more reliable the observation is, so measurement that is free from error is a correct measure (McDaniel & Gates, 2010:313). There are three tests of reliability that the researcher can use (McDaniel & Gates, 2010:313-316; Zikmund & Babin, 2010b:334-335; Eiselen et al., 2007:112; Malhotra, 2007:284): o Test-retest reliability which measures the stability of results and is usually used to determine whether the results are consistent when the same questionnaire is provided to the same respondent at different times. The test-retest reliability is thus obtained by repeating the measurement with the same instrument. A correlation coefficient is calculated to determine whether the results are similar. The higher the correlation coefficient, the more reliable are the results. o Alternative-forms reliability is also called equivalent form reliability and measures two corresponding sets of scales among the same group of respondents at two different time intervals. Reliability is then assessed based on the results of the two sets of scales. o Internal consistency reliability assesses the ability to produce similar results when different samples are used to measure a phenomenon during the same time period. This reliability test is based on the concept that the various statements in the scale measure some aspect of the construct, and these statements should be consistent in measuring the construct. It therefore focuses on the level of internal consistency between the set of statements and the 260 |

complete scale. Two types of internal consistency tests are available: the split- half reliability test, which divides the scale into two halves and correlates the results with one another; and the coefficient alpha, or Cronbach’s alpha coefficient test, which calculates and average for all the possible split-half coefficients by splitting the scale statements in different ways. By calculating the Cronbach’s alpha coefficient, it is possible to determine how each statement in the factor corresponds to the scale as a whole, as well as to each one of the other statements.

For the purpose of this study, Cronbach’s alpha coefficients were calculated to determine whether the results for each scale used in the questionnaire, i.e. the choice construct and its different factors, the perceived value construct and its factors, as well as for the intention to enrol construct (Appendix K), were reliable. As mentioned above, this statistic (Cronbach’s alpha coefficient) provides an indication of the average correlation among all of the items that make up the scale. Values range from 0 to 1, with higher values indicating greater reliability. While different levels of reliability are required, depending on the nature and purpose of the scale, a minimum level of 0.7 Cronbach alpha coefficient values are recommended, where a 0.7 Cronbach alpha coefficient values are dependent on the number of items in the scale. If the Cronbach alpha coefficient is less than 0.7, the scale is not considered reliable (Pallant, 2010:6; Eiselen et al., 2007:112). When there are a small number of items in the scale (fewer than 10), Cronbach alpha coefficient values can be quite small. In this situation it may be better to calculate and report the mean inter item correlation for the items. Optimal mean inter-item correlation values range from 0.2 to 0.4 (Pallant, 2010:6). The Cronbach’s alpha coefficient results obtained for the constructs in the study are discussed in Chapter 6 (Sections 6.7.5; 6.8.4 & 6.9.4).

 Determining whether to use parametric vs. non-parametric tests to test hypotheses

After examining the nature of distribution, the researcher needs to consider whether parametric or non-parametric tests will be employed. Parametric tests are hypothesis-testing procedures that assume that the variables of interest are measured on at least an interval scale (Malhotra, 2009:515; Malhotra, 2007:478). Parametric tests are also employed when group sizes under investigation are to a

261 | certain extent equal in size and relatively large, and the results are normally distributed (Pallant, 2010:208; Erceg-Hurn & Mirosevich, 2008:594; Eiselen et al., 2007:79-80). Parametric techniques include independent-samples t-tests, paired- samples t-test, one-way between groups ANOVA, one-way repeated-measures ANOVA, two-way analysis of variance (between two groups), multivariate analysis of variance (MANOVA) and analysis of covariance (Pallant, 2010:204).

In contrast, non-parametric tests assume that the variables are measured on a nominal or ordinal scale and they are more suitable for smaller samples (less than 30) (Pallant, 2010: 204, 206; Malhotra, 2007:478). Non-parametric tests are also employed when group sizes under investigation are unequal in size and relatively small, and the results are not normally distributed (Pallant, 2010:208; Erceg-Hurn & Mirosevich, 2008:594; Eiselen et al., 2007:79-80).

If the results are normally distributed parametric tests are used to test the hypotheses, while non-parametric tests are used when the results are not normally distributed. As noted previously, if a sample size is larger than 30 it is not imperative for the distribution to be normal (Eiselen et al., 2007:79-80).

Non-parametric tests can be further classified based on whether one, two, or more samples are involved. The number of samples is determined based on how the data is treated for the purpose of analysis, not based on how the data was collected (Malhotra, 2007:478). Non-parametric tests are used for non-metric data (i.e. nominal and ordinal) and include Chi-Square, K-S, Runs, Binomial (for one sample), Chi-Square, Mann-Whitney U Tests, Median, K-S (two independents samples), and Sign, Wilcoxon, McNemar and Chi-Square for two paired samples (Malhotra, 2007:478). Table 5.13 differentiates between non-parametric tests and parametric tests.

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Table 5.13 The differences between non-parametric tests and parametric tests

Non-parametric tests Parametric tests

Deals with smaller samples Deals with bigger samples

Results are NOT normally distributed Results are normally distributed

Nominal and ordinal scales (non-metric data) Interval scale

Independent-samples t-tests, paired samples t- Chi-Square, K-S Runs, Binomial, Mann- test, one way ANOVA, two-way analysis of Whitney U Test, Median, Sin, Wilcoxon, variance, multivariate analysis of variance McNemar (MANOVA) and analysis of covariance When group sizes are relatively small and When groups sizes are relatively large and equal unequal in size in size

Source: Adapted from Pallant (2010:204, 206-208); Erceg-Hurn and Mirosevich (2008:594); Eiselen, Uys and Potgieter (2007:80); and Malhotra (2007:478).

 Testing the stated hypotheses

A hypothesis is an assumption or theory guess that a researcher makes about some characteristic of the population being investigated (McDaniel & Gates, 2010: 521). To determine whether a result obtained in a sample is due to chance or whether it is a reflection of what is happening in the population, the researcher uses hypothesis testing. Typically, two hypotheses are stated, i.e. a null-hypothesis (the sample results are due to chance alone) and an alternative hypothesis (the sample results reflect what is happening in the population). By using the sample results, a test statistic is calculated that enables the researcher to either accept or reject a hypothesis (Eiselen et al., 2007:69-70).

Before addressing each one of the hypothesis tests available to researchers in more detail, it is necessary to define the confidence interval and level of significance that were used during the study. The confidence interval is the representation of the true population value, meaning that if the entire relevant population between the confidence interval level specified have been asked the same question, they would have chosen that particular answer (McDaniel & Gates, 2010:457). A fundamental trend of statistical inference is that it is possible for a number to be different in a mathematical sense, but not significantly different in a statistical sense (McDaniel & Gates, 2010:520).

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Thus, the confidence interval refers to the likelihood that the measured results are correct, the level of significance (also known as the alpha (α) level) indicates the likelihood that the null hypothesis is rejected (Cooper & Schindler, 2006:478, 540- 541). For the purpose of this study, a confidence interval of 95% and a significance level of 5% (i.e. 0.05) were used.

Together with a tests statistic (as mentioned above), a probability also knows as an exceedance probability or a p-value, is calculated (Eiselen et al., 2007:70). The p- value can indicate the level of significance, better explained below: o The p-value is the probability that the result is due to chance alone. o If the p-value is less than 0.05, it implies that it is highly unlikely that the result is due to chance alone, i.e. the null-hypothesis will then be rejected. o If the p-value is larger than 0.05, the null-hypothesis will be accepted. o The ‘cut-off’ value of 0.05 is referred to as the level of significance or alpha (α) level (Eiselen et al., 2007:70).

Once the significance level test is done, the researcher reports on whether the null- hypothesis is accepted or rejected, and a number of main findings based on the results are stated (Chapter 6). Table 5.14 presents a summary of the secondary research objectives, the hypotheses formulated, the related questions, and the appropriate statistical test used. This Table indicates that only three statistical techniques were needed to test the stated hypotheses for this study. The tests include independent-samples t-tests, Kruskal-Wallis Tests and Mann-Whitney U Tests. These three tests are discussed next.

o Independent-samples t-tests

A t-test can be used when the researcher would like to determine whether there is a statistically significant difference among a number of groups, and the parametric test is used for interval-scaled data with normal distribution (Pallant, 2010:105). T-tests are used when there are only two groups (e.g. males/females) or two time points (e.g. pre-intervention, post-intervention) (Pallant, 2010:204). There are different types of t-tests available in SPPS, however the two that are most commonly used are (Pallant, 2010:239):

264 | o Independent-samples t-test can be used when the researcher needs to compare the mean scores of two different groups of people or conditions. o Paired-samples t-test can be used when the researcher needs to compare the mean scores for the same group of people on two different occasions, or when there are matched pairs.

For the purpose of this study, independent-samples t-tests were used when data was normally distributed, when group sizes under investigation were relatively equal in size and relatively large (Pallant, 2010:208; Erceg-Hurn & Mirosevich, 2008:594; Eiselen et al., 2007:79-80). In the first instance the p-value for Levene’s test was investigated. A p-value of more than 0.05 indicates that equal variances can be assumed. If the p-value is smaller than 0.05 (for the Levene’s test) equal variances cannot be assumed. A corresponding p-value smaller than 0.05 it indicates that there is a significant difference between the means. The means are thus interpreted and reported (Pallant, 2010:241-242). o Mann-Whitney U Tests

A Mann-Whitney U Test is a non-parametric test used to test whether two of the mean ranks of two groups are significantly different. It is used when the assumption of normality cannot be made, when group sizes are relatively small and when group sizes are relatively unequal (Pallant, 2010:208, 227; Eiselen et al., 2007:82). For the purpose of this study the Mann-Whitney U Test was used to test for differences between two independent groups, particularly between the following independent groups:

o Differences between home language: o Afrikaans vs English; o Afrikaans vs Indigenous; and o English vs Indigenous. o Differences between expected average grade for grade 12 groups: o students expecting an A average grade vs students expecting a B average grade; o students expecting an A average grade vs students expecting a C average grade;

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o students expecting an A average grade vs students expecting a D-F average grade; o students expecting a B average grade vs students expecting a C average grade; o students expecting a B average grade vs students expecting a D-F average grade; and o students expecting a C average grade vs students expecting a D-F average grade. o Differences between prospective university students with parents who attended university vs those with parents who did not attend university. o Kruskal-Wallis Test

The Kruskal-Wallis Test is a non-parametric test that allows the comparison between three or more groups. The mean ranks for the groups are compared. If the p-value is less than 0.05 it indicates that there is a significant difference between at least two groups. However, it was still not clear at this stage which of the groups are statistically significantly different from one another. To determine this, a Mann-Whitney U Tests between the pairs of groups is conducted (Pallant, 2010; 234-235). To control for Type 1 errors (rejecting the null hypotheses when it is, in fact, true is referred to as a Type 1 error) (Pallant, 2010:207), Bonferroni adjustments were made to the p-values resultant from the Mann-Whitney U Tests. This involved setting a more stringent p-value level for each comparison to keep the p-value across all the tests at a reasonable level, the p-value (0.05 in this instance) was divided by the number of comparisons planned (Pallant, 2010:209). For the purpose of this study, the Bonferroni adjustments to the p- values were made as follows: o Differences between language groups (Afrikaans, English and Indigenous languages) the p-value of 0.05 /3 = 0.0167. Thus if the p-value is smaller than 0.0167, there is a significant difference. o Differences between expected average grades for grade 12 (A, B, C and D-F) the p-value of 0.05/4 = 0.0125. Thus if the p-value is smaller than 0.0125, there is a significant difference.

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 Conducting Exploratory Factor Analysis (EFA)

The EFA is a data reduction technique that assists the researcher in reducing the number of statements to a few interpretable factors (Pallant, 2010:181, Eiselen et al., 2007:105). The key objective of an EFA is to simplify the data, and to determine the underlying factors in a set of opinion-related questions (McDaniel & Gates, 2010:616; Eiselen et al., 2007:104-105). It analyses the correlations (Pearson product moment) between pairs of variables, (items or statements) and identifies groups of variables in such a way that variables in the same group are highly correlated with one another, but uncorrelated with the variables in another group (Eiselen et al., 2007:104-105).

Initially, an EFA was conducted to uncover underlying factors that naturally occur in the data. This was followed by an EFA that reduced the statements into five factors as dictated by the literature, and a second order EFA was conducted to validate the factors. All three EFAs followed a similar process.

To ensure that an EFA provides valid results, the sample size needs to be examined, however it is not the only determinant to ensure that an EFA will provide valid results (Eiselen et al., 2007:105). Other practical considerations include the method of extraction, method of rotation, the Bartlett’s Test of Sphericity and the Kaiser-Meyer- Olkin (KMO) measure of sampling adequacy that assess the factorability of the data (Pallant, 2010:183).

o Sample size

There is little agreement among authors concerning how large a sample should be, however the recommendation is the larger the better (Pallant, 2010:182). It is suggested that a sample should have at least 300 respondents for factor analysis, however the number of respondents per variable (or statement) is also a good guideline (Pallant, 2010:183, Eiselen et al., 2007:105). The number of respondents should be at least four times the number of variables (questions, items or statements) and ideally the ratio of respondents to the number of questions in the factor analysis should be 10 to 1. Thus, 10 respondents are

267 | needed for every question to be included in the factor analysis (to enable a factor analysis) (Eiselen et al., 2007:105). For example, Section B2 of this study’s questionnaire consists of 57 statements, thus is a sample of 570 respondents (10 respondents x 57 statements) necessary to warrant a factor analysis. o Data suitability for conducting a factor analysis

It is necessary to determine the appropriateness or ‘factorability’ of the data before EFA can be conducted. KMO and Bartlett’s Test of Sphericity are measures used to determine the suitability of data for a factor analysis (Pallant, 2010:187).

Bartlett’s Test of Sphericity tests the null hypotheses that the variables are uncorrelated. When the p value is < 0.05 not all variables are uncorrelated and the null hypothesis can be rejected. The KMO index ranges from 0 to 1, with 0.6 suggesting ample correlation between variables to warrant a continuation with the factor analysis (Malhotra, 2007:614; Pallant, 2010:183).

The Anti-image Correlation statistic is also used to decide whether factor analysis is feasible. For a variable to be retained in the proposed factor analysis, the Measures of Sampling Adequacy (MSA) should be equal to or should exceed 0.6. In such a case sufficient correlation exists between this variable and at least one other variable for it to be retained in the factor analysis (Eiselen et al., 2007:107).

The next issue to investigate is the communalities. To determine how much a single variable has in common with all the remaining variables, communality is investigated. It is a measure of the relationship between a particular variable and the set of remaining variables in the analysis (Zikmund & Babin, 2010b: 627; Meyers, Gamst & Guarino, 2006:490-491). Communalities provide information about how much of the variance in each item is explained. A value of less than 0.3 indicates that the variable does not fit well with the other variables. This information can be used to improve or refine a scale and variables can then be deleted from the scale. When removing items with low communality, values tend to increase the total variance explained (Pallant, 2010:198).

268 | o Method of extraction

The most commonly used approaches to extract the underlying factors from the data, are Principal Components Analysis (Pallant, 2010:183) and according to Eiselen et al., (2007:108). Principal Axis Factoring. For the purpose of this study, Principal Axis Factoring has been deemed most suitable. o Method of rotation and interpretation

Rotation is used to assist with the interpretation or identification of the variables contributing most to a factor (Eiselen et al., 2007:105). There are two main approaches to rotation, resulting in either orthogonal (uncorrelated) or oblique (correlated) factor solutions (Pallant, 2010:185). For the purpose of this study, an orthogonal rotation method, namely the Varimax rotation was used. o Total Variance Explained

To determine the number of factors extracted during the process the eigenvalues are considered. The number of eigenvalues greater than 1 indicates the number of factors extracted (Pallant, 2010:192; Eiselen et al., 2007: 107). o Rotated Factor Matrix

The rotated factor matrix is furthermore analysed to determine the variables that load on a particular factor (Eiselen et al., 2007:108-109). Once this has been established, the researcher labels each factor with an applicable name describing all the variables included in each factor (Pallant, 2010:198). The loading of a particular variable on a factor can be assessed by using the guidelines provided by Comrey and Lee (1992). According to the authors a loading of > 0.70 is excellent, > 0.63 is very good, > 0.55 is good, > 0.45 is fair, and > 0.32 is considered poor. Meyers et al. (2006:508) also refer to Comrey and Lee’s (1992) guidelines, but suggest that sample size should also be considered. Meyers et al. (2006:508) recommend that with samples larger than 200 respondents, a cut-off of 0.40 should be considered as a rough guide for retaining a variable within a factor. Based on Comrey and Lee’s (1992) guidelines and considering Meyers et

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al.’s (2006:508) recommendations, all variables (choice statements) with a factor loading of below 0.43 were omitted from the specific factor before each individual variable’s or choice statement’s correlation with the ‘choice’ factor (scale) as a whole was investigated (Chapter 6, Section 6.4.3).

 Conducting second order Exploratory Factor Analysis (EFA)

Second-order exploratory factor analysis can be described as a further factor analysis of the results of an initial factor analysis. Specifically, the initial (or first- order) factor means, variances, and covariances are re-analysed into more general or abstracted variables (Tisak & Tisak, 2005). The 2nd order EFA assures that the set of common factors are entirely responsible for the measured variables (Chen, Sousa & West, 2005:487). Second-order EFA can furthermore test whether the hypothesised factors account for the pattern of relations between the first EFA’s factors, and it puts a structure on the pattern of covariance between the initial EFA’s factors, explaining the covariance in a more parsimonious way with fewer parameters (Chen et al., 2005:473). Thus, it can be argued that the objective of a 2nd order EFA serves to bring simplification of the interpretation, to validate the factors obtained through the EFA as explained above, and to determine if further variable extractions are necessary (Chapter 6, Section 6.4.3.1) (Chen et al., 2005:473). The same process as with the initial EFA, was followed here.

 Performing Confirmatory Factor Analysis (CFA)

Confirmatory factor analysis (CFA) is similar to exploratory factor analysis (EFA) as it is also used to examine the relationships between a set of measured variables and a smaller set of factors that might account for the variables (Ary et al., 2006:393). For the purpose of this study, the EQS statistical programme was used to conduct CFA analysis. EQS is a Microsoft Windows programme that can be used for all stages of the analysis from data entry and screening to exploratory statistical analyses to SEM (Kline, 2011:81).

The difference between CFA and EFA, is that with CFA the researcher has advance knowledge and strong theoretical expectations about the factor structure before performing the analysis. By performing an EFA, the underlying factor structure is

270 | identified, and by performing CFA the factor structure of a set of observed variables is verified. The CFA provides a test of how well the theory about the factor structure fits the actual observations (Zikmund & Babin, 2010b:625; Ary et al., 2006:393; Suhr, 2006a:1).

CFA is an analytical tool of choice for developing and refining measurement instruments, assessing construct validity, identifying method effects, and evaluating factor invariance across time and groups. CFA also allows researchers to specify precise and highly complex hypotheses arising from theory, regarding the phenomenon under study (Jackson, Gillaspy & Purc-Stephenson, 2009:6, 9; Bartholomew, Steele, Moustaki & Galbraith, 2008:290).

CFA is part of the larger family of methods known as structural equation modelling (SEM). It is imperative when conducting SEM to first evaluate the measurement model to assess whether the measured variables accurately reflect the desired constructs of factors. The output of the CFA allows the researcher to evaluate the factor model overall and at the level of individual variable and factor relationships. Also, in many cases the CFA assists in the identification of measurement model problems and issues that can be rectified before conducting SEM (Jackson et al., 2009:6; Ary et al., 2006:393).

CFA relies on several statistical tests to determine the adequacy of model fit to the data. For the purpose of this study, the root mean square error of approximation (RMSEA) and the comparative fit index (CFI) measures were used to determine the adequacy of model fit to the data (Meyers et al., 2006:558; Suhr, 2006a:2). o The RMSEA is the average of the residuals between the observed correlation/covariance from the sample and the expected model estimated for the population. Values range from 0 to 1 with a smaller RMSEA value indicating better model fit. Values less than 0.08 are acceptable, however, values greater than 0.10 are generally unacceptable (Meyers et al., 2006:559). When evaluating RMSEA, which has been recognised as one of the most informative criteria for evaluating model fit, a more detailed interpretation of evaluating this index is to follow the following criteria, where if the value is less than 0.08 it indicates a good fit, if the value is 0.08 to 0.10 it indicates a moderate fit, and

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greater than 0.10 indicates poor fit (Meyers et al., 2006:562). The RMSEA fit measure tends to perform well with respect to detecting model misspecification and lack of dependence on sample size (Jackson et al., 2009:10). o The CFI is the comparative fit index that ranges from 0 to 1 with a larger value indicating better model fit. Acceptable model fit is indicated by a CFI value of 0.90 or greater (Suhr, 2006a:2). The CFI can further be interpreted where a good fit > 0.90; adequate but marginal fit = 0.80 to 0.89; poor fit = 0.60 to 0.79; very poor fit < 0.60 (Meyers et al., 2006:560).

 Performing Structural Equation Modelling (SEM)

Structural equation modelling (SEM) is a sophisticated technique that allows the researcher to test various models concerning the interrelationships among a set of variables. This modelling technique examines a series of dependence relationships simultaneously, thus the structure of interrelationships among a diverse set of variables can be examined. SEM also expresses the structure in a series of equations and the overall fit of the model to the data can also be tested (Pallant, 2010:105; Shiu et al., 2009:649-650).

SEM can be thought of as the union of CFA and path analysis. The reason for this marriage is because in SEM there are really two types of models: a measurement model and a structural model. The model as a whole is evaluated by a variety of goodness-of-fit indices, however SEM also evaluates the measurement and the structural model separately because it is possible that they may differentially fit the data (Wuensch, 2009:2; Ary et al., 2006:393; Meyer et al., 2006:613).

The first model involves a confirmatory factor analysis for each latent variable (called the measurement model) (Meyers et al., 2006:613; Anderson & Gerbing, 1988:414). The structural model is a path analysis that investigates at the causal relationships between the major variables of interest in the theory (Meyers et al., 2006:613).

The first step in SEM is to create a graphical depiction of the model portraying how the various variables fit together. The second step is to verify such a model (or theory), however, two conditions must be met before this verification can take place (Blunch, 2008:4):

272 | o The variables that are making up the model need to be defined conceptually; and o The variables must be defined operationally (meaning the construct instruments to measure the concepts need to be added together).

Once a model is proposed (and the relationships between the variables have been hypothesised), these relationships are tested using EQS software to estimate and evaluate the structural portion of the model (Hoe, 2008:77). The estimates of the relationships between the variables in the model are calculated by using any one of a number of estimation methods, however, for the purpose of this study the Maximum Likelihood (ML) that estimates the parameters of the whole system in one by using all available data (Blunch, 2008:80-81), is used. The ML describes the statistical principle that underlies the derivation of parameter estimates; the estimates are the ones that maximise the likelihood that the data (the observed covariances) was drawn from the specified population (Kline, 2011:154).

Fit measures need to be employed, and in SEM the fit measures need to be cautiously interpreted. As with CFA, there are several indicators of goodness-of-fit that can be interpreted. For the purpose of this study, the root mean squared approximation of error (RMSEA) that should be less than 0.08 to indicate acceptable fit, and comparative fit index (CFI) that should be greater than 0.90 to indicate good fit, are reported and interpreted (Hoe, 2008:77; Meyers et al., 2006:562, 615). In addition the relative chi-square ratio or X2/df ratio that should be less than 3.0 indicating a good fit are also reported and interpreted (Rotgangs & Schmidt, 2011:470; Hoe, 2008:77; Schreiber, Stage, King, Nora & Barlow, 2006:330). The chi- square ratio should be used with caution as it is affected by the size of correlations between pairs of variables (larger correlations generally cause a poorer fit), and it is furthermore sensitive to the sample size (Hooper, Coughlan & Mullen, 2008:54; Meyers et al., 2006:557, 561).

Even if an adequate to good model fit is observed, the researcher needs to investigate the SEM results for any possible error messages that highlight any problems such as possible problems relating to singularity or multicollinearity. Singularity is evident when one of the factors (variables) is a perfect linear combination of the other factors (variables), it results in a singular matrix which cannot be inverted and that causes the analysis to crash (Wuensch, 2009:1). 273 |

Another possible problem with the model could relate to multicollinearity. Multicollinearity refers to a condition that exists when more than two predictors or independent factors (variables) correlate strongly with one another, thus what appears to be separate factors (variables), actually measure the same thing (Kline, 2011:51; Meyers et al., 2006:180). To overcome the problems associated with singularity and multicollinearity, the next step in the SEM analysis involves the investigation of the paths among the factors (or variables) in order to evaluate their statistical significance and secondly, their strength using standardised path coefficients (Hoe, 2008:79; Meyers et al., 2006:615; Schreiber, Stage, King, Nora & Barlow, 2006:327; Lleras, 2005:25-27).

The causal paths can be evaluated in terms of statistical significance and strength using standardised path coefficient that ranges between -1 and +1. Based on a p- value of 0.05, the test statistic generated from the EQS output should be greater than ± 1.96 to indicate that the null hypothesis can be rejected (meaning that the structural coefficient is not zero). The strength of the relationships among the variables is reviewed after the statistical significance of the standardised paths has been investigated. Standardised paths should be at least 0.20 and ideally above 0.30 in order to be considered meaningful (Hoe, 2008:79). A standardised path coefficient with absolute values less than 0.10 may indicate a ‘small’ effect, values around 0.30 a ‘medium’ effect and values greater than 0.50 a ‘large’ effect (Suhr, 2006b:4).

Once the overall model fit and causal paths have been evaluated the correlations among independent variables are reviewed. A correlation of 0.80 is typical an indication of multicollinearity and the usual approach to deal with multicollinearity is to eliminate one of more variables from consideration (Kline 2011:54; Paul, 2005- 2006:4; Meyers et al., 2006:181; Wulder, 1998:44).

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Table 5.14 A summary of the secondary research objectives, hypotheses, related questions in questionnaire and statistical techniques used in this study

Appropriate statistical test to Secondary research objective Hypothesis Related questions test related hypotheses To gain insight into the South African higher education (HE) environment. Objective achieved through information obtained from the literature review

To provide an overview of the extant literature related to the main constructs of the study, namely the choice factors influencing university choice, the Objective achieved through information obtained from the literature review perceived value universities offer and prospective students’ intention to enrol at their chosen university.

H1: Female and male prospective university students differ significantly in terms of their level of agreement Section B2 (all the regarding the influence of different choice factors on statements) Independent-samples t-test their university choice. Section A3

H : Prospective university students with different 2 Section B2 (all the home languages differ significantly in terms of their Kruskal-Wallis Tests, and statements) level of agreement regarding the influence of different Mann-Whitney U Tests Section A4 choice factors on their university choice. To measure the extent to which the H : Prospective university students who take different choice factors, identified through 3 mathematics as a subject in grade 12 differ the literature review, influence significantly from prospective university students who Section B2 (all the prospective students’ university choice. take mathematics literacy as a subject in grade 12 in statements) Independent-samples t-test

terms of their level of agreement regarding the Section A5 influence of different choice factors on their university choice.

H4. Prospective university students with different expected average grades for grade 12 differ Section B2 (all the significantly in terms of their level of agreement Kruskal-Wallis Tests, and statements) regarding the influence of different choice factors on Mann-Whitney U Tests Section A6 their university choice.

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Appropriate statistical test to Secondary research objective Hypothesis Related questions test related hypotheses

H5. Prospective university students with parents who went to university differ significantly from prospective university students with parents who did not go to Section B2 (all the university and in terms of their level of agreement statements) Mann-Whitney U Test regarding the influence of different choice factors on Section A7 their university choice.

H6. Female and male prospective university students differ significantly in terms of their level of Section C (all the agreement regarding the perceived value that their statements) Independent-samples t-test chosen university offers. Section A3

H7. Prospective university students with different home languages differ significantly in terms of their Section C (all the Kruskal-Wallis Tests, and level of agreement regarding the perceived value that statements) Mann-Whitney U Tests their chosen university offers. Section A4

H8. Prospective university students who take mathematics as a subject in grade 12 differ significantly from prospective university students who Section C (all the To assess the value prospective take mathematics literacy as a subject in grade 12 in statements) Independent-samples t-test students perceive they will derive from terms of their level of agreement regarding perceived Section A5 their chosen university. value that their chosen university offers.

H9. Prospective university students with different expected average grades for grade 12 differ Section C (all the significantly in terms of their level of agreement Kruskal-Wallis Test and Mann- statements) regarding perceived value that their chosen university Whitney U Test Section A6 offers.

H10. Prospective university students with parents who went to university differ significantly from prospective Section C (all the university students with parents who did not go to statements) Mann-Whitney U Tests university and in terms of their level of agreement Section A7 regarding perceived value that their chosen university offers.

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Appropriate statistical test to Secondary research objective Hypothesis Related questions test related hypotheses

H11. Female and male prospective university students differ significantly in terms of their level of Section D (all the agreement regarding their intention to enrol at their statements) Independent-samples t-test chosen university. Section A3

H12. Prospective university students with different home languages differ significantly in terms of their Section D (all the Kruskal-Wallis Test and Mann- level of agreement regarding their intention to enrol at statements) Whitney U Test their chosen university. Section A4

H13. Prospective university students who take mathematics as a subject in grade 12 differ significantly from prospective university students who Section D (all the take mathematics literacy as a subject in grade 12 in statements) Independent-samples t-test To gauge the intention of prospective terms of their level of agreement regarding their Section A5 students to enrol at their chosen intention to enrol at their chosen university. university.

H14. Prospective university students with different expected average grades for grade 12 differ significantly in terms of their level of Section D (all the Kruskal-Wallis Test and Mann- agreement regarding their intention to enrol at their statements) Whitney U Test chosen university. Section A6

H15. Prospective university students with parents who went to university differ significantly from prospective university students with parents who did not go to Section D (all the university and in terms of their level of statements) Mann-Whitney U Test agreement regarding their intention to enrol at their Section A7 chosen university.

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Appropriate statistical test to Secondary research objective Hypothesis Related questions test related hypotheses To determine whether groups of Sections A3, A4, A5, prospective students who exhibit A6 and A7 – different demographic characteristics demographic differ significantly from each other in A3 & A5 - Independent- information terms of the influence of different choice samples t-test Section B2 – choice factors on their university choice, the H to H A4 & A6 - Kruskal-Wallis Test 1 15 factors perceived value that their chosen and Mann-Whitney U Test Section C – university offers and their level of A7 – Mann-Whitney U Test perceived value agreement regarding their intention to Section D – enrol at their chosen university. intention to enrol

H16. There are significant and positive interrelationships between the choice factors To determine the interrelationships prospective university students consider when Section B2 , C and between the main constructs of the study Interpreting the standardised choosing a university, the perceived value they expect D in order to propose a model to explain path coefficient to derive from, and their intention to enrol at their prospective students’ university choice chosen university.

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5.6 Report preparation and presentation

The last step in Malhotra’s (2007:10-11) research process is to report on the preparation of the results and to present the results. This is presented in Chapter 6.

5.7 Conclusion

Chapter 5 addresses the research methodology followed in the study and outlines the marketing research process as proposed by Malhotra (2007:78). A quantitative study that is descriptive in nature was employed, using a paper-based, drop-off self- administered questionnaire to collect data.

A judgemental sampling technique was used to select the sampling units. Six fieldworkers assisted with the collection of data, and SPSS and EQS were used to assist with data preparation and analysis. A data analysis strategy was designed that first addressed the descriptive statistics, after which the distribution, validity, and reliability of the results were discussed. The three statistical techniques that were used for hypotheses testing were the independent-samples t-test, Kruskal-Wallis Test and Mann-Whitney U Test.

A brief overview was given on the essential issues surrounding exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modelling (SEM). The process of conducting an EFA, CFA and SEM was discussed.

Chapter 6 presents the results and subsequent main findings of the study.

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CHAPTER 6 Discussion and interpretation of results

6.1 Introduction

The purpose of this chapter is to discuss and interpret the results that were obtained through the empirical research phase of the study, as explained in Chapter 5. This chapter commences with a discussion on the sample realisation rate, followed by a discussion of several aspects related to the respondents’ schooling, university preference, as well as their demographic profile. The chapter is then divided into a number of main sections, including three sections presenting the results for each of the main constructs of the study.

Each of the three sections addressing a main construct of the study, presents the descriptive results as well as the distribution of the results for all the statements measuring the particular construct. Furthermore, the results of the factor analysis or data reduction techniques that were utilised to uncover, validate and confirm the constructs or their underlying factors are presented.

Subsequently, the results of the reliability analysis for the respective constructs or their underlying factors uncovered, validated and confirmed are reported and overall mean scores for these are presented. The results of the hypothesis testing, based upon these overall mean scores are furthermore presented.

The chapter finally presents a section reporting on the results for the model tested and concludes with a summary of the main findings of this chapter.

6.2 Sample realisation rate

Since the purpose of the study is to develop a model for prospective university students, it is imperative that the sample is representative of prospective university students. It was subsequently decided to select 1 500 grade 12 scholars who were attending any of the top 250 public Gauteng schools during 2011, and were planning to attend a university or university of technology as part of the sample (Chapter 5,

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Section 5.5.5, Step 4). In total 1 733 respondents from 14 schools completed the questionnaire. Of the 1 733 questionnaires, 1 476 could be included for analysis and interpretation purposes as only these respondents indicated that they were planning to attend a university or a university of technology (Chapter 5, Section 5.5.5, Step 3). Table 6.1 presents the proposed and realised sample for the study.

Table 6.1 Sample realisation rate

Proposed Number of Overall Proposed Number of Type of public number of public number of number of qualified school public schools respondents respondents respondents* schools realised realised Afrikaans schools (Afrikaans is the 5 500 6 993 854 language of instruction) English schools (English is the 5 500 4 363 355 language of instruction) African schools (attended mostly by scholars speaking an African/indigenous- 5 500 4 377 267 home language, although language of instruction might be English. Total 15 1500 14 1733 1476

Realisation Rate 100% 100% 93% 115% 98.4%

*Qualified respondents are all the respondents who are planning to attend a university or university of technology

Considering the results obtained in Table 6.1, it is evident that the majority of the respondents who participated in the study and who were planning to attend a university/university of technology were mostly from Afrikaans schools, followed by English and then “African” schools. A total realisation rate of 98.4% is achieved with a total of 1 476 questionnaires that will be included for analysis and interpretation.

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6.3 Planned university attendance

Table 6.2 provides a breakdown of the 1 476 respondents who indicated that they were planning to study at a university/university of technology.

Table 6.2 Respondents’ planned university/university of technology attendance

Variable N % Yes, I am planning to apply to study at a university or 1427 85.9 university of technology in South Africa Yes, a university, but NOT in South Africa 49 2.9 No, I am not intending to study at a university or a university 188 11.2 of technology TOTAL 1664 100

It is evident from Table 6.2 that the majority of respondents were planning to apply to study at a university or a university of technology in South Africa (85.9%), with a small percentage of 2.9% who wished to study outside of South Africa. It is this group of respondents who are included for analysis and interpretation purposes (1427 + 49 = 1476).

One of the main reasons respondents indicated for not wishing to study in South Africa, was mostly because of the type of course(s) that was offered overseas. A total of 15 scholars indicated that they were intending to study music overseas.

6.4 Schools respondents are attending

Table 6.3 provides insight into the schools the respondents came from.

Table 6.3 Schools in Gauteng from where respondents originate

School in Gauteng Ranking N % 1.Hoërskool Waterkloof 1 276 18.7 2.Hoërskool Menlopark 2 116 7.9 3.Hoërskool Monument 9 149 10.1 4.Hoërskool Florida 10 101 6.8 5.Parktown Girls High 13 43 2.9 6.Hoërskool Kempton Park 16 42 2.8 7.Hoërskool Noordheuwel 17 170 11.5 8.Benoni High School 18 232 15.7 9.Mondeor High School 24 36 2.4

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School in Gauteng Ranking N % 10.Jeppe High School for Boys 121 45 3.1 11.Mokgome High School 194 70 4.7 12.Kwadedangenlale High School 243 39 2.6 13.Thutolore Secondary School 244 99 6.7 14.Moletsane High School 250 58 3.9 *Top 250 Total 1476 100 schools list * The top 250 schools list consists out of a top 190 Gauteng schools list and a top 60 Sowetan schools list (Appendix B and Appendix J)

It is evident from Table 6.3 that nine out of the 14 schools are in the top 25 schools in Gauteng and these first nine schools represented 1 165 of the 1 476 respondents who were planning to apply to study at a university or university of technology. Thus, a total of 79% of the respondents represented the top 25 Gauteng schools.

6.5 Demographic profile of respondents

Table 6.4 summarises the demographic profile of the respondents.

Table 6.4 Demographic profile of respondents

Variable Overall Gender N % Female 815 55.7 Male 647 44.3 Total 1462 100 Home Language N % Afrikaans 808 55 English 284 19.3 Nguni (IsiZulu, IsiXhosa IsiSwati, IsiNdebele) 132 9.0 Sotho (SeSotho s Leboa, Sesotho, Setswana) 227 15.5 TshiVenda / XiTsonga 5 0.3 Other 13 0.9 Total 1469 100 Overall expected average grade for grade 12 N % A (80%-100%) 295 20.4 B (70%-79%) 445 30.7 C (60%-69%) 475 32.8 D (50%-59%) 199 13.8 E (40%-49%) 29 2.0 F (34%-39%) 5 0.3 Total 1448 100

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Maths subject taken at school N % Mathematics 917 62.5 Mathematics literacy 550 37.5 Total 1467 100 Did any of your parents/guardians attend a N % university/university of technology/college? Yes 948 65.6 No 497 34.4 Total 1445 100

It is evident from Table 6.4 that although the majority of respondents are female (55.7%), the genders are fairly equally represented in the sample. The majority of respondents are Afrikaans speaking (54.7%), followed by English (19.2%), Sotho (15.4%), Nguni (8.9%) and TshiVenda/XiTsonga (0.3%) respectively. The majority of respondents had Mathematics (63.5%) as a school subject and half of the respondents (50%) were expecting an average grade for grade 12 in the top 70% - 100% (A and B) grade bracket, with 20.4% expecting an A, 30.7% a B, and 32.8% a C grade. Also, the majority of respondents’ parents or guardians (65.6%) had attended a university, or university of technology or a college, while 34.4% of respondents indicated that neither their parents (nor their guardians) had attended any post-school (grade 12) education.

6.6 Respondents’ most preferred university/university of technology

Table 6.5 provides a list of the universities (or universities of technology) in rank order from 1 indicating the most popular university with this sample.

Table 6.5 The most preferred university/university of technology of respondents

Rank University/university of Technology N % position 1 University of Pretoria (UP) 437 30.9 2 University of Johannesburg (UJ) 293 20.8 3 North-West University (NWU) 152 10.7 4 University of the Witwatersrand (Wits) 115 8.1 5 University of Stellenbosch (US) 67 4.7 6 University of Cape Town (UCT) 63 4.4 7 University of South Africa (UNISA) 50 3.5 8 Tshwane University of Technology (TUT) 46 3.2 9 Rhodes University (RU) 13 0.9

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Rank University/university of Technology N % position 10 University of the Free State (UFS) 11 0.8 11 University of Limpopo 10 0.7 12 Other 160 11.3 Total 1417 100

It is evident from the results presented in Table 6.5 that the University of Pretoria (UP) was the first choice of the majority of respondents, with 30.8% choosing UP, followed by the University of Johannesburg (UJ) in second place with 20.7% choosing UJ, and North West University (NWU) in third place with 10.7% choosing NWU.

The ‘other’ option of universities/universities of technology chosen in this sample represented a long list of possible alternative Higher Educational Institutions (HEIs). The other HEIs that are the most popular with respondents are in the first place Damelin with 13 respondents (0.9%), Vaal University of Technology is second with 10 respondents (0.7%), CTI Education group is third with 9 respondents (0.6%), and in the fourth place is jointly The Academy of Sound Engineering with 5 respondents (0.3%) and Monash University with 5 respondents (0.3%).

Based upon the results presented in the preceding sections, the following profile of respondents taking part in the study can be presented:

The profile of the typical respondent in this sample can be summarised as 88.8% planning to apply to study at a university/university of technology, 55.7% is female and 43% is male, the majority of respondents are Afrikaans-speaking (54.7%), followed by English (19.2%) and Sotho (15.4%), the majority of respondents had Mathematics (62.1%) as a grade 12 subject and a large percentage of the group (32.2%) expected a C (between 60% and 69%) average grade and 30.1% of respondents expected a B (between 70% and 79%) average grade, the majority of respondents’ parents (65.6%) did attend a university/university of technology or a college, and the first choice or most preferred university of this sample was the University of Pretoria (UP) with 30.8% of respondents choosing UP, followed by the

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University of Johannesburg (UJ) (20.7% of respondents choosing UJ), and in the third place is North-West University (NWU) (10.7% of respondents choosing NWU). The following section (Section B of the questionnaire) addresses the first main construct namely, the choice construct.

6.7 The choice construct

Section B of the questionnaire provided respondents with a list of 57 statements to enable the researcher to determine those who are most influential when a decision regarding a university/university of technology to attend, is taken (choice construct). Respondents were asked to indicate, on a seven-point Likert-type scale, where one is 'strongly disagree' and seven is 'strongly agree', the extent to which they agreed with each one of the 57 statements presented to them.

This section firstly presents the descriptive results for each statement to determine which statements respondents agreed with the most. Following this discussion, the distribution of results is assessed in order to determine whether parametric or non- parametric tests should be used to test the hypotheses formulated for the study. Following this, the results of the data reduction techniques are presented. The results of an exploratory factor analysis (EFA), a 2nd order EFA, as well as a confirmatory factor analysis (CFA) are presented before the reliabilities for the resulting factors are tested. Finally overall mean scores are calculated for reliable factors before they are subjected to hypothesis testing aimed at uncovering significant differences between groups of respondents who took part in the study.

6.7.1 Descriptive results

Table 6.6 provides the descriptive results of Section B2 of the questionnaire. In table format the count (N), mean, low-box score, top-box score and standard deviation for all 57 choice statements are presented.

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Table 6.6 The respondents’ choice statements influencing their university decision

Low- Top- State- Standard Statement N Mean box box ment deviation score score B1 It has excellent lecturers 1421 5.85 0.7 30.1 1.202 B2 Its lecturers are knowledgeable experts 1419 5.92 0.4 38.3 1.129 It is known that only the very intelligent B3 1414 4.52 8.2 15.0 1.708 study there B4 Only good students get in 1415 4.52 6.4 15.9 1.708 Academic programmes are nationally B5 1407 5.96 1.2 48.0 1.293 known It offers many good cultural experiences B6 1409 5.65 3.2 40.6 1.545 (fine arts, music, theatre, etc.) B7 It offers a variety of courses 1418 6.25 0.4 57.9 1.116 It offers the courses that I am interested B8 1423 6.55 0.8 76.5 1.021 in B9 It offers courses with a good reputation 1413 6.30 0.6 58.5 1.066 It offers courses that the job market is B10 1414 6.18 0.7 53.3 1.135 interested in It is committed to social service (involved B11 1399 5.37 2.1 26.5 1.399 with local community) B12 Sport teams have a good reputation 1400 5.33 5.6 30.9 1.675 There are good sporting opportunities at B13 1399 5.45 5.9 35.2 1.691 the university B14 It is committed to academic excellence 1413 6.10 0.5 47.1 1.119 B15 It offers a world-class education 1416 6.06 0.6 47.7 1.151 Its qualifications are internationally B16 1407 6.02 1.3 51.3 1.299 recognised It is a reputable institution (in South B17 1392 6.19 1.1 54.5 1.142 Africa) B18 Its qualifications are reputable 1399 6.10 0.6 48.0 1.107 It has a positive image with possible B19 1406 6.11 0.8 47.4 1.106 employers Its admission requirements are high (i.e. B20 students must do well in grade 12 to get 1417 5.78 1.7 39.2 1.332 in) B21 Hostel/residential facilities are attractive 1406 5.56 3.2 36.9 1.526 B22 The campus looks attractive 1407 5.95 1.3 47.3 1.314 B23 The buildings look attractive 1406 5.90 0.9 42.9 1.274 B24 The campus looks prestigious 1382 5.84 0.5 38.4 1.235 Its buildings and grounds are well B25 1399 6.05 0.4 45.2 1.124 maintained B26 The sports facilities are up to date 1379 5.59 3.4 34.8 1.503 It has good resources for students B27 1394 6.22 0.8 52.7 1.075 (computers, library, etc.) B28 It offers a safe environment 1395 6.01 0.7 45.0 1.178 The recreation facilities (e.g. student B29 1390 5.90 0.5 39.6 1.181 centre) look attractive I will find a job after completing my B30 1397 6.16 0.6 52.1 1.138 qualification Studying at this university will make it B31 1397 6.16 0.8 51.2 1.122 possible to find a job after qualifying

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Low- Top- State- Standard Statement N Mean box box ment deviation score score Studying at this university will enhance B32 1394 6.13 0.9 48.4 1.117 chances of employment opportunities Studying at this university will increase B33 1397 6.07 0.8 43.7 1.096 career prospects Studying at this university will provide B34 1395 5.81 1.4 36.8 1.272 better salary prospects Current students’ perception of this B35 1384 5.98 0.9 41.9 1.180 university is positive My friends’ perception of this university is B36 1384 5.79 2.0 39.5 1.380 positive My friends also consider studying at this B37 1390 5.52 5.0 38.9 1.704 university My parents’ perception of this university B38 1390 5.99 1.9 48.3 1.331 is positive My schoolteachers’ perceptions of this B39 1375 5.83 1.2 39.1 1.310 university are positive The particular university’s B40 representatives are positive about the 1386 6.02 0.7 44.7 1.174 university Others from my cultural group are B41 1381 5.78 3.0 43.1 1.500 present on campus B42 My culture will be respected 1385 5.97 1.6 47.4 1.320 B43 I will feel at home at this university 1382 5.84 1.7 42.2 1.350 I will be able to express my culture at this B44 1383 5.77 2.2 39.8 1.402 university All population groups are represented on B45 1384 5.91 1.3 45.4 1.312 the campus B46 It is known that there is NO racism 1381 5.50 3.4 37.4 1.588 B47 I will be taught in English 1378 5.56 6.0 45.9 1.788 The distance of the university from home B48 1389 4.66 15.0 25.8 2.097 is not too far The university’s campus is easily B49 1390 5.47 3.5 34.6 1579 accessible (transport) The university’s campus is located near B50 1394 5.68 1.8 37.7 1.416 shops/malls The university’s campus is close to B51 1383 5.70 1.0 36.1 1.333 health services (hospitals, dentists, etc.) Accommodation (other than residence) is B52 1383 5.72 1.1 38.5 1.372 near campus The cost of tuition at the university is B53 1388 5.36 2.6 26.7 1.481 fairly priced Financial aid and scholarships are B54 1380 5.93 1.1 45.3 1.292 available at the university Studying at the university is value for B55 1396 5.86 1.4 42.0 1.324 money My parents/guardians are able to afford B56 1395 5.14 8.4 32.0 1.872 the university There will be the opportunity for part-time B57 1393 5.44 2.3 30.9 1.479 jobs (nearby campus)

Respondents overall exhibit the strongest level of agreement with the statements ‘It offers the courses that I am interested in.’ (mean = 6.55), ‘It offers courses with a

288 | good reputation’ (mean = 6.30), ‘It offers a variety of courses’ (mean = 6.25), and ‘It has good resources for students (computers, library, etc.)’ (mean = 6.22). Statements with the lowest level of agreement include ‘It is known that only the very intelligent study there’ (mean = 4.52), ‘only good students get in’ (mean = 4.52), ‘The distance of the university from home is not too far’ (mean = 4.66), ‘Only good students get in’ (mean = 4.67), and ‘My parents/guardians are able to afford the university’ (mean = 5.14).

Main finding C1: The type of courses, the variety of courses, the reputation of courses, and the university’s resources for students (computers, library, etc.) are the most important choice statements influencing respondents’ university choice.

6.7.2 Distribution of results

Before the hypotheses can be subjected to further statistical analysis, it is necessary to determine whether the 57 choice statements are normally distributed. As explained in Chapter 5 (Section 5.5.6.3) it is not imperative to measure distribution of the results if the sample size is larger than (30) (i.e. 1 476 respondents in this sample who indicated that they were planning to attend a university/university of technology) (Eiselen, Uys & Potgieter, 2007:79). However, the skewness and kurtosis for each of the statements in Section B are presented in Table 6.7.

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Table 6.7 Skewness and Kurtosis of choice statement results

State- Statement Skewness Kurtosis ment B1 It has excellent lecturers -1.013 0.993 B2 Its lecturers are knowledgeable experts 1.113 1.407 B3 It is known that only the very intelligent study there -0.386 -0.469 B4 Only good students get in -0.461 -0.298 B5 Academic programmes are nationally known -1.357 1.738 It offers many good cultural experiences (fine arts, music, B6 -1.209 0.971 theatre, etc.) B7 It offers a variety of courses -1.806 3.490 B8 It offers the courses that I am interested in -2.980* 9.900** B9 It offers courses with a good reputation -2.029* 5.007 B10 It offers courses that the job market is interested in -1.709 3.356 B11 It is committed to social service (involved with local community) -0.759 0.458 B12 Sport teams have a good reputation -1.028 0.412 B13 There are good sporting opportunities at the university -1.162 0.657 B14 It is committed to academic excellence -1.507 2.709 B15 It offers a world-class education -1.381 2.104 B16 Its qualifications are internationally recognised -1.498 2.208 B17 It is a reputable institution (in South Africa) -1.789 3.937 B18 Its qualifications are reputable -1.434 2.481 B19 It has a positive image with possible employers -1.566 3.287 Its admission requirements are high (i.e. students must do well in B20 -1.225 1.559 grade 12 to get in) B21 Hostel/residential facilities are attractive -1.093 0.809 B22 The campus looks attractive -1.430 1.973 B23 The buildings look attractive -1.277 1.597 B24 The campus looks prestigious -1.117 1.096 B25 Its buildings and grounds are well maintained -1.330 1.816 B26 The sports facilities are up to date -1.237 1.255 B27 It has good resources for students (computers, library, etc.) -1.824 4.329 B28 It offers a safe environment -1.372 2.173 B29 The recreation facilities (e.g. Student centre) look attractive -1.114 1.205 B30 I will find a job after completing my qualification -1.669 3.198 Studying at this university will make it possible to find a job after B31 -1.721 3.597 qualifying Studying at this university will enhance chances of employment B32 -1.710 3.841 opportunities B33 Studying at this university will increase career prospects -1.541 3.331 B34 Studying at this university will provide better salary prospects -1.306 1.944 B35 Current students’ perception of this university is positive -1.437 2.508 B36 My friends’ perception of this university is positive -1.336 1.665 B37 My friends also consider studying at this university -1.196 0.604 B38 My parents’ perception of this university is positive -1.645 2.805 B39 My schoolteachers’ perceptions of this university are positive -1.301 1.644

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State- Statement Skewness Kurtosis ment The particular university’s representatives are positive about the B40 -1.455 2.443 university B41 Others from my cultural group are present on campus -1.442 1.712 B42 My culture will be respected -1.532 2.350 B43 I will feel at home at this university -1.370 1.877 B44 I will be able to express my culture at this university -1.329 1.639 B45 All population groups are represented on the campus -1.338 1.736 B46 It is known that there is NO racism -1.023 0.474 B47 I will be taught in English -1.184 0.436 B48 The distance of the university from home is not too far -0.540 -1.010 B49 The university’s campus is easyily accessible (transport) -1.011 0.471 B50 The university’s campus is located near shops/malls -1.118 0.960 The university’s campus is close to health services (hospitals, B51 -0.981 0.640 dentists, etc.) B52 Accommodation (other than residence) is near campus -1.082 0.813 B53 The cost of tuition at the university is fairly priced -0.882 0.401 B54 Financial aid and scholarships are available at the university -1.345 1.743 B55 Studying at the university is value for money -1.343 1.782 B56 My Parents/guardians are able to afford the university -0.875 -0.250 B57 There will be the opportunity for part-time jobs (nearby campus) -0.894 0.436

The results indicate that only two statements (*statements B8 and B9) have a skewness of more than two, however all the other statements have a skewness of less than two, meaning that 55 of the 57 statements are normally distributed (West et al., 1995:74). Also, only one statement has a kurtosis of less than seven (**statement B8) and the remaining 56 statements have a kurtosis of less than seven, indicating that the 56 statements are normally distributed (West et al., 1995:74).

Researchers generally expect a certain level of skewness of the results since opinion-related statements are often positively or negatively skewed. For purposes of further analyses it was therefore decided to retain these statements.

In addition to determining whether the results are normally distributed, it is also necessary to employ an exploratory factor analysis (EFA) to determine if statements are relevant and if a large number of choice statements can be reduced to a more manageable number of factors. The following section presents the results of the EFA conducted for the 57 choice construct statements in more detail.

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6.7.3 Exploratory Factor Analysis (EFA)

Initially an EFA was conducted and 14 factors were uncovered (Appendix K). Since the number of factors was impractical for further analysis, the researcher drew on the theory (Chapter 3) and advise of a statistical consultant and it was decided to conduct an EFA reducing the statements to five factors. The results are subsequently presented. Table 6.8 portrays the KMO and Bartlett’s Test of Sphericity results.

Table 6.8 KMO and Bartlett’s test results for five factor extraction

Test Value of Forced EFA Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.959 Approx. Chi-square 34107.413 Bartlett’s Test of DF 1596 Sphericity Sig. 0.00

It is evident from Table 6.8 that the 57 statements are suitable for factor analysis. The KMO is 0.959, which is above 0.6, and Bartlett’s Test of Sphericity is 0.00, which is smaller than 0.05 (Pallant, 2010:187). The MSA for a particular statement indicated that no statements measured less than 0.6 (Eiselen et al., 2007:107), and therefore no statements were omitted at this stage.

The next output to investigate is the Communalities. Communalities provide information about how much of the variance in each item is explained. A low value, thus less than 0.3, could indicate that the statement does not fit well with the other statements. Three choice statements were omitted for further analysis, B2.47 (communality = 0.036), B2.3 (communality = 0.154) and B2.4 (communality = 0.131). Using Principal Axis Factoring as extraction method and Varimax for rotation, the total variance explained results, are presented in Table 6.9.

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Table 6.9 Total Variance explained Table

Extraction sums of Rotation sums of squared Initial eigenvalues squared loadings loadings Dimen- Cumu- Cumu- Cumu- sion % of % of % of Total lative Total lative Total lative Variance Variance Variance % % % 1 18 898 34 996 34 996 18 397 34 069 34 069 7 405 13 713 13 713 2 3 115 5 768 40 764 2 691 4 983 39 052 6 186 11 456 25 169 3 2 468 4 571 45 336 1 948 3 608 42 660 4 878 9 033 34 202 4 1 978 3 663 48 999 1 518 2 810 45 470 3 680 6 816 41 018 5 1 530 2 834 51 833 1 137 2 106 47 576 3 541 6 558 47 576

It is evident from Table 6.9 that five factors explain a total of 47.57% of the variance. Factor 1 (or component) contributes 34.06%, factor 2 contributes 39.05%, factor 3 contributes 42.66%, and factor 4 contributes 45.47% of the total variance. Factor 5 contributes 47.57% of the total variance explained.

Table 6.10 indicates the factor loadings for the statements for each of the five underlying choice factors with appropriate labels assigned to them, before any statements were omitted.

Table 6.10 Rotated Factor Matrix for the EFA including all the statements

Factor Factor loading Statement 0.685 It offers courses with a good reputation 0.556 It offers courses that the job market is interested in 0.600 It is committed to academic excellence 0.626 It offers a world-class education 0.628 Its qualifications are internationally recognised 0.642 It is a reputable institution (in South Africa) 0.686 Its qualifications are reputable 0.598 It has a positive image with possible employers 1. Proficiency 0.530 It has excellent lecturers (14 statements) 0.534 Its lecturers are knowledgeable experts 0.585 Academic programmes are nationally known 0.518 It offers a variety of courses 0.584 It offers the courses that I am interested in The particular university’s representatives are positive about the 0.447 university Omitted earlier because I will be taught in English commu-nality = 0.036

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Factor Factor loading Statement 0.444 My friends’ perception of this university is positive 0.518 My culture will be respected 0.506 I will feel at home at this university 0.575 I will be able to express my culture at the university 0.559 The university’s campus is easily accessible (transport) 0.527 The university’s campus is located near shops/malls The university’s campus is close to health services (hospitals, 0.499 dentists, etc.) 0.436 Accommodation (other than residence) is near the campus 0.568 The cost of tuition at the university is fairly priced 2. Accessibility 0.475 Studying at the university is value for money (social, cultural and financial) 0.531 My parents/guardians are able to afford the university (21 statements) 0.525 There will be the opportunity for part-time jobs (nearby campus) 0.471 The distance of the university from home is not too far 0.426 My school teachers’ perception of this university is positive 0.427 All population groups are represented on the campus 0.427 Others from my cultural group are present on campus 0.419 Current students’ perception of this university is positive 0.403 Financial aid and scholarships are available at the university 0.396 It is known that there is NO racism 0.367 My friends also consider to study at this university 0.331 My parents’ perception of this university is positive 0.724 The campus looks attractive 0.754 The buildings look attractive 3. Physical 0.721 The campus looks prestigious evidence 0.635 Its buildings and grounds are well maintained (7 statements) 0.470 The recreation facilities e.g. student centre) looks attractive 0.449 It has good resources for students (computers, library, etc.) 0.454 It offers a safe environment 0.843 Sport teams have a good reputation 0.844 There are good sporting opportunities at the university 0.695 The sports facilities are up to date 0.484 Hostel/residential facilities are attractive 4. Prestige/ 0.391 It is committed to social service (involved with local community) prominence Its admission requirements are high (i.e. students must do well (9 statements) 0.362 in grade 12 to get in) It offers many good cultural experiences (fine arts, music, 0.359 theatre, etc.) 0.311 Only good students get in 0.269 It is known that only the very intelligent study there 0.620 I will find a job after completing my qualification Studying at this university will make it possible to find a job after 0.673 5. Future qualifying Studying at this university will enhance chances of employment employability 0.744 (5 statements) opportunities 0.645 Studying at this university will increase career prospects 0.620 Studying at this university will provide better salary prospects

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It is evident from Table 6.10 that factor 1 (proficiency) consists of 14 statements, with factor loadings all above a 0.43, factor 2 (accessibility) consists out of 21 statements of which eight statements have a factor loading lower than 0.43, factor 3 (physical evidence) consists of 7 statements, with factor loadings all above a 0.43, factor 4 (prestige/prominence) consists of nine statements, of which five factors have a factor loading lower than 0.43, and factor 5 (future employability) consists of five statements, with factor loadings all above 0.43.

Table 6.11 indicates all the choice statements (or items) with loadings above 0.43 and it excludes the choice statements with factor loadings below 0.43 since such low loadings are considered poor (Chapter 5, Section 5.5.6.3).

Table 6.11 Rotated Factor Matrix for the EFA including retained statements

Factor Factor Statement loading 0.685 It offers courses with a good reputation 0.556 It offers courses that the job market is interested in 0.600 It is committed to academic excellence 0.626 It offers a world-class education 0.628 Its qualifications are internationally recognised 0.642 It is a reputable institution (in South Africa) 0.686 Its qualifications are reputable 1. Proficiency (14 statements) 0.598 It has a positive image with possible employers 0.530 It has excellent lecturers 0.534 Its lecturers are knowledgeable experts 0.585 Academic programmes are nationally known 0.518 It offers a variety of courses 0.584 It offers the courses that I am interested in The particular university’s representatives are positive about the 0.447 university

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Factor Factor Statement loading 0.444 My friends’ perception of this university is positive 0.518 My culture will be respected 0.506 I will feel at home at this university 0.575 I will be able to express my culture at the university 0.559 The university’s campus is easily accessible (transport)

2. Accessibility 0.527 The university’s campus is located near shops/malls (social, cultural The university’s campus is close to health services (hospitals, dentists, 0.499 and financial) etc.) (13 statements) 0.436 Accommodation (other than residence) is near the campus 0.568 The cost of tuition at the university is fairly priced 0.475 Studying at the university is value for money 0.531 My parents/guardians are able to afford the university 0.525 There will be the opportunity for part-time jobs (nearby campus) 0.471 The distance of the university from home is not too far 0.724 The campus looks attractive 0.754 The buildings look attractive 0.721 The campus looks prestigious 3. Physical evidence 0.635 Its buildings and grounds are well maintained (7 statements) 0.470 The recreation facilities (e.g. student centre) look attractive 0.449 It has good resources for students (computers, library, etc.) 0.454 It offers a safe environment 0.843 Sport teams have a good reputation 4. Prestige/ 0.844 There are good sporting opportunities at the university prominence (4 statements) 0.695 The sports facilities are up to date 0.484 Hostel/residential facilities are attractive 0.620 I will find a job after completing my qualification Studying at this university will make it possible to find a job after 0.673 5. Future qualifying Studying at this university will enhance chances of employment employability 0.744 (5 statements) opportunities 0.645 Studying at this university will increase career prospects 0.620 Studying at this university will provide better salary prospects

Table 6.11 indicates that all 14 statements of factor 1 (proficiency), 13 of the initial 21 statements of factor 2 (accessibility), all 7 statements of factor 3 (physical evidence), 4 of the original 9 statements of factor 4 (prestige/prominence) and all 5 statements of factor 5 (future employability) were retained. Thus 42 of the original 57 choice

296 | statements, representing five choice factors, were included for validation using a 2nd order EFA of which the results are subsequently reported.

6.7.3.1 Second Order Exploratory Factor Analysis (EFA)

A 2nd order EFA is a further factor analysis of the results of the initial factor analysis to assure that the set of common factors are entirely responsible for the measured variables (Chen, Sousa & West, 2005:487; Tisak & Tisak, 2005).

Table 6.12 portrays the KMO and Bartlett’s Test of Sphericity results.

Table 6.12 KMO and Bartlett’s test results for 2nd order Factor Analysis

Value of Value of Value of Value of Value of Prestige/ Future Proficiency Accessi- Physical nd nd Promi- employ- Test 2 order bility 2 evidence nd nd nence ability 2 EFA order EFA 2 order nd 2 order order EFA (1) (2) EFA (3) EFA (4) (5) Kaiser-Meyer-Olkin Measure of Sampling 0.932 0.898 0.886 0.773 0.853 Adequacy. Approx. Chi- 9056.094 6237.051 6127.421 2809.741 4186.986 Bartlett’s Test square of Sphericity DF 91 78 21 6 10 Sig. 0.000 0.000 0.000 0.000 0.000

Table 6.12 indicates that the KMO value of all five factors is above 0.6 (factor 1 = 0.932; factor 2 = 0.898; factor 3 = 0.886; factor 4 = 0.773; factor 5 = 0.853), and that the Bartlett’s Test of Sphericity of all the factors is smaller than 0.05 (all factors = 0.000), and thus serve as indication that all the statements in each choice factor is suitable for factor analysis (Pallant, 2010:187).

The MSA for all the statements contained in the five factors (proficiency, accessibility, physical evidence, prestige/prominence and future employability) indicates that no statements measured less than 0.6 (Eiselen et al., 2007:107), and therefore no statements were omitted at this stage.

With regard to the communalities, none of the particular statements for each for the five choice factors (proficiency, accessibility, physical evidence, prestige/prominence

297 | and future employability) had a value less than 0.3, and therefore no statements were omitted from any of the five factors (Pallant, 2010:198).

To determine if any of the five factors (proficiency, accessibility, physical evidence, prestige/prominence and future employability) obtained through the EFA should be considered for further factor extraction, the total variance explained output of the 2nd order EFA was considered.

The eigenvalue output of proficiency (factor 1) indicated that this factor consists of two sub-factors and that these two sub-factors explain a total of 51.06% of the variance. On analysing the Rotated Factor Matrix, it is evident that sub-factor 1 consists of 12 statements and sub-factor 2 consists of two statements. As it is difficult to work with a sub-factor with only two statements, it was decided to retain the proficiency factor as one factor consisting of 14 statements for further analysis. The eigenvalue output of accessibility (factor 2) indicated that this factor consists of three possible sub-factors, with the three sub-factors explaining a total of 48.22% of the variance. Table 6.13 portrays the three sub-factors for the accessibility factor (factor 2) as portrayed by the Rotated Factor Matrix output.

Table 6.13 Three sub-factors for the accessibility factor (Factor 2)

Cultural acceptance My friends’ perception of this university is positive My culture will be respected I will feel at home at this university I will be able to express my culture at the university Accessibility-location The university’s campus is easily accessible (transport) The university’s campus is located near shops/malls The university’s campus is close to health services (hospitals, dentists, etc.) Accommodation (other than residence) is near the campus Accessibility-price The cost of tuition at the university is fairly priced Studying at the university is value for money My parents/guardians are able to afford the university There will be the opportunity for part-time jobs (nearby campus) The distance of the university from home is not too far

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On analysing the Rotated Factor Matrix, it is evident that sub-factor one consists of four statements explaining culture, sub-factor two consists of four statements explaining location, and sub-factor three consists of five statements explaining price (Table 6.13). At this point it was decided to split factor 2 (accessibility) into three factors and to label them culture accessibility, accessibility-location and accessibility- price.

The eigenvalue for physical evidence (factor 3) contributed 57.74% of the total variance, prestige/prominence (factor 4) contributed 61.17% of the total variance, and future employability (factor 5) contributed 63.59% of total variance. On further investigating factors 3, 4 and 5’s Factor Matrices, it is evident that none of these factors should be further divided into additional factors and these three factors remain the same for CFA.

It can be concluded from the 2nd order EFA findings that the initial five factors of proficiency, accessibility, physical evidence, prestige/prominence and future employability could further be refined into seven choice factors. As already explained (Section 6.4.3.2), it was decided to keep proficiency with its 14 statements, physical evidence with its seven statements, prestige/prominence with its four statements and future employability with its five statements, however accessibility (factor 2) with its original 13 statements was further refined and sub-divided into three factors. Accessibility’s three new factors are portrayed in Table 6.13 and they are labelled: cultural acceptance (4 statements), accessibility-location (4 statements) and accessibility-price (5 statements). Employing a confirmatory factor analysis (CFA) will further validate these seven new factors with their relative statements. Table 6.14 portrays all the seven factors with their relevant statements retained for confirmatory factor analysis (CFA).

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Table 6.14 Statements retained after 2nd order EFA

Proficiency (factor 1) It offers courses with a good reputation It offers courses that the job market is interested in It is committed to academic excellence It offers a world-class education Its qualifications are internationally recognised It is a reputable institution (in South Africa) Its qualifications are reputable It has a positive image with possible employers It has excellent lecturers Its lecturers are knowledgeable experts Academic programmes are nationally known It offers a variety of courses It offers the courses that I am interested in The particular university’s representatives are positive about the university Cultural acceptance (factor 2) My friends’ perception of this university is positive My culture will be respected I will feel at home at this university I will be able to express my culture at the university Accessibility-location (factor 3) The university’s campus is easily accessible (transport) The university’s campus is located near shops/malls The university’s campus is close to health services (hospitals, dentists, etc.) Accommodation (other than residence) is near the campus Accessibility-price (factor 4) The cost of tuition at the university is fairly priced Studying at the university is value for money My parents/guardians are able to afford the university There will be the opportunity for part-time jobs (nearby campus) The distance of the university from home is not too far Physical evidence (factor 5) The campus looks attractive The buildings look attractive The campus looks prestigious Its buildings and grounds are well maintained The recreation facilities (e.g. student centre) looks attractive It has good resources for students (computers, library, etc.) It offers a safe environment Prestige/prominence (factor 6) Sport teams have a good reputation There are good sporting opportunities at the university The sports facilities are up to date Hostel/residential facilities are attractive

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Future employability (factor 7) I will find a job after completing my qualification Studying at this university will make it possible to find a job after qualifying Studying at this university will enhance chances of employment opportunities Studying at this university will increase career prospects Studying at this university will provide better salary prospects

6.7.4 Confirmatory Factor Analysis (CFA)

By means of confirmatory factor analysis (CFA), the seven factors of the choice construct - as initially extracted through an exploratory factor analysis (EFA) (Section 6.4.3.1) and validated through a 2nd order exploratory factor analysis (EFA) (Section 6.4.3.2) - were verified and refined (Ary et al., 2006:393). In order to determine the extent of the fit-of-the-measurement model (verification), two indices namely, the root mean square error of approximation (RMSEA) and the comparative fit index (CFI) are considered. The model fit is furthermore improved (refined) by the removal of one or more statements from a factor. Table 6.15 presents the CFA results, which include the number of statements omitted, and number of statements remaining within each factor, as well as the fit indices.

Table 6.15 Fit indices for CFA models of the choice factors

Number of Statements Factor remaining CFI RMSEA omitted statements 1. Reputation (was proficiency) 6 8 0.909 0.075 2. Cultural acceptance None 4 1.000 0.007 3. Accessibility – location None 4 0.977 0.087 4. Accessibility – price 1 4 0.998 0.023 5. Physical evidence 2 5 0.969 0.091 6. Prestige/prominence None 4 0.977 0.104 7. Future employability None 5 0.947 0.096

It is evident from the fit indices that all seven factors exhibit a good fit after a number of statements were omitted from three of the seven factors. The fit indices of the CFA indicate that the cultural acceptance factor (factor 2) exhibits the best fit to the model (CFI = 1.000; RMSEA = 0.007). The accessibility-price factor (factor 4) portrays the second best fit to the model (CFI = 0.998; RMSEA = 0.023). The factor with the poorest fit in relation to its factor counterparts is the prestige/prominence factor (factor 6) that portrays a RMSEA value of 0.104, however its CFI value still

301 | indicates (0.977) a very good fit, and for this reason this factor was retained for further analysis. Table 6.16 provides an exposition of the final set of factors and corresponding statements for the choice construct.

Table 6.16 Final set of factors and corresponding statements retained after CFA

Reputation (was proficiency) (factor 1) It offers courses with a good reputation It offers courses that the job market is interested in It is committed to academic excellence It offers a world-class education Its qualifications are internationally recognised It is a reputable institution (in South Africa) Its qualifications are reputable It has a positive image with possible employers Cultural acceptance (factor 2) My friends’ perception of this university is positive My culture will be respected I will feel at home at this university I will be able to express my culture at the university Accessibility-location (factor 3) The university’s campus is easily accessible (transport) The university’s campus is located near shops/malls The university’s campus is close to health services (hospitals, dentists, etc.) Accommodation (other than residence) is near the campus Accessibility-price (factor 4) The cost of tuition at the university is fairly priced Studying at the university is value for money My parents/guardians are able to afford the university There will be the opportunity for part-time jobs (nearby campus) Physical evidence (factor 5) The campus looks attractive The buildings look attractive The campus looks prestigious Its buildings and grounds are well maintained The recreation facilities (e.g. student centre) look attractive Prestige/prominence (factor 6) Sport teams have a good reputation There are good sporting opportunities at the university The sports facilities are up to date Hostel/residential facilities are attractive

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Table 6.14 portrays that factor 1 originally consisted of 14 (after the 2nd order EFA) statements, however the CFA results indicated that six of the statements had to be omitted, leaving eight statements to improve fit (Table 6.15 & Table 6.16). After examining the content of the remaining eight statements retained as indicated in Table 6.16, it was evident that these statements related more to the perceived reputation of the preferred university and not to proficiency anymore. For this reason the label was changed to reputation. The CFA further indicated that one statement had to be omitted from factor 4 (the accessibility-price factor), and two statements had to be omitted from factor 5 (the physical evidence factor) to improve fit. After these statements had been omitted, accessibility-price consisted of four statements and physical evidence of five statements (Table 6.15 & Table 6.16). The remaining four choice factors (cultural acceptance, accessibility–location, prestige/prominence and future employability) retained all their statements.

Main finding C2: The results of a CFA indicate that all seven factors of the choice construct exhibit a good fit after a number of statements were omitted from three of the factors.

6.7.5 Reliability of choice factors

In reliability analysis the researcher is interested in how well the responses of each choice statement in a factor (or scale of items) corresponds to that of the other statements and to the choice scale as a whole. A Cronbach’s alpha coefficient is calculated to indicate the reliability of the scale. A value above 0.7 are considered acceptable, however, values above 0.8 are preferable (Pallant, 2010:100). A value below 0.7 is not considered reliable (Eiselen et al., 2007:112). For the purpose of this study, the Cronbach’s alpha coefficient of the scales of each of the seven factors is portrayed in Table 6.17.

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Table 6.17 Cronbach’s alpha coefficients for the seven choice factors

Factor Number of Cronbach’s alpha coefficient statements 1. Reputation 8 0.894 2. Cultural acceptance 4 0.829 3. Accessibility-location 4 0.781 4. Accessibility-price 4 0.723 5. Physical evidence 5 0.901 6. Prestige/prominence 4 0.854 7. Future employability 5 0.888

It is evident from Table 6.17 that all the Cronbach’s alpha coefficients for all seven factors are higher than 0.7 and consequently the results are considered reliable. The next step is to calculate and report on the overall mean scores for each of the seven factors.

Main finding C3: The results of the Cronbach’s alpha coefficients for the seven choice factors indicate that the scales of all seven choice factors are reliable.

6.7.6 Overall mean score for factors

Table 6.18 presents the results of the overall mean score for each of the seven factors, as well as if they are below, equal or above mid-point of the scale. Since a seven-point Likert-type scale has been used in the questionnaire, and the mid-point is 3.5.

Table 6.18 Overall mean score for each of the seven choice factors

Factor Number of statements Overall mean score 1. Reputation 8 6.16 2. Cultural acceptance 4 5.85 3. Accessibility-location 4 5.65 4. Accessibility-price 4 5.46 5. Physical evidence 5 5.93 6. Prestige/prominence 4 5.48 7. Future employability 5 6.07

It is evident from the results presented in Table 6.18 that all seven choice factors realised a mean of higher to the midpoint of the scale (3.5). The highest mean score per statement is for factor 1, reputation (mean = 6.16), the second highest for factor

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7, future employability (mean = 6.07) and the third highest mean score is for factor 5, physical evidence (mean = 5.93). Although it is above the mid-point of 3.5, the lowest mean score per statement is for factor 4, accessibility-price (mean = 5.46).

Main finding C4: Each of the seven choice factors realised an overall mean score above the mid-point (3.5), indicating that all seven factors play an important role in influencing prospective university students’ university choice.

6.7.7 Testing for significant differences between groups

This section presents the results for the alternative hypotheses formulated for the study. The section focuses specifically on reporting on significant differences between groups, based upon demographics with regard to the main constructs of the study, namely the factors influencing university choice.

6.7.7.1 Hypothesis 1

H1. Female and male prospective university students differ significantly in terms of their level of agreement regarding the influence of different choice factors on their university choice.

Independent-samples t-tests were performed to determine whether significant differences exist between males and female regarding the influence of the different choice factors on their university choice. Table 6.19 presents the subsequent results for the seven choice factors concerned.

Table 6.19 Gender-based differences with respect to the choice factors

Choice factor Gender N Mean t-value p-value

Female 808 6.22 1. Reputation 5.206 0.000* Male 639 5.98 Female 795 5.91 2. Cultural acceptance 3.465 0.001* Male 623 5.71 Female 794 5.72 3. Accessibility – location 3.234 0.001* Male 622 5.52 Female 794 5.50 4. Accessibility – price 1.826 0.068 Male 623 5.39 Female 800 6.00 5. Physical evidence 3.584 0.000* Male 624 5.80

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Choice factor Gender N Mean t-value p-value

Female 803 5.54 6. Prestige/prominence 2.236 0.026* Male 639 5.38 Female 800 6.12 7. Future employability 2.670 0.008* Male 626 5.98 * p-value < 0.050

Based upon the results presented in Table 6.19, the following can be reported:  Female prospective students (mean = 6.22) agree significantly more than male prospective students (mean = 5.98; p-value = 0.000) that reputation influences their university choice.  Female prospective students (mean = 5.91) agree significantly more than male prospective students (mean = 5.71; p-value = 0.001) that cultural acceptance influences their university choice.  Female prospective students (mean = 5.72) agree significantly more than male prospective students (mean = 5.52; p-value = 0.001) that accessibility-location influences their university choice.  There is not a significant difference between female prospective students (mean = 5.50) and male prospective students (mean = 5.39; p-value = 0.068) with regard to their level of agreement that accessibility-price influences their university choice.  Female prospective students (mean = 6.00) agree significantly more than male prospective students (mean = 5.80; p-value = 0.000) that physical evidence influences their university choice.  Female prospective students (mean = 5.54) agree significantly more than male prospective students (mean = 5.38; p-value = 0.026) that prestige/prominence influences their university choice.  Female prospective students (mean = 6.12) agree significantly more than male prospective students (mean = 5.98; p-value = 0.008) that future employability influences their university choice.

Hypothesis 1 that female and male prospective university students differ significantly in terms of the extent to which different choice factors influence their decision- making, can therefore be accepted with respect to six of the seven choice factors

306 | namely, reputation, cultural acceptance, accessibility-location, physical evidence, prestige/prominence and future employability.

Main finding CH1: Female prospective students agree significantly more than male prospective students that six choice factors namely reputation, cultural acceptance, accessibility-location, physical evidence, prestige/prominence and future employability influence their university choice.

6.7.7.2 Hypothesis 2

Kruskal-Wallis Tests were performed to determine whether such significant differences exist. Where significant differences were evident, Mann-Whitney U Tests were performed to uncover the pairs of home language groups between which the significant differences exist. The resultant p-values were interpreted once the Bonferroni adjustments were made. Table 6.20 presents the results of the Kruskal- Wallis Tests for the seven choice factors concerned. Appendix L contains the detailed results with respect to the subsequent Mann-Whitney U Tests performed.

Table 6.20 Home language differences with respect to the choice factors

Choice factor Home language N Mean rank p-value Afrikaans 801 739.23

1. Reputation English 283 748.52 0.083 Indigenous 370 686.03 languages Afrikaans 786 770.07

2. Cultural acceptance English 280 680.02 0.000* Indigenous 360 616.04 languages Afrikaans 785 760.35

3. Accessibility – location English 281 665.91 0.000* Indigenous 358 644.15 languages Afrikaans 783 794.72

4. Accessibility – price English 281 710.12 0.000* Indigenous 360 535.53 languages

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Choice factor Home language N Mean rank p-value Afrikaans 790 733.78

5. Physical evidence English 280 677.61 0.134 Indigenous 362 708.87 languages Afrikaans 800 766.90

6. Prestige/prominence English 281 644.26 0.000* Indigenous 368 695.57 languages Afrikaans 789 740.03

7. Future employability English 280 711.29 0.036* Indigenous 365 673.55 languages * p-value < 0.050

Based upon the results presented in Table 6.20, the following can be reported:

 There is not a significant difference between prospective university students with different home languages with regard to their level of agreement that reputation influences their university choice (p-value = 0.083).  There are significant differences between prospective university students with different home languages with regard to their level of agreement that cultural acceptance influences their university choice (p-value = 0.000). Afrikaans prospective students (mean rank = 770.07) agree significantly more than English prospective students (mean rank = 680.02; p-value = 0.001) and prospective university students who speak an indigenous home language (mean rank = 616.04; p-value = 0.000) that cultural acceptance influences their university choice.  There are significant differences between prospective university students with different home languages with regard to their level of agreement that accessibility-location influences their university choice (p-value = 0.000). Afrikaans prospective students (mean rank = 760.35) agree significantly more than English prospective students (mean rank = 665.91; p-value = 0.001) and prospective university students who speak an indigenous home language (mean rank = 644.15; p-value = 0.000) that accessibility-location influences their university choice.  There are significant differences between prospective university students with different home languages with regard to their level of agreement that

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accessibility-price influences their university choice (p-value = 0.000). Afrikaans prospective students (mean rank = 794.72) agree significantly more than English prospective students (mean rank = 710.12; p-value = 0.002) and prospective university students who speak an indigenous home language (mean rank = 535.53; p-value = 0.000) that accessibility-price influences their university choice. It is also evident that English prospective students (mean rank = 710.12) agree significantly more than those prospective university students who speak an indigenous home language (mean rank = 535.53; p-value = 0.000) that accessibility-price influences their university choice.  There is not a significant difference between prospective university students with different home languages with regard to their level of agreement that physical evidence influences their university choice (p-value = 0.134).  There are significant differences between prospective university students with different home languages with regard to their level of agreement that prestige/prominence influences their university choice (p-value = 0.000). Afrikaans prospective students (mean rank = 733.78) agree significantly more than English prospective students (mean rank = 677.61; p-value = 0.000) and prospective university students who speak an indigenous home language (mean rank = 708.87; p-value = 0.005) that prestige/prominence influences their university choice.  There is a significant difference between prospective university students with different home languages with regard to their level of agreement that future employability influences their university choice (p-value = 0.036). Afrikaans prospective students (mean rank = 740.03) agree significantly more than those prospective students who speak an indigenous language (mean rank = 673.55; p- value = 0.010) that future employability influences their university choice.

Hypothesis 2 that prospective students with different home languages differ significantly in terms of the extent to which different choice factors influence their decision-making, can therefore be accepted with respect to five of the seven choice factors namely, cultural acceptance, accessibility-location, accessibility-price, prestige/prominence, and future employability.

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Main finding CH2a: Afrikaans prospective university students agree significantly more than English prospective university students and those prospective university students speaking an indigenous language that four choice factors namely cultural acceptance, accessibility-location, accessibility-price and prestige/prominence influence their university choice.

Main finding CH2b: Afrikaans prospective university students furthermore agree significantly more than those prospective university students speaking an indigenous language that future employability influences their university choice.

Main finding CH2c: English prospective university students agree significantly more than those prospective university students speaking an indigenous language that accessibility-price influences their university choice.

6.7.7.3 Hypothesis 3

Independent-samples t-tests were performed to determine whether significant differences exist between students who take mathematics as a subject in grade 12 and those students who take mathematics literacy as a subject in grade 12 regarding the influence of the different choice factors on their university choice. Table 6.21 presents the subsequent results for the seven choice factors concerned.

Table 6.21 Subject-choice (mathematics vs mathematics literacy) differences with respect to the choice factors

Maths or Maths Choice factor N Mean t-value p-value literacy Maths 911 6.23 1.Reputation 6.117 0.000* Maths literacy 543 5.93 Maths 901 5.90 2.Cultural acceptance 3.301 0.001* Maths literacy 523 5.76

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Maths or Maths Choice factor N Mean t-value p-value literacy Maths 900 5.73 3.Accessibility – location 3.938 0.000* Maths literacy 522 5.49 Maths 901 5.53 4.Accessibility – price 3.351 0.001* Maths literacy 521 5.31 Maths 902 5.94 5.Physical evidence 1.128 0.260 Maths literacy 528 5.88 Maths 909 5.55 6.Prestige/prominence 2.784 0.005* Maths literacy 539 5.34 Maths 905 6.15 7.Future employability 4.151 0.000* Maths literacy 527 5.92 * p-value < 0.050

Based upon the results presented in Table 6.21, the following can be reported:  Prospective students who take mathematics as a subject in grade 12 (mean = 6.23) agree significantly more than prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.93; p-value = 0.000) that reputation influences their university choice.  Prospective students who take mathematics as a subject in grade 12 (mean = 5.90) agree significantly more than prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.76; p-value = 0.001) that cultural acceptance influences their university choice.  Prospective students who take mathematics as a subject in grade 12 (mean = 5.73) agree significantly more than prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.49; p-value = 0.000) that accessibility- location influences their university choice.  Prospective students who take mathematics as a subject in grade 12 (mean = 5.53) agree significantly more than prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.31; p-value = 0.001) that accessibility- price influences their university choice.  There is not a significant difference between prospective students who take mathematics as a subject in grade 12 (mean = 5.94) and prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.88; p-value = 0.260) with regard to their level of agreement that physical evidence influences their university choice.  Prospective students who take mathematics as a subject in grade 12 (mean =

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5.55) agree significantly more than prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.34; p-value = 0.005) that prestige/prominence influences their university choice.  Prospective students who take mathematics as a subject in grade 12 (mean = 6.15) agree significantly more than prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.92; p-value = 0.000) that future employability influences their university choice.

Hypothesis 3 that prospective university students who take mathematics as a subject in grade 12 differ significantly from prospective university students who take mathematics literacy as a subject in grade 12 in terms of the extent to which different choice factors influence their decision-making, can therefore be accepted with respect to six of the seven choice factors namely, reputation, cultural acceptance, accessibility-location, accessibility-price, prestige/prominence and future employability.

Main finding CH3: Prospective university students who take mathematics as a subject in grade 12 agree significantly more than prospective university students who take mathematics literacy as a subject in grade 12 that six choice factors namely reputation, cultural acceptance, accessibility-location, accessibility-price, prestige/prominence and future employability influence their university choice.

6.7.7.4 Hypothesis 4

Kruskal-Wallis Tests were performed to determine whether such significant differences exist. Where significant differences were evident, Mann-Whitney U Tests were performed to uncover the pairs of expected average grades for grade 12 groups between which the significant differences exist. The resultant p-values were interpreted once the Bonferroni adjustments were made. Table 6.22 presents the results of the Kruskal-Wallis Tests for the seven choice factors concerned. Appendix

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L contains the detailed results with respect to the subsequent Mann-Whitney U Tests performed.

Table 6.22 Expected average grades for grade 12 differ with respect to the choice factors

Choice factor Expected grade N Mean rank p-value A (80%-100%) 295 900.47 B (70%-79%) 442 738.40 1.Reputation 0.000* C (60%-69%) 468 649.51 D-F (34%-59%) 230 584.12 A (80%-100%) 293 795.67 B (70%-79%) 433 735.55 2.Cultural acceptance 0.000* C (60%-69%) 455 656.83 D-F (34%-59%) 224 612.66 A (80%-100%) 293 795.37 B (70%-79%) 433 722.87 3.Accessibility – location 0.000* C (60%-69%) 454 653.98 D-F (34%-59%) 223 636.57 A (80%-100%) 292 760.62 B (70%-79%) 433 717.41 4.Accessibility – price 0.001* C (60%-69%) 456 692.80 D-F (34%-59%) 222 613.75 A (80%-100%) 293 757.37 B (70%-79%) 435 720.61 5.Physical evidence 0.013* C (60%-69%) 456 687.28 D-F (34%-59%) 227 649.32 A (80%-100%) 294 795.74 B (70%-79%) 441 722.39 6.Prestige/prominence 0.000* C (60%-69%) 468 701.47 D-F (34%-59%) 226 623.58 A (80%-100%) 293 828.99 B (70%-79%) 435 732.24 7.Future employability 0.000* C (60%-69%) 458 658.93 D-F (34%-59%) 227 598.16 * p-value < 0.050

Based upon the results presented in Table 6.22, the following can be reported:

 There are significant differences between prospective university students with different expected grades for grade 12 with regard to their level of agreement that reputation influences their university choice (p-value = 0.000). Students expecting an A grade average (mean rank = 900.47), agree significantly more than students expecting a B grade average (mean rank = 738.49; p-value = 0.000), students

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expecting a C grade average (mean rank = 649.51; p-value = 0.000), and students expecting a D-F grade average (mean rank = 584.12; p-value = 0.000) that reputation influences their university choice. Students expecting a B grade average (mean rank = 738.40) also agree significantly more than students expecting a C grade average (mean rank = 649.51; p-value = 0.001) and students expecting a D-F grade average (mean rank = 584.12; p-value = 0.000) that reputation influences their university choice.  There are significant differences between prospective university students with different expected grades for grade 12 with regard to their level of agreement that cultural acceptance influences their university choice (p-value = 0.000). Students expecting an A grade average (mean rank = 795.67) agree significantly more than students expecting a C grade average (mean rank = 656.83; p-value = 0.000) and students expecting a D-F grade average (mean rank = 612.66; p- value = 0.000) that cultural acceptance influences their university choice. Students expecting a B grade average (mean rank =735.55) also agree significantly more than students expecting a C grade average (mean rank = 656.83; p-value = 0.003) and students expecting a D-F grade average (mean rank = 612.66; p-value = 0.000) that cultural acceptance influences their university choice.  There are significant differences between prospective university students with different expected grades for grade 12 with regard to their level of agreement that accessibility-location influences their university choice (p-value = 0.000). Students expecting an A grade average (mean rank = 795.37) agree significantly more than students expecting a C grade average (mean rank = 653.98; p-value = 0.000), and students expecting a D-F grade average (636.57; p-value = 0.000) that accessibility-location influences their university choice. Students expecting a B grade average (mean rank = 722.87) agree significantly more than students expecting a C grade average (mean rank = 653.98; p-value = 0.010), and students expecting a D-F grade average (636.57; p-value = 0.009) that accessibility-location influences their university choice.  There are significant differences between prospective university students with different expected grades for grade 12 with regard to their level of agreement that accessibility-price influences their university choice (p-value = 0.001). Students

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expecting an A grade average (mean rank = 760.62) agree significantly more than students expecting a D-F grade average (mean rank = 613.75; p-value = 0.000) that accessibility-price influences their university choice. Students expecting a B grade average (mean rank = 717.41) also agree significantly more than students expecting a D-F grade average (mean rank = 613.75; p-value = 0.002) that accessibility-price influences their university choice.  There is a significant difference between prospective university students with different expected grades for grade 12 with regard to their level of agreement that physical evidence influences their university choice (p-value = 0.013). Students expecting an A grade average (mean rank = 757.37) agree significantly more than students expecting a D-F grade average (mean rank = 649.32; p-value = 0.003) that physical evidence influences their university choice.  There are significant differences between prospective university students with different expected grades for grade 12 with regard to their level of agreement that prestige/prominence influences their university choice (p-value = 0.000). Students expecting an A grade average (mean rank = 795.74) agree significantly more than students expecting a C grade average (mean rank = 701.47; p-value = 0.002), and students expecting a D-F grade average (mean rank = 623.58;p- value = 0.000) that prestige/prominence influences their university choice. Students expecting a B grade average (mean rank = 722.39) agree significantly more than students expecting a D-F grade average (mean rank = 623.58; p-value = 0.003) that prestige/prominence influences their university choice.  There are significant differences between prospective university students with different expected grades for grade 12 with regard to their level of agreement that future employability influences their university choice (p-value = 0.000). Students expecting an A grade average (mean rank = 828.99) agree significantly more than students expecting a B grade average (mean rank = 732.24; p-value = 0.002), than students expecting a C grade average (mean rank = 658.93; p-value = 0.00), and students expecting a D-F grade average (mean rank = 598.16; p- value = 0.000) that future employability influences their university choice. Also, students expecting a B grade average (mean rank = 732.24) agree significantly more than students expecting a C grade average (mean rank = 658.93; p-value =

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0.006) and students expecting a D-F grade average (mean rank = 598.16; p- value = 0.000) that future employability influences their university choice.

Hypothesis 4 that prospective students with different expected grades for grade 12 differ significantly in terms of the extent to which different choice factors influence their decision-making, can therefore be accepted with respect to all seven choice factors namely, reputation, cultural acceptance, accessibility-location, accessibility- price, physical evidence, prestige/prominence, and future employability.

Main finding CH4a: Students expecting an A grade average and students expecting a B grade average agree significantly more than students expecting a C grade average and a D-F average that two choice factors namely reputation and future employability influence their university choice. Also, students expecting an A grade average agree significantly more than students expecting a B grade average that reputation and future employability influence their university choice.

Main finding CH4b: Students expecting an A grade average and students expecting a B grade average agree significantly more than students expecting a C grade average and a D-F average that two choice factors namely cultural acceptance and accessibility-location influence their university choice.

Main finding CH4c: Students expecting an A grade average agree significantly more than students expecting a C grade average and a D-F average that prestige/prominence influences their university choice. Also, students expecting a B grade average agree significantly more than students expecting a D-F grade average that prestige/prominence influences their university choice.

Main finding CH4d: Students expecting an A grade average and students expecting a B grade average agree significantly more than students expecting a D-F grade average that accessibility-price influences their university choice.

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Main finding CH4e: Students expecting an A grade average agree significantly more than students expecting a D-F grade average that physical evidence influences their university choice.

Main finding CH4f: There are no significant differences between students expecting a C grade average and students expecting a D-F grade average regarding choice factors that influence their university choice.

6.7.7.5 Hypothesis 5

To test this hypothesis, Mann-Whitney U Tests were performed to determine whether such differences exist between prospective students with parents who attended a university and those prospective university students with parents who did not attend a university. Table 6.23 presents the results of the Mann-Whitney U Tests for the seven choice factors concerned.

Table 6.23 Prospective students with parents who attended and those with parents who did not attend university differ with respect to the choice factors

Parents who went vs parents Choice factor N Mean rank p-value who did not go to university Yes 942 752.00 1.Reputation 0.000* No 490 648.25 Yes 922 736.48 2.Cultural acceptance 0.000* No 481 635.91 Yes 920 737.36 3.Accessibility – location 0.000* No 480 629.86 Yes 920 761.26 4.Accessibility – price 0.000* No 481 585.73 Yes 924 722.13 5.Physical evidence 0.028* No 485 672.37 Yes 938 729.10 6.Prestige/prominence 0.047* No 488 683.51

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Parents who went vs parents Choice factor N Mean rank p-value who did not go to university Yes 925 731.66 7.Future employability 0.001* No 486 657.16 * p-value < 0.050

Based upon the results presented in Table 6.23, the following can be reported:

 Prospective university students with parents who went to university (mean rank = 752.00) agree significantly more than prospective students with parents who did not go to university (mean rank = 648.25; p-value = 0.000) that reputation influences their university choice.  Prospective university students with parents who went to university (mean rank = 736.48) agree significantly more than prospective students with parents who did not go to university (mean rank = 635.91; p-value = 0.000) that cultural acceptance influences their university choice.  Prospective university students with parents who went to university (mean rank = 737.36) agree significantly more than prospective students with parents who did not go to university (mean rank = 629.86; p-value = 0.000) that accessibility- location influences their university choice.  Prospective university students with parents who went to university (mean rank = 761.26) agree significantly more than prospective students with parents who did not go to university (mean rank = 585.73; p-value = 0.000) that accessibility-price influences their university choice.  Prospective university students with parents who went to university (mean rank = 722.13) agree significantly more than prospective students with parents who did not go to university (mean rank = 672.37; p-value = 0.028) that physical evidence influences their university choice.  Prospective university students with parents who went to university (mean rank = 729.10) agree significantly more than prospective students with parents who did not go to university (mean rank = 683.51; p-value = 0.047) that prestige/prominence influences their university choice.  Prospective university students with parents who went to university (mean rank = 731.66) agree significantly more than prospective students with parents who did

318 | not go to university (mean rank = 657.16; p-value = 0.001) that future employability influences their university choice.

Hypothesis 5 that prospective university students with parents who went to university and those prospective university students with parents who did not go to university differ significantly in terms of the extent to which different choice factors influence their decision-making, can therefore be accepted with respect to all seven choice factors namely, reputation, cultural acceptance, accessibility-location, accessibility- price, physical evidence, prestige/prominence and future employability.

Main finding CH5: Prospective university students with parents who went to university agree significantly more than prospective university students with parents who did not go to university that all seven choice factors namely reputation, cultural acceptance, accessibility-location, accessibility-price, physical evidence, prestige/prominence and future employability influence their university choice.

6.8 Perceived value construct

Section C of the questionnaire presented a perceived value scale (or factor) with 17 perceived value statements. Prospective students had to indicate to what extent they agree with the statements regarding the university/university of technology at which they would most like to study. Respondents were asked to indicate, on a seven-point Likert-type scale, where one indicated ‘strongly disagree’ and seven ‘strongly agree’, the extent to which they agree with each one of the 17 statements presented to them.

Firstly, the descriptive results are discussed to determine which perceived value statements have the highest mean scores. Following this discussion, the distribution of results is indicted to allow for parametric testing. A confirmatory factor analysis (CFA) is presented, followed by presenting the reliability of the two perceived value factors, the overall mean score for these two perceived value factors is discussed and lastly, the researcher tests for significant differences between groups.

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6.8.1 Descriptive results

Table 6.24 provides the descriptive results of Section C of the questionnaire. In table format the count (N), mean, low-box score, top-box score and standard deviation for the statements of all 17 perceived value factors presented.

Table 6.24 The respondents’ perceived value influencing their university decision

Low- Top- State- Perceived value that chosen university Standard N Mean box box ment offers Deviation score score The benefit of attending this university will C1 1377 5.63 1.9 36.7 1.395 outweigh the financial cost I am happy to make financial sacrifices to C2 1381 5.78 1.4 39.8 1.348 attend this university The price paid for studying at this university is C3 1379 5.39 2.0 27.0 1.435 reasonable I am happy that the price of the university is C4 1375 5.73 0.6 33.2 1.245 an indication of good quality I am happy to give up some of my interests to C5 1376 5.61 2.5 34.7 1.467 attend this university The benefit of attending the university will C6 outweigh the time sacrifices (less time with 1379 5.71 1.7 36.8 1.364 friends and family) I will achieve my career goals (because I C7 1369 6.12 0.7 50.8 1.152 study at this university) C8 The university will perform to my expectations 1358 6.00 0.4 40.6 1.100 The staff at the university will provide service C9 1354 5.85 0.6 34.0 1.127 as I expect C10 I will gain the knowledge that I need 1359 6.26 0.4 53.2 .995 I will feel a sense of ‘belonging when C11 1350 5.91 0.9 40.7 1.208 attending the university’ The reputation of the university will influence C12 1359 6.02 0.6 44.3 1.152 the value of my degree I believe that employers have good things to C13 1350 6.09 0.7 45.7 1.085 say about the university The university will give me a good experience C14 1358 6.11 0.7 49.2 1.145 (enjoyment, feel good, pleasure relaxed) The price I have to pay for the university is C15 1354 5.89 1.2 40.0 1.244 worth the money The facilities (library, computer labs etc.) will C16 1354 6.08 0.3 46.1 1.091 meet my expectations Compared to what I have to give up, the C17 overall ability of the university to satisfy my 1353 5.97 0.7 41.5 1.143 want and needs is very high

It is evident from Table 6.24 that respondents overall exhibit the strongest level of agreement with the statements ‘I will gain the knowledge that I need’ (mean = 6.26), ‘I will achieve my career goals because I study at this university’ (mean = 6.12), and ‘The university will give me a good experience’ (enjoyment, feel good, pleasure

320 | relaxed) (mean = 6.11). Statements with the lowest level of agreement include ‘The price paid for studying at this university is reasonable’ (mean = 5.39), and ‘I am happy to give up some of my interests to attend this university’ (mean = 5.61).

Main finding PV1: Prospective university students value the attainment of knowledge most, followed by the value of achieving career goals as a result of studying at their chosen university.

Main finding PV2: Prospective students value the reasonable price paid for their education the least, and although they agree that they are happy to give up some of their interests to attend their chosen university, it is not so strongly valued as gaining knowledge.

6.8.2 Distribution of results

As explained in Section 6.4.2 earlier in this chapter, it is necessary to test whether statements are normally distributed before hypotheses can be tested. For this purpose it will be determined whether the 17 perceived value statements are normally distributed, as this will give an indication to whether parametric or non- parametric tests are suitable to use for hypotheses testing. Although this large sample (i.e. 1 476 respondents) does not necessitate testing of results’ distribution (Eiselen et al., 2007:79), the skewness and kurtosis for each of the statements in Section C are presented in Table 6.25.

Table 6.25 A summary of perceived value factors’ skewness and kurtosis results

State- Perceived value that chosen university offers Skewness Kurtosis ment The benefit of attending this university will outweigh the C1 -0.976 0.774 financial cost I am happy to make financial sacrifices to attend this C2 -1.172 1.205 university C3 The price paid for studying at this university is reasonable -0.841 0.413 I am happy that the price of the university is an indication of C4 -0.988 0.823 good quality I am happy to give up some of my interests to attend this C5 -1.168 1.035 university It offers many good cultural experiences (fine arts, music, C6 -1.150 1.202 theatre, etc.) I will achieve my career goals (because I study at this C7 -1.544 2.725 university)

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State- Perceived value that chosen university offers Skewness Kurtosis ment C8 The university will perform to my expectations -1.278 2.006 C9 The staff at the university will provide service as I expect -1.056 1.410 C10 I will gain the knowledge that I need -1.699 3.977 I will feel a sense of ‘belonging’ when attending the C11 -1.249 1.806 university’ The reputation of the university will influence the value of my C12 -1.358 2.077 degree I believe that employers have good things to say about the C13 -1.470 2.952 university The university will give me a good experience (enjoyment, C14 -1.521 2.622 feel good, pleasure relaxed) C15 The price I have to pay for the university is worth the money -1.340 2.061 The facilities (library, computer labs, etc.) will meet my C16 -1.272 1.687 expectations Compared to what I have to give up, the overall ability of the C17 -1.256 1.883 university to satisfy my want and needs is very high

Table 6.25 indicates that all 17 perceived value statements are normally distributed. As a result, all the statements are therefore retained for statistical analysis.

6.8.3 Confirmatory Factor Analysis (CFA) for the perceived value construct

By means of confirmatory factor analysis (CFA) the two factors with their relevant statements of the perceived value construct were verified (Ary, Jacobs, Razavieh & Sorensen, 2006:393). Table 6.26 presents the CFA results which include the number of statements omitted and number of statements remaining within each factor as well as the fit indices. The extent of the fit of the measurement model (verification) uses two indices namely, the root mean square error of approximation (RMSEA) and the comparative fit index (CFI).

Table 6.26 Fit indices for CFA models of the perceived value factor

Number of Statements Factor Perceived value factor remaining CFI RMSEA omitted statements 1 Perceived willingness to sacrifice None 6 0.957 0.079 2 Perceived benefits None 11 0.924 0.068

It is evident from the fit indices that both perceived value factors exhibit a good fit and that it was not necessary to remove any of the statements to improve fit. The fit indices of the CFA indicate that the perceived willingness to sacrifice factor (factor 1) exhibits the best fit to the model if the CFI value is considered (CFI 322 |

= 0.957; RMSEA = 0.079) and that the perceived benefits factor (factor 2) exhibits the best fit to the model if the RMSEA value is considered (CFI = 0.924; RMSEA = 0.068). It can be concluded that both these perceived value factors are equally strong when considering fit. Table 6.27 presents the CFA results that include the statements retained within each perceived value factor.

Main finding PV3: The results of a CFA indicate that both perceived value factors (perceived willingness to sacrifice and perceived benefits) exhibit a good fit and all the statements are retained for further analysis.

Table 6.27 Perceived value statements retained after CFA

Factor Statement The benefit of attending this university will outweigh the financial cost I am happy to make financial sacrifices to attend this university 1. Perceived The price paid for studying at this university is reasonable willingness to sacrifice I am happy that the price of the university is an indication of good quality I am happy to give up some of my interests to attend this university It offers many good cultural experiences (fine arts, music, theatre, etc.) I will achieve my career goals (because I study at this university) The university will perform to my expectations The staff at the university will provide service as I expect I will gain the knowledge that I need I will feel a sense of ‘belonging’ when attending the university The reputation of the university will influence the value of my degree 2. Perceived benefits I believe that employers have good things to say about the university The university will give me a good experience (enjoyment, feel good, pleasure relaxed) The price I have to pay for the university is worth the money The facilities (library, computer labs, etc.) will meet my expectations Compared to what I have to give up, the overall ability of the university to satisfy my want and needs is very high

6.8.4 Reliability of perceived value factors

As explained in Section 6.4.4, reliability determines how well the response of each perceived value statement (or scale of items) corresponds to that of the other statements and to the perceived value scale (factors) as a whole. Thus, the reliability of the scale needs to be determined (Eiselen et al., 2007:112). A Cronbach’s alpha

323 | coefficient is used to indicate the reliability of the perceived value scales (factors), where values above 0.7 are considered acceptable and reliable (Pallant, 2010:100; Eiselen et al., 2007:112). For the purpose of this study, the reliability of both the perceived value factors are portrayed in Table 6.28.

Table 6.28 A summary of the results of the Cronbach’s alpha coefficient for the perceived value factors

Factor Number of Cronbach’s alpha coefficient statements 1. Perceived willingness to sacrifice 6 0.848 2. Perceived benefit 11 0.923

It is evident from the results in Table 6.28 that both the perceived value factors are reliable as the Cronbach’s alpha coefficient for both scales calculated a value greater than 0.7 (Pallant, 2010:100). Consequently, hypotheses tests can be done on the two scales.

Main finding PV4: The results of the Cronbach’s alpha coefficients for both perceived value factors indicate that both these scales are reliable.

6.8.5 Overall mean score for factors

Table 6.29 presents the results of the overall mean score for each of the two perceived value factors as well as if they are below, equal or above the mid-point of the scale. As explained in Section 6.4.5, a seven-point Likert-type scale has been used in the questionnaire, and therefore the mid-point is 3.5.

Table 6.29 A summary of the overall mean score for each of the perceived value factors

Perceived value factors Number of statements Average mean score per statement 1. Perceived willingness to sacrifice 6 5.6 2. Perceived benefit 11 6.0

It is evident from the results presented in Table 6.29 that both perceived value factors realised a mean of higher to the midpoint of the scale (3.5). The highest mean score per statement is for factor 2, perceived benefit (mean = 6.0) and,

324 | although factor 1, perceived willingness to sacrifice, is above the mid-point of 3.5, it represents the lowest mean score per statement of the two factors (mean = 5.6).

Main finding PV5: Both the perceived value factors realised an overall mean score above the mid-point (3.5).

6.8.6 Testing for significant differences between groups

This section presents the results for the alternative hypotheses formulated for the study. The section focuses specifically on reporting on significant differences between groups, based upon demographics, with regard to the main constructs of the study, namely perceived value.

6.8.6.1 Hypothesis 6

H6. Female and male prospective university students differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers.

Independent-samples t-tests were performed to determine whether significant differences exist between males and female regarding the influence of the different perceived value factors on their university choice. Table 6.30 presents the subsequent results for the two perceived value factors.

Table 6.30 Gender-based differences with respect to the perceived value factors

Perceived value factor Gender N Mean t-value p-value

1. Perceived willingness to Female 788 5.68 1.712 0.087 sacrifice Male 614 5.59 Female 778 6.10 2. Perceived benefits 3.922 0.000* Male 607 5.92 * p-value < 0.050

Based upon the results presented in Table 6.30, the following can be reported:  There is not a significant difference between female prospective students (mean = 5.68) and male prospective students (mean = 5.59; p-value = 0.087) with regard to the perceived sacrifice they are willing to make in order to attend their chosen university.

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 Female prospective students (mean = 6.10) agree significantly more than male prospective students (mean = 5.92; p-value = 0.000) with regard to the perceived benefits they believe they will derive from attending their chosen university.

Hypothesis 6 that female and male prospective university students differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers, can therefore be accepted with respect to the perceived benefits factor.

Main finding PVH6a: Female prospective students agree significantly more than male prospective students with regard to the perceived benefits they believe they will derive from attending their chosen university.

Main finding PVH6b: There is not a significant difference between female prospective students and male prospective students with regard to the perceived sacrifices they are willing to make in order to attend their chosen university.

6.8.6.2 Hypothesis 7

H7. Prospective university students with different home languages differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers.

Kruskal-Wallis Tests were performed to determine whether such significant differences exist. Where significant differences were evident, Mann-Whitney U Tests were performed to uncover the pairs of home language groups between which the significant differences exist. The resultant p-values were interpreted once the Bonferroni adjustments had been made. Table 6.31 presents the results of the Kruskal-Wallis Tests for the two perceived value factors concerned. Appendix L contains the detailed results with respect to the subsequent Mann-Whitney U Tests performed.

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Table 6.31 Home language differences with respect to the perceived value factors

Home Perceived value factor N Mean rank p-value language Afrikaans 772 726.02 1. Perceived willingness to English 277 720.77 0.006* sacrifice Indigenous 359 645.66 languages Afrikaans 765 707.58 2. Perceived benefits English 272 704.32 0.231 Indigenous 354 664.59 languages * p-value < 0.05

Based upon the results presented in Table 6.31, the following can be reported:  There are significant differences between prospective university students with different home languages with regard to the perceived sacrifice they are willing to make in order to attend their chosen university (p-value = 0.006). Afrikaans prospective students (mean rank = 726.02) agree significantly more than those prospective university students who speak an indigenous home language (mean rank = 645.66; p-value = 0.002) with regard to the perceived sacrifice they are willing to make in order to attend their chosen university.  There is not a significant difference between prospective university students with different home languages with regard to the perceived benefits they believe they will derive from attending their chosen university (p-value = 0.231).

Hypothesis 7 that prospective students with different home languages differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers, can therefore be accepted with respect to the perceived willingness to sacrifice factor.

Main finding PVH7a: Afrikaans prospective university students agree significantly more than those prospective university students speaking an indigenous language with regard to the perceived sacrifice they are willing to make in order to attend their chosen university.

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Main finding PVH7b: There are no significant differences between Afrikaans prospective students and English prospective students, or between English prospective students and those prospective students speaking an indigenous language with regard to the perceived sacrifices they are willing to make in order to attend their chosen university.

Main finding PVH7c: There is not a significant difference between prospective university students with different home languages with regard to the perceived benefits they believe they will derive from attending their chosen university.

6.8.6.3 Hypothesis 8

Independent-samples t-tests were performed to determine whether significant differences exist between students who take mathematics as a subject in grade 12 and those students who take mathematics literacy as a subject in grade 12 regarding the influence of the different perceived factors on their university choice. Table 6.32 presents the subsequent results for the seven choice factors concerned.

Table 6.32 Subject-choice (mathematics vs mathematics literacy) differences with respect to the perceived value factors

Maths or Perceived value factor N Mean t-value p-value Maths literacy 1. Perceived willingness to Maths 891 5.74 4.399 0.000* sacrifice Maths literacy 514 5.47 Maths 882 6.08 2. Perceived benefits 3.264 0.001* Maths literacy 508 5.92 * p-value < 0.050

Based upon the results presented in Table 6.32, the following can be reported:  Prospective students who take mathematics as a subject in grade 12 (mean = 5.74) agree significantly more than prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.47; p-value = 0.000) with regard to the

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perceived sacrifice they are willing to make in order to attend their chosen university.  Prospective students who take mathematics as a subject in grade 12 (mean = 6.08) agree significantly more than prospective students who take mathematics literacy as a subject in grade 12 (mean = 5.92; p-value = 0.001) with regard to the perceived benefits they believe they will derive from attending their chosen university.

Hypothesis 8 that prospective university students who take mathematics as a subject in grade 12 differ significantly from prospective university students who take mathematics literacy as a subject in grade 12 in terms of their level of agreement regarding the perceived value that their chosen university offers, can therefore be accepted with respect to the perceived willingness to sacrifice factor.

Main finding PVH8: Prospective university students who take mathematics as a subject in grade 12 agree significantly more than prospective university students who take mathematics literacy as a subject in grade 12 with regard to the perceived sacrifice they are willing to make and with regard to the perceived benefits they believe they will derive from attending their chosen university.

6.8.6.4 Hypothesis 9

Kruskal-Wallis Tests were performed to determine whether such significant differences exist. Where significant differences were evident, Mann-Whitney U Tests were performed to uncover the pairs of expected average grades for grade 12 groups between which the significant differences exist. The resultant p-values were interpreted once the Bonferroni adjustments had been made. Table 6.33 presents the results of the Kruskal-Wallis Tests for the two perceived value factors concerned. Appendix L contains the detailed results with respect to the subsequent Mann- Whitney U Tests performed.

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Table 6.33 Expected average grades for grade 12 differ with respect to the perceived value factors

Perceived value factor Expected grade N Mean rank p-value A (80%-100%) 290 803.38 1. Perceived willingness to B (70%-79%) 427 730.69 0.000* sacrifice C (60%-69%) 448 634.18 D-F (34%-59%) 221 597.72 A (80%-100%) 292 795.01 B (70%-79%) 425 703.49 2. Perceived sacrifice 0.000* C (60%-69%) 443 626.37 D-F (34%-59%) 211 625.11 * p-value < 0.050

Based upon the results presented in Table 6.33, the following can be reported:  There are significant differences between prospective university students with different expected grades for grade 12 with regard to the perceived sacrifice they are willing to make in order to attend their chosen university (p-value = 0.000). Students expecting an A grade average (mean rank = 803.38), agree significantly more than students expecting a B grade average (mean rank = 730.69; p-value = 0.0120), than students expecting a C grade average (mean rank = 634.18; p- value = 0.000), and students expecting a D-F grade average (mean rank = 597.72; p-value = 0.000) with regard to the perceived sacrifice they are willing to make in order to attend their chosen university. Students expecting a B grade average (mean rank = 730.69) also agree significantly more than students expecting a C grade average (mean rank = 634.18; p-value = 0.001) and students expecting a D-F grade average (mean rank = 597.72; p-value = 0.000) with regard to the perceived sacrifice they are willing to make in order to attend their chosen university.  There are significant differences between prospective university students with different expected grades for grade 12 with regard to their level of agreement that perceived benefits influences their university choice (p-value = 0.000). Students expecting an A grade average (mean rank = 795.01), agree significantly more than students expecting a B grade average (mean rank = 703.49; p-value = 0.001), than students expecting a C grade average (mean rank = 626.37; p-value = 0.000), and students expecting a D-F grade average (mean rank = 625.11; p- value = 0.000) with regard to the perceived benefits they believe they will derive from attending their chosen university. Students expecting a B grade average

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(mean rank = 730.69) also agree significantly more than students expecting a C grade average (mean rank = 634.18; p-value = 0.003) and students expecting a D-F grade average (mean rank = 597.72; p-value = 0.016) with regard to the perceived benefits they believe they will derive from attending their chosen university.

Hypothesis 9 that prospective students with different expected grades for grade 12 differ significantly in terms of their level of agreement regarding perceived value that their chosen university offers, can therefore be accepted with respect to both the perceived sacrifice they are willing to make and the perceived benefits they believe they will derive from attending their chosen university.

Main finding PVH9a: Students expecting an A grade average and students expecting a B grade average agree significantly more than students expecting a C grade average and students expecting a D-F average with regard to the perceived sacrifice they are willing to make and with regard to the perceived benefits they believe they will derive from attending their chosen university. Also, students expecting an A grade average agree significantly more than students expecting a B grade average with regard to the perceived sacrifice they are willing to make and with regard to the perceived benefits they believe they will derive from attending their chosen university.

Main finding PVH9b: There are no significant differences between students expecting a C grade average and students expecting a D-F grade average with regard to the perceived sacrifice they are willing to make and with regard to the perceived benefits they believe they will derive from attending their chosen university.

6.8.6.5 Hypothesis 10

H10. Prospective university students with parents who went to university differ significantly from prospective university students with parents who did not go to university and in terms of their level of agreement regarding perceived value that their chosen university offers.

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To test this hypothesis, Mann-Whitney U Tests were performed to determine whether such differences exist between prospective students with parents who attended a university and those prospective university students with parents who did not attend a university. Table 6.34 presents the results of the Mann-Whitney U Tests for the seven choice factors concerned.

Table 6.34 Prospective students with parents who attended and those with parents who did not attend university differ with respect to the perceived value factors

Parents who went vs parents Perceived value factor N Mean rank p-value who did not go to university 1. Perceived willingness to Yes 910 727.68 0.000* sacrifice No 475 626.57 Yes 901 709.05 2. Perceived benefits 0.002* No 468 638.70 * p-value < 0.050

Based upon the results presented in Table 6.34, the following can be reported:

 Prospective university students with parents who went to university (mean rank = 727.68) agree significantly more than prospective students with parents who did not go to university (mean rank = 626.57; p-value = 0.000) with regard to the perceived sacrifice they are willing to make in order to attend their chosen university.  Prospective university students with parents who went to university (mean rank = 709.05) agree significantly more than prospective students with parents who did not go to university (mean rank = 638.70; p-value = 0.002) with regard to the perceived benefits they believe they will derive from attending their chosen university.

Hypothesis 10 that prospective university students with parents who went to university and those prospective university students with parents who did not go to university differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers, can therefore be accepted with respect to both the perceived sacrifice they are willing to make and the perceived benefits they believe they will derive from attending their chosen university. 332 |

Main finding PVH10: Prospective university students with parents who went to university agree significantly more than prospective university students with parents who did not go to university that both the perceived sacrifice they are willing to make and the perceived benefits they believe they will derive from attending their chosen university.

6.9 Intention to enrol

Section D of the questionnaire offered an intention to enrol scale with five intention to enrol statements. Prospective students had to indicate to what extent they agreed with the statements regarding the university/university of technology at which they would most like to study. Respondents were asked to indicate, on a seven-point Likert-type scale, where one indicated ‘strongly disagree’ and seven ‘strongly agree’, the extent to which they agreed with each one of the five statements presented to them.

The following section presents the descriptive results for each intention to enrol statement to determine which statements respondents agreed with the most. Following this discussion, the distribution of results is assessed in order to determine whether parametric or non-parametric tests should be used to test the hypotheses formulated for the study. Following this, the results of the confirmatory factor analysis (CFA) are presented, followed by presenting the reliability of the intention to enrol factor (scale), the overall mean score for the factor, and lastly, the researcher tests for significant differences between groups.

6.9.1 Descriptive results

Table 6.35 provides the descriptive results of Section D of the questionnaire. In Table format the count (N), mean, low-box score, top-box score and standard deviation for all five intention to enrol factor statements are presented.

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Table 6.35 The respondents’ intention to enrol

Low- Top- State- Standard Statement N Mean box box ment deviation score score I would feel guilty if I go to another D1 1363 3.67 27.9 16.5 2.163 university If it is up to me I would never go to D2 1357 4.68 13.4 30.4 2.104 another university Whenever possible, I would avoid going D3 1360 4.73 11.0 27.8 1.996 to another university If a place is available at this university, I D4 1357 6.0 1.9 54.2 1.409 will attend it I do not like the idea of going to another D5 1361 4.86 10.1 30.9 1.974 university

It is evident from Table 6.35 that respondents overall exhibit the strongest level of agreement with the statements ‘If a place is available at this university, I will attend it’ (mean = 6.0), followed by ‘I do not like the idea of going to another university’ (mean = 4.86). Statements with the lowest level of agreement include ‘I would feel guilty if I go to another university’ (mean = 3.67), and ‘if it is up to me I would never go to another university’ (mean = 4.68).

Main finding IE1: Prospective university students agree strongly that availability at their chosen university will influence their intention to enrol.

6.9.2 Distribution of results

Section 6.4.2 earlier in this chapter explained that it is necessary to test whether statements are normally distributed before hypotheses can be tested. For this purpose it will be determined whether the five intention to enrol statements are normally distributed, as this will give and indication to whether parametric or non- parametric tests are suitable to use for hypotheses testing. Although this large sample (i.e. 1 476 respondents) does not necessitate testing of results’ distribution (Eiselen et al., 2007:79), the skewness and kurtosies for each of the statements in Section D are presented in Table 6.36.

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Table 6.36 A summary of intention to enrol factors’ skewness and kurtosis results

State- Statement Skewness Kurtosis ment D1 I would feel guilty if I go to another university 0.158 -1.286 D2 If it is up to me I would never go to another university -0.456 -1.078 Whenever possible, I would avoid going to another D3 -0.483 -0.917 university D4 If a place is available at this university, I will attend it -1.563 2.075 D5 I do not like the idea of going to another university -0.569 -0.789

Table 6.36 indicates that all five intention to enrol statements are normally distributed. All the statements have a skewness of less than two and all the statements have a kurtosis of less than seven, indicating that they are all normally distributed (West, Finch & Curran, 1995:74). As a result, all the statements are therefore retained for statistical analyses.

6.9.3 Confirmatory Factor Analysis (CFA) for the intention to enrol construct

By means of confirmatory factor analysis (CFA), the one intention to enrol factor with its relevant statements is verified (Ary et al., 2006:393). Table 6.37 presents the CFA results, which include the number of statements that are retained as well as the fit indices. As already explained in Sections 6.4.4 and 6.5.3, the fit of the measurement model (verification) uses two indices namely, the root mean square error of approximation (RMSEA) and the comparative fit index (CFI).

Table 6.37 Fit indices for CFA models of the intention to enrol factor

Number of Statements Factor remaining CFI RMSEA omitted statements 1. Intention to enrol None 5 0.984 0.072

It is evident from the fit indices that the intention to enrol factor exhibits a good fit and that it is not necessary to remove any of the statements to improve fit (CFI = 0.984; RMSEA = 0.072). Although the results of the CFA measurement indicate that statement D1.4 (If a place is available at this university, I will attend it) can be omitted to strengthen the fit of the model even further, it will be retained together with the other four statements for further analyses as it is an existing intention to buy

335 | factor that has only been adapted to suit this study. Table 6.38 presents the CFA results that include the statements retained within the intention to enrol factor.

Table 6.38 CFA results indicating the intention to enrol statements retained

Factor Statements

I would feel guilty if I go to another university If it is up to me I would never go to another university 1. Intention to enrol factor Whenever possible, I would avoid going to another university If a place is available at this university, I will attend it I do not like the idea of going to another university

Main finding IE2: The results of a CFA indicate that four of the possible five intention to enrol statements should be retained for better fit, however as fit is still good if all the statements are retained, it was decided to retain all five intention to enrol statements for further analyses.

6.9.4 Reliability of intention to enrol factors

Sections 6.4.4 and 6.5.4 already explained that reliability determines how well the response of each statement (or scale of items) corresponds to that of the other statements and to the scale as a whole. In this case, the reliability of the intention to enrol statements and the intention to enrol scale as a whole is tested. Table 6.39 presents the results of the Cronbach’s alpha coefficient for the intention to enrol scale (factor).

Table 6.39 A summary of the results of the Cronbach’s alpha coefficient for the intention to enrol factor

Cronbach’s alpha Factor Number of statements coefficient

1. Intention to enrol 5 0.813

It is evident from the results in Table 6.39 that the intention to enrol factor (or scale) is reliable as the Cronbach’s alpha coefficient value is greater than 0.7 (Pallant, 2010:100). Consequently, hypotheses tests can be conducted on this scale.

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Main finding IE3: The results of the Cronbach’s alpha coefficients for the intention to enrol factor (scale) indicate that this scale is reliable.

6.9.5 Overall mean score for factor

Table 6.40 presents the results of the overall mean score for the intention to enrol factor as well as if the statements are below, equal or above mid-point of the scale. As explained in Section 6.4.5 and 6.5.5, a seven-point Likert-type scale has been used in the questionnaire, and therefore the mid-point is 3.5.

Table 6.40 A summary of the overall mean score for the intention to enrol factor

Average mean score per Factor Number of statements statement

1. Intention to enrol 5 4.79

It is evident from the results presented in Table 6.40 that the intention to enrol factor realised a mean of higher to the midpoint of the scale (3.5). The average mean score per statement realised at 4.79.

Main finding IE4: The intention to enrol factor realised an overall mean score above the mid-point (3.5).

6.9.6 Testing for significant differences between groups

6.9.6.1 Hypothesis 11

H11. Female and male prospective university students differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university.

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Independent-samples t-tests were performed to determine whether significant differences exist between males and female regarding the influence of the intention to enrol factor on their university choice. Table 6.41 presents the subsequent results for the intention to enrol factors concerned.

Table 6.41 Gender-based differences with respect to the intention to enrol factors

Factor Gender N Mean t-value p-value

Female 772 4.81 1. Intention to enrol 0.689 0.491 Male 603 4.76 p-value < 0.050

Based upon the results presented in Table 6.41, the following can be reported:  There is not a significant difference between female prospective students (mean = 4.81) and male prospective students (mean = 4.76; p-value = 0.491) in terms of their level of agreement regarding their intention to enrol at their chosen university.

Hypothesis 11 that female and male prospective university students differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university, can therefore be rejected with respect to the intention to enrol factor.

Main finding IEH11: There is no significant difference between female and male prospective students in terms of their level of agreement regarding their intention to enrol at their chosen university.

6.9.6.2 Hypothesis 12

H12. Prospective university students with different home languages differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university.

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Bonferroni adjustments had been made. Table 6.42 presents the results of the Kruskal-Wallis Tests for the two intention to enrol factors concerned. Appendix L contains the detailed results with respect to the subsequent Mann-Whitney U Tests performed.

Table 6.42 Home language difference with respect to the intention to enrol factor

Home Intention to enrol N Mean rank p-value language Afrikaans 764 687.61 English 271 641.31 1. Intention to enrol 0.110 Indigenous 346 737.41 languages p-value < 0.050

Based upon the results presented in Table 6.42, the following can be reported:  There is not a significant difference between prospective university students with different home languages in terms of their level of agreement regarding their intention to enrol at their chosen university (p-value = 0.110).

Hypothesis 12 that prospective students with different home languages differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university, can therefore be rejected with respect to the intention to enrol factor.

Main finding IEH12: There is not a significant difference between prospective students with different home languages relating to intention to enrol influencing their university choice.

6.9.6.3 Hypothesis 13

Independent-samples t-tests were performed to determine whether significant differences exist between students who take mathematics as a subject in grade 12

339 | and those students who take mathematics literacy as a subject in grade 12 regarding the influence of the intention to enrol factor on their university choice. Table 6.43 presents the subsequent results for the intention to enrol factor concerned.

Table 6.43 Subject-choice (mathematics vs mathematics literacy) differences with respect to the intention to enrol factor

Maths or Intention to enrol factor Maths N Mean t-value p-value literacy Maths 875 4.77 Intention to enrol -.0585 0.559 Maths literacy 505 4.82 * p-value < 0.050

Based upon the results presented in Table 6.43, the following can be reported:  There is not a significant difference between prospective university students who take mathematics as a subject in grade 12 (mean = 4.77) and prospective university students who take mathematics literacy as a subject in grade 12 (mean = 4.82; p-value = 0.559) in terms of their level of agreement regarding their intention to enrol at their chosen university.

Hypothesis 13 that prospective university students who take mathematics as a subject in grade 12 differ significantly from prospective university students who take mathematics literacy as a subject in grade 12 in terms of their level of agreement regarding their intention to enrol at their chosen university, can therefore be rejected with respect to the intention to enrol factor.

Main finding IEH13: There is not a significant difference between prospective university students who take mathematics as a subject in grade 12 and prospective university students who take mathematics literacy as a subject in grade 12 in terms of their level of agreement regarding their intention to enrol at their chosen university.

6.9.6.4 Hypothesis 14

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Kruskal-Wallis Tests were performed to determine whether such significant differences exist. Where significant differences were evident, Mann-Whitney U Tests were performed to uncover the pairs of expected average grades for grade 12 groups between which the significant differences exist. The resultant p-values were interpreted once the Bonferroni adjustments had been made. Table 6.44 presents the results of the Kruskal-Wallis Tests for the intention to enrol factor concerned. Appendix L contains the detailed results with respect to the subsequent Mann- Whitney U Tests performed.

Table 6.44 Expected average grades for grade 12 differences with respect to the intention to enrol factors

Factor Expected grade N Mean rank p-value A (80%-100%) 292 687.27 B (70%-79%) 422 662.39 1. Intention to enrol 0.501 C (60%-69%) 440 681.33 D-F (34%-59%) 208 712.53 p-value < 0.050

Based upon the results presented in Table 6.44, the following can be reported:  There are no significant differences between prospective students with different expected grades in terms of their level of agreement regarding their intention to enrol at their chosen university (p-value = 0.501).

Hypothesis 14 that prospective students with different expected grades for grade 12 differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university, can therefore be rejected with respect to the intention to enrol factor.

Main finding IEH14: There are no significant differences between prospective university students with different expected grades for grade 12 in terms of their level of agreement regarding their intention to enrol at their chosen university.

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6.9.6.5 Hypothesis 15

H15. Prospective university students with parents who went to university differ significantly from prospective university students with parents who did not go to university and in terms of their level of agreement regarding their intention to enrol at their chosen university.

To test this hypothesis, Mann-Whitney U Tests were performed to determine whether significant differences exist between prospective students with parents who attended university and those prospective university students with parents who did not attend university. Table 6.45 presents the results of the Mann-Whitney U Tests for the intention to enrol factor concerned.

Table 6.45 Prospective students with parents who attended and those with parents who did not attend university differ with respect to the perceived value factors

Parents who went or did Factor N Mean rank p-value not go to university Yes 897 677.13 1. Intention to enrol 0.707 No 462 685.57 * p-value < 0.050

Based upon the results presented in Table 6.45, the following can be reported:  There is not a significant difference between prospective university students with parents who went to university (mean rank = 677.13) and prospective university students with parents who did not go to university (mean rank = 685.57; p-value = 0.707) in terms of their level of agreement regarding their intention to enrol at their chosen university.

Hypothesis 15 that prospective university students with parents who went to university and those prospective university students with parents who did not go to university differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university, can therefore be rejected with respect to the intention to enrol factor.

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Main finding IEH15: There is not a significant difference between prospective university students with parents who went to university and prospective university students with parents who did not go to university with respect to the intention to enrol factor.

This section concludes the discussion on the results of the hypotheses tests relating to the three main components of the theoretical model: choice, perceived value and intention to enrol. The following section will discuss the testing of the theoretical model by applying structural equation modelling (SEM).

6.10 Testing the theoretical model

By means of structural equation modelling (SEM) the theoretical model as proposed in Chapter 4, Section 4.7 was tested. The causal relationships among the proposed theoretical model’s three constructs, namely the choice construct, perceived value construct and intention to enrol construct were tested using EQS software applying the maximum likelihood (ML) estimation (Kline, 2011:81; Suhr, 2006b:4). This section reports the results of the theoretical (measurement) model and the structural model that were tested (Figure 6.1).

Figure 6.1 The theoretical model

6.10.1 The measurement model

Table 6.46 presents the fit indices for the measurement model. As discussed in Chapter 5, Section 5.5.6.3, the extent of the fit of the measurement model is assessed through three indices namely, the relative chi-square ratio (X2/df), the root

343 | mean square error approximation (RMSEA) and the comparative fit index (CFI) (Hooper, Coughlan & Mullen, 2008:54-55; Hoe, 2008:78; Meyers, Gamst & Guarino, 2006:558-562).

Table 6.46 Fit indices for the measurement model

Fit indices Fit indices value

Chi-square/degrees of freedom (relative chi- 2 X /df = 4118.84/ 1447 = 2.845 square ratio) CFI 0.888 RMSEA 0.037

It is evident from Table 6.46 that the relative chi-square ratio or X2/df ratio (2.845) is less than 3.0 and therefore indicates good fit (Rotgangs & Schmidt, 2011:470; Hoe, 2008:77; Schreiber, Stage, King, Nora & Barlow, 2006:330). Although the chi-square ratio indicates a good fit, it should be used with caution as it is affected by the size of correlations between pairs of variables (larger correlations generally cause a poorer fit), and it is furthermore sensitive to the sample size (Hooper et al., 2008:54; Meyers et al., 2006:557, 561). For this reason CFI and RMSEA were also investigated. According to these measures the model exhibits an adequate albeit marginal fit when the CFI is considered (CFI = 0.888), and a good fit when the RMSEA is considered (RMSEA = 0.037) (Meyers et al., 2006:559-560).

Although an adequate to good model fit can be observed, the EQS software flagged a problem related to singularity. When one of the factors (variables) is a perfect linear combination of the other factors (variables), it results in a singular matrix which cannot be inverted and that causes the analysis to crash (Wuensch, 2009:1).

Another possible problem with the model relates to multicollinearity. Multicollinearity refers to a condition that exists when more than two predictors or independent factors (variables) correlate strongly with one another, thus what appears to be separate factors (variables), actually measure the same thing (Kline, 2011:51; Meyers et al., 2006:180).

To overcome the problems associated with singularity and multicollinearity, the next step involved the investigation of the paths among the factors (or variables) in order

344 | to evaluate their statistical significance and secondly, their strength using standardised path coefficients that range between -1.0 and 1.0 (Hoe, 2008:79; Meyers et al., 2006:615; Schreiber et al., 2006:327; Lleras, 2005:25-27). The subsequent results indicate that the paths among the independent factors are statistically significant (p-value < 0.05) and that the test statistics generated for all the factors exhibited a value greater than ± 1.96 (Hoe, 2008:79; Hooper et al., 2008:56; Suhr, 2006b:2; Hox & Bechger, 1998:357)(Appendix M): More specifically, the following results were observed:  Both the factors accessibility-price (standardised path coefficient = 2.728) and accessibility-location’s (standardised path coefficient = 1.488) paths to perceived willingness to sacrifice exhibit standardised path coefficient values that are not between -1.0 and 1.0 (Hoe, 2008:79; Meyers et al., 2006:615).  On further examining the strength of the choice factors’ paths to perceived benefits, the accessibility-price factor (standardised path coefficient = 1.456) is again outside the parameters of -1.0 and 1.0, indicating that there is not a significant path, while the accessibility-location (standardised path coefficient = 0.810) indicates a significant path to perceived benefits.

In order to determine which of the two factors needed to be omitted from the model to increase the strength of the relationships among the factors, correlations among independent variables were reviewed. The results indicated, a strong correlation (0.901) between accessibility-price and accessibility-location. A correlation of 0.80 is typical of multicollinearity (Meyers et al., 2006:181; Wulder, 1998:44). Based on these results it was decided to remove the accessibility-price factor from the model and to run the model again (Kline, 2011:54).

6.10.2 The structural model

Table 6.47 compares the fit indices for the structural model that excludes accessibility-price, with those for the measurement model.

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Table 6.47 Fit indices for the structural SEM

Measurement model Fit indices Structural model X2/df = Chi-square/degrees of X2/df = 4118.84/ 1447 = 2.845 freedom (relative chi-square X2/df = 3719.381/ 1425 = 2.610 ratio) 0.888 CFI 0.888 0.037 RMSEA 0.037

Table 6.47 indicates that the CFI and RMSEA goodness-of-fit measures remained exactly the same for both the structural model (when accessibility-price was omitted), and the measurement model. Thus, based on these measures the structural model exhibit and adequate but marginal fit when the CFI is considered (CFI = 0.888), and a good fit when the RMSEA is considered (RMSEA = 0.037)(Meyers et al., 2006:559-560). When the chi-square ratio (X2/df) for the structural model is investigated (X2/df = 2.610), the X2/df value is 3.0 or less indicating a good fit (Rotgangs & Schmidt, 2011:470; Hoe, 2008:77; Schreiber et al., 2006:330). Upon comparing the X2/df values of the measurement (X2/df = 2.845) and the structural model (X2/df = 2.610) (Table 6.47), it is evident that the X2/df is lower for the structural model (X2/df = 2.610), indicating a better fit and is considered to be the preferable model to use (Meyers et al., 2006:562).

Before the statistical significance of paths could be determined, the researcher again evaluated the results for any evidence that could flag possible multicollinearity between factors. As multicollinearities could not be uncovered, the statistical significance of the standardised paths among the factors was investigated. These test statistics generated from the EQS output furthermore indicated all the factors having a value greater than ± 1.96 for all factors (variables), meaning that all the independent factors are statistical significant (Hoe, 2008:79; Hooper et al., 2008:56; Suhr, 2006b:2; Hox & Bechger, 1998:357) (Appendix M).

Table 6.48 portrays the robust statistic of the significant paths of the choice factors to perceived willingness to sacrifice and to perceived benefits, as well as between both perceived willingness to sacrifice and perceived benefits to intention to enrol where significant direct paths can be observed.

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Table 6.48 Significant direct paths evident in the structural model

Path Significant paths p-value for path 1  7 Reputation  perceived willingness to sacrifice 2.478* 2  7 Cultural acceptance  perceived willingness to sacrifice 3.315* 3  7 Accessibility-location  perceived willingness to sacrifice 6.882* 4  7 Physical evidence  perceived willingness to sacrifice -0.694 5  7 Prestige/prominence  perceived willingness to sacrifice -1.634 6  7 Future employabilityperceived willingness to sacrifice 1.905 1  8 Reputation  perceived benefits 2.531* 2  8 Cultural acceptance  perceived benefits 5.523* 3  8 Accessibility-location  perceived benefits 6.016* 4  8 Physical evidence perceived benefits 2.461* 5  8 Prestige/prominence  perceived benefits -2.359* 6  8 Future employability  perceived benefits 4.176* 7  9 Perceived willingness to sacrifice  intention to enrol 2.225* 8  9 Perceived benefits  intention to enrol 12.386* p-value > ±1.96

It is evident from Table 6.48 that for 11 of the 14 paths there are statistical significant direct paths between these factors. Three factors namely physical evidence, prestige/prominence and future employability did not portray significant direct paths to perceived willingness to sacrifice and were therefore omitted from the model (p- value in these instances were less than ±1.96). However, all six choice factors (reputation, cultural acceptance, accessibility-location, physical evidence, prestige/prominence and future employability) indicate significant direct paths to perceived benefits. It is evident from Table 6.48 that both perceived value factors (perceived willingness to sacrifice and perceived benefits) indicate a statistical significant direct path to intention to enrol.

Once the statistical significance of direct paths had been established, the following step was to investigate the strength of relationships or paths among the factors (variables) by reviewing the standardised paths. Table 6.49 portrays the structural model’s standardised paths with their path coefficients for all the paths and the size of the direct effects between the factors (Hoe, 2008:79).

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Table 6.49 Standardised paths coefficient

Standardised Size of Dimen- Significant paths path direct sion coefficient effect** 1 7 Reputation  perceived willingness to sacrifice 0.150* Small 2 7 Cultural acceptance  perceived willingness to sacrifice 0.177* Small 3 7 Accessibility-location  perceived willingness to sacrifice 0.405* Medium 4 7 Physical evidence  perceived willingness to sacrifice -0.030 N/A* 5 7 Prestige/prominence  perceived willingness to sacrifice -0.053 N/A* 6 7 Future employability  perceived willingness to sacrifice 0.102 N/A* 1  8 Reputation  perceived benefits 0.138* Small 2  8 Cultural acceptance  perceived benefits 0.269* Small 3  8 Accessibility-location  perceived benefits 0.276* Small 4  8 Physical evidence  perceived benefits 0.087 Small 5  8 Prestige/prominence  perceived benefits -0.062 Small 6  8 Future employability  perceived benefits 0.193* Small 7  9 Perceived willingness to sacrifice  0.063* Small 8  9 Perceived benefits  intention to enrol 0.393* Medium

* paths are not statistically significant ** standardised path coefficients with values less than 0.10 = small effect, values around 0.30 = a medium effect and values around and >0.50 indicate a large effect (Suhr, 2006b:4)

It is evident from Table 6.49 that two paths exhibit a medium direct effect and the remaining nine paths have a small direct effect between the factors concerned. It is furthermore evident from Table 6.49 that the standardised path coefficients for all paths that are significant and are positive, range between 0.405 and 0.087, with one negative path (-0.062) and the exception of three paths that exhibit a non-significant relationship between the factors.

It can finally be concluded that the structural model exhibits an adequate (CFI = 0.888) to a good fit (RMSEA = 0.037) (Meyers et al., 2006:559-560). However, when the chi-square ratio (X2/df) is compared with the measurement (X2/df = 2.845) and the structural model (X2/df = 2.610) (Table 6.X), it is evident that the structural model (X2/df= 2.610) indicates a better fit and is accepted as the preferable model (Meyers et al., 2006:562).

It can be argued that the choice constructs have a statistical significant direct effect and path (thus relationship) to perceived value, and perceived value to intention to

348 | enrol, that will be confirmed with the following hypothesis discussion. The structural model is presented in Figure 6.2.

Figure 6.2 The structural model

Based upon the abovementioned results, the following main findings are presented:

 Main finding SEM1a: Three choice factors namely, reputation, cultural acceptance and accessibility-location exhibit a significant direct path to perceived willingness to sacrifice.  Main finding SEM1b: All six choice factors exhibit a significant direct path to perceived benefits.  Main finding SEM1c: The perceived willingness to sacrifice factor exhibits a significant direct path to intention to enrol.

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 Main finding SEM1d: The perceived benefit factor exhibits a significant direct path to intention to enrol.

6.10.3 Hypothesis 16

Based upon the results and main findings presented in Section 6.10 hypothesis 16 that there are significant and positive interrelationships between the choice factors prospective university students consider when choosing a university, the perceived value they expect to derive from their choice, and their intention to enrol at their chosen university, can therefore be accepted.

Main finding H16: There is an interrelationship between the choice factors prospective university students consider when choosing a university, the perceived value they expect to derive from their choice, and their intention to enrol at their chosen university.

6.11 Summary of findings

The purpose of this section is to provide a summary of the main findings uncovered in the study. The main findings relating to the descriptive results are summarised first, followed by the main findings pertaining to hypotheses testing.

6.11.1 Main findings pertaining to the descriptive results

The main findings for the descriptive analysis are summarised according to the various three main components of the theoretical model as portrayed in the questionnaire namely: choice (Section B of questionnaire), perceived value (Section C of the questionnaire) intention to enrol (Section D of the questionnaire) and also includes the main findings pertaining the tested choice model after structural equation modelling (SEM) was employed.

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6.11.1.1 Choice factor o Main finding C1: The type of courses, the variety of courses, the reputation of courses, and the university’s resources for students (computers, library, etc.) are the most important choice statements influencing respondents’ university choice. o Main finding C2: The results of a CFA indicate that all seven factors of the choice construct exhibit a good fit after a number of statements were omitted from three of the factors. o Main finding C3: The results of the Cronbach’s alpha coefficients for the seven choice factors indicate that the scales of all seven choice factors are reliable. o Main finding C4: Each of the seven choice factors realised an overall mean score above the mid-point (3.5), indicating that all seven factors play an important role in influencing prospective university students’ university choice.

6.11.1.2 Perceived value factor o Main finding PV1: Prospective university students value the attainment of knowledge most, followed by the value of achieving career goals as a result of studying at their chosen university. o Main finding PV2: Prospective students value the reasonable price paid for their education the least, and although they agree that they are happy to give up some of their interests to attend their chosen university, it is not so strongly valued as gaining knowledge. o Main finding PV3: The results of a CFA indicate that both perceived value factors (perceived willingness to sacrifice and perceived benefits) exhibit a good fit and all the statements are retained for further analysis. o Main finding PV4: The results of the Cronbach’s alpha coefficients for both perceived value factors indicate that both these scales are reliable. o Main finding PV5: Both the perceived value factors realised an overall mean score above the mid-point (3.5).

6.11.1.3 Intention to enrol factor o Main finding IE1: Prospective university students agree strongly that availability at their chosen university will influence their intention to enrol.

351 | o Main finding IE2: The results of a CFA indicate that four of the possible five intention to enrol statements should be retained for better fit, however, as fit is still good if all the statements are retained, it was decided to retain all five intention to enrol statements for further analysis. o Main finding IE3: The results of the Cronbach’s alpha coefficients for the intention to enrol factor (scale) indicate that this scale is reliable. o Main finding IE4: The intention to enrol factor realised an overall mean score above the mid-point (3.5).

6.11.1.4 The structural model o Main finding SEM1a: Three choice factors namely, reputation, cultural acceptance and accessibility-location exhibit a significant direct path to perceived willingness to sacrifice. o Main finding SEM1b: All six choice factors exhibit a significant direct path to perceived benefits. o Main finding SEM1c: The perceived willingness to sacrifice factor exhibits a significant direct path to intention to enrol. o Main finding SEM1d: The perceived benefit factor exhibits a significant direct path to intention to enrol.

The various main findings formulated for hypotheses testing are summarised in the following section under the three main headings, choice, perceived value and intention to enrol.

6.11.2 Main findings pertaining to hypotheses testing

The purpose of this section is to summarise all the main findings pertaining to hypothesis testing and includes the hypotheses as well as the main finding(s) formulated for each hypothesis.

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6.11.2.1 Choice factors

H1. Female and male prospective university students differ significantly in terms of their level of agreement regarding the influence of different choice factors on their university choice. o Main finding CH1: Female prospective students agree significantly more than male prospective students that six choice factors namely reputation, cultural acceptance, accessibility-location, physical evidence, prestige/prominence and future employability influence their university choice.

H2. Prospective university students with different home languages differ significantly in terms of their level of agreement regarding the influence of different choice factors on their university choice. o Main finding CH2a: Afrikaans prospective university students agree significantly more than English prospective university students and those prospective university students speaking an indigenous language that four choice factors namely cultural acceptance, accessibility-location, accessibility-price and prestige/prominence influence their university choice. o Main finding CH2b: Afrikaans prospective university students agree significantly more than those prospective university students speaking an indigenous language that future employability influences their university choice. o Main finding CH2c: English prospective university students agree significantly more than those prospective university students speaking an indigenous language that accessibility-price influences their university choice.

H3. Prospective university students who take mathematics as a subject in grade 12 differ significantly from prospective university students who take mathematics literacy as a subject in grade 12 in terms of their level of agreement regarding the influence of different choice factors on their university choice. o Main finding CH3: Prospective university students who take mathematics as a subject in grade 12 agree significantly more than prospective university students who take mathematics literacy as a subject in grade 12 that six choice factors namely reputation, cultural acceptance, accessibility-location, accessibility-price, prestige/prominence and future employability influence their university choice.

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H4. Prospective university students with different expected average grades for grade 12 differ significantly in terms of their level of agreement regarding the influence of different choice factors on their university choice. o Main finding CH4a: Students expecting an A grade average and students expecting a B grade average agree significantly more than students expecting a C grade average and a D-F average that two choice factors namely reputation and future employability influence their university choice. Also, students expecting an A grade average agree significantly more than students expecting a B grade average that reputation and future employability influence their university choice. o Main finding CH4b: Students expecting an A grade average and students expecting a B grade average agree significantly more than students expecting a C grade average and a D-F average that two choice factors namely cultural acceptance and accessibility-location influence their university choice. o Main finding CH4c: Students expecting an A grade average agree significantly more than students expecting a C grade average and a D-F average that prestige/prominence influences their university choice. Also, students expecting a B grade average agree significantly more than students expecting a D-F grade average that prestige/prominence influences their university choice. o Main finding CH4d: Students expecting an A grade average and students expecting a B grade average agree significantly more than students expecting a D-F grade average that accessibility-price influences their university choice. o Main finding CH4e: Students expecting an A grade average agree significantly more than students expecting a D-F grade average that physical evidence influences their university choice. o Main finding CH4f: There are no significant differences between students expecting a C grade average and students expecting a D-F grade average regarding choice factors that influence their university choice.

354 | o Main finding CH5: Prospective university students with parents who went to university agree significantly more than prospective university students with parents who did not go to university that all seven choice factors namely reputation, cultural acceptance, accessibility-location, accessibility-price, physical evidence, prestige/prominence and future employability influence their university choice.

6.11.2.2 Perceived value factor

H6. Female and male prospective university students differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers. o Main finding PVH6a: Female prospective students agree significantly more than male prospective students with regard to the perceived benefits they believe they will derive from attending their chosen university. o Main finding PVH6b: There is not a significant difference between female prospective students and male prospective students with regard to the perceived sacrifices they are willing to make in order to attend their chosen university.

H7. Prospective university students with different home languages differ significantly in terms of their level of agreement regarding the perceived value that their chosen university offers. o Main finding PVH7a: Afrikaans prospective university students agree significantly more than those prospective university students speaking an indigenous language with regard to the perceived sacrifice they are willing to make in order to attend their chosen university. o Main finding PVH7b: There are no significant differences between Afrikaans prospective university students and English prospective students, or between English prospective students and those prospective students speaking an indigenous language with regard to the perceived sacrifice they are willing to make in order to attend their chosen university.

355 | o Main finding PVH7c: There is not a significant difference between prospective university students with different home languages with regard to the perceived benefits they believe they will derive from attending their chosen university.

H8. Prospective university students who take mathematics as a subject in grade 12 differ significantly from prospective university students who take mathematics literacy as a subject in grade 12 in terms of their level of agreement regarding perceived value that their chosen university offers. o Main finding PVH8: Prospective university students who take mathematics as a subject in grade 12 agree significantly more than prospective university students who take mathematics literacy as a subject in grade 12 with regard to the perceived sacrifice they are willing to make and with regard to the perceived benefits they believe they will derive from attending their chosen university.

H9. Prospective university students with different expected average grades for grade 12 differ significantly in terms of their level of agreement regarding perceived value that their chosen university offers. o Main finding PVH9a: Students expecting an A grade average and students expecting a B grade average agree significantly more than students expecting a C grade average and students expecting a D-F average with regard to the perceived sacrifice they are willing to make and with regard to the perceived benefits they believe they will derive from attending their chosen university. Also, students expecting an A grade average agree significantly more than students expecting a B grade average with regard to the perceived sacrifice they are willing to make and with regard to the perceived benefits they believe they will derive from attending their chosen university. o Main finding PVH9b: There are no significant differences between students expecting a C grade average and students expecting a D-F grade average with regard to the perceived sacrifice they are willing to make and with regard to the perceived benefits they believe they will derive from attending their chosen university.

H10. Prospective university students with parents who went to university differ significantly from prospective university students with parents who did not go to

356 | university in terms of their level of agreement regarding perceived value that their chosen university offers. o Main finding PVH10: Prospective university students with parents who went to university agree significantly more than prospective university students with parents who did not go to university that both the perceived sacrifice they are willing to make and the perceived benefits they believe they will derive from attending their chosen university.

6.11.2.3 Intention to enrol factor

H11. Female and male prospective university students differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university. o Main finding IEH11: There is no significance difference between female and male prospective students in terms of their level of agreement regarding their intention to enrol at their chosen university.

H12. Prospective university students with different home languages differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university. o Main finding IEH12: There is not a significant difference between prospective students with different home languages relating to intention to enrol influencing their university choice.

H13. Prospective university students who take mathematics as a subject in grade 12 differ significantly from prospective university students who take mathematics literacy as a subject in grade 12 in terms of their level of agreement regarding their intention to enrol at their chosen university. o Main finding IEH13: There is not a significant difference between prospective university students who take mathematics as a subject in grade 12 and prospective university students who take mathematics literacy as a subject in grade 12 in terms of their level of agreement regarding their intention to enrol at their chosen university.

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H14. Prospective university students with different expected average grades for grade 12 differ significantly in terms of their level of agreement regarding their intention to enrol at their chosen university. o Main finding IEH14: There are no significant differences between prospective university students with different expected grades for grade 12 in terms of their level of agreement regarding their intention to enrol at their chosen university.

H15. Prospective university students with parents who went to university differ significantly from prospective university students with parents who did not go to university in terms of their level of agreement regarding their intention to enrol at their chosen university. o Main finding IEH15: There is not a significant difference between prospective university students with parents who went to university and prospective university students with parents who did not go to a university with respect to the intention to enrol factor.

6.11.2.4 The structural model

H16. There are significant and positive interrelationships between the choice factors prospective university students consider when choosing a university, the perceived value they expect to derive from their choice, and their intention to enrol at their chosen university. o Main finding SEMH16: There is an interrelationship between the choice factors prospective university students consider when choosing a university, the perceived value they expect to derive from their choice, and their intention to enrol at their chosen university.

The following step in the research process is to take the results discussed and to make a number of recommendations in terms of how universities can utilise the knowledge gained in their marketing strategies. A number of recommendations and managerial implications are discussed in the following chapter.

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6.12 Conclusion

This chapter discusses and interprets the results obtained in the research study and addresses the sample realisation rate and demographic profile. Descriptive results, distribution of results, reliability of factors, overall mean score for factors and testing for significant differences for each of the three main components (factors) of the proposed choice model are discussed, namely choice, perceived value and intention to enrol. An EFA, 2nd order EFA as well as CFA for the choice factor and a CFA for both the Perceived Value and Intention to Enrol factor are explained and prove that the results are valid. Cronbach’s alpha coefficient for all three factors resulted in a value greater than 0.7, confirming the reliability of the results. Testing the theoretical model by the application of SEM with its reliabilities, presents findings that the proposed choice model for universities can be accepted and applied to the HE environment in South Africa. The hypothesis testing highlights the various hypotheses, the results obtained and the main finding(s) pertaining to each hypothesis. This chapter concludes with a summary of the main findings relating to the descriptive results of and hypothesis testing.

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CHAPTER 7

Overview, conclusions and recommendations

7.1 Introduction

The purpose of this chapter is to draw a number of conclusions, and suggest specific recommendations based on the results obtained in Chapter 6 and the preceding literature review presented in Chapters 2 to 4. This chapter commences with an overview of the study consisting of a brief literature overview, presenting the objectives and providing a brief overview of the methodology used in this study. A flow diagram is furthermore included, which links the objectives of the research study with the questions in the questionnaires, the resulting main findings, conclusions and the recommendations made. In addition, a number of limitations of the research study are discussed after which the chapter concludes with recommendations for future research.

7.2 Overview of the study

Against the backdrop of increased competition in the Higher Education (HE) landscape globally, Higher Education Institutions (HEIs) such as universities, realise the need to embrace marketing related ideas and practices to acquire and retain students (Harrison-Walker, 2009:103; Opoku, Hultman & Salehi-Sangari, 2008:138; Hemsley-Brown & Oplatka, 2006:316; Veloutsou, Lewis & Paton, 2004:160; Kotler & Fox, 1985:8-10). In order to attract the ‘right’ prospective students in this increasingly competitive environment, university marketers need to engage in consumer behaviour research to gain an understanding of the choice factors that are most influential in selecting a university (Petruzzellis & Romanazzi, 2010:141; Varley & Pal, 2009:311; Maringe, 2006:466; Freeman & Thomas, 2005:154; Veloutsou et al., 2004:160; Ahola & Kokko, 2001:199; Baldwin & James, 2000:141). Also, determining the prospective student’s subsequent perception of value is key since it is a determining force guiding the decision-making process (Ledden & Kalafatis, 2010:142; Blocker & Flint, 2007:249; Cronin, 2003:333).

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Choice is a function of multiple independent consumption values (LeBlanc & Nguyen, 1999:1888). It is thus important to grasp the concept of value, or value as perceived by the prospective student as it is also an accurate indicator of the student’s intent to enrol (Ledden, Kalafatis & Samouel, 2007:966; Patterson & Spreng, 1997:416).

It was therefore suggested that a model be developed to assist universities in gaining an understanding of the prospective university student’s selection-process when alternatives are evaluated. The model could assist universities in determining the key choice factors influencing prospective students’ university choice and the resultant perceived value they believe their choice university offers. This model could also further assist universities in gauging the impact that perceived value has on the prospective student’s intention to enrol at their chosen university.

To enable the researcher to gain an understanding of the different components of the theoretical model, a literature review was conducted to explore literature related to: (1) the HE landscape and its challenges (Chapter 2), (2) the decision-making process and choice factors of prospective university students as well as existing choice models (Chapter 3), and lastly, (3) perceived value and the willingness or intention to enrol (Chapter 4).

7.2.1 Literature overview

A brief summary of each of the abovementioned components of the model (choice factors, perceived value and intention to enrol) is presented in the following section. However, before these components are addressed, a brief overview of the HE landscape in which this study was conducted is presented.

7.2.1.1 The Higher Education (HE) landscape

HE plays a critical role in enhancing standards of living and it forms the foundation upon which individual countries’ economies are based. A surge in the demand for highly skilled and technologically competent individuals, has further contributed to the speed at which the growth in HE has taken place. This demand has brought forward the privatisation of HE and new types of HEIs. This growth is also evident in

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South Africa, where just over 800 000 undergraduate students enrolled in 2010 at the 23 public HEIs, and the National Development Plan forecasts that 1 067 776 students will enrol in 2016.

It was further evident from the literature review that universities are operating in a very different market than during the 1990s. There are the effects of globalisation, funding issues and increased scarcity of funds, massification and the trend of widening access, coupled with students and prospective students acting as customers with specific needs and wants.

The term customer in the university context is most often used in talking about attracting and serving students and prospective students. Prospective students are more discriminating and investigative in their selection and more demanding with the universities they choose. These customers are satisfied when the offered products and services meet their needs, desires and requests, or create customer value.

If a university does not actively respond to its potential customer base, others will Marketing research plays an important role in understanding and meeting prospective students’ needs. The challenge is further for universities to determine how to attract and retain the ‘right’ students. The ‘right’ student can be defined as the prospective student with potential and the greater ability student. They start searching for relevant information early in their decision-making process when choosing a university. It is thus key for university marketers to understand the prospective students’ behaviour while in the decision-making process, and the choice factors influencing prospective university students’ choice (Chapter 2).

7.2.1.2 Decision-making process of prospective university students

Making a choice or decision is an integral part of consumer behaviour. For this reason, consumer behaviour theory and the levels of the consumer decision-making process were investigated. The emphasis is to understand the choice factors considered during the stage of evaluating alternatives. Grasping consumer behaviour and the decision-making process in the educational context can help university marketers to better segment and target their market(s). Student or

362 | prospective student choice models were also reviewed to gain knowledge on current themes and challenges that might implicate the researcher’s own model.

It can be concluded from reviewing ten choice models, that choice is a dynamic, interactive process and a multi-dimensional concept that involves a wide range of factors. Understanding university choice and choice factors is important, as they have potential implications for universities’ future practice, policy, and research. Prospective university students have several options to choose from. This choice stimulates competition amongst universities and now more universities need to “raise their game” to attract the ‘right’ students.

It is therefore important for universities to understand what students desire and expect from the university they choose, in order to better market to them. A comprehensive set of choice factors can result in an improved prediction of students’ university choice and provides university marketers with a more accurate picture of the characteristics students believe are important in the university selection process.

Furthermore, the intangible, non-observable qualities in HE make it more difficult to assess and compare different universities, and prospective students are often influenced by perceptions and values held by not only themselves, but those significant others, such as friends, parents or teachers who can sway decisions, as can career officers. What prospective students value also becomes important and plays a role in university choice (Chapter 3).

7.2.1.3 Perceived value and willingness/intention to enrol

Understanding value and how the prospective student perceives it, is important as it drives consumer decision-making. The concept of value, however, is complex to define and to understand. The reason provided for complexity is because it is an abstract.

Value is a preferential judgment and an overall assessment by the prospective students of how a specific university will meet their needs and desires. Perceived value is defined as a trade-off between what customers receive such as quality,

363 | benefits and utilities, and what they sacrifice such as price, opportunity, cost, time and effort. The perceived value definition adopted for the purpose of this study is the notion that it is a trade-off between perceived benefits received and perceived costs or sacrifices incurred.

It is essential for universities to understand the concept of value and to create value if they want to gain sustainable competitive advantage. Knowledge of prospective students’ embedded values permits a university to position itself better and to be more strategic in how the university evaluates, develops and tailors its offerings to prospective students. The university with the highest perceived value that matches the prospective students’ wants and needs, will succeed to attract the prospective student.

In order to obtain a thorough understanding of how perceived value links to intention to enrol, the researcher reviewed fifteen existing value-intention frameworks and models (Chapter 4, Section 4.5). Except for three models, the remaining twelve value intention frameworks or models portrayed a strong or positive link from perceived value to an actual outcome (Chapter 4, Section 4.6). The conclusion is drawn that perceived value is an important factor in customers’ evaluations of post- purchase behaviour and that perceived value is a good predictor of intention to purchase (Chapter 4).

7.2.2 Objectives of the study

The primary objective of the study is to propose a model to explain prospective students’ university choice.

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value universities offer, and prospective students’ intention to enrol at their chosen university. o To measure the extent to which the different choice factors, identified through the literature review, influences prospective students’ university choice. o To assess the value prospective students perceive they will derive from their chosen university. o To gauge the intention of prospective students to enrol at their chosen university. o To determine whether groups of prospective students who exhibit different demographic characteristics differ significantly from each other in terms of the influence of different choice factors on their university choice, the perceived value that their chosen university offers, and their level of agreement regarding their intention to enrol at their chosen university. o To determine the interrelationships between the main constructs of the study in order to propose a model to explain prospective students’ university choice.

7.2.3 Methodology overview

For the purpose of this study, a cross-sectional descriptive research design was followed to obtain information from grade 12 scholars (elements) attending public schools (units) in Gauteng. Furthermore, grade 12 scholars who considered studying at a university/university of technology and who had the ability to enter a university, thus high enough grades to be accepted at a university (passing with matric exemption) were targeted. A non-probability, judgmental sampling technique was employed to select sampling units and a census of selected sampling units was conducted. Judgement sampling, is a sampling technique that requires an experienced individual to select the sample based on his or her judgement about some appropriate characteristics required of the sample element (Zikmund & Babin, 2010b:424). A paper-based self-administered drop-off questionnaire was employed to collect data and distributed to 14 selected public schools in Gauteng. Of the 1 733 questionnaires, 1 476 could be included for analysis and interpretation purposes as only these respondents indicated that they were planning to attend a university /university of technology (Chapter 5, Section 5.5.5, Step 3).

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Data was entered in SPSS, and after data had been checked for accuracy, completeness, and validity, the distribution of results was examined (Malhotra, 2007:436). For the purpose of this study, multivariate statistical techniques were suitable for analysing data. An exploratory factor analysis (EFA), a second-order exploratory factor analysis (2nd order EFA), confirmatory factor analysis (CFA) and structural equation modelling (SEM) were conducted. Data had to be reduced and simplified (EFA), verified (2nd order EFA), and refined (CFA). This was followed with an inferential analysis process, as statistical procedures were used to generalise the quantitative results obtained of the sample to make judgements about the whole population (Burns & Bush, 2006:426).

Three statistical techniques were employed to test the stated hypotheses for this study. These tests include independent-samples t-tests (parametric test), Kruskal- Wallis and Mann-Whitney U Tests (non-parametric tests).

By employing SEM the theoretical model was tested (Pallant, 2010:105; Malhotra, 2007:609:4; Tisak & Tisak, 2005; Chen, Sousa & West, 2005:487). The following section presents the conclusions and recommendations of the study.

7.3 Conclusions and recommendations for secondary objectives

This section addresses the conclusions and recommendations for each of the secondary objectives formulated for the study.

7.3.1 Secondary objective 1

To gain insight into the South African higher education (HE) environment.

HEIs, such as universities, operate in fast changing markets. Universities are facing increased competition from new types of institutions and online and for-profit private institutions. Overall success or failure is likely to be determined by how well these universities make the transition from local to regional to global players, while not losing sight of their educational objectives and their roles as developers and disseminators of knowledge and wisdom (Chapter 2).

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The literature review highlights the main challenges of the HE landscape: globalisation, funding issues (scarcity of funds), massification that refers to the mass demand for HE in general and for widening access to HE, and the phenomenon of students acting as customers, all impacting on the future of South African universities (Chapter 2, Section 2.4).

A key challenge for universities is to determine how to attract and retain students using marketing methods. A good understanding of students’ decision-making processes creates a sound basis for addressing the real, rather than the perceived needs. The quest for best possible students is on, and this places greater emphasis on universities to understand the factors that influence university decision before enrolment, thus in the pre-purchase phase (Chapter 2, Section 2.5). Based on the discussion above, the following conclusions can be drawn:  Conclusion 1.1: The main HE landscape challenges, as identified through the extant literature review (Chapter 2) of the HE landscape can be summarised as: o Globalisation which leads to an increase in the mobility of students. o Universities are experiencing a decrease in government subsidies and decreasing financial resources that have lead to increasing costs for universities. o Readdressing inequality in Higher Education opportunity leads to the widening access of HE. o Widening access of HE in South Africa specifically, leads to a greater number of poor students seeking financial assistance to attend a university. o The increasing demand for HE has resulted in the massive expansion in the number of students (massification). o Prospective students are becoming more knowledgeable about their university choices, and discerning customers who place more emphasis and concern on receiving quality education and value for money.  Conclusion 1.2: Universities can only attract prospective students and the ‘right’ prospective students if they understand their needs and wants (Chapter 2).

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Based on the literature review findings (Chapter 2), a number of recommendations are suggested:  Recommendation 1.1: Universities need to constantly adapt and reposition themselves to be global players.  Recommendation 1.2: Universities have to find new ways to generate income and to seek an increasing proportion of their funding from industry. Some departments at universities embark on industry-related courses to fill the financial gap.  Recommendation 1.3: Universities need to communicate the type of bursaries, funding options and scholarships that are possibly available to prospective students and implement transparent processes and procedures for prospective students to apply for these.  Recommendation 1.4: Since widening access leads to the increased likelihood of underprepared students entering HE, universities need to develop programmes to prepare these students specifically for (1) university life, (2) as well as providing them with the necessary life skills. In most instances, universities will need to prepare students for the academic pressures they will be experiencing by providing them with the right skills to cope in this environment. Universities need to implement the following suggested strategies to prepare students for university life: o Developing academic initiatives such as bridging/foundation or extended curriculum programmes that will help students to overcome poor schooling. o Sending students on writing courses and courses to cope with learning in a second language, usually English. o Sending students on basic computer and Internet courses, ensuring they know how to access web-based class notes and material. o Orientating students regarding attending lectures, taking notes and providing these students with the opportunity to learn about different study methods.

Universities also need to consider teaching students life skills that include:

o Communicating effectively in a wide variety of forms and contexts for a wide range of purposes and using multiple media and technologies. Practically,

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once such skill is teaching students to prepare and deliver good presentations. o Working with others respectfully and effectively to create, use and share knowledge and solutions. This can be achieved through classwork and group work. o Developing skills for becoming self-directed, independent students and workers who can adapt to change, manage projects, and take responsibility for their work.  Recommendation 1.5: Universities need to address prospective students as customers who have clearly articulated wants and needs. This can be achieved by university marketers following some suggested marketing techniques: o Gaining an understanding of prospective students’ (as customers) needs and preferences to enable marketers to anticipate and respond to these. o Adopting a marketing orientation to become a university that generates market intelligence pertaining to current and future needs of customers, dissemination of intelligence within the organisation, and to respond to these findings.  Recommendation 1.6: For universities to understand prospective students’ needs and wants, universities need to follow a studentcentric marketing approach, by determining the needs and wants of their customers; prospective students.

7.3.2 Secondary objective 2

To provide an overview of the extant literature related to the main constructs of the study, namely the choice factors influencing university choice, the perceived value universities offer and prospective students’ intention to enrol at their chosen university.

Drawing from the literature review in terms of choice (Chapter 3), the following conclusions can be made:  Conclusion 2.1: The identification of a comprehensive set of choice factors can result in an improved prediction of prospective students’ university choice and

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provides university marketers with a more accurate picture of the characteristics students believe are important in the university selection process.  Conclusion 2.2: Prospective students’ decision-making process is complex as the outcome involves the filtering of many layers of information that even for prospective students with access to quality information, is a challenging task.

Based on the literature review (Chapter 3) findings, in terms of choice a number of recommendations are suggested:  Recommendation 2.1: For universities to attract new and the ‘right’ prospective students they need to engage in consumer behaviour research, by: o Gaining an understanding of the prospective students’ decision-making processes. o Determining the choice factors that are most influential in selecting a university.  Recommendation 2.2: A comprehensive set of choice factors should be obtained, by: o Researching the prospective university students’ requirements, finding out the motivation of why they choose to enrol at a certain university.  Recommendation 2.3: Universities should reduce the complexity of decision- making, by: o Communicating the information that is truly important to prospective students. o Communicating information that these prospective students want and value. o Simplifying the university’s positioning through clear identification and communication of what will be ‘bought’ (or gained) when enrolling at the particular university.

Drawing from the literature review (Chapter 4) in terms of perceived value, the following conclusions can be made:  Conclusion 2.4: Value as perceived by prospective students drives consumer decision-making.  Conclusion 2.5: The exchange of value to prospective students will result in these students giving their ‘choice’ to the university offering with the highest value in return.

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 Conclusion 2.6: Prospective students determine perceived value, and this takes place as a trade-off between ‘give’ and ‘get’ factors. These ‘give’ and ‘get’ factors each consist out of the following: o The ‘give’ factor is also known as the sacrifice factor and consists of perceived monetary sacrifice and perceived non-monetary sacrifice. In the HE context, non-monetary sacrifices include sacrifices such as time, energy and effort (Chapter 4, Section 4.3.1.2). o The ‘get’ factor is also known as the benefit factor and consists of perceived quality or functional value, emotional value, social value, epistemic value, conditional value, reputational and image value and market value (Chapter 4, Section 4.3.1.1).

Based on the literature review (Chapter 4) findings, in terms of perceived value a number of recommendations are suggested:  Recommendation 2.4: Universities need to determine what prospective students value when choosing which university to attend.  Recommendation 2.5: Universities need to create value that matches prospective students’ needs to attract these prospective students.  Recommendation 2.6: Marketers should consider the trade-off of both the ‘give’ and ‘get’ factors that constitute perceived value, by: o Reducing time, effort and search costs that in turn should reduce perceived sacrifice and thereby increase perceptions of value. o Enhancing the value of the ‘get’ factors, particularly by: o Providing quality degrees and delivering exceptional service quality. o Providing social activities at the particular university that prospective students find interesting, as well as the possibility of including groupwork into the curriculum. o Providing courses that contribute to the high value of prospective students’ education (epistemic value). o Providing campus facilities that are up-to-date and maintained (conditional value). o Building and then communicating a favourable reputation of the university.

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Drawing from the literature review (Chapter 4) in terms of intention to enrol, the following conclusions can be made:  Conclusion 2.7: Perceived value is a good predictor of ‘intention’, such as the intention to buy.  Conclusion 2.8: Particularly, the university image has a strong influence on purchase intention.

Based on the literature review findings (Chapter 4), in terms of intention to enrol the following recommendation is suggested:  Recommendation 2.7: Universities marketers need to define the university’s image and develop clear branding strategies and campaigns to communicate this.

7.3.3 Secondary objective 3

To measure the extent to which the different choice factors, identified through the literature review, influence prospective students’ university choice.

The literature review (Chapter 3) indicates that several choice factors are integral in the higher educational decision-making process. The choice to attend a specific university is also widely viewed as a complex and multi-stage process.

Ten student university choice models were investigated (Chapter 3, Section 3.3.2). It is evident from the models that prospective students typically enter into an information search process followed by an evaluation of alternatives. Prospective students furthermore evaluate their alternatives by comparing and analysing the key factors before a decision is taken.

The literature review reveals several key choice factors that influence prospective students’ university choice (Chapter 3, Table 3.2). The nine key factors include: the course and programme offered, employment opportunities, image, quality, facilities, location, safety, price/cost and reputation (Chapter 3, Section 3.4.2).

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With respect to the empirical results of the study, the main findings of the descriptive results reveal that the type of courses, the variety of courses, the reputation of courses and good university resources for students (computers, Library, etc.) are the most important choice factors influencing respondents’ university choice (main finding C1, Chapter 6, Section 6.7.1). The results further indicate (1) that the choice construct consists of seven factors, namely reputation, cultural acceptance, accessibility-location, accessibility-price, physical evidence, prestige/prominence and future employability (main finding C2, Chapter 6, Section 6.7.4), (2) all factors were measured with reliable scales (main finding C3, Chapter 6, Section 6.7.5), (3) overall mean scores above the mid-point of the scale were realised for all factors concerned (main finding C4, Chapter 6, Section 6.7.6).

The latter indicates that the seven factors are all important in influencing prospective students’ university choice. The three factors with the highest mean scores are reputation, future employability and physical evidence. The lowest mean score was realised by the accessibility-price factor (Chapter 6, Sections 6.7.4 & 6.7.6).

Hypothesis testing revealed significant differences between prospective university students, based upon their demographic characteristics, and in terms of different choice factors that influence their university choice (H1 to H5, Chapter 6, Section 6.7.7). There are significant differences between respondents based upon gender, home language, mathematics or mathematics literacy as subject taken in grade 12, expected average grade for grade 12, and parents who went to university and those who did not go to university (H1 to H5, Chapter 6, Section 6.7.7) with respect to the extent to which different choice factors influence their decision-making (main findings CH1 to CH5, Chapter 6, Section 6.11.2).

Based on the discussion above, a number of conclusions can be drawn:  Conclusion 3.1: The key choice factors confirmed through CFA, influencing prospective university students are: o reputation o future employability o physical evidence o cultural acceptance 373 |

o accessibility-location o prestige/prominence o accessibility-price (Since the accessibility-price factor was not retained as part of the structural model due to multicollinearity, which is the strong correlations between accessibility-price and accessibility-location (Chapter 6, Section 6.10), subsequent recommendations are not made).  Conclusion 3.2: The three key choice factors, influencing prospective university students’ university choice are: o reputation (which includes the type and reputation of the courses) o future employability o physical evidence  Conclusion 3.3: Significant differences exist between prospective university students in terms of the different choice factors that influence their university choice, based upon gender, home language, whether mathematics or mathematics literacy as grade 12 subject is taken, the average grade for grade 12 they expect, and whether prospective university students’ parents went to university or did not go to university.

Based on the major findings, a number of recommendations are suggested:  Recommendation 3.1: Universities need to build their reputation as the more favourable the prospective students’ perception of the reputation, the higher the students’ loyalty will be and the better the likelihood of applying at that particular university (Petruzzellis & Romanazzi, 2010:152). Reputation can be enhanced by: o Evaluating teaching quality and academic quality by employing well-known lecturers and skilled lecturers who can provide the quality. o Providing the information on the specific departments and courses, specifically promoting internship programmes that some of these departments organise, communicate the successes of past students and the companies they currently work at, as well as promoting staff’s successes. o Ensuring information is available and covers topics such as the university’s reputation and staff’s reputation and achievements.

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o Developing a clear brand position within the chosen market. Universities need to decide what it is that makes them different, or what they do differently or better, and this needs to be communicated to prospective students to position a particular image of the university in the minds of the prospective student. Universities could focus on quality, specifically communicating the quality educational services and quality support services they render, by providing specific examples such as an innovative online application process that tracks the prospective students’ application and communicates the progress by sending an update via sms or email (or twitter). o Building branding and strategic image campaigns that go beyond basic student recruitment and begin to explore a genuine, institution-wide focus on the characteristics that can form a university’s strengths and competitive advantages. o Focusing on activities that support a clear mission that fulfils a perceived need within a well-conceptualised market segment. o Building a brand that has a positive image with possible employers, thus interaction is necessary between the university and the possible employers of students. o Considering the type, variety and reputation of courses on offer. o Evaluating if the courses on offer, are the courses that the job market is interested in.  Recommendation 3.2: Since cultural acceptance was confirmed as a choice factor, universities need to create an emotionally safe environment that will make prospective students feel at home, by: o Avoiding the creation of a single overriding culture through the uniform assimilation of cultures, but to respect and accommodate the diversity of individuals and communities. o Providing prospective students with the platform to have interactions (social areas, campus newspapers, campus radio stations, other social activities that are university related) and to build relationships with fellow prospective students. These interactions will create a sense of belonging. o Drawing on common interests that are displayed by everyone such as sport, cultural interests (a university theatre production or art exhibition), and

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acceptance of constitutional values, love of the country and all its people, and advancement of economic interest.  Recommendation 3.3: Since accessibility-location was confirmed as a choice factor, university marketers need to employ the following marketing strategies to address issues relating to accessibility-location, by: o Promoting the various transport options and possibilities available to students, and ensuring that information regarding transport is easily available and accessible. Timetables and pricelists of buses and shuttles to the university, as well as between different campuses should be communicated. o Communicating the availability of accommodation (other than residence) near the campus. o Communivating the location of shops/malls and health services that are near the campus.  Recommendation 3.4: Since, the physical environment in which the HE institution provides its services constitute an important choice factor in the prospective students’ decision-making process, universities need to ensure that facilities are attractive and up to standard, by: o Maintaining the standard and quality of library facilities. o Implementing and maintaining sophisticated academic technology (access to the most recent databases and latest model laptops with relevant IT programmes on the laptops. Lecturers furthermore need to be educated and trained in using web books, tablets and smart phones, utilising the Internet and how to integrate this into their lectures and how to use it for research). o Implementing and maintaining sophisticated lecturing technology (attractive lecturing halls with up-to-date technology for presentations, playing voice and video clips, availing access to Internet facilities, developing of cellphone/smart phone, tablets and web books applications for students to access course material and messages. Lecturers furthermore need easily accessible computer facilities and computer programmes to post class notes, articles or videos for students. Students will probably in future collaborate with their fellows on in-class projects in real-time).

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o Maintaining classroom buildings, grounds and residence halls, ensuring that everything is clean, gardens are well kept, buildings are regularly painted, and that facilities are conducive as working environment.  Recommendation 3.5: Since prestige/prominence was confirmed as a choice factor, university marketers need to employ the following marketing strategies to address issues relating to prestige/prominence, by: o Ensuring that hostel or residential facilities are maintained and that surrounding areas are clean, the gardens are well kept and the furniture in reception areas in particular is clean, intact and functional. o Raising awareness of the successes of sports teams, the sports facilities available to students, and the possible sporting opportunities at the university. o Raising awareness of the successes of any cultural activities, a choir’s special performances or prizes won, or students performing exceptionally. o Communicating special visits or linkages of famous or prominent people with the university. o Communicating alumni’s success stories, specifically those who have become famous or prominent people.  Recommendation 3.6: Since prospective students consider future employability as an important choice factor when selecting a university, universities are advised to assist in the preparation of the students to become more employable by: o Providing internships (or the opportunity of internships). o Providing prospective students with information related to the university’s role in preparing students for careers, programmes/courses and price, as this type of information is considered as being critical for decision-making. o Evaluating the academic preparation of prospective students’ chosen careers, as this should be based on the real needs expressed by the current South African and global job market.  Recommendation 3.7: Because significant differences exist between prospective university students in terms of different choice factors (H1 to H5, Chapter 6, Section 6.8.2), universities need to understand prospective students’ needs and wants and adapt their marketing strategies to attract and entice the relevant students (Moogan, 2011:572), by: o Differentiating market-offerings to enable targeting of distinct markets.

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o Selecting target markets and developing a distinct position. o Assigning marketing mixes to create planned positions.

7.3.4 Secondary objective 4

To assess the value prospective students perceive they will derive from their chosen university.

The literature review (Chapter 4) indicates that choice factors alone are not enough to understand prospective students’ complex evaluation of possible universities, but that value has also become important. Value, as perceived by prospective students, influences purchase (or enrolment) when the perceived benefits obtained outweigh the perceived sacrifices incurred (Chapter 4, Section 4.1).

Fifteen value-intention frameworks and models were investigated (Chapter 4, Section 4.5). It is evident from these models that nine of these models’ authors agree that perceived value is a trade-off between benefits received and sacrifices incurred (Chapter 4, Section 4.6). The benefits factor evaluated by prospective students include perceived quality and functional value, social value, emotional value, epistemic value, conditional value, reputation and image, and market value received (Chapter 4, Section 4.3.1.1). Particularly, prospective university students’ perception of the image of a university will increase the perceived value of that particular university. The sacrifices factor as evaluated by prospective students includes monetary and non-monetary sacrifices the customer or prospective student incurred. Non-monetary sacrifices also include associated risk assumptions regarding the purchase including time costs, search costs, learning costs, emotional costs and cognitive and physical effort, coupled with social and psychological risks (Chapter 4, Section 4.3.1.2, Section 4.4).

With respect to the empirical results of the study, the main findings of the descriptive results reveal that prospective university students value the attainment of knowledge most, followed by the value of achieving career goals as a result of studying at their chosen university (main finding PV1, Chapter 6, Section 6.8.1). The results further indicate that prospective students value the reasonable price paid for their education

378 | the least, and although they agree that they are prepared to give up some of their interests to attend their chosen university, it is not as strongly valued as gaining knowledge (main finding PV2, Chapter 6, Section 6.8.1).

The results further indicate that: (1) the perceived value construct consists of two factors, namely perceived benefits received and perceived willingness to sacrifice (main finding PV3, Chapter 6, Section 6.8.3), (2) all factors were measured with reliable scales (main finding PV4, Chapter 6, Section 6.8.4), (3) overall mean scores above the mid-point of the scale were realised for all factors concerned (main finding PV5, Chapter 6, Section 6.8.5).

The latter indicates that both perceived value factors are important when prospective students regard the perceived value that their chosen university offers. The factor with the highest mean score is perceived benefits, indicating that benefits offered by the chosen university, and as perceived by prospective students, play a greater role when perceived value is concerned (Chapter 6, Section 6.8.5).

Hypothesis testing revealed significant differences between prospective university students, based upon their demographic characteristics, and in terms of their level of agreement regarding the perceived value that their chosen university offers (main findings PVH6 to PVH10, Chapter 6, Section 6.11.2). There are significant differences between respondents based upon gender, home language, mathematics or mathematics literacy as subject taken in grade 12, expected average grade for grade 12 and parents who went to university and those who did not go to university (H6 to H10, Chapter 6, Section 6.8.6) with respect to the level of agreement regarding the perceived value that their chosen university offers (main findings PVH6 to PVH10, Chapter 6, Section 6.11.2).

Based on the discussion above, a number of conclusions can be drawn:  Conclusion 4.1: Prospective university students value the attainment of knowledge most, followed by the value of achieving career goals as a result of studying at their chosen university (main finding PV1, Chapter 6, Section 6.8.1).

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 Conclusion 4.2: The key perceived value factors (or dimensions) confirmed through CFA, are: o perceived willingness to sacrifice, and o perceived benefits received (main finding PV3, Chapter 6, Section 6.8.3).  Conclusion 4.3: Significant differences exist between prospective university students in terms of their level of agreement regarding perceived benefits they believe they will derive from attending their chosen university, based upon gender, whether mathematics or mathematics literacy as grade 12 subject is taken, the average grade they expect for grade 12, and whether prospective university students’ parents went to university or did not go to university (main findings PVH6, and PVH8 to PVH10, Chapter 6, Section 6.11.2).  Conclusion 4.4: Significant differences exist between prospective university students in terms of their level of agreement regarding the perceived sacrifices they are willing to make in order to attend their chosen university based upon home language, whether mathematics or mathematics literacy as grade 12 subject is taken, the average grade they expect for grade 12, and whether prospective university students’ parents went to university or did not go to university (main findings PVH7 to PVH10, Chapter 6, Section 6.11.2).  Conclusion 4.5: In two instances no significant differences between prospective students with regard to the following two perceived value factors are evident: o There are no significant differences between prospective students with different home languages with regard to the perceived benefits they believe they will derive from attending their chosen university (main finding PVH7c, Chapter 6, Section 6.8.6.2). o There are no significant differences between female and male prospective university students with regard to the perceived sacrifices they are willing to make in order to attend their chosen university (main finding PVH6b, Chapter 6, Section 6.11.2).  Conclusion 4.6: Prospective university students’ perception of the image of a university will particularly increase the perceived value of that particular university (Chapter 4, Section 4.3.1.1 and Section 4.5).

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Based on the discussion above, a number of recommendations can be made:  Recommendation 4.1: University marketers should incorporate the main reason for prospective students wanting to attend a university into their marketing campaigns, which is to gain the knowledge that prospective students believe they need to be successful in their chosen careers, by o Communicating the value prospective students will receive through the attainment of knowledge at the specific university under question.  Recommendation 4.2: Because significant differences exist between prospective university students in terms of their level of agreement regarding perceived benefits they believe they will derive from attending their chosen university, based upon gender, whether mathematics or mathematics literacy as grade 12 subject is taken, the average grade they expect for grade 12, and whether prospective university students’ parents went to university or did not go to university (main findings PVH6, and PVH8 to PVH10, Chapter 6, Section 6.11.2), universities should adapt marketing strategies by: o Differentiating market-offerings to enable segmentation and targeting of distinct markets. o Developing tailor-made communication strategies to reach these distinct markets. o Focusing marketing resources on areas which prospective students value.  Recommendation 4.3: Because significant differences exist between prospective university students in terms of their level of agreement regarding the perceived sacrifice they are willing to make in order to attend their chosen university based upon home language, whether mathematics or mathematics literacy as grade 12 subject is taken, the average grade for grade 12 they expect, and whether prospective university students’ parents went to university or did not go to university (main findings PVH7 to PVH10, Chapter 6, Section 6.11.2), universities should adapt marketing strategies by: o Incorporating all the recommendations made at ‘recommendation 4.2’. o Communicating quality, because perceived value in the form of quality at the one end of the spectrum will outweigh the sacrifices prospective students are willing to make on price (fees).

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o Communicating value for money (and creating value for money), to improve the perception that the price (fees) paid for attending university seems reasonable.  Recommendation 4.4: When no significant differences between prospective students with regard to the following two instances are evident, university marketers need not to differentiate their marketing offering with respect to: o The different home languages and the perceived benefits these prospective students believe they will derive from attending their chosen university (main finding PVH7c, Chapter 6, Section 6.8.6.2). o Female and male prospective university students with regard to the perceived sacrifices these prospective university students are willing to make in order to attend their chosen university (main finding PVH6b, Chapter 6, Section 6.11.2).  Recommendation 4.5: University marketers need to build a positive image of the university that includes the value of the degree, and the perception of learning that occurs, by: o Communicating the benefits derived from studying at that particular university.

7.3.5 Secondary objective 5

To gauge the intention of prospective students to enrol at their chosen university.

The literature review (Chapter 4) indicates that perceived value is an accurate indicator of prospective students’ intention to enrol .This is further evident from twelve of the value-intention frameworks and models reviewed in this study. These twelve value-intention frameworks and models portray a strong or positive link from perceived value to an actual outcome such as the intention to buy, the intention to re-purchase, the intention to deliver positive word-of-mouth, the intention to recommend, and the outcome of satisfaction (Chapter 4, Section 4.5).

With respect to the empirical results of the study, the main findings of the descriptive results reveal that prospective university students agree strongly that an available place at their chosen university will influence their intention to enrol (main finding IE1, Chapter 6, Section 6.9.1). The results further indicate that prospective students

382 | are not driven by guilt, as the statement with the lowest level of agreement was given to “I would feel guilty if I go to another university” (Chapter 6, Section 6.9.1).

The results further indicate that: (1) that the intention to enrol construct consists of one factor (main finding IE2, Chapter 6, Section 6.9.3), (2) the factor was measured with a reliable scale (main finding IE3, Chapter 6, Section 6.9.4), (3) an overall mean score above the mid-point of the scale was realised for all factors concerned (main finding IE4, Chapter 6, Section 6.9.5).

Hypothesis testing revealed there are no significant differences between prospective university students, based upon their demographic characteristics, and in terms of their level of agreement regarding the intention to enrol at their chosen university (main findings IEH11 to IEH15, Chapter 6, Section 6.11.2). There are no significant differences between respondents based upon gender, home language, mathematics or mathematics literacy as subject taken in grade 12, expected average grade for grade 12, and parents who went to university and those who did not go to university (main findings IEH11 to IEH15, Chapter 6, Section 6.11.2) with respect to their level of agreement regarding the intention to enrol at their chosen university.

Based on the discussion above, a number of conclusions can be drawn:  Conclusion 5.1: Prospective students exhibited the strongest level of agreement with the intention to enrol statement that if a place is available at their chosen university, they will attend it (main finding IE1, Chapter 6, Section 6.9.1). It can thus be concluded that the perception of an available ‘spot’ at a prospective student’s chosen university impacts upon the decision when considering enrolling at the chosen university.  Conclusion 5.2: There are no significant differences between respondents based upon gender, home language, mathematics or mathematics literacy as subject taken in grade 12, expected average grade for grade 12, and parents who went to university and those who did not go to university (main findings IEH11 to IEH15, Chapter 6, Section 6.11.2) with respect to their level of agreement regarding the intention to enrol at their chosen university.

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 Conclusion 5.3: The perceived image of a university has a direct relation to purchase intention in HE (Chapter 4, Section 4.4).

Based on the discussion above, the following recommendations can be made:  Recommendation 5.1: University marketers need to ensure that a transparent application process exists and that it is communicated to prospective students. Information material sent or communicated to prospective students needs to state how many places on different courses (or programmes) are available, and what the selection criteria and deadlines are.  Recommendation 5.2: Marketers do not have to differentiate their marketing offering when no significant differences between prospective students with regard to intention to enrol exist.  Recommendation 5.3: University marketers should anticipate prospective students’ behaviour, as those prospective students with a behaviour tendency to enrol at a specific university would avoid going to another university. This could be achieved by: o Communicating with the prospective students who are likely to enrol by giving them assurance that the particular university values their possible enrolment.  Recommendation 5.4: Perceived value is a good predictor of intention to enrol, and marketers need to determine which perceived benefits and perceived sacrifices prospective students weigh and consider when arriving at the decision of which university they intent to enrol at. Communication strategies should focus on these selected perceived benefits prospective students value (attaining knowledge, achieving career goals).  Recommendation 5.5: Universities should provide service quality as it is strongly linked to value creation in HE, and it has an indirect effect on prospective students’ behavioural intentions. Practically, service quality means providing efficient support services such as access to relevant staff, friendly help from relevant staff, and easily accessible information from websites.

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7.3.6 Secondary objective 6

To determine the interrelationships between the main constructs of the study in order to propose a model to explain prospective students’ university choice

The theoretical model was tested by the application of structural equation modelling (SEM). SEM evaluates the measurement model (the first model that involves a CFA for each latent variable) and the structural model separately (Chapter 5, Section 5.5.6.3). This statistical technique is furthermore used to test the causal relationships among the theoretical model’s three constructs, namely the choice construct, perceived value construct and intention to enrol construct (Chapter 6, Section 6.10).

With respect to the empirical results of the study, the main findings of the measurement model relating to the choice factors and perceived value revealed that the choice factor, accessibility-price had to be removed from the model because of multicollinearity (Chapter 6, Section 6.10.1). The choice factors retained for the structural model included six of the original seven choice factors: reputation, cultural acceptance, accessibility-location, physical evidence, prestige/prominence and future employability (Chapter 6, Section 6.10.2). Further analysis indicated that three choice factors namely, reputation, cultural acceptance and accessibility-location exhibit a significant direct path to perceived willingness to sacrifice (main finding SEM1a, Chapter 6, Section 6.10.2). All six choice factors exhibit a significant direct path to perceived benefits (main finding SEM1b, Chapter 6, Section 6.10.2).

Furthermore, with respect to the empirical results of the study, the main findings relating to the perceived value factors and intention to enrol revealed that the perceived willingness to sacrifice factor exhibits a significant direct path to intention to enrol (main finding SEM1c, Chapter 6, Section 6.10.2). Also, the perceived benefit factor exhibits a significant direct path to intention to enrol (main finding SEM1d, Chapter 6, Section 6.10.2).

The results therefore revealed that there are significant and positive interrelationships between the choice factors prospective university students

385 | consider when choosing a university, the perceived value they believe to derive from their choice, and their intention to enrol at their chosen university (main finding SEMH16, Chapter 6, Section 6.11.2).

Based on the previous discussion, a number of conclusions can be drawn:  Conclusion 6.1: Six choice factors exhibit a significant direct path to perceived benefits, while only three choice factors exhibit a significant direct path to perceived willingness to sacrifice (main finding SEM1a and SEM1b, Chapter 6, Section 6.11.2, Figure 6.2).

The significant paths between the choice factors and perceived benefits are as follows: o Reputation perceived benefits o Cultural acceptance perceived benefits o Accessibility-location perceived benefits o Physical evidence perceived benefits o Prestige/prominence perceived benefits o Future employability perceived benefits It is therefore evident that reputation, cultural acceptance, accessibility-location, physical evidence, prestige/prominence, and future employability influence the perceived benefits the prospective students believe they derive from their chosen university. Prospective students believe they derive benefits of functional value, social value, epistemic value, emotional value, conditional value, image and reputational value because of their chosen university’s reputation, cultural acceptance, accessibility-location, physical evidence, prestige/prominence and future employability.

The significant paths between the choice factors and perceived willingness to sacrifice are as follows: o Reputation perceived willingness to sacrifice o Cultural acceptance perceived willingness to sacrifice o Accessibility-location perceived willingness to sacrifice It is therefore evident that these three choice factors influence prospective students’ willingness to sacrifice their time and interests, and they are willing to 386 |

make financial sacrifices for the reputation, cultural acceptance and for their chosen university’s accessibility-location.  Conclusion 6.2: Both perceived value factors exhibit a significant direct path to intention to enrol (main finding SEM1c and SEM1d, Chapter 6, Section 6.11.2, Figure 6.2).

The significant paths between perceived value and intention to enrol are as follows: o Perceived benefits intention to enrol o Perceived willingness to sacrifice intention to enrol It is thus evident that these two perceived value factors influence prospective students’ intention to enrol. Thus, the perceived benefits (functional value, social value, epistemic value, emotional value, conditional value, image and reputational value) students believe they will derive from their chosen university and the sacrifices these prospective students are willing to incur (sacrifice their time and interests, and they are willing to make financial sacrifices), will also influence their intention to enrol at their chosen university.  Conclusion 6.3: There is an interrelationship between the choice factors prospective university students consider when choosing a university, the perceived value they believe to derive from their choice, and their intention to enrol at their chosen university (main finding SEMH16, Chapter 6, Section 6.11.2).

Since recommendations regarding the main constructs, i.e. choice factors, perceived value and intention to enrol, have already been addressed under the various secondary objectives, they will not be repeated again in this section. However, it is important that marketers consider the model as a whole as interrelationships between these main constructs do exist.  Recommendation 6.1: Universities should put equal effort into understanding the choice factors influencing prospective students’ university choice and the perceived value prospective students believe their chosen university offers as interrelationships exit between these constructs.

Figure 7.1 presents the structural model with its relevant significant paths as tested and verified by employing structural equation modelling (SEM).

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Figure 7.1 The structural model

In summary it can be said that the structural model (Figure 7.1) indicates that all six choice factors have a significant path to perceived benefits, but only three choice factors have a significant path and relationship to perceived willingness to sacrifice. The reputation, cultural acceptance, accessibility-location, physical evidence, prestige/prominence, and future employability influence the perceived benefits (functional value, social value, epistemic value, emotional value, conditional value, image and reputational value) the prospective students believe they derive from their chosen university. Three choice factors, namely reputation, cultural acceptance and accessibility-location influence prospective students’ willingness to sacrifice their time and interests, as well as their financial sacrifices for the reputation, cultural acceptance and for their chosen university’s accessibility-location.

It is furthermore evident that both the perceived benefits students believe they derive from their chosen university, and the sacrifices these prospective students are willing to incur, influence their intention to enrol at their chosen universities.

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Existing educational choice models (Chapter 3, Section 3.3.2.2) indicated that prospective students evaluate their alternatives and during this phase the choice factors are compared and analysed. The literature review revealed nine key choice factors including course and programme offered, employment opportunities, image, quality, facilities, location, safety, price/cost (fees) and reputation. These are very similar choice factors to this particular study. However, results of this study revealed that cultural acceptance currently plays a greater role in South African than was evident in the literature review.

Reviewing existing value-intention frameworks or models (Chapter 4, Section 4.6) revealed that most authors agreed that perceived value is the trade-off between benefits received and sacrifices made. This finding is consistent with the findings of this study. It was further evident that most of the discussed value-intention frameworks or models in the literature review, indicated a strong or positive link from perceived value to an actual outcome. Specifically, the explored literature portrayed the actual outcome achieved as a result of perceived value derived as: positive- word-of-mouth, recommendations, satisfaction, loyalty, re-purchasing of a product or service, or the actual purchase of a product or service. The results of the structural model also indicated a positive link or outcome between perceived value to an actual outcome, however, in this instance the outcome was the prospective student’s intention to enrol.

7.4 Linking the objectives, hypotheses, questions in the questionnaire, the findings, conclusions and recommendations

Figure 7.2 provides a flow diagram that summarises how the primary and secondary objectives, questions in the questionnaire, hypotheses, findings, conclusions and recommendations are linked together.

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Figure 7.2 A Summary of the primary and secondary objectives, dbertonquestions in the questionnaire, hypotheses, findings, conclusions and recommendations.

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Since the conclusions and recommendations for the research study have been finalised, it is necessary to highlight some of the limitations that the researcher faced during the study.

7.5 Limitations

No research study is without limitations and a number of limitations relating to the literature review and empirical research of the study are identified in the following paragraphs.

7.5.1 Limitations of the literature review

The following limitations to the literature review are identified: o Although a limited number of research studies have been conducted on choice factors in the South African higher education (HE) landscape, their related marketing strategies mainly focused on communication strategies and not marketing strategies. o Studies conducted to determine prospective students’ choice factors have mostly been conducted with first-year students who have already been through the decision-making process and not with the scholars who are currently in the process of choosing a university. o Limited research has been done on the prospective student as customer in South Africa, thereby forcing the researcher to rely more on international sources for the literature review. o Limited research has been done on prospective students’ perceived value and what they value when choosing a university in South Africa, thereby forcing the researcher to rely more on international sources for the literature review. o The majority of literature covering intention to purchase, or willingness to buy is mostly international and limited information could be found on this topic in the HE landscape either internationally or in South Africa. o Limited research has been done on intention to enrol in the HE landscape and a number of assumptions had to be made by adapting theory to the HE landscape. o Although choice models and value-intention frameworks and models have been investigated to provide a foundation for the researcher’s own model, limited

391 |

research specifically addresses prospective students’ choice factors, perceived value and intention to enrol in a single study, either internationally or in South Africa. o Few models have been suggested for HEIs, specifically universities, and tested by employing structural equation modelling (SEM). The researcher had to rely on research studies conducted in the psychology field to gain knowledge on how SEM can be applied to build and test theoretical models.

A number of limitations associated with the empirical research is also identified in the following paragraph.

7.5.2 Limitations of the empirical research

A number of limitations should be highlighted, following the empirical research part of the study. o Due to time and budget constraints, the study had to focus on a sample only representing Gauteng. With a bigger budget and more time, the researcher would have been able to conduct the research on a bigger scale, thereby increasing the representation of other provinces and perhaps uncovering more subtle differences between prospective students. o The list of choice factors presented to prospective students has not included ‘process’-type statements like service encounters with faculty staff and application related questions, as not all scholars would have been through the entire decision-making process at the time of completing the questionnaire. o Due to the nature of this study (non-probability sampling), the non-response error and sampling error could not be determined. o Although the initial questionnaire (Appendix H3) was pre-tested with grade 12 scholars in 2009, the refined pilot questionnaire (Appendix H2) could have been pre-tested not only with first year students but with grade 12 scholars too. o Due to the sensitive nature of ‘culture’, especially in the South African context, it is suggested that future studies should explore the meaning of culture and the role it plays in prospective students’ university choice in more detail. o Although every effort has been made to include representatives from all the home language groups in South Africa, more Afrikaans speaking prospective students

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participated in the study.

Knowing the limitations of the study, it is also possible to make a few recommendations in terms of possible future research.

7.6 Recommendations for future research

A number of recommendations for future research are suggested, including: o A similar study can be conducted with prospective students planning to study different courses/degrees to determine if there are similarities with the main findings from this study. o A similar study can be conducted by targeting equal numbers of prospective students speaking English, Afrikaans and African home languages planning to determine if there are similarities with the main findings from this study. o A similar study can be conducted with prospective students from other provinces to determine if there are similarities with the main findings from this study. o A similar study can be implemented to determining the similarities or differences between prospective South African students and prospective students from other countries. o It could prove valuable to conduct in-depth research by employing both quantitative and qualitative research techniques on each of the choice factors to gain an even better understanding of how prospective students define and value them. o It could prove valuable to conduct in-depth research by employing both quantitative and qualitative research techniques on defining and refining the perceived value factor in the HE context. o It could prove valuable to conduct in-depth research by employing both quantitative and qualitative research techniques on defining and refining the intention to enrol factor in the HE context. o Literature on related aspects in higher education marketing should be expanded by means of relevant research in South Africa. o South African higher education institutions should engage in longitudinal research studies on aspects such as prospective students’ socio-demographic information, reasons for choosing a university, determining what prospective

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students value, and how they view intention to enrol to improve their understanding of their prospective students’ needs in order to implement more effective marketing and communication strategies. o ‘Process’-type choice factors can be included in the model to determine the possible influence and relative importance of these when prospective students consider attending a university/university of technology. o Researchers should revisit statements included in the final questionnaire to ensure face or content validity of statements included in the scales for the purposes of their particular study. o Researchers should revisit the scale questions in the final questionnaire and consider the possible inclusion of the “don’t know” option.

7.7 Conclusion

Chapter 7 addresses the various secondary objectives, related findings, conclusions, recommendations and limitations of the study. After providing a brief overview of the study, a number of conclusions are drawn for each secondary objective, which are based on the main findings formulated in Chapter 6. In addition, a number of recommendations pertaining to the conclusions are formulated in order to give universities direction in terms of how their marketing strategies should be adapted when attracting new and the ‘right’ prospective students. Thereafter, Figure 7.2 provides a summary of the various secondary objectives and links them with the questions in the questionnaire, the hypotheses, the related main findings, conclusions and recommendations. This chapter concludes with a number of limitations pertaining to the literature review and the empirical analysis, as well as possible future research opportunities.

The research contributes to the the body of knowledge on the interrelationships between the choice factors prospective students consider when choosing a university, the perceived value they expect to derive from their choice, and their intention to enrol at their chosen university. Higher Education Institutions (HEIs) such as universities and university marketers could consider and employ recommendations made in this study, as attracting the ‘right’ students become more difficult in an increasingly competitive Higher Education (HE) landscape.

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The findings of this study provide an understanding into the choice factors that are most important to students when choosing a university that include the type of courses, the variety of courses, the reputation of courses, and the university’s resources for students (computers, library, etc). It is also evident that prospective students are willing to incur price sacrifices to gain access to a reputable university. The findings furthermore provides an understanding into the perceived value prospective students expect, and the attainment of knowledge followed by the value of achieving career goals as a result of studying at a prospective student’s chosen university are valued the most. A reasonable price paid for education was valued the least. This study also provides insight into the intention to enrol factor where prospective students agree strongly that availability at their chosen university will influence their intention to enrol. Results could be used for effective planning and resource allocation for recruitment, communication and marketing.

The findings and recommendations in this final chapter contribute to a growing knowledge on higher education marketing. In particular, the research adds to the understanding of why students choose a higher education institution in South Africa and what they value most that will ultimately lead to their intention to enrol. South African universities could use the information from the study to become more marketing-oriented and to adapt their marketing mix to correspond with the findings of the study.

To summarise, the purpose of the study is to develop a model to explain prospective students’ university choice. This is achieved by better understanding prospective university students’ choice factors influencing university choice and the perceived value they believe their chosen university offers. Perceived value is the trade-off between perceived willingness to sacrifice and perceived benefits received that ultimately leads to prospective students’ intention to enrol at their chosen university.

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Choice factors and the perceived value that influence prospective university students’ intention to enrol - a choice model

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APPENDIX A

Public Higher Education Institutions in South Africa

Cape Peninsula University of Technology Founded: 1 January 2005 1. Number of students: Over 28 000 Location: Western Cape 2. Central University of Technology Founded: 1981 Number of students: Over 10 100 Location: Free State 3. Durban University of Technology Founded: 2002 Number of students: Over 20 000 Location: KwaZulu-Natal 4. Nelson Mandela Metropolitan University Founded: 1 January 2005 Number of students: Over 23 700 Location: Eastern Cape and Western Cape 5. North-West University Founded: 1 January 2004 Number of students: Over 47 000 Location: North-West and Gauteng 6. Rhodes University Founded: 1904 Number of students: Approximately 6 000 Location: Eastern Cape 7. Tshwane University of Technology Founded: 1 January 2004 Number of students: Approximately 60 000 Location: Gauteng, Mpumalanga, Limpopo and North-West 8. University of Cape Town Founded: 1829 Number of students: Approximately 20 000 Location: Western Cape 9. University of Fort Hare Founded: 1916 Number of students: Over 7 050 Location: Eastern Cape 10. University of the Free State Founded: 1904 Number of students: 21 000 Location: Free State 11. University of KwaZulu-Natal Founded: 1 January 2004 Number of students: Approximately 42 000 Location: Durban, Pinetown and Pietermartizburg, KwaZulu-Natal 12. University of Johannesburg Founded: 1 January 2005 Number of students: Approximately 45 000 Location: Johannesburg, Gauteng 13. University of Limpopo Founded: 1 January 2005 Number of students: Approximately 16 500 Location: Limpopo and Gauteng

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14. University of Pretoria Founded: 1908 Number of students: 50 000 Location: Gauteng 15. University of South Africa Founded: 1 January 2004 Number of students: Over 200 000 Location: All provinces 16. University of Stellenbosch Founded: 1866 Number of students: Approximately 22 000 Location: Western Cape 17. University of Venda for Science and Technology Founded: 1982 Number of students: Over 9 800 Location: Limpopo 18. University of the Western Cape Founded: 1959 Number of students: Over 14 200 Location: Bellville, Western Cape 19. University of the Witwatersrand Founded: 1922 Number of students: Approximately 25 000 Location: Johannesburg, Gauteng 20. University of Zululand Founded: 1960 Number of students: Over 10 000 Location: KwaZulu-Natal 21. Vaal University of Technology Founded: 1966 Number of students: Approximately 16 000 Location: Gauteng, North-West, Mpumulanga and Northern Cape 22. Walter Sisulu University for Technology and Science Founded: 1 June 2005 Number of students: Approximately 25 000 Location: Eastern Cape Mangosuthu University of Technology Founded: 1979 23. Number of students: 9 798 Location: KwaZulu-Natal

Source: Adapted from: http://www.southafrica.info/about/education/universities.htm#.UNn25aU6bLY (Accessed on 28 April 2010)

AND

Source: Adapted from: Higher Education South Africa, 2010. About us, list of public universities. Available atline at: http://www.hesa.org.za/hesa/index.php/about-us/universities (Accessed on 24 August 2010).

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APPENDIX B

Top Gauteng school list 2010/2011 criteria for determining the top schools

The top 250 Gauteng school list consisted out of a top 190 Gauteng school list and an additional top 60 Sowetan school list. These lists were compiled and provided by the Deparment of Education (Department of Education, 2011). The below ‘algorithms’ were used by the Deparment of Education to compile these lists.

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Top schools (1179) - Full-time A2010/11

Cert -

100 100

- 100% type -

- Disadv no name name Centre Centre Centre District District Pass % Pass Quintile Dinaledi Position 2011/03/01 District no. District TotalWrote Centre Prev Prev Total Passed Total Total Entered Total Pass 80 Pass No. of passes passes of No. Pass 80 Pass Bachelor Wrote Bachelor NSC Pass Total No. of HE Bach of No. Bachelor Entered Bachelor Total Pass H Pass Total Total Pass Diploma Pass Total Total Pass Bachelor Pass Total

TSHWANE HOËRSKOOL 1 4 SOUTH DISTRICT 8210245 WATERKLOOF N Public School Y 5 346 345 344 323 322 311 32 1 0 835 1490 99.71 TSHWANE HOËRSKOOL 2 4 SOUTH DISTRICT 8210195 MENLOPARK N Public School Y 5 252 250 249 210 208 222 26 1 0 749 1220 99.60 TSHWANE HOËRSKOOL 3 4 SOUTH DISTRICT 8210187 GARSFONTEIN N Public School Y 5 330 328 328 309 307 262 65 1 0 569 1159 100.00 TSHWANE HOËRSKOOL 4 4 SOUTH DISTRICT 8210161 ELDORAIGNE N Public School Y 5 321 320 320 308 307 236 84 0 0 552 1108 100.00 TSHWANE AFRIKAANSE HOER 5 4 SOUTH DISTRICT 8230110 MEISIESKOOL N Public School Y 5 197 196 196 167 167 193 3 0 0 653 1042 100.00 TSHWANE PRETORIA HIGH 6 4 SOUTH DISTRICT 8231324 SCHOOL FOR GIRLS Y Public School Y 5 264 263 263 193 193 247 16 0 0 531 1041 100.00 TSHWANE PRETORIA BOYS HIGH 7 4 SOUTH DISTRICT 8231316 SCHOOL Y Public School Y 5 298 297 295 278 277 270 25 0 0 442 1007 99.33 TSHWANE HOËRSKOOL 8 4 SOUTH DISTRICT 8210252 ZWARTKOP N Public School Y 5 306 304 302 299 298 224 70 8 0 439 965 99.34 GAUTENG WEST HOËRSKOOL 9 2 DISTRICT 8250258 MONUMENT N Public School Y 5 273 272 270 256 255 200 69 1 0 440 910 99.26 JOHANNESBURG 10 12 WEST DISTRICT 8250233 HOËRSKOOL FLORIDA N Public School Y 5 291 287 284 255 251 188 95 1 0 431 903 98.95 TSHWANE HOËRSKOOL 11 4 SOUTH DISTRICT 8210229 CENTURION Y Public School Y 5 247 245 245 239 237 215 30 0 0 387 847 100.00 JOHANNESBURG NORTHCLIFF HIGH 12 10 NORTH DISTRICT 8140285 SCHOOL N Public School Y 5 260 260 260 259 259 192 65 3 0 389 841 100.00 JOHANNESBURG PARKTOWN GIRLS HIGH 13 10 NORTH DISTRICT 8131136 SCHOOL N Public School Y 5 205 203 203 188 187 197 6 0 0 426 826 100.00 TSHWANE SUTHERLAND HIGH 14 4 SOUTH DISTRICT 8211144 SCHOOL Y Public School Y 5 221 220 218 217 216 187 31 0 0 418 823 99.09 TSHWANE AFRIKAANSE HOER 15 4 SOUTH DISTRICT 8230128 SEUNSKOOL N Public School Y 5 197 196 196 182 181 182 14 0 0 434 812 100.00 EKURHULENI HOËRSKOOL KEMPTON 16 6 NORTH DISTRICT 8260125 PARK N Public School Y 5 310 305 299 283 279 173 120 6 0 339 811 98.03 GAUTENG WEST HOËRSKOOL 17 2 DISTRICT 8250266 NOORDHEUWEL N Public School Y 5 234 231 231 233 230 197 34 0 0 376 804 100.00

424 |

Cert -

100 100

- 100% type -

Total Pass Bachelor Pass Total EKURHULENI 18 6 NORTH DISTRICT 8310151 BENONI HIGH SCHOOL N Public School Y 5 274 273 273 268 267 201 65 7 0 281 755 100.00 TSHWANE HOËRSKOOL 19 3 NORTH DISTRICT 8240077 OVERKRUIN N Public School Y 5 228 227 227 211 210 186 40 1 0 332 745 100.00 JOHANNESBURG RAND PARK HIGH 20 10 NORTH DISTRICT 8151241 SCHOOL N Public School Y 5 274 270 266 273 269 200 58 8 0 254 720 98.52 JOHANNESBURG GLENVISTA HIGH 21 11 SOUTH DISTRICT 8110403 SCHOOL N Public School Y 5 230 230 230 229 229 173 51 6 0 311 714 100.00 JOHANNESBURG FOURWAYS HIGH 22 10 NORTH DISTRICT 8150466 SCHOOL N Public School Y 5 231 230 230 231 230 196 30 4 0 284 710 100.00 TSHWANE HOËRSKOOL OOS- 23 3 NORTH DISTRICT 8220178 MOOT N Public School Y 5 263 261 258 250 248 179 75 4 0 255 692 98.85 JOHANNESBURG MONDEOR HIGH 24 14 CENTRAL 8120840 SCHOOL Y Public School Y 5 299 298 290 291 290 176 89 25 0 224 690 97.32 TSHWANE HOËRSKOOL 25 3 NORTH DISTRICT 8230383 WONDERBOOM N Public School Y 5 280 276 273 268 264 160 99 14 0 252 685 98.91 TSHWANE 26 3 NORTH DISTRICT 8240069 HOËRSKOOL MONTANA N Public School Y 5 261 261 258 259 259 172 83 3 0 241 671 98.85 SEDIBENG WEST HOËRSKOOL 27 8 DISTRICT 8330191 TRANSVALIA N Public School Y 5 204 203 201 189 188 164 34 3 0 291 656 99.01 GAUTENG WEST KRUGERSDORP HIGH 28 2 DISTRICT 8250563 SCHOOL Y Public School Y 5 243 241 238 240 238 196 34 8 0 196 630 98.76 TSHWANE CRAWFORD COLLEGE Independent 29 4 SOUTH DISTRICT 8230219 PRETORIA N School Y 0 121 119 119 99 97 116 3 0 0 391 626 100.00 EKURHULENI HOËRSKOOL DR E G 30 16 SOUTH 8160564 JANSEN N Public School Y 5 241 240 240 219 219 133 103 4 0 248 621 100.00 EKURHULENI HOËRSKOOL MARAIS 31 16 SOUTH 8340166 VILJOEN N Public School Y 5 256 253 251 253 250 142 107 2 0 227 620 99.21 EKURHULENI BOKSBURG HIGH 32 16 SOUTH 8160234 SCHOOL Y Public School Y 4 290 286 283 288 284 163 114 6 0 171 617 98.95 JOHANNESBURG KING EDWARD VII 35 9 EAST DISTRICT 8130765 SCHOOL N Public School Y 5 193 193 192 191 191 161 29 2 0 234 587 99.48 JOHANNESBURG 37 9 EAST DISTRICT 8400128 PONELOPELE N Public School Y 0 327 322 304 327 322 177 108 19 0 93 574 94.41 EKURHULENI 38 6 NORTH DISTRICT 8260117 HOËRSKOOL JEUGLAND N Public School Y 5 236 230 224 217 212 118 100 6 0 217 559 97.39 GAUTENG EAST HOËRSKOOL 39 5 DISTRICT 8350116 HUGENOTE N Public School Y 4 240 239 239 237 236 130 106 3 0 174 543 100.00

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Cert -

100 100

- 100% % type -

- no name name Centre Centre Centre District District Pass Quintile Dinaledi Position District no. District TotalWrote Centre Prev Prev Disadv Total Passed Total Total Entered Total Pass 80 Pass No. of passes passes of No. Pass 80 Pass Bachelor Wrote Bachelor NSC Pass Total No. of HE Bach of No. Bachelor Entered Bachelor Total Pass H Pass Total Total Pass Diploma Pass Total

Total Pass Bachelor Pass Total EKURHULENI EDENGLEN HIGH 40 6 NORTH DISTRICT 8160390 SCHOOL Y Public School Y 5 231 231 223 229 230 154 57 12 0 145 522 96.54 TSHWANE WILLOWRIDGE HIGH 41 4 SOUTH DISTRICT 8211235 SCHOOL N Public School Y 5 184 182 181 168 166 129 51 1 0 210 520 99.45 TSHWANE PRO ARTE ALPHEN 42 4 SOUTH DISTRICT 8210203 PARK N Public School Y 5 162 161 161 156 155 134 25 2 0 224 519 100.00 JOHANNESBURG ALLEN GLEN HIGH 43 12 WEST DISTRICT 8252189 SCHOOL N Public School Y 5 233 231 228 219 217 118 98 12 0 173 519 98.70 EKURHULENI EDENVALE HIGH 44 6 NORTH DISTRICT 8160416 SCHOOL Y Public School Y 5 199 196 196 190 187 162 30 4 0 151 509 100.00 SIR PIERRE VAN EKURHULENI RYNEVELD HIGH 45 6 NORTH DISTRICT 8260562 SCHOOL Y Public School Y 5 235 235 218 231 231 107 76 35 0 180 505 92.77 JOHANNESBURG 46 10 NORTH DISTRICT 8150623 HOËRSKOOL LINDEN N Public School Y 5 120 119 119 101 101 99 18 2 0 274 492 100.00 SEDIBENG WEST 47 8 DISTRICT 8330175 HOËRSKOOL DRIEHOEK N Public School Y 5 175 173 173 164 162 120 53 0 0 196 489 100.00 JOHANNESBURG BRYANSTON HIGH 48 9 EAST DISTRICT 8150201 SCHOOL N Public School Y 5 196 191 189 195 190 140 44 5 0 155 484 98.95 GAUTENG WEST HOËRSKOOL 49 2 DISTRICT 8270124 RIEBEECKRAND N Public School Y 4 214 213 210 199 198 109 97 4 0 165 484 98.59 GAUTENG WEST RANDFONTEIN HIGH 50 2 DISTRICT 8270488 SCHOOL N Public School Y 5 167 166 166 167 166 100 65 1 0 216 482 100.00 JOHANNESBURG JEPPE GIRLS HIGH 51 9 EAST DISTRICT 8130641 SCHOOL Y Public School Y 5 155 155 155 151 151 145 10 0 0 181 481 100.00 52 15 TSHWANE WEST 8240044 HOËRSKOOL AKASIA N Public School Y 5 195 195 194 192 192 121 68 5 0 160 475 99.49 TSHWANE LYTTELTON MANOR 53 4 SOUTH DISTRICT 8210906 HIGH SCHOOL Y Public School Y 5 207 205 200 205 203 142 52 6 0 129 471 97.56 EKURHULENI BRACKEN HIGH 54 16 SOUTH 8340125 SCHOOL Y Public School Y 5 188 186 183 188 186 120 58 5 0 168 471 98.39 TSHWANE HOER TEGNOLOGIESE 55 3 NORTH DISTRICT 8220152 SKOOL JOHN VORSTER N Public School Y 5 232 231 224 232 231 81 125 18 0 153 458 96.97 SEDIBENG EAST HOER VOLKSKOOL 56 7 DISTRICT 8340174 HEIDELBERG N Public School Y 5 200 197 195 191 188 101 88 6 0 154 450 98.98 EKURHULENI ALBERTON HIGH 57 16 SOUTH 8340083 SCHOOL N Public School Y 5 211 208 208 208 205 134 71 3 0 105 447 100.00 EKURHULENI PHUMLANI SECONDARY 58 16 SOUTH 8341099 SCHOOL Y Public School Y 3 304 299 248 304 299 122 87 39 0 73 443 82.94

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Cert -

100 100

- 100% type -

- no name name Centre Centre Centre District District Pass % Pass Quintile Dinaledi Position District no. District TotalWrote Centre Prev Prev Disadv Total Passed Total Total Entered Total Pass 80 Pass No. of passes passes of No. Pass 80 Pass Bachelor Wrote Bachelor NSC Pass Total No. of HE Bach of No. Bachelor Entered Bachelor Total Pass H Pass Total Total Pass Diploma Pass Total Total Pass Bachelor Pass Total TSHWANE MAKGETSE HIGH 59 3 NORTH DISTRICT 8911026 SCHOOL Y Public School Y 3 335 327 286 184 179 119 113 54 0 36 441 87.46 JOHANNESBURG FLORIDA PARK HIGH 60 12 WEST DISTRICT 8250142 SCHOOL Y Public School Y 5 269 264 241 268 264 94 112 35 0 97 432 91.29 TSHWANE RATSHEPO SEC 61 3 NORTH DISTRICT 8911767 SCHOOL N Public School Y 3 228 226 223 0 0 144 73 6 0 65 432 98.67 EKURHULENI ZITIKENI SECONDARY 62 6 NORTH DISTRICT 8261479 SCHOOL Y Public School Y 3 464 454 269 464 454 81 97 91 0 75 425 59.25 JOHANNESBURG LENASIA MUSLIM Independent 63 11 SOUTH DISTRICT 8111583 SCHOOL N School Y 0 110 109 108 110 109 98 10 0 0 217 423 99.08 EKURHULENI SUNWARD PARK HIGH 64 16 SOUTH 8161521 SCHOOL Y Public School Y 5 164 163 160 161 160 115 44 1 0 148 423 98.16 GAUTENG EAST HOËRSKOOL 65 5 DISTRICT 8310359 STOFFBERG N Public School Y 5 181 181 181 177 177 105 71 5 0 136 422 100.00 TSHWANE THE GLEN HIGH 66 4 SOUTH DISTRICT 8211151 SCHOOL Y Public School Y 5 178 177 175 172 171 135 37 3 0 104 414 98.87 TSHWANE CORNERSTONE Independent 67 4 SOUTH DISTRICT 8220582 COLLEGE SEC. SCHOOL N School Y 0 130 130 129 126 126 120 9 0 0 164 413 99.23 JOHANNESBURG EQINISWENI 68 9 EAST DISTRICT 8260760 SECONDARY SCHOOL Y Public School Y 2 250 246 205 250 246 66 76 63 0 141 412 83.33 JOHANNESBURG HOËRSKOOL 69 10 NORTH DISTRICT 8150631 RANDBURG N Public School Y 5 122 121 120 121 120 90 30 0 0 200 410 99.17 REITUMETSE 70 15 TSHWANE WEST 8241174 SECONDARY SCHOOL Y Public School Y 4 266 266 237 266 266 110 101 26 0 62 409 89.10 JOHANNESBURG HOËRSKOOL 71 12 WEST DISTRICT 8250274 ROODEPOORT N Public School Y 5 178 176 173 177 175 90 67 16 0 145 408 98.30 EKURHULENI 72 16 SOUTH 8340190 HOËRSKOOL DINAMIKA N Public School Y 5 193 192 184 165 164 87 87 10 0 135 406 95.83 EKURHULENI LIVERPOOL 73 6 NORTH DISTRICT 8310060 SECONDARY SCHOOL N Public School Y 4 218 211 200 217 210 103 83 14 0 100 403 94.79 TSHWANE 74 4 SOUTH DISTRICT 8210211 HOËRSKOOL UITSIG N Public School Y 5 171 169 169 169 167 102 61 6 0 130 401 100.00 EKURHULENI HOËRSKOOL 75 6 NORTH DISTRICT 8310318 BRANDWAG N Public School Y 5 175 174 173 175 174 94 74 5 0 130 397 99.43 JOHANNESBURG PARKTOWN BOYS HIGH 76 9 EAST DISTRICT 8131128 SCHOOL Y Public School Y 4 141 140 139 141 140 113 24 2 0 141 393 99.29 TSHWANE CLAPHAM HIGH 77 3 NORTH DISTRICT 8220103 SCHOOL Y Public School Y 5 160 160 158 157 157 112 42 4 0 123 393 98.75

427 |

Cert -

100 100

- 100% type -

- no name name Centre Centre Centre District District Quintile Pass % Pass Dinaledi Position District no. District TotalWrote Centre Prev Prev Disadv Total Passed Total Total Entered Total Pass 80 Pass No. of passes passes of No. Pass 80 Pass Bachelor Wrote Bachelor NSC Pass Total No. of HE Bach of No. Bachelor Entered Bachelor Total Pass H Pass Total Total Pass Diploma Pass Total Total Pass Bachelor Pass Total 78 15 TSHWANE WEST 8230375 HOËRSKOOL TUINE N Public School Y 5 232 229 225 216 214 101 108 16 0 66 392 98.25 JOHANNESBURG SIR JOHN ADAMSON 79 14 CENTRAL 8121061 HIGH SCHOOL N Public School Y 5 241 239 230 241 239 112 95 23 0 48 390 96.23 HOËRSKOOL 80 15 TSHWANE WEST 8230359 LANGENHOVEN N Public School Y 5 209 208 204 207 206 94 100 10 0 90 388 98.08 SEDIBENG EAST GENERAL SMUTS HIGH 81 7 DISTRICT 8330126 SCHOOL Y Public School Y 4 330 327 267 330 327 64 146 57 0 56 387 81.65 JOHANNESBURG GREENSIDE HIGH 82 10 NORTH DISTRICT 8130427 SCHOOL N Public School Y 5 163 161 159 160 159 114 38 7 0 104 377 98.76 JOHANNESBURG 83 11 SOUTH DISTRICT 8121137 THE HILL HIGH SCHOOL N Public School Y 5 178 178 176 173 173 106 59 11 0 94 376 98.88 EKURHULENI HOËRSKOOL 84 16 SOUTH 8160606 OOSTERLIG N Public School Y 5 154 151 149 141 138 94 55 0 0 133 376 98.68 HOËRSKOOL GERRIT 86 15 TSHWANE WEST 8240051 MARITZ Y Public School Y 5 233 232 212 225 224 77 101 34 0 83 372 91.38 EKURHULENI NORKEM PARK HIGH 87 6 NORTH DISTRICT 8260471 SCHOOL Y Public School Y 5 218 217 197 218 217 84 78 35 0 91 372 90.78 TSHWANE HANS KEKANA SEC 88 3 NORTH DISTRICT 8910484 SCHOOL Y Public School Y 1 363 355 261 363 355 93 117 51 0 11 365 73.52 EKURHULENI 89 16 SOUTH 8340182 HOËRSKOOL ALBERTON N Public School Y 5 177 176 171 173 172 91 64 16 0 103 365 97.16 EKURHULENI LESIBA SECONDARY 90 6 NORTH DISTRICT 8311258 SCHOOL N Public School Y 4 257 254 226 257 254 75 104 47 0 55 356 88.98 GAUTENG WEST 91 2 DISTRICT 8250191 HOËRSKOOL BASTION N Public School Y 4 185 183 173 156 154 90 74 9 0 93 356 94.54 TSHWANE HOËRSKOOL HENDRIK 92 3 NORTH DISTRICT 8230334 VERWOERD N Public School Y 5 181 180 177 179 178 101 68 8 0 77 355 98.33 JOHANNESBURG NIRVANA SECONDARY 93 14 CENTRAL 8110262 SCHOOL N Public School Y 5 221 220 168 221 220 82 63 23 0 98 348 76.36 JOHANNESBURG Independent 94 9 EAST DISTRICT 8133694 BLUE HILLS COLLEGE N School Y 0 92 92 92 84 84 63 25 4 0 190 345 100.00 GAUTENG WEST 95 2 DISTRICT 8252867 LODIRILE SEC SCHOOL N Public School Y 2 169 169 165 169 169 65 85 15 0 109 339 97.63 GAUTENG EAST SPRINGS GIRLS HIGH 96 5 DISTRICT 8350488 SCHOOL N Public School Y 5 134 132 132 134 132 115 16 1 0 91 338 100.00

428 |

Cert -

100 100

- 100% type -

98 15 TSHWANE WEST 8911263 MODIRI SEC SCHOOL N Public School Y 4 212 212 178 212 212 98 67 13 0 52 328 83.96 JOHANNESBURG SANDRINGHAM HIGH 99 9 EAST DISTRICT 8131441 SCHOOL Y Public School Y 5 193 189 177 193 189 90 68 19 0 60 327 93.65 GAUTENG EAST HOËRSKOOL JOHAN 100 5 DISTRICT 8350124 JURGENS N Public School Y 4 182 179 177 150 148 75 95 7 0 73 325 98.88 EKURHULENI TEMBISA SECONDARY 101 6 NORTH DISTRICT 8261305 SCHOOL Y Public School Y 4 313 305 237 313 305 61 107 69 0 27 325 77.70 GAUTENG WEST 102 2 DISTRICT 8930376 FOCHVILLE H/S N 131 131 129 131 131 85 40 4 0 110 324 98.47 JOHANNESBURG HOËRSKOOL 103 14 CENTRAL 8120576 PRESIDENT N Public School Y 5 151 151 150 151 151 88 59 3 0 85 323 99.34 JOHANNESBURG 104 14 CENTRAL 8111203 REASOMA SEC SCHOOL Y Public School Y 5 302 295 208 302 295 62 101 45 0 53 323 70.51 JOHANNESBURG 105 9 EAST DISTRICT 8131326 QUEENS HIGH SCHOOL Y Public School Y 5 243 238 207 241 237 78 85 44 0 37 322 86.97 JOHANNESBURG 106 9 EAST DISTRICT 8150987 MIDRAND HIGH SCHOOL Y Public School Y 5 160 158 152 159 157 98 45 9 0 71 321 96.20 HOËRSKOOL 107 15 TSHWANE WEST 8240085 PRETORIA-NOORD N Public School Y 5 154 154 151 147 147 81 68 2 0 88 320 98.05 NGAKA MASEKO SEC 109 15 TSHWANE WEST 8911498 SCHOOL N Public School Y 5 229 229 201 229 229 76 90 35 0 41 318 87.77 SEDIBENG WEST 110 8 DISTRICT 8320036 HOËRSKOOL SUIDERLIG N Public School Y 5 155 155 151 155 155 66 81 4 0 99 316 97.42 TSHWANE 111 3 NORTH DISTRICT 8913607 SIKHULULEKILE N Public School Y 4 196 194 181 196 194 99 57 25 0 35 315 93.30 EKURHULENI LETHUKUTHULA 112 16 SOUTH 8340844 SECONDARY N Public School Y 3 165 164 160 165 164 88 55 17 0 64 312 97.56 GAUTENG 113 1 NORTH DISTRICT 8210179 HOËRSKOOL ERASMUS N Public School Y 5 131 130 126 128 127 87 33 6 0 98 311 96.92 JOHANNESBURG FRED NORMAN SEC 114 11 SOUTH DISTRICT 8111500 SCHOOL N Public School Y 5 219 216 195 219 216 68 101 26 0 45 308 90.28 TSHWANE HOËRSKOOL 115 4 SOUTH DISTRICT 8210237 VOORTREKKERHOOGTE N Public School Y 5 233 233 213 230 230 54 108 51 0 41 308 91.42 SEDIBENG EAST HOËRSKOOL DRIE 116 7 DISTRICT 8330167 RIVIERE N Public School Y 5 138 136 134 138 136 72 54 8 0 100 306 98.53

429 |

Cert -

100 100

- 100% type -

- no name name Centre Centre Centre District District Pass % Pass Quintile Dinaledi Position District no. District TotalWrote Centre Prev Prev Disadv Total Passed Total Total Entered Total Pass 80 Pass No. of passes passes of No. Pass 80 Pass Bachelor Wrote Bachelor NSC Pass Total No. of HE Bach of No. Bachelor Entered Bachelor Total Pass H Pass Total Total Pass Diploma Pass Total Total Pass Bachelor Pass Total JOHANNESBURG LENASIA SECONDARY 117 14 CENTRAL 8110155 SCHOOL N Public School Y 4 170 170 149 170 170 61 65 23 0 96 306 87.65 EKURHULENI WILLOWMOORE HIGH 118 6 NORTH DISTRICT 8310847 SCHOOL Y Public School Y 5 153 153 152 153 153 104 43 5 0 50 306 99.35 SETUMO-KHIBA SEC 119 15 TSHWANE WEST 8911984 SCHOOL N Public School Y 4 273 270 221 0 0 64 98 59 0 21 306 81.85 EKURHULENI 120 6 NORTH DISTRICT 8160598 HOËRSKOOL GOUDRIF N Public School Y 5 131 131 130 131 131 70 56 4 0 104 304 99.24 JOHANNESBURG JEPPE HIGH SCHOOL 121 9 EAST DISTRICT 8130633 FOR BOYS Y Public School Y 5 163 161 154 159 157 84 54 16 0 62 300 95.65 JOHANNESBURG PRINCESS HIGH 122 12 WEST DISTRICT 8250951 SCHOOL Y Public School Y 5 157 157 134 157 157 35 63 36 0 131 300 85.35 JOHANNESBURG IVORY PARK 123 9 EAST DISTRICT 8400009 SECONDARY SCHOOL Y Public School Y 2 257 253 213 257 253 70 72 71 0 16 299 84.19 HOLY TRINITY SEC 124 15 TSHWANE WEST 8910512 SCHOOL N Public School Y 1 143 142 136 143 142 84 43 9 0 78 298 95.77 SEDIBENG EAST 125 7 DISTRICT 8330159 HOËRSKOOL DR MALAN N Public School Y 5 163 159 158 137 135 71 81 6 0 66 295 99.37 HOËRSKOOL TSHWANE STAATSPRESIDENT C R 126 3 NORTH DISTRICT 8220194 SWART N Public School Y 5 172 170 157 172 170 59 79 19 0 76 292 92.35 EKURHULENI INQAYIZIVELE 127 6 NORTH DISTRICT 8260877 SECONDARY SCHOOL Y Public School Y 4 176 173 164 176 173 92 51 21 0 35 291 94.80 EKURHULENI GERMISTON HIGH 128 16 SOUTH 8160457 SCHOOL N Public School Y 5 160 157 148 160 157 75 58 15 0 67 290 94.27 THUTO-KE-MAATLA EKURHULENI COMPREHENSIVE 129 6 NORTH DISTRICT 8261362 SCHOOL Y Public School Y 4 266 252 181 266 252 67 67 47 0 42 290 71.83 JOHANNESBURG WEST RIDGE HIGH 130 12 WEST DISTRICT 8251173 SCHOOL Y Public School Y 5 177 174 162 177 174 82 61 19 0 45 289 93.10 GAUTENG WEST MOSUPATSELA 131 2 DISTRICT 8251736 SECONDARY SCHOOL Y Public School Y 4 233 228 178 150 148 85 71 22 0 23 286 78.07 JOHANNESBURG Independent 132 14 CENTRAL 8111906 AL-AQSA SCHOOL N School Y 0 70 70 70 70 70 69 1 0 0 147 286 100.00 EKURHULENI MASITHWALISANE 133 16 SOUTH 8161901 SECONDARY SCHOOL N Public School Y 4 307 300 231 307 300 41 132 58 0 13 285 77.00 JOHANNESBURG WENDYWOOD HIGH 134 9 EAST DISTRICT 8151928 SCHOOL Y Public School Y 5 112 112 111 107 107 83 27 1 0 90 284 99.11

430 |

Cert -

100 100

- 100% type -

no name name Centre Centre Centre District District Pass % Pass Quintile Dinaledi Position District no. District TotalWrote Centre Prev Prev Disadv Total Passed Total Total Entered Total Pass 80 Pass No. of passes passes of No. Pass 80 Pass Bachelor Wrote Bachelor NSC Pass Total No. of HE Bach of No. Bachelor Entered Bachelor Total Pass H Pass Total Total Pass Diploma Pass Total Total Pass Bachelor Pass Total JOHANNESBURG BHUKULANI 135 14 CENTRAL 8121236 SECONDARY SCHOOL N Public School Y 3 138 135 128 138 135 72 44 12 0 81 281 94.81 SOSHANGUVE 136 15 TSHWANE WEST 8241315 TECHNICAL CENTRE Y Public School Y 4 180 179 160 180 179 63 82 15 0 58 281 89.39 JOHANNESBURG RAUCALL SECONDARY 137 10 NORTH DISTRICT 8132811 SCHOOL Y Public School Y 5 94 94 91 94 94 64 22 5 0 123 278 96.81 GAUTENG 138 1 NORTH DISTRICT 8920186 EKANGALA Y Public School Y 3 292 287 191 261 258 62 93 36 0 23 276 66.55 GAUTENG EAST BRAKPAN HIGH 139 5 DISTRICT 8310201 SCHOOL N Public School Y 5 187 186 179 186 185 56 97 26 0 40 275 96.24 EKURHULENI WORDSWORTH HIGH 140 6 NORTH DISTRICT 8310854 SCHOOL N Public School Y 5 137 136 136 137 136 94 41 1 0 45 275 100.00 JOHANNESBURG EMSHUKANTAMBO SEC 142 10 NORTH DISTRICT 8121301 SCHOOL Y Public School Y 3 306 299 179 306 299 56 72 51 0 37 272 59.87

143 15 TSHWANE WEST 8230300 HILLVIEW HIGH SCHOOL Y Public School Y 5 156 155 152 148 147 56 82 14 0 63 271 98.06 GAUTENG EAST 144 5 DISTRICT 8310300 HOËRSKOOL DIE ANKER N Public School Y 5 187 178 168 151 146 45 109 14 0 58 271 94.38 SEDIBENG WEST ESOKWAZI SECONDARY 145 8 DISTRICT 8320515 SCHOOL Y Public School Y 2 216 213 183 216 213 58 73 52 0 28 269 85.92 SEDIBENG EAST RIVERSIDE HIGH 148 7 DISTRICT 8330597 SCHOOL N Public School Y 5 164 163 157 164 163 82 68 7 0 27 266 96.32 TSHWANE HOËRSKOOL DIE 149 4 SOUTH DISTRICT 8210153 WILGERS N Public School Y 5 136 129 128 131 124 75 48 5 0 63 266 99.22 JOHANNESBURG SOUTHVIEW HIGH 150 11 SOUTH DISTRICT 8110197 SCHOOL N Public School Y 4 228 222 159 228 222 39 57 63 0 67 265 71.62 JOHANNESBURG MORRIS ISAACSON 151 14 CENTRAL 8132571 SECONDARY SCHOOL N Public School Y 3 314 307 175 314 307 61 58 56 0 29 265 57.00 JOHANNESBURG PJ SIMELANE 152 12 WEST DISTRICT 8251777 SECONDARY SCHOOL Y Public School Y 4 194 193 165 194 193 59 80 26 0 41 265 85.49 EKURHULENI MABUYA SECONDARY 153 6 NORTH DISTRICT 8311290 SCHOOL N Public School Y 3 234 230 197 234 230 52 93 52 0 15 264 85.65 GAUTENG WEST MANDISA SHICEKA 154 2 DISTRICT 8252262 SECONDARY SCHOOL Y Public School Y 4 193 187 152 193 187 65 73 14 0 45 262 81.28 JOHANNESBURG WAVERLEY GIRLS HIGH 155 9 EAST DISTRICT 8151910 SCHOOL Y Public School Y 5 164 159 149 164 159 83 55 11 0 26 258 93.71 EKURHULENI 156 6 NORTH DISTRICT 8400138 PHOMOLONG N Public School Y 1 244 241 157 244 241 54 61 42 0 47 258 65.15

431 |

Cert -

100 100

- 100% type -

- no name name Centre Centre Centre District District Pass % Pass Quintile Dinaledi Position District no. District TotalWrote Centre Prev Prev Disadv Total Passed Total Total Entered Total Pass 80 Pass No. of passes passes of No. Pass 80 Pass Bachelor Wrote Bachelor NSC Pass Total No. of HE Bach of No. Bachelor Entered Bachelor Total Pass H Pass Total Total Pass Diploma Pass Total Total Pass Bachelor Pass Total JOHANNESBURG VUWANI SECONDARY 157 14 CENTRAL 8111450 SCHOOL Y Public School Y 3 212 208 159 212 208 52 61 46 0 45 256 76.44 RAND JOHANNESBURG MEISIESKOOL/GIRLS 158 9 EAST DISTRICT 8131334 SCHOOL N Public School Y 4 144 144 144 144 144 76 55 13 0 36 256 100.00 GAUTENG EAST BUHLEBEMFUNDO 159 5 DISTRICT 8310938 SECONDARY SCHOOL Y Public School Y 3 221 215 178 221 215 56 83 39 0 22 256 82.79 EKURHULENI TIISETSONG 160 16 SOUTH 8341370 SECONDARY SCHOOL N Public School Y 3 159 153 148 159 153 53 78 17 0 55 256 96.73 TSHWANE TSHWANE MUSLIM Independent 161 4 SOUTH DISTRICT 8232157 SCHOOL N School Y 0 57 57 57 51 51 53 2 2 0 145 255 100.00 EKURHULENI UNITY SECONDARY 162 6 NORTH DISTRICT 8311613 SCHOOL N Public School Y 4 167 163 142 167 163 46 57 39 0 67 255 87.12 IR LESOLANG SEC 163 15 TSHWANE WEST 8910553 SCHOOL N Public School Y 1 260 256 178 260 256 53 77 48 0 22 253 69.53 JOHANNESBURG HOËRSKOOL DIE 164 12 WEST DISTRICT 8250217 ADELAAR N Public School Y 5 122 121 121 117 116 59 57 5 0 71 251 100.00 JOHANNESBURG LESHATA SECONDARY 165 11 SOUTH DISTRICT 8330969 SCHOOL Y Public School Y 2 170 168 149 95 93 70 59 20 0 31 250 88.69 EKURHULENI PONEGO SECONDARY 166 16 SOUTH 8341123 SCHOOL N Public School Y 4 208 205 158 208 205 51 59 48 0 41 250 77.07 EKURHULENI HOËRSKOOL 167 6 NORTH DISTRICT 8260109 BIRCHLEIGH N Public School Y 5 148 142 134 148 142 63 62 9 0 50 247 94.37 JOHANNESBURG ROOSEVELT HIGH 168 10 NORTH DISTRICT 8131367 SCHOOL Y Public School Y 5 142 140 138 141 139 72 54 12 0 37 247 98.57 JOHANNESBURG AHA-THUTO 169 11 SOUTH DISTRICT 8330696 SECONDARY SCHOOL Y Public School Y 2 191 184 124 188 184 47 47 30 0 75 246 67.39 EKURHULENI THUTO-LESEDI 170 16 SOUTH 8162073 SECONDARY SCHOOL Y Public School Y 4 208 203 178 205 201 47 98 33 0 20 245 87.68 JOHANNESBURG 172 14 CENTRAL 8120501 FOREST HIGH SCHOOL N Public School Y 5 231 226 172 229 224 44 79 49 0 27 243 76.11 GAUTENG WEST CARLETON JONES HIGH 174 2 DISTRICT 8270041 SCHOOL N 158 158 151 157 157 59 80 12 0 33 243 95.57 SEDIBENG EAST HOËRSKOOL 175 7 DISTRICT 8330209 VEREENIGING N Public School Y 5 117 115 115 97 95 47 64 4 0 81 243 100.00 SEDIBENG EAST 176 7 DISTRICT 8330183 HOËRSKOOL OVERVAAL N Public School Y 5 106 106 105 104 104 62 42 1 0 75 242 99.06

177 5 GAUTENG EAST 8350470 SPRINGS BOYS HIGH N Public School Y 5 101 101 99 101 101 66 28 5 0 77 242 98.02

432 |

Cert -

100 100

- 100% type -

Total Pass Bachelor Pass Total EKURHULENI REIGER PARK 179 16 SOUTH 8160069 SECONDARY SCHOOL N Public School Y 2 150 126 104 150 126 18 50 36 0 118 240 82.54 EKURHULENI DINWIDDIE HIGH 180 16 SOUTH 8160317 SCHOOL Y Public School Y 5 140 139 126 138 137 35 62 29 0 79 240 90.65 EKURHULENI HOËRSKOOL 181 16 SOUTH 8160614 VOORTREKKER Y Public School Y 4 129 129 129 128 128 61 65 3 0 49 239 100.00 LETHULWAZI EKURHULENI COMPREHENSIVE 182 16 SOUTH 8161893 SCHOOL N Public School Y 5 156 156 124 156 156 72 34 18 0 43 239 79.49 CENTRAL SECONDARY 183 15 TSHWANE WEST 8240598 SCHOOL N Public School Y 3 195 191 160 195 191 49 79 32 0 30 239 83.77 GAUTENG EAST 184 5 DISTRICT 8350371 NIGEL HIGH SCHOOL N Public School Y 5 164 163 151 163 162 57 72 22 0 28 236 92.64 EKURHULENI FUMANA SECONDARY 186 16 SOUTH 8340604 SCHOOL N Public School Y 3 251 243 156 251 243 34 74 48 0 45 235 64.20 JOHANNESBURG ALTMONT TECHNICAL 187 14 CENTRAL 8121210 HIGH SCHOOL N Public School Y 1 151 151 132 151 151 47 61 24 0 55 234 87.42 SEDIBENG EAST HOER TEGNIESE SKOOL 188 7 DISTRICT 8330142 VEREENIGING N Public School Y 5 121 120 118 117 116 53 53 12 0 62 233 98.33 WALLMANSTHAL 189 15 TSHWANE WEST 8241547 SECONDARY N Public School Y 4 162 159 144 162 159 49 73 22 0 39 232 90.57 GAUTENG WEST HOËRSKOOL JAN 190 2 DISTRICT 8270116 VILJOEN N Public School Y 4 126 126 123 126 126 42 65 16 0 67 232 97.62

433 |

Top SOWETO schools (1179) - Full-time A2010/11

100 100

100

type

- % no Cert NSC Pass Pass Bach name name Wrote Centre Centre Centre District District Quintile Entered Position Dinaledi Diploma Bachelor Bachelor Bachelor Bachelor District no District Total Pass Pass Total Pass Total TotalWrote No. of HE No. of Total Pass Pass Total Centre Prev Disadv Total Passed Total Total Entered Total Pass Pass 80 Total Pass H Pass Total Pass 80 Pass No. of passes passes No. of

JOHANNESBURG BHUKULANI Public 135 14 CENTRAL 8121236 SECONDARY SCHOOL N School Y 3 138 135 128 138 135 72 44 12 0 81 281 94.81 JOHANNESBURG ST MATTHEWS PRIVATE Independent 141 14 CENTRAL 8133033 SECONDARY SCHOOL N School Y 0 116 116 115 116 116 84 30 1 0 75 274 99.14 JOHANNESBURG Public NORTH EMSHUKANTAMBO SEC School 142 10 DISTRICT 8121301 SCHOOL Y Y 3 306 299 179 306 299 56 72 51 0 37 272 59.87 JOHANNESBURG MORRIS ISAACSON Public 151 14 CENTRAL 8132571 SECONDARY SCHOOL N School Y 3 314 307 175 314 307 61 58 56 0 29 265 57.00 JOHANNESBURG PJ SIMELANE Public 152 12 WEST DISTRICT 8251777 SECONDARY SCHOOL Y School Y 4 194 193 165 194 193 59 80 26 0 41 265 85.49 JOHANNESBURG MOKGOME Public 157 14 CENTRAL 8111450 SECONDARY SCHOOL Y School Y 3 212 208 159 212 208 52 61 46 0 45 256 76.44 JOHANNESBURG ALTMONT TECHNICAL Public 187 14 CENTRAL 8121210 HIGH SCHOOL N School Y 1 151 151 132 151 151 47 61 24 0 55 234 87.42 JOHANNESBURG THOMAS MOFOLO Public 193 14 CENTRAL 8111377 SECONDARY SCHOOL N School Y 4 217 213 170 217 213 46 81 43 0 13 229 79.81 JOHANNESBURG VUWANI SECONDARY Public 194 12 WEST DISTRICT 8140814 SCHOOL N School Y 3 210 209 147 210 209 37 52 58 0 45 229 70.33 JOHANNESBURG Public NORTH SELELEKELA SEC School 206 10 DISTRICT 8132902 SCHOOL N Y 3 268 252 134 268 252 41 61 32 0 47 222 53.17 JOHANNESBURG MATSELISO Public 216 12 WEST DISTRICT 8140780 SECONDARY SCHOOL N School Y 3 198 191 127 198 191 32 52 43 0 55 214 66.49 JOHANNESBURG Independent NORTH IMMACULATA School 224 10 DISTRICT 8140566 SECONDARY SCHOOL N Y 0 115 115 110 115 115 66 40 4 0 27 203 95.65 JOHANNESBURG PROGRESS Public NORTH COMPREHENSIVE School 236 10 DISTRICT 8121715 SCHOOL N Y 3 231 220 134 231 220 34 52 48 0 31 199 60.91 JOHANNESBURG MAPHUTHA SEC Independent 237 9 EAST DISTRICT 8400150 SCHOOL N School Y 0 155 153 138 155 153 42 70 26 0 17 197 90.20 JOHANNESBURG Public NORTH FONS LUMINIS SEC School 240 10 DISTRICT 8140525 SCHOOL Y Y 5 196 194 131 196 194 46 52 33 0 17 194 67.53 JOHANNESBURG KWADEDANGENDLALE Public 243 14 CENTRAL 8110510 SECONDARY SCHOOL Y School Y 3 174 174 107 174 174 45 41 21 0 40 192 61.49

434 |

100 100

100

type

JOHANNESBURG THUTOLORE Public 244 12 WEST DISTRICT 8141002 SECONDARY SCHOOL N School Y 3 211 204 121 211 204 24 47 50 0 47 192 59.31 JOHANNESBURG Public NORTH THABA-JABULA School 252 10 DISTRICT 8121798 SECONDARY SCHOOL N Y 3 156 152 119 156 152 45 42 32 0 21 185 78.29 JOHANNESBURG SEANA MARENA Public 273 14 CENTRAL 8111237 SECONDARY SCHOOL N School Y 3 201 193 138 201 193 26 62 50 0 10 174 71.50 JOHANNESBURG TETELO SECONDARY Public 274 14 CENTRAL 8111328 SCHOOL N School Y 5 127 126 107 127 126 50 48 9 0 17 174 84.92 JOHANNESBURG LETSIBOGO Public 282 12 WEST DISTRICT 8140715 SECONDARY SCHOOL Y School Y 4 120 120 99 120 120 47 41 11 0 26 172 82.50 JOHANNESBURG Public NORTH BONA COMPREHENSIVE School 285 10 DISTRICT 8131961 SCHOOL N Y 3 184 182 90 184 182 29 36 25 0 50 169 49.45 KELOKITSO Public JOHANNESBURG COMPREHENSIVE School 303 12 WEST DISTRICT 8140624 SCHOOL N Y 4 153 150 92 153 150 19 37 36 0 46 157 61.33 JOHANNESBURG ST MARTIN DE PORRES Independent 311 12 WEST DISTRICT 8133009 COMBINED SCHOOL N School Y 0 73 72 71 73 72 43 20 8 0 41 155 98.61 JOHANNESBURG IBHONGO SECONDARY Public 312 14 CENTRAL 8121392 SCHOOL N School Y 2 169 162 86 169 162 17 32 37 0 51 154 53.09 JOHANNESBURG FIDELITAS Public NORTH COMPREHENSIVE School 326 10 DISTRICT 8140517 SCHOOL N Y 3 116 116 90 116 116 45 32 13 0 14 149 77.59 AURORA Public JOHANNESBURG COMPREHENSIVE School 335 14 CENTRAL 8110536 SCHOOL N Y 3 165 156 119 165 156 21 47 51 0 6 146 76.28 JOHANNESBURG LAMULA JUBILEE Public 363 12 WEST DISTRICT 8140681 SECONDARY SCHOOL N School Y 3 205 201 94 205 201 26 36 32 0 17 137 46.77 JOHANNESBURG SENAOANE Public 364 14 CENTRAL 8111260 SECONDARY SCHOOL N School Y 4 269 261 96 269 261 25 37 34 0 16 137 36.78 JOHANNESBURG LOFENTSE Public NORTH COMPREHENSIVE School 365 10 DISTRICT 8132449 SCHOOL N Y 3 135 130 97 135 130 27 50 20 0 13 137 74.62 JOHANNESBURG PHEFENI SECONDARY Public 366 12 WEST DISTRICT 8132704 SCHOOL N School Y 3 130 123 79 130 123 16 41 22 0 42 137 64.23 JOHANNESBURG PACE SECONDARY Public 381 14 CENTRAL 8121665 PRIVATE SCHOOL N School Y 0 146 138 102 120 114 11 51 40 0 17 130 73.91

435 |

100 100

100

type

JOHANNESBURG Public NORTH DIEPDALE SECONDARY School 383 10 DISTRICT 8140434 SCHOOL N Y 3 136 133 80 136 133 27 32 21 0 23 130 60.15 JOHANNESBURG MEADOWLANDS Public 390 12 WEST DISTRICT 8140806 SECONDARY SCHOOL N School Y 3 173 171 97 173 171 26 44 27 0 6 129 56.73 JOHANNESBURG KWA-MAHLOBO Public 410 12 WEST DISTRICT 8140665 SECONDARY SCHOOL N School Y 3 101 101 85 101 101 25 49 11 0 13 123 84.16 JOHANNESBURG FORTE SECONDARY Public 415 12 WEST DISTRICT 8251405 SCHOOL Y School Y 4 120 119 79 120 119 27 31 21 0 15 121 66.39 JOHANNESBURG DALIWONGA Public 425 14 CENTRAL 8132043 SECONDARY SCHOOL N School Y 4 107 106 77 107 106 28 34 15 0 10 115 72.64 JOHANNESBURG THABO SECONDARY Public 443 14 CENTRAL 8111344 SCHOOL Y School Y 4 160 153 73 160 153 23 32 18 0 15 111 47.71 JOHANNESBURG LAVELA SECONDARY Public 451 14 CENTRAL 8110874 SCHOOL N School Y 3 142 139 83 141 139 17 39 27 0 9 109 59.71 JOHANNESBURG ORLANDO WEST Public 452 12 WEST DISTRICT 8132688 SECONDARY SCHOOL N School Y 3 129 126 74 129 126 20 33 21 0 15 109 58.73 JOHANNESBURG DR BW VILAKAZI Public 483 14 CENTRAL 8110601 SECONDARY SCHOOL N School Y 3 73 72 64 73 72 28 31 5 0 9 101 88.89 JOHANNESBURG MNCUBE SECONDARY Public 484 14 CENTRAL 8132498 SCHOOL N School Y 3 172 168 78 172 168 7 32 38 1 16 101 46.43 JOHANNESBURG Public NORTH ORLANDO SECONDARY School 485 10 DISTRICT 8132670 SCHOOL N Y 3 179 172 69 179 172 23 19 27 0 9 101 40.12 MAFORI MPHAHLELE Public JOHANNESBURG COMPREHENSIVE School 497 14 CENTRAL 8121525 SCHOOL N Y 3 71 71 57 71 71 22 21 14 0 18 97 80.28 JOHANNESBURG LETARE SECONDARY Public 505 14 CENTRAL 8121491 SCHOOL N School Y 3 131 128 80 131 128 8 35 37 0 6 94 62.50 JOHANNESBURG Public NORTH School 509 10 DISTRICT 8121608 MUSI COMPREHENSIVE N Y 4 102 97 66 102 97 19 35 12 0 8 93 68.04 JOHANNESBURG GEORGE KHOSA Public 521 12 WEST DISTRICT 8251413 SECONDARY SCHOOL N School Y 4 103 101 68 103 101 20 35 13 0 1 89 67.33 JOHANNESBURG JABULANI TECHNICAL Public 526 14 CENTRAL 8121442 SECONDARY SCHOOL Y School Y 3 124 122 39 124 122 3 28 8 0 44 86 31.97 JOHANNESBURG MOLETSANE Public 527 14 CENTRAL 8121574 SECONDARY SCHOOL Y School Y 4 218 214 61 218 214 21 19 21 0 4 86 28.50

436 |

100 100

100

type

JOHANNESBURG EMDENI SECONDARY Public 538 14 CENTRAL 8110643 SCHOOL N School Y 3 129 121 63 129 121 16 22 25 0 5 84 52.07 JOHANNESBURG ANCHOR Public 553 12 WEST DISTRICT 8131862 COMPREHENSIVE N School Y 3 124 122 61 124 122 17 20 24 0 3 81 50.00 JOHANNESBURG SEKANONTOANE Public 561 14 CENTRAL 8111252 SECONDARY SCHOOL N School Y 4 111 103 58 111 103 15 29 14 0 6 79 56.31 JOHANNESBURG MAPETLA HIGH Public 565 14 CENTRAL 8110999 SCHOOL N School Y 3 140 137 57 140 137 7 23 27 0 14 78 41.61 JOHANNESBURG DR BEYERS NAUDE Public 575 14 CENTRAL 8132746 SECONDARY SCHOOL N School N 3 72 70 48 72 70 9 23 16 0 18 75 68.57 JOHANNESBURG NALEDI SECONDARY Public 581 14 CENTRAL 8111112 SCHOOL N School Y 4 122 118 43 122 118 13 14 16 0 17 73 36.44 JOHANNESBURG Public NORTH BOPASENATLA School 592 10 DISTRICT 8140426 SECONDARY SCHOOL N Y 3 57 56 44 57 56 13 16 15 0 11 68 78.57 JOHANNESBURG NGHUNGHUNYANI Public 604 14 CENTRAL 8111120 COMPREHENSIVE N School Y 3 88 86 54 88 86 8 24 22 0 3 65 62.79 JOHANNESBURG Public NORTH School 636 10 DISTRICT 8140848 NAMEDI SEC SCHOOL N Y 2 51 51 40 51 51 12 19 9 0 2 54 78.43 JOHANNESBURG EMADWALENI Public 641 12 WEST DISTRICT 8132167 SECONDARY SCHOOL N School Y 3 66 66 39 66 66 8 21 10 0 6 53 59.09 JOHANNESBURG VERITAS SECONDARY Public 651 12 WEST DISTRICT 8141143 SCHOOL N School Y 3 59 58 36 59 58 8 13 15 0 5 49 62.07 JOHANNESBURG MADIBANE Public NORTH COMPREHENSIVE School 659 10 DISTRICT 8140756 SCHOOL N Y 2 38 37 29 38 37 8 13 8 0 8 45 78.38 JOHANNESBURG PHAFOGANG Public 673 14 CENTRAL 8121681 SECONDARY SCHOOL N School Y 4 118 113 32 118 113 5 15 12 0 0 37 28.32 FONTANUS Public JOHANNESBURG COMPREHENSIVE School 678 14 CENTRAL 8110692 SECONDARY SCHOOL N Y 3 88 86 26 87 85 2 11 13 0 5 33 30.23 JOHANNESBURG PRUDENS SECONDARY Public 709 14 CENTRAL 8111195 SCHOOL N School Y 3 46 46 19 46 46 3 13 3 0 0 22 41.30 JOHANNESBURG ADELAIDE TAMBO (J C Public 716 14 CENTRAL 8133652 MERKIN WHITE CITY N School Y 0 30 30 14 30 30 1 5 8 0 2 17 46.67 JOHANNESBURG LOBONE SECONDARY Public 720 14 CENTRAL 8132431 SCHOOL N School Y 3 22 19 11 22 19 3 5 3 0 1 15 57.89 743 12 JOHANNESBURG 8251900 SIZWILE SCHOOL N Special Y 0 6 6 1 0 0 0 0 0 1 0 1 16.67

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APPENDIX C

A summary of value-intention frameworks or models.

Refer to Chapter 4 for the entire discussion.

Content 1 Dodds, Monroe and Grewal’s (1991) Product Evaluation Model. Sheth, Newman and Gross’ (1991) Consumption Values influencing consumer choice 2 behaviour. Cronin, Brady, Brand, Hightower and Shemwell’s (1997) Conceptualisation of their service 3 value model. Teas and Agarwal’s (2000) Conceptual model of extrinsic-cue effects on perceived quality, 4 perceived sacrifice and perceived value. Cronin, Brady and Hult’s (2000) Synthesised and conceptualised the effects of quality, 5 satisfaction, and value on consumers’ behavioural intentions. 6 Petrick’s (2002) SERV-PERVAL scale for measuring perceived value. 7 Petrick’s (2004) Model of the variables related to behavioural intentions. Sánchez, Callarisa, Rodríguez and Moliner (2006) GLOVAL scale of measurement of the 8 overall perceived value of purchase

C.1. Introduction

Although fifteen value-intention frameworks or models have been identified from the literature study, not all fifteen have been discussed in Chapter 4, Section 4.5. The following section briefly reviews the remaining eight value-intention frameworks and models that were not included in Section 4.5, however, the relevant information has been addressed and included in the earlier sections of Chapter 4. The models that follow have also been included in Table 4.3 (Chapter 4) that provides a summary of all the fifteen value-intention frameworks/models’ elements. These remaining eight value-intention frameworks/models are discussed following a sequential approach to portray how frameworks/models have evolved over time.

C.1.1. Dodds, Monroe and Grewal’s (1991) Product evaluation model

Dodds et al. (1991) tested the direct and indirect relationships between three extrinsic product cues (price, brand name, and store name) to evaluate variables (perceived quality and perceived product value), as well as buyers’ willingness to buy. Their research holds promise as a conceptual framework for and lays the basis for (i) isolating the theoretical reasons for when buyers use price, brand, store or intrinsic product information as indicators of quality; (ii) determining how quality 438 | perceptions influence value perceptions, purchase intentions, and product choice, and (iii) how monetary and non-monetary perceived sacrifices influence value perceptions, purchase intentions and choice. Their conceptualisation also suggested that certain interrelationships between perceived quality, perceived value and willingness to buy exist.

Dodds et al. (1991:317) were able to compare the relative effect of combining price, brand name, and store name by using relatively higher price and infrequently purchased products (calculators and stereo headsets/walkmans). Their findings suggested that consumers are less likely to rely on the presence of a price-quality relationship for a particular product class in order to rely more on the familiar information cues of brand and store name to assess the product’s worth. Dodds et al. (1999:317) provided an explanation that if a consumer who has never bought a stereo headset player or who bought a calculator five years ago may use price to categorise the product as very high quality, average or poor quality if only price information is available. However, a consumer who lacks knowledge about the product may use store and brand name information (if available) to make the quality assessment, thus relying less on the price cue. Their study also showed evidence that brand and store information combined with price, have a positive effect on buying intentions (Dodds et al., 1999:317). Thus, it can be concluded that weighing the perceived quality received and sacrifice made (price), leads to perceived value, which in effect has a positive relationship on willingness to buy (Figure C.1).

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Figure C.1 Dodds, Monroe and Grewal’s (1991) Product evaluation model.

C.1.2. Sheth, Newman and Gross’ (1991) Five consumption values. Influencing consumer choice.

Sheth, Newman and Gross (1991) argue that consumer choice is a function of multiple consumption values and that these consumption values make differential contributions in any given choice situation (Figure C.2). These authors further argue that consumption values are independent and contribute ‘incrementally’ to choice. Consumers are usually willing to accept less of one value in order to obtain more of another (trading off) as it is not desirable to maximise all five consumption values at one given time (Sheth et al., 1991:163).

The five consumption values that they identified influencing consumer choice behaviour are functional value, social value, emotional value, epistemic value, and conditional value. A decision to buy or not to buy may be influenced by any or all of the five consumption values (Sheth et al., 1999:160). These consumption values will not be defined in this section, as they have already been discussed and described earlier in this Chapter 4.

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Figure C.2 Sheth, Newman and Gross’ (1991) Five consumption values. Influencing consumer choice.

Sheth et al.’s (1991) theory of consumption values has been operationallsed and tested in more than 200 consumer choice situations, and it has consistently demonstrated predictive validity that it may be used to predict consumption behaviour, as well as describe and explain it. They also argue that the theory may be applied to any consumer choice situation of interest IF the context is one of individual decision-making (not dyadic or group choice), systematic decision-making and voluntary decision-making (Lai, To, Lung & Lai, 2012:272; Sheth et al., 1991:169).

C.1.3. Cronin, Brady, Brand, Hightower and Shemwell’s (1997) Conceptualisation of their service value model.

These authors’ (Cronin et al., 1997) research results were presented on a hypothesis-by-hypothesis basis where H1 suggested that adding a direct measure of service value to models of the consumer decision-making process, which are based on service quality, and sacrifice will increase the variance in consumers’ purchase intention (Figure C.3). They called their model “The value added model”. In operationalising the model, a ten-item service quality scale, a four-item sacrifice scale (price, effort, time and perceived risk), and the three-item purchase intentions scale were employed. The results indicated that their structural model appeared to fit the data well and that there was a sizable increase in the variance explained in

441 | purchase intentions by the introduction of the direct measure of service value. Service value thus explained a unique portion of the variance in purchase intentions, which is not accounted for individually by either service quality or sacrifice perceptions (Cronin et al., 1997:383).

They conducted their study across several service industries, including spectator sports, participation sports, entertainment, health care, long-distance carriers and the fast-food industry Their study suggested that consumers integrate their perception of what they ‘get’ (benefits) and what they must ‘give up’ (costs/sacrifices) in a service purchasing situation to arrive at a decision whether or not to buy from a given service provider. Thus, service value, just like consumer satisfaction, may also be an integrating decision-making construct for consumers (Cronin et al., 1997:379, 384- 385).

Figure C.3 Cronin, Brady, Brand, Hightower and Shemwell’s (1997) Value added model.

C.1.3. Teas and Agarwal’s (2000) Conceptual model of extrinsic-cue effects on perceived quality, perceived sacrifice and perceived value

Teas and Agarwal’s (2000) study was included in this section because it is built on Dodds et al.’s (1991) model. Although Teas and Agarwal’s (2000) model doesn’t address all the elements and components of Dodds et al.’s (1991) model, such as the linkage of perceived value to willingness to buy, it extended the Dodds et al.

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(1991) model by examining linkages involving perceived sacrifice that was specified but not tested in the Dodds et al. (1991) study. The concept of sacrifice was measured from a budget constraint perspective to allow for the possibility that the perception of sacrifice will vary depending on an individual’s financial situation (Teas & Agarwal, 2000:282).

Teas and Agarwal (2000) also studied the extent to which perceived quality and sacrifice mediate the relationships between the extrinsic cues and perceived value. Dodds et al. (1991) formulated a model in which perceived quality and perceived sacrifice mediate linkages between brand name, store name, price and perceived value. Teas and Agarwal (2000:278) also added an additional extrinsic cue – country of origin (Figure C.4).

The finding of Teas and Agarwal’s (2000:286) study indicated that perceived sacrifice is found to be a significant predictor of perceived value, and that the effects of the extrinsic product cues (price, brand, store, and country of origin) on perceived value are mediated by perceived quality and sacrifice.

Teas and Agarwal (2000) collected data via two questionnaires from 530 undergraduate students attending a major Midwestern university in the United States of America. The products under research, were handheld business calculators and wrist watches.

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Figure C.4 The effect of Teas and Agarwal’s (2000) Conceptual model of extrinsic product-cues effects on consumers’ perceived quality, perceived sacrifice and perceived value.

C.1.4. Cronin, Brady and Hult (2000) Synthesised and conceptualised the effects of quality, satisfaction, and value on consumers’ behavioural intentions.

Cronin et al.’s (2000) study reported an empirical assessment of a model of service encounters that simultaneously considers the direct effect of the variables, quality, satisfaction and value on behavioural intentions. They extended the Cronin et al. (1997) model by adding satisfaction, and built their study on service marketing theory and assessed the relationships between the mentioned constructs across multiple service industries. Their findings concluded that service quality, service value and satisfaction may all be directly related to behavioural intentions when all of these variables are considered collectively. In essence, the indirect effects of the service quality and value constructs enhanced their impact on behavioural intentions.

In the industry-specific analyses of Cronin et al.’s (2000) study, the relationship between service value and behavioural intentions was significant in all six industry samples (Spectator Sports, Participative Sports, Entertainment, Health Care, Long-

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Distance Carrier and the Fast-food industry), while satisfaction influenced behavioural intentions directly in all industries except health care. Service quality had a direct effect on consumers’ behavioural intentions in four of the six industries with the exceptions being the health care and long-distance carrier industries.

Figure C.5 Cronin, Brady and Hult’s (2000) Model on the effects of quality, value, and customer satisfaction on consumer behavioural intentions in service environments.

C.1.5. Petrick’s (2002) SERV-PERVAL scale for measuring perceived value

Petrick’s (2002) purpose of his study was to develop a multi-dimensional scale for the measurement of perceived value of a service. He believed that the leisure and tourism providers could benefit from refined measures of the construct, since perceived value has been found to be an important indicator of repurchase intentions.

Petrick (2002:121) argued that some perceived value frameworks help in the understanding of perceived value, but that they do not offer measures for collecting perceived value data. Most commonly perceived value is measured by using a self-

445 | reported uni-dimensional measure and the problem with a one-dimensional measurement is that it assumes that customers have a shared meaning of value. Petrick (2002:122) stated that the assumption of shared meaning of value is problematic as quality and value are not well differentiated from each. He argues that a one-dimensional scale lacks validity and it provides no specific direction on how to improve value. For this reason, although difficult to quantify perceived value, Petrick (2002:122) argued that a multi-dimensional measure for perceived value should be devised.

Students from undergraduate tourism classes were asked to rate each of the 25 perceived value items previously developed by the author under cruise passengers. Students had to respond to a questionnaire by rating each of the 25 items as they relate them to lunch at a well-known fast-food restaurant. After validation, confirmatory factor analysis was employed that resulted in a model that had a good fit of the data. The scale was given the name SERV-PERVAL (Service Perceived Value). The SERV-PERVAL scale is thus a multi-dimensional scale that operationalises perceived value as a five-dimensional construct. Petrick (2002) developed this measuring scale of perceived value for restaurants and identified five dimensions that contribute to the formation of value: quality, emotional response, monetary price, behavioural price and reputation (Sánchez et al., 2006:395, 397; Petrick, 2004:399) (Figure C.6).

The quality dimension was defined as a consumer’s judgement about a product or service’s overall excellence or superiority, while emotional response was defined as a descriptive judgment regarding the pleasure that a product or service gives the consumer. Monetary price was defined as the price of a service as encoded by the consumer, while behavioural price or non-monetary price was defined as the price of obtaining a service that included the time and effort used to search for the service. Reputation was defined as the prestige or status of a product or service as perceived by the consumer, and it was based on the image of the supplier (Petrick, 2004:399; Petrick, 2002: 125; Dodds et al., 1991).

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Figure C.6 Petrick’s (2002) Hypothetical model portraying post-experience perceived value in the service choice process.

Petrick’s (2002) scale was found to be both reliable and valid (Petrick, 2004:399). It was concluded that the scale consisted of five interrelated, but unique dimensions and that perceived value was positively and significantly related to each of the five dimensions (Petrick, 2002:131).

C.1.6. Petrick’s (2004) model on the roles of quality, value, and satisfaction in predicting cruise passengers’ behavioural intentions

The purpose of Petrick’s 2004 study was to examine the relationships between satisfaction, perceived value, and quality in their prediction of intentions to repurchase and positive word-of-mouth publicity. These three mentioned constructs have been tested from three distinctly different perspectives, resulting in three competing modes: (1) the satisfaction model, (2) the perceived value model, and (3)

447 | the quality model. These three models were used to assess which one best explains cruise passengers’ behavioural intentions. The results revealed that the quality model most accurately fitted the data and that quality was the best predictor of intentions to repurchase (Petrick, 2004:397).

Although quality was in this situation the best predictor of intentions to repurchase, Petrick (2004:404) argues that it doesn’t mean that satisfaction and perceived value are not good predictors of repurchase intentions. He argues that both perceived value and quality are antecedents of cruise passengers’ satisfaction in the prediction of behavioural intentions. Their study’s findings further revealed that quality and value are cognitive responses to a service experience, while satisfaction is an emotional response, thus quality and perceived value lead to satisfaction which inevitably leads to behavioural intentions. The study also indicated that cruise passengers with higher intentions to repurchase are more likely to speak positively about their experiences (WOM) (Petrick, 2004:405) (Figure C.7).

In contrast with Petrick’s 2002 work, it was found that reputation in this particular situation was not a good predictor of cruise passengers’ perceived value. This finding appears to be due to little or no deviation in the scores given by respondents (Petrick, 2004:405).

The differences between Petrick’s 2002 and 2004 models are: o The 2002 model mostly focussed on post-experience perceived value, while the 2004 model’s focus was on examining the relationships between satisfaction, perceived value and quality in prediction intentions to repurchase and positive word of mouth. o The 2004 model included a satisfaction construct, that was not evident in the 2002 model. o The 2002 model included ‘reputation’ as a perceived value construct, while this construct was left out in the 2004 model. o The 2002 model portrayed perceived value as leading to purchase intentions and word-of-mouth simultaneously, while the 2004 model portrayed that customers with a higher intention to buy again, are more likely to speak positively about their experiences (WOM). 448 | o The 2002 model indicated that only perceived value lead to repurchase intentions, while the 2004 model revealed that satisfaction, quality and perceived value can all lead to repurchase intentions.

Figure C.7 Petrick’s (2004) Model on the variables (quality, value and satisfaction) related to cruise passengers’ behavioural intentions

C.1.7. Sánchez, Callarisa, Rodríguez and Moliner’s (2006) GLOVAL scale of measurement of the overall perceived value of purchase.

GLOVAL stands for GLObal purchase perceived VALue (Sánchez et al., 2006:395). Sánchez et al.’s (2006) model is a multi-item measure of marketing constructs that paid special attention to identifying the cognitive and affective dimensions of overall perceived value of a purchase. Their scale was constructed on the basis of Sheth at al.’s (1991) model, and the PERVAL scale by Sweeney & Soutar (2001) (Moliner et al., 2007:1400). It is for this reason that the GLOVAL scale is included in this section. Although the authors (Sánchez et al., 2006) haven’t stipulated that perceived value leads to consumer behaviour intention, Sheth et al.’s (1991) model does and the PERVAL scale is developed by Sweeney and Soutar (2001) where it is proven that ‘emotions’ have a strong link to willingness to buy (intention to buy). It was further determined that Petrick (2002, 2004) adapted the PERVAL scale to arrive at the

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SERV-PERVAL scale and his scale leads to repurchase intentions and positive word-of-mouth. The researcher would like to assume, that if Sánchez et al. (2006) have included and tested the possible linkage between perceived value and consumer behaviour intention, they most probably would have found evidence that there is a strong link, as Sheth et al. (1991), Petrick (2002, 2004) and Sweeney and Soutar (2001), have found although to a lesser extent.

Sánchez et al.’s (2006) scale was developed for the overall perceived value of a purchase consisting initially of 40 items, following the structure proposed by Sweeney and Soutar (2001). By means of a rigorous procedure, they developed a scale of measurement of the perceived overall value of a purchase through 24 items grouped into six dimensions. These six dimensions include: Functional value (establishment), Functional value (personnel), Functional value (product), Functional value (price), Emotional value and social value that all lead to perceived value of the purchase. They called it the GLOVAL scale, and proposed that it can be specifically used as an instrument of measurement of the perceived value of a tourism package at academic and professional level (Sánchez et al., 2006:398) (Figure C.8).

The authors positioned the holistic conception of the purchase, specifically measuring a tourist’s evaluation of a purchase experience, specifically post- purchased perceived value. It was found that a tourist valuation of a purchase experience does not separate the experience of consumption from that of purchase, but evaluates them as a single whole. It is for this reason that an instrument of measurement was designed that will take into account all the variables (Sánchez et al., 2006:404).

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Figure C.8 Sánchez, Callarisa, Rodríguez and Moliner (2006) GLOVAL scale of measurement of the overall perceived value.

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APPENDIX D

Perceived value measurement scales

The next table presents a summary of the perceived value measurement scales and their statements that were investigated for the purpose of this study

Perceived value scale used in this study Benefit factors of perceived value: 1 Functional Value Scales with their statements 2 Social Value Scales with their statements 3 Emotional Value Scales with their statements 4 Epistemic Value Scales with their statements 5 Reputational Value Scales with their statements 6 Conditional Value Scales with their statements Sacrifice factors of perceived value: 1 Monetary Scales with their statements 2 Non-monetary Scales with their statements

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A summary of the different perceived value scales’ constructs and their suggested statements

Functional value scale statements Petrick’s Ledden, (2002) Sánchez, Brown and Kalafatis Seth, SERV- Callarisa, Mazzarol Cronin, Teas and Sweeney and In THIS study Zeithaml, Newman PERVAL Rodríguez (2009) applying Brady and Agarwal and Soutar Samouel (suggested (1988) and Gross scale & and Moliner’s PERVAL Hult (2000) (2000) (2001) (2007) by I Lubbe) (1991) Petrick’s, (2006) (education (education (2004) Serv- GLOVAL specific) specific) perval Functional Functional Value II (result Service Functional Functional Value Perceived Functional Functional of the Value (very Quality Value Quality value Statements value Value value product/percei low..) (quality) (quality) used in final ved quality) questionnaire

The facilities Overall , the The tourism My degree (library, Has an value of this Is package will allow me ..has consistent computer labs acceptable Low price Reliability facility’s Reliability outstanding purchased to earn a quality etc.) will meet standard of services to quality was well good/better my quality me is… organized salary expectations (service) Compared to Compared to what I had to Value as what I had to give up, the The quality of whatever give up, the The degree overall ability the tourism the overall ability Workman- Is very is a good ..is well made of this Durability Is well made package was consumer of this facility ship reliable investment in university to maintained wants in to satisfy my my future* satisfy my throughout product wants and wants and needs is …… needs is very high (service)

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Functional value scale statements (..continues) Petrick’s Ledden, (2002) Sánchez, Brown and Kalafatis Seth, SERV- Callarisa, Mazzarol Cronin, Teas and Sweeney and In THIS study Zeithaml, Newman PERVAL Rodríguez (2009) applying Brady and Agarwal and Soutar Samouel (suggested (1988) and Gross scale & and Moliner’s PERVAL Hult (2000) (2000) (2001) (2007) by I Lubbe) (1991) Petrick’s, (2006) (education (education (2004) Serv- GLOVAL specific) specific) perval The Relative to knowledge I The staff at the Value in other tourism will have ..has an university will quality packages acquired on acceptable provide It performs Is very obtained for Price Quality purchased it my course standard of service as I consistently dependable the price has an will enable quality expect paid acceptable me to do my (consistent level of quality future job performance) better * What Value as “product” can The university what do, i.e. My degree will perform to Has poor ..has poor consumer Tobacco Depend- Is very The result was will lead to my workman- workmanship (*) gets for stops ability consistent as expected promotion in expectations ship(*) what he nervousness, my future* (it is gives keeps me dependable) busy etc. I will achieve my career * see notes, My degree goals these authors ..would NOT Would not will allow me (because I also included last a long Durability last a long to achieve study at this 3 additional time(*) time(*) my career university) functional goals (Can also be a values conditional value)

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Notes on functional value:

Dodds and Monroe (1985): Higher prices for instance will lead to greater perceived quality Dodds and Monroe (1985). Dodds, Kent, Monroe and Grewal (1991): They described the perceived value construct as the trade-off between perceived quality and perceived sacrifice. Perceived quality included dimensions such as reliability, workmanship, a quality product, dependable and durable. Perception of the store and the perception of the brand is also included as indicators of perceived quality. Cronin, Brady, Brand, Hightower and Shemwell (1997): state that the perceived value construct consists of a service quality and sacrifice construct. The service quality construct consists out of a ten-item service quality scale. This scale is NOT included in the above table as the study focused on post-purchase evaluation and the researcher’s study is on pre-purchase (pre-enrolment) perceptions. It was also evident that Teas and Agarwal’s (2000) model as well as Cronin, Brady and Hult’s (2000) model both

455 | included the dimensions mentions in Cronin et al., 1997 model. They are: reliable, consistent and dependable service, service in a timely manner, employees that are competent, employees that are approachable, employees that are courteous, polite, and respectful, employees that listen, employees that are trustworthy, believable, and honest, the facility that provides an environment that is free from danger, the physical facilities and employees are neat and clean, and the employees make the effort to understand my needs. Teas and Agarwal (2000): Identified two Perceived value constructs, perceived quality and perceived sacrifice. Chu and Lu (2007) added perceived usefulness as a functional value, stating it focuses on functional and convenience benefits. Perceived usefulness is the degree to which the consumer believes that the service would fulfill the certain purpose. Perceived playfulness describes the degree to which the consumer believes that enjoyment could be derived when using the product/service. Sánchez, Callarisa, Rodríguez and Moliner’s (2006) included in total four types of functional value. Only Functional value relating to quality has been included in the above table. The other Functional Value scales used by these authors included functional value of the travel agency (installations), Functional Value of Contact personnel of the travel agency (professionalism) and Functional Value price. It is important to remember, that these authors studied post-purchase perceived and the researcher’s particular study, will study pre-purchase perceptions. For this reason, Functional Value relating to professionalism and installations will not be included in this study’s Perceived Value Scale.

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Social value scale statements Ledden, Petrick’s Kalafatis Brown and (2002) Sánchez, and Mazzarol Seth, SERV- Callarisa, Samouel In THIS Cronin, Brady Teas and Sweeney (2009) Zeithaml, Newman PERVAL Rodríguez and (2007) study and Hult Agarwal and Soutar applying (1988) and Gross scale & Moliner’s (education (suggested (2000) (2000) (2001) PERVAL (1991) Petrick’s, (2006) specific) by I Lubbe) (education (2004) Serv- GLOVAL Ledden & specific) perval Kalafatis (2010) Social Value statement Social Value used in the N/A Social Value N/A N/A Social value N/A Social Value Social Value final questionnaire About the Taking the People who (Same as I will feel a association tourism are important Sweeney and sense of with one or Would help package to me think Soutar (2001) ‘belonging’ more specific me to feel improved the that taking was adapted when groups ( a acceptable way I am my course is to this attending the sense of perceived by a good thing situation university belonging) others to do People who People who influence Would take that type what I do improve the of tourism think that way I am packages taking my perceived obtain social course is a approval good idea

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Social value scale statements (..continues) Ledden, Petrick’s Kalafatis Brown and (2002) Sánchez, and Mazzarol Seth, SERV- Callarisa, Samouel In THIS Cronin, Brady Teas and Sweeney (2009) Zeithaml, Newman PERVAL Rodríguez and (2007) study and Hult Agarwal and Soutar applying (1988) and Gross scale & Moliner’s (education (suggested (2000) (2000) (2001) PERVAL (1991) Petrick’s, (2006) specific) by I Lubbe) (education (2004) Serv- GLOVAL Ledden & specific) perval Kalafatis (2010) My Using the current/future Would make services of the employer will a good travel agency see me in a . impression has improved better light on other the way other when I have people people finished my perceive me degree My family The tour and friends Would give operator’s will se me in its owner packages are a better light social taken by many when I have approval people that I finished my know degree The social interaction with fellow students on

my course makes my studies more interesting

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important in helping me through my course (*) reverse scored ALSO authors applied Sweeney &

Soutar’s (2001) PERVAL scale

Notes on Social Value:

Dodds and Monroe (1985) – they do not mention social value at all. Dodds, Kent, Monroe and Grewal (1991): – they do not mention social value at all. Cronin, Brady, Brand, Hightower and Shemwell (1997) - they do not mention social value at all.

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Emotional value scale statements Ledden, Petrick’s Kalafatis Brown and (2002) Sánchez, and Mazzarol Seth, SERV- Callarisa, Samouel In THIS Cronin, Brady Teas and Sweeney (2009) Zeithaml, Newman PERVAL Rodríguez and (2007) study and Hult Agarwal and Soutar applying (1988) and Gross scale & Moliner’s (education (suggested (2000) (2000) (2001) PERVAL (1991) Petrick’s, (2006) specific) by I Lubbe) (education (2004) Serv- GLOVAL Ledden & specific) perval Kalafatis (2010) Emotional Not an Value emotional Emotional statement Emotional scale as Emotional Emotional Emotional Emotional N/A N/A Value used in the value such, but value Value Value Value Value final mention in question- Satisfaction naire The Enjoyment – (Same as university will defined as I am Sweeney and give me a About the delighted, I feel proud comfortable Soutar (2001) good association happy joyful Is one that I It makes me that I’m with the was adapted experience with one’s (this was would enjoy feel good taking my tourism to this (enjoyment, feelings mention in the course package situation in feel good, satisfaction education pleasure, scale) relaxed) The personnel Taking my Would make were always course has It gives me me want to willing to satisfy boosted my pleasure use it my wishes as a self customer confidence

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Emotional value scale statements (..continues) Ledden, Petrick’s Kalafatis Brown and (2002) Sánchez, and Mazzarol Seth, SERV- Callarisa, Samouel In THIS Cronin, Brady Teas and Sweeney (2009) Zeithaml, Newman PERVAL Rodríguez and (2007) study and Hult Agarwal and Soutar applying (1988) and Gross scale & Moliner’s (education (suggested (2000) (2000) (2001) PERVAL (1991) Petrick’s, (2006) specific) by I Lubbe) (education (2004) Serv- GLOVAL Ledden & specific) perval Kalafatis (2010)

Is one that I Taking my The personnel would feel It gives me a course has . gave me a relaxed sense of joy fulfilled an positive feeling about using ambition

My performance on my I felt relaxed in Would make It makes me course the travel me feel good feel delighted depends agency upon my personal effort The personnel Taking my course has Would give It gives me didn’t pressure given me a me pleasure happiness me to decide sense of self- quickly achievement

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Emotional value scale statements (..continues) Zeithaml, Seth, Cronin, Brady Teas and Sweeney Petrick’s Sánchez, Ledden, Brown and In THIS (1988) Newman and Hult Agarwal and Soutar (2002) Callarisa, Kalafatis Mazzarol study and Gross (2000) (2000) (2001) SERV- Rodríguez and and (2009) (suggested (1991) PERVAL Moliner’s Samouel applying by I Lubbe) scale & (2006) (2007) PERVAL Petrick’s, GLOVAL (education (education (2004) Serv- specific) specific) perval Ledden & Kalafatis (2010) I took this course for .

the personal challenge

Notes on Emotional value:

Dodds and Monroe (1985) – they do not mention emotional value at all. Dodds, Kent, Monroe and Grewal (1991) – they do not mention emotional value at all. Cronin, Brady, Brand, Hightower and Shemwell (1997) - they do not mention emotional value at all. Chu & Lu (2007) – The do not mention emotional value at all.

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Epistemic Value Scale statements Ledden, Petrick’s Kalafatis Sánchez, Brown and (2002) and Callarisa, Mazzarol Seth, SERV- Samouel In THIS Cronin, Teas and Sweeney Rodríguez (2009) Zeithaml, Newman PERVAL (2007) study Brady and Agarwal and Soutar and applying (1988) and Gross scale & (education (suggested Hult (2000) (2000) (2001) Moliner’s PERVAL (1991) Petrick’s, specific) by I Lubbe) (2006) (education (2004) Serv- Ledden & GLOVAL specific) perval Kalafatis (2010) Epistemic NOT as Value such an statement Epistemic epistemic Epistemic N/A N/A N/A N/A N/A N/A used in the Value scale, but Value final mention in question- Satisfaction naire Is about the Interested in (Same as desire for the service/ The content Sweeney knowledge I will gain the product , of my course and Soutar (referring to knowledge could be keeps me (2001) was curiosity, that I need ( interpreted interested adapted to novelty, and as curious. this situation knowledge) I learn new things from my course The course content contributes to .

the high value of my education

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Epistemic value scale statements (..continues) Ledden, Petrick’s Kalafatis Sánchez, Brown and (2002) and Callarisa, Mazzarol Seth, SERV- Samouel In THIS Cronin, Teas and Sweeney Rodríguez (2009) Zeithaml, Newman PERVAL (2007) study Brady and Agarwal and Soutar and applying (1988) and Gross scale & (education (suggested Hult (2000) (2000) (2001) Moliner’s PERVAL (1991) Petrick’s, specific) by I Lubbe) (2006) (education (2004) Serv- Ledden & GLOVAL specific) perval Kalafatis (2010) The academic guidance I receive from my lecturers has enhanced the value of my degree

Notes on Epistemic Value:

Dodds and Monroe (1985), Dodds, Kent, Monroe and Grewal (1991), Cronin, Brady, Brand, Hightower and Shemwell (1997), Chu and Lu (2007) – they do not mention epistemic value at all.

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Reputation value scale statements Petrick’s Sánchez, Ledden, Kalafatis Seth, Cronin, Teas Sweeney (2002) SERV- Callarisa, and Samouel Brown and Newman Brady In THIS study Zeithaml, and and PERVAL scale Rodríguez (2007) (education Mazzarol (2009) and and (suggested by I (1988) Agarwal Soutar & Petrick’s, and Moliner’s specific) applying PERVAL Gross Hult Lubbe) (2000) (2001) (2004) Serv- (2006) Ledden & (education specific) (1991) (2000) perval GLOVAL Kalafatis (2010) Epistemic Value Not as such, Reputation Label it “Image” statement used in N/A N/A N/A N/A N/A N/A mention Image value Value the final question-

naire The reputation of The reputation of xxx influence the Xxx has good the university will value of my xxx is reputation influence the value well respected of my degree degree The image projected by xxx Xxx is well has in influence thought of on the value of my degree I believe that I believe that employers would employers have Xxx has status have positive good things to say things to say about the university about xxx I believe that xxx Xxx is has a good reputable reputation

Notes on Reputational Value:

Dodds and Monroe (1985), Dodds, Kent, Monroe and Grewal (1991), Cronin, Brady, Brand, Hightower and Shemwell (1997), Chu and Lu (2007) – they do not mention reputational value at all.

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Conditional value scale statements Ledden, Petrick’s Kalafatis Sánchez, Brown and (2002) and Callarisa, Mazzarol Seth, SERV- Samouel In THIS Cronin, Teas and Sweeney Rodríguez (2009) Zeithaml, Newman PERVAL (2007) study Brady and Agarwal and Soutar and applying (1988) and Gross scale & (education (suggested Hult (2000) (2000) (2001) Moliner’s PERVAL (1991) Petrick’s, specific) by I Lubbe) (2006) (education (2004) Serv- Ledden & GLOVAL specific) perval Kalafatis (2010) Conditional Value Define statement Conditional N/A N/A conditional N/A N/A N/A N/A N/A used in the value value final questionnaire Refers to the The support I will achieve experience of materials my career the given supplied to goals situation, as me on my (because I the result of course study at this the specific helped me university) situation The xxx campus and its facilities have contributed to the value of my course

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Conditional Value Scale statements Ledden, Petrick’s Kalafatis Sánchez, Brown and (2002) and Callarisa, Mazzarol Seth, SERV- Samouel In THIS Cronin, Teas and Sweeney Rodríguez (2009) Zeithaml, Newman PERVAL (2007) study Brady and Agarwal and Soutar and applying (1988) and Gross scale & (education (suggested Hult (2000) (2000) (2001) Moliner’s PERVAL (1991) Petrick’s, specific) by I Lubbe) (2006) (education (2004) Serv- Ledden & GLOVAL specific) perval Kalafatis (2010) The convenience of the campus’s

location has contributed to the value of m course Study-group work has been a

beneficial part of my course.

Notes on Conditional Value:

Dodds and Monroe (1985), Dodds, Kent, Monroe and Grewal (1991), Cronin, Brady, Brand, Hightower and Shemwell (1997), Chu and Lu (2007) – they do not mention conditional value at all.

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Monetary scale statements Ledden, Petrick’s Kalafatis Brown and (2002) Sánchez, and Mazzarol Seth, SERV- Callarisa, Samouel In THIS Cronin, Brady Teas and Sweeney (2009) Zeithaml, Newman PERVAL Rodríguez and (2007) study and Hult Agarwal and Soutar applying (1988) and Gross scale & Moliner’s (education (suggested (2000) (2000) (2001) PERVAL (1991) Petrick’s, (2006) specific) by I Lubbe) (education (2004) Serv- GLOVAL Ledden & specific) perval Kalafatis (2010) Monetary Monetary Functional Monetary N/A Sacrifice Sacrifice Price N/A Price Price value price Sacrifice I am happy to make Authors did financial not include the I am happy sacrifices I sacrifice or that the price Price (talks The price It is a good It was a good taking m price element of the about It is a good charge to use Price product for purchase for course of Sweeney university is objective buy this facility is… the price the price paid because I and Soutar’s an indication price) believe I will (2001) of good benefit from PERVAL quality it in the long scale term The monetary price paid for The price I The tourism my course is have to pay It offers package It is worth the reasonable for the value for purchased was money when I university is money reasonably consider worth the priced what I am money getting out of it

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Monetary scale statements (..continues) Ledden, Petrick’s Kalafatis Brown and (2002) Sánchez, and Mazzarol Seth, SERV- Callarisa, Samouel In THIS Cronin, Brady Teas and Sweeney (2009) Zeithaml, Newman PERVAL Rodríguez and (2007) study and Hult Agarwal and Soutar applying (1988) and Gross scale & Moliner’s (education (suggested (2000) (2000) (2001) PERVAL (1991) Petrick’s, (2006) specific) by I Lubbe) (education (2004) Serv- GLOVAL Ledden & specific) perval Kalafatis (2010) When considering I am happy The price was the monetary to make It would be It is fairly the main price of my financial

economical priced criterion for the course, I sacrifices to decision believe that attend this the quality is university good The course The price fee paid for It is It is represents a studying at reasonably reasonably considerable this priced priced amount of university is money reasonable Aside from the course fee, doing It is this course

economical will involve considerable additional expense.

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Notes on Monetary Sacrifice:

Dodds and Monroe (1985) – the only sacrifice mentioned in their study is price. Dodds, Kent, Monroe and Grewal (1991) – perception of price is the only Perceived Sacrifice element that is mentioned in their study. Cronin, Brady, Brand, Hightower and Shemwell (1997) – mention monetary and non-monetary as well as risk factors. The monetary sacrifice refers to the price charged to use the facility (in dollar cost). Chu and Lu (2007) – distinguishes between monetary and non-monetary costs. Price is frequently used as the key measure representing what consumers have to pay money to obtain a product.

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Non-monetary scale statements Ledden, Petrick’s Kalafatis Brown and (2002) Sánchez, and Mazzarol Seth, SERV- Callarisa, Samouel In THIS Cronin, Brady Teas and Sweeney (2009) Zeithaml, Newman PERVAL Rodríguez and (2007) study and Hult Agarwal and Soutar applying (1988) and Gross scale & Moliner’s (education (suggested (2000) (2000) (2001) PERVAL (1991) Petrick’s, (2006) specific) by I Lubbe) (education (2004) Serv- GLOVAL Ledden & specific) perval Kalafatis (2010) Non- N/A Sacrifice N/A N/A Behavioural N/A Non- N/A monetary Price monetary Price Sacrifice Time costs The time Only Only It is easy to Only included I had to give Authors did The benefit required to use discussed discussed buy functional value up some not include the of attending this facility is… price price price other sacrifice or the university interests of price element will outweigh mine in order of Sweeney the time to do my and Soutar’s sacrifices course* (2001) (less time PERVAL with friends scale and family) Search costs The effort that It required The I must make to little energy monetary receive the to purchase price paid for services my course is offered is… reasonable when I consider what I am getting out of it

471 |

Non-monetary scale statements (..continues) Ledden, Petrick’s Kalafatis Brown and (2002) Sánchez, and Mazzarol Seth, SERV- Callarisa, Samouel In THIS Cronin, Brady Teas and Sweeney (2009) Zeithaml, Newman PERVAL Rodríguez and (2007) study and Hult Agarwal and Soutar applying (1988) and Gross scale & Moliner’s (education (suggested (2000) (2000) (2001) PERVAL (1991) Petrick’s, (2006) specific) by I Lubbe) (education (2004) Serv- GLOVAL Ledden & specific) perval Kalafatis (2010) Non- Non- Behavioural monetary N/A Sacrifice N/A N/A N/A monetary N/A Price Price Sacrifice When considering the monetary Psychic It is easy to price of my costs shop for course, I believe that the quality is good. It required Effort costs little effort to buy It is easily

bought

Notes on Non-monetary Sacrifice:

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Cronin, Brady, Brand, Hightower and Shemwell (1997) – mention monetary and non-monetary as well as risk factors. The non- monetary sacrifice refers to the effort, the time, the financial risk, the personal or physical risk, poor performance, social risk and psychological risk. Chu and Lu (2007) – distinguishes between monetary and non-monetary costs. Non-monetary costs include physical or psychological efforts. In an online music setting, perceived ease of use captured this non-monetary cost

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The perceived value scale used in the final questionnaire

Perceived value statement Type of statement Author Ledden, Kalafatis and The benefit of attending this Non-monetary Samouel (2007) (education university will outweigh the financial Sacrifice specific), Ledden & Kalafatis cost (2010) Ledden, Kalafatis and I am happy to make financial Samouel (2007) (education Monetary Sacrifice sacrifices to attend this university specific), Ledden & Kalafatis (2010) Sweeney and Soutar (2001), The price paid for studying at this Petrick’s (2002) SERV- Monetary Sacrifice university is reasonable PERVAL scale & Petrick (2004) Ledden, Kalafatis and Samouel (2007) (education I am happy that the price of the specific) university is an indication of good Monetary Sacrifice Ledden & Kalafatis (2010), quality Petrick’s (2002) SERV- PERVAL scale & Petrick (2004) Ledden, Kalafatis and Samouel (2007) (education I am happy to give up some of my Non-monetary specific), Ledden & Kalafatis interests to attend this university Sacrifice (2010), Cronin, Brady, Brand, Hightower & Shemwell (1997) The benefit of attending the university will outweigh the time Non-monetary Zeithaml (1988), Cronin, Brady sacrifices Sacrifice and Hult (2000) (less time with friends and family) Ledden, Kalafatis and Samouel (2007) (education I will achieve my career goals Functional Value/ specific) (because I study at this university) Conditional Value Ledden & Kalafatis (2010), Seth, Newman and Gross (1991) (used their definition), The university will perform to my Sánchez, Callarisa, Rodríguez Functional Value expectations and Moliner’s (2006) GLOVAL Sweeney and Soutar (2001), The staff at the university will provide Petrick’s (2002) SERV- Functional Value service as I expect PERVAL scale & Petrick (2004) Seth, Newman and Gross (1991) (used their definition), Ledden, Kalafatis and I will gain the knowledge that I need Epistemic Value Samouel (2007) (education specific) Ledden & Kalafatis (2010) (learn things)

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Perceived Value statement Type of Statement Author Ledden, Kalafatis and Samouel (2007) (education specific) The reputation of the university will Reputational Value Ledden & Kalafatis (2010), influence the value of my degree Petrick’s (2002) SERV- PERVAL scale & Petrick (2004) Ledden, Kalafatis and Samouel (2007) (education specific) I believe that employers have good Reputational Value Ledden & Kalafatis (2010), things to say about the university Petrick’s (2002) SERV- PERVAL scale & Petrick (2004) Seth, Newman and Gross (1991) (used their definition), The university will give me a good Cronin, Brady and Hult (2000), experience (enjoyment, feel good, Emotional Value Sweeney and Soutar (2001), pleasure, relaxed) Petrick’s (2002) SERV- PERVAL scale & Petrick (2004) Sweeney and Soutar (2001), Petrick’s (2002) SERV- PERVAL scale & Petrick (2004), Ledden, Kalafatis and The price I have to pay for the Monetary Sacrifice Samouel (2007) (education university is worth the money specific) Ledden & Kalafatis (2010), Sánchez, Callarisa, Rodríguez and Moliner’s (2006) GLOVAL The facilities (library, computer labs Service Value/ Cronin, Brady and Hult (2000) etc) will meet my expectations Functional Value Compared to what I have to give up, the overall ability of the Service Value/ Cronin, Brady and Hult (2000) university to satisfy my wants and Functional Value needs is very high

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A summary of the different behavioural intention scales and their suggested statements

Behavioural intention statements Petrick’s Dodds, (2002) Brady, Grewal, Espejel, Dodds, Sweeney, SERV- In THIS Dodds and Brand, Monroe Cronin, Fandos Zeithaml Monroe Soutar and PERVAL Chu and study Monroe Hightower and Brady and and (1988) and Grewal Johnson scale and Lu (2007) (suggested (1985) and Krishnan Hult (2000) Flavián (1991) (1999) Petrick’s, by I Lubbe) Shemwell (1998) (2008) (2004) (1997) Serv-perval Willing- ness to Willing- Behaviour- Purchase Willing- Willing- Re- Purchase Buying buy ness to al Intention to Purchase intentions ness to ness to purchase intention intention construct buy Intentions enrol measures buy buy intention scale (no state- indicators scale ments) Although If I were The The NO SCALE The State that going to buy Willingness The probability I would probability with likelihood I intend to perceived a bicycle, I would feel to buy likelihood of that I will consider that I will statements, that I would continue value leads the guilty if I go increase purchasing use this buying this use this perceived pay for buying the to purchase probability to another with low this product facility’s product at facility’s value a online product (no state- of buying university price is… services this store… services determinant music is ments) this model again is… again is… of purchase high is… intentions If I were going to buy The There is a The Willingness The ….and My this product, likelihood strong likelihood If a retailer decrease probability perceived willingness I would I would that I would likelihood that I would suggests with low- to that I would value a to buy never go to consider recommend that I will recommend me this medium- consider determinant online another buying this this facility’s buy this this facility’s olive oil I price buying this of repeat music is university model at services to product at services to would buy it models bicycle is… visitation very high the price a friend is… this store a friend is… shown

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Behavioural intention statements Dodds and Zeithaml Dodds, Dodds, Grewal, Sweeney, Cronin, Petrick’s Chu and Espejel, In THIS Monroe (1988) Monroe Brady, Monroe Soutar and Brady and (2002) Lu (2007) Fandos study (1985) and Grewal Brand, and Johnson Hult (2000) SERV- and (suggested (1991) Hightower Krishnan (1999) PERVAL Flavián by I Lubbe) and (1998) scale and (2008) Shemwell Petrick’s, (1997) (2004) Serv-perval Willing- ness to Willing- Behaviour- Purchase Willing- Willing- Re- Purchase Buying buy ness to al Intention to Purchase intentions ness to ness to purchase intention intention construct buy Intentions enrol measures buy buy intention scale (no state- indicators scale metns) Perceived quality will Whenever increase In near possible, I At the price The If I had to If a friend or and future, I would avoid shown, I likelihood do it over relative perceived would going to would that I would again, I recommend value and consider another consider purchase would make me this willingness purchasing university buying the this bicycle the same olive oil I to buy will online (continue at product is… choice would buy it decrease as music this price university) increases My If a place is The favourable available at probability opinion this that I would toward this university, I consider olive oil will would buying the lead me to prefer to go product is… buy it in the to it future

477 |

Notes on behavioural intention scales:

Teas and Agarwal (1991), Sweeney and Soutar (2001), Sánchez, Callarisa, Rodríguez and Moliner’s (2006) GLOVAL, Ledden, Kalafatis and Samouel (2007) (education specific), Ledden and Kalafatis (2010), and Brown and Mazzarol (2009) - their models stop at perceived value. No willingness to buy or intention to buy scales or paths was included in their study.

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APPENDIX E

Behavioural intention scale (Willingness to enrol scale)

The next table presents a summary of the behavioural intention scales and their statements that were investigated for the purpose of this study.

Content 1 Behavioural intention scales with their statements. 2 Behavioural intention scale with its statements that were used in this study.

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A summary of the different behavioural intention scales and their suggested statements

Behavioural intention statements Petrick’s Dodds, (2002) Brady, Grewal, Espejel, Dodds, Sweeney, SERV- In THIS Dodds and Brand, Monroe Cronin, Fandos Zeithaml Monroe Soutar and PERVAL Chu and study Monroe Hightower and Brady and and (1988) and Grewal Johnson scale and Lu (2007) (suggested (1985) and Krishnan Hult (2000) Flavián (1991) (1999) Petrick’s, by I Lubbe) Shemwell (1998) (2008) (2004) (1997) Serv-perval Willing- ness to Willing- Behaviour- Purchase Willing- Willing- Re- Purchase Buying buy ness to al Intention to Purchase intentions ness to ness to purchase intention intention construct buy Intentions enrol measures buy buy intention scale (no state- indicators scale metns) Although If I were The The NO SCALE The State that going to buy Willingness The probability I would probability with likelihood I intend to perceived a bicycle, I would feel to buy likelihood of that I will consider that I will statements, that I would continue value leads the guilty if I go increase purchasing use this buying this use this perceived pay for buying the to purchase probability to another with low this product facility’s product at facility’s value a online product (no state- of buying university price is… services this store… services determinant music is ments) this model again is… again is… of purchase high is… intentions If I were going to buy The There is a The Willingness The ….and My this product, likelihood strong likelihood If a retailer decrease probability perceived willingness I would I would that I would likelihood that I would suggests with low- to that I would value a to buy never go to consider recommend that I will recommend me this medium- consider determinant online another buying this this facility’s buy this this facility’s olive oil I price buying this of repeat music is university model at services to product at services to would buy it models bicycle is… visitation very high the price a friend is… this store a friend is… shown

480 |

481 |

Notes on behavioural intention scales:

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The behavioural intention scale used in the final questionnaire

Behavioural intention statement Author I would feel guilty if I go to another university Bruner II, Hensel & James (2005)

Bruner II, Hensel & James, I would never go to another university (2005), Espejel, Fandos and Flavián (2008) Bruner II, Hensel & James (2005), Espejel, Fandos and Flavián (2008), Doods, Monroe and Whenever possible, I would avoid going to another Grewal (1991), Dodds, Brady, Brand, Hightower and Shemwell university (1997), Grewal, Monroe and Krishnan (1998), Cronin, Brady and Hult (2000) (it is about the probability of going…) Bruner II, Hensel & James (2005), Espejel, Fandos and Flavián If a place is available at this university, I would prefer to (2008), Cronin, Brady and Hult (2000), Chu and Lu (2007), go to it Sweeney, Soutar and Johnson (1999), Grewal, Monroe and Krishnan (1998) Bruner II, Hensel & James (2005), I do not like the idea of going to another university Espejel, Fandos and Flavián (2008), Cronin, Brady and Hult (2000)

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APPENDIX F

Choice factor statements and its corresponding theoretical dimensions and theoretical constructs.

Statement Factor Possible construct 1. It has excellent lecturers Its lecturers are knowledgeable 2. Factor 1: experts QUALITY - of the Only the very intelligent study 3. institution QUALITY there (include statements 12 & 4. Only good students get in 13) Academic programmes are 5. nationally known It offers many good cultural Factor 8: Cultural Cultural 6. experiences association/belong association/belong 7. It offers a variety of courses It offers courses that I am 8. interested in Factor 2: QUALITY – of It offers courses with a good QUALITY 9. the courses reputation It offers courses that the job 10. market is interested in It is committed to social service Factor 8: Cultural Cultural 11. association/belong association/belong Sport teams have a good 12. reputation Factor 1: QUALITY of the QUALITY There are good sporting institution 13. opportunities It is committed to academic 14. excellence 15. If offers a world-class education Its qualifications are 16. internationally recognised Factor 3: Academic It is a reputable institution (in 17. REPUTATION of the REPUTATION South Africa) university 18. Its qualifications are reputable It has a positive image with 19. possible employers Its admission requirements are 20. high Hostel/residential facilities are 21. attractive 22. The campus looks attractive 23. The buildings look attractive Factor 4: QUALITY of the 24. The campus looks prestigious facilities (physical QUALITY Its buildings and grounds are attractiveness) 25. well maintained The sports facilities are up to 26. date It has good resources for 27. QUALITY students (computers etc) Factor 5: Quality 28. It offers a safe environment Factor 5: QUALITY of the The recreation facilities (e.g. QUALITY 29. Student centre) looks attractive facilities (university environment) I will find a job after completing 30. Factor 6: Job prospects JOB PROSPECTS my qualification

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Statement Factor Possible construct Studying at this university will 31. make it possible to find a job after qualifying Studying at this university will 32. enhance chances of employment opportunities Studying at this university will 33. increase career prospects Studying at this university will 34. provide better salary prospects Current students’ perception at 35. this university is positive My friends perception of this 36. university is positive My friends also consider to study 37. at this university Factor 7: REPUTATION My parents’ perception of this (image and brand of the REPUTATION 38. university is positive institution) My school teachers’ perception 39. of this university is positive The particular university’s 40. representatives are positive about the university Others from my cultural group 41. are present on campus 42. My culture will be respected I will feel at home at this 43. university Factor 8: Cultural I will be able to express my association/belonging Cultural 44. culture at the university (include statements 6 and association/belonging All population groups are 11) 45. represented on campus It is known that there is NO 46. racism 47. I will be taught in English The distance of the university 48. from home is not too far The university’s campus is easy 49. accessible (transport) The university’s campus is LOCATION 50. Factor 9: Location located near shops/malls

The university’s campus is close 51. to health services (hospitals, dentists) Accommodation (other than 52. residence) is near the campus The cost of tuition at the 53. university is fairly priced Financial aid and scholarships 54. are available at the university Studying at this university is 55. Factor 10: Affordability value for money QUALITY (price) My parents/guardians are 56. able to afford the university There will be the opportunity 57. for part-time jobs (near campus)

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APPENDIX G

Coding of the questionnaire’s relevant sections

Content Coding of the HEIs in South Africa (List of Public Higher Education Institutions in South G1 Africa with their relevant codes used for capturing this information). Coding of Gauteng Public High Schools that form part of the sample (List of the Gauteng G2 High Schools used in this study, with their relevant codes)

G1: Coding of the HEIs in South Africa

List of HEIs in South Africa Code Cape Peninsula University of Technology (CPUT) 1 Durban University of Technology (DUT) 2 University of Cape Town (UCT) 3 University of Fort Hare (UFH) 4 University of Free State (UFS) 5 University of Johannesburg (UJ) 6 University of KwaZulu-Natal (UKZN) 7 University of Limpopo (UL) 8 University of Pretoria (UP) 9 University of Stellenbosch (US) 10 University of South Africa (UNISA) 11 University of the Western Cape Town (UWC) 12 University of the Witwatersrand (Wits) 13 University of Zululand (UZ) 14 Nelson Mandela Metropolitan University (NMMU) 15 North-West University (NWU) 16 Rhodes University (RU) 17 Tshwane University of Technology (TUT) 18 Walter Sisulu University for Technology and Science (WSU) 19 OTHER (Please specify): 20

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G2: Coding of Gauteng Public High Schools that form part of the sample

Type of School School’s Name Code Afrikaans Hoërskool Florida 1 Afrikaans Hoërskool Kempton Park 2 Afrikaans Hoërskool Menlopark 3 Afrikaans Hoërskool Monument 4 Afrikaans Hoërskool Noordheuwel 5 Afrikaans Hoërskool Waterkloof 6 English Benoni High School 7 English Jeppe High School for Boys 8 English Parktown Girls High School 9 English Modeor High School 10 “African” Kwadedangenlale High School 11 “African” Mokogome High School 12 “African” Moletsane High School 13 “African” Thutolore High School 14

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APPENDIX H

Questionnaires

Content H1 Final questionnaire used in this study H2 Pilot questionnaire H3 Initial pilot questionnaire (distributed in 2008 to 38 Gauteng public schools) Refined questionnaire (instrument) portraying remaining statements for each scale H4 retained after Confirmatory Factor Analysis (CFA)

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Only for office use:

2011 - Factors Influencing Prospective Students’ University Choice

The researcher would like to understand what influences the prospective students’ University/University of Technology choice. What choice factors are important and what do YOU value when making the choice of which university to enrol at?

Your participation in this survey is completely anonymous and voluntary. You are not required to include your name on the questionnaire and you do not have to answer a question if you find it objectionable. Your responses will also be treated as confidential. The questionnaire should not take longer than 15 minutes to complete.

When answering the questions please report on your own experience, opinion and perspective.

Thank you for taking the time to complete this questionnaire.

If you have any questions relating to this questionnaire, please contact Mrs Isolde Lubbe on 082 921 3257.

PLEASE INDICATE YOUR ANSWER BY MARKING A CROSS (X) IN THE APPROPRIATE SPACE PROVIDED.

Section A – Background information

A1. Are you planning to apply to study at a University or University of Technology in the future? (Mark one option only) a. Yes, a University or University of Technology in South Africa 1 IF you have answered YES, please continue with the questionnaire b. Yes, a University, but NOT in South Africa 2 IF you have answered yes, please indicate why you are intending to study outside South Africa? Please continue with the questionnaire ______c. No, I am not intending to study at a University or University of Technology 3 IF you have answered NO, you do NOT have to complete the questionnaire.

A2. Please write down the name of the school that you are currently attending in grade 12:

A3. Indicate your gender Female 1 Male 2

A4. What is your home language (choose the language in which you generally communicate with your parents/ family)? Choose only ONE option Afrikaans 1 English 2 Nguni (IsiZulu, IsiXhosa, IsiSwati, IsiNdebele) 3 Sotho (SeSotho sa Leboa, Sesotho,Setswana) 4 TshiVenda/XiTsonga 5 Other (Please specify): 6

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A5. Specify which ONE of the following subjects you take at school: Maths 1 Maths literacy 2

A6. Indicate your overall expected average grade for grade 12? A (80 – 100%) 1 B (70 – 79%) 2 C (60 – 69%) 3 D (50 – 59%) 4 E (40 – 49%) 5 F (34 – 39%) 6

A7. Did any of your parents/guardians attend a university/university of technology/college? Yes 1 No 2

Section B – Factors influencing your preferences of a University/ University of Technology.

B1. Please write down the name of the university/ university of technology that you would most like to study at.

THINK ABOUT THIS UNIVERSITY THAT YOU WOULD MOST LIKE TO STUDY AT (YOUR FIRST CHOICE) WHEN ANSWERING ALL THE FOLLOWING QUESTIONS:

B2. To what extent do you agree with the following statements regarding the university/university of technology that you would most like to study at? Please indicate your answers using the 7-point response scale provided where 1 indicates strongly disagree and 7 strongly agree. Factors influencing your decision Strongly disagree Strongly agree 1 It has excellent lecturers 1 2 3 4 5 6 7 2 Its lecturers are knowledgeable experts 1 2 3 4 5 6 7 3 It is known that only the very intelligent study there 1 2 3 4 5 6 7 4 Only good students get in 1 2 3 4 5 6 7 5 Academic programmes are nationally known 1 2 3 4 5 6 7 6 It offers many good cultural experiences (fine arts, music, theatre, etc.) 1 2 3 4 5 6 7 7 It offers a variety of courses 1 2 3 4 5 6 7 8 It offers the courses that I am interested in 1 2 3 4 5 6 7

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Factors influencing your decision (Question B.2 Continues..) Strongly disagree Strongly agree 9 It offers courses with a good reputation 1 2 3 4 5 6 7 10 It offers courses that the job market is interested in 1 2 3 4 5 6 7 11 It is committed to social service (involved with local community) 1 2 3 4 5 6 7 12 Sport teams have a good reputation 1 2 3 4 5 6 7 13 There are good sporting opportunities at the university 1 2 3 4 5 6 7 14 It is committed to academic excellence 1 2 3 4 5 6 7 15 It offers a world-class education 1 2 3 4 5 6 7 16 Its qualifications are internationally recognised 1 2 3 4 5 6 7 17 It is a reputable institution (in South Africa) 1 2 3 4 5 6 7 18 Its qualifications are reputable 1 2 3 4 5 6 7 19 It has a positive image with possible employers 1 2 3 4 5 6 7 Its admission requirements are high (ie students must do well in 20 1 2 3 4 5 6 7 grade 12 to get in) 21 Hostel/residential facilities are attractive 1 2 3 4 5 6 7 22 The campus looks attractive 1 2 3 4 5 6 7 23 The buildings look attractive 1 2 3 4 5 6 7 24 The campus looks prestigious 1 2 3 4 5 6 7 25 Its buildings and grounds are well maintained 1 2 3 4 5 6 7 26 The sports facilities are up to date 1 2 3 4 5 6 7 27 It has good resources for students (computers, Library, etc) 1 2 3 4 5 6 7 28 It offers a safe environment 1 2 3 4 5 6 7 29 The recreation facilities (e.g. Student centre) look attractive 1 2 3 4 5 6 7 30 I will find a job after completing my qualification 1 2 3 4 5 6 7 31 Studying at this university will make it possible to find a job after qualifying 1 2 3 4 5 6 7 32 Studying at this university will enhance chances of employment opportunities1 2 3 4 5 6 7 33 Studying at this university will increase career prospects 1 2 3 4 5 6 7 34 Studying at this university will provide better salary prospects 1 2 3 4 5 6 7 35 Current students’ perception at this university is positive 1 2 3 4 5 6 7 36 My friends’ perception of this university is positive 1 2 3 4 5 6 7 37 My friends also consider to study at this university 1 2 3 4 5 6 7 38 My parents’ perception of this university is positive. 1 2 3 4 5 6 7 39 My school teachers’ perception of this university is positive. 1 2 3 4 5 6 7 40 The particular university’s representatives are positive about the university 1 2 3 4 5 6 7 41 Others from my cultural group are present on campus 1 2 3 4 5 6 7 42 My culture will be respected 1 2 3 4 5 6 7 43 I will feel at home at this university 1 2 3 4 5 6 7 44 I will be able to express my culture at the university 1 2 3 4 5 6 7 45 All population groups are represented on the campus 1 2 3 4 5 6 7 46 It is known that there is NO racism 1 2 3 4 5 6 7 47 I will be taught in English 1 2 3 4 5 6 7 48 The distance of the university from home is not too far 1 2 3 4 5 6 7 49 The university’s campus is easy accessible (transport) 1 2 3 4 5 6 7 50 The university’s campus is located near shops/malls 1 2 3 4 5 6 7 51 The university’s campus is close to health services (hospitals, dentists etc)1 2 3 4 5 6 7 52 Accommodation (other than residence) is near the campus 1 2 3 4 5 6 7 53 The cost of tuition at the university is fairly priced 1 2 3 4 5 6 7 54 Financial aid and scholarships are available at the university 1 2 3 4 5 6 7 55 Studying at the university is value for money 1 2 3 4 5 6 7 56 My parents/guardians are able to afford the university 1 2 3 4 5 6 7 57 There will be the opportunity for part-time jobs (nearby campus) 1 2 3 4 5 6 7

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Section C – The value offered by your chosen university/ university of technology

C1. To what extent do you agree with the following statements regarding the university/university of technology that you would most like to study at? Please indicate your answers using the 7-point response scale provided where 1 indicates strongly disagree and 7 strongly agree. Value Strongly disagree Strongly agree The benefit of attending this university will outweigh the financial cost 1 2 3 4 5 6 7 I am happy to make financial sacrifices to attend this university 1 2 3 4 5 6 7 The price paid for studying at this university is reasonable 1 2 3 4 5 6 7 I am happy that the price of the university is an indication of good quality 1 2 3 4 5 6 7 I am happy to give up some of my interests to attend this university 1 2 3 4 5 6 7 The benefit of attending the university will outweigh the time sacrifices 1 2 3 4 5 6 7 (less time with friends and family) I will achieve my career goals (because I study at this university) 1 2 3 4 5 6 7 The university will perform to my expectations 1 2 3 4 5 6 7 The staff at the university will provide service as I expect 1 2 3 4 5 6 7 I will gain the knowledge that I need 1 2 3 4 5 6 7 I will feel a sense of ‘belonging’ when attending the university 1 2 3 4 5 6 7 The reputation of the university will influence the value of my degree 1 2 3 4 5 6 7 I believe that employers have good things to say about the university 1 2 3 4 5 6 7 The university will give me a good experience (enjoyment, feel good, 1 2 3 4 5 6 7 pleasure, relaxed) The price I have to pay for the university is worth the money 1 2 3 4 5 6 7 The facilities (library, computer labs etc) will meet my expectations 1 2 3 4 5 6 7 Compared to what I have to give up, the overall ability of the 1 2 3 4 5 6 7 university to satisfy my wants and needs is very high

Section D – Willingness to enrol at your preferred university

D1. To what extent do you agree with the following statements regarding the university/university of technology that you would most like to study at? Please indicate your answers using the 7-point response scale provided where 1 indicates strongly disagree and 7 strongly agree. Behavioural intention scale Strongly disagree Strongly agree I would feel guilty if I go to another university 1 2 3 4 5 6 7 If it is up to me, I would never go to another university 1 2 3 4 5 6 7 Whenever possible, I would avoid going to another university 1 2 3 4 5 6 7 If a place is available at this university, I will attend it 1 2 3 4 5 6 7 I do not like the idea of going to another university 1 2 3 4 5 6 7

Thank you for your participation!

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APPENDIX H

Pilot questionnaire

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Only for office use: (PILOT QUESTIONNAIRE)

2011 - Factors Influencing Prospective Students’ University Choice

The questionnaire consists of four short sections:

Section A records information about you as a learner. Section B finds out about factors influencing your preferences of a University/University of Technology. Section C asks you about what you value when evaluating which university to attend. Section D asks you about your willingness to enrol at a university.

When answering the questions please report on your own experience, opinion and perspective.

Thank you for taking the time to complete this questionnaire.

If you have any questions relating to this questionnaire, please contact Mrs I Lubbe on 082 921 3257.

PLEASE INDICATE YOUR ANSWER BY MARKING A CROSS (X) IN THE APPROPRIATE SPACE PROVIDED.

Section A – Background information

A1. Are you planning to apply to study at a University or University of Technology in the future? (Mark one option only) d. Yes, a University or University of Technology in South Africa 1 IF you have answered YES, please continue with the questionnaire e. Yes, a University, but NOT in South Africa IF you have answered yes, please indicate why you are intending to study outside South Africa? Please continue with the questionnaire 2 ______f. No, I am not intending to study at a University or University of Technology 3 IF you have answered NO, you do NOT have to complete the questionnaire.

A2. Please write down the name of the school that you are currently attending in grade 12:

A3. Indicate your gender Female 1 Male 2

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A5. Specify which ONE of the following subjects you take at school: Maths 1 Maths literacy 2

A6. Indicate your overall expected average grade for grade12? A (80 – 100%) 1 B (70 – 79%) 2 C (60 – 69%) 3 D (50 – 59%) 4 E (40 – 49%) 5 F (34 – 39%) 6

A7. Did any of your parents/guardians attend a university/university of technology/college? Yes 1 No 2

Section B – Factors influencing your preferences of a University/University of Technology.

B1. From the following list of South African universities, please choose the ONE university/university of technology that you would most like to study at (your first choice university). (Mark with a X ) Cape Peninsula University of Technology (CPUT) 1 Durban University of Technology (DUT) 2 University of Cape Town (UCT) 3 University of Fort Hare (UFH) 4 University of Free State (UFS) 5 University of Johannesburg (UJ) 6 University of KwaZulu-Natal (UKZN) 7 University of Limpopo (UL) 8 University of Pretoria (UP) 9 University of Stellenbosch (US) 10 University of South Africa (UNISA) 11 University of the Western Cape Town (UWC) 12 University of the Witwatersrand (Wits) 13 University of Zululand (UZ) 14 Nelson Mandela Metropolitan University (NMMU) 15 North-West University (NWU) 16 Rhodes University (RU) 17 Tshwane University of Technology (TUT) 18 Walter Sisulu University for Technology and Science 19 (WSU) OTHER (Please specify): 20

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THINK ABOUT YOUR PREFERRED UNIVERSITY (FIRST CHOICE) WHEN ANSWERING ALL THE FOLLOWING QUESTIONS:

B2. Below is a list of factors which could have influenced your decision to choose your first choice university (the university you really would like to study at). How important would you rate the following factors in affecting your decision when choosing your first choice university/ university of technology to further your education?. Please indicate your answer using the 7-point response scale provided where 1 indicates NOT important and 7 VERY important. Not Important Very important 1 It has excellent lecturers 1 2 3 4 5 6 7 2 Its lecturers are knowledgeable experts 1 2 3 4 5 6 7 3 It is known that only the very intelligent study at the university 1 2 3 4 5 6 7 4 It is tough to get into the particular university 1 2 3 4 5 6 7 5 It has nationally known academic programmes 1 2 3 4 5 6 7 6 It is active in social issues 1 2 3 4 5 6 7 7 It offers many good cultural experiences (fine arts, music, theatre, etc.) 1 2 3 4 5 6 7 8 It offers a variety of courses 1 2 3 4 5 6 7 9 It offers the courses that I am interested in 1 2 3 4 5 6 7 10 It offers courses with a good reputation 1 2 3 4 5 6 7 11 It offers courses that the job market is interested in 1 2 3 4 5 6 7 12 It is committed to social service (involved with local community) 1 2 3 4 5 6 7 13 Sport teams have a good reputation 1 2 3 4 5 6 7 14 There are good sporting opportunities at the university 1 2 3 4 5 6 7 15 It is committed to academic excellence 1 2 3 4 5 6 7 16 It offers a world-class education 1 2 3 4 5 6 7 17 Its qualifications are internationally recognised 1 2 3 4 5 6 7 18 It is a reputable institution (in South Africa) 1 2 3 4 5 6 7 19 Its qualifications are reputable 1 2 3 4 5 6 7 20 It has a positive image 1 2 3 4 5 6 7 21 Its admission requirements are high (ie students must do well in 1 2 3 4 5 6 7 grade 12 to get in) 22 Hostel/residential facilities are attractive 1 2 3 4 5 6 7 23 The campus looks attractive 1 2 3 4 5 6 7 24 The buildings look attractive 1 2 3 4 5 6 7 25 The campus looks prestigious 1 2 3 4 5 6 7 26 Its buildings and grounds are well maintained 1 2 3 4 5 6 7 27 The sports facilities are up to date 1 2 3 4 5 6 7 28 It has good resources for students (computers, Library, etc) 1 2 3 4 5 6 7 29 It offers a safe environment 1 2 3 4 5 6 7 30 The recreation facilities (e.g. Student centre) look attractive 1 2 3 4 5 6 7 31 I will find a job after completing my qualification 1 2 3 4 5 6 7 32 The institution should have the ability to place me in a job after qualifying 1 2 3 4 5 6 7 33 There should be available employment opportunities after graduation 1 2 3 4 5 6 7 34 I should have better career prospects after studying at that university 1 2 3 4 5 6 7 35 I expect a better salary after completing my qualification at that university 1 2 3 4 5 6 7 36 Current students’ perception at that university is positive 1 2 3 4 5 6 7 37 My friends’ perception of the institution is positive 1 2 3 4 5 6 7 38 My friends also consider to study at the institution 1 2 3 4 5 6 7 39 My parents’ perception of the institution is positive. 1 2 3 4 5 6 7 40 My school teachers’ perception of the institution is positive. 1 2 3 4 5 6 7 41 The particular university’s representatives are positive about the university1 2 3 4 5 6 7 42 Others from my cultural group are present on campus 1 2 3 4 5 6 7 43 My culture will be respected 1 2 3 4 5 6 7 44 I will feel at home at the chosen university 1 2 3 4 5 6 7 45 I will be able to express my culture at the chosen university 1 2 3 4 5 6 7

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Question B.2 Continues…(Factors affecting your choice…) Not Important Very important 46 All population groups are represented on the campus 1 2 3 4 5 6 7 47 It is known that there is NO racism 1 2 3 4 5 6 7 48 I should be taught in English 1 2 3 4 5 6 7 49 The distance of the university from home 1 2 3 4 5 6 7 50 The university’s campus’ is easy accessible (transport) 1 2 3 4 5 6 7 51 The university’s campus is located near shops/malls 1 2 3 4 5 6 7 52 The university’s campus is close to health services (hospitals, dentists etc) 1 2 3 4 5 6 7 53 Availability of accommodation other than residence near campus 1 2 3 4 5 6 7 54 The cost of tuition at the university 1 2 3 4 5 6 7 55 The availability of financial aid and scholarships at the university 1 2 3 4 5 6 7 56 Perceived value for money 1 2 3 4 5 6 7 57 My parents/guardian’s financial ‘health’ influence the university decision 1 2 3 4 5 6 7 58 Part-time jobs near chosen university to help me earn money for studies 1 2 3 4 5 6 7

Section C – What do you value when evaluating which university to attend?

C1. Keep your ‘first choice’ university in mind when answering the following questions. On a scale of 1 to 7 where 1 is ‘strongly disagree’ and 7 is strongly agree, indicate the extent to which you agree with each of the following statements Value Strongly disagree Strongly agree The university will perform to my expectations 1 2 3 4 5 6 7 The staff at the university will provide a service as I expect 1 2 3 4 5 6 7 I will enjoy attending the university 1 2 3 4 5 6 7 I will feel a sense of ‘belonging’ when attending the university 1 2 3 4 5 6 7 The university has a good reputation 1 2 3 4 5 6 7 The benefit of attending the university will outweigh the social sacrifices 1 2 3 4 5 6 7 (less time with friends and family) The university offers value for money 1 2 3 4 5 6 7 The benefit of attending this university will outweigh the financial cost 1 2 3 4 5 6 7 The facilities (library, computer labs etc) will meet my expectations 1 2 3 4 5 6 7 Compared to what I have to give up, the overall ability of the 1 2 3 4 5 6 7 university to satisfy my wants and needs is very high

Section D – Willingness to enrol at a university.

D1. Keep your ‘first choice’ university in mind when answering the following questions. On a scale of 1 to 7 where 1 is ‘strongly disagree’ and 7 is strongly agree, indicate the extent to which you agree with each of the following statements Behavioural intention scale Strongly disagree Strongly agree I would feel guilty if I go to another university 1 2 3 4 5 6 7 I would never go to another university 1 2 3 4 5 6 7 Whenever possible, I would avoid going to another university 1 2 3 4 5 6 7 If a place is available at this university, I would prefer to go to it 1 2 3 4 5 6 7 I do not like the idea of going to another university 1 2 3 4 5 6 7

Thank you for your participation! The end

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APPENDIX H3

Initial pilot questionnaire (2009)

The purpose of the initial pilot study, conducted during 2009, was to assist the University of Johannesburg with a research project to understand why prospective students, especially from the Afrikaans schools, choose a specific university/university of technology to further his/her education. Specifically, the aim was to determine which university(ies) prospective students would apply to and what factors influence these choices.

The sample consisted of 2 700 prospective students (grade 12 scholars) from 38 Gauteng schools. These 38 Gauteng schools represented 20 Afrikaans schools (Afrikaans is the language of instruction) and 18 English schools (English is the language of instruction). The majority of respondents were female (59%), however the genders were fairly equally represented in the sample. The majority of respondents were Afrikaans speaking (63%), followed by English (22%), Nguni (7%) and Sotho speaking (6%).

The results of the open-ended questions indicated that respondents choose their ‘first choice’ university because it is ‘closer to home’ (30%), followed by ‘reputation’ (21.9%) and then ‘availability of courses’ (21%). The results further indicated that the most important deciding factor influencing university choice is ‘finding a job after completing my qualification’, followed by ‘international recognition’ (2), ‘world class education’ (3), ‘racism should be non existing’ (4), ‘a safe environment’ (5) ‘knowledgeable lectures ‘(6), ‘excellent library and associated resources’ (7), ‘I want to feel at home’ (8), and ‘my culture should be respected’ (9).

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Only for office use:

(Initial pilot questionnaire) 2009 Choosing a University Questionnaire

The researcher would like to understand why the prospective student chooses a specific University/University of Technology to further his/her education. Specifically, I am interested in what Universities you would and would not apply to and what factors influence these choices.

Your participation in this survey is voluntary and you do not have to answer a question if you find it objectionable. Your responses will be treated as confidential. You are not required to include your name on the questionnaire. We estimate it will take you about 15 minutes to complete the questionnaire.

The questionnaire consists of three parts:

Section A records information about you as a learner.

Section B finds out about factors influencing your preferences of a University/University of Technology.

Section C asks you about your perceptions of local Universities.

When answering questions please report on your own experience, opinion and perspective. Answer the question by placing an X in the space provided.

Thank you for taking the time to complete this questionnaire.

If you have any questions relating to this questionnaire, please contact Mrs I Lubbe on 082 921 3257.

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PLEASE INDICATE YOUR ANSWER BY MAKING A CROSS (X) IN THE SPACE PROVIDED.

Section A – Background information

This section of the questionnaire records background and biographical information.

A1. Are you planning to apply to study at a University or University of Technology in the future? (Mark one option only) Yes, a University in South Africa IF you have answered YES, please continue with the questionnaire Yes, a University of Technology in South Africa IF you have answered YES, please continue with the questionnaire Yes, a University, but NOT in South Africa IF you have answered yes, please indicate why you are intending to study outside South Africa? Please continue with the questionnaire ______No, I am not intending to study at a University or University of Technology IF you have answered NO, you do NOT have to complete the questionnaire. Please explain why you do not want to apply to study at a University ______

A2. Please write down the name of the school that you are currently attending:

A3. Indicate your gender Female Male

A4. What is your race? Indian Black Coloured White Other (Please specify) ______

A5. What is your home language (choose the language in which you generally communicate with your parents/ family)? Choose only one option Afrikaans English Nguni (IsiZulu, IsiXhosa, IsiSwati, IsiNdebele) Sotho (SeSotho sa Leboa, Sesotho,Setswana) TshiVenda/XiTsonga Other (Please specify): ______

A6. Specify which ONE of the following subjects you take at School. Maths Maths literacy

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Section B

In this section the researcher wants to find out what you know about South African Universities, whether you have decided what you want to study when you apply to a University, and which University (Universities) you are thinking of applying to, and why.

B1. Do you know what you want to study? (Mark one option only) Yes, I have a very clear idea (please continue with question B2) Yes, but I haven’t made up my mind (please continue with question B2) No, no idea yet (please continue with question B3)

B2. If you answered ‘yes’ to B1 above, please indicate what you want to study (Mark one option only) Art and Design and Architecture (e.g. Fashion, Fine Art, Graphic Design, Architecture etc.) Economic & Financial Sciences (e.g. Accounting, Investment Management, Econometrics, Taxation etc.) Education (e.g. Educational Psychology, Teaching etc.) Engineering and Built Environment (e.g. Engineering, Town and Regional planning, Mine Surveying, Construction Management etc.) Health Sciences (e.g. Nursing, Optometry, Sport Management, Radiography, Somatology etc.) Humanities (e.g. Social work, Anthropology, Philosophy, Politics, Corporate Communications, Marketing Communications, Psychology, Languages, Theology, Journalism etc.) Law Management (e.g. Entrepreneurship, Marketing Management (BCom), Industrial Psychology, Transport, Logistics, Human Resource Management etc.) Science (e.g. Life and Environmental Sciences, Geography, Chemistry, Physical Science, Zoology, Food Technology, Statistics, Geology, Biochemistry, Mathematics etc.) Other (Please specify eg Medical Doctor OR Actuary Or Veterinary OR …….etc. etc.): ______

B3.From the following list of South African universities, please rank the 3 (THREE) universities which you would most like to study at. Write the number 1 (one) next to the university you most prefer, the number 2 (two) next to your second choice and the number 3 (three) next to your third choice. Cape Peninsula University of Technology (CPUT) Durban University of Technology (DUT) University of Cape Town (UCT) University of Fort Hare (UFH) University of Free State (UFS) University of Johannesburg (UJ) University of KwaZulu-Natal (UKZN) University of Limpopo (UL) University of Pretoria (UP) University of Stellenbosch (US) University of South Africa (UNISA) University of the Western Cape Town (UWC) University of the Witwatersrand (Wits) University of Zululand (UZ) Nelson Mandela Metropolitan University (NMMU) North-West University (NWU) Rhodes University (RU) Tshwane University of Technology (TUT) Walter Sisulu University for Technology and Science (WSU) OTHER (Please specify):

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B4.Please state the reason to WHY you have chosen your above mentioned 3 (THREE) universities in question B3 as the institutions you would most like to study at:. Preferred university, because

Second choice university, because

Third choice university, because

B5. How significant would you rate the following factors in affecting your decision when choosing a university/ university of technology to further your education?. Please indicate your answer using the 7- point response scale provided where 1 indicates NO significance and 7 VERY significant.

NOT significant VERY significant It should offer a world-class education 1 2 3 4 5 6 7 Its qualifications should be internationally recognised. 1 2 3 4 5 6 7 It should be a reputable institution (in South Africa). 1 2 3 4 5 6 7 Its qualifications should be reputable 1 2 3 4 5 6 7 It should be known as a FIRST rate university 1 2 3 4 5 6 7 Its lecturers should be knowledgeable experts 1 2 3 4 5 6 7 It should have enabling facilities 1 2 3 4 5 6 7 I should be taught in English. 1 2 3 4 5 6 7 I should be able to find a job after completing my qualification. 1 2 3 4 5 6 7 Its admission requirements should be high (ie students must do 1 2 3 4 5 6 7 well in grade 12 to get in) It should offer a safe environment. 1 2 3 4 5 6 7 My teachers’ perception of the institution should be positive. 1 2 3 4 5 6 7 My friends’ perception of the institution should be positive. 1 2 3 4 5 6 7 My parents’ perception of the institution should be positive. 1 2 3 4 5 6 7 Its buildings and grounds should be well maintained 1 2 3 4 5 6 7 The campus(es) should look “attractive” 1 2 3 4 5 6 7 My friends should also consider the institution to further their 1 2 3 4 5 6 7 education Others from my cultural group should be present on campus 1 2 3 4 5 6 7 My culture should be respected 1 2 3 4 5 6 7 I should feel at home at the chosen institution 1 2 3 4 5 6 7 I should be able to express my culture 1 2 3 4 5 6 7 All population groups are represented on the campus 1 2 3 4 5 6 7 Racism should be non existing 1 2 3 4 5 6 7 The campus’ location is important 1 2 3 4 5 6 7 It should be a “hip” environment 1 2 3 4 5 6 7 Sport teams should have a good reputation 1 2 3 4 5 6 7 Excellent library and associated resources 1 2 3 4 5 6 7

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B6. If you are accepted into a University, what is your preferred language of tuition? English Afrikaans Zulu Sepedi Other (Please specify): ______

B7. Which ONE of the following options will be the main contributor towards paying for your studies? Bursary from university Bursary from a company/ business Loan from the NSFAS (National Student Financial Aid Scheme) Loan from a bank Self Parents Other (please specify):______

______

Section C

This section of the questionnaire determines YOUR perception of two Universities namely, the University of Johannesburg (UJ) and YOUR first choice university (If UJ is NOT your first choice university).

C1. Would you consider applying to study at the University of Johannesburg? Yes No

C2. Please give reasons for your response to C1

C3. IF you are considering the University of Johannesburg (UJ), PLEASE answer this question. If you are NOT considering UJ, then please go to question C4.

Please consider ALL the listed locations of campuses below and please indicate if the location will influence your decision to study at UJ. Mark with an X. DO NOT know YES, I will study there NO, I will NOT where it is study there Soweto Doornfontein (JHB) Auckland Park (JHB) Known as Kingsway Campus Bunting Road (JHB)

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C4. We would like to compare YOUR perception of the University of Johannesburg (UJ) to YOUR university of first choice (if not UJ). Please indicate with a cross (X) to what extent you agree with each of the following statements for BOTH your first choice university AND the UJ. (Please use the response scale provided; 1 Strongly disagree, 2 Disagree, 3 Neither agree nor disagree, 4 Agree and 5 Strongly agree).)

YOUR first choice university (Name of University of university):______Johannesburg ______

Strongly Strongly Disagree agree Neither disagree nor Agree Strongly agree Strongly disagree Disagree agree Neither disagree nor Agree Strongly agree disagree 1 2 3 4 5 It provides a world-class education. 1 2 3 4 5 1 2 3 4 5 It has a good reputation (as an institution in 1 2 3 4 5 South Africa). 1 2 3 4 5 The degrees have a good reputation in South 1 2 3 4 5 Africa. 1 2 3 4 5 My chosen qualification (degree/diploma) is 1 2 3 4 5 available at … 1 2 3 4 5 My chosen qualification (degree/diploma) if 1 2 3 4 5 obtained from … has a good reputation in South Africa. 1 2 3 4 5 It is one of the top 2 universities in South Africa. 1 2 3 4 5 1 2 3 4 5 The degrees are internationally recognised. 1 2 3 4 5 My chosen qualification (degree/diploma) if obtained from …. is internationally recognised 1 2 3 4 5 It has safe campuses. 1 2 3 4 5 1 2 3 4 5 Accommodation on campus is safe. 1 2 3 4 5 1 2 3 4 5 It is predominantly an English institution. 1 2 3 4 5 1 2 3 4 5 It has multi-cultural campuses. 1 2 3 4 5 1 2 3 4 5 My culture is well represented at …. 1 2 3 4 5 1 2 3 4 5 It is a modern university. 1 2 3 4 5 1 2 3 4 5 It is a university my friends also consider to 1 2 3 4 5 further their studies 1 2 3 4 5 All the campuses are well located. 1 2 3 4 5 1 2 3 4 5 My parents think it is a reputable institution. 1 2 3 4 5 1 2 3 4 5 A teacher at school told me that it has a good 1 2 3 4 5 reputation. 1 2 3 4 5 My friends think it is a reputable institution. 1 2 3 4 5 1 2 3 4 5 It has the appropriate financial support like 1 2 3 4 5 bursaries in place to help me. 1 2 3 4 5 The … responds promptly to applications. 1 2 3 4 5 1 2 3 4 5 Its buildings are well maintained. 1 2 3 4 5

THANK YOU!!!

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Slegs vir kantoorgebruik:

(Oorspronklike vraelys) 2009 – Vraelys: om ’n universiteit te kies

Die navorser wil verstaan hoekom die voornemende student ’n spesifieke universiteit/universiteit van tegnologie kies om verder te studeer. Ek stel spesifiek belang in watter universiteite jy voor aansoek sal doen en nie aansoek sal doen nie en watter faktore hierdie keuses beïnvloed.

Jou deelname aan hierdie opname is vrywillig en jy hoef nie ’n vraag te beantwoord indien jy dit aanstootlik vind nie. Jou antwoorde sal vertroulik hanteer word. Jy hoef nie jou naam op die vraelys in te vul nie. Ons skat dit sal jou ongeveer 15 minute neem om die vraelys in te vul.

Die vraelys bestaan uit drie dele:

Afdeling A samel inligting oor jou as ŉ leerder in.

Afdeling B vind uit oor faktore wat jou voorkeure vir ’n universiteit/universiteit van tegnologie beïnvloed.

Afdeling C vra uit oor jou persepsies van plaaslike universiteite.

Wanneer vrae beantwoord word, antwoord vanuit jou eie ervaring en perspektief en gee jou eie mening. Beantwoord die vrae deur ’n X in die ruimte wat verskaf is, te trek.

Dankie dat jy die tyd afgestaan het om hierdie vraelys in te vul.

Indien jy enige vrae met betrekking tot hierdie vraelys het, skakel asseblief mev Lubbe by 082 921 3257.

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DUI ASSEBLIEF JOU ANTWOORD AAN DEUR ’N KRUIS (X) IN DIE RUIMTE WAT VERSKAF IS, TE TREK.

Afdeling A – Agtergrond inligting

Hierdie afdeling van die vraelys samel agtergrond- en biografiese inligting in.

A1. Beplan jy om in die toekoms aan ’n universiteit of ’n universiteit van tegnologie te studeer? (Merk slegs een keuse.) Ja, ’n universiteit in Suid-Afrika Indien jy JA geantwoord het, gaan asseblief voort met die vraelys. Ja, ’n universiteit van tegnologie in Suid-Afrika Indien jy JA geantwoord het, gaan asseblief voort met die vraelys. Ja, ’n universiteit, maar NIE in Suid-Afrika nie Indien jy ja geantwoord het, verduidelik hoekom jy beoog om buite Suid-Afrika te studeer? Gaan asseblief met die vraelys aan. ______Nee, ek beoog nie om aan ’n universiteit of universiteit van tegnologie te studeer nie. Indien jy NEE geantwoord het, hoef jy NIE die vraelys in te vul nie. Verduidelik asseblief hoekom jy nie wil aansoek doen om aan ’n universiteit te studeer nie. ______

A2. Skryf asseblief die naam van die hoërskool wat jy tans bywoon neer.

A3. Dui jou geslag aan Vroulik Manlik

A4. Wat is jou ras? Indiër Swart Kleurling Wit Ander (spesifiseer asseblief): ______

A5. Wat is jou huistaal (kies die taal waarin jy gewoonlik met jou ouers/familie kommunikeer)? Kies slegs een opsie. Afrikaans Engels Nguni (IsiZulu, IsiXhosa, SiSwati, IsiNdebele) Sotho (Sesotho sa Leboa, Sesotho, Setswana) Tshivenda/Xitsonga Ander (spesifiseer asseblief): ______

A6. Spesifiseer watter EEN van die volgende vakke jy op skool neem. Wiskunde Wiskundige Geletterdheid

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Afdeling B

In hierdie afdeling wil die navorser uitvind wat jy van Suid-Afrikaanse universiteite weet, of jy al besluit wat jy wil studeer wanneer by ŉ universiteit aansoek doen en by watter universiteit(e) jy beplan om aansoek te doen en hoekom.

B1. Weet jy wat jy wil studeer? (Merk slegs een opsie.) Ja, ek het ’n goeie idee (gaan asseblief met vraag B2 aan) Ja, maar ek het nog nie finaal besluit nie (gaan asseblief met vraag B2 aan) Nee, weet nog glad nie (gaan asseblief met vraag B3 aan)

B2. Indien jy “ja” in B1 hierbo geantwoord het, dui asseblief aan wat jy wil studeer.(Merk slegs een opsie.) Kuns, Ontwerp en Argitektuur (bv. Mode, Beeldende Kuns, Grafiese Ontwerp, Argitektuur, ens.) Ekonomiese en Finansiële Wetenskappe (bv. Rekeningkunde, Beleggingsbestuur, Ekonometrie, Belasting, ens.) Opvoedkunde (bv. Opvoedkundige Sielkunde, Onderwys, ens.) Ingenieurswese en die Bou-omgewing (bv. Ingenieurswese, Stads- en streeksbeplanning, Mynopmeting, Konstruksiebestuur, ens.) Gesondheidswetenskappe (bv. Verpleegkunde, Optometrie, Sportbestuur, Radiografie, Somatologie, ens.) Geesteswetenskappe (bv. Maatskaplike Werk, Antropologie, Filosofie, Politiek, Korporatiewe Kommunikasie, Bemarkingskommunikasie, Sielkunde, Tale, Teologie, Joernalistiek, ens.) Regte Bestuur (bv. Entrepreneurskap, Bemarkingsbestuur (BCom), Bedryfsielkunde, Vervoer. Logistiek, Menslikehulpbronbestuur, ens.) Natuurwetenskappe (bv. Lewens- en omgewingswetenskappe, Geografie, Chemie, Fisiese Wetenskappe, Dierkunde, Voedseltegnologie, Statistiek, Geologie, Biochemie, Wiskunde, ens.) Ander (spesifiseer asseblief, bv. Medies OF Aktuariële Wetenskappe OF Veeartsenykunde OF ... ens.) ______

B3.Uit die volgende lys van Suid-Afrikaanse universiteite, plaas asseblief die 3 (DRIE) universiteite waaraan jy die graagste wil studeer in ’n rangorde. Skryf die syfer 1 langs die universiteit wat jy die meeste verkies, die syfer 2 langs jou tweede keuse en die syfer 3 langs jou derde keuse. Kaapse Skiereiland Universiteit van Tegnologie (CPUT) Durban Universiteit van Tegnologie (DUT) Universiteit van Kaapstad (UK) Fort Hare-universiteit (UFH) Universiteit van die Vrystaat (UV) Universiteit van Johannesburg (UJ) Universiteit van KwaZulu-Natal (UKZN) Universiteit van Limpopo (UL) Universiteit van Pretoria (UP) Universiteit van Stellenbosch (US) Universiteit van Suid-Afrika (UNISA) Universiteit van Wes-Kaapland (UWK) Universiteit van die Witwatersrand (Wits) Universiteit van Zoeloeland (UZ) Nelson Mandela Metropolitaanse Universiteit (NMMU) Noordwes-Universiteit (NWU) Rhodes-universiteit (RU) Tshwane Universiteit van Tegnologie (TUT) Walter Sisulu Universiteit vir Tegnologie en Wetenskap (WSU) Ander (spesifiseer asseblief):

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B4.Noem asseblief die rede HOEKOM jy bogenoemde 3 (DRIE) universiteite in vraag B3 as die instellings gekies het waar jy die graagste wil studeer. Voorkeur universiteit (dus eerste keuse universiteit), omdat

Tweede keuse van ’n universiteit, omdat

Derde keuse van ’n universiteit, omdat

B5. Hoe belangrik ag jy die volgende faktore wat hul invloed op jou besluit betref wanneer ’n universiteit/universiteit van tegnologie vir verdere studie gekies word? Dui asseblief jou antwoord aan deur die sewepuntresponskaal wat verskaf is, te gebruik, waar 1 GLAD nie belangrik is nie en 7 BAIE belangrik is.

NIE NIE nie belangrik BAIE belangrik Dit moet onderrig van wêreldgehalte bied. 1 2 3 4 5 6 7 Hulle kwalifikasies moet internasionaal erken word. 1 2 3 4 5 6 7 Dit moet ’n agtenswaardige (‘reputable’) instelling (in Suid-Afrika) wees. 1 2 3 4 5 6 7 Hulle kwalifikasies moet agtenswaardig (‘reputable’) wees. 1 2 3 4 5 6 7 Dit moet as ’n EERSTEKLAS-universiteit bekendstaan. 1 2 3 4 5 6 7 Die dosente moet goed ingeligte deskundiges wees 1 2 3 4 5 6 7 Dit moet bevoegdheidsgewende (‘enabling’) fasiliteite hê. 1 2 3 4 5 6 7 Ek moet onderrig in Engels ontvang. 1 2 3 4 5 6 7 Ek moet ’n werk kry nadat ek my kwalifikasie verwerf het. 1 2 3 4 5 6 7 Die toelatingsvereistes moet hoog wees (d.i. studente moet goed in 1 2 3 4 5 6 7 graad 12 presteer om toelating te kry). Dit moet ’n veilige omgewing bied. 1 2 3 4 5 6 7 My onderwysers se persepsie van die instelling moet positief wees. 1 2 3 4 5 6 7 My vriende se persepsie van die instelling moet positief wees. 1 2 3 4 5 6 7 My ouers se persepsie van die instelling moet positief wees. 1 2 3 4 5 6 7 Die geboue en terrein moet goed in stand gehou word. 1 2 3 4 5 6 7 Die kampus(se) moet “aantreklik” lyk. 1 2 3 4 5 6 7 My vriende moet ook die instelling oorweeg vir hul verdere studie. 1 2 3 4 5 6 7 Daar moet ander van my kulturele groep op die kampus wees. 1 2 3 4 5 6 7 My kultuur moet gerespekteer word. 1 2 3 4 5 6 7 Ek moet tuis by die gekose instelling voel. 1 2 3 4 5 6 7 Ek moet my kultuur kan uitleef. 1 2 3 4 5 6 7 Alle bevolkingsgroepe moet op die kampus verteenwoordig word. 1 2 3 4 5 6 7 Rassisme moet nie bestaan nie. 1 2 3 4 5 6 7 Die kampus se ligging is belangrik. 1 2 3 4 5 6 7 Dit moet ’n byderwetse (“hip”) omgewing wees. 1 2 3 4 5 6 7 Sportspanne moet goeie aansien geniet. 1 2 3 4 5 6 7 Uitstekende biblioteek en verwante hulpbronne. 1 2 3 4 5 6 7

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B6. Indien jy by ’n universiteit aanvaar word, wat is jou voorkeuronderrigtaal? Engels Afrikaans IsiZulu Sepedi Ander (spesifiseer asseblief): ______

B7. Watter EEN van die volgende opsies sal die hoofbydraer wees om vir jou studie te betaal? Beurs van die universiteit Beurs van ’n maatskappy/besigheid Lening van NSFAS (Nasionale Studente- Finansiële Hulpskema) Lening van ’n bank Self Ouers Ander (spesifiseer asseblief):______

______

Afdeling C

Hierdie afdeling van die vraelys bepaal JOU persepsie van twee universiteite, naamlik die Universiteit van Johannesburg (UJ) en JOU eerste keuse (voorkeur) universiteit (indien UJ NIE jou eerste keuse universiteit is nie).

C1. Sal jy dit oorweeg om aansoek te doen om aan die Universiteit van Johannesburg te studeer? Ja Nee

C2. Gee asseblief redes vir jou antwoord in C1

C3. INDIEN jy die Universiteit van Johannesburg (UJ) oorweeg, beantwoord ASSEBLIEF hierdie vraag. Indien jy NIE UJ oorweeg nie, gaan asseblief na vraag C4.

Oorweeg asseblief AL die gelyste liggings van onderstaande kampusse en dui asseblief aan of die ligging jou besluit sal beïnvloed om daar te studeer. Merk met ’n X. WEET NIE waar JA, ek sal daar NEE, ek sal NIE daar dit is nie studeer studeer nie Soweto Doornfontein (Jhb) Auckland Park (Jhb) Ook bekend as Kingsway Kampus Buntingweg (Jhb)

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C4. Ons wil graag JOU persepsie van die Universiteit van Johannesburg (UJ) met JOU eerste keuse (voorkeur) universiteit (indien nie UJ nie) vergelyk. Dui asseblief met ‘n kruis (X) aan tot watter mate stem jy met elkeen van die stellings vir BEIDE jou eerste keuse (voorkeur) universiteit EN die UJ saam. (Gebruik asb.die responsskaal; 1 Stem glad nie saam nie, 2 Stem nie saam nie, 3 Stem nóg saam, nóg nie saam nie, 4 Stem saam en 5 Stem ten volle saam)

JOU eerste keuse universiteit (Naam van Universiteit van universiteit) Johannesburg ______

Stem glad nie nie glad Stem saam nie saam nie Stem nie Stem nóg saam nóg nie saam nie saam Stem volle ten Stem saam nie glad Stem saam nie saam nie Stem nie Stem nóg saam nóg nie saam nie saam Stem volle ten Stem saam 1 2 3 4 5 Die verskaf onderrig van wêreldgehalte. 1 2 3 4 5 1 2 3 4 5 Dit het ’n goeie reputasie (as ’n instelling in Suid- 1 2 3 4 5 Afrika). 1 2 3 4 5 Die grade het ’n goeie reputasie in Suid-Afrika. 1 2 3 4 5 1 2 3 4 5 My gekose kwalifikasie (graad/diploma) is 1 2 3 4 5 beskikbaar by… 1 2 3 4 5 My gekose kwalifikasie (graad/diploma) indien 1 2 3 4 5 by … verwerf word, het ’n goeie reputasie in Suid-Afrika. 1 2 3 4 5 Dit is een van die twee topuniversiteite in Suid- 1 2 3 4 5 Afrika 1 2 3 4 5 Die grade word internasionaal erken. 1 2 3 4 5 My gekose kwalifikasie (graad/diploma) indien by … verwerf word, word internasionaal erken. 1 2 3 4 5 Dit het veilige kampusse. 1 2 3 4 5 1 2 3 4 5 Akkommodasie op die kampus is veilig. 1 2 3 4 5 1 2 3 4 5 Dit is hoofsaaklik ’n Engelse instelling. 1 2 3 4 5 1 2 3 4 5 Dit het multikulturele kampusse. 1 2 3 4 5 1 2 3 4 5 My kultuur word goed verteenwoordig by... 1 2 3 4 5 1 2 3 4 5 Dit is ’n moderne universiteit. 1 2 3 4 5 1 2 3 4 5 Dit is ’n universiteit wat my vriende ook vir 1 2 3 4 5 verdere studie oorwee.g. 1 2 3 4 5 Al die kampusse is goed geleë. 1 2 3 4 5 1 2 3 4 5 My ouers dink dit is ’n agtenswaardige 1 2 3 4 5 (‘reputable’)instelling. 1 2 3 4 5 ’n Onderwyser by die skool het vir my gesê dit 1 2 3 4 5 het ’n goeie reputasie. 1 2 3 4 5 My vriende dink dit is ’n agtenswaardige 1 2 3 4 5 instelling. 1 2 3 4 5 Dit het geskikte finansiële ondersteuning, soos 1 2 3 4 5 beurse, om my te help. 1 2 3 4 5 Die … reageer onmiddellik op aansoeke. 1 2 3 4 5 1 2 3 4 5 Die geboue word goed in stand gehou. 1 2 3 4 5

DANKIE!!!

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APPENDIX H4

Refined questionnaire (2013) Portraying scales with the remaining statements for each scale retained after Confirmatory Factor Analysis (CFA)

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Only for office use:

(Refined questionnaire) 2013 - Factors Influencing Prospective Students’ University Choice

When answering the questions please report on your own experience, opinion and perspective.

Thank you for taking the time to complete this questionnaire.

If you have any questions relating to this questionnaire, please contact Mrs Isolde Lubbe on 082 921 3257.

PLEASE INDICATE YOUR ANSWER BY MARKING A CROSS (X) IN THE APPROPRIATE SPACE PROVIDED.

Section A – Background information

A1. Are you planning to apply to study at a University or University of Technology in the future? (Mark one option only) g. Yes, a University or University of Technology in South Africa 1 IF you have answered YES, please continue with the questionnaire h. Yes, a University, but NOT in South Africa 2 IF you have answered yes, please indicate why you are intending to study outside South Africa? Please continue with the questionnaire ______i. No, I am not intending to study at a University or University of Technology 3 IF you have answered NO, you do NOT have to complete the questionnaire.

A2. Please write down the name of the school that you are currently attending in grade 12:

A3. Indicate your gender Female 1 Male 2

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A5. Specify which ONE of the following subjects you take at school: Maths 1 Maths literacy 2

A6. Indicate your overall expected average grade for grade 12? A (80 – 100%) 1 B (70 – 79%) 2 C (60 – 69%) 3 D (50 – 59%) 4 E (40 – 49%) 5 F (34 – 39%) 6

A7. Did any of your parents/guardians attend a university/university of technology/college? Yes 1 No 2

Section B – Factors influencing your preferences of a University/ University of Technology.

B1. Please write down the name of the university/ university of technology that you would most like to study at.

THINK ABOUT THIS UNIVERSITY THAT YOU WOULD MOST LIKE TO STUDY AT (YOUR FIRST CHOICE) WHEN ANSWERING ALL THE FOLLOWING QUESTIONS:

B2. To what extent do you agree with the following statements regarding the university/university of technology that you would most like to study at? Please indicate your answers using the 7-point response scale provided where 1 indicates strongly disagree and 7 strongly agree. Factors influencing your decision Strongly disagree Strongly agree 1 It offers courses with a good reputation 1 2 3 4 5 6 7 2 It offers the courses that I am interested in 1 2 3 4 5 6 7 3 It is committed to academic excellence 1 2 3 4 5 6 7 4 It offers world-class education 1 2 3 4 5 6 7 5 Its qualifications are internationally recognised 1 2 3 4 5 6 7 6 It it a reputable institution in South Africa 1 2 3 4 5 6 7 7 Its qualifications are reputable 1 2 3 4 5 6 7 8 It has a positive image with possible employers 1 2 3 4 5 6 7

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Factors influencing your decision (Question B.2 Continues..) Strongly disagree Strongly agree 9 My friends’ perception of this university is positive 1 2 3 4 5 6 7 10 My culture will be respected 1 2 3 4 5 6 7 11 I will feel at home at this university 1 2 3 4 5 6 7 12 I will be able to express my culture ate the university 1 2 3 4 5 6 7 13 The university’s campus is easily accessible (transport) 1 2 3 4 5 6 7 14 The university’s campus is located near shops/malls 1 2 3 4 5 6 7 15 The university’s campus is close to health services (hospitals, dentists) 1 2 3 4 5 6 7 16 Accommodation (other than residence) is near the campus 1 2 3 4 5 6 7 17 The cost of tuition at the university is fairly priced 1 2 3 4 5 6 7 18 Studying at the university is value for money 1 2 3 4 5 6 7 19 My parents/guardians are able to afford the university 1 2 3 4 5 6 7 20 There will be the opportunity for part-time jobs (nearby campus) 1 2 3 4 5 6 7 21 The campus looks attractive 1 2 3 4 5 6 7 22 The buildings look attractive 1 2 3 4 5 6 7 23 The campus looks prestigious 1 2 3 4 5 6 7 24 Its buildings and grounds are well maintained 1 2 3 4 5 6 7 25 The recreation facilities (e.g. student centre) look attractive 1 2 3 4 5 6 7 26 Sport teams have a good reputation 1 2 3 4 5 6 7 27 There are good sporting opportunities at the university 1 2 3 4 5 6 7 28 The sport facilities are up to date 1 2 3 4 5 6 7 29 Hostel/residential facilities are attractive 1 2 3 4 5 6 7 30 I will find a job after completing my qualification 1 2 3 4 5 6 7 Studying at this university will make it possible to find a job 31 1 2 3 4 5 6 7 after qualifying Studying at this university will enhance chances of employment 32 1 2 3 4 5 6 7 opportunities 33 Studying at this university will increase career prospects 1 2 3 4 5 6 7 34 Studying at this university will provide better salary prospects 1 2 3 4 5 6 7

Section C – The value offered by your chosen university/ university of technology

C1. To what extent do you agree with the following statements regarding the university/university of technology that you would most like to study at? Please indicate your answers using the 7-point response scale provided where 1 indicates strongly disagree and 7 strongly agree. Value Strongly disagree Strongly agree The benefit of attending this university will outweigh the financial cost 1 2 3 4 5 6 7 I am happy to make financial sacrifices to attend this university 1 2 3 4 5 6 7 The price paid for studying at this university is reasonable 1 2 3 4 5 6 7 I am happy that the price of the university is an indication of good quality 1 2 3 4 5 6 7 I am happy to give up some of my interests to attend this university 1 2 3 4 5 6 7 It offers many good cultural experiences (fine arts, music, theatre, etc.) 1 2 3 4 5 6 7 I will achieve my career goals (because I study at this university) 1 2 3 4 5 6 7 The university will perform to my expectations 1 2 3 4 5 6 7 The staff at the university will provide service as I expect 1 2 3 4 5 6 7 I will gain the knowledge that I need 1 2 3 4 5 6 7 I will feel a sense of ‘belonging’ when attending the university 1 2 3 4 5 6 7 The reputation of the university will influence the value of my degree 1 2 3 4 5 6 7 I believe that employers have good things to say about the university 1 2 3 4 5 6 7 The university will give me a good experience (enjoyment, feel good, 1 2 3 4 5 6 7 pleasure, relaxed) The price I have to pay for the university is worth the money 1 2 3 4 5 6 7 The facilities (library, computer labs etc) will meet my expectations 1 2 3 4 5 6 7 Compared to what I have to give up, the overall ability of the 1 2 3 4 5 6 7 university to satisfy my wants and needs is very high

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Section D – Willingness to enrol at your preferred university

Thank you for your participation!

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APPENDIX I

Approval letter from the Department of Education

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APPENDIX J

Top Gauteng schools’ list

Indicating which schools has been included in this study’s sample.

Content Top 15 Afrikaans Schools (DOE, 2011) Top 15 English Schools (DOE, 2011) Top 15 “African” Schools (DOE, 2011)

NOTE:

All schools with a “✔” in the column that states “Included in FINAL data analysis”, have been included into the sample and final analysis.

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Top Afrikaans schools (for 2011 study)

packed

no name name Centre Centre Centre District District Quintile Dinaledi Position District no.District data analysys questionn

thisnumber of grade 12grade exam naires received TotalEntered for Usable question Included in FINAL Researcher

TSHWANE HOëRSKOOL 1 4 8210245 5 346 300 ✔ 45 SOUTH DISTRICT WATERKLOOF N TSHWANE HOëRSKOOL 2 4 8210195 5 252 200 ✔ 43 SOUTH DISTRICT MENLOPARK N TSHWANE HOëRSKOOL 3 4 8210187 5 330 SOUTH DISTRICT GARSFONTEIN N TSHWANE HOëRSKOOL 4 4 8210161 5 321 SOUTH DISTRICT ELDORAIGNE N TSHWANE AFRIKAANSE HOëR 5 4 8230110 5 197 SOUTH DISTRICT MEISIESKOOL N TSHWANE HOëRSKOOL 8 4 8210252 5 306 SOUTH DISTRICT ZWARTKOP N GAUTENG WEST HOëRSKOOL 9 2 8250258 5 273 280 ✔ 99 DISTRICT MONUMENT N JOHANNES- 1 HOëRSKOOL 10 BURG WEST 8250233 5 291 300 ✔ 58 2 FLORIDA DISTRICT N TSHWANE HOëRSKOOL 11 4 8210229 5 247 SOUTH DISTRICT CENTURION Y TSHWANE AFRIKAANSE HOëR 15 4 8230128 5 197 SOUTH DISTRICT SEUNSKOOL N EKURHULENI HOëRSKOOL 16 6 8260125 5 310 300 ✔ 85 NORTH DISTRICT KEMPTON PARK N GAUTENG WEST HOëRSKOOL 17 2 8250266 5 234 240 ✔ 116 DISTRICT NOORDHEUWEL N TSHWANE HOëRSKOOL 19 3 8240077 5 228 NORTH DISTRICT OVERKRUIN N TSHWANE HOëRSKOOL OOS- 23 3 8220178 5 263 NORTH DISTRICT MOOT N TSHWANE HOëRSKOOL 25 3 8230383 5 280 NORTH DISTRICT WONDERBOOM N *Notes: Menlo Park Hoërskool was NOT originally in the sample, however a fieldworker had to visit the school for other reasons and took some questionnaires with that was completed.

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Top English schools (for 2011 study)

rs.

ai packed no

name

Quintile Dinaledi Position Centre District no.District Centre data analysis District nameDistrict questionn thisnumber of grade 12grade exam naires received TotalEntered for Usable question Included in FINAL Researcher

TSHWANE *PRETORIA HIGH 6 4 SOUTH 8231324 Y 5 264 ✖ SCHOOL FOR GIRLS DISTRICT TSHWANE PRETORIA BOYS 7 4 SOUTH 8231316 Y 5 298 HIGH SCHOOL DISTRICT JOHANNES- NORTHCLIFF HIGH 12 10 BURG NORTH 8140285 N 5 260 SCHOOL DISTRICT JOHANNES- PARKTOWN GIRLS 13 10 BURG NORTH 8131136 N 5 205 150 ✔ 39 HIGH SCHOOL DISTRICT TSHWANE SUTHERLAND HIGH 14 4 SOUTH 8211144 Y 5 221 230 SCHOOL DISTRICT EKURHULENI BENONI HIGH 18 6 NORTH 8310151 N 5 274 280 ✔ 231 SCHOOL DISTRICT JOHANNES- RAND PARK HIGH 20 10 BURG NORTH 8151241 N 5 274 280 SCHOOL DISTRICT JOHANNES- GLENVISTA HIGH 21 11 BURG SOUTH 8110403 N 5 230 SCHOOL DISTRICT JOHANNES- FOURWAYS HIGH 22 10 BURG NORTH 8150466 N 5 231 SCHOOL DISTRICT JOHANNES- MONDEOR HIGH 24 14 BURG 8120840 Y 5 299 250 ✔ 44 SCHOOL CENTRAL GAUTENG KRUGERSDORP 28 2 8250563 Y 5 243 WEST DISTRICT HIGH SCHOOL EKURHULENI BOKSBURG HIGH 32 16 8160234 Y 4 290 SOUTH SCHOOL JOHANNES- KING EDWARD VII 35 9 BURG EAST 8130765 N 5 193 SCHOOL DISTRICT SIR JOHN ADAMSON 79 14 JOHANNES- 8121061 HIGH SCHOOL JHB N 5 241 BURG CENTRAL CENTRAL JOHANNES- **JEPPE HIGH 121 9 BURG EAST 8130633 SCHOOL FOR Y 4 163 150 ✔ 70 DISTRICT BOYS *Notes: Pretoria High School for Girls never returned their questionnaires. The school phoned to let the researcher know that they are not willing to take part in this research project. ** Jeppe High School for Boys was included into the sample at the last moment. When the researcher realised that Pretoria High School for Girls cannot be convinced to participate, the questionnaires were distributed to this school as it was an English-speaking public school that the fieldworkers visited in the following week.

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Top Sowetan Schools (for 2011 study)

rs.

no name name number of Centre Centre Centre District District Quintile Dinaledi Position received District no District data analysis questionn this grade 12grade exam TotalEntered for Included in FINAL Researcher packed Usable questionnrs.

JOHANNES- BHUKULANI 135 14 BURG 8121236 SECONDARY N 3 138 CENTRAL SCHOOL JOHANNES- EMSHUKANTAMBO 142 10 BURG NORTH 8121301 Y 3 306 SEC SCHOOL DISTRICT JOHANNES- *MORRIS ISAACSON 151 14 BURG 8132571 SECONDARY N 3 314 310 ✖ CENTRAL SCHOOL JOHANNES- PJ SIMELANE 152 12 BURG WEST 8251777 SECONDARY Y 4 194 DISTRICT SCHOOL JOHANNES- VUWANI 157 14 BURG 8111450 SECONDARY Y 3 212 CENTRAL SCHOOL JOHANNES- ALTMONT 187 14 BURG 8121210 TECHNICAL HIGH N 1 151 CENTRAL SCHOOL JOHANNES- THOMAS MOFOLO 193 14 BURG 8111377 SECONDARY N 4 217 CENTRAL SCHOOL JOHANNES- MOKGOME 194 12 BURG WEST 8140814 SECONDARY N 3 210 220 ✔ 70 DISTRICT SCHOOL JOHANNES- SELELEKELA SEC 206 10 BURG NORTH 8132902 N 3 268 SCHOOL DISTRICT JOHANNES- MATSELISO 216 12 BURG WEST 8140780 SECONDARY N 3 198 DISTRICT SCHOOL JOHANNES- PROGRESS 236 10 BURG NORTH 8121715 COMPREHENSIVE N 3 231 DISTRICT SCHOOL JOHANNES- FONS LUMINIS SEC 240 10 BURG NORTH 8140525 Y 5 196 SCHOOL DISTRICT JOHANNES- KWADEDANGENDLA 243 14 BURG 8110510 LE SECONDARY Y 3 174 180 ✔ 45 CENTRAL SCHOOL JOHANNES- THUTOLORE 244 12 BURG WEST 8141002 SECONDARY N 3 211 220 ✔ 100 DISTRICT SCHOOL JOHANNES- MOLETSANE 250 10 BURG 8E+06 SECONDARY Y 4 218 200 ✔ 62 CENTRAL SCHOOL *Notes: Morris Isaacson Secondary School have received questionnaires and this school was willing to participate in the study, however, questionnaires were never received from this school.

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