Success in the Protean Career: a Predictive Study of Professional Artists and Tertiary Arts Graduates
SUCCESS IN THE PROTEAN CAREER: A PREDICTIVE STUDY OF PROFESSIONAL ARTISTS AND TERTIARY ARTS GRADUATES
Ruth Bridgstock
Submitted in fulfilment of the requirements for the award of the Degree of Doctor of Philosophy, 2007
Centre for Learning Innovation, Faculty of Education Queensland University of Technology
ii
KEYWORDS
Career success, protean career, boundaryless career, career development, career transition, school to work transition, university to work transition, work experience, higher education, career self-management, career management, career education, artists, arts graduates, creative industries, creative workforce, graduate attributes, employability, generic skills, transferable skills, scale development, confirmatory factor analysis, structural equation modelling, content analysis, decision tree, regression tree, CART. iv
ABSTRACT
In the shift to a globalised creative economy where innovation and creativity are increasingly prized, many studies have documented direct and indirect social and economic benefits of the arts. In addition, arts workers have been argued to possess capabilities which are of great benefit both within and outside the arts, including (in addition to creativity) problem solving abilities, emotional intelligence, and team working skills (ARC Centre of Excellence for Creative Industries and Innovation,
2007).
However, the labour force characteristics of professional artists in Australia and elsewhere belie their importance. The average earnings of workers in the arts sector are consistently less than other workers with similar educational backgrounds, and their rates of unemployment and underemployment are much higher (Australian
Bureau of Statistics, 2005; Caves, 2000; Throsby & Hollister, 2003). Graduating students in the arts appear to experience similar employment challenges and exhibit similar patterns of work to artists in general. Many eventually obtain work unrelated to the arts or go back to university to complete further tertiary study in fields unrelated to arts (Graduate Careers Council of Australia, 2005a).
Recent developments in career development theory have involved discussion of the rise of boundaryless careers amongst knowledge workers. Boundaryless careers are characterised by non-linear career progression occurring outside the bounds of a single organisation or field (Arthur & Rousseau, 1996a, 1996b). The protean career is an extreme form of the boundaryless career, where the careerist also possesses strong internal career motivations and criteria for success (Baruch, 2004;
Hall, 2004; Hall & Mirvis, 1996). It involves a psychological contract with one’s self rather than an organisation or organisations. The boundaryless and protean career
literature suggests competencies and dispositions for career self-management and career success, but to date there has been minimal empirical work investigating the predictive value of these competencies and dispositions to career success in the boundaryless or protean career.
This program of research employed competencies and dispositions from boundaryless and protean career theory to predict career success in professional artists and tertiary arts graduates. These competencies and dispositions were placed into context using individual and contextual career development influences suggested by the Systems Theory Framework of career development (McMahon & Patton,
1995; Patton & McMahon, 1999, 2006a). Four substantive studies were conducted, using online surveys with professional artists and tertiary arts students / graduates, which were preceded by a pilot study for measure development.
A largely quantitative approach to the program of research was preferred, in the interests of generalisability of findings. However, at the time of data collection, there were no quantitative measures available which addressed the constructs of interest. Brief scales of Career Management Competence based on the Australian
Blueprint for Career Development (Haines, Scott, & Lincoln, 2003), Protean Career
Success Orientation based on the underlying dispositions for career success suggested by protean career theory, and Career Development Influences based on the
Systems Theory Framework of career development (McMahon & Patton, 1995;
Patton & McMahon, 1999, 2006a) were constructed and validated via a process of pilot testing and exploratory factor analyses. This process was followed by confirmatory factor analyses with data collected from two samples: 310 professional artists, and 218 graduating arts students who participated at time 1 (i.e., at the point of undergraduate course completion in October, 2005). vi
Confirmatory factor analyses via Structural Equation Modelling conducted in
Study 1 revealed that the scales would benefit from some respecification, and so modifications were made to the measures to enhance their validity and reliability.
The three scales modified and validated in Study 1 were then used in Studies 3 and 4 as potential predictors of career success for the two groups of artists under investigation, along with relevant sociodemographic variables.
The aim of the Study 2 was to explore the construct of career success in the two groups of artists studied. Each participant responded to an open-ended question asking them to define career success. The responses for professional artists were content analysed using emergent coding with two coders. The codebook was later applied to the arts students’ definitions. The majority of the themes could be grouped into four main categories: internal definitions; financial recognition definitions; contribution definitions; and non-financial recognition definitions. Only one third of the definition themes in the professional artists’ and arts graduates’ definitions of career success were categorised as relating to financial recognition. Responses within the financial recognition category also indicated that many of the artists aspired only to a regular subsistence level of arts income (although a small number of the arts graduates did aspire to fame and fortune).
The second section of the study investigated the statistical relationships between the five different measures of career success for each career success definitional category and overall. The professional artists’ and arts graduates’ surveys contained several measures of career success, including total earnings over the previous 12 months, arts earnings over the previous 12 months, 1-6 self-rated total employability, 1-6 self-rated arts employability, and 1-6 self-rated self-defined career success. All of the measures were found to be statistically related to one
another, but a very strong statistical relationship was identified between each employability measure and its corresponding earnings measure for both of the samples. Consequently, it was decided to include only the earnings measures
(earnings from arts, and earnings overall) and the self-defined career success rating measure in the later studies.
Study 3 used the career development constructs validated in Study 1, sociodemographic variables, and the career success measures explored in Study 2 via
Classification and Regression Tree (CART - Breiman, Friedman, Olshen, & Stone,
1984) style decision trees with v-fold crossvalidation pruning using the 1 SE rule.
CART decision trees are a nonparametric analysis technique which can be used as an alternative to OLS or hierarchical regression in the case of data which violates parametric statistical assumptions.
The three optimal decision trees for total earnings, arts earnings and self defined career success ratings explained a large proportion of the variance in their respective target variables (R2 between 0.49 and 0.68). The Career building subscale of the Career Management Competence scale, pertaining to the ability to manage the external aspects of a career, was the most consistent predictor of all three career success measures (and was the strongest predictor for two of the three trees), indicating the importance of the artists’ abilities to secure work and build the external aspects of a career. Other important predictors included the Self management subscale of the Career Management Competence scale, Protean Career Success
Orientation, length of time working in the arts, and the positive role of interpersonal influences, skills and abilities, and interests and beliefs from the Career Development
Influences scale. Slightly different patterns of predictors were found for the three different career success measures. viii
Study 4 also involved the career development constructs validated in Study 1, sociodemographic variables, and the career success measures explored in Study 2 via
CART style decision trees. This study used a prospective repeated measures design where the data for the attribute variables were gathered at the point of undergraduate course completion, and the target variables were measured one year later. Data from a total of 122 arts students were used, as 122 of the 218 students who responded to the survey at time 1 (October 2005) also responded at time 2 (October 2006).
The resulting optimal decision trees had R2 values of between 0.33 and 0.46.
The values were lower than those for the professional artists’ decision trees, and the trees themselves were smaller, but the R2 values nonetheless indicated that the arts students’ trees possessed satisfactory explanatory power. The arts graduates’ Career building scores at time 1 were strongly predictive of all three career success measures at time 2, a similar finding to the professional artists’ trees. A further similarity between the trees for the two samples was the strong statistical relationship between Career building, Self management, and Protean Career Success
Orientation. However, the most important variable in the total earnings tree was arts discipline category. Technical / design arts graduates consistently earned more overall than arts graduates from other disciplines. Other key predictors in the arts graduates’ trees were work experience in arts prior to course completion, positive interpersonal influences, and the positive influence of skills and abilities and interests and beliefs on career development.
The research program findings represent significant contributions to existing knowledge about artists’ career development and success, and also the transition from higher education to the world of work, with specific reference to arts and
creative industries programs. It also has implications for theory relating to career success and protean / boundaryless careers. x
TABLE OF CONTENTS
CHAPTER 1 1
Introduction to the Research 1
Background to the Research 1
The Importance of the Arts 1
Artists’ Working Lives 2
Tertiary Arts Graduates’ Transitions to the World of Work 3
Opportunities for Research into Artists’ Careers 5
Definitions of Key Terms 6
Artist 6
Arts Graduate 7
Boundaryless Career 8
Career 8
Career Development Influences 8
Career Management Competencies / Skills, Career Self-Management 9
Career Success 9
Employability 10
Graduate Attributes 10
Graduate Transition to the World of Work 10
Protean Career 11
Protean Career Success Orientation 11
Predictors of Artists’ and Arts Graduates’ Career Success 11
Career Self-Management Skills 12
Protean Career Success Orientation 13
Career Development Influences 14
Research Questions and Overview of the Studies 15
Study 1 16
Study 2 17
Study 3 18
Study 4 19
Research Significance and Contributions 19
Theoretical Contributions 20
Methodological Contributions 21
Substantive Contributions 22
Organisation of the Thesis Document 23
CHAPTER 2 25
Literature Review: Career Development Theory 25
New Conceptions of Career 26
Boundaryless Careers and Employability 27
The Subjective Career 29
The Protean Career 30
Theoretical Understandings of Career Development 33
Structure Theories of Career: Traits and Types 34
Process Theories of Career: Development and the Individual 38
Theories Embracing Structure, Process and Context 43
Recent Developments in Career Theory 50
Calls for Theory Convergence and Integration 50
Systems Theory as an Integrative Theoretical Framework 51
The Development of the Systems Theory Framework of Career Development 53
Chapter Summary 55
CHAPTER 3 56
Literature Review: Career Success and Successful Tertiary Graduate Transitions to the
World of Work 56
Career Success 56
Objective Measures of Career Success 57 xii
Subjective Measures of Career Success 59
The Relationship between Subjective and Objective Measures of Career Success 62
Predictors of Career Success 63
Skills as Predictors of Career Success 67
Career Self-Management Skills as Predictors of Career Success 68
The Australian Blueprint for Career Development 75
Underlying Dispositions and Characteristics as Predictors of Career Success 79
The University to World-of-Work Transition 84
Graduate Attributes 85
Broadening the Concepts of Graduate Attributes and Employability 87
Predictors of Successful Graduate Transitions to the World of Work 90
Career Self-Management Skills and Underlying Dispositions and Characteristics as Predictors
of Successful Graduate Transitions 90
Contextual Predictors of Successful Transitions 93
Chapter Summary 95
CHAPTER 4 97
Literature Review: Artists’ Careers 97
Definitions of the Artist and the Arts 97
The Arts Sector in Australia 99
The Social and Economic Importance of the Arts 100
Artists’ Career Patterns: A Protean Career in Arts 101
Mobility, Occupational Roles and Sources of Income 103
Personal Responsibility For Career Development 104
Career Motivations and Measures of Success 105
The Protean Tertiary Arts Graduate 108
Predictors of Career Success for Professional Artists and Arts Graduates 110
The Present Program of Study 113
Study 1: Research Question 1 116
Study 2: Research Question 2 116
Study 3: Research Question 3 117
Study 4: Research Question 4 117
Chapter Summary 118
CHAPTER 5 120
Method and Methodological Considerations 120
Data Collection 120
Self-Report Surveys 121
Limitations of Survey Research 122
Online Surveys 124
Ethical Considerations 126
Sampling: Professional Artists 127
Defining ‘Professional Artist’ 127
Artist Categories 128
Current Professional Activity 132
Recruitment 133
Sample Characteristics 134
Sampling: Arts Students 137
Defining ‘Arts Student’ 137
Arts Student Categories 138
Recruitment 138
Sample Characteristics 140
Survey Composition 143
Protean Career Success Orientation 145
Career Management Competence 146
Career Development Influences 148 xiv
Career Success 152
Sociodemographic Variables 153
Analysis Design 157
Study 1 157
Study 2 159
Study 3 161
Study 4 163
Chapter Summary 164
CHAPTER 6 166
Study 1: Development and Validation of Career Development Influences, Protean Career
Success Orientation and Career Management Competence Scales 166
Data Analysis 166
Career Development Influences Scale 167
Data Screening Procedures 167
Confirmatory Factor Analyses: Six Factor Structure 174
Model Respecification 178
Confirmatory Factor Analysis: Respecified Five Factor Structure 184
Protean Career Success Orientation Scale 187
Data Screening Procedures 188
Confirmatory Factor Analyses: Single Factor Structure 191
Model Respecification 193
Confirmatory Factor Analysis: Respecified Single Factor Structure 196
Career Management Competence Scale 197
Data Screening Procedures 198
Confirmatory Factor Analyses: Three Factor Structure 201
Model Respecification 204
Confirmatory Factor Analysis: Two Factor Structure 205
Confirmatory Factor Analysis: Respecified Two Factor Structure 208
Discussion of Findings 210
Career Development Influences Scale 211
Protean Career Success Orientation Scale 213
Career Management Competence Scale 215
Chapter Summary 219
CHAPTER 7 220
Study 2: Definition and Exploration of the Career Success Construct in Professional
Artists and Arts Graduates 220
Content Analysis of Artists’ Definitions of Career Success 221
Content Analysis Procedure 221
Coding of Professional Artists’ Responses 221
Themes in Professional Artists’ Responses 222
Professional Artists’ Broad Career Success Definition Categories 224
Description of Professional Artists’ Definitions of Career Success 227
Internal Definitions 227
Financial Recognition Definitions 228
Non-Financial Recognition Definitions 229
Contribution Definitions 229
Coding of Arts Graduates’ Responses 230
Arts Graduates’ Response Theme Subcategories 230
Description of Arts Graduates’ Definitions of Career Success 232
Internal Definitions 232
Financial Recognition Definitions 233
Non-Financial Recognition Definitions 234
Contribution Definitions 234
Other Definitions 235 xvi
Relationships Between Measures of Career Success 235
Professional Artist Career Success Measure Correlations 236
Arts Graduate Career Success Measure Correlations 240
Discussion 244
Chapter Summary 249
CHAPTER 8 250
Study 3: Prediction of Professional Artists’ Career Success from Career Development
Measures 250
Decision Tree Method 250
Overview of the Decision Tree Method 251
Choice of Algorithms: CART 252
Decision Tree Results 254
Descriptive Statistics for Variables Included in the Analyses 254
Total Earnings Decision Tree Results 262
Arts Earnings Decision Tree Results 264
Self Defined Career Success Rating Decision Tree Results 267
Discussion 270
Total Earnings 273
Arts Earnings 274
Self Defined Career Success Rating 275
Links Between Decision Tree Findings and Extant Literature 276
Chapter Summary 279
CHAPTER 9 281
Study 4: Prediction of Arts Students’ Successful Transitions to Work from Career
Development Influences and Constructs 281
Decision Tree Method 282
Decision Tree Results 283
Descriptive Statistics for Variables Included in the Analyses 283
Total Earnings Decision Tree Results 291
Arts Earnings Decision Tree Results 293
Self Defined Career Success Rating Decision Tree Results 296
Discussion 298
Total Earnings 301
Arts Earnings 302
Self Defined Career Success Rating 303
Links Between Decision Tree Findings and Extant Literature 304
Chapter Summary 310
CHAPTER 10 312
Discussion and Conclusion of the Research Program 312
Overview of the Research Program 312
Study 1 314
Study 2 316
Study 3 318
Study 4 321
Implications for Artists’ Careers 324
Implications for Artists’ Employers / Clients 326
Implications for Universities and Tertiary Students 327
Theoretical Contributions of the Program of Study 329
Factorial Structure of Scales 329
Career Success 332
Predictors of Artists’ and Arts Graduates’ Career Success 333
Graduate Attributes 334
Methodological Contributions of the Program of Study 335 xviii
Scale Development 335
Decision Trees 336
A Prospective Approach to the Study of Tertiary Graduate Outcomes 336
Limitations of the Research Program 337
Recommendations for Future Research 339
Conclusion 341
REFERENCES 343
LIST OF FIGURES
2.1 The Systems Theory Framework of career development 52
6.1 Path diagram for the hypothesised six factor model of the CDI 168
6.2 Standardised estimates and SMCs for the six factor model: 175
Professional artists sample
6.3 Standardised estimates and SMCs for the six factor model: Arts 176
students sample
6.4 Path diagram for the respecified five factor model of the CDI 181
6.5 Standardised estimates and SMCs for five-factor model of CDI: 183
Professional artists sample (N = 310)
6.6 Standardised estimates and SMCs for five factor model of CDI: 185
Arts students sample (N = 218)
6.7 Path diagram for the hypothesised single factor model of PCSO 188
6.8 Standardised estimates and SMCs for single-factor model of 191
PCSO: Professional artists sample (N = 310)
6.9 Standardised estimates and SMCs for single-factor model of 192
PCSO: Arts students sample (N = 218)
6.10 Path diagram for respecified single factor model of the PCSO 195
6.11 Standardised estimates and SMCs for respecified single-factor 196
model of the PCSO: Arts students sample (N = 218)
6.12 Standardised estimates and SMCs for respecified single-factor 196
model of the PCSO: Professional artists sample (N = 310) xx
6.13 Path diagram for the hypothesised three factor model of CMC 198
6.14 Standardised estimates and SMCs for three factor model of 202
CMC: Professional artists sample (N = 310)
6.15 Standardised estimates and SMCs for three factor model of 203
CMC: Arts students sample (N = 218)
6.16 Path diagram for the respecified two factor model of CMC 205
6.17 Standardised estimates and SMCs for respecified two factor 207
model of the CMC: Professional artists sample (N = 310)
6.18 Standardised estimates and SMCs for respecified two factor 209
model of the CMC: Arts students sample (N = 218)
8.1 Optimal decision tree for total earnings 262
8.2 Optimal decision tree for earnings from arts 265
8.3 Optimal decision tree for self defined career success rating 268
9.1 Optimal decision tree for total earnings 291
9.2 Optimal decision tree for earnings from arts 294
9.3 Optimal decision tree for self defined career success rating 296
LIST OF TABLES
1.1 The Program of Study By Research Question 17
2.1 Key Attributes of Traditional vs Protean Careers 31
3.1 Significant Predictors of Objective and Subjective Career 65
Success From 140 Studies
3.2 Significant Career Competency Predictors of Marketability and 73
Career Success in Eby et al.'s (2003) Study
3.3 Top-Level Structure of the Australian Blueprint for Career 76
Development
3.4 The Relationship Between ABCD Competencies, Performance 77
Indicators and Learning Stages: Sample For One Competency
4.1 Employment Status of Australian Artists Working From Within 104
Their Principal Artistic Occupation Versus The General Working
Population
5.1 Professional Artist Categories 130
5.2 Professional Artist Sample Sizes by Category 135
5.3 Professional Artist Characteristics: Sample and Population 136
Statistics
5.4 Arts Student Categories by Discipline of Program of Study 138
5.5 Arts Student Characteristics: Sample and Population Statistics 142
5.6 Substantive Research Measures Used to Address Research 144 xxii
Questions in the Present Investigation
5.7 Sociodemographic Research Measures Used in the Present 156
Investigation
5.8 Study 1 Analysis Design 158
5.9 Study 2 Analysis Design 160
5.10 Study 3 Analysis Design 162
5.11 Study 4 Analysis Design 164
6.1 Item Means and Standard Deviations: CDI in Professional Artists 170
Sample and Arts Students Sample
6.2 Inter-item Correlations: CDI in Professional Artists Sample, N = 172
310
6.3 Inter-item Correlations: CDI in Arts Students Sample, N = 218 173
6.4 Fit Indices for Six Factor Model of CDI Using Two Data Sets 178
6.5 Fit Indices for Six Factor Model of CDI and Respecifications: 182
Professional Artists Sample (N = 310)
6.6 Internal Consistency of the Respecified CDI Five Factor Model: 184
Professional Artists Sample (N = 310)
6.7 Fit Indices for Respecified Five Factor Model of CDI with Arts 186
Students (N = 218)
6.8 Internal Consistency of the Respecified CDI Five Factor Model: 187
Arts Students Sample (N = 218)
6.9 Item Means and Standard Deviations: PCSO in Professional 189
Artists Sample and Arts Students Sample
6.10 Inter-item Correlations: PCSO in Professional Artists Sample, N 190
= 310
6.11 Inter-item Correlations: PCSO in Arts Students Sample, N = 218 190
6.12 Fit Indices for Single Factor Model of PCSO 193
6.13 Fit Indices for Single Factor Model of PCSO and 195
Respecification: Arts Students Sample (N = 218)
6.14 Fit Indices for Single Factor Model of PCSO and 197
Respecification: Professional Artists (N = 310)
6.15 Item Means and Standard Deviations: CMC in Professional 199
Artists Sample and Arts Students Sample
6.16 Inter-item Correlations: CMC in Professional Artists Sample, N 200
= 310
6.17 Inter-item Correlations: CMC in Arts Students Sample, N = 218 201
6.18 Fit Indices for Three Factor Model of CMC 204
6.19 Fit Indices for Three Factor Model of CMC and 206
Respecifications: Professional Artists (N = 310)
6.20 Internal Consistency of the Respecified CMC Two Factor 208
Model: Professional Artists Sample (N = 310)
6.21 Fit Indices for Respecified Two Factor Model of CMC with Arts 210
Students (N = 218)
6.22 Internal Consistency of the Respecified CMC Two Factor 210 xxiv
Model: Arts Students Sample (N = 218)
6.23 Final Career Development Influences Scale Items and Factors 211
6.24 Final Protean Career Success Orientation Scale Items and 214
Factors
6.25 Final Career Management Competence Scale Items and Factors 217
7.1 Themes Identified in Professional Artists’ Definitions of Career 224
Success
7.2 Categories of Professional Artists Definitions of Career Success 225
Themes
7.3 Major Elements of Professional Artists’ Definitions of Career 226
Success
7.4 Major Elements of Arts Graduates’ Definitions of Career 232
Success
7.5 Descriptive statistics for professional artists’ success measures 237
7.6 Spearman’s ρ correlation coefficients for professional artists’ 239
success measures
7.7 Descriptive statistics for arts graduates’ success measures 241
7.8 Spearman’s ρ Correlation Coefficients for Arts Graduates’ 243
Success Measures
8.1 Descriptive Statistics for Target Variables Included in the 254
Decision Tree Analysis
8.2 Descriptive Statistics for Career Development Measure Attribute 256
Variables Included in the Decision Tree Analysis
8.3 Descriptive Statistics for Sociodemographic Attribute Variables 257
Included in the Decision Tree Analysis
8.4 Target Variable Means, Standard Deviations and Nonparametric 259
Tests of Difference by Categorical Variable Levels
8.5 Spearman’s Bivariate Correlation Coefficients for Continuous 260
and Ordinal Variables Included in the Decision Tree Analysis
8.6 Attribute Variables Included in the Decision Trees 261
8.7. Relative Importance Of Variables In Constructing The Optimal 264
Total Earnings Decision Tree
8.8. Relative Importance of Variables in Constructing the Optimal 267
Earnings from Arts Decision Tree
8.9. Relative Importance Of Variables In Constructing The Optimal 270
Self Defined Career Success Rating Decision Tree
8.10 Summary of Attribute Variable Roles in the Decision Trees 272
9.1 Descriptive Statistics for Target Variables Included in the 284
Decision Tree Analysis
9.2 Descriptive Statistics for Career Development Measure Attribute 285
Variables Included in the Decision Tree Analysis
9.3 Descriptive Statistics for Sociodemographic Attribute Variables 286
Included in the Decision Tree Analysis
9.4 Target Variable Means, Standard Deviations and Nonparametric 288 xxvi
Tests of Difference by Categorical Variable Levels
9.5 Spearman’s Bivariate Correlation Coefficients for Continuous 289
and Ordinal Variables Included in the Decision Tree Analysis
9.6 Attribute Variables Included in the Decision Trees 290
9.7. Relative Importance Of Variables In Constructing The Optimal 293
Total Earnings Decision Tree
9.8. Relative Importance of Variables in Constructing the Optimal 295
Earnings from Arts Decision Tree
9.9. Relative Importance Of Variables In Constructing The Optimal 298
Self Defined Career Success Rating Decision Tree
9.10 Summary of Attribute Variable Roles in the Decision Trees 300
LIST OF APPENDICES ON CD
APPENDIX A Online Survey Instruments
APPENDIX B Ethical Clearances
APPENDIX C Participating Artists’ Professional Organisations and Networks
APPENDIX D Pilot Study Information and Exploratory Factor Analyses xxviii
LIST OF ACRONYMS
ACCI Australian Chamber of Commerce and Industry
BCA Business Council of Australia
CART Classification and Regression Tree
CCI ARC Centre for Excellence in Creative Industries and Innovation
CDI Career Development Influences
CMC Career Management Competence
OLS Ordinary Least Squares
PCSO Protean Career Success Orientation
SEM Structural Equation Modelling
STF Systems Theory Framework of career development
LIST OF PUBLICATIONS ARISING FROM THIS RESEARCH
Journal Articles
Bridgstock, R. (2005). Australian artists, starving and well-nourished: What can we learn from the prototypical protean career? Australian Journal of Career Development, 14(3), 40-48.
Bridgstock, R. (2007). Development of a brief measure of career development influences based on the Systems Theory Framework of Career Development. Australian Journal of Career Development, 16(3), 19-30.
Bridgstock, R. (2008). The graduate attributes we’ve overlooked: Enhancing tertiary graduate employability through career management skills. Higher Education Research and Development, in press.
Refereed Conference Papers
Bridgstock, R. (2005). What influences the decision to teach? A quantitative application of the Systems Theory Framework of career development. Paper presented at the New Researchers for New Times Conference, Brisbane.
Bridgstock, R. (2006). Follow your (employable) bliss: The challenge of the Australian creative / performing arts graduate. Paper presented at the Australian Association of Career Counsellors Conference, Sydney.
Bridgstock, R. (2007). Human capital predictors of career success in the Creative Industries: A regression tree approach. Paper presented at the EIDOS EMERGE Conference, Brisbane Powerhouse, Brisbane.
xxx
Bridgstock, R. (2007). Self-motivated and self-managing: Predicting tertiary arts graduate career success using a prospective regression tree approach. Paper presented at the AIC Partnership for World Graduates Conference, Melbourne, November 2007.
Brow, J., Hearn, G., & Bridgstock, R. (2007). 60Sox and the 2bobmob: Mentoring in an online learning community for Creative Industries graduates. Paper presented at the AIC Partnership for World Graduates Conference, Melbourne, November 2007.
Non-Refereed Conference Presentations
Bridgstock, R. (2006). Influences on artists’ career development and success. Poster presentation at the Institute for Creative Industries and Innovation Research Symposium, Brisbane.
Bridgstock, R. (2007). Creative workforce: Building a creative capacity. Creative Workforce Symposium presentation at Digital Literacy and Creative Innovation in a Knowledge Economy Research Symposium, Brisbane.
STATEMENT OF ORIGINAL AUTHORSHIP
The work contained in this thesis has not previously been submitted for a degree or diploma at any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.
Signed: ......
Date: ...... xxxii
ACKNOWLEDGEMENTS
Although PhD study is often seen as a solitary journey, numerous people contributed in meaningful ways to the completion of this project – not to mention regularly reminding me of the joys in learning to be a researcher. First I would like to thank my principal supervisor Wendy Patton, who throughout my candidature encouraged and nurtured my academic growth in the development of this thesis, and more broadly – providing timely career counselling when necessary!
Many people were instrumental to the data collection process. Dean Bensted
(PHP programmer extraordinaire), the QUT Student Information Systems staff, and the residents of B Block 5th floor (who participated uncomplainingly in recruitment material envelope-stuffing parties) spring particularly to mind. Also, without the timely action of Stephen Towers and the academic staff of the Creative Industries
Faculty at QUT, and also the managers of the participating professional arts organisations, my sample sizes would have been considerably smaller. Many thanks.
I would also like to thank Erica McWilliam, who kindly provided scholarship support, part-time research employment, academic mentoring, annual song-and- dance acts, and reminders when needed that, “girls can do anything!”
Thanks must also go to some of my fellow doctoral candidates. Drs Shane
Dawson, Amanda Mergler, Lee Tennent and Lyndal O’Gorman, and almost-Drs
Jennifer Puay Leng Tan and David Emmett provided invaluable inspiration, support, advice and numerous cappuccinos during my many long days and nights of PhD candidature. Finally, I would like to acknowledge the support of my family and friends outside QUT, without whom this PhD would never have been completed.
This document is dedicated to Michael and approximately 299,999 other
Australian artists, in appreciation of your important creative work. 1
CHAPTER 1
Introduction to the Research
The first chapter of this document provides an overview of the research at hand, the central aim of which was to explore the value of a number of career development constructs and influences in predicting career success in artists. Using samples of professional artists and graduating tertiary arts students, this program of doctoral research involved a comprehensive exploration of conceptions of career success held by the samples of artists, and also an investigation of the statistical relationships between the participants’ definitions and commonly accepted measures of career success such as earnings. Nonparametric techniques were then employed to determine the predictive value of a number of constructs suggested by career development literature to the measures of artistic career success.
This first chapter commences by positioning the present research within the arts and career development theory milieux. It also provides concise definitions for specialised terms used in this document.
Background to the Research
The Importance of the Arts
Article 27 of the Universal Declaration of Human Rights (United Nations,
1948) affirms the right of every human being to freely participate in the cultural life of the community and to enjoy the arts. The value of artistic participation (both expression and reception) to individuals is agreed be wide ranging, from enhanced development of learning skills and academic performance, to improved psychological and physical health, to greater individual receptivity to new 2 perspectives and tolerance for others (Belfiore & Bennett, 2007; Bryce, Mendelovits,
Beavis, McQueen, & Adama, 2004; McCarthy, Ondaatje, Zakaras, & Brooks, 2004b)
In addition, it is now widely acknowledged that the arts plays a vital role in contributing to broad social and economic goals, particularly in the worldwide shift to innovation and knowledge-based economies where creativity and innovation are increasingly prized (Budd, 1995; Czikszentmihalyi, 1997; Radich, 1992; Stone,
Bikson, Moini, & McArthur, 1999). Many studies document the direct and indirect economic benefits of the arts to communities, including employment and spending, and attraction of organisations and people to areas where the arts are available
(Florida, 2003; Heilbrun & Gray, 2001; Myerscough, 1988). In the last few years, the economic value of creative workers has also increasingly been emphasised. In this era of the ‘creative workforce’ (ARC Centre of Excellence for Creative Industries and Innovation, 2007; Florida, 2003), which is often located within the ‘creative industries’ (Caves, 2000; Cunningham, 2002), and in the broader context of the
‘creative economy’ (Howkins, 2002), artists are argued to possess capabilities which are of great economic benefit both within and outside the arts, including, in addition to creativity, problem solving abilities, emotional intelligence, and team working skills (ARC Centre of Excellence for Creative Industries and Innovation, 2007;
Moga, Burger, Hetland, & Winner, 1999).
Artists’ Working Lives
Despite ongoing rhetoric regarding the importance of the arts to individuals, society and the economy, the cultural sector continues to struggle against intense economic pressures. Very few artistic ventures are profitable, and it is difficult to predict whether an artistic product or service will become a ‘hit’ (Hodsoll, 2002).
Demand for artistic products appears to be strongly affected by the potential 3 audience’s disposable income levels and previous experience with the arts, and the political / social forces at play (Levy-Garboua & Montmarquette, 2003; Throsby,
1994a). The sector is highly dependant upon public funding and subsidies, which are argued to favour certain groups of recipients over others (e.g., opera and ballet as opposed to orchestras; more conservative works as opposed to experimental ones) and are also strongly affected by the government’s wider objectives, which vary markedly by government ideology and over time (Frey, 2003).
The arts sector displays labour force characteristics which have been described as ‘exceptional’ (Abbing, 2002). The average earnings of workers in the arts sector are consistently less than other workers with comparable educational and demographic profiles, and artists also experience much higher rates of unemployment and underemployment (Australia Council for the Arts, 2005b;
Australian Bureau of Statistics, 2005; Caves, 2000; Throsby & Hollister, 2003).
Artists may be prepared to undertake poorly paid casual or part-time work in an unrelated field while unemployed or underemployed as an artist in order to sustain their practice (Papandrea, 2004). Further, economic studies suggest that if artists receive extra income from any source, they will tend to use this income to work more hours in arts rather than spend fewer hours working. Faced with the prospect of additional income, they may even reduce hours at supplementary jobs in order to maximise time working in their arts practice, resulting in a similar level of income to before the new income was introduced (Abbing, 2002; Throsby, 1994b).
Tertiary Arts Graduates’ Transitions to the World of Work
There have been fewer studies of the working lives of tertiary arts graduates than of professional artists, but evidence suggests that graduating arts students experience similar employment challenges and exhibit similar patterns of work to 4 more established artists. Tertiary courses in the arts and creative industries attract large and increasing numbers of students, yet arts graduates reaching the world of work seem to experience very high levels of underemployment and very low incomes, and many eventually obtain work unrelated to their degrees or go back to university in order to complete further tertiary study in fields unrelated to arts
(Graduate Careers Council of Australia, 2005a). Other arts graduates persevere, perhaps willing to sacrifice earnings and career stability in order to continue work in the arts for non-economic reasons (Throsby, 1994b), or mindful of the very few who have a big break into arts ‘superstardom’ (Benhamou, 2003; Rengers, 2002).
Recent moves towards making public funding for universities contingent on positive graduate outcomes (Department of Education Science and Training, 2007b;
Higher Education Funding Council for England, 2002) have meant that universities are under significant pressure to demonstrate that their students are employable.
Providers of tertiary arts courses are at a disadvantage in this because of the arts labour market characteristics just described. This disadvantage is compounded by several issues. First, governments have chosen to define ‘positive graduate outcomes’ restrictively, as the proportion of graduates in full-time employment four months after course completion, when the Graduate Destination Survey is administered (Graduate Careers Council of Australia, 2006b).
Second, universities have approached the issue of graduate employability in a narrow and limited way, choosing to emphasise in their students the development of generic / transferable skills (e.g., verbal communication, working with technology) that might appeal to the widest variety of employers (Australian Chamber of
Commerce and Industry, 2002; Bennett, 2002; Bowden, Hart, King, Trigwell, &
Watts, 2000; Kearns, 2001). While these generic / transferable skills are important, in 5 all probability they only represent a fraction of the influences on graduate employability (Brown, Hesketh, & Williams, 2003) and other positive graduate outcomes. Based on this information, the outlook for tertiary arts graduates, providers of arts education, providers of higher education in general, and the future creative economy, appears somewhat uncertain.
Opportunities for Research into Artists’ Careers
The preceding descriptions of artists’ and tertiary arts graduates’ careers lead to a number of questions that are insufficiently addressed by the extant literature.
Some of these questions are: Why do so many artists pursue careers in a profession where the economic conditions are so unfavourable? Why do many artists invest heavily in their artistic educations and the creation of works of art when the financial returns are usually low and unpredictable? Do artists choose arts because they aspire to a ‘holy grail’ of fame and fortune in arts, or are they motivated by factors unrelated to financial or non-financial recognition? Is success in the arts just a matter of being talented and getting a ‘lucky break’, or are there aspects of the artist and their social or environmental context that systematically predict career success in the arts? What should universities’ priorities be in supporting their arts students to achieve positive outcomes?
The program of research presented in this document investigates two substantive research inquiries emerging from these questions, across four key research questions. The first inquiry relates to career success in the arts. It investigates artists’ and arts graduates’ definitions of career success, and explores how their ratings of self-defined career success relate to established measures of career success such as earnings and self-assessments of employability. The second inquiry uses constructs and influences suggested by career development theory to 6 predict career success (using several of the measures explored in the first inquiry) in the two groups of artists studied. As the result of the findings of the second inquiry, several key recommendations are able to be made regarding key ways to support and develop tertiary arts students and professional artists in their careers.
Definitions of Key Terms
The terms presented in this section are frequently used in this thesis, and have specific meanings which have arisen from the literature (e.g., ‘boundaryless career’) or have been created by the researcher for the purposes of this study (e.g., ‘protean career success orientation’). It is therefore worthwhile to define these terms in the introduction. The terms are presented in alphabetical order for ease of reference.
Each of the terms is clarified further in Chapters 2 through 5 of this document.
Artist
The present program of study uses a definition of ‘artist’ based on previous work undertaken by: the Australia Council for the Arts (2005b); the Australian
Bureau of Statistics (1997); and, Throsby and Hollister (2003). For the purposes of the present study, there are four conditions necessary for an individual to meet the definition of ‘professional artist’, two of which pertain to the nature of artistic activities undertaken, and two of which pertain to professional practice. They are:
1. The artist is engaged in one or more of the following fields:
(i) creative arts (where the emphasis is on creation of original cultural expression);
(ii) performing arts (where the emphasis is on creative interpretation of an existing piece of creative artwork, e.g.. musicians, actors); 7
(iii) community cultural development (where the emphasis is on building a cultural community through art - Flood, 1998); and
(iv) technical or design arts (where the emphasis is on technical expertise as well as creative output, e.g., multimedia or fashion design).
2. The artist engages in creative work in at least one of these fields (not only
performing support, administrative, managerial or educational functions).
3. During the last 5 years the artist has created a professional work of art (sold,
performed, exhibited, published, filmed, broadcast or otherwise produced a
professional work), or has received a government or similar grant to produce
a professional artistic work.
4. The artist regards themselves as being engaged in creating a serious and
substantial body of artistic work.
Arts Graduate
Arts graduates in the present study were recruited at the point of course completion (October, 2005) from relevant Bachelor of Fine Arts, Bachelor of
Creative Industries or Graduate Certificate / Graduate Diploma programs at
Queensland University of Technology. They were eligible to participate at time 1 providing that they were planning to work in the arts during the subsequent year. The arts students were placed into three occupational categories according to the arts discipline of their program of study. These categories, as with the professional artists’ sample, were creative artists, performing artists, and technical artists. A second data collection procedure was undertaken with these participants one year after course completion (October, 2006). They were eligible to participate at time 2 if 8 they had attempted to work in arts over the preceding 12 months since course completion.
Boundaryless Career
The boundaryless career (Arthur, Hall, & Lawrence, 1989; Arthur, Inkson, &
Pringle, 1999; Arthur & Rousseau, 1996a, 1996b) in contrast with a ‘traditional’,
‘hierarchical’, ‘organisational’ career, is one which is not bounded by a single organisation or field, and is marked by less vertical movement, a less ordered progression, and less stability. "Put simply, boundaryless careers are the opposite of
‘organisational careers’ - careers conceived to unfold in a single employment setting"
(Arthur & Rousseau, 1996a, p.5).
Career
“[Career is]… the individual’s lifelong progression in learning and work”
(Watts, 1998, p.2), where ‘learning’ can be formal or informal, ‘work’ can include all paid and unpaid work roles, and ‘progression’ can be any kind of movement which retains a sense of development. The definition of career has recently been expanded from being synonymous with an ‘occupation’ to accommodate changes in the work world (see the definition of ‘boundaryless career’) and subjective notions of career; that is, that people personally construct their careers and make their own meanings from them (Collin & Watts, 1996; Herr, 1992; Miller-Tiedeman & Tiedeman, 1990;
Richardson, 1993).
Career Development Influences
Career development influences are key components of the Systems Theory
Framework of career development (McMahon & Patton, 1995; Patton & McMahon, 9
1997, 1999, 2006a). They are the dynamic intrapersonal, social-contextual, and environmental-contextual factors relevant to the career development process. These influences are the elements of the individual’s career development system.
Career Management Competencies / Skills, Career Self-Management
Career management skills and competencies are the abilities suggested by theory to be required in order for an individual to maximise employability and successfully navigate their own career. Several career management competence frameworks exist, the most well-known of which is deFillippi and Arthur’s (1994;
1996) competencies for the boundaryless career. These authors posited six classes of career management competencies: knowing what, knowing where, knowing how, knowing when, knowing why, and knowing who. However, the most comprehensive exposition and classification of career management skills for the Australian context were suggested in the draft Australian Blueprint for Career Development (Haines et al., 2003).
Career Success
Career success is the accumulated positive work and psychological outcomes resulting from one’s career experiences (Seibert & Kramer, 2001). Career success has traditionally been operationalised by variables which can be seen and evaluated by others, such as salary level or number of promotions, known as ‘objective career success’ (Judge, Cable, Boudreau, & Bretz, 1995). With recent acknowledgement by career theorists of the importance of derivation of personal meaning in careers,
‘subjective career success’, the individual’s subjective judgements about their career attainments (Burke, 2001; Ng, Eby, Sorensen, & Feldman, 2005), has assumed an increasingly dominant role in the career success literature. 10
Employability
Employability is defined as the careerist’s ability to create or obtain work. It is work-specific adaptability that enables workers to identify and realise career opportunities (Fugate, Kinicki, & Ashforth, 2004), an important construct in the boundaryless career world where job security is becoming rarer and the onus is on the individual to create a career for themselves. Recently, policy makers have started to emphasise ‘employability skills’ (Atkins, 1999; Australian Chamber of Commerce and Industry, 2002; Yorke, 2004), defined as the skills required to maximise employability. The most common conception of employability skills is transferable or generic skills which might be useful in a wide range of situations (such as information literacy, working with technology, and written and verbal communication).
Graduate Attributes
Graduate attributes are “the qualities, skills and understandings a university community agrees its students would desirably develop during their time at the institution and, consequently, shape the contribution they are able to make to their profession and as a citizen” (Bowden et al., 2000, para 1). Graduate attributes can be divided into two main categories: (i) those which pertain to an individual’s capacity for citizenship; and (ii) those which pertain to an individual’s capacity for employability.
Graduate Transition to the World of Work
Graduate transition is the process involved in moving from higher education to work upon completion of a tertiary course. Graduate transitions are most commonly discussed with reference to graduate employability and graduate 11 employment outcomes, but they also involve major life and identity transitions for most students. The present research operationalises successful graduate transitions in terms of the subjective and objective career success measures used in this study: total earnings; earnings from arts; total employability; employability in arts; and, self- defined career success.
Protean Career
The protean career is an extreme form of the boundaryless career, in conjunction with strong internal career motivations and measures of success (Baruch,
2004). It involves a psychological contract with one’s self rather than an organisation or organisations. The protean careerist, taking personal responsibility for their career development, continually ‘refashions’ themselves according to their personal needs and demands of the world of work. The term ‘protean’ comes from the name of the
Greek god Proteus, who could change his shape at will.
Protean Career Success Orientation
Seven related underlying attributes or dispositions are commonly argued in protean career literature to lead to individual career success in the context of the non- traditional career pattern. These are: strong internal motivations; self-directedness; proactivity; resilience and adaptability; openness to career opportunities; a positive self image; and a positive interpersonal orientation. For the purposes of the present study, these seven underlying attributes comprise protean career success orientation.
Predictors of Artists’ and Arts Graduates’ Career Success
The primary purpose of this research was to investigate the predictive value of certain constructs and influences suggested by career theory to career success in artists and tertiary arts graduates. The constructs and influences chosen as predictors 12 arose from relatively recent developments in career theory which have begun to recognise:
- the changing nature of many careers from linear and organisationally based to
‘boundaryless’ (Arthur & Rousseau, 1996a; Sullivan, 1999) and individually
managed;
- the importance of subjective, holistic and process-oriented views of career;
and,
- shortcomings in extant career development theories which tend to emphasise
only one or a few influences on career development and thus have limited
explanatory power in an exploratory study such as this one.
Career Self-Management Skills
The first group of arts career success predictors chosen for the present research emerged from discussions of the changing world of work in advanced economies. Since the mid 1990s, commentators have observed a decline in traditional organisationally-based careers amongst knowledge workers, and a corresponding rise in ‘boundaryless’ careers, characterised by lateral or advancing movements within, between or outside organisations, and various types of paid employment such as short-term contracts, job-sharing and self-employment (Arthur
& Rousseau, 1996b; deFillippi & Arthur, 1994; Hall, 1996; Knowdell, 1996). The boundaryless careerist experiences low levels of employment security and cannot rely on organisationally-based career development opportunities. Chapter 4 of this thesis presents the argument that a significant number of artists and tertiary arts graduates experience boundaryless careers. 13
Employability, the ability to create or obtain work, is a central concept to the boundaryless careerist (e.g., Bridges, 1995; Kanter, 1989; Mirvis & Hall, 1996), who must constantly look around and ahead for the next suitable career opportunity or opportunities (Jones & deFillippi, 1996). It follows logically that boundaryless careerists who possess certain types of skills, such as the ability to effectively network with professional contacts or locate career-related information, would be more successful than others.
The career development literature suggests a range of skills or competencies for successful career self-management in the era of the boundaryless career
(deFillippi & Arthur, 1994, 1996; Hache, Redekopp, & Jarvis, 2000; Haines et al.,
2003; Kuijpers & Scheerens, 2006; Kuijpers, Schyns, & Scheerens, 2006). The present study used a research-designed survey measure based on the Australian
Blueprint for Career Development (Haines et al., 2003) to investigate whether career self-management competence predicted career success in the artists and arts graduates under study.
Protean Career Success Orientation
A deficit in most career self-management skill frameworks is the relative lack of consideration of dispositional and other psychological characteristics which underlie the successful development and application of career self-management skills. This shortcoming is addressed by protean career theory, which emphasises the dispositions, identities, attitudes and beliefs associated with success in a non- traditional career (Briscoe & Hall, 2006; Briscoe, Hall, & DeMuth, 2006; Hall &
Chandler, 2005; Hall & Mirvis, 1996). 14
Protean career theory suggests that successful protean careerists possess a number of related underlying dispositions or attributes. The most commonly documented of these dispositions include: strong internal motivations (Gagné,
Senecal, & Koestner, 1997); self-directedness (Briscoe & Hall, 2006; Hall &
Chandler, 2005); proactivity (Briscoe et al., 2006; Eby, Butts, & Lockwood, 2003); resilience and adaptability (Lounsbury et al., 2003); openness to career opportunities
(Chiaburu, Baker, & Pitariu, 2006); a positive self-image (Judge & Bono, 2001); and, a positive interpersonal orientation (Seibert & Kramer, 2001).
These underlying dispositions are associated with varying degrees of empirical support, and have also received different levels of attention in the theoretical literature, as discussed in Chapter 3. The research described in the present document involved the development and validation of a brief survey measure based on these dispositions, which was then used to predict career success in the artists’ and arts graduates’ samples.
Career Development Influences
It is of value to place career self-management skills and protean career success orientation into a theoretical career development context in predicting artists’ and tertiary arts graduates’ career success. However, relatively little is known about the career development of artists, apart from information from labour market and economic modelling studies (Australian Bureau of Statistics, 2004; Graduate Careers
Council of Australia, 2005a; Throsby & Hollister, 2003). There is little to guide a choice of one theoretical formulation, particularly considering that many of the career development theories still focus on only one or a few aspects of the individual, their context, or processes acting within or between them (Savickas, 2001a, 2001b). 15
The Systems Theory Framework of career development (McMahon & Patton,
1995; Patton & McMahon, 1997, 1999, 2006a), which encompasses all of the influences described in the major theories, provides a broad, overarching view of the career development processes acting on and within boundaryless or protean careerists. As such, it may be used to examine a wide variety of systems and subsystems relating to career development and success in artists. Protean career success orientation (Hall, 1976, 1996, 2004; Hall & Mirvis, 1996) and career management competence (deFillippi & Arthur, 1994, 1996; Hache et al., 2000;
Haines et al., 2003) can then be contextualised into the wider influences on artists’ career development. Data obtained from this broad exploratory stage of study into the non-traditional career can be used to direct later targeted studies.
A measure of career development influences based on the Systems Theory
Framework of career development (McMahon & Patton, 1995; Patton & McMahon,
1997, 1999, 2006a) was developed and validated as part of the present doctoral research. Along with career self-management competence, protean career success orientation and a number of sociodemographic variables, this brief scale was then employed in the exploration of predictors of career success in artists and arts graduates.
Research Questions and Overview of the Studies
Four substantive studies were conducted in this program of research, using an online survey method with professional artists and tertiary arts students / graduates.
These studies were preceded by a pilot study which assisted in the development of the research instruments (refer to Appendix D for details). The first study examined the psychometric properties of researcher-designed survey measures of the career development constructs and influences under investigation: Career Management 16
Competence; Protean Career Success Orientation; and Career Development
Influences. The second study explored the notion of career success in the two samples of participants. Studies 3 and 4 determined which of the measured career development constructs and influences predicted artistic career success in the two groups. Table 1.1 presents an outline of the four studies comprising this program of research.
Study 1
The first study answered the research question: Are the following researcher constructed measures sufficiently valid and reliable when used with the study samples? a) career development influences; b) protean career success orientation; c) career management competence.
No quantitative measures were available to address the constructs of interest, so brief scales based on theory were constructed and validated via pilot testing and exploratory factor analyses, followed by confirmatory factor analyses via Structural
Equation Modelling with data collected from the artist groups: 310 professional artists, and the 218 graduating arts students who participated at time 1 (i.e., at the point of course completion). Confirmatory factor analyses revealed that the scales would benefit from respecification, so changes were made to the measures to enhance their validity and reliability. The three scales modified and validated in
Study 1 were then employed in Studies 3 and 4 as potential predictors of career success for the two cohorts of artists under investigation, along with relevant sociodemographic variables. 17
Table 1.1
The Program of Study by Research Question Study Research Question Samples Measures Analysis Techniques 1 Are the following researcher Professional Career Development Confirmatory constructed measures sufficiently artists Influences factor analysis valid and reliable when used with Arts students Protean Career Reliability the study samples? at time 1 (at Success Orientation analysis a) career development influences course Career Management b) protean career success completion) Competence orientation c) career management competence 2 How can career success in the Professional Career Success Content arts be defined? artists analysis Arts students Descriptive at time 2 (one statistics year after course completion) 3 Which of the measured career Professional Career Development Decision tree development influences and artists Influences analysis constructs predict career success Protean Career in professional artists? Success Orientation Career Management Competence Career Success Sociodemographic variables 4 Which of the measured career Arts students Career Development Decision tree development influences and at time 1 (at Influences analysis constructs measured at course Protean Career undergraduate course completion completion) Success Orientation predict successful transition to the Arts students Career Management world of arts work? at time 2 (one Competence year after Career Success course Sociodemographic completion) variables
Study 2
The second study of the program of research answered the research question:
How can career success in the arts be defined? Two research subquestions were addressed in this study, namely: (i) What are the artists’ definitions of career success? and (ii) What is the statistical relationship between the measures of career success employed in the present study? The broader aim of the study was to identify 18 the career success measures which would be most appropriate to use in the ensuing two studies.
The professional artists’ and arts graduates’ surveys contained several measures of career success, including: (i) total earnings over the previous 12 months;
(ii) arts earnings over the previous 12 months; (iii) 1-6 self-rated total employability;
(iv) 1-6 self-rated arts employability; and (v) 1-6 self-rated self-defined career success. Each participant also responded to an open-ended question asking them to define career success.
The responses for professional artists were content analysed using emergent coding with two coders. The codebook was later applied to the arts students’ definitions. Once themes emerged, sociodemographic differences in career success definitions were explored.
The second section of the study investigated the statistical relationships between the five different measures of career success for each career success definitional category and overall. Nonparametric Spearmans correlation procedures were used to do this.
Study 3
Study 3 answered the research question: Which of the measured career development influences and constructs predict career success in professional artists?
The study used the career development constructs validated in Study 1, sociodemographic variables, and the career success measures explored in Study 2 via
Classification and Regression Tree (CART - Breiman et al., 1984) style decision trees with v-fold crossvalidation pruning using the 1 SE rule. CART decision trees are a nonparametric analysis technique which can be used as an alternative to OLS or 19 hierarchical regression in the case of data which violates parametric statistical assumptions. In the regression trees, the Protean Career Success Orientation scale
(PCSO), Career Management Competence scale (CMC), Career Development
Influences scale (CDI), career success definitions, and sociodemographic measures for the 310 artists were entered as attribute (predictor) variables, and the career success measures were entered as target (criterion) variables.
Study 4
Study 4 answered the research question: Which of the measured career development influences and constructs measured at undergraduate course completion predict successful transition to the world of arts work? Like Study 3, this study used the career development constructs validated in Study 1, sociodemographic variables, and the career success measures explored in Study 2 via CART style decision trees. However, unlike Study 3, which employed a cross-sectional design where professional artists provided data regarding the attribute (predictor) and target
(criterion) variables at once, Study 4 used a prospective repeated measures design where the data for the attribute variables were gathered at the point of undergraduate course completion and the target variables were measured one year later. Data from a total of 122 arts students were used, as 122 of the 218 students who responded to the survey at time 1 (October 2005) also responded at time 2 (October 2006).
Research Significance and Contributions
The research program findings represent significant contributions to existing knowledge, in terms of theory relating to career success, the nature of protean and boundaryless careers, career self-management competence, and the relationships between various influences on artists’ career development. In addition, through the 20 development of three brief scales, the use of a one-year prospective approach to investigate career outcomes in tertiary graduates, and the application of CART style decision trees (Breiman et al., 1984) for nonparametric prediction, strong methodological contributions are made. Further, as indicated below, the research makes substantive contributions to what is known about artists’ career development and success, and also the transition from higher education to the world of work, with specific applicability to arts and creative industries programs.
Theoretical Contributions
The program of study contributes to theory in four key ways. First, the confirmed factorial structures of the three measures as examined in Study 1 provide insights into the nature of career self-management competence, the dispositions and attributes argued to underlie the development and application of these competencies, and the relationships between influences acting within an individual’s career development system.
Second, the findings of Study 2 extend theory pertaining to career success in arts, and in the protean career more broadly. The nature of subjective career success in artists was explored, and links investigated between subjective career success and other aspects of the artist (such as age and gender), and also objective measures of career success such as earnings. Some previous work investigating the nature of subjective and objective career success has been conducted (Arthur, Khapova, &
Wilderom, 2005; Heslin, 2005), but Study 2 described in this document is amongst the first to explore subjective and objective career success amongst careerists navigating non-traditional, non-organisationally based careers. 21
Third, Studies 3 and 4 contribute to knowledge regarding connections between career self-management skills, career motivations and measures of success, and career success in the non-traditional career. Recent theoretical literature suggests that links between these constructs may exist (e.g., Hall & Chandler, 2005), and the simple correlations and decision trees depicted in Chapters 8 and 9 empirically demonstrate these relationships.
Fourth, the program of study makes contributions to extant theory regarding graduate attributes, extending the notion of ‘employability skills’ (Atkins, 1999;
Australian Chamber of Commerce and Industry, 2002; Yorke, 2004) to include a broader range of competencies and attributes which are linked with positive graduate outcomes. The present studies show that there are a range of important influences on graduate outcomes beyond the discipline-specific and transferable skills for employability commonly discussed in the graduate attributes literature (Bennett,
2002; Bowden et al., 2000; Dahlgren, Hult, Dahlgren, af Segerstad, & Johansson,
2006; Garcia-Aracil, Mora, & Vila, 2004).
Methodological Contributions
The present program of study developed and validated three brief quantitative scales. No quantitative measures relating to: (i) career self-management competence;
(ii) underlying dispositions and attributes for career success; or (iii) broad influences on career development, existed at the time of writing. The scales developed in this research can be further refined and used in subsequent research studies.
Second, the research demonstrated that a one-year, online, prospective approach to investigate career outcomes in tertiary graduates is possible and that it can yield valuable information regarding graduate outcomes and also regarding links 22 between skills and knowledge developed at university and post-graduation success.
The prospective one-year approach provides clear advantages over the limited graduate data collection procedures currently undertaken 4 months after course completion (Graduate Careers Council of Australia, 2006b).
The use of CART style decision trees (Breiman et al., 1984) for prediction of career success in the artists under study is also an important contribution to research methodology. Although decision trees are well known in data mining, and fields as diverse as law, science and medicine (Furnkrantz, Petrak, & Trappl, 1997; Gibb,
Auslander, & Griffin, 1993; Yohannes & Webb, 1999), they have not commonly been employed in the social sciences. Decision tree methodologies may prove useful when the data shows serious violations of the statistical assumptions needed for parametric regression modelling. Decision trees can also can be used as effective profiling tools, for instance to identify arts students who may be at risk of poor graduate outcomes and therefore will require additional support during a tertiary program in arts.
Substantive Contributions
The studies described in this document add to what is known about artists’ and arts graduates’ experiences and beliefs regarding their careers. This is a significant addition to the literature which has hitherto largely been restricted to economic modelling research (Menger, 1999, 2001; Rengers, 2002; Rengers &
Madden, 2000) and small scale, predominantly qualitative studies (Brooks &
Daniluk, 1998; Stohs, 1991). The present research identifies influences and constructs which can enhance career outcomes for professional artists and tertiary arts graduates, and also identifies constructs and influences which represent risk 23 factors to artists’ career development and success. Minimal systematic quantitative research into these issues had previously been undertaken.
There are strong practical implications arising from the research for career education in the arts, tertiary arts / creative industries education programs, and also for employers wishing to capitalise on the desirable creative workforce skills and abilities artists possess. The research raises broader issues about the importance of career education, the responsibilities of universities in preparing students for the world of work, and the roles of artists in the creative economy and contemporary society.
Organisation of the Thesis Document
Chapter 1 presented a discussion of the background and context to the research program, including the social and economic importance of the arts to knowledge-based economies, and the working patterns and career challenges faced by professional artists and tertiary arts graduates. In addition, it provided concise definitions of the specialised terms used in this thesis document. An overview of the research questions and the techniques used to answer these questions was included, and also a short section outlining the significance of the research.
Chapters 2 through 4 review the literature pertinent to the present research.
Chapter 2 commences by examining career development theory, including recent moves towards theory convergence and integration, and recognition of new patterns of career in advanced economies. Chapter 3 outlines extant literature relating to career success and successful tertiary graduate transitions to the world of work.
Chapter 4 relates the theory discussed in Chapters 2 and 3 to the career development and career success of artists and tertiary arts students. 24
The methodological issues relating to this program of research are presented in Chapter 5. Considerations relating to online survey research comprising both qualitative and quantitative approaches are discussed, and ethical issues arising in the research are also reviewed. This chapter also summarises the research design and analyses used in the research, along with sampling of the professional artist and arts graduate participants.
Chapters 6 through 9 report the findings of the four studies conducted in this research program. Chapter 6 relates to Study 1, the primary aim of which was to examine the psychometric properties of the researcher-designed measures with the participants under study. Chapter 7 pertains to Study 2, which explored the construct of career success with relation to the professional artists and arts graduates. Chapter
8 outlines Study 3, which assessed the predictive value of the career development constructs and influences explored in Study 1 to career success in professional artists using a decision tree approach. Chapter 9 summarises Study 4, which involved the use of a decision tree approach to assess the predictive value of the career development influences and constructs explored in Study 1 to career success in tertiary arts graduates.
Finally, Chapter 10 discusses the research findings and broader implications of the findings for artists’ careers, protean and boundaryless careers, career development theory, higher education, and research methods. Limitations of the current studies and possible future research projects are also included. 25
CHAPTER 2
Literature Review: Career Development Theory
This chapter, in conjunction with Chapters 3 and 4, provides a theoretical basis for this program of study. The aim of the present chapter is to present an overview of career development theory. It commences by exploring recent changes in conceptions of career, and the challenges inherent in the new ‘boundaryless’
(Arthur et al., 2005; Arthur & Rousseau, 1996a; deFillippi & Arthur, 1994, 1996;
Sullivan, 1999) career pattern. Current theories of career development are then considered, and placed into context using the Systems Theory Framework of career development (McMahon & Patton, 1995; Patton & McMahon, 1997, 1999, 2006a), a meta-theoretical framework which provides an overarching frame for existing career development theories. A measure based on the Systems Theory Framework is developed and employed as part of this program of research, in order to investigate the predictive value of a broad range of career development influences on artists’ career success.
Chapter 3 presents a discussion of current thinking about career success, including a critical review of the various skills and dispositions suggested by various theorists to be necessary for careerists to flourish in an era of ‘employability security’
(Opengart & Short, 2002) rather than employment security. The emphasis on employability has extended to the tertiary education sector, with universities being placed under increasing pressure to demonstrate positive graduate outcomes. Chapter
3 concludes by giving an overview of the strategies currently undertaken by universities to facilitate student transitions to the workforce and promote graduate employability. 26
Chapter 4 examines the literature surrounding the career development of artists, and connects career development theories and constructs outlined in chapters
2 and 3 to the unusual circumstances of arts careers. Using labour force statistics and published studies of artists’ working lives, the career patterns of professional artists and tertiary arts graduates are examined. Links with boundaryless and protean career theory, career success theory, and graduate employability theory are made. Chapter 4 concludes with a discussion of the research questions to be addressed in this program of research. It has the overall objective of investigating which, if any, of several potentially relevant career development constructs predict professional artists’ career success and arts graduates’ successful transition to the workforce.
New Conceptions of Career
Like many good words in the English language, the term [career] is richly
ambiguous. It can describe a neat progression up the hierarchy; but we also
refer to ‘careering about’ (Watts, 1998, p.2).
The concept of career has traditionally been synonymised with ‘occupation’, or ‘vocation’, and has been associated with professional paid work involving linear advancement within one field, or even one organisation (Adamson, Doherty, &
Viney, 1998; Arthur et al., 1989). This idea of career has recently expanded and developed in line with changes to: (i) the way work is structured and organised
(Arnold & Jackson, 1997), known as the ‘boundaryless career’ (Arthur et al., 2005;
Arthur & Rousseau, 1996a; deFillippi & Arthur, 1994, 1996; Sullivan, 1999); and (ii) more developed understandings of the ways that people personally construct their working lives and obtain meaning from them, termed the ‘subjective career’ (Collin
& Young, 1986, 2000; Miller-Tiedeman, 1999a; Peiperl, Arthur, Goffee, & Morris,
2000; Savickas, 2001a; Savickas, 2002; Watts, 1998). 27
Boundaryless Careers and Employability
The current career development literature contains much discussion of the changing world of work in most advanced economies (e.g., Arnold, 1997; Arnold et al., 2005; Collin & Young, 2000; Hall, 1996). These changes have been attributed to technological advances and the shift to a post-industrial ‘knowledge economy’, globalisation, deregulation of labour markets, changing workforce demographics, and a rise in post-compulsory education (Niles, Edwin, & Hartung, 2001; Storey,
2000). They have been noted to particularly affect knowledge workers (Peiperl,
Arthur, & Anand, 2002; Peiperl et al., 2000; Royal & Althauser, 2003; Sullivan,
1999), and those who work primarily with information or who develop and use knowledge in the workplace (Drucker, 1995).
By the mid 1990s, alternatives to the traditional linear career ‘bounded’ by orderly employment arrangements and upward progress through a single firm or occupation were increasingly discussed (Arthur & Rousseau, 1996b; deFillippi &
Arthur, 1994; Hall, 1996; Knowdell, 1996). As McMahon, Patton and Tatham (2003, p.5) note, “work is often no longer characterised by a set of tasks which are mastered once, and a career is no longer characterised by a vertical process of advancement within one organisation”. Instead, careers increasingly appear to involve a series of lateral or advancing movements within, between and outside organisations, occupations and paid employment (the idea of a ‘multidirectional career’ (Baruch,
2004), associated with both formal and informal educational experiences. Periods of paid employment may include casual work, short-term contracts, job sharing and self-employment in combination or isolation (Arnold et al., 2005; Storey, 2000). The career mobility and flexibility thus described are the conditions which comprise the boundaryless career (Arthur & Rousseau, 1996a), although some authors have 28 argued that the term is misleading, as rather than signalling a dissolution of career boundaries, these careers are merely differently bounded and organised and characterised by different kinds of employer - employee relationships (King, Burke,
& Pemberton, 2005).
The decline of traditional employment relationships has given rise to new conceptions of the employer-employee psychological contract. The boundaryless career is typified by finite-term transactional relationships with employers (who, in many cases could be more appropriately termed ‘clients’), and therefore employers are, “abandoning their implicit promises of orderly promotion and long-term job security” (Stone, 2001, p.1). The individual is challenged to rely less on organisationally-based career development and instead actively to navigate their own career path – to seek employment security no longer, but to opt instead for security in employability (Bridges, 1995; Jarvis, 2003; Kanter, 1989; Mirvis & Hall, 1996).
As the careerist takes personal responsibility for their career development, personal and professional networking becomes much more important (Burt, 1992;
Raider & Burt, 1996). Individuals in boundaryless careers recurringly seek jobs and information regarding new job opportunities (Jones & deFillippi, 1996; Kram, 1996;
Raider & Burt, 1996) and so will often depend on their personal and professional contact networks for information about the next lead in the career journey. It is also in the boundaryless careerist’s interests to acquire skills that are transferable from employment opportunity to employment opportunity (Arthur et al., 1999; Hall, 1996;
Peiperl et al., 2002; Peiperl et al., 2000), and reflect the latest technological and conceptual advancements. Thus, it has been suggested that the new employment relationship is based on the exchange of work for worker upskilling and networking / increased marketability (Altman & Post, 1996). 29
The Subjective Career
As non-traditional work patterns and boundarylessness (Arthur et al., 2005;
Arthur & Rousseau, 1996a; deFillippi & Arthur, 1994, 1996; Sullivan, 1999) have increasingly become the norm, career theorists have also begun to acknowledge more holistic, subjective and process-oriented views of career (Hall, 1976, 1996; Harley,
Muller-Camen, & Collin, 2004). Careers comprise significant internal components
(attitudes, orientations, and perceptions) as well as external components (visible, observable activities, behaviours, or events comprising a person's work history). A significant school of thought in career theory now posits that careers can be viewed as the subjective construction of the individual, and that people create their own definitions of work in their lives (Collin & Watts, 1996; Herr, 1992; Miller-
Tiedeman & Tiedeman, 1990; Richardson, 1993). Miller-Tiedeman’s concept of
‘lifecareer’ (Miller-Tiedeman, 1999a, 1999b) has become a definitive term used to express an individual interpretation of career and integration of work and other aspects of an individual’s life.
A career is now viewed as something one has for a lifetime (Gysbers, 1997) and includes the many interrelated and overlapping work-related roles that a person occupies. Wolfe and Kolb (1984) discussed career as “involv(ing) one’s whole life, not just occupation … it concerns him or her in the ever-changing contexts of his or her life … self and circumstances – evolving, changing, unfolding in mutual interaction” (p.9). While the traditional notion of career choice is still key to discussions of career development, all career-related behaviours are also acknowledged to be relevant, as well as the intrapersonal and contextual factors influencing these behaviours, and the processes associated with these influences
(Patton & McMahon, 2006). Thus, recent career development theory has blurred the 30 distinction between work and private life, with paid employment being one system embedded in other complex and dynamic systems (e.g., family, peers, community, education) comprising people’s lives (Miller-Tiedeman, 1999a; Patton & McMahon,
1999, 2006a; Super, 1990).
Further, theorists (Arnold et al., 2005) have observed that in some careers the boundary between work and non-work is indistinct or non-existent, as in the case of career development which involves acquisition of significant personal meaning and life direction. To create a personally meaningful career, individuals reflect on their experiences and make changes necessary to keep their careers aligned with personal values and interests (Savickas, 1999).
The Protean Career
Protean career theory is strongly linked to both boundaryless career theory and the subjective career. The protean career has been suggested to be an extreme form of both career boundarylessness and career subjectivity (Baruch, 2004). A protean careerist creates and maintains a psychological contract with themselves rather than an organisation or organisations. They assume ultimate responsibility for their own career development and repeatedly ‘shapeshift’ in accordance with demands from the employment market and their own desires. The term protean comes from the name of the Greek god Proteus, who could change his shape at will.
For the protean careerist (Briscoe & Hall, 2006; Hall, 1976, 1996, 2004; Hall
& Chandler, 2005; Hall & Mirvis, 1996), the emphasis is on a personally meaningful career path which expresses human potential. Protean careerists often have a strong sense of purpose, or ‘calling’ (Hall & Chandler, 2005). The protean careerist’s personal identity is intertwined with their career (Baker & Aldrich, 1996); their 31 career is a form of self-expression, is a vehicle to personal growth, and provides a means by which they can achieve personally meaningful goals. The idea of an internal, psychological (or indeed spiritual) impetus for career is also evident in the works of other writers, notably Campbell’s (1988) ‘follow your bliss’, the ‘career path with heart’ (Potter, 1995; Shepard, 1984) and Miller-Tiedeman’s (1999a)
‘personally meaningful life-work’.
The key attributes of the protean careerist discussed below are presented in
Table 2.1, which summarises the nine career elements across five categories drawn from the protean career literature (Arthur & Rousseau, 1996a; Briscoe & Hall, 2006;
Briscoe et al., 2006; Hall, 1996, 2004; Mohrman & Cohen, 1995) and discussed in this chapter. These elements distinguish the protean career pattern from that of the traditional career.
Table 2.1
Key Attributes of Traditional vs Protean Careers
Traditional Career Protean Career
Mobility/ Security Low mobility, high job security High mobility, lower job security
Firm-specific skills Transferable skills, knowledge and abilities
Occupational Roles One occupational role Several occupational roles
Source of Income Employer Employers and Clients
Salary/ wages Contracts or invoices
Single source of income Multiple sources of income
Career Motivation and Hierarchical position, salary Subjective, psychological Measures of Success motivations and measures of success
Responsibility for Career Organisational responsibility for Personal responsibility for career Development career development development
Personal and professional Personal and professional networks networks not as important very important 32
Individuals with a protean orientation are less concerned with maximising their chances for promotions, salary and greater power within one working context than traditional careerists may be, and are more motivated by autonomy, personal values, and psychological success (Briscoe & Hall, 2006; Briscoe et al., 2006; Mirvis
& Hall, 1996). Protean careerists assume ultimate personal responsibility for their careers (Briscoe et al., 2006; Comfort, 1997) and make autonomous career choices according to internal and self-determined criteria (although these choices are influenced by, and in turn influence, the various other social, familial and systems the individual operates within).
A protean career pattern thus comprises an acute form of boundarylessness with the attendant individual responsibility for career development and employability, but the protean career can be distinguished from the boundaryless career by virtue of its emphasis on internal motivations and measures of career success (Briscoe & Hall, 2006; Briscoe et al., 2006; Hall, 2004; Hall & Chandler,
2005). Protean career theory also tends to emphasise the dispositions, identities, attitudes and beliefs leading to psychological success in a career (Briscoe et al.,
2006; Hall & Chandler, 2005; Hall & Mirvis, 1996), whereas literature emanating from boundaryless career theory suggests skills and competencies needed for employability (e.g., deFillippi & Arthur, 1994, 1996). Both points of view are potentially valuable in discussions of career success in an era of the non-traditional career pattern, and will be explored further in chapter 3 of this document.
This first section of Chapter 2 has provided an overview of current perceptions of career. Once synonymised with ‘occupation’, the term ‘career’ is now considered to be a far more inclusive concept, encompassing all internal and external aspects of a person’s work-related life, which for knowledge workers seem to include 33 an increasing diversity of employment arrangements, career directions and motivations for work. The next section of this chapter presents an overview of the diverse theoretical perspectives on individual career development, culminating with a discussion of recent attempts at theory integration and convergence.
Theoretical Understandings of Career Development
Career development theory has had a relatively short history, with vocational guidance (the precursor to career counselling) only established in the early 1900s. It covers an immensely broad theoretical area: “[t]he total constellation of psychological sociological, educational, physical, economic and chance factors that combine to shape the career of the individual over the lifespan” (Sears, 1982, p. 139).
While a significant number of theories have been suggested to account for career development, most emphasise different aspects of the individual, contextual factors, or processes that might be acting on or within individuals. The four principal fields which contribute to career theory are psychology, sociology, education, and management (Peiperl et al., 2000).
A division is often made between career development theories primarily relating to content, which focus on individual characteristics, contexts and occupations, and theories primarily relating to development, which focus on the processes involved in an unfolding career across a life span (Brown, 2002b; Brown
& Brooks, 1991, 1996; Niles, 2002; Patton & McMahon, 1999, 2006a). Recent theories such as social cognitive career theory (Lent, Brown, & Hackett, 1994, 2000,
2002) and cognitive information processing theory (Peterson, Sampson, Reardon, &
Lenz, 2002; Reardon, Lenz, Sampson, & Peterson, 2000) better acknowledge the interaction of career structures and processes. 34
Structure Theories of Career: Traits and Types
The earliest theories of career development (e.g., Parsons, 1909) were related to structure – namely, the prediction of career development from characteristics of the individual, often emphasising traits such as personality, interests or values, and the match between a person and their work environment. These theories continue to dominate career counselling practice (Osipow & Fitzgerald, 1996; Sharf, 2002).
Several theories of this type will be outlined in this literature review:
- ‘Trait and factor’ theories, which assess characteristics of individuals and
jobs and then attempts to match them. Five-factor personality theory
(John, 1990; McAdams, 1992; McCrae & Costa, 1996, 1997) is used
herein as an example of a trait and factor theory;
- Holland’s typological theory (Holland, 1963, 1966, 1973, 1985, 1987,
1997);
- Work adjustment theory (Dawis, 1996, 2002, 2005; Dawis & Lofquist,
1984); and
- Brown’s values-based theory (Brown, 2002a, 2003b; Brown & Crace,
1996).
Trait and factor theories of career development are based on the idea that individuals are different, that these differences can be tested, and that their different attributes and capacities can be matched to different jobs which require a certain
‘profile’ of worker. A good match will ensure not only satisfactory performance in a job, but also satisfaction on the part of the worker. Attributes of the worker which are often considered in trait and factor theories include interests (Consulting
Psychologists Press, 2004), general and specific aptitudes / abilities (Walsh & Betz, 35
2001), values (Macnab, Bakker, & Fitzsimmons, 2005), work styles (Jackson &
Gray, 1998) and personality (McCrae & John, 1992; Myers, McCaulley, Quenk, &
Hammer, 1998). In turn, jobs are often differentiated in terms of worker characteristics, experience, and skills/ knowledge required (Farr, Ludden, & Mangin,
1998; Hammer, 1993; Holland, 1994a).
The five factor model of personality (Gottfredson, Jones, & Holland, 1993;
McCrae & John, 1992) is an example of a trait and factor theory which historically has enjoyed considerable support. It has become the most widely used and extensively researched model of personality throughout mainstream psychology as well as in the career development field (John, 1990; McAdams, 1992; McCrae &
Costa, 1996, 1997). However, like most trait and factor theories, it has been criticised for failing to adequately consider contextual variables in career development (e.g.,
Brown, 1990), and for its inadequate coverage of career growth and change (e.g.,
Zunker, 1994).
The five factor model is a hierarchical model of personality traits with five broad factors, which represent personality at the broadest level of abstraction. Each factor (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) summarises several more specific facets (e.g., sociability), which, in turn, subsume a large number of even more specific traits (e.g., talkative, outgoing). The Big Five model is then used to predict an individual’s job choice (Howard & Howard, 2001), job performance (Hurtz & Donovan, 2000), and even overall job and career satisfaction (Judge, Heller, & Mount, 2002).
Holland’s theory was originally conceptualised as a trait and factor theory and has been criticised for suffering from similar shortcomings to other trait and factor theories (Zunker, 1994). In response to such criticisms, the theory has begun to 36 be ‘renovated’ (Holland, 1994b) to encompass processes involved in change and development, and other structural aspects such as values, goals, interests and beliefs
(Holland, 1997). The essence of the theory remains in the trait and factor field, with
Holland maintaining that by late adolescence most people come to resemble a combination of six vocational personality / interest types: Realistic, Investigative,
Artistic, Social, Enterprising, or Conventional (Holland, 1987). Work environments are described in commensurate and complementary terms on the basis of the types of people working in them and the types of work activities undertaken there (Spokane
& Cruza-Guet, 2005). From this, the degree of fit or congruence between the person and the work environment can be calculated (Young, Tokar, & Subich, 1998). Also important are the concepts of ‘differentiation’, which refers to how well crystallised and clearly defined the individual’s type is (Holland, 1987) and ‘identity’, which refers to the level of stability and clarity an individual has about their interests, values and goals.
Like Holland’s model, work adjustment theory (Dawis, 1996, 2002, 2005;
Dawis & Lofquist, 1984) relies on the assessment of individual traits in order to quantify the degree of ‘match’ between an individual and their work environment.
However, work adjustment theory places a greater emphasis on adjustment over time than the theories previously outlined; “a continuous and dynamic process by which a worker seeks to achieve and maintain correspondence with a work environment”
(Dawis & Lofquist, 1984, p.237). In correspondence, both the individual’s requirements and the work environment’s needs are met; the worker is both
“satisfied and satisfactory” (Dawis, 1996, p.82). If there is discorrespondence, the dissatisfied party or parties attempt/s to create correspondence by adjusting reinforcers and / or abilities. The individual worker’s requirements are termed 37
‘reinforcers’, and can include (depending on their values) salary, prestige, and working conditions (Dawis, 2002). The employer’s requirements, on the other hand, are for the worker’s abilities and skills. Ongoing satisfaction and satisfactoriness are argued to predict tenure of the employment relationship in this model (Dawis, 2005), and this may limit the model’s usefulness in examining the boundaryless or protean career.
Brown’s recent values-based theory (Brown, 2002a, 2003b; Brown & Crace,
1996), the final of the theories presented here under the ‘structure’ subheading, acknowledges far more completely than the preceding theories the concepts of ongoing development and contextual influences within which career development
(and also career success) occurs. It is included with the structure theories because of the theory’s emphasis on a particular trait, that of work and cultural values. As
Brown (2002a, p.48) indicated, “values are beliefs that are experienced by the individual as standards that guide how he or she should function; they are cognitive structures but they also have behavioural and effective dimensions”. A value system, then, encompasses all such beliefs held by an individual including work values and those arising from their culture. In this theory, values are posited to be primary factors in choosing, deriving satisfaction from, and advancing in a career (Brown,
1996a). However, values may be constrained or assisted in this by contextual or developmental variables such as socioeconomic status, aptitudes, life roles other than worker, self-efficacy, and family or group influence (Brown, 2003b). In its focus on both traits and developmental variables, this theory makes important links between the structure theory way of seeing traits as isolated and static determinants of career choice, and other theories about the context and processes involved in career development. 38
Process Theories of Career: Development and the Individual
In contrast with the majority of theories of structure just outlined, process- orientated theories of career development focus on careers as evolving, often through developmental stages or phases. These theories imply a predictable life cycle to career, and suggest an underlying order in the unfolding of an individual’s life
(Hagestad & Neugarten, 1985), although few of these theories suggest that progress through career stages can only be a forward movement (Patton & McMahon, 1999,
2006a).
Four process-orientated theories will be discussed here:
- The early theories of Ginzberg (1972; 1984; Ginzberg, Ginzberg,
Axelrad, & Herma, 1951);
- Super’s life-span, life space theory (Super, 1980; 1990; 1992; 1994;
Super & Knasel, 1981; Super & Nevill, 1984; 1996);
- Tiedeman and Miller-Tiedeman’s career development theories which
emphasise the individual (Miller-Tiedeman, 1997, 1999a; Miller-
Tiedeman & Tiedeman, 1979, 1990; Tiedeman & O'Hara, 1963);
- Gottfredson’s theory of circumspection and compromise (Gottfredson,
1981, 1996, 2002).
Ginzberg and colleagues were some of the earliest theorists to deviate from static trait and factor theories. The initial formulation of the theory (Ginzberg et al.,
1951) construed vocational choice as an irreversible process occurring in clear marked periods through childhood and adolescence and characterised by a series of compromises between wishes and possibilities. The first of these periods, the fantasy phase (up to age 10 to 12), involves the arbitrary expression of occupational 39 preferences in childhood. The next phase, the tentative period (up to 17 or 18), comprises four substages: interest, capacity, value, and transition. Thus, consideration of occupational choice is initially based on what the child likes to do, followed by an awareness of their skills and aptitudes, followed by a discovery that different career options have different extrinsic and intrinsic value, and finally a transition into the final stage, that of realistic choice.
The realistic phase, too, is comprised of several stages, beginning with an exploration substage, which involves implementing their occupational choices and evaluating feedback on their implementations, followed by crystallisation of career choices and patterns (between the ages of 19 to 21), and concluding with specification, within which the individual chooses a position or profession.
Ginzberg (1972; 1984) subsequently reformulated his theory in response to criticisms that it did not adequately address career as a lifelong process. The newer formulations involved an assertion that occupational decisions are a life-span phenomenon, not simply a short-term process restricted to adolescence, and that rather than involving successive compromises, that people make decisions about jobs with the aim of finding the best possible fit between their needs and the opportunities and constraints of the world of work.
However, significant criticisms of this theory remain, including the absence of adult empirical data supporting the theory, and a lack of explanation of the developmental processes influencing career choice (Osipow & Fitzgerald, 1996).
This theory has had declining influence on the career literature in the last 30 years, although the importance of developmental processes in careers has subsequently been emphasised by several major theories, and some of the constructs included in 40
Ginzberg’s (1972; 1984; Ginzberg et al., 1951) theory have been further developed in them.
Super’s life-span theory (1980; 1990) is one of the most widely accepted process-oriented views of career development and is strongly influenced by
Ginzberg’s process theory. The career development ‘maxicycle’ is, according to
Super, characterised by progression through five stages: growth, exploration, establishment, maintenance, and decline (disengagement) corresponding to childhood, adolescence, adulthood, middle adulthood and old age, and these stages may in turn be divided into a) the fantasy, tentative, and realistic phases of the exploratory stage, and b) the trial and stable phases of the establishment stage. A mini-cycle takes place during transitions from one stage to the next or due to destabilisation associated with socioeconomic or personal events (such as illness).
Career development occurs as an individual either meets the challenges of predictable developmental tasks, which may bear relation to chronological age, or alternatively, faces a situation which demands adaptation and which are not linearly predictive. Successful adaptation to major developmental tasks associated with each stage prepares individuals for the challenges of the next developmental stage.
Readiness to cope with these developmental tasks has been termed ‘career maturity’, denoting readiness of the adolescent to make educational and vocational choices
(Super, 1990).
It has been argued that alternative terms such as ‘career choice readiness’
(Savickas, 1993), or ‘career adaptability’ (Super & Knasel, 1981) might be substituted for career maturity when describing career development in adults. Career adaptability is defined as a sense of planfulness and readiness to deal with changing work and working conditions (Super & Knasel, 1981). 41
The exploration stage in Super’s theory, spanning the ages of fourteen to twenty-four, is composed of the tentative substage, a transition substage, and an uncommitted trial substage. Three career development tasks – crystallisation, specification (choosing a specific career), and implementation (study and beginning employment) are undertaken during this time. The establishment stage follows the exploration stage, and is associated with the gaining of employment, becoming familiar with work culture, and performing satisfactorily. Then the individual’s position is consolidated, followed by advancement and promotion. A period of maintenance occurs thereafter, followed by disengagement. It should be noted that recycling, which involves revisiting issues from a previous career stage and experiencing growth, re-exploration and reestablishment (as with the boundaryless career) may actually enhance adaptability and coping power (Smart & Peterson,
1997), and is not seen as problematic.
In recognition of the importance of contextual elements to career development, Super added the idea of ‘life space’ to his ‘lifespan’ model of career development, denoting “the constellation of social positions and (interactive) roles enacted by the individual” (Super et al., 1996, p.128). Nevill and Super (1986) described six major life roles: leisurite, student, child, homemaker, worker, and citizen. As such, an individual might be an artist, a son, a husband and a student concurrently, and these roles interact reciprocally to shape each other throughout life.
Self-concept (how individual view themselves and their situation) is at the core of Super’s (1980; 1990; 1992; 1994; Super & Knasel, 1981; Super & Nevill,
1984; Super et al., 1996) developmental theory of career. For Super, how individuals perceive themselves and interact is a reflection of personality, needs, values and interests. These perceptions change over the lifespan, through processes such as self- 42 differentiation, role playing, exploration, and reality testing. Interaction with society brings about development of the self-concept as the individual interacts with influences such as family, peers, school, and co-workers.
Gottfredson’s (1981; 1996; 2002) theory of career development emphasises gender and social class background, and therefore adds to Super’s developmental theories which appear somewhat underdeveloped in these areas. Gottfredson (1981;
1996) presented a model of four developmental stages beginning in early childhood and ending in late adolescence, with a process of circumscription and compromise.
Circumscription is “progressive elimination of unacceptable alternatives”
(Gottfredson, 1996, p.187) through self-concept development, which involves progressive influences from age 6 of size and power, gender, social background, ability, and future lifestyle. Compromise is an individual process of vetting career alternatives based on limiting external factors such as insufficient academic performance.
The self is also a key concept in Tiedeman’s perspective on the process of career. Drawing on White’s (1952) individualistic approach to the study of human lives and Erikson’s (1959) study of ego and psychosocial development, Tiedeman’s early work with O’Hara (1963) emphasised the gradual development, and then commitment to, a choice by processes of differentiation and reintegration (Miller-
Tiedeman & Tiedeman, 1990). Anticipating a choice involves four phases: exploration, crystallisation, choice, and then clarification. Once the choice has been made, there is a period of adjustment to it, comprising induction, reformation, and integration.
More recent work by Miller-Tiedeman and Tiedeman (1990), drawing from the individualistic perspective of the original theoretical formulation, asserts that 43
“life is career” (p.331). Miller-Tiedeman (Miller-Tiedeman, 1999a) criticised existing career development theories as not catering to the client’s perspective. She contended that the individual should be their own career theory maker, based on their lived-in-the-moment process (Miller-Tiedeman, 1997, 1999a). This ‘spiritual approach’ (Savickas, 1997) to careers helps clients to trust themselves and their inner wisdom over external or ‘common’ reality (Miller-Tiedeman & Tiedeman, 1979), and develop their careers naturally.
Theories Embracing Structure, Process and Context
Over the last 20 years, significant changes have occurred in the way career development is viewed. Theories emphasising either content or process variables are no longer seen as adequate in explaining career development, resulting in amendments to Holland’s matching theory to incorporate change over time (Holland,
1994b, 1996, 1997), and to Super’s developmental theory in terms of ‘life space’ as well as ‘life span’ (Super, 1990). In pursuit of increasing explanatory power and to accommodate changes in the world of work, recent theories often include an increasingly large number of individual and contextual variables which interact in complex ways. Five main theories will be discussed in this section:
- Krumboltz’s social learning theory (Krumboltz, 1979; Krumboltz &
Worthington, 1999; Mitchell, Levin, & Krumboltz, 1999; Mitchell &
Krumboltz, 1990, 1996);
- Social cognitive career theory (Hackett & Betz, 1981; Lent & Brown, 1996;
Lent et al., 1994, 2002; Lent & Hackett, 1987, 1994); 44
- Cognitive information-processing theory (Peterson, Sampson, Reardon, &
Lenz, 1991; Peterson et al., 2002; Reardon et al., 2000; Sampson, Lenz,
Reardon, & Peterson, 1999);
- Vondracek’s developmental-contextual approach (Vondracek, 1990, 1995,
2001; Vondracek & Fouad, 1994; Vondracek & Kawasaki, 1995;
Vondracek, Lerner, & Schulenberg, 1986);
- The contextualist approach of Young et al. (Valach & Young, 2002; Young
& Valach, 2000; Young, Valach, & Collin, 2002)
Drawing on the work of Bandura (1969), Krumboltz (1979) and a number of his colleagues (Mitchell et al., 1999; Mitchell & Krumboltz, 1990) have applied the principles of social learning theory in an attempt to explicate the individual and environmental factors which contribute to career. Variables consistent with the tenets of social learning theory are clustered under four elements purported to influence career decision-making: genetic endowment and special abilities, environmental conditions and events, learning experiences, and task approach skills (Mitchell &
Krumboltz, 1990). Genetic endowment includes innate characteristics such as physical appearance, abilities or talents, ethnicity, and gender; these characteristics make certain skills easier to develop given the right environmental conditions (e.g., a child with innate music aptitudes is likely to respond well to music instruction if it is given). Environmental conditions are those social, cultural, educational, occupational, political, geographical, and economic factors which affect the individual, whether in a planned or an unplanned way (Mitchell & Krumboltz, 1996).
According to Krumboltz (1979), the individual will develop career preferences through instrumental (direct) and associative (based on observation or 45 classical conditioning) learning experiences. Task approach skills arise from an interaction of genetic endowment factors, environmental factors, and learning experiences. These are the skills, attitudes, work values, standards, cognitive processes, and affective responses applied to each new task or problems which the individual faces (Mitchell et al., 1999; Mitchell & Krumboltz, 1996). Based on their learning experiences, individuals will also make self-generalisations about their abilities, interests and values, and world generalisations.
Like Krumboltz’s theory of career development, social cognitive career theory (Hackett & Betz, 1981; Lent & Brown, 1996; Lent et al., 1994, 2002; Lent &
Hackett, 1987, 1994) is based on Bandura’s social learning theory. Both theories make use of Bandura’s triadic reciprocal interaction system (environment, personal factors, behaviours), and both emphasise the role of learning experiences and cognitions in career development. However, while Krumboltz focuses on learning behaviours, social cognitive career theory is based on a later version of social learning theory (Bandura, 1986) and also self-efficacy theory (Hackett & Betz,
1981), and emphasises cognitive processes that moderate career actions. Lent and colleagues (Brown & Lent, 1996; Lent & Brown, 1996; Lent & Hackett, 1994) have suggested that, although traits like aptitudes, values, abilities and personality are important in the development of career interests and choices, they are always mediated by beliefs and expectations. Their focus is on the connection between one’s learning experiences and the social cognitive variables of self-efficacy, outcome expectations and goals in predicting career-related actions and attainments.
A feedback loop between performance attainment and further expectations, interests, goals and actions is posited, with acknowledgement given to the individual’s 46 background and contextual influences such as financial support or opportunities for skill development.
As an individual develops expertise in a particular endeavour and meets with success, their self-efficacy (beliefs about their ability to exercise control over outcomes) is reinforced. Beliefs and expectations develop about future continued success in the use of this particular expertise. As a result, they are likely to develop goals that involve continuing involvement in that activity. Through an evolutionary process beginning in early childhood and continuing throughout adulthood, they will narrow the scope to successful endeavours to form career goals and choices.
One of the most popular applications of self-efficacy theory is the study of career decision self-efficacy (formerly career decision-making self-efficacy). Career decision self-efficacy is defined as the individual’s belief that he or she can successfully complete tasks necessary to making successful career decisions (Betz,
2001; Betz & Hackett, 1986). The study of career decision self-efficacy has demonstrated that individuals’ beliefs about their education and career capabilities are significantly related to the range and type of career options considered, and participation in career decision-making tasks and behaviours (Betz, 2001).
Subsequent studies have shown that high levels of career decision self-efficacy are linked to greater career exploration (Blustein, 1989), career maturity (Luzzo, 1995) career commitment (Betz & Serling, 1993), and more adaptive career beliefs (Luzzo
& Day, 1999).
While the cognitive information-processing approach (Peterson et al., 1991,
2002; Reardon et al., 2000; Sampson et al., 1999) also emphasises the importance of cognitions to career development, it highlights thought and memory processes rather 47 than beliefs. The theory suggests ways that individuals can think about career decision making that will improve their ability to make good career decisions.
The three basic components of this approach are the knowledge domains, decision-making skills domain, and the meta-cognitions domain. The knowledge domains involve self knowledge and occupational knowledge, and these correspond directly to trait and factor theory. However, the inclusion of the decision making skills domain sets this theory apart from the trait and factor theories.
The capabilities that enable individuals to process information about themselves and their occupations are known as generic information processing skills
(Reardon et al., 2000). These skills are known by the acronym CASVE, which stands for communication, analysis, synthesis, valuing, and execution. They are outlined in a cycle, as follows: internal or external signals, such as dissatisfaction, communicate to the individual that something needs to be done differently; the self and occupational knowledge domains are examined to discover the causes of the problem and new learning may be required.; from this a list of likely alternatives is crystallised.; and finally, the individual evaluates the alternatives created (Sampson et al., 1999).
The final domain of the cognitive information-processing model is the executive processing domain, in which individuals examine how they think and behave. Peterson and Sampson (Peterson et al., 2002; Sampson et al., 1999) described three types of meta-cognition: self-talk, self awareness (of what the individual is doing and why they are doing it), and monitoring and control of the
CASVE process.
The information processing model has strengths in terms of its derivation from a strong theoretical base (Hunt, 1971; Newell & Simon, 1972), and its 48 relationship to existing theoretical constructs. However, the model does not explain how the knowledges and skills interact within the individual. Also, the model does not suggest ages of development of the skills it describes.
Vondracek, Lerner and Schulenberg (1986) attempted to address some concerns regarding the existing developmental theories of career development about the failure of career theory to take into account the dynamic nature of the interaction between individuals and their ever-changing contexts, and their use of normative frameworks which assume that career development stages are universal and linear.
Vondracek et al’s (1986) framework states that the environment differentially inhibits or encourages an individual’s abilities to capitalise on personal characteristics and translate them into career outcomes (contextual affordance), and that as the individual’s context changes, so does the individual, and so, too will any career decision made. In addition, changes within the individual at one level may well affect other levels of that individual.
Vondracek and colleagues (Vondracek & Kawasaki, 1995; Vondracek et al.,
1986) also emphasised self-determinism and agency in the individual. They argued that the environment is constantly changing, and the individual’s actions will not only be affected by its chaotic and reflexive nature, but also in accordance with his or her own unique characteristics (at many levels, including biological, social, psychological, community and cultural), and that this will occur throughout the lifespan. Although the developmental-contextual model is not complete (Vondracek
& Kawasaki, 1995), the ideas it presents regarding career development throughout the lifespan, and the dynamic interaction of elements both within and outside the individual, are important to the development of career theory. 49
The final theory to be examined in this section is the action-theoretical contextualist approach to career, formulated by Young and colleagues (Young &
Valach, 2000; Young et al., 2002). This theory extends understandings of ‘action’ as discussed by social-cognitive career theory, social learning theory and other theories of goal-directed behaviour. The contextualist action theory (Young et al., 2002) takes
‘action’ to mean purposive intentional behaviour as well as the dynamic processes occurring in career development, acting between the individual and their context.
According to Young et al. (2000), there are four action systems which are progressively longer in duration and more complex: individual action; joint action; project; and career. The final three of these action systems are socially embedded.
Each of the action systems can be interpreted from three perspectives, which are manifest behaviour (overt, observable behaviour), conscious cognitions
(including all cognitive and emotional processes that occur), and social meaning (the meaning of the action to the self and others). Finally, actions are organised into elements, functional steps, and goals. Elements are physical and verbal behaviour such as words or movements. They are grouped into functional steps which have meaning, and functional steps are further contextualised into goals, which represent the individual’s general intent.
While this theory has strengths in terms of its theoretical applicability to the boundaryless, non-traditional career, and its usefulness in career counselling (Valach
& Young, 2002; Young et al., 2002), it has been criticised for avoiding the concept of causation, and therefore having no explanation for development and process, and for suggesting that we can only understand the present moment in an individual’s career development (Brown, 1996b). 50
Recent Developments in Career Theory
It can be observed from the above theoretical summaries that career development theories have arisen from diverse paradigms and emphasise vastly different aspects of career development, ranging from initial occupational choices to lifelong career progression, and highlighting certain features of the careerist, their context, and processes acting within and between each.
Very recent theoretical developments in this field have concentrated in two main areas. First, some attempts have been made to accommodate changes in the world of work (e.g., boundarylessness) and the ways in which career is now viewed.
Thus, theories which originally pertained to occupational choice at a single point now acknowledge that career is a lifelong process (e.g., Holland, 1994b), and is much broader then hitherto suspected. For instance, work-related values are increasingly no longer seen as universal, invariant, and isolated — rather, they are ever-changing and reconstructed in concert with life-roles (Patton, 2000).
Second, theorists have become concerned about the breadth and explanatory power of existing theories. Some writers have pointed out shortcomings in the major theoretical views when dealing with the effects of variables such as race (Pringle &
Mallon, 2003; Worthington, Flores, & Navarro, 2005), gender (Betz, 2005; Burke,
2001; Gates, 2002; Hopfl & Hornby Atkinson, 2000), sexual orientation (Pope &
Barrett, 2002), and disability (Szymanski & Parker, 1996), and have made amendments to existing theories or created new theories to accommodate them.
Calls for Theory Convergence and Integration
Recognising the disjointed and segmented nature of career theory, Osipow
(1990) called for career theory synthesis, or ‘convergence’, whereby overlaps and 51 equivalences in theories are identified, and linkages drawn between them (Dawis,
1994). Attempts at convergence of existing theories have resulted in movement towards the integration of several major theories (Chen, 2003; Savickas & Lent,
1994). For instance, Savickas (2001b) suggested a four-level model for integrating
Holland’s RIASEC career personality traits (Holland, 1973) with Super’s career maturity work (Super et al., 1996), and with theories regarding self-definitional narratives and selective optimisation processes.
In exploring an individual’s career-related behaviours, scholars agree that it is important to examine the relationship between all relevant elements in the career development process (Chen, 2003; Patton & McMahon, 2006a; Savickas & Lent,
1994). In doing so, it is important that contributions from the largest possible number of theories are considered. Unfortunately, the career development field remains somewhat disparate and segmented.
Systems Theory as an Integrative Theoretical Framework
The Systems Theory Framework of career development, or STF (McMahon,
2002; McMahon & Patton, 1995; Patton & McMahon, 1999, 2006a) has been suggested to provide cross-theoretical linkages between existing theories of career development. Its proponents argue that it places each extant theory in the context of other theories so that their interconnectedness can be demonstrated, while allowing for the depth needed to account for specific concepts (McMahon, 2002). This framework is depicted in Figure 2.1. 52
Figure 2.1
The Systems Theory Framework of career development
Note. From Patton, W., & McMahon, M. (1999). Career development and systems theory: A new relationship. Pacific Grove, CA: Brooks/Cole. Reprinted with permission.
The centre of the STF is a system representing the individual, containing a range of intrapersonal features influencing career development which will be different for each individual. Examples of influences from extant career theories falling within this system include personality, interests and beliefs (Holland, 1985;
McCrae & John, 1992), values (Brown & Crace, 1996), self-role concept (Super,
1990), age (as exemplified in Super’s life-span theory, 1990), world-of-work knowledge, and cognitive variables such as self-efficacy and outcome expectations
(Lent & Hackett, 1994).
In the STF, the individual system exists as part of a much larger system known as the contextual system. The contextual system is divided into the social 53 system (the other people systems with which the individual interacts) and the environmental / societal system (the environment and society). The social contextual system includes such variables as community structure, family variables like birth order, socio-economic status, and family encouragement, and the individual’s educational experience. The broader environmental / societal contextual system includes such variables as geographical location, government policies, and the employment market. These contextual variables are acknowledged by extant career development theory in such concepts as Super’s life-roles (1990), Lent et al.’s (1994) contextual affordances, and the interactional model of Vondracek (e.g.,Vondracek &
Fouad, 1994).
The systems within the STF are permeable and influence one another recursively, that is, they are interrelated, and act on each other, although not necessarily in reciprocal ways. In systems theory, the emphasis is on the reciprocal interactions between elements or subsystems of the system (Bronfenbrennner, 1977) and the changes that occur over time as the result of these interactions. In the STF, influences and changes occur over time as a process of constant evolution, and it is emphasised that they will occur in a nonlinear way (Patton & McMahon, 1999,
2006a). This is reflective of extant theories such as Super’s life span model (Super,
1990). Finally, the unpredictable influence of chance – luck, fortune, accident or happenstance, (as discussed by Mitchell et al., 1999; Neault, 2000) – is also included in STF as possibly affecting any part or parts in combination of the system, which in turn may influence other parts.
The Development of the Systems Theory Framework of Career Development
First presented as a contextual model for understanding adolescent career decision-making (McMahon, 1992), the STF has been progressively refined and 54 developed over the ensuing 15 years as a qualitative tool for career assessment and counselling, and as a tool for research. The initial framework comprised 12 individual influences, 5 social-contextual influences, and 5 environmental / societal contextual influences. In line with developments in: (i) constituent career development theories such as greater acknowledgement of the influence of values
(e.g., Brown & Crace, 1996), of globalisation (e.g., Brown, 2003a) and the need for a well-developed body of world-of-work knowledge (e.g., Haines et al., 2003); and (ii) as the result of STF research in career assessment and counselling contexts
(McMahon, 2005; McMahon, Patton, & Watson, 2003; Patton & McMahon, 1997), the present formulation depicts 16 individual influences, 6 social-contextual influences, and 6 environmental / societal contextual influences.
One criticism of career development literature until the inception of the STF was its failure to adequately address the needs of individuals falling outside the category of “white, able-bodied middle-class Western male” (Patton & McMahon,
1999, p.88). The STF has shown much promise in the area of application to the career development of women (Patton, 1997; Patton & McMahon, 1999), Aboriginal people (Sarra, 1997), Chinese people (Back, 1997), South African people
(McMahon, Watson, & Patton, 2004) and in multicultural contexts generally (Arthur
& McMahon, 2005). While the individual is central to the framework, it may be customised to accommodate individuals whose career development occurs within collective cultures (McMahon, 2002). Its application to contextual influences such as rural locations (Collett, 1997) and socioeconomic disadvantage (Taylor, 1997) has been described. Of paramount importance to the present program of study is its ability to accommodate non-traditional career paths and the roles of both individual and contextual influences in shaping careers. 55
McMahon (2002) argued that with the STF’s emphasis on the individual, holism and connectedness, it can accommodate constructivist approaches to career practice while not excluding positivist traditions, thus allowing, in theory, for statistical validation of the STF as a broad-brush quantitative research tool, as well as a qualitative research and career assessment instrument (e.g., McMahon, Watson, &
Patton, 2005). The present program of study in part involves the development of a brief quantitative measure based on the STF, which is used to investigate the maximal range of influences on artists’ career development and career success, thus providing a background and context for more targeted measures relating to career self-management skills and protean career orientation.
Chapter Summary
This chapter has provided an overview of current career development theory, with a particular emphasis on recent developments relating to boundarylessness, subjective careers and the protean careerist, and career development theory convergence and integration. The next chapter presents an examination of current conceptualisations of career success, and successful university to work transition. It also puts forward a review of the constructs and approaches that have been used to predict success and successful transition to date, in both traditional and boundaryless career contexts. Chapter 4 links the career development and career success literature discussed in Chapters 2 and 3 with the unusual circumstances of artists’ careers, and outlines the questions which are addressed in the present program of study. 56
CHAPTER 3
Literature Review: Career Success and Successful Tertiary Graduate Transitions to the World of Work
Chapter 3 reviews theory and empirical findings relating to career success and successful tertiary graduate transitions to the world of work. In so doing, various perspectives on what constitutes career success and successful graduate transition are compared. This chapter also presents an examination of the academic literature relating to predictors of career success and successful graduate transition, with specific reference to the skills of the careerist and other psychological factors.
Predictors of success for the boundaryless and protean career patterns discussed in
Chapter 2, which may require higher levels of career self-management than the traditional career, are emphasised in this discussion.
Career Success
Career success is defined as the accumulated positive work and psychological outcomes resulting from one’s career experiences (Seibert & Kramer, 2001).
Objective career success reflects externally verifiable attainments in areas such as work performance, pay, position, and promotions (Judge et al., 1995). It is this view of career success that has been the most widely accepted across a range of societies and over the longest period of time (Nicholson, 2000). A decade ago, Arthur and
Rousseau (1996b) reported that over 75% of the career-related articles published in major journals between 1980 and 1994 focussed on objective perspectives on career success.
However, as Hughes (1958) asserted, there is another perspective from which one can view career success – that of the person who is engaged in his or her career.
Subjective career success is defined by an individual’s reaction to his or her career 57 experiences, such as job or career satisfaction (Greenhaus, Parasuraman, &
Wormley, 1990; Judge, Higgins, Thoresen, & Barrick, 1999). In line with the recent theoretical emphasis on individual constructions of, and meanings from, career (as outlined in Chapter 2), recent literature has sought to provide a better understanding of subjective career success, by weighing up the strengths and weaknesses of each vantage point (Arthur et al., 2005; Eby et al., 2003; Gunz & Heslin, 2005; Heslin,
2003, 2005; Judge et al., 1995; Judge et al., 1999; Mayotte, 2003; Ng et al., 2005;
Seibert & Kramer, 2001). In protean career theory, which argues that careerists can work for reasons quite distinct from money or hierarchical position (Hall, 1976,
1996, 2004; Hall & Mirvis, 1996), the idea of subjective career success has become central, to the extent that one group of theorists has elevated the role of subjective career success over objective career success (Aryee, Chay, & Tan, 1994).
Objective Measures of Career Success
Salary, salary growth, and promotions are the most widely used and readily accessible indicators of objective career success (for a comprehensive review and a meta-analysis, see respectively Arthur et al., 2005; Ng et al., 2005). These indicators have the advantage of being easily measured, and widely understood. However, organisational changes and the increasing prevalence of boundaryless careers have reduced the relevance of some of these traditional indicators (Heslin, 2005). For instance, organisational downsizing, delayering and outsourcing have resulted in increasing numbers of ‘consultants’, ‘contractors’, and the self-employed, for whom hierarchical position in an organisation has little meaning (Inkson, Heising, &
Rousseau, 2001).
Objective criteria of career success may also be strongly affected by factors external to the individual, such as pay agreements and Awards, competition between 58 firms, and other labour market conditions (Heslin, 2005). These factors will affect the salary and hierarchical positions of workers independently of the individual worker’s performance.
In addition, the commonly used measures of objective career success may not be the only, or even the most important, objective criteria careerists use to measure their success. For example, teachers may base their appraisals of career success on the learning outcomes of their students, and academics may base their appraisals of career success on publications and peer recognition. These measures of career success may not lead to an increase in pay or promotions, and yet they are still valuable as indicators of career success for those individuals (Heslin, 2005).
Many people also appear to have less objectively measurable career-related goals. Subjective career theorists have observed that through work people often aspire to a sense of purpose (Cochran, 1990; Hall & Chandler, 2005), work / life balance (Clark, 2001; Rapoport, Bailyn, Fletcher, & Pruitt, 2002), and ‘flow’, a state of deep satisfaction and productivity associated with a fine balance of challenge and skills (Czikszentmihalyi, 1997). Other theorists documented instances in individuals’ career lives where, although high pay and promotions were being received, people still felt depressed, overwhelmed, or alienated (Burke, 1999; Hall, 2002). Equally, some individuals with very low pay or in underemployment circumstances reported deep feelings of satisfaction, or that they had “done their personal best” (Hall &
Mirvis, 1996, p.26). It is clear from this literature that measures of career success need to take into account individual priorities and individual perceptions – the concept of subjective career success. 59
Subjective Measures of Career Success
Subjective career success is most often operationalised in studies as job or career satisfaction — the affect resulting from evaluating one’s job or career situation (Dawis & Lofquist, 1984). However, Heslin (2003; 2005) argued that using job satisfaction as a proxy for subjective career success is problematic because, while a person’s feelings about their present job and their career may be correlated, a job is only one part of a career (which evolves over time and encompasses non-job variables such as family and health, as discussed in Chapter 2). The broader construct of career satisfaction is therefore a better measure of career success, although it, too, suffers some shortcomings. Chief of these is the use of standardised measures which assess constructs which may be superfluous to certain types of careerists. For example, measures of career satisfaction often contain items relating to ‘hierarchical success’ (Peluchette & Jeanquart, 2000) or ‘advancement’ (Greenhaus et al., 1990) which may not be relevant to careerists who are self-employed (Tullar, 2001), part time or contract workers (Inkson et al., 2001) or simply value other aspects of their careers more (Aronsson, Bejerot, & Haerentam, 1999), as in the case of the protean careerist.
Further, the standardised measures in use at present (deVanna, 1984;
Greenhaus et al., 1990; Peluchette & Jeanquart, 2000) have been criticised for being limited and unidimensional. There is increasing evidence to suggest that there are multiple coexisting dimensions which comprise subjective career success (Eby et al.,
2003; Parker & Arthur, 2002). Some theorists have suggested that individuals may align themselves with one or a combination of ‘career anchors’ (Schein, 1996), also known as career orientations (Derr, 1986; Derr & Laurent, 1989; Driver, 1982). 60
These career anchors or orientations are values the careerist finds to be the most important, such as autonomy, service to a cause, or security.
What constitutes subjective career success would appear to be dependent upon an individual’s personal attributes, background and context, although very few empirical studies have been undertaken in this area to date. Brookes, Grauer et al.
(2003) compared practising and academic organisational psychologists and discovered that the practitioners valued affiliation, structure, and money significantly more than the academics, who valued autonomy and science. An in-depth study of
North Plains American Indians revealed the importance of contribution to the well- being of others to this group of careerists, in comparison with the population at large
(Juntunen et al., 2001).
Gender differences may exist in conceptions of career success, and definitions of subjective career success may also change with age. Sturges (1999) asked 36 managers to define career success, and discovered major differences in the definitions given by both age and sex. Sturges’ findings suggested that material criteria for career success reduce in importance with age, and are replaced by an emphasis on influence (‘leaving a mark’) and autonomy. The study also found that female managers were more likely than male managers to use internal and intangible criteria for career success, whereas male managers tended to define career success as being associated with pay and advancement. A recent study by Dyke and Murphy
(2006), comprising 40 in-depth interviews with successful managers, supported these findings, with the predominant themes in the female managers’ definitions of career success found to be work-life balance and the development of positive relationships at work. Male managers’ definitions of career success, by contrast, most commonly fell into the realm of material success and financial rewards. 61
In terms of subjective career success outside the traditional, organisational career pattern, one might reasonably expect that individuals who undertake boundaryless careers would include their employability levels (i.e., their ability to create or obtain work) as an immediate criterion for subjective career success. This may also be the case for protean careerists, who, as discussed in Chapter 2, experience extreme forms of boundarylessness in their careers (Baruch, 2004).
Protean career theory also explicitly suggests that protean careerists have strong internal, psychological motivations and derive measures of success according to personal values (Briscoe & Hall, 2006; Briscoe et al., 2006; Mirvis & Hall, 1996).
The exact nature and extent of these internal measures of success and motivations amongst protean careerists is as yet unclear. However, it is evident that for the protean careerist there are potential tensions between the careerist’s drive to do personally meaningful work and the employability imperative (Lips-Wiersma &
McMorland, 2006), as in the case of the protean careerist who is strongly motivated to work in a field in which there are very few job opportunities.
A comprehensive literature search only revealed one study which examined conceptions of subjective career success amongst individuals who seemed not to be undertaking strictly ‘traditional’ patterns of career (Lee et al., 2006). This study explored definitions of career success amongst 87 part-time professionals in the U.S. and Canada. The most frequently occurring themes in the definitions given by the part-time professionals were: (i) the ability to have a life outside work (equivalent to
Dyke and Murphy’s (2006) ‘work-life balance’); (ii) having an impact / making a contribution; and (3) continuing to grow professionally. Although upward mobility and financial / non-financial recognition themes were present, they were not prominent in the definitions given. 62
Recognising that research into subjective career success is in its infancy,
Arthur et al. (2005) and Heslin (2005) have called for further systematic qualitative studies of how people making their careers in different contexts conceptualise their success, and how subjective career success might relate to objective career success.
The present program of study undertakes this task with cohorts of professional artists and arts graduates, who are argued to fulfil the definition of protean careerist as outlined in Chapter 2. Chapter 4 presents a detailed justification for this assertion, based on labour force statistics and empirical research.
The Relationship between Subjective and Objective Measures of Career Success
Recently, some authors have begun to discuss the idea that personally perceived career success and objective success may in some ways be related to one another. Arthur et al. (2005) reviewed 68 papers from 1992-2002 relating to career success; of these, 37% acknowledged the objective career’s impact on the subjective career in some way, most commonly by stating that individuals interpret their career success based on objective accomplishments (Judge et al., 1995). Nineteen percent of the articles considered the influences of specific subjective variables onto objective career success, such as hypothesised relationships between personality (e.g.,
Boudreau, Boswell, & Judge, 2001) or attitudes (e.g., Orpen, 1995) and objective career success. Only 9% overall explored two-way interdependence.
Ng et al.’s (2005) meta-analysis examining predictors of career success found that, while subjective career success and objective career success measures (salary and promotion) were moderately positively correlated, they were empirically distinct, with different patterns of predictors for each. Hall and Chandler (2005) outlined a feedback loop model which attempted to explain the conceptual relationship between objective and subjective success, identity change and self-confidence in the case of 63 the worker who is motivated by a ‘calling’, that is, careerists who work for the fulfilment of doing so, and believe the work contributes to a better world (an idea closely related to the concept of the protean career). The authors asserted that objective success in all workers is related to subjective success, career self- confidence and sense of purpose. These in turn affect career goals and career behaviours. However, according to Hall and Chandler’s (2005) model, workers who are ‘called’ to their careers show a far stronger cyclical relationship between subjective career success, career identity, self-confidence, sense of purpose, and further career goals. Propositions suggested by this model have yet to be formally tested.
Predictors of Career Success
Numerous studies have sought to describe the precursors of career success.
Most of these have done so in a traditional, organisational career context, or at least in circumstances where it is unclear whether the sample studied are engaging in boundaryless careers or more traditional career paths (e.g., Cannella & Shen, 2001;
Kirchmeyer, 2002; Tremblay, St-Onge, & Toulouse, 1997; Wallace, 2001).
Predictors of success for the traditional, organisational career (and indeed what constitutes career success in the first place, as discussed previously in this chapter) may be quite different from those for non-traditional patterns of career (Eby et al.,
2003). Boundaryless and protean careerists experience higher levels of career mobility than traditional careerists, and are challenged to take personal responsibility for their careers and maintain employability. Success in this context may well be linked to a quite distinct set of skills, dispositions and contextual influences from those associated with success in the traditional career pattern. 64
The most commonly investigated influences in studies of predictors of career success fall into the categories of human capital (training, work experience, education, mentoring), demographics (age, sex, marital status, children) or dispositions (personality, mental ability) (Judge et al., 1995; Judge et al., 1999; Ng et al., 2005; Tharenou, 1997). Ng et al. (2005) conducted a meta-analysis of 140 empirical articles relating to predictors of subjective career success (career satisfaction) and objective career success (salary, promotions). No distinction was made between traditional patterns of career and boundaryless or protean careers in the meta-analysis. The study’s 26 predictor variables were grouped into ‘human capital’, ‘organisational sponsorship’, ‘sociodemographic’, and ‘stable individual differences’ categories. The significant predictors of each type of career success from the study are summarised in Table 3.1. 65
Table 3.1
Significant Predictors of Objective and Subjective Career Success From 140 Studies Predictors Salary Promotions Career Satisfaction Human Capital Hours worked + + + Work centrality + + + Job tenure + - Organisation tenure + + Work experience + + Willingness to transfer jobs / locations + + International experience + + Education level + + + Political knowledge and skills + + Social capital (number and quality of professional + + + contacts) Organisational Sponsorship Career sponsorship + + + Supervisor support + + Training and skill development opportunities + + + Organisation size + + Sociodemographics Gender (Male) + + Race (Caucasian) + + Marital status (Married) + + + Age (Older) + + Stable Individual Differences Neuroticism - - - Conscientiousness + + + Extroversion + + + Agreeableness - - + Openness to experience + + Proactivity + + + Locus of control + - + Note. Data is from Ng, T. W., Eby, L. T., Sorensen, K. L., & Feldman, D. C. (2005). Predictors of objective and subjective career success: a meta analysis. Personnel Psychology, 58, 367-408. ‘+’ = positive predictors, ‘-’= negative predictors, correlations significant at least p<.05 Although there were some commonalities between the predictors of the three measures of career success, the patterns of predictors were quite different for salary level, number of promotions and career satisfaction. Salary and promotions were positively related to all of the human capital measures, except that job tenure and political knowledge / skills were negatively related to number of promotions.
However, job and organisational tenure, work experience, international experience 66 and willingness to transfer jobs or locations were not found to be related to career satisfaction at all. In terms of organisational sponsorship and support measures, career sponsorship and training opportunities were positive predictors of all career success measures, but supervisor support was not a predictor of number of promotions (although it was a predictor of both salary level and career satisfaction).
Organisation size was a positive predictor of promotions and salary level, but not career satisfaction.
The only universally positive sociodemographic predictor included in Ng et al’s (2005) meta-analysis was marital status, with married workers being more successful using both objective and subjective measures. Caucasian workers had higher career satisfaction levels and salaries but did not experience significantly more promotions than non-Caucasian workers. Males and older workers had higher salaries and more promotions than females and younger workers, but there was no gender or age difference found for career satisfaction.
Dispositional / personality traits which were positive predictors of all three measures of career success included Conscientiousness, Extroversion and
Proactivity. Neuroticism was a negative predictor of all three career success measures. However, Agreeableness was a negative predictor of objective career success and a positive predictor of subjective career success. Locus of control positively predicted career satisfaction and salary level but negatively predicted number of promotions. Openness to experience also positively predicted career satisfaction and salary level, but there was no statistical relationship found between openness to experience and number of promotions.
One group of career development influences in an individual’s social- contextual system (Patton & McMahon, 1999, 2006a) not explicitly included in Ng 67 et al’s (2005) meta-analysis but which has been documented to be related to both subjective and objective career success, is social support. Personal support (e.g., from friends and family) and work peer support have been found to be salient predictors of both objective and subjective career success (Peluchette, 1993), although there may be gender differences in the types of social support relied upon and the predictive value of different types of social support. For instance, in his study of predictors of career success amongst 425 administrative personnel, Nabi (2001) found that support from colleagues was a stronger predictor of career success amongst men, whereas personal support was a stronger predictor of career success amongst women.
Skills as Predictors of Career Success
Reflecting recent shifts from stable employment and organisational career development to worker mobility and individually constructed / managed careers, lists of individual competencies for career development and career success have increasingly been produced on behalf of employers’ organisations (Australian
Chamber of Commerce and Industry, 2002), education providers (Australian Council for Educational Research, 1999; Graduate Careers Council of Australia, 2005b), and government bodies (Haines et al., 2003). These resources are intended to be comprehensive lists of the skills and competencies required for an individual’s career development and career success in an era of the boundaryless career.
In many of these lists the emphasis is on generic skills that can be transferred
(i.e., used within various employment contexts) (Atkins, 1999; Australian Chamber of Commerce and Industry, 2002; Fugate et al., 2004). Proponents of the generic / transferable skill approach argue that in the context of a rapidly changing information- and knowledge-intensive economy, employable workers must not only 68 maintain and develop knowledge and skills which are specific to their own discipline or occupation, but must also possess generic skills, dispositions and attributes which are transferable to many occupational situations and areas. Generic / transferable skills have also been variously known as ‘core skills’, ‘key competencies’, or
‘underpinning skills’ (Mayer, 1992). These skills include written and verbal communication, numeracy, and technological literacy (Atkins, 1999).
Career Self-Management Skills as Predictors of Career Success
Although generic / transferable skills form an important subset of the skills necessary for career self-management, effective career management has also been suggested to involve knowledge about one’s self and the world of work, and the competencies necessary to successfully navigate life and career (Jarvis, 2003;
McMahon, Patton, & Tatham, 2003). Career self-management competence can be viewed as the ability to build a career; to intentionally manage the interaction of work, learning and other aspects of the individual’s life throughout the lifespan
(Haines et al., 2003; Watts, 1998; Webster, Wooden, & Marks, 2004). Career self- management at the broadest level involves creating realistic and personally meaningful career goals, identifying and engaging in strategic work decisions and learning opportunities, achieving personally effective work/ life balance, and appreciating the broader relationships between work, the economy and society.
Career self-management skills can be divided into several sub-types. A small number of competing career self-management taxonomies have been developed
(deFillippi & Arthur, 1994, 1996; Hache et al., 2000; Haines et al., 2003; Kuijpers &
Scheerens, 2006; Kuijpers et al., 2006), and these will be examined later in this section. Generally speaking however, in addition to the category of transferable / generic skills already mentioned, there are: 69
(i) discipline–specific skills, which originate in specific domains,
disciplines or subject matter areas and are necessary for performance in
specialised fields;
(ii) internally focussed self-management skills, which relate to an
individual’s perceptions and appraisals of themselves, their careers and
other aspects of their lives, a concept that is closely related to that of ‘career
identity’ (Arthur et al., 1999; Jones & deFillippi, 1996); and
(iii) externally focussed career building skills, including the ability to make
informed career decisions, engage in learning opportunities as needed, and
secure or create work and build a career.
Relatively few writers have so far discussed the nature of career self- management skills, and how they might develop (deFillippi & Arthur, 1994, 1996;
Jarvis, 2003; Jarvis, Zielke, & Cartright, 2003; Kobylarz & Hayslip, 1996). Further, the utility of career self-management skills has not yet been conclusively demonstrated, particularly in the boundaryless or protean career context and relating to subjective career success.
Links between career self-management skills and constructs like employability, career success, and broader economic productivity are intuitively appealing and have been strongly argued theoretically (Gillie & Gillie Isenhour,
2003; Mayston, 2002). For instance, it has been suggested that the acquisition of career building skills and knowledge will result in more realistic expectations of the labour market (Watts, 1999) and fewer mismatches between labour market supply and demand resulting in poor employment outcomes (Mayston, 2002; Watts, 1999). 70
However, there have been very few studies that have attempted to demonstrate these links empirically. The present program of study addresses this hiatus by investigating the predictive value of a brief measure of career self- management competence to subjective and objective career success in artists and arts graduates.
In terms of previous research into the relationship between career self- management skills and career success, Ng et al’s (2005) meta-analysis previously outlined in this chapter included social capital (operationalised as number and quality of professional contacts) as the only career self-management construct. Social capital was found to be a strong positive predictor of both objective and subjective career success, implying that the abilities required to create professional networks would also be positive predictors of career success. Other recent studies have also demonstrated the effect of social capital on perceived (Eby et al., 2003) and actual employability (Brown & Konrad, 2001; Marmaros & Sacerdote, 2002; Nabi, 2003), although most of these studies examined social capital and career success in the traditional, non-boundaryless career.
Three career self-management competence taxonomies and the empirical support for each are critically discussed in this section. The taxonomies are:
(i) deFillippi and Arthur’s (1994; 1996) 6 ‘knowing’ career
competencies for the boundaryless career, which became the
foundation for the Intelligent Career Model (Arthur, Claman, &
deFillippi, 1995; Parker, 2002) often used in career counselling;
(ii) the 6 career competencies suggested by Kuijpers and colleagues
(Kuijpers, 2002; Kuijpers & Scheerens, 2006; Kuijpers et al., 71
2006), used in a recent quantitative study of 1600 employees in 16
Dutch companies; and
(iii) the draft Australian Blueprint for Career Development (Haines et
al., 2003; Miles Morgan Australia, 2003), a national framework of
career competencies currently undergoing trials in a wide variety
of Australian educational and career contexts.
Arguably, the best-known classification of career management competencies for boundaryless careers was created by deFillippi and Arthur (1994) and Jones and deFillippi (1996), who identified six classes of competencies which, they suggested, interact to predict success in the boundaryless career: knowing what, knowing why, knowing how, knowing when, knowing where, and knowing whom. Using the classification system outlined previously in this chapter, knowing why roughly corresponds to self-management skills, and the remainder fall within the category of career building skills. These ‘knowing’ competencies became the basis for the
‘intelligent career’ framework (Arthur et al., 1995; Parker, 2002) used in career counselling.
Knowing whom involves creating social capital by creating strategic personal and professional relationships with those who might provide opportunities and important resources. Much of one’s professional network will develop from working in collaboration with individuals on projects (a key aspect of ‘knowing what’ as discussed below).
Knowing why refers to self-knowledge – understanding the personal motives, interests, and meanings for pursuing a particular career, career goals and expectations. This competency is closely related to the concept of career identity.
Some writers have argued that knowing why will often entail pursuing one’s career 72 with ‘a passion’, in line with the concept of a protean career (Arthur et al., 1999; deFillippi & Arthur, 1994; Jones & deFillippi, 1996). At the very least the boundaryless careerist needs to be comfortable with the relationship between their work and self-identity (e.g., balancing career and family).
Knowing what and knowing where refer to knowing one’s industry – what opportunities and threats exist and what factors are critical to success. ‘Knowing what’ involves a knowledge of ‘the rules of the game’ (Jones & deFillippi, 1996), that is industry structure, beliefs, norms, values, and culture. ‘Knowing where’ involves being able to effectively identify and choose the best opportunities for advancement in terms of geography, projects, and role.
Knowing when refers to the pacing and timing of the development of one’s career within the context of the industry. Knowing how long to stay in a role, when to exploit a new employment or training opportunity, and the ability to move quickly once an opportunity is identified all fall within the bounds of ‘knowing when’.
‘Knowing when’ is of course critical to the development of ‘knowing how’, that is, the skills and abilities required to take advantage of opportunities as they arise.
Knowing how refers to skills also emphasised in the traditional career – the skills needed for performance in one’s work roles. In the boundaryless career, as previously discussed, there is much greater emphasis on generic skills which can be transferred from one working context to another, as opposed to firm-specific skills,
In terms of empirical support for a link between these competencies as outlined and boundaryless or protean career success, Eby and colleagues (2003) showed that three of deFillipi and Arthur’s (1994; 1996) ‘knowing’ career competencies were strong predictors of perceived career satisfaction, and one aspect of employability ('internal / external marketability' (Eby et al., 2003)) with a large 73 sample of university alumni in the early stages of their careers. The significant predictors from the study are described in Table 3.2.
Table 3.2
Significant Career Competency Predictors of Marketability and Career Success in
Eby et al.'s (2003) Study
‘Knowing’ competency Predictors used Knowing why Proactive personality Openness to experience Career insight Knowing whom Experience with a mentor Networking Knowing how Career / job related skills Career identity
Note. Table summarised from Eby, L., Butts, M., & Lockwood, A. (2003). Predictors of success in the era of the boundaryless career. Journal of Organizational Behavior, 24(6), 689-708.
However, there are a number of shortcomings of Eby et al.’s (2003) study which limit the utility of the findings. First, while the investigation was among the first to explicitly use career management competencies in studying boundaryless career success, they used Greenhaus’s (1990) five-item unidimensional scale of career satisfaction, which, as discussed previously in this chapter, includes questions which may be irrelevant to the protean careerist’s conceptions of career success such as, ‘I am satisfied with the progress I have made toward meeting my goals for income’.
Second, the ‘knowing’ competencies were difficult to operationalise in the study. A total of seven predictor measures were employed in the survey instrument, and it was impossible to determine to what extent each of the ‘knowing’ competencies was effectively captured by the scales chosen. For instance, it might be argued that career identity, defined as the links between an individual’s motivations, 74 interests and competencies with career roles (Meijers, 1998), might be better placed in the category of ‘knowing why’.
Third, the population of university alumni studied by Eby and colleagues
(2003), while likely to be comprised of a proportion of boundaryless or protean careerists, was also likely to contain a large number of individuals engaged in traditional, organisational careers. It is therefore difficult to assess from the results which predictors are particularly relevant to the boundaryless or protean careerist, and which are more useful to the traditional careerist, or indeed whether there is any difference.
A recent large scale Dutch study (Kuijpers & Scheerens, 2006; Kuijpers et al., 2006) of 1,579 employees in 16 companies examined the predictive value of six career management competencies suggested by the authors on subjective and objective career success, in conjunction with a number of personal / sociodemographic variables. In terms of the personal / sociodemographic variables, career support at work and career support at home were key predictors of internal and external career success. In addition, three career building – related competencies were found to predict both subjective and objective career success: career- actualisation-ability (the ability to realise one’s own career goals); career control
(career-related planning ability); and networking (setting up contacts that are relevant to one’s career). However, there was no statistical relationship found between career success and two further competencies which approximately corresponded with self- management skills previously discussed in this section: career reflection (reviewing one’s own competencies with respect to one’s career), and work exploration
(orientation towards matching one’s own identify and competencies with a work 75 situation). The final competency, motivation reflection (reviewing one’s own desires relating to one’s career), was negatively correlated with the career success measures.
While the findings of the Dutch study (Kuijpers & Scheerens, 2006; Kuijpers et al., 2006) are informative, there is evidence that the competencies investigated are not as exhaustive as those suggested by frameworks such as the Australian Blueprint for Career Development discussed later in this section (Haines et al., 2003). Second, the constituents of the six competencies were not clearly defined by the authors, and the competencies are not easily translatable into indicators that can be built into career education / development or evaluation programs. Finally, Kuijpers and colleagues investigated the predictive value of career competencies on career success within traditionally structured organisations, and did not explicitly consider career development competencies for the boundaryless or protean career.
The Australian Blueprint for Career Development
The Australian Blueprint for Career Development (ABCD), currently in draft form (Haines et al., 2003), and the Canadian Blueprint for Life / Work Designs
(Hache et al., 2000) upon which it was based, are at present the most comprehensive resources available which document the career competencies all individuals need in order to manage life, learning and work for all careerists (see Table 3.3). The
Australian Blueprint was developed in response to a recommendation in the OECD
Country Paper on Career Guidance for Australia in 2002 (Organisation for Economic
Cooperation and Development, 2002) for systemisation and augmentation of career education and development resources for Australians at all stages of career. It has subsequently been further developed and refined extensively by career practitioners and related professional associations. The Blueprint is now undergoing trials in a 76 number of educational and career development contexts, such as high schools, technical and further education colleges, universities, and professional development organisations (Department of Education Science and Training, 2007a).
Table 3.3
Top-Level Structure of the Australian Blueprint for Career Development
Personal Management Learning and Work Career Building Exploration 3 Areas
Build and maintain a positive Participate in life-long Secure/ create and maintain self image learning supportive of work career goals Make career enhancing decisions
Interact positively and Locate and effectively use Maintain balanced life and effectively with others career information work roles 11 Competencies Change and grow throughout Understand the Understand the changing life relationship between work, nature of life and work roles society and the economy Understand, engage in and manage the career building processes
Note. Derived from Haines, K., Scott, K., & Lincoln, R. (2003). Australian blueprint for career development: Draft prototype. Retrieved March 20, 2005, from http://www.dest.gov.au/directory/publications/australian_blueprint.pdf
Performance indicators have been included in the ABCD to describe the specific skills, knowledge and attitudes that individuals need to develop to achieve a career competency, as shown in Table 3.4. In addition, the Blueprints contain a four stage learning taxonomy which facilitates career development from early childhood to adulthood. This taxonomy (‘Acquisition’, Application’, ‘Personalisation’, and
‘Actualisation’) was based on existing learning taxonomies such as Bloom (1956; 77
Krathwohl, Bloom, & Bertram, 1973), and the CONDUCT professional development model (Redekopp, 1999), and is intended to facilitate the application of the Blueprint documents across the lifespan and career development process.
Table 3.4
The Relationship Between ABCD Competencies, Performance Indicators and
Learning Stages: Sample For One Competency
CAREER COMPETENCY 7: SECURE/CREATE AND MAINTAIN WORK
7.3 Develop abilities to seek, obtain/create and maintain work
Stage I – Acquisition 7.3.1 Explore skills, knowledge and attitudes required to locate, interpret and use information about work opportunities. 7.3.2 Explore skills, knowledge and attitudes that are transferable from one work role to another. 7.3.3 Explore work search tools and skills required to seek, obtain/create and maintain work (job application forms, résumés, portfolios, job interviewing, proposals, cover letters, etc.). 7.3.4 Explore specific work opportunities in terms of working conditions and safety hazards, benefits, etc. 7.3.5 Explore employability skills and attributes necessary to obtain and maintain work 7.3.6 Explore services or initiatives that support transitions to different settings. 7.3.7 Understand that work opportunities often require flexibility and adaptability (e.g., relocating, learning new skills). 7.3.8 Explore volunteering as a proactive job search and personal development strategy. Stage II – Application 7.3.9 Demonstrate skills, knowledge and attitudes in preparing personal marketing documentation (e.g., résumés, proposals, portfolios, cover letters). 7.3.10 Demonstrate the skills, knowledge and attitudes necessary for a successful work interview. 7.3.11 Demonstrate employability skills and attributes necessary to obtain and maintain work 7.3.12 Experience volunteering as a proactive job search or personal development strategy. Stage III – Personalisation 7.3.13 Evaluate work opportunities in terms of working conditions, benefits, etc., that are important to you. 7.3.14 Acknowledge your personal set of skills, knowledge and attitudes that contribute to seeking, obtaining/creating and maintaining work. Stage IV – Actualisation 7.3.15 Create and engage in work opportunities reflective of your personal set of skills, knowledge and attitudes. 7.3.16 Adapt current or try new work search skills and tools. Note. Derived from Haines, K., Scott, K., & Lincoln, R. (2003). Australian blueprint for career development: Draft prototype. Retrieved March 20, 2005, from http://www.dest.gov.au/directory/publications/australian_blueprint.pdf 78
The development of the ABCD from the Canadian Blueprint was informed by an issues paper (McMahon, Patton, & Tatham, 2003) which synthesised Australian theoretical, policy and practice perspectives. Once a draft prototype was completed, it was then evaluated and workshopped (Miles Morgan Australia, 2003) by 208 representatives from stakeholders in Australia’s career development community, ranging from Centrelink (Australia’s national social security body), to the Australian
National Training Authority, the Australian Association of Career Counsellors,
Universities, Technical and Further Education Colleges, and CRS Australia
Rehabilitation. Of particular focus during these workshops were competency accuracy, completeness, and wording. The outcomes of this evaluation were that the competencies appeared sufficiently comprehensive and valid, when minor modifications to performance indicator wording were made.
Despite the significant potential of the ABCD as a comprehensive, systematic and developmentally oriented taxonomy of competencies necessary for career self- management, and the widespread support given to it by the government and career education providers alike (Miles Morgan Australia, 2003), no empirical work has to date been undertaken to examine whether the competencies proposed in the ABCD are in fact predictive of subjective or objective career success, either in the traditional, organisational career context or for the boundaryless career pattern. The present program of study involves the development, validation and application of a brief research measure based on the ABCD competencies in predicting several different measures of career success, thus evaluating the value of the ABCD career competencies further than the workshopping and trial processes have so far undertaken. 79
A significant shortcoming of most competency-based taxonomies and approaches to career self-management is the lack of attention paid to precursor dispositional and other psychological characteristics which underlie the successful development and application of career self-management skills. However, this shortcoming is addressed by protean career theory, which emphasises the dispositions, identities, attitudes and beliefs leading to subjective success in a career
(Briscoe & Hall, 2006; Briscoe et al., 2006; Hall & Chandler, 2005; Hall & Mirvis,
1996).
Underlying Dispositions and Characteristics as Predictors of Career Success
The protean career centres on psychological career success and internal career motivations, in conjunction with self-managed career development, in a context of extreme boundarylessness (Briscoe & Hall, 2006; Hall & Chandler, 2005;
Hall & Mirvis, 1996). Protean career theory suggests that successful protean careerists possess a number of related underlying dispositions or attributes. The most commonly documented of these dispositions include: strong internal motivations; self-directedness; proactivity; resilience and adaptability; openness to career opportunities; a positive interpersonal orientation; and a positive self-image.
These underlying dispositions are associated with varying degrees of empirical support, and have also received different levels of attention in the theoretical literature. Although predictive links between strong internal career motivations and career success are the subject of two recent theoretical models (Hall
& Chandler, 2005; Quigley & Tymon, 2006), propositions arising from these models have not yet been tested. In terms of empirical evidence, Gagne et al. (1997) demonstrated that intrinsic motivation was a significant predictor of career empowerment among Canadian technical and telemarketing workers. Another study 80
(Thomas & Tymon, 1997) found significant relationships between intrinsic motivation and job satisfaction, reduced stress levels and work performance in employees. However, neither of these studies pertained to protean or boundaryless careers, or broader definitions of career success.
Self-directedness has repeatedly been argued to be a fundamental component of both subjective and objective success in the boundaryless or protean career
(Arthur et al., 2005; Briscoe & Hall, 2006; Hall & Chandler, 2005; Hall & Mirvis,
1996). Briscoe and colleagues (2006) found support for this assertion via their study of boundaryless and protean career attitudes. Their ‘career self-directedness’ subscale, containing items such as ‘I am in charge of my own career’, and ‘I depend on myself to move my career forward’, was related to positive career outcomes in undergraduate students, MBA students, and business executives. Further investigation into self-directedness and career outcomes is however needed in order to explore the relationships between being self-directed, career self-management skills, and subjective versus objective career success in samples which clearly comprise a significant proportion of protean and boundaryless careerists.
The constructs of proactivity and openness to opportunities have both been examined in many studies relating to personality determinants of career success. The findings of Ng et al’s (2005) meta-analysis outlined previously in this chapter indicated that both proactivity and openness to opportunities were positive predictors of salary levels and career satisfaction across 140 studies, although no significant relationship was found between openness to opportunities and number of promotions received. Proactive personality and openness to experience have also been found to be associated with career self-management behaviours (Chiaburu et al., 2006) and 81 positive career outcomes in the boundaryless or protean career (Briscoe et al., 2006;
Eby et al., 2003).
Resilience and adaptability have also been recognised by protean career theorists as important to career success in careers where individuals must repeatedly create or obtain work (Briscoe & Hall, 2006; Hall, 1996; Hall & Chandler, 2005).
There is some large scale research-based support for this contention. For instance,
Lounsbury and colleagues (2003) investigated the relationship between 15 broad personality traits and career satisfaction in 6,000 individuals in career transition. In their sample, emotional resilience was strongly related to both job and career satisfaction.
A positive interpersonal orientation is most often studied in terms of
‘agreeableness’, one of the Big Five personality traits relating to cooperation and friendliness (Gottfredson et al., 1993; McCrae & John, 1992). The meta-analytical study by Ng et al. (2005) found that agreeableness positively predicted career satisfaction, but provided less support for the idea that agreeableness predicted objective career success measures. Seibert and Kramer (2001) demonstrated that there may well be a complex relationship between agreeableness and objective career success. Their study of diverse careerists showed an interaction between agreeableness and how much interpersonal contact was required at work in predicting career success.
However, positive interpersonal orientation has been argued by some to be a somewhat different construct to agreeableness. Filsinger (1981) discussed positive personal orientation as corresponding to ‘likeableness’, and other writers have linked positive interpersonal orientations to sociability and interpersonal warmth dimensions of extraversion, another Big Five personality trait (Costa & McCrae, 82
1988), which has been positively linked with objective career success and, less consistently, career satisfaction (Judge et al., 1999; Ng et al., 2005).
Positive self-image has been the subject of several recent theoretical models relating self-image to career self-management and career success in the boundaryless or protean career (Hall & Chandler, 2005; King, 2004; Quigley & Tymon, 2006).
Empirical studies of career success and positive self-image have often used the related construct of self-efficacy (an individual’s belief that they are able to perform across a variety of situations), or career self-efficacy (a individual’s belief that they are able to perform well in managing their career) (Day & Allen, 2004; Judge &
Bono, 2001). Judge and Bono (2001) proposed that positive self-image or positive self-concept is comprised of emotional stability, self-esteem and locus of control as well as self-efficacy. In their meta-analytic study, these four traits were strongly correlated with one another and also predicted both job satisfaction and job performance. However, career satisfaction or other measures of subjective career success were not included in the study, and the meta-analysis referred to studies of the traditional-organisationally-based career.
Seven related dispositions are argued in protean career theory to lead to career success in the non-traditional career pattern, although studies to date of these dispositions in relation to career success in non-traditional careers are few. A brief scale of protean career success orientation, based on these dispositions, is developed as part of the present study, to be used in conjunction with the career self- management competence scale in predicting career success.
The preceding review has revealed several significant gaps in the career success literature. First, the nature of subjective career success and its relationship with objective career success remains unclear, particularly within boundaryless and 83 protean careers. Second, although there is agreement in the literature that possession of career self-management skills and certain types of underlying dispositions and characteristics will enhance career outcomes, there have been very few studies that have attempted to make a link between the skills and disposition constructs and either subjective or objective career success. A major deficiency in the literature to date is that although there has been theoretical and descriptive discussion regarding boundaryless and protean careers, data-driven studies of boundaryless and protean careerists are virtually non-existent. This doctoral study attempts to address these deficits, by using predictive statistical modelling to explore the relationships between career self-management skills, dispositions / characteristics for career success outlined in this chapter, broader career development influences, and subjective and objective career success in professional artists, who are argued in Chapter 4 to be engaged in protean careers. Further, the same constructs will be used to explore tertiary arts students’ success in the transition to the world of work.
There are strong parallels between the literature relating to career success in general and the literature relating to successful university to work transition. As will be discussed in the next section of this chapter, the university to world-of-work transition literature also tends to rely on objective measures of career success within a traditional, organisationally-based career context, and competency based approaches in both areas place great emphasis on transferable / generic skills. The next section of this chapter will critically examine the competencies and other attributes currently considered to be necessary to successful transition to the world of work from higher education, particularly with relation to the transition from university to a boundaryless or protean career, as is the case for the participants involved in the present program of study. 84
The University to World-of-Work Transition
Universities play a central role in preparing tertiary students for the world of work. This role has been highlighted in recent government decisions (particularly in the United Kingdom, Australia and Canada) to make public funding for universities partially contingent upon demonstrable graduate outcomes (e.g., Department of
Education Science and Training, 2007b), with an emphasis on the production of employable, ‘work ready’ graduates who are competent within their disciplinary fields and possess the abilities necessary to negotiate a world of work which is increasingly in flux (Barrie, 2006; Bowden et al., 2000).
In both Australia and the United Kingdom, graduates’ first-destination employment status a few months after course completion is used as the primary graduate employability performance indicator (Department of Education Science and
Training, 2005b; Higher Education Funding Council for England, 2002). This suggests that graduate full-time employment rates have become, in many instances, easily measurable proxies for graduate employability. In both countries, universities are under significant funding pressure for their graduates to find permanent, full-time employment quickly.
Use of first-destination data in this way is problematic. Rather than indicating a graduate’s ability to create or obtain work, these statistics tend to indicate information about the short-term graduate employment market in a particular region
(Coleman & Keep, 2001; Knight & Yorke, 2003) or for a particular occupational grouping. They also do not recognise the increasing prevalence of non-traditional employment patterns such as the boundaryless or protean careers discussed in
Chapter 2. 85
Universities have had a tendency to engage with the government-driven graduate employability agenda by re-examining which attributes their graduates should possess. They then have used the desirable attributes in mapping exercises whereby university-identified graduate attributes are identified in units of study. The mapping process shows where and how the attributes are taught, identifying gaps and developing curricula to address these gaps (Australian Vice-Chancellors' Committee,
2004; Chanock, 2003; Clerehan, Chanock, Moore, & Prince, 2003).
Graduate Attributes
Bowden’s (2000) commonly cited definition states that graduate attributes are, “the qualities, skills and understandings a university community agrees its students would desirably develop during their time at the institution and, consequently, shape the contribution they are able to make to their profession and as a citizen” (para 1). Each Australian university has constructed its own unique list of desirable graduate attributes. The Australian Government and employers’ organisations have contributed lists of their own (Australian Chamber of Commerce and Industry, 2002). There is extensive variation in the composition of these capability lists between universities (Australian Council for Educational Research,
1999) and disciplines (Bowden et al., 2000). Very few attempts have so far been made to identify commonalities between various lists, provide a research-based synthesis of attributes (cf. Barrie, 2004; Nunan, 1999) or identify deficiencies in lists, in part because of disparate understandings of what is meant by the various categories of attribute included.
It is clear, however, that Bowden’s (2000) definition encompasses two main types of attributes: (i) those which pertain to an individual’s capacity for citizenship
(including involvement in democratic processes, social cohesion, equity and human 86 rights, and ecological sustainability) and thus ability to contribute towards a well- functioning society (Rychen & Salganik, 2005), and (ii) those which pertain to an individual’s capacity to create or obtain and maintain work (Harvey, 2001; McQuaid
& Lindsay, 2005) and therefore contribute to economic productivity. This second
‘employability’ agenda, as the main impetus for the recent interest in graduate attributes, is part of the move towards developing “human capital to meet the needs of the ‘new knowledge economy’” (Curtis & McKenzie, 2001, p.vii).
The most widely acknowledged ‘employability skills’ in university, policy, and employer graduate attribute lists (e.g., the 'Employability Skills Framework' -
ACCI and BCA, 2002) are generic or transferable skills previously discussed in this chapter. Such skills are used in many occupational situations, and therefore are argued by universities to maximise graduates’ attractiveness to the widest range of potential employers. These skills include information literacy, working with technology, written and verbal communication, working in teams, and numeracy.
Although researchers have content-analysed graduate job advertisements (Bennett,
2002) or employed a direct questioning approach to determine which transferable / generic skills employers value the most (Australian Chamber of Commerce and
Industry, 2002; Department of Employment Training and Youth Affairs, 2000;
Graduate Careers Council of Australia, 2005b), very few studies have attempted to demonstrate that well-developed transferable / generic skills actually lead to enhanced graduate employability (Dahlgren et al., 2006; Garcia-Aracil et al., 2004).
In part this seems to be because of disagreement over which transferable / generic skills should be included and how they should be measured, and difficulty in disentangling the effects of these skills from other characteristics of the graduate and the employment market. 87
Broadening the Concepts of Graduate Attributes and Employability
Narrow notions of employability emphasising skills and dispositions which might make a student attractive to potential graduate employers have, understandably, often been espoused by employer organisations. The Confederation of British Industry (1999) defined employability as being “the possession by the individual of the qualities and competencies required to meet the changing needs of employers and customers” (p.1). Similarly, the Australian Chamber of Commerce and Industry (ACCI) and Business Council of Australia (BCA) represent employability skills as “skills required not only to gain employment, but also to progress within an enterprise so as to achieve one’s potential and contribute successfully to enterprise strategic directions” (ACCI and BCA, 2002, p.1).
Analogous definitions have been adopted and promoted in Australian Government policy documents (e.g., Department of Education Science & Training, 2004;
Department of Employment Training and Youth Affairs, 2000), and are also found in joint university and business publications (Hager, Holland, & Beckett, 2002).
The narrow ‘generic / transferable skills for employability’ approach currently taken by universities to prepare students for the world of work is flawed for several related reasons. First, the world of work involves significantly more than first employment destinations, particularly in the era of the boundaryless career where many graduates, as knowledge workers, can expect to create or obtain work many times, work in multiple fields and in multiple geographic regions, and train and retrain throughout their lives (McMahon, Patton, & Tatham, 2003).
In addition to inadequate recognition of the nature and demands of the world of work, the existing approach to graduate preparation for the workforce does not acknowledge the importance of the subjective career. The movement from education 88 to work represents a major life and identity transition for most students (Organisation for Economic Cooperation and Development, 2000). Successful transition to the world of work is a highly individual experience, and the acquisition of a full-time job post-graduation is not an adequate indicator of success (Yorke, 2004). Recently, theorists have attempted to incorporate notions of subjective career and identity into the graduate attributes literature (Branch, 2000; Holmes, 2001; Taylor, Millwater, &
Nash, 2007; Wood, 2004), but these models are yet to be adopted by mainstream higher education at the time of writing.
A consequence of the limitations in current conceptualisations of university to world-of-work preparation is that the lists of employability skills emphasising transferable / generic skills do not encompass the full range of competencies and dispositions required for the graduate to move successfully into the world of work
(Chanock, 2003). As previously noted in this chapter, effective career self- management involves the ability to intentionally manage the interaction of work, learning and other aspects of the individual’s life (Haines et al., 2003; Watts, 1998;
Webster et al., 2004). Apart from transferable / generic and discipline-specific skills, competencies for career self-management also include internally focussed self- management skills and externally focussed career building skills.
Many university graduates seem to be under-prepared for the bewildering array of shifting employment and training options from which they must construct a career (Lamb & McKenzie, 2001; Organisation for Economic Cooperation and
Development, 2002). This is especially the case in Australia, given that pathways into the world of work are often individually rather than institutionally constructed, and given that the graduate labour market is becoming more fluid, with graduate 89 occupational destinations becoming increasingly diverse (Andrews & Wu, 1998;
Lamb, Long, & Baldwin, 2002).
Some recent national and international policy documents make mention of career education as the basis for positive graduate outcomes. A key recommendation of the OECD’s (2002) Review of Career Guidance Policies Country Note for
Australia was that further investigation was required to understand, “how university careers services can work with teaching departments to help students link learning with career development, and of the resources they need to perform this role effectively” (p.18). The same review commented that “many students in (Australian) tertiary education appear to have little idea of why they are there or where it is leading” (p.18). In February 2006, the Council of Australian Governments (2006) instigated a national reform agenda aimed at raising living standards by lifting the nation’s productivity and workforce participation. They agreed that a key way to underpin Australia’s future prosperity was to “increase the proportion of young people making a smooth transition from school to work” (p.1), and requested that strategies be developed to ensure that policies and programs relating to pathways from education to work be developed. Career self-management competency frameworks such as the ABCD (Haines et al., 2003) may prove to be significant resources in broadening the concept of graduate attributes, although little theoretical or empirical work has so far been undertaken to link career self-management skills and graduate career outcomes, whether in the context of traditional / organisationally based careers, or boundaryless / protean careers (Mayston, 2002; Watts, 1998, 2005,
2006). 90
Predictors of Successful Graduate Transitions to the World of Work
The final section of this chapter considers the current research regarding predictors of successful tertiary graduate transitions to the world of work. Although links between career self-management skills, certain underlying dispositions and characteristics, and career success generally and in the boundaryless or protean career context have previously been discussed in this chapter, it is worthwhile to review the evidence regarding the predictive value of these skills and characteristics to university graduate career outcomes.
Career Self-Management Skills and Underlying Dispositions and Characteristics as
Predictors of Successful Graduate Transitions
It has been suggested that the acquisition of career self-management skills by tertiary students will result in more realistic expectations of the labour market (Watts,
1999) and fewer mismatches between labour market supply and demand resulting in poor employment outcomes and low levels of career satisfaction (Mayston, 2002;
Watts, 1999). A student who is aware of a high unemployment rate in an occupation or geographical location can draw on their self-management and career building skills to construct alternative career scenarios involving different locations, training options, occupational choices, or work modes through the process of proactive career management. However, as noted by several scholars in the career development field
(Fretz, 1981; Mayston, 2002; Sagen, Dallam, & Laverty, 2000; Watts, 2006), it can be extremely difficult to conclusively demonstrate a link between graduate outcomes and career self-management abilities (not to mention career education experiences), because of difficulties in operationalising the complex construct of career self- management competence, and the multiplicity of potential confounding and random effects which can affect the career development of graduates. 91
Much of the evidence of positive career outcomes emanating from career self-management skills in tertiary graduates is therefore indirect. Hughes, Bosley,
Bowes and Bysshe (2002) reviewed more than 40 primarily UK or US-based studies investigating the economic effects of career guidance. They concluded that, while there were significant challenges involved in evaluating the impact of career education provision in separation from other contributory factors, there was also moderate-to-high level evidence for economic benefits of career guidance in higher education through improved student course choice, course retention and learning outcomes, and in the wider population through lower unemployment rates, reduced job-search times, lower worker turnover rates, and improved productivity.
Studies of graduate career self-management also tend to emphasise behaviours (which are easier to measure) rather than the skills and dispositions underlying these behaviours. Conclusions are not able to be drawn from these studies regarding levels of self-management competence in graduates. In addition, these studies often exclusively use objective measures of career success such as pay levels or employment outcomes. Nonetheless, studies which investigate potential links between graduate career self-management behaviours and career outcomes can be persuasive, and the findings of three such studies will be outlined.
Werbel (2000) investigated the relationships between work exploration behaviours, job search intensity, and objective career success (operationalised as initial salary) in 219 college graduates. Work exploration included such behaviours as obtaining information on the labour market and opportunities in a field of interest, or having conversations with knowledgeable individuals. Job search intensity was measured by such items as revisions to, and submission of, resumes, preparation for job interviews and use of a campus placement service. Werbels (2000) found that 92 work exploration behaviours were strongly related to job search intensity, and that both were related to the graduates’ income attainment. However, the study found no relationship between career self-exploration and income attainment, where self- exploration included reflecting on integrating the graduate’s past with future career, and focussing their career thoughts on themselves.
In another study of career self-management behaviours amongst tertiary graduates (Orazem, Werbel, & McElroy, 2003), career information seeking behaviour, career planning behaviour, and job search intensity were found to significantly predict actual starting pay in 149 employed tertiary graduates from a variety of disciplines. Interestingly, Orazem et al (2003) found that, while starting pay did not vary by nationality or age, females’ starting salaries were lower than males’ starting salaries, and that this effect was strongly related to the job search measures and the pay levels female graduates expected. Female graduates who engaged in more intensive career planning had pay expectations and starting salary levels that were equivalent to those of the male graduates.
In a third similar study, Saks and Ashforth (1999) examined the effects of job search behaviours, including preparatory (prior to graduation) and active (current) job search behaviour and job search intensity on the employment status of 384 recent business, computer science and information technology graduates at the time of graduation and four months later. They also included the dispositional variables of self-esteem, job search self-efficacy, and perceived control over job search outcomes.
All three of the job search behaviours predicted employment outcomes (albeit at different times) , and job search self-efficacy (the graduate’s confidence in performing tasks relating to the job search process) predicted the behaviours as well as employment outcomes. The dispositional factors of self esteem and perceived 93 control over outcomes were not significantly related to employment outcomes in this study.
From these studies, it would seem that at least some career self-management behaviours and certain objective measures of graduate success are connected, and that dispositions such as self-efficacy might also be important to graduate career outcomes. However, there is a paucity of studies regarding the predictive power of graduate career self-management skills and dispositions such as openness to opportunities, proactivity and resilience. In addition, little reference in the empirical literature has been made to graduates who are embarking on a boundaryless / protean career.
There are two major bodies of literature relating to social or environmental / societal contextual influences on graduate career outcomes which are of relevance to the present discussion. This literature relates to the role of work experience, and the role of mentoring for career development of tertiary students.
Contextual Predictors of Successful Transitions
One key area of study into contextual predictors of successful transitions is the role of work experience prior to undergraduate course completion. Work- integrated learning programs (‘practica’) have traditionally been implemented in vocationally-oriented courses which lead to professional accreditation, and have become increasingly popular in broader university programs in recent years as well.
In 2000, Jancauskas et. al. reported that 20 of Australia’s 38 publicly funded universities offered some type of work-integrated learning program in fields as diverse as engineering, journalism and sports medicine. Although many models of 94 work-integrated learning exist, the foundation of all such programs is work-based practical application and integration of what is learned at university.
The potential benefits of work-integrated learning have been argued to be numerous, and extend to all levels of employability-related skills (Fallows, 2000).
Work-integrated learning can provide the opportunity to learn or deepen highly relevant generic and discipline-specific skills such as teamwork and communication
(Crebert, Bates, Bell, Patrick, & Cragnolini, 2004). Experience within a workplace can also provide students with unparalleled first-hand knowledge of the world of work, including industry specific know-how, business operations, the establishment of professional networks, and an enhanced resumé (Crebert et al., 2004). In terms of self-management skills, work integrated learning can assist students to develop confidence and increase motivation levels (Te Wiata, 2001) and also to reflect on their personal interests and values and identify whether a particular career choice is appropriate for them.
Longitudinal studies have demonstrated links between student participation in work integrated learning programs and graduate employment outcomes (Cranmer,
2006; Harvey, Moon, Geall, & Bower, 1997). However, theorists have suggested that not all work-integrated learning programs are equally effective. For benefits to be realised, the program needs to be structured and organised so that expected outcomes are clear, and that effective and flexible partnerships be formed and maintained between the student, the workplace, and the university (Orrell, 2004; Smith & Betts,
2000).
Another key area of research into contextual predictors of graduate career outcomes centres around the concept of mentoring, which has long been used in business and academic environments to enhance individual career and skills 95 development (Theobold, Nancarrow, & McCowan, 1999). Its potential role in successful tertiary world of work transition extends from enhanced awareness of the world of work and workplace requirements, to the creation of career networks and social capital (and the skills associated with this), to increased confidence in work- related situations (Theobold et al., 1999). Mentoring is defined as, “a developmental, caring, sharing, and helping relationship where one person invests time, know-how, and effort in enhancing another person’s growth, knowledge, and skills … in ways that prepare the individual for greater achievement in the future” (Shea, 1994, p.13).
The mentor, usually a person experienced in the field or industry, shares knowledge and experiences with a less experienced mentee who engages in a process of self management and world-of-work learning.
Much literature presents arguments for the benefits of mentoring among adults. Hansford, Tennant and Ehrich (2000) reviewed 159 articles written from 1986 and concluded that generally speaking, educational mentoring resulted in positive academic and work outcomes. However, studies which specifically link mentoring experiences at university with graduate outcomes are harder to come by, and the results have so far been somewhat inconclusive (Sagen et al., 2000). It has been argued that aspects of the mentoring relationship strongly influence how successful it will be (Simonsen, 1997). A successful mentoring relationship emphasises reciprocity (Limerick, Heywood, & Daws, 1994), and mutually agreed responsibility and goals.
Chapter Summary
Chapter 3 has provided a critical summary of current thinking regarding career success and successful higher education to world-of-work transition. It has examined the evidence regarding the skills and dispositions thought to be required to 96 be successful in the move from education to work, and more generally. Deficits in the literature were identified, including a lack of knowledge regarding the relationships between subjective and objective career success, particularly within the boundaryless or protean career, and a relative scarcity of empirical evidence regarding the role of career self-management competence.
Chapter 4 describes artists’ careers, and sets out an argument based on labour force statistics and economic studies will be presented for the idea that many professional artists experience protean careers. Based on this argument, this chapter contains the suggestion that professional artists and tertiary arts graduates can be used to study the predictive value of career self-management skills, suggested underlying dispositions / characteristics, and contextual influences on career success in the protean career. The chapter concludes with an overview of the four research questions under investigation in this program of study. 97
CHAPTER 4
Literature Review: Artists’ Careers
The previous two chapters of this document have outlined theory and empirical literature regarding career development, career success, and successful tertiary graduate transitions to the world of work, with specific reference to non- traditional patterns of career. Chapter 4 connects the theory discussed in these chapters with what is known about the unusual circumstances of arts careers. Using data from the Australian Bureau of Statistics and the Australia Council for the Arts in conjunction with published studies of artists’ working lives, an argument is made that many artists can be seen as protean careerists. Potential predictors of career success in professional artists and arts graduates as protean careerists are considered, including career self-management skills, and dispositional and contextual factors.
This chapter concludes with the four substantive research questions to be answered in this research program.
Definitions of the Artist and the Arts
Before engaging in a discussion of arts careers and the working lives of artists, it is necessary to undertake some definitional work. UNESCO’s International
Conference on the Status of the Artist (1980) employed the following definition:
(An artist is) … any person who creates or gives expression to, or recreates
works of art, who considers his [sic] artistic creation to be an essential part of
his life, who contributes in this way to the development of art and culture and
who is or asks to be recognised as an artist, whether or not he is bound by any
relations of employment or association. 98
While this definition is appealing because of its emphasis on creative work and its inclusivity, a study of professional artists must necessarily make some delineation between artistic and non-artistic fields; creative and non-creative work; and professional and amateur practice.
In terms of distinguishing between artistic and non-artistic fields of practice, the present study follows the lead of the Australian Bureau of Statistics (1997) in including creative artists such as authors and visual artists, performing artists such as actors and dancers, technical artists such as designers, illustrators and film directors, and community cultural development workers, who build a cultural community through arts practice (Flood, 1998). These categories are discussed in depth in
Chapter 5 of this document. A further important delineation within these categories is that only individuals engaged in creative work are considered to be artists, as opposed to individuals whose input is necessary to the production of the arts but for whom creative input is not the primary focus, such as front-of-house staff, accountants, agents and so on (Throsby, 2001).
Professional activity is another important component of the definition of
‘professional artist’. In many activities, such as sport, the distinction between professional and amateur rests on whether the individual pursues the activity as a paid job or as an unpaid leisure pastime. However, the financial criterion is inadequate as a discriminator in the case of artists, as many professional artists hold multiple jobs both inside and outside the arts, and some professional artists such as novelists may receive little or no remuneration from arts for significant stretches of time (Flood, 1998). The criterion for professional practice adopted by Throsby
(Throsby & Hollister, 2003; Throsby, 2001) and the Australia Council for the Arts
(2005a; 2005b) is therefore adopted for the purposes of the present investigation: the 99 professional artist is committed to building a substantial body of arts work within the previously outlined categories, and this arts work represents a major aspect of the artist’s life.
Professional artists therefore comprise creative workers within creative, performing or technical / design arts or community cultural development fields, who are engaged in the creation of a serious and substantial body of artistic work. How this definition is operationalised in the present program study is outlined in Chapter
5.
The Arts Sector in Australia
The 2005-2006 annual report of the Australia Council for the Arts (the
Australian Government’s arts industry body) indicated that in that financial year, the total size of the arts and related industries sector in this country was $12.8 billion.
During this time, there were 300,000 people principally employed in the cultural and arts sector, with a total of 958,000 (9% of the total working population) having obtained some paid work in this sector. In 2005, Australia had about 45,000 practising professional artists, with a five-year sector growth rate of approximately
13 per cent (Australia Council for the Arts, 2006).
It is clear that the arts sector, whilst not large, is an established one that continues to experience growth. The arts in Australia, as with most other Western economies, is strongly subsidised by both the Federal Government and State
Governments. In 2005-2006, the total amount of Australia Council spending on arts and related activities at all levels was $151.5 million (Australia Council for the Arts,
2006). 100
The Social and Economic Importance of the Arts
Faced with intense competition for audiences and financial support, arts advocates have increasingly sought to make a case for the arts in terms of their social effects. Researchers have offered much evidence suggesting that participation in the arts can produce strong benefits both at the individual and the community level
(Belfiore & Bennett, 2007), including development of learning skills and improved academic performance (Bryce et al., 2004; Deasy, 2002), enhancement of psychological and physical health (Karkou & Glasman, 2004), and the promotion of social interaction and community building, thus creating social capital (Matarasso,
1997). Some authors have also argued for the value of the intrinsic benefits of the arts, such as pleasure, meaning-making and emotional stimulation, and that these intrinsic benefits can flow outwards to the community level (McCarthy, Ondaatje,
Zakaras, & Brooks, 2004a).
With the recent government emphasis on economic returns for public sector support of many areas, economic justifications for the arts have also become the subject of much discussion (Reeves, 2002). In an early study in the United Kingdom,
Myerscough (1988) demonstrated that direct spending on the arts led to spending in other sectors of the economy, which in turn led to enhanced wealth and job creation.
Many other studies flowed from this, showing direct economic benefits of the arts, such as employment and spending, and also indirect benefits, including the attraction of organisations and people to areas where the arts are available (Florida, 2003;
Heilbrun & Gray, 2001; Klamer, 2002).
In the last five years, discussions of the economic benefits of the arts have begun to be subsumed into literature addressing the value of the ‘creative industries’
(ARC Centre of Excellence for Creative Industries and Innovation, 2007; 101
Cunningham, 2002; Garnham, 2005). The creative industries include the arts and also many other fields where creativity is prized such as information technology and communications (Caust, 2003; Hesmondhalgh, 2007). The creative industries are agreed to be essential to the growth of the post-industrial knowledge economy, also known as the ‘creative economy’ (Howkins, 2002). Between 60 and 80% of economic growth is argued to come from innovation and new knowledge associated with the creative economy (Mulgan, 2006), and the source of this innovation is the
‘creative workforce’ (Florida, 2003), individuals who are involved in turning latent symbolic value of their work into economic and social assets.
In this era of the creative industries, artists have been suggested to possess attributes and capabilities that are of great economic benefit to fields well outside their core arts practice (ARC Centre of Excellence for Creative Industries and
Innovation, 2007). Aside from their creative faculties, artists have also been touted as having well-developed problem-solving skills (Moga et al., 1999), emotional intelligence, and team-working skills (ARC Centre of Excellence for Creative
Industries and Innovation, 2007). The present program of study concentrates on the working lives of artists operating within their art forms, but also accommodates the possibility that many artists will also work outside the arts in some capacity.
Artists’ Career Patterns: A Protean Career in Arts
As outlined in the beginning of Chapter 2 of this document, the protean career has a number of hallmarks which arise from the extreme boundarylessness protean careerists experience, in conjunction with their strong internal career motivations. The hallmarks of a protean career are as follows: 102
(i) Mobility / security, with the protean careerist experiencing higher
job mobility and lower job security than the traditional careerist;
(ii) Occupational role/s, where the protean careerist may occupy
multiple roles and the traditional careerist occupies one only;
(iii) Source of income, where the protean careerist often has clients
rather than employers, and earns money through multiple employment
contracts or invoices rather than a single salary;
(v) Responsibility for career development, where the protean
careerist takes personal responsibility for managing their own career;
(iv) Career motivations and measures of success, where the protean
careerist has subjective, psychological motivations and measures of career
success as opposed to using ‘traditional’ criteria such as salary or
promotions.
This section of the present document will outline an argument for artists to be defined as protean careerists, using detailed arts employment statistics from
Australia. Australian data is used because it is the most comprehensive available
(Shaw, 2004; Throsby & Hollister, 2003), and it is most relevant to the present study of Australian artists. There is, however, substantial evidence to suggest that artists in
Finland (Arts Council of Finland, 2003), New Zealand (Creative New Zealand,
2003), the United States (Alper, Wassal, & Jeffri, 1996; Jeffri, 1988; Jeffri &
Greenblatt, 1997), and the United Kingdom (Davies & Lindley, 2003) face very similar employment issues to Australian artists.
Commencing in 1983, the Australia Council for the Arts commissioned a longitudinal study of the economic circumstances of over 1,000 professional 103
Australian artists recruited via arts organisations (Throsby, 1983; Throsby &
Hollister, 2003; Throsby & Mills, 1989; Throsby & Thompson, 1994). Drawing on phone interviews, the study documented artists’ working conditions, earnings, multiple job-holding, and employment patterns. The most recent phase of Throsby’s study (Throsby & Hollister, 2003) reported on data from 1986 to 2001.
Mobility, Occupational Roles and Sources of Income
The first three attributes of the protean career relate to the extreme boundarylessness that protean careerists experience. Protean careerists have low job security, occupy many occupational roles, and have multiple sources of income
(Arthur & Rousseau, 1996a; Briscoe & Hall, 2006; Briscoe et al., 2006; Hall, 1996,
2004; Mohrman & Cohen, 1995).
Australian employment statistics suggest that professional artists commonly experience these circumstances in their working lives. According to Throsby and
Hollister (2003), professional artists in Australia have had four different serious occupations in the arts, on average. About two thirds of these jobs were undertaken in the artist’s own general art form (e.g., a writer might work variously as a novelist, a screenwriter and a non-fiction writer), and one third in other artforms (such as craft, music, or dancing). The majority of artists (63%) work at more than one job simultaneously, with 56% holding two jobs and 7% holding 3 jobs (Throsby &
Hollister, 2003). By comparison, in 2002 only 5.2% of the general working population held a second job (Australian Bureau of Statistics, 2002). Forty-three percent of artists who take on additional work do so in an arts-related field, and 32% undertake work in an area not related to arts. Throsby and Hollister (2003) entitled the most recent wave of their longitudinal study of the working lives of Australian artists ‘Don’t Give Up Your Day Job’ with good reason. 104
Some distinction has been made in the literature between ‘creative’ and
‘performing’ artists (Rengers & Madden, 2000) in terms of the nature of work arrangements typically undertaken, with performing artists more frequently doing contract work for per hour wages, and creative artists more often experiencing self- employment with piecemeal reimbursement (Rengers, 2002; Rengers & Madden,
2000). Both types of artists, however, experience far higher levels of freelance / self- employed work than the general population and far lower levels of permanent wage earning (Australian Bureau of Statistics, 2002), as outlined in Table 4.1. Four in five artists have an Australian Business Number and do work for clients through their own businesses (Throsby & Hollister, 2003).
Table 4.1
Employment Status of Australian Artists Working From Within Their Principal
Artistic Occupation Versus The General Working Population
Creative Artists Performing Artists General Working Population Working for salary 9% 14% 58% or wages – permanent Working for salary 6% 20% 20% or wages – casual Working freelance or 82% 64% 19% self-employed Other 3% 2% 3% Note. Data source for columns 1 and 2 is Throsby, C. D., & Hollister, V. (2003). Don't give up your day job: an economic study of professional artists in Australia. Sydney: Australia Council for the Arts. Data source for column 3 is Australian Bureau of Statistics. (2002). 6359.0 Forms of employment, Australia. Retrieved August 20th, 2004, from http://www.abs.gov.au/Ausstats/
Personal Responsibility For Career Development
The experience of extreme boundarylessness and the associated lack of long- term affiliation with any one employer has been argued often to result in the protean careerist taking ultimate responsibility for their own career development (Briscoe et 105 al., 2006; Comfort, 1997). This appears to be the case for artists. “The apprentice artist’s tasks are to build her body of work and the skills and sensitivity to produce it, and to get it known and accepted” (Caves, 2000, p. 5).
In Throsby and Hollister’s (2003) study, three quarters of the professional artists agreed that they were the most active promoter of their work and themselves, with only 5% identifying their employer as their most active promoter, and 8% identifying their agent or dealer. Given the large proportion of self-employed artists as discussed previously, this finding is not entirely surprising.
Career Motivations and Measures of Success
In addition to experiencing boundarylessness in the world of work, the protean careerist is also suggested to possess strong internal motivations and psychological measures of career success, as opposed to traditional criteria for career success such as hierarchical position in an organisation, or salary level.
Although the proposition that artists are often motivated in their careers by non-traditional, psychological factors has not been formally tested, it appears to be commonly accepted in literature relating to the arts and artists. Statements about the
‘career calling’ (Hall & Chandler, 2005) of arts are widespread. As Davidson (2004, p.4) stated, “the driving force that attracts so many people to this industry is passion
– a simple, yet complete, desire for the job”. Similarly, Caplin (1988, p.11) commented, “art is not so much a business as it is a calling … it is a matter of producing something because one is obsessed with producing it, and only then wondering … what should be done with it”. The concept is summed up by the slogan, “art for art’s sake”, originally attributed to the 19th century French novelist 106
Théophile Gautier, and widely adopted by cultural theorists (e.g., Caves, 2000, p.4;
Royseng, Mangset, & Borgen, 2007, p.2).
Arts labour market statistics do suggest unusual and distinctive career motivations amongst artists. For instance, the number of artists continues to grow at a much higher rate than other occupational groups, despite persistently low and often declining rates of monetary compensation in the arts (e.g., Menger, 1999, 2001;
Papandrea, 2004; Rengers & Madden, 2000). Worldwide, the arts sector is also characterised by comparatively high rates of unemployment and underemployment
(e.g., intermittent work, voluntary work, and part-time work).
Thorsby and Hollister (2003) reported that about one-third of professional arts workers experienced unemployment between 1996 and 2001, and that the median income of an arts worker in 2001 was $30,000, with a median arts-related income of $15,700 per annum. By comparison, the median annual income across all occupations in 2001 was $36,600, and across professional categories with professional training time comparable to that of artists (4-6 years, e.g., teachers, lawyers, scientists) was $43,700. Though artists themselves are often deeply dissatisfied with their financial positions (e.g., Greffe, 2002), they persevere.
Throsby and Hollister (2003) showed that in Australia, the typical artist spends just over 80% of their time at arts work of some type, and yet only earns two-thirds of their income from these sources.
The observation that many artists are willing to supplement their insufficient paid work in the arts with work outside the arts in order to meet financial obligations, and will continue to spend substantial time working and training in their art whilst doing so, has been formalised in an economic ‘work preference model’ (Throsby,
1994b) which has been tested in Australia, the United States, and Denmark 107
(Papandrea, 2004; Rengers, 2002; Rengers & Madden, 2000; Robinson &
Montgomery, 2000; Throsby, 1994b). The artists’ work preference model shows that artists who receive more income (including from sale of their art, subsidies or grants, from a partner or from work outside the arts) do not use it to work fewer hours in arts in order to have more leisure time, or to buy consumer goods. Instead, they use it to work more hours in arts (Abbing, 2003). This economic modelling of artists’ work behaviour is strong evidence for a non-traditional career success orientation in artists.
Although there are very few studies examining the reasons behind artists’ work preferences, general suggestions have been made in terms of ‘psychic income’
(Thurow, 1978), ‘flow’ (Czikszentmihalyi, 1997), the therapeutic benefits of art as an occupation (Burleigh, 1996), and contributing to a greater good (Smith, Arendt,
Lahman, Settle, & Duff, 2006). It would appear that the new developments in career theory emphasising personally meaningful life / work (Miller-Tiedeman, 1999a),
‘following your bliss’ (Campbell & Moyers, 1988; Henderson, 2000), and having a
‘career path with heart’ (Potter, 1995) might well be relevant to many artists.
However, other writers have suggested that artists are motivated by external factors such as recognition by peers, in addition to internal factors (Abbing, 2003).
The present program of study addresses the question of artists’ conceptions of career success, by asking a group of professional artists and a group of tertiary arts graduates to define career success for themselves, and then by exploring the relationship between this subjective measure and the objective career success measures of overall salary and arts salary levels. 108
The Protean Tertiary Arts Graduate
Less is known about the working lives of university arts graduates than professional artists, although what little information there is suggests that many tertiary arts graduates also experience boundarylessness moving into the world of work, and are motivated in their careers by intrinsic, psychological factors. The primary source of data on Australian tertiary graduate careers is the Graduate
Destination Survey, or GDS (e.g., Graduate Careers Council of Australia, 2002,
2005a; Graduate Careers Council of Australia, 2006b), which is conducted four months after course completion. Unfortunately, graduates are not tracked beyond the four month mark, and the GDS only asks basic questions relating to field of study, employment status and earnings. It does not ask about graduates’ career goals, attempts to obtain employment, skills, or experiences of the world of work.
The arts continue to be an attractive option for tertiary students in Australia.
There is a large and increasing supply of arts graduates each year, with over 50,000 students enrolled in arts courses in 2005 (Department of Education Science and
Training, 2006). Although little information is available about the career aims of arts students in Australia, a United Kingdom-based study of 97 arts students found that they generally prioritised a career characterised by interesting work within their own discipline with a strong element of creativity, rather than salary, prestige, status and job security (Brown, 2007).
All tertiary graduates can expect to experience some uncertainty as they transition into the world of work from university. However, most can look forward to full-time employment quickly. In 2006, 82% of Australian bachelors degree graduates obtained full-time employment within four months of completing their degrees, with a median starting salary of about $40,800 (Graduate Careers Council of 109
Australia, 2006a, 2006b). Four months after course completion, an additional 12% percent of tertiary graduates were working part-time or casually while they sought full-time employment.
The GDS survey consistently shows that arts graduates (including creative, performing and some technical arts graduates in fields such as design and film & television) have the lowest levels of full-time employment, the highest levels of unemployment, and the lowest salaries. A total of 38% of arts graduates were actively seeking full-time employment four months from course completion in 2006, and 26% were working on a part-time or casual basis while seeking further work. In line with the labour force statistics for artists in general, applied arts graduates employed full-time earned just $33,000 per year on average, lower than any other fully-qualified occupational grouping (Graduate Careers Council of Australia, 2006a,
2006b).
While there is some statistical information available regarding arts graduates’ short-term employment outcomes (Graduate Careers Council of Australia, 2005a), and there are some overseas studies of longer term graduate outcomes (Behle &
Davies, 2005; Brown, 2007), relatively little has been documented about Australian arts students’ experiences as they transition to the world of work. This is somewhat surprising, considering that arts graduates experience lower levels of full-time employment than other tertiary graduates, and that public funding for universities is now partially contingent upon these graduate outcomes (e.g., Department of
Education Science and Training, 2007b).
This program of study attempts to partially redress this situation, by documenting the employment outcomes of a sample of graduates in arts at twelve months after their course completion, as well as examining the predictive value of a 110 number of constructs suggested by career development theory to be relevant to protean careerists to career success (as discussed in Chapter 3), such as career self- management competence, and dispositions and characteristics like openness to opportunities and proactivity.
The next section of this chapter will discuss the existing literature on predictors of career success in the arts, both for professional artists and tertiary arts graduate transitions to the world of work. The review suggests that work in this area has so far been limited to a few finite areas, and that although career self- management (or at least what is commonly known as ‘arts business management’ or
“entrepreneurialism” (Fillis, 2000, 2004; McLarty, 2005)) is a known concept in arts education, there are few studies of the uptake or effects of arts business management education programs.
Predictors of Career Success for Professional Artists and Arts Graduates
When Throsby and Hollister (2003) asked Australian artists to identify the most important factor in advancing their professional development, the most common response was ‘talent’ (31%), followed by ‘social support’ (26%), and
‘training’ (24%). Twelve percent indicated that the role of ‘happenstance’, defined as a lucky break or critical timing (Mitchell et al., 1999; Neault, 2000) was most important. No career self-management or proactive career-related behaviours were mentioned by the respondents in response to this question.
These findings could be taken to indicate that artists are less aware than they could be of the effect they personally have on their career development and potential career success, a suggestion that is borne out by other studies of professional artists and arts students in the United Kingdom (Brown, 2007; Freakley & Neelands, 2003).
Freakley and Neelands (2003) reported on an action research project which aimed to 111 increase performing artists’ appreciations of the extent they needed to work entrepreneurially. Operating from a position that each artist should see themselves as a small firm dependant on the quality of its reputation and trading relations (Menger,
1999), the project explored ideas of artistic supply, demand and ‘trading’ with participants, explored qualities of successful practitioners, and used self-evaluation to identify new directions and professional development needs. The authors observed that not only were participants unfamiliar with the notion of the artist as a micro- business, offering products and services and developing relationships with buyers and distributors, but they also felt considerable initial resistance to the idea, feeling that it might ‘stifle’ the creative process.
It has also been suggested that artists can experience reluctance to engage in proactive business- or employment-seeking behaviours because of a perception that this might result in less ‘authentic’ art (Abbing, 2002; Caplin, 1988; Royseng et al.,
2007). This tension between the artist’s intrinsic motivations and the employability imperative – the desire to supply artistic products for which there is demand (Caves,
2000; Greffe, 2002) provides a fertile ground for further investigation into artists’ working lives.
The concept of teaching small business / entrepreneurial skills to artists is not new (Caplin, 1988; Casewit, 1981). Various professional bodies and tertiary institutions provide business education and support for artists and arts students in
Australia, ranging from work experience, to professional development planning, to assistance with the development of portfolios, to marketing and pricing (e.g., Arts
Hub, 2005; Australia Council for the Arts, 2002; Fuel 4 Arts, 2004; Q Music, 2004;
Victorian College of the Arts, 2004). These resources are, in the main, piecemeal and designed to address specific career and business management requirements. Further, 112 it is unclear to what extent artists make use of them, and what effect they have on artists’ career self-management and business skills or their career success.
There is some acknowledgement of the potential of artists’ networking skills and social capital on career success in arts. There are enormous numbers of self-help books published each year on networking and self-promotion for the artist (e.g.,
Davidson, 2004; Dickman, 1997; Forster, 1993; Hadden, 1998; Letts, 1996). Studies of how artists use their social and professional networks to develop their careers are not numerous, but there is a small amount of empirical evidence that an artist’s ability to network does have an impact on whether they continue to be employed in the arts, and how successful they are in the fields of film music composition
(Faulkner, 2003), writing (Anheier, 1995), and visual art (Giuffre, 1999; Greffe,
2002). Though professional artists appear to agree in principle that ‘knowing whom’
(deFillippi & Arthur, 1994, 1996) is important to the successful protean career in arts, a comprehensive review of the literature revealed no studies to date on the source of this notion in the psyche of artists, and whether (and if so, to what extent) they implement it and other skills in their personal career management.
A small number of authors (Fillis, 2004; O’Reilly, 2005) have recently begun to suggest that career self-management skills, including both product / world-of- work-oriented and self-oriented skills and processes within a broader life context will enhance the career success of artists. They suggest that in addition to skills and competencies, aspects of an artist’s identity and their dispositions / characteristics will also have an effect on the artist’s career outcomes. These authors argue that artists’ creativity can potentially enhance their entrepreneurial abilities (Fillis, 2004;
O’del, 2003), and that other dispositions such as proactivity may also be influential in determining an artist’s success. 113
A large body of literature has investigated the traits professional artists possess, with a particular interest in the link between personality and creativity
(Feist, 1998; Marchant-Haycox & Wilson, 1992; Whitesel, 1984). Much of this work has centred around personality traits and creativity or mental illness and creativity, in an attempt to understand the nature of artistic creativity. However, very few if any of these studies have examined whether any underlying dispositional characteristics in artists are predictive of their career success.
It can be seen that while some aspects of the careerist or their behaviours have been suggested to be connected to arts career success, such as entrepreneurialism and networking / social capital, there are significant deficiencies in the extant literature. There is a lack of theory-driven empirical work demonstrating the links between aspects of the artist and success in arts, both in professional artist cohorts and in emerging artists who are graduating from tertiary arts programs.
Further, the existing studies tend to emphasise the importance of artists’ employability or business success, to the exclusion of the aspirations and career goals of the artist. This doctoral study redresses this issue by presenting a systematic investigation of the predictive value of a number of constructs suggested by career development literature on both subjective and objective career success in professional artists and tertiary arts graduates. The potential predictors investigated include: career self-management skills; underlying dispositions / characteristics suggested by protean career theory; and individual / contextual influences on career development.
The Present Program of Study
The literature reviews conducted in Chapters 2, 3 and 4 have demonstrated the presence of a number of opportunities for further research. These opportunities 114 centre around the successful navigation of non-traditional, ‘boundaryless’ or
‘protean’ patterns of career, which are argued to be increasingly prevalent amongst knowledge workers in developed economies. As noted previously in this chapter, the working lives of artists display a striking congruence with the attributes of a protean career. It appears that many artists have experienced the life of the protean careerist since before the concept of the protean career became popular.
A study into predictors of career success in the protean career of arts will necessarily be somewhat exploratory and descriptive in nature. As can be observed from the preceding literature review, relatively little is known about the working lives of artists, apart from what is documented by large-scale employment statistics
(Australian Bureau of Statistics, 2004; Creative New Zealand, 2003; Graduate
Careers Council of Australia, 2005a; Jeffri & Greenblatt, 1997; Throsby & Hollister,
2003) and, to a lesser extent, economic modelling (Papandrea, 2004; Rengers, 2002;
Rengers & Madden, 2000; Robinson & Montgomery, 2000; Throsby, 1994b).
A number of constructs are suggested by career development literature as being potentially advantageous to an individual who is undertaking a career characterised by boundarylessness, and therefore may be of use in an investigation of predictors of career success in artists. These constructs include: (i) career self- management skills, comprising generic / transferable, discipline specific, self- management and career building skills; and (ii) underlying dispositions and characteristics such as internal motivations, self-directedness, proactivity and resilience. The most comprehensive formulation of career self-management competencies is the Australian Blueprint for Career Development, or ABCD (Haines et al., 2003). The ABCD has been assessed and developed by an extensive process of stakeholder review, and has been modified for the Australian context (Miles Morgan 115
Australia, 2003). Therefore, the Australian Blueprint has been chosen in the present program of study as the most appropriate framework to use in assessing career management competencies in the Australian protean or boundaryless careerist. As there are no existing quantitative measures of career self-management skills based on the ABCD, a brief measure is developed and refined through the present doctoral research.
Seven underlying dispositions were identified in the protean career literature as being conducive to career success in the non-traditional career, although empirical studies of the predictive value of these underlying dispositions to career success are few, and studies of these dispositions in boundaryless or protean careerists are virtually non-existent. A brief measure pertaining to these underlying dispositions, named ‘protean career success orientation’, is also developed and validated as part of this research program.
It is of value to place career self-management skills and the careerist’s underlying dispositions / characteristics in a theoretical career development context.
Regrettably, with minimal literature available regarding development and success in the boundaryless or protean career, there is little theory to guide a conclusive study to choose one theoretical formulation, particularly considering that many of the career development theories still focus on only one or a few aspects of the individual, their context, or processes acting within or between them (Savickas, 2001a, 2001b). Thus, the Systems Theory metatheoretical framework of career development (McMahon &
Patton, 1995; Patton & McMahon, 1997, 1999, 2006a), which encompasses all of the influences described in the major theories, is appropriate to use to provide a broad, overarching view of the career development processes acting on and within boundaryless or protean careerists. Data obtained from this broad exploratory stage 116 of study into the non-traditional career can then later be used to direct conclusive and in-depth research using a finite number of theoretical formulations, as appropriate.
Study 1: Research Question 1
Three brief quantitative measures are developed for use in predictive modelling of artists’ career success as part of the present research. The first substantive research question under investigation is as follows:
1. Are the following researcher constructed measures sufficiently valid and
reliable when used with the study samples?
a) career development influences, derived from the STF
b) protean career success orientation, derived from protean career theory
c) career management competence, derived from the ABCD
An investigation into predictors of career success will unavoidably involve some consideration of the nature of career success. Recent developments in career theory suggest that alternative viewpoints should be employed when studying career success, and that in the protean career, subjective ideas of career success are probably more valuable than objective constructs such as salary and hierarchical position (Arthur et al., 2005; Hall & Chandler, 2005; Heslin, 2003, 2005). However, existing formulations of subjective career success are in the main based on traditional notions of objective career success which may be less relevant to the protean careerist (Greenhaus et al., 1990; Peluchette & Jeanquart, 2000).
Study 2: Research Question 2
Responding to calls from Arthur et al. (2005) and Heslin (2005) for further exploration regarding conceptualisations of career success with different populations 117 and different contexts, artists and arts graduates participating in the present study were asked to self-define and then rate their own career success. In addition, where appropriate, participants were asked to provide details of their income (arts-related and non-arts related), and self-rate their level of employability in arts and overall, where employability is defined as the participant’s ability to create or obtain work
(Harvey, 2001). The relationships between these various subjective and objective measures of career success are explored. Thus, the second research question is as follows:
2. How can career success in the arts be defined?
Study 3: Research Question 3
In the third study within this program of research, the predictive value of the career self-management competence, protean career success orientation, and career development influences measures previously discussed within research question 1 are investigated with relation to the career success measures explored under research question 2. This investigation is among the first to attempt to empirically demonstrate a link between career self-management constructs and dispositions, and career success. The third research question is:
3. Which of the measured career development influences and constructs
predict career success in professional artists?
Study 4: Research Question 4
The fourth study once again employs the career self-management, protean career success orientation and career development influences measures, this time in predicting successful transitions to the world of work in tertiary graduates, where transition to the world of work is defined as career success twelve months from the 118 point of course completion. This study represents a significant addition to the graduate attributes literature, which traditionally has only acknowledged the value of generic / transferable skills to graduate careers, and has only measured graduate outcomes four months after course completion. The fourth research question to be addressed by this research is:
4. Which of the measured career development influences and constructs
measured at undergraduate course completion predict successful transition
to the world of arts work?
Chapter Summary
Chapter 4 has reviewed the literature regarding what is known about the careers of artists and arts graduates in Australia. The career patterns of artists have been examined using statistics from the Australian Bureau of Statistics and the
Australia Council for the Arts, and links made with the career development and career success literature outlined in Chapters 2 and 3. Based on these links, an argument has been made for artists and tertiary arts graduates to be viewed as protean careerists, and that constructs such as career self-management and protean career success orientation will therefore be relevant to a study of career success in artists. Four substantive research questions to be addressed in four sequential studies were outlined, as follows:
1. Are the following researcher constructed measures sufficiently valid
and reliable when used with the study samples?
(a) career development influences
(b) protean career success orientation
(c) career management competence 119
2. How can career success in the arts be defined?
3. Which of the measured career development influences and constructs
predict career success in professional artists?
4. Which of the measured career development influences and constructs
measured at undergraduate course completion predict successful
transition to the world of arts work?
The next chapter of this document, Chapter 5, presents the methodological considerations involved in the research, and justifies the approaches taken to answering the research questions. The chapter also contains a description of the samples of professional artists and tertiary arts graduates under study. 120
120
CHAPTER 5
Method and Methodological Considerations
Chapter 5 outlines the method chosen to explore the research questions, and important methodological considerations pertinent to this research program. Each of the four sequential studies introduced at the end of the previous chapter are discussed in depth, beginning with a rationale for the quantitative, questionnaire-based research approach employed. Issues relating to the study samples are then discussed, including definitions of the artistic populations under investigation, sample selection, and sample representativeness. Construct operationalisation is then considered, incorporating scale composition and how issues of measure reliability and validity are addressed. Finally, the principal research questions, associated hypotheses and analysis techniques performed will be outlined.
Data Collection
A single data collection procedure was undertaken for the professional artists’ sample, with the online questionnaire available and active recruiting conducted from
October to December in 2006. However, the tertiary arts students’ sample filled out questionnaires at two points in time, in a prospective repeated measures design. They were initially recruited at the end of their undergraduate 3 or 4 year arts courses, and filled in their initial online survey in October 2005. The students, now graduates, were contacted one year later by various means, and completed another online survey in October 2006. This design enabled the relationships between various constructs of interest measured at course completion (e.g., the students’ career self-management competence) and their levels of career success one year after course completion to be described. 121
Self-Report Surveys
The present program of study relied on surveys as the method of data collection. Participants self-completed online questionnaires containing the research measures, which included Likert scale, check box (multiple response) and radio button (single response) questions, and open-ended text boxes in which participants typed responses. A research design based on surveys was chosen for a number of reasons.
First, survey research has long been established as an effective method of measuring the characteristics, attitudes and perceptions of a population. Researchers use quantitative surveys as a scientifically sound method in which to interview a representative sample instead of an entire population (Salant & Dillman, 1994).
Nesbary (2000) defines survey research as, “the process of collecting representative sample data from a larger population and using the sample to infer attributes of the population” (p. 10). The main purpose of a survey is to estimate, with significant precision, the percentage of population that has a specific attribute by collecting data from a small proportion of the total population (Dillman, 2000; Wallen & Fraenkel,
2001). A choice of survey research in the present study allowed generalisations to be made from the study participants to the broader population of graduating and recently graduated arts students and practising professional artists, and potentially to protean careerists in other fields as well.
Monette, Sullivan and DeJong (1998) championed the flexibility surveys provides, noting that the data collection technique can be used for exploratory, descriptive, explanatory, and evaluative studies. While exploratory and descriptive research is often associated with qualitative research methods such as focus groups and interviews, these methods do not permit inferences to be made to the wider 122 population and do not describe the variations between individuals in a population
(Creswell, 2002). Exploratory and descriptive statistical techniques and open-ended questions allow for a holistic picture while allowing inferences and generalisations to be drawn.
The use of open-ended questions in survey research has traditionally been limited, with proponents suggesting that open-ended questions are more difficult to interpret than closed-ended questions, and even that open-ended questions might measure the participant’s ability to articulate a response, not their underlying attitudes (Geer, 1988). However, recent evidence seems to suggest that open-ended questions, if well-constructed, can be more reliable and valid than equivalent closed- ended questions (reviewed in Krosnick, 1999). Open-ended questions are certainly preferred when undertaking exploratory work with little empirical background (Fink,
2003). In the present investigation, open-ended questions were employed to investigate artists’ ideas about the construct of career success, an area which has received little scrutiny in the past. The open-ended questions were supported by quantitative items relating to known objective and subjective career success measures, including earnings and employability ratings.
Limitations of Survey Research
One of the limitations of survey research pertains to the issue of self-perception regarding the self-evaluative and self-descriptive items on the questionnaire. The accuracy with which an individual assesses his or her level of knowledge or ability, or indeed the level of influence a variable has had upon them, may in some cases not be as high as others’ assessments or externally-observable variables (Donaldson &
Grant-Vallione, 2002; Miller & Ross, 1975). Self-serving bias, one common type of self-evaluation bias, is evident in the "above average" effect (Kruger, 1999), whereby 123 well over half of survey respondents typically rate themselves in the top 50 percent of drivers (Svenson, 1981), and also the top 50 percent of a variety of other desirable qualities such as intelligence, organisation, ethics, logic, and health (Myers, 1998).
It is difficult to determine whether research findings have been influenced by this kind of bias, and it is also difficult to mitigate the potential effects of self-serving bias. The present investigation attempted to reduce the possible effects of self- serving bias by the use of anonymous (or at least fully confidential) self-completion surveys. Aquilino (1994) showed that respondents were less likely to report with bias on self-administered questionnaires than by those conducted over the telephone.
Epstein, Barker, and Kroutil (2001) found that respondents reported a more accurate list of mental health symptoms when the mode of administration was computer assisted self-reporting in comparison with responses when administered by an interviewer.
Another potential source of self-perception error in survey research is common method bias. A substantial body of literature (e.g., Podsakoff, MacKenzie,
Lee, & Podsakoff, 2003; Smither, Collins, & Buda, 1989) suggests that people try to maintain consistent attitudes and cognitions, and wish to appear consistent and rational in their responses to questionnaires. According to Podsakoff et al. (2003), people may look for commonalities between predictor variables and the criteria variables, and provide answers which are reflective of their theories regarding these commonalities as well as the actual covariation between the constructs. For instance, an artist might rate their career management skills highly, and then rate their arts employability levels more highly than they would have if the career management skills questions had not been present, because of a belief that career management skills and arts employability should be theoretically linked. 124
This ‘consistency effect’ is likely to occur when respondents are required to provide retrospective accounts of their attitudes, perceptions and / or behaviours. It is less likely to occur when items are separated in some way (temporally, proximally, psychologically, or methodologically). Therefore it is less likely to be a problem when a prospective, repeated measures design is used, as with the arts students’ data collection procedures. In addition, in the present study the career success rating measures were separated from the career development scales in the surveys by various response items, including two open-ended questions.
Online Surveys
For all of the substantive studies, participants filled out online surveys. The surveys are included in Appendix A, and were located at the following URLs:
http://www.australianartistssurvey.org/artists/ (professional artists)
http://www.australianartistssurvey.org/students/ (arts students at time 1)
http://www.australianartistssurvey.org/graduates/ (arts students at time 2)
An online survey was considered the best way to approach final year undergraduate arts students, as many of these students did not attend large group classes in their final semester of study. Instead, the students worked individually or in small groups on portfolio-based work which often did not involve on-campus attendance. However, these students all had student email addresses provided by the university, and were expected to check their email regularly to receive essential course and assessment information.
It was also decided to place the professional artists’ survey online. When artists’ professional organisations were initially contacted regarding possible recruitment of their members, it was discovered that most of the organisations 125 preferred to contact their membership via email, and that newsletters were most frequently sent out in electronic format. Indeed, some of the major artists’ networking and promotion organisations (e.g., artsConnect, ArtsHub) had an exclusively online presence.
There were a number of possible repercussions of the online data collection method. First, there is some evidence to suggest that potential participants may be less likely to fill out online surveys than paper-based surveys. Cook, Heath and
Thompson (2000) conducted a meta-analysis of online surveys from social sciences journals between 1994 and 2000, and reported that response rates to online surveys tend to be somewhat lower than mail-based surveys, at 25-30%. However, Cook et al’s (2000) analysis was conducted on surveys of the general population rather than university or professional organisation-specific mailing lists, and thus it was expected that response rates for the present surveys, endorsed by QUT and artists organisations and pertaining to issues of strong relevance to the participants, would be significantly higher. This was indeed the case for the arts students, with a response rate at time 1 of 49.43%. Actual response rates were not able to be calculated for the professional artists because of the nature of the recruitment process.
Second, generalising from internet samples to the larger population can be problematic. Although the large demographic differences between internet users and non-users that existed in previous decades have diminished, the two populations still differ on many demographic, social, and psychological dimensions. For example,
66% of Australians had access to the internet either at home or at work in 2005.
These internet users were more likely to be young, well-educated, employed and live in metropolitan areas than Australians generally (Australian Bureau of Statistics, 126
2006). This means that the artists studied in this program of study may not be typical of artists in the Australian population at large. The data on the general population of internet users suggests that the artist participants will tend to have higher incomes and levels of employment, be younger, more urbanised and better skilled than other artists (Australian Bureau of Statistics, 2006). The study samples and general artistic populations are compared on key demographic variables in the sections on sample characteristics later in this chapter.
There are also security risks associated with online surveys. Because data is entered via the internet, there is a chance that contact details and other confidential information might be appropriated by malicious individuals, either during the data collection process or when the collected data is stored on a server connected to the internet (Kraut et al., 2004). Potential respondents may be wary of these possible security risks and decide not to participate, or behave differently than they would otherwise, for example by not giving correct contact details.
In order to minimise online security risks in the present program of study, confidential data entered online was downloaded immediately to a secure database.
Data was deidentified for analysis purposes, except where the repeated measures design required that the records retained identifying information in order to match data sets. In this case the identifying information was kept in one file and data in another, with an arbitrary code number to link the two. Participants were informed of these security measures during the recruitment process.
Ethical Considerations
This program of study complied with the ethical guidelines of the Queensland
University of Technology and received separate ethical clearances from the Human 127
Research Ethics Committee for each of the three online surveys and the pilot study.
These approvals are included in the present document in Appendix B.
All participants in the studies were volunteers, and effort was made to ensure they were informed of the nature of the project before they agreed to participate.
Information sheets, brochures, posters, and survey cover sheets provided details of the research for participants in each study where relevant. Participants were advised they were free to withdraw from the research at any time without comment or penalty.
Sampling: Professional Artists
This section of Chapter 5 discusses the participants involved in the study: criteria for eligibility, recruitment processes, characteristics of the samples, and comparisons with published data to explore representativeness issues. The sampling for the professional artists will be discussed first, followed by a discussion of the arts students’ sample.
Defining ‘Professional Artist’
Varying criteria have been used to define artists for the purposes of statistical collection, assessing employment opportunities and extending grant assistance.
Further blurring of definitions occurs because the skills associated with art practice contribute to many other industries. As noted in Chapter 4, artists often span different artistic and non-artistic professions either by choice or through necessity. Further, a single instance of artistic practice may require the artist to draw on skills from multiple artforms which make categorisation difficult.
For this study, as with previous work conducted by the Australia Council for the Arts (Throsby & Hollister, 2003) and discussed in Chapter 4, the definition of 128
‘professional artist’ contained two parts. First, potential participants identified which, if any, of 12 listed artistic categories they identified with professionally. Second, for each category selected they were asked questions relating to current professional activity within Australia. If participants selected at least one of the 12 categories and indicated current professional activity within Australia, they were eligible to participate in the study. If participants selected more than one category, they were then asked which of the categories they selected subsumed their main or principal artistic occupation (i.e., the occupation that they were engaged in most of the time).
Artist Categories
Throsby and Hollister (2003) identified 120 occupations within eight categories of artist, seven of which then were able to be aggregated into two broader categories, creative artists and performing artists. The present study drew on the categories provided by Throsby and Hollister, but also included four additional categories of artist, falling within a broad technical / design arts grouping. In
Throsby and Hollister’s (2003) study ‘new / digital media’ artists were subsumed within the various other categories, but the Australian Bureau of Statistics Australian
Standard Classification of Occupations (ASCO) (Australian Bureau of Statistics,
1997) separates technical / design artists within its ‘Artists and Related
Professionals’ code. These additional categories of artist were included in order to explicitly investigate digital and technical artistic careers, and in order to ensure that the discipline profile of the professional artist population studied was as similar as possible to the arts students’ population profile. In the QUT arts students’ population under investigation, 37.64% undertook digital / technical arts courses.
The relationships between the occupational groupings used by Throsby and
Hollister (2003) and those employed in the present study, including the technical / 129 digital artist categories, are depicted in Table 5.1. For comparison purposes, the relevant ASCO classifications (Australian Bureau of Statistics, 1997) are also included. 130
Table 5.1
Professional Artist Categories
Professional Artist Australian Standard Professional Artist Categories Types (Throsby & Classification of – Present Study Hollister, 2003) Occupations (Australian Bureau of Statistics, 1997) Writers Authors and Related Writer Professionals Visual artists Photographers Visual Artist Craft Practitioners Visual Arts and Craft Craft Practitioner/ Artisan Professionals Composers Composer/ Songwriter/ Music
Creative Creative Artists Arranger Actors Actors, Dancers and Actor/ Performer/ Director Directors Related Professionals Dancers Dancer/ Choreographer Choreographers Musicians Musicians and Related Musician/ Singer
Performing Artists Singers Professionals* Film, Television, Radio and Filmmaker (other than Stage Directors** Director) Technical, Stage or Production Manager Designers and Illustrators Interaction/ Information/ Multimedia Designer Fashion Designer Technical / Design Artists Community Cultural Community Cultural Development Workers Development Worker Note. In the present study, community cultural development workers are classified by main art form into one of the three broad artist categories. * The ASCO classification of Musicians and Related Professionals includes musicians, singers, composers, music arrangers and songwriters ** The ASCO classification of Film, Television, Radio and Stage Directors includes filmmakers, technical, stage or production managers and directors
The distinction between the first two categories of artist, creative artists and performing artists, hinges on the nature of the creative activity undertaken. Creative artists primarily undertake the creation of original cultural expression (Throsby &
Hollister, 2003). While performing artists also require significant creative ability to undertake their work, their skills are mostly employed in interpreting existing creative works (Kogan, 2002). A distinction has also been made between creative and performing artists in terms of their work patterns. For example, although both 131 types of artist have been argued to experience boundaryless careers, it has been observed that creative artists tend to engage in commission-based self employment, and performing artists tend to be casual or fixed-term employees (Rengers &
Madden, 2000). This adds further weight to the argument for a performing and a creative arts category. It should be noted that a few occupational groupings are difficult to classify according to this system (for example, improvisational musicians).
‘Technical / design artists’ have also been included in the present investigation. This category comprises individuals for whom significant digital or technical design skill is required in addition to creative abilities. These artists include filmmakers, fashion designers, technical stage or production managers, and interaction, information and multimedia designers. Data from the annual Graduate
Destination Survey (Graduate Careers Council of Australia, 2006a, 2006b) and the
Australian Bureau of Statistics (2005) indicate that many technical / digital artists face similar career challenges to creative and performing artists, including underemployment and strong competition for work, a relatively high proportion of casual / part-time work and self-employment, and strong personal identification with their work (Jones & deFillippi, 1996; Tremblay, 2003). There may be some overlap between technical / design artists and the other categories, particularly with respect to the creative arts category. For instance, depending on the tools and techniques employed, an animator might be most appropriately classified into either the creative artists category or the technical / design artists category.
The final type of artist, the community cultural development worker, engages in work which emphasises building a cultural community through artistic practice
(Flood, 1998). These workers are classifiable by their primary art form into creative, 132 performing or technical / design arts categories. In the present investigation only three participants identified themselves as community cultural development workers
– two visual arts youth workers and a performing arts community artist. These participants were assigned to the creative artists and performing artists categories respectively.
Current Professional Activity
Data was collected from participants regarding current professional activity within each category of art form. These questions were included in order to distinguish between practising, professional artists and hobbyists. The present investigation used similar current professional activity criteria as Throsby and
Hollister (2003); participants were asked whether, at some time during the past five years, they had sold, performed, exhibited, published, filmed, broadcast or otherwise produced a professional work in each artistic occupational category they selected.
They were also asked whether, during the last five years, they had received a government or similar grant to produce a professional artistic work. A timeframe of five years was used because in some fields the process of artistic production can take extended periods of time, for example novel writing or film score composition.
The second criterion for current professional activity used by Throsby and
Hollister (2003) was that during the last five years the participant had been engaged in creating a serious and substantial body of work their field/s, except if they had undertaken full-time training in arts during that period of time. It was decided, for the purposes of the present investigation, to ask whether the participants regarded themselves as professional artists who were engaged in creating a serious and substantial body of artistic work in their field/s. The reason for this change was that
Throsby and Hollister’s (2003) definition excludes emerging artists who, despite 133 being engaged in professional practice, may have worked in the arts for less than five years, and artists who may have experienced a hiatus from professional practice during the previous five year period.
In 12 cases, participants identified themselves as currently practising in multiple fields (e.g., creative writing and multimedia), and fulfilled the professional practice criteria for each. In order to allow for categorisation of occupation for each artist, the participant was asked to identify which of the occupations they selected was their current main artistic occupation, and their artistic category was coded from this.
Recruitment
Professional artist participants were sourced through various professional arts organisations, informal communities, galleries and other similar venues that professional artists were likely to visit. A list of these organisations is located in
Appendix C. An electronic advertisement inviting professional artists to participate in an anonymous online survey about their careers was placed on arts organisation web sites, and sent to membership lists via email newsletters. In addition, recruitment business cards were placed in Brisbane and Sydney metropolitan galleries, artists’ organisations, and stores selling visual art supplies, or performing arts / musical equipment.
The recruitment materials included information about: the intended aims of the project; eligibility to participate; anonymity, confidentiality and data security measures; and details of an incentive prize draw. Participant names and contact details were placed into a separate dataset from that used in the main study. A lottery 134 procedure was then employed. One random artist participant was presented with a
$200 voucher for artists’ materials or equipment after the study was complete.
It is recognised that a proportion of practising professional artists were not reachable by these means, as in the case of artists who do not: belong to professional organisations; use the internet; or, live in certain metropolitan areas. This may have affected the representativeness of the sample of professional artists, and consequently the generalisability of the findings to the broader population of Australian professional artists.
Sample Characteristics
A total of 372 individuals responded to the online survey. Forty-two of these individuals (11.29%) did not meet the criterion relating to professional activity in the last five years, and were screened from the sample. Another thirteen (3.49%) were screened out at the question relating to the creation of a serious and substantial body of artistic work. A further 19 participants (5.11%) indicated that their main artistic practice fell outside the eleven categories of professional artist provided. These participants typed their artistic occupation into a text box, and the responses were coded by the researcher.
Nine were categorised as creative artists: two poets, a textile artist, a paper artist, a sculptor, and four photographic artists. Another three were categorised as technical / design artists: two 3D designers, and a digital illustrator. Seven responses were not able to be categorised. These included a youth worker, two arts managers, a curator, an art teacher, a storyteller, and an arts administrator. The records for these participants were not included in the studies. 135
After screening, the professional artists dataset from which analyses were conducted comprised a total of 310 records, of which 122 (39.35%) were creative artists, 85 (27.42%) were performing artists and 103 (33.23%) were technical / design artists. Aggregated 2001 Census data (Australian Bureau of Statistics, 2003) shows that in 2001 37.30% of Australians whose main, full-time occupation was professional artist were creative artists, 23.90% were performing artists and 38.80% were technical / design artists. Although performing artists appeared to be slightly overrepresented and technical artists slightly underrepresented in the sample, the chi- square statistic indicated that the differences were not statistically significant at p <
.05, at χ2(2) = 4.42, p = 0.20; Cramer’s φ = .008. The sample sizes for each of the 11 categories of artist included in the present investigation are presented in Table 5.2.
Table 5.2
Professional Artist Sample Sizes by Category
Creative Artists (N = 122) Performing Artists (N = 85) Technical / Design Artists (N = 103) Writer 38 Actor/ Performer/ 28 Fashion Designer 11 Director Visual Artist 44 Dancer/ Choreographer 23 Filmmaker (other than Director) 28 Craft Practitioner/ 14 Musician/ Singer 33 Technical, Stage or Production 25 Artisan Manager Composer/ Songwriter/ 24 Community Cultural 1 Interaction/ Information/ 36 Music Arranger Development Worker Multimedia Designer Community Cultural 2 Uncategorised 3 Development Worker Uncategorised 9
Note. ‘Uncategorised’ denotes records where participants indicated that their occupation fell outside the 11 categories of artist provided. Their occupations were coded into broad categories by the researcher.
Sociodemographic data were collected from the professional artists’ sample.
These data were used within the main studies (documented in Survey Composition in this chapter), and to explore the issue of potential sampling bias. Correspondence 136 between sample characteristics and known characteristics of the artistic population can be taken to suggest representativeness and consequently the potential for generalisabilty of the investigation’s findings to a wider population of Australian artists. The variables used for comparison were: age; gender; income; and educational background because comparison data were available for these variables from other studies. Population statistics were drawn from occupational data obtained in the 2001 Australian Census (Australian Bureau of Statistics, 2003) where available. It should be noted that the Census data set only includes ‘main job’ artists, that is, those who indicated that in Census week arts was their main occupation.
Comparison data were also drawn from the large scale Australia Council for the Arts professional artists survey (Throsby & Hollister, 2003).
The sociodemographic comparisons revealed that the characteristics of the present sample of professional artists were consistent with those described by previous data collections, as shown in Table 5.3.
Table 5.3
Professional Artist Characteristics: Sample and Population Statistics
Population Data for Australia Council Study Sample Artists (Australian Artists Survey N = 310 Bureau of Statistics, (Throsby & Hollister, 2001, 2003) 2003) N = 61,950 N = 1062 Female 43% 44% 46.13% Median age -- 50 37 Modal age group 25 - 34 (26%) 35 – 44 (27%) 35 – 44 (36%) 35 – 44 (26%) 45 – 54 (27%) Has undertaken formal -- 76% 79.35% training in art form Mean income from arts -- $17,100 $16,603 Proportion arts income of -- 46% 48.20% total income Modal total income group $300-$499 per -- $300-499 per week week (35%) (30%) Note. Blank cells indicate that data was unavailable from this source 137
The present study participants were slightly more likely than expected to be female and formally educated. They had very similar levels of income to the
Australia Council sample, but on average were younger. One possible explanation for these demographic differences is that one of the professional activity screening criteria was different between the two studies. The present investigation only required current professional artistic practice and did not expect that participants had been engaged in creating a serious and substantial body of work for the previous five years.
A variety of other sociodemographic data were also collected from the professional artists’ sample. On average, the professional artists had worked in arts for 13.79 years (SD = 9.75), ranging from 0 to 45 years. Only 20% of the professional artists did not work outside their arts practice at all; 37.10% indicated that they sustained a concurrent ‘career’ outside arts, and 42.90% did some work outside their art form but did not term this work a ‘career’. The majority of the professional artists were married or partnered (65.16%). The sample was strongly urbanised, with 85.16% living in a metropolitan region. A very small number of the professional artists indicated that English was their second language (6.77%), that they identified as an Aboriginal or Torres Strait Islander (2.58%), or that they had a disability (1.61%).
Sampling: Arts Students
Defining ‘Arts Student’
Arts student participants were all drawn from Faculty of Creative Industries courses at the Queensland University of Technology (QUT), a large university located in metropolitan Brisbane, Australia. Students who were graduating from 138 relevant Bachelor of Fine Arts, Bachelor of Creative Industries or Graduate
Certificate / Graduate Diploma programs within the Faculty were eligible to participate, providing that they were planning to work in the arts during the subsequent year. Students who were not completing their courses in second semester
2005 and moving on to the workforce in their arts discipline/s were not eligible to participate and were screened from the sample.
Arts Student Categories
The arts students were placed into three occupational categories according to the arts discipline of their program of study. These categories, as with the professional artists’ sample, were creative artists, performing artists, and technical artists. The categories of arts student by discipline are tabulated in Table 5.4 below.
Table 5.4
Arts Student Categories by Discipline of Program of Study
Creative Arts Performing Arts Technical / Design Arts
Visual Arts Acting Technical Production
Creative Writing Dance Communication Design
Drama / Performance Studies Film and Television
Music Sound Design
Recruitment
A number of methods were used to recruit arts students for the first data collection in October 2005. All eligible students were contacted by at least one method. First, an invitation email containing the URL for the survey was sent to students from relevant disciplines via central Faculty mailing lists. A reminder email was sent to the students one week later. In addition, if a relevant on-campus class of 139 some type was being held during the final two weeks of semester, the researcher visited to inform students about the purpose of the project and invite them to fill out the survey online. An advertisement containing the survey URL was placed on the
Faculty of Creative Industries intranet home page for the final two weeks of semester and one week of exam preparation break. Paper notices advising of the study were placed on noticeboards in discipline common rooms, group work areas, studios and rehearsal spaces.
Participants at time 1 were asked to provide contact details for themselves at time 2 (October 2006). Most commonly, the students gave an email address, although a few gave mobile phone numbers or paper mail addresses. Recruitment emails (or text messages, where applicable) for the time 2 survey were sent out to the students in the first week of October, 2006. Because a number of the supplied email addresses had become ‘stale’ (i.e., the recruitment email bounced back) over the ensuing twelve months, last known paper mail addresses for all Creative Industries students from relevant disciplines who completed study in Semester 2, 2005 were retrieved from the QUT student enrolment database (with permission). Personally addressed invitation letters were sent to the students who were identified as having participated in the study at time 1.
The recruitment materials for the arts students were similar to those used with the professional artists. The materials at both time 1 and time 2 comprised information about: the intended aims of the project; eligibility to participate; confidentiality and data security measures; the survey URL; and, details of an incentive prize draw which was conducted at time 1 and again at time 2. If they wished to participate in the prize draw, arts student names and contact details were placed into a separate dataset from that used in the main study. A lottery procedure 140 was then employed. One random arts student participant at each time of data collection was presented with a $200 voucher for artists’ materials or equipment after the study was completed.
Sample Characteristics
According to data supplied by the QUT Division of Finance and Resource
Planning for semester 2, 2005 (QUT Strategic Information and Analysis Section,
2007), the population of potentially eligible final year QUT students was 441. At the time of recruitment in October 2005, it was unknown what proportion of these students would be attempting to work in the arts during the subsequent year. The cohort number of 441 did not include approximately 400 final year Creative
Industries students who were studying in disciplines outside those listed in Table 5.4, including journalism and media / mass communication. It also did not include a number of Creative Industries students for whom the major field of Creative
Industries study was not able to be ascertained (e.g., interdisciplinary Bachelor of
Creative Industries courses with no major noted).
Because recruitment emails were sent to all final year Creative Industries students, some ineligible students filled in the survey. Their records were removed from the time 1 dataset. A screening question asking about planned primary career- related activity in 2006 resulted in further record removal; the students who indicated that they were not planning on entering the arts workforce during the next year were not included in the investigation.
At time 2, a proportion of participants from time 1 did not fill out the follow- up survey. Some were unreachable via the email address or mobile telephone number they provided at time 1, and some recruitment letters were returned to the researcher 141 as the student no longer lived at the address. A further proportion did receive the recruitment materials but did not respond to the second survey. Exact numbers regarding attrition due to non-contact versus non-response are unavailable.
A further screening question was included in the questionnaire which was administered at time 2. It asked the following: “Over the last year, did you attempt to work in your arts field/s?” A small number of arts students who had indicated at time
1 that they planned on entering the arts workforce in 2006 did not attempt to do so, and records for these students were removed from the dataset.
At time 1, the dataset contained a total of 218 usable records from eligible students, a response rate of 49.43%. This figure was reduced to 180 when students who did not plan to enter the arts workforce during the next year were removed. At time 2, 128 valid records were returned, representing 71.11% of the time 1 sample.
This figure reduced to 122 when 6 records for students who had not attempted to work in their arts field/s since course completion were removed.
Sociodemographic data were collected from the arts students, and used to explore sample representativeness issues. Demographic indicators were obtainable from QUT enrolment data for final year students in the disciplines of interest, and the results of the Graduate Destination Survey (Graduate Careers Council of Australia,
2006a, 2006b), which is conducted with all Australian graduates four months after course completion. Thus, the arts students’ sample was able to be compared with
QUT Creative Industries data and to a wider population of Australian arts graduates
(although the GDS ‘visual and performing arts’ category included graduates in many other fields in addition to those under study, such as museum studies and art conservation). The variables used for comparison were: age; gender; income; and percentage looking for work. 142
Table 5.5 depicts the indicators used in the comparisons. Compared with the
GDS data, the study sample had a slightly lower proportion of females. The age, salary and employment statistics were, however, very similar for the GDS reporting and the present study sample.
In terms of other sociodemographic variables collected from students in the sample, nearly all of the students at time 1 (89.91%) reported that they had some or significant paid work experience outside the arts, and 43.45% said that they had some or significant paid work experience within arts. Another third (34.43%) had unpaid work experience in the arts. A minority of the students had English as a second language (7.37%), indicated that they had a disability (4.10%), or were of
Aboriginal or Torres Strait Islander origin (3.27%). At time 2, 86.07% of the graduates were living in a metropolitan region of Australia.
Table 5.5
Arts Student Characteristics: Sample and Population Statistics
Graduate QUT Strategic Study Sample Study Sample Destination Survey Information and Time 1 (2005), Time 2 (2006), N (Graduate Careers Analysis data, all N = 218 = 122 Council of eligible students Australia, 2006a, (2007), N = 441 2006b) Visual and Performing Arts categorya, N = 3104 Female 69.20% 67.35% 65.14% 62.30%
Median age -- 25 years 24 years 25 years
Median total salary if in $32,000 -- -- $29,000 full-time employment
% looking for work 39.7% -- -- 40.98%
% working full-time 60.3% -- -- 59.02%
Note. Blank cells indicate that data was unavailable from this source. a includes surveyed graduates from the fields of: computer graphics; conservation of art and cultural material; crafts; dance; dramatic arts; fashion; film and photographic arts; fine arts; graphic arts and 143 design; industrial design; museum studies; music; visual and performing arts, 4 months after course completion.
Survey Composition
The three survey instruments employed in the present program of investigation comprised multiple measures. The present investigation employed three researcher developed career development constructs / influences measures which were included in the professional artists’ survey and the arts students time 1 survey. These were: the
Career Development Influences (CDI) scale; the Protean Career Success Orientation
(PCSO) scale; and the Career Management Competence (CMC) scale, as developed by the researcher and piloted in May 2005, prior to the main body of the present investigation. Various measures of career success were included in the surveys, in order to explore both subjective and objective dimensions of career success in the artists. The measures employed in the present investigation are presented by survey in Table 5.6. 144
Table 5.6
Substantive Research Measures Used to Address Research Questions in the Present
Investigation
Item Item type Survey Professional Arts Students Arts Students Artists Time 1 (Oct 2005) Time 2 (Oct 2006) Career development Career Development 1-6 Likert scale Influences (CDI): 26 items Protean Career Success 1-6 Likert scale Orientation (PCSO): 7 items Career Management 1-6 Likert scale Competence (CMC): 11 items Career success Earnings from arts last 12 Numeric months Total earnings last 12 Numeric months Own definition of career Open-ended success Rating of current career 1-6 Likert scale success – own definition Rating of arts 1-6 Likert scale employability Rating of overall 1-6 Likert scale employability
The development of the new research measures followed the course most commonly used by researchers in measure design (Anderson & Kanuka, 2003;
Cohen & Manion, 1994; de Vaus, 1995; Fink, 1995, 2006; Seale, 2004): item construction was conducted by the researcher, and in the absence of existing instruments upon which to base items, the items were drawn from theory, as outlined in Chapters 2 and 3. This stage was followed by reviews from an ‘expert panel’ of colleagues who made suggestions as to wording, item selection and response options.
The measures were then tested by a pilot sample of 168 first year Education students, who also provided further feedback regarding face validity and instrument length, and completed the survey instrument. Exploratory factor analytical techniques and 145 reliability analyses were then conducted with the pilot study data. The results of these analyses are presented in Appendix D.
Measure development and description for each scale is discussed in more detail in the immediately following sections of this chapter. The psychometrics of these measures are examined with the professional artists’ and arts students’ samples in Chapter 6.
Protean Career Success Orientation
The brief Protean Career Success Orientation (PCSO) measure employed in the present program of investigation was designed to assess the extent to which the participants agreed that they possessed each of the underlying dispositions or characteristics suggested in the literature to be necessary for success in the protean career, as discussed in Chapter 3 of this document (Arthur et al., 2005; Briscoe &
Hall, 2006; Hall & Chandler, 2005; Hall & Mirvis, 1996). PCSO was measured on 6 point Likert scales ranging from ‘strongly disagree’ to ‘strongly agree’. Seven attributes were identified in the extant literature, as discussed in Chapter 3. One item for each attribute identified was included in the scale, as follows:
To what extent do you agree with the following with respect to your arts career?
1. I am self-directed and take personal responsibility 2. I am proactive 3. I am internally motivated 4. I have a positive interpersonal orientation 5. I am resilient and adaptable 6. I am open to opportunities 7. I have a positive self image
The PCSO instrument piloted with 168 Education students was identical to that used in the main study with professional artists and arts students, except that the 146 generic term ‘career’ was substituted for ‘arts career’, and participants responded on a 1-4 Likert scale. The scale was amended to 1-6 for the main study to improve scale discriminability and reliability (deVellis, 2003; Masters, 1974).
Preliminary analyses showed that the pilot study data were suitable for factoring, with a Kaiser-Meyer-Olkin value of .88 and a significant Bartlett’s
Sphericity Test value (χ2 (21) = 369.74, p < .0001). Exploratory factor analyses using principal axis factoring yielded a single factor solution for the scale, with an eigenvalue of 3.75 and accounting for 46.6% of the variance. Using factor loadings above .45 for power of 0.8 and an alpha level of .05 (Hair, Anderson, Tatham, &
Black, 1998), all variables loaded onto the single protean career success orientation factor. The scale possessed sufficient internal consistency, with a Cronbach’s alpha coefficient of 0.85. Consequently, all 7 piloted items were included in the main study, and the single factor solution was tested with the professional artists and arts students in confirmatory factor analyses during Study 1 (see Chapter 6).
Career Management Competence
The brief measure of Career Management Competence was based on the 11 competencies outlined in the draft Australian Blueprint for Career Development
(ABCD) (Haines et al., 2003), due to its relevance to the Australian context and its comprehensiveness, as outlined in Chapter 3. Clarifying descriptors for each competency were provided with each item, summarising the definitions for each competency provided by the ABCD’s authors (Haines et al., 2003, pp. 22-23).
Six-point Likert scales ranging from “not at all” to “very” were used to ask about the participants’ confidence in their abilities and skills for each of the 11 competencies, as follows: 147
How confident are you in your abilities and skills in the below areas?
1. Building and maintaining a positive self-image Knowing who you are & what influences you, staying positive, understanding how self-image has an impact on goals and decisions 2. Interacting positively and effectively with others Understanding and maintaining positive relationships, being able to express yourself in an appropriate manner, knowing how to solve interpersonal problems 3. Changing and growing throughout your life Understanding that our motivations and aspirations change throughout our lives, that change and growth can impact on our physical and mental health and vice versa, knowing how to adapt to changes and manage stress 4. Participating in life-long learning supportive of your career goals Knowing what influences life and work successes, understanding how to improve skills and strengths, knowing about learning opportunities, behaving in ways that contribute to achieving your goals 5. Locating and effective use of career information Knowing where and how to access career information, and how to use it, knowing what working conditions you want, understanding the requirements of work settings 6. Understanding the relationship between work, society and the economy Understanding about how work contributes to our community, society, and ourselves; understanding how trends affect work, understanding how organizations operate 7. Securing or creating and then maintaining work Understanding the importance of personal qualities on getting/ keeping/ creating work, being able to articulate your skills, being able to transfer your skills between work settings, developing work search tools and skills 8. Make career enhancing decisions Understanding how choices are made, how personal beliefs and attitudes affect decision-making, knowing how to problem- solve, being able to explore alternatives, being able to evaluate the impact of decisions 9. Maintain balanced life and work roles Being aware of the various roles we may have and the responsibilities linked to those roles, how these roles impact upon our lifestyles, determining the value of work, family and leisure activities and making choices about a balance of these 10. Understanding the changing nature of life and work roles Understanding the changing life roles of people in work and home settings, understanding how these roles are important to family and society, exploring and considering non-traditional life/ work scenarios, working to eliminate stereotypes, biases and discriminatory behaviours 11. Understand, engage in, and manage the career building process Being able to define your preferred future and create career scenarios in step with it, being able to set goals and short-term plans, and apply coping strategies and new career scenarios during transition periods e.g. starting a family, losing a job
The pilot study four-point Likert scale data obtained from Education students obtained a reasonable Kaiser-Meyer-Olkin sampling adequacy figure of .78, and a significant Bartlett’s Sphericity test value (χ2 (55) = 383.60, p < .0001). Principal axis factoring with oblique (oblimin) rotation yielded a three factor model when factors with eigenvalues greater than one were retained. These factors accounted for
63.51% of the variance (Hair et al., 1998; Tabachnik & Fidell, 2001). Factor loadings greater than .45 were included, as per Hair et al.’s (1998) recommendation for sample sizes between 150 and 200 at a statistical power of 0.8 and an alpha of .05. 148
Factor 1. Self management (5 items, eigenvalue 4.27, alpha .83)
Items: Building and maintaining a positive self image; changing and growing throughout life; participating in lifelong learning supportive of career goals; maintaining balanced life and work roles; understanding the changing nature of life and work roles. Factor 2. Career building (4 items, eigenvalue 1.64, alpha .80)
Items: Locating and effectively using career information; securing or creating and maintaining work; making career enhancing decisions; understand, engage in, and manage the career building process. Factor 3. Relationships (2 items, eigenvalue 1.08, alpha .82)
Items: Interacting positively and effectively with others; understanding the relationship between work, society and the economy This factorial structure was tested with professional artists and arts students using a confirmatory factor analysis procedure in Study 1, outlined in Chapter 6.
Career Development Influences
The value of placing protean career success orientation and career management skills into a theoretical career development context in predicting artists’ career success was discussed in Chapter 4. Relatively little is known about career development in the protean career, and the present program of study is exploratory in nature. The Systems Theory Framework of career development (McMahon & Patton,
1995; Patton & McMahon, 1997, 1999, 2006a), which includes all of the influences described in the major theories, is appropriate to use to provide a broad, overarching view of the individual and contextual influences on artists’ career development.
Findings from this exploratory stage of research can be used to guide future, targeted studies using specific relevant theoretical formulations.
The Systems Theory Framework of career development (McMahon, 2002;
McMahon & Patton, 1995; Patton & McMahon, 1999) is comprised of 16 intra- individual influences, 6 social influences, and 6 societal-environmental influences which are correlated and influence one another recursively. The career development influences measure piloted with Education students contained Likert-scale “level of 149 influence on your career development” items for each of the 28 influences.
Clarifying descriptors were included for influences where necessary, for example,
“values – what is important to you e.g., prestige, risk, autonomy, responsibility”.
During the pilot study with Education students, the 4-point scale ran from ‘weak influence’ to ‘strong influence’, but preliminary ‘declared’ (participatory) pretesting of the instrument prior to the main pilot study with five colleagues (as suggested by de Vaus, 1995) showed that participants might have trouble conceptualising level of influence without valence. In addition, it was decided that it would be in the interests of the measure’s predictive value for the scale to run from ‘strongly negative’ to
‘strongly positive’.
The final measure employed with the professional artists and arts students asked participants to rate the influence of each of the items on their career development on 1-6 Likert scales ranging from “strongly negative” to “strongly positive”. The items were as follows: 150
How negative or positive an influence has each of the following had on your arts career
development so far? This can include choices you have made about study, work, or any other
aspects of your career development and career progression.
a) Gender b) Health - both physical and psychological c) Self-concept - your idea of your important roles e.g. student, homemaker, community member, worker d) Ability – your potential to acquire the skills you need e) Aptitudes – the quickness or ease with which you can learn a skill you need f) Disability g) Personality h) Age i) Ethnicity j) Physical attributes k) World-of-work knowledge – what you know about work environments and how to find and secure work l) Interests m) Skills n) Beliefs – your ongoing ideas about yourself and the world o) Values – what is important to you e.g. prestige, risk, autonomy, responsibility p) Sexual orientation q) Family r) Peers – friends and colleagues s) Community groups t) Educational institutions u) Workplaces v) Media w) Geographical location x) Political decisions y) Employment market z) Historical trends aa) Socioeconomic status bb) Globalisation – increased global connectivity, integration and interdependence
In the pilot study, the career development influences instrument was shown to possess adequate psychometric properties in terms of reliability. A Cronbach’s alpha of .93 was obtained. The Kaiser-Meyer-Olkin measure of sampling adequacy and the 151
Bartlett’s test of sphericity values (.86 and χ2 (325) = 2029.22, p < .0001, respectively) indicated that the data were factorable. When exploratory factor analysis (Tabachnik & Fidell, 2001) was conducted with principal axis factoring and oblique (oblimin) rotation, a six factor model solution was found, which accounted for 66% of the variance (Hair et al., 1998; Tabachnik & Fidell, 2001) and included
22 of the original items. Factor loadings greater than .45 were included, as per Hair et al.’s (1998) recommendation for sample sizes between 150 and 200 at a statistical power of 0.8 and an alpha of .05. Factors were interpreted as follows:
Factor 1. Environmental-societal influences (6 items, eigenvalue 9.34, alpha .90)
Items: historical trends, political decisions, socioeconomic status, globalisation, media, geographical location Factor 2. Interests and beliefs (4 items, eigenvalue 2.96, alpha .80)
Items: interests, beliefs, values, personality Factor 3. Interpersonal influences (2 items, eigenvalue 1.55, alpha .84)
Items: family, peers Factor 4. Skills and abilities (3 items, eigenvalue 1.26, alpha .75)
Items: ability, aptitudes, skills Factor 5. Age and experience (3 items, eigenvalue 1.18, alpha .74)
Items: age, world of work knowledge, physical attributes Factor 6. Gender and health (2 items, eigenvalue 1.11, alpha .74)
Items: health, gender
In the main, the pilot study factor analysis supported the idea of three career development systems. The factor solution showed one environmental-societal system factor (factor 1), four intrapersonal system factors (factors 2, 4, 5 and 6), and one social system factor. This six-factor solution was then tested with professional artists and arts students in Study 1, outlined in Chapter 6. 152
Career Success
Multiple measures of career success were applied in the current program of study. An exploratory approach was taken, because of deficiencies in existing conceptualisations and measures of career success, particularly as they apply to protean careerists. For instance, many frequently used measures of objective, externally observable career success are probably not relevant to artists (e.g., hierarchical position in an organisation). Also, objective measures such as pay levels and continuity of employment are strongly affected by factors outside the artist’s control, such as labour market conditions.
Subjective career success is based on an individual’s subjective judgements of the positive outcomes of his / her work experiences. It is most frequently operationalised in the literature as career satisfaction (Baruch, 2004; Burke, 1999;
Judge et al., 1995; Ng et al., 2005), and measured by a five-item scale developed by
Greenhaus et al. (1990). Unfortunately, this scale was developed for use with organisational managers, and thus is probably of limited usefulness with artists, individuals who have substantially different career experiences and motivations than
‘traditional’ careerists.
In order to explore artists’ conceptions of career success, participants were asked the following open-ended question: “How would you define career success in your field/s of arts?” They were then asked to rate their career success in arts on a 1-
6 Likert scale (ranging from ‘not successful at all’ to ‘highly successful’) using their own definitions.
Some established measures of career success were included in the surveys, in order to explore the relationship between career success as it is defined by artists and more usual measures of career success, and also to compare the predictors of each 153 type. The most relevant of the measures of career success as suggested by the literature (e.g., Bridges, 1995; Fugate et al., 2004; Kanter, 1989; Mirvis & Hall,
1996) was arts employability and overall employability, with arts employability defined as the artist’s ability to create or obtain arts work, and employability overall defined as the artists’ ability to create or obtain work of all kinds. Participants were therefore asked to indicate on a 1-6 Likert scale how employable they believed they were in their artistic field/s, and generally (ranging from ‘not at all employable’ to
‘highly employable’).
Measures of objective career success were also included. Participants were asked how much they earned from their career in arts, and from all sources of income. Two earnings measures were included because previous work by Throsby and Hollister (2003) and others suggested that many artists have multiple concurrent employment arrangements within and outside arts. Consequently, total income may not necessarily be a good indicator of arts career success in artists. However, total income is the most commonly used measure of career success in the literature (Gunz
& Heslin, 2005).
Sociodemographic Variables
A large number of sociodemographic variables were included in the surveys in addition to the sociodemographic measures. Parsimony was a major consideration in selection of the demographic variables to be used in the analysis studies. Although it may be tempting to include as many predictors as possible in regression procedures, this practice can inflate the type 1 error rate (i.e., ‘capitalise on chance’ by increasing the likelihood that there will be significant predictors in the model).
This section considers the sociodemographic variables included in the study and provides a rationale for the inclusion or non-inclusion of each sociodemographic 154 item in the career success prediction models employed in Studies 3 and 4. The sociodemographic variables used in the professional artists’ and arts students’ predictive models are summarised in Table 5.7. Comprehensive descriptive statistics for each of these variables are presented in Chapters 8 and 9, at the beginning of the predictive modelling sections.
Arts discipline category was included as a predictor in the arts students’ and professional artists’ models, as previous research has suggested that creative, performing and technical artists may have somewhat different patterns of work and experience slightly different career demands (Australian Bureau of Statistics, 2005;
Throsby & Hollister, 2003; Tremblay, 2003). In addition, the sample sizes for the disciplines were large enough to be suitable for further analysis.
Previous research also suggested that levels of work experience might predict a more successful transition to the world of work for the arts students’ sample
(Cranmer, 2006; Harvey et al., 1997). The arts students’ time 1 survey asked about levels of work experience both in arts and outside arts. Nearly all of the students
(89.91%) reported that they had some or extensive paid work experience outside arts, so the variable was not suitable for inclusion in the predictive models. However, the arts work experience variable was suitable for inclusion. A significant spread in levels of work experience was evident in the sample, with 22.13% of students reporting that they had no arts work experience, 31.97% reporting that they had some unpaid work experience, 31.97% reporting that they had some paid work experience, and 11.48% reporting that they had significant paid work experience in arts.
All of the arts students were at the point of completing a tertiary course in arts at time 1. However, the professional artists had variable levels of education in their artforms, with 20.65% not having undertaken formal study in their art form, and it 155 was thought that level of arts education might feasibly be a predictor of career success. Formal education level was therefore included in the professional artists’ predictive model.
Professional artists were also asked about whether they worked or sustained a career outside arts. Work outside arts will certainly have an effect on an artist’s total earnings, one of the measures of career success used in the present program of study.
In addition, work outside arts may have an effect on arts earnings, as time the artist spends working outside arts will reduce the time they can spend working in their art form (Abbing, 2003; Throsby, 1994b). In the sample of professional artists under study, 20% did not work outside arts at all, 42.90% did some work outside arts, and
37.10% indicated that they had a ‘career’ outside arts in addition to their arts practice.
Length of time (number of years) working in the arts was also a strong potential predictor of career success in the professional artists cohort, and exhibited a good spread of values (0 – 45 years, mean = 13.79, SD = 9.75). Artists who have spent many years in arts may be more successful than other artists. The bidirectional nature of the potential relationship was noted: successful artists may also be likely to remain in the arts for longer.
Gender, as a basic sociodemographic variable, was included in both the student and professional artist predictive models. Some gender differences in career self-management and career success have previously been observed in other populations of careerists (Dyke & Murphy, 2006; Sturges, 1999), and it is reasonable to suggest that there might also be gender differences observed in the current samples. 156
Age of the participants was also included as a variable in the models. The
potential covariation between level of work experience, career self-management
skills, and (in the case of the professional artists) number of years working in the arts
with age was noted. However, as noted in Chapter 8, a key strength of the decision
tree approach used in the studies is its robustness to high correlations amongst
predictor variables. The algorithm simply chooses the variable with the best
predictive power at each decision point, and identifies where other variables have
almost as good predictive power through ‘competing splits’.
Table 5.7
Sociodemographic Research Measures Used in the Present Investigation
Item Item type Survey Professional Arts Students Arts Students Artists Time 1 (Oct 2005) Time 2 (Oct 2006) Sociodemographic variables Arts discipline Arts students: coded from course type Professional artists: coded from main artistic occupation Gender Single response categorical Age Numeric Work experience in arts Single response categorical Current work outside arts Single response categorical Arts educational Multiple response background – formal categorical education Length of time working in Numeric the arts – number of years
In some cases, sociodemographic variables included in the survey were not
used in the main studies because the samples were fairly homogenous with respect to
that variable. In both samples, a very small number of participants identified 157 themselves as Aboriginal or Torres Strait Islanders, having a disability, or being of non-English speaking background. These variables were therefore not included in the predictive models. Other variables were considered to have limited predictive power, and in the interests of parsimony were not incorporated into the models. These variables included region of residence, and marital status.
Analysis Design
Quantitative survey data was analysed using descriptive and inferential tests available in SPSS version 12.0, AMOS version 15.0 for confirmatory factor analyses via SEM, and SAS version 9.1 (Enterprise Miner version 4.1) for decision tree analyses. A hand coding procedure was employed for content analysis of the open- ended question, and the binary ‘dummy’ codes for each theme were then entered into
SPSS for the purposes of future analyses.
Study 1
Study 1, outlined in Chapter 6, examines the psychometric properties of the career development measures created and piloted by the researcher prior to this investigation. The purpose of this study was to ensure that the researcher-developed measures quantified concepts they were intended to measure (validity), and that error associated with the measurement was minimised (reliability) (de Vaus, 1995; Litwin,
1995). The analysis design for Study 1 is presented in Table 5.8. 158
Table 5.8
Study 1 Analysis Design
Chapter Research Question Samples Measures Analysis Techniques
6 Are the following researcher Professional Career Development Confirmatory constructed measures sufficiently artists Influences Scale factor valid and reliable when used analysis, Arts students Protean Career with the study samples? reliability at time 1 (at Success Orientation analysis a) career development influences course Scale completion) b) protean career success Career Management orientation Competence Scale c) career management competence
Internal consistency reliability was measured using Cronbach’s alpha, which is an assessment of homogeneity across all items (Fink, 2003, 2006; Litwin, 1995; Salant
& Dillman, 1994). Factorial validity testing, a type of construct validity testing, was conducted to further gauge the construct validity of the researcher designed measures
(Froman, 2001). Construct validity is the extent to which inferences from scores on a test can be made in relation to the construct of interest (Litwin, 1995), and in the instance of factorial validity means that the factors obtained when the data is factor- analysed correspond to those suggested by the underlying theory used to construct the measure, and also previous empirical work (Messick, 1995).
In Study 1, confirmatory factor analysis procedures via structural equation modelling using maximum likelihood estimation were conducted with both the professional artists’ sample and the arts students’ sample. The factorial structures suggested by exploratory factor analyses carried out with the pilot study data were used. Minor factorial model misspecifications (i.e., limited small instances where the hypothesised model and the tested data do not fit) are common when confirmatory factor analysis is undertaken, and post hoc respecification and modification 159 procedures are therefore often employed (Byrne, 2001). This process was undertaken with the three measures in the present investigation.
Study 2
Study 2, presented in Chapter 7, involved an exploration of the construct of career success in professional artists and arts graduates. First, the professional artists’ and arts graduates’ definitions of career success were systematically content analysed and the major theme categories were dummy coded for each participant record. This procedure enabled the categorisation of participants by their definitions of career success. Then, for each category of participant, correlational analyses were performed between the career success measures in order to explore the statistical relationships between them. The analysis design for this study is outlined in Table
5.9. 160
Table 5.9
Study 2 Analysis Design Chapter Research Question
7 How can career success in the arts be defined?
Research Subquestion Samples Measures Analysis Techniques
What are the artists’ definitions of Professional Open-ended career Content career success? artists success definition analysis Arts students item at time 2 (one year after course completion)
What is the statistical relationship Professional Career success Non- between the measures of career artists definition category parametric success employed in the present Arts students Self-defined career correlations study? at time 2 (one success rating for each success year after Overall definition course employability rating completion) category Arts employability rating Earnings in arts Total earnings
Content analysis is the systematic, quantitative, and replicable analysis of message characteristics (Neuendorf, 2002). The characteristics analysed can be manifest content, the “elements that are physically present and countable” (Gray &
Densten, 1998, p.420) such as words, or latent content, the unobserved concepts or themes which are represented by manifest content. Content analysis can be as simple as doing word-frequency count, operating on the assumption that the most commonly mentioned words are the most important (Carley, 1990). It gains in complexity and interest when words with similar meanings or connotations are grouped together into categories (Weber, 1990). Some content analysts argue that for a content analysis procedure to be valid, these categories must be mutually exclusive and exhaustive
(U.S. General Accounting Office, 1996). 161
The analysis of content variables can occur by using theory and past research to look for variables of interest (a priori coding). Coding can also occur in a grounded, ‘emergent’ way whereby a qualitative scrutiny of a subset of the data is undertaken, and common variables emerge (Stemler, 2001). Content analysis with two coders was chosen as the analysis approach for the open-ended questions in this program of study for a number of reasons. First, it codes data in ways which can be used in quantitative analysis (Krippendorff, 1980; Stemler, 2001; Weber, 1990). The open-ended responses to the survey questions can therefore be included in quantitative descriptive and inferential tests along with the closed-ended questions.
Second, content analysis is a transparent research method which places an emphasis on reliability and validity. Like other quantitative data, coded content analysed data can be generalised to wider populations, and content analysed studies can be replicated and followed up (Bryman, 2004).
In the second part of Study 2, the participants were categorised according to their definitions of career success, as previously content analysed. Bivariate correlations were then conducted between the four measures of career success for each category of participant: self-defined career success rating, arts employability rating, earnings in arts and total earnings. Because the ratings measures were ordinal and the earnings measures displayed non-normal distributions, non-parametric
Spearmans bivariate correlational procedures were appropriate (Gibbons,
Chakraborti, & Gibbons, 2003; Tabachnik & Fidell, 2001). Bonferroni adjustments were used to control the type 1 error rate across the multiple correlations.
Study 3
In Study 3, presented in Chapter 8, a decision tree approach was used to predict career success in professional artists from the career development measures 162 validated in Study 1, career success definition category from Study 2, and sociodemographic variables of interest. The earnings and self defined career success ratings were used as the career success measures, and the employability measures were not, because of the very high correlations between earnings levels and the employability ratings. The analysis design for this study is shown in Table 5.10.
The decision tree method employed in Study 3 is known as a regression tree.
A regression tree is used to predict the values of a continuous response variable (e.g., earnings) or ordinal response variable (e.g., self defined career success rating) from continuous and categorical predictors.
Table 5.10
Study 3 Analysis Design
Chapter Research Question Sample Measures Analysis Techniques
8 Which of the measured career Professional Predictor variables: Decision development influences and artists tree analysis Career Development constructs predict career Influences (CART) success in professional artists? Protean Career Success
Orientation Career Management Competence Career success definition category Sociodemographic variables Criterion variables: 1. Self-defined career success rating 2. Earnings in arts 3. Total earnings
The classic computational algorithm for regression trees (CART) was popularised by Breiman, Friedman, Olshen, & Stone (1984; see also Hastie,
Tibshirani, & Friedman, 2001; Ripley, 1996). At each step, the program finds a 163 logical split condition to assign observations to a number of ‘child nodes’. The final results of using tree methods for classification or regression can be summarised in a series of logical if-then conditions. The inverted tree structure results generated from
CART analysis are easily interpretable.
A regression tree approach was chosen for this study because unlike traditional parametric regression techniques (Tabachnik & Fidell, 2001), regression trees do not rely on statistical assumptions of any kind for the predictor and criterion variables. For instance, there is no assumption that the underlying relationships between the predictor variables and the dependent variable are linear, and that they
(and their errors) are normally distributed. CART is not affected by outliers, and can accommodate and make use of collinearity (via ‘surrogate splits’) and variable interactions (Breiman et al., 1984).
Study 4
Study 4, in Chapter 9, also employed a CART decision tree approach to predict career success in arts graduates from the career development measures validated in Study 1, career success definition category from Study 2 (earnings and self defined career success but not employability, as per Study 3 with professional artists), and sociodemographic variables of interest. All of the predictor variables used in the arts students regression tree analysis were measured at time 1 (October
2005), and the career success measures were measured at time 2, one year later. The analysis design for this study is presented in Table 5.11. 164
Table 5.11
Study 4 Analysis Design
Chapter Research Question Sample Measures Analysis Techniques 9 Which of the measured career Arts Predictor variables: Decision development influences and students at tree analysis Career Development constructs measured at time 1 (at Influences (CART) undergraduate course course completion predict successful completion) Protean Career Success transition to the world of arts Orientation
work? Career Management Competence Career success definition category Sociodemographic variables
Arts Criterion variables: students at 1. Self-defined career time 2 (one success rating year after course 2. Earnings in arts completion) 3. Total earnings
Chapter Summary
This chapter has presented the methodological issues associated with the program of study at hand. Four research questions are addressed over four sequential studies, with the overall aim of determining which of several potential predictors of career success are relevant in two populations of artists: tertiary arts students who are undertaking a university-to-work transition, and practising professional artists. From this, key skills and other factors can be identified which will enhance the career development and success of artists and arts graduates and can be built into education and career development programs for the arts.
165
This chapter summarised the present investigation’s online survey approach, prospective / cross sectional quantitative research design, sampling procedures, survey composition and measure development, and data analysis techniques.
Chapters 6 - 9 will outline the results of the four sequential studies, and Chapter 10 will present a discussion of the various study findings and an overview of the research implications.
166
CHAPTER 6
Study 1: Development and Validation of Career Development Influences, Protean Career Success Orientation and Career Management Competence Scales
The primary objective of Study 1 is to examine and where necessary refine the researcher-developed measures to be used in the subsequent Studies. Study 1 addresses the first research question of the present program of inquiry: Are the following researcher constructed measures sufficiently valid and reliable when used with the study samples? a) career development influences; b) protean career success orientation; c) career management competence.
Data Analysis
In the present study, the psychometric properties of the researcher-developed measures of career development influences (CDI), protean career success orientation
(PCSO) and career management competence (CMC) were evaluated using two data sets. The evaluation was conducted with data obtained from the professional artists questionnaire (N = 310) and the arts students questionnaire (at time 1, N = 218).
These sample sizes were deemed to be adequate using the common rules of thumb of
5 to 10 times as many cases as variables for sufficient factor analytical power
(Gorsuch, 1983), and a minimum of 200 cases for structural equation modelling
(SEM) (Curran, Bollen, Paxton, Kirby, & Chen, 2002).
Confirmatory factor analyses via SEM using maximum likelihood estimation were conducted to verify the factorial composition of each scale. Where a less than adequate model fit was detected, model respecification was conducted. Cronbach’s alpha reliability coefficient was calculated to assess the internal consistency of the scales, and full path diagrams showing standardised estimates and squared multiple correlations were included to provide further evidence of the scales’ construct 167 validity. Data were analysed using the Statistical Package for the Social Sciences
(SPSS) 14.0.0, and confirmatory modelling was undertaken using Analysis of
Moment Structures (AMOS) 6.0.0.
Career Development Influences Scale
Based on exploratory factor analyses conducted with data obtained in the pilot study of Education Faculty students (as summarised in Chapter 5 and Appendix
D), it was expected that an oblique six-factor solution would be identified when confirmatory factor analyses were conducted with the CDI scale data, and that an acceptable level of internal consistency would be observed. The hypothesised six- factor model path diagram is presented in Figure 6.1.
Data Screening Procedures
Prior to analysis, the data were checked for violations of assumptions of univariate and multivariate normality, linearity and factorability. McDonald and Ho
(2002) maintained that much of social and behavioural sciences data are non-normal in nature, and that maximum likelihood estimation and its associated statistics seem fairly robust against moderate violations of this type, at least in terms of parameter estimation. Arbuckle (2005) indicated that when conducting a confirmatory factor analysis using AMOS, it is usually sufficient to establish multivariate normality of the observed variables.
To determine the impact of outliers on normality of the variables measured, all outliers, including both univariate outliers on the total CDI scale and its subscales, and also multivariate outliers across subscales were removed from the data sets. This resulted in the removal of 11 professional artists from one data set and 7 arts students from the other data set. The data were then analysed with the outliers removed. 168
Removal of the outliers made no major difference to results, with significance levels remaining consistent and no substantive differences in the relative magnitude of correlations. It was decided to retain the outliers in the samples.
Figure 6.1
Path diagram for the hypothesised six factor model of the CDI 169
Most of the item distributions exhibited moderate negative skewing.
Tabachnik and Fidell (2001) argued that, “if all variables are skewed to about the same moderate extent, improvements of analysis with transformations are often marginal” (p. 81), and therefore the majority of items were not transformed to correct for this non-normality. However, items on three subscales exhibited greater than average skewing towards the positive end of the distribution in both data sets (using descriptive indicators and the Kolmorogov-Smirnov inferential test), as was also observed in the pilot study data. These subscales were skills and abilities, interpersonal influences, and interests and beliefs. Logarithmic transformations and reflection were conducted with the variables which initially exhibited excessive skewness: aptitudes, abilities, skills, interests, beliefs, peers, and family. These transformations successfully corrected the skewing in these variables, and the transformations were retained for the remainder of the analyses. The standardised item means and standard deviations, after transformations where appropriate, are presented in Table 6.1. 170
Table 6.1
Item Means and Standard Deviations: CDI in Professional Artists Sample and Arts
Students Sample
Professional artists sample (N = 310) Arts students sample (N = 218) Mean SD Mean SD 1. historical trends 3.26 1.11 3.39 1.00 2. political decisions 3.40 1.15 3.46 0.92 3. socioeconomic status 3.39 1.17 3.67 1.02 4. globalization 3.55 1.11 3.72 0.99 5. media 3.50 1.23 3.80 1.00 6. geographical location 3.72 1.35 3.93 1.15 7. ability* 3.33 1.11 3.32 1.16 8. aptitudes 3.92 0.98 4.03 1.04 9. skills* 4.06 0.88 4.32 1.05 10. family* 3.11 1.12 3.47 1.28 11. peers* 4.27 1.05 4.27 1.10 12. age 4.01 1.16 4.20 1.09 13. world of work knowledge 4.01 1.21 4.08 1.20 14. physical attributes 4.11 1.01 4.07 1.09 15. health 4.21 1.42 3.97 1.21 16. gender 3.98 1.15 3.95 0.99 17. interests* 3.23 0.89 3.78 1.17 18. beliefs* 3.86 1.13 3.41 1.17 19. values 4.07 1.08 3.62 1.23 20. personality 3.80 1.15 3.78 1.18 Note. Scale range 1-6. * logarithmically transformed and reflected items
Assumptions for linearity and homoscedasticity were examined in each cohort separately using bivariate scatterplots. With 20 variables in each cohort, over
600 scatterplots were possible. A sample of scatterplots, including the variables most expected to exhibit departure from linearity (i.e., those transformed in the previous section), were therefore examined. Minimal evidence of nonlinearity or heteroscedasticity was found. 171
In order to investigate potential multicollinearity within the data sets, Pearson correlations amongst the observed variables were conducted, as shown in Tables 6.2 and 6.3. Although many of the correlations between the variables were statistically significant in both data sets, no variables were very highly correlated (above .90).
When collinearity diagnostic tests were performed, no evidence of collinearity assumption violation was found.
The variables of ethnicity, sexual orientation and disability, for which a high proportion of missing values was found in all data sets, were not included in the model to be tested, and therefore were not included in the analysis. For the remainder of the variables, missing values represented 2.5% of the data in the professional artists data set, and 2.3% in the arts students data set, and no distinct ‘patterns’ of missing data were observed (McDonald & Ho, 2002). In order to retain as many cases as possible, these missing values were replaced with the series mean. 172
Table 6.2
Inter-item Correlations: CDI in Professional Artists Sample, N = 310
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1. historical trends 1.00 2. political decisions 0.34 1.00 3. socioeconomic status 0.47 0.35 1.00 4. globalisation 0.25 0.28 0.45 1.00 5. media 0.19 0.29 0.18 0.19 1.00 6. geographical location 0.10 0.32 0.22 0.22 0.13 1.00 7. ability 0.07 0.09 0.16 0.13 0.21 0.13 1.00 8. aptitudes 0.06 0.09 0.12 0.14 0.20 0.04 0.52 1.00 9. skills 0.09 0.14 0.07 0.16 0.18 0.12 0.43 0.52 1.00 10. family 0.08 0.04 0.10 0.16 0.10 0.13 0.26 0.12 0.14 1.00 11. peers 0.12 0.19 0.07 0.13 0.22 0.12 0.24 0.22 0.27 0.47 1.00 12. age 0.04 0.18 0.10 0.10 0.21 0.28 0.23 0.06 0.18 0.20 0.26 1.00 13. world of work knowledge 0.15 0.14 0.21 0.10 0.20 0.23 0.24 0.25 0.24 0.10 0.18 0.16 1.00 14. physical attributes 0.06 0.04 0.03 0.02 0.10 0.12 0.15 0.02 0.13 0.10 0.15 0.42 0.15 1.00 15. health 0.10 0.08 0.12 0.08 0.08 0.07 0.25 0.08 0.15 0.20 0.20 0.44 0.18 0.40 1.00 16. gender 0.12 0.11 0.06 0.13 0.12 0.10 0.24 0.08 0.18 0.18 0.18 0.43 0.07 0.36 0.49 1.00 17. interests 0.09 0.12 0.04 0.11 0.08 0.16 0.23 0.20 0.46 0.17 0.20 0.14 0.08 0.10 0.06 0.23 1.00 18. beliefs 0.19 0.20 0.16 0.02 0.12 0.23 0.33 0.24 0.35 0.16 0.22 0.22 0.18 0.17 0.12 0.13 0.37 1.00 19. values 0.13 0.11 0.13 0.06 0.07 0.24 0.35 0.26 0.37 0.24 0.23 0.23 0.20 0.20 0.15 0.15 0.36 0.60 1.00 20. personality 0.11 0.13 0.11 0.12 0.16 0.12 0.25 0.24 0.24 0.20 0.34 0.27 0.23 0.15 0.19 0.20 0.25 0.35 0.33 1.00 173
Table 6.3
Inter-item Correlations: CDI in Arts Students Sample, N = 218
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 1. historical trends 1.00 2. political decisions 0.25 1.00 3. socioeconomic status 0.53 0.30 1.00 4. globalization 0.44 0.24 0.44 1.00 5. media 0.41 0.20 0.26 0.25 1.00 6. geographical location 0.11 0.16 0.17 0.04 0.09 1.00 7. ability 0.22 0.04 0.08 0.04 0.15 0.02 1.00 8. aptitudes 0.07 0.04 0.08 0.08 0.02 0.08 0.63 1.00 9. skills 0.19 0.08 0.16 0.11 0.24 0.09 0.66 0.67 1.00 10. family 0.21 0.10 0.06 0.05 0.15 0.28 0.30 0.24 0.35 1.00 11. peers 0.15 0.02 0.06 0.10 0.14 0.09 0.25 0.25 0.37 0.54 1.00 12. age 0.22 0.14 0.10 0.12 0.01 0.06 0.04 0.01 0.11 0.09 0.13 1.00 13. world of work knowledge 0.15 0.03 0.15 0.10 0.19 0.12 0.21 0.26 0.35 0.14 0.22 0.15 1.00 14. physical attributes 0.19 0.20 0.09 0.12 0.06 0.04 0.03 0.01 0.07 0.07 0.09 0.53 0.10 1.00 15. health 0.21 0.06 0.15 0.10 0.05 0.12 0.14 0.14 0.18 0.16 0.19 0.41 0.25 0.38 1.00 16. gender 0.10 0.01 0.02 0.05 0.12 0.07 0.07 0.06 0.03 0.17 0.13 0.43 0.14 0.30 0.38 1.00 17. interests 0.18 0.05 0.14 0.13 0.11 0.04 0.25 0.33 0.47 0.08 0.08 0.20 0.17 0.10 0.15 0.01 1.00 18. beliefs 0.17 0.10 0.10 0.14 0.08 0.10 0.16 0.22 0.28 0.07 0.10 0.13 0.20 0.05 0.12 0.09 0.56 1.00 19. values 0.17 0.04 0.13 0.18 0.16 0.10 0.17 0.18 0.34 0.08 0.08 0.01 0.24 0.03 0.12 0.01 0.62 0.64 1.00 20. personality 0.13 0.05 0.14 0.05 0.07 0.01 0.31 0.34 0.35 0.03 0.05 0.13 0.11 0.06 0.02 0.01 0.47 0.39 0.28 1.00 174
Confirmatory Factor Analyses: Six Factor Structure
The first step in model assessment was examination of the model parameter estimates. The feasibility of estimated values and appropriateness of the standard errors were assessed, and then the statistical significance of parameter estimates was tested, in order to assess whether all of the included variables were statistically important to the model. The standardised parameter estimates plus the Squared
Multiple Correlations (SMCs) and error values for the 6-factor model are presented for the two data sets in Figures 6.2 and 6.3.
SMC values, denoted in italics in Figures 6.2 and 6.3, can be interpreted as the percentage variance explained in each endogenous variable (item) by its relative latent construct (factor) (Schreiber, Stage, King, Nora, & Barlow, 2006). The age and experience construct explained relatively little variance in world of work knowledge (7% for the professional artists’ sample and 6% for the arts students’ sample); a number of the SMC values for environmental / societal influences were also moderately low.
The factor loadings for the model are shown as arrows between the latent constructs and the endogenous variables in Figures 6.2 and 6.3. Comrey and Lee
(1992) suggested that in factor analysis parameter estimates of .70 are excellent, .63 are very good, .55 are good, .45 fair and .32 poor. According to these guidelines, world of work knowledge, media, political decisions and geographical location exhibited less than ‘fair’ factor loadings with at least one data set, although the
AMOS output reported all loadings were significant at p < .01 or better. The majority of factor loadings were ‘very good’ to ‘excellent’ across both data sets, with the most 175 consistently strong factor loadings observed with the interpersonal influences and skills and abilities factors.
Figure 6.2
Standardised estimates and SMCs for the six factor model: Professional artists sample
The correlations between the factors were acceptably moderate in both data sets, with the exception of the correlation between the age and experience and 176 gender and health factors (.89 for the professional artists’ sample and .88 for the arts students’ sample). The high correlation between these two factors indicated poor discriminant validity, and the potential for multicollinearity and inflated errors if the factors were used in a regression equation. In this situation, it has been suggested to collapse the two factors and test to see whether the model fit worsens (Bollen, 2002).
Figure 6.3
Standardised estimates and SMCs for the six factor model: Arts students sample 177
Chi-square goodness-of-fit tests assessing overall model fit were also conducted. The results indicated that the model fit was not adequate for either data set, at χ2 (155) = 340.40, p < .0001 for the professional artists’ sample and χ2 (155) =
286.09, p < .0001 for the arts students’ sample. However, the chi-square goodness- of-fit statistic has been documented often to be affected by sample size, with larger samples finding poor fit more often than warranted (Arbuckle, 2005; Byrne, 2001;
Ullman, 2001). With adjustment for degrees of freedom, the chi-square statistics indicated a fairly good fit, at 2.20 and 1.85 respectively. A good overall model fit is usually indicated by a χ2 / df value of smaller than or equal to 3 (Byrne, 2001).
It is possible to have a model that is statistically acceptable (in that it shows
an adequate chi-square value) but has a poor fit in certain parts of the model, little
predictive power, or poor theoretical value. It is therefore good practice to report a
number of test statistics, with the greater the number of tests supporting the model
fit, the greater the confidence in the hypothesised model (Kline, 1998). The other
fit indices most commonly employed in the literature, as documented by
Abramson, Rahman and Buckley (2005), are of several types. One set of test
statistics compares the hypothesised model with an independence, or null model
where the constructs are uncorrelated; these include the Non-Normed Fit Index
(NNFI) and the Comparative Fit Index (CFI). The test statistics which compare the
observed covariance matrix against that constructed through the model include the
adjusted goodness of fit index (AGFI), and the standardised root mean squared
residual (SRMR). The root mean square error of approximation (RMSEA)
compares observed and predicted covariance matrices and includes a model
parsimony criterion. 178
The fit indices for the hypothesised six factor model across the two samples of interest are shown in Table 6.4. The NNFI and CFI results, when compared to a critical value of .90, indicated a borderline suboptimal fit for the hypothesised model when compared to the null model for both samples. The RMSEA, with a critical value of < = .08 indicated a fairly good parsimonious model fit, but the AGFI
(critical value > = .90) was also borderline. The SRMR, with a critical value of = <
.05, indicated a less than adequate fit.
Table 6.4
Fit Indices for Six Factor Model of CDI Using Two Data Sets
Data set NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Professional artists (N = .79 .87 .06 .88 .08
310)
Arts students (N = 218) .80 .89 .06 .90 .07
Taken together, the indices showed that the model would benefit from some respecification in order to improve the fit. An examination of the standardised covariance residuals for the model also suggested the need for model respecification
(Byrne, 2001), with several values greater than the cut point of 2.58.
Model Respecification
With the analyses of the two samples revealing that the model was somewhat inconsistent with the data, the analyses then entered an exploratory, rather than confirmatory, mode. The aim of model respecification was to identify the source of 179 misfit in the original model and to determine a model that better describes the sample data (Byrne, 2001), with the ultimate objective of finding a model that was substantively meaningful and statistically well-fitting (Abramson et al., 2005). In the interests of maximising the validity of the model results and minimising the likelihood of model overfitting (where parameters which represent weak non- replicable effects can be included in the model and standard errors inflated), the model respecification was conducted with one sample of participants (the professional artists), and once a satisfactory model was generated, a further confirmatory factor analysis with the revised model was conducted with the remaining sample (arts students).
Models can be ‘trimmed’ or ‘built’ by removing or adding direct effects, and can also be modified by reconfiguring the relationships between variables. However, it is advisable to limit model changes due to the increased risk of making a Type I error (Byrne, 2001), and for clarity to make changes to the model one parameter at a time (Ullman, 2001). It is also generally preferred to respecify models only when theoretically congruent to do so (Schreiber et al., 2006).
Nine large standardised covariance residuals of greater than 2.58 were observed in the matrix. Seven of these involved the world of work knowledge variable, indicating that this variable was associated with many other latent and measured variables in the model, and was therefore a candidate for deletion, as per
Anderson and Gerbing’s (1988) guidelines.
Modification index values were examined in order to reveal cross-loadings and misspecified error covariances (where there is systematic error in item responses or item redundancy). The parameter with the highest three modification index values was world of work knowledge, which cross-loaded onto the interests and beliefs 180 factor, the skills and abilities factor, and the environmental-societal influences factor as well as the hypothesised loading onto the age and experience factor.
The modification index values and the standardised covariance residuals, combined with the item’s low SMC values and poor factor loadings, suggested that world of work knowledge was an important and complex variable which was strongly related to many of the measured and unmeasured constructs in the six-factor model of career development influences.
It was therefore decided to trim world of work knowledge from the model altogether, leaving the parameters of age and physical attributes linked to the latent construct of age and experience. The respecified model was then compared with the originally hypothesised 6-factor model for fit.
With adjustment for degrees of freedom, the chi-square statistic indicated a better fit for the respecified model, at χ2/ df (137) = 2.04 than observed with the original model. The rest of the indicators also showed an improved and acceptable model fit for the respecified model (see Table 6.5).
In a second stage of respecification, the age and experience and gender and health factors, which were found to be correlated to an unacceptably high degree when the six-factor model was tested, were then collapsed into one factor, physical characteristics, with a total of four associated endogenous variables: health, gender, age and physical attributes, as shown in Figure 6.4. It was not expected that this respecification would improve model fit, but rather would ensure better instrument discriminant validity and provide a more parsimonious solution. The model was then retested to assess whether the fit was appreciably poorer after the second respecification. 181
Figure 6.4
Path diagram for the respecified five factor model of the CDI
With adjustment for degrees of freedom, the chi-square statistic indicated a similarly acceptable fit for the second respecified model over the first respecification and the original model, at χ2 / df (142) = 2.04. The rest of the indicators continued to 182 show an acceptable model fit for the second respecified model, with the NNFI value continuing to show a borderline result (see Table 6.6).
Table 6.5
Fit Indices for Six Factor Model of CDI and Respecifications: Professional Artists
Sample (N = 310)
NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Original model .79 .87 .06 .88 .08
Respecified model: world of work .84 .90 .05 .91 .06
knowledge removed
Respecified model: age and .83 .90 .05 .90 .06
experience and gender and health
collapsed into one factor
The final model, post-respecification, is presented in Figure 6.5, and shows consistently ‘fair’ or better factor loadings using Comrey and Lee’s (1992) guidelines. Media and geographical location continued to exhibit lower factor loadings than this, but the loadings were still statistically significant at p < .0001.
Good factor correlations were observed with the five factor model in the professional artists’ sample. Internal consistency for the scales was measured by Cronbach’s alpha. The coefficients for the five factor scales, as shown in Table 6.6, varied between .69 (environmental / societal influences and interpersonal influences) and
.76 (physical characteristics), with no evidence of coefficient improvement should an item be dropped from any of the scales. A commonly used statistical guideline 183 suggested by Nunnally (1978) proposed that in new research areas and initial scale development, coefficient alphas of around .70 are acceptable, and it was therefore decided to retain the respecified model rather than perform further exploratory scale analyses. The total 19 item scale obtained an acceptable alpha level of .82.
Figure 6.5
Standardised estimates and SMCs for five-factor model of CDI: Professional artists’ sample (N = 310) 184
Table 6.6
Internal Consistency of the Respecified CDI Five Factor Model: Professional Artists
Sample (N = 310)
Scale Name Cronbach’s α
Environmental / societal influences .69
Skills and abilities .75
Interpersonal influences .69
Physical characteristics .76
Interests and beliefs .70
Total CDI scale .82
Confirmatory Factor Analysis: Respecified Five Factor Structure
The respecified model was then tested with the arts students’ sample, once again using a confirmatory factor analysis approach with maximum likelihood estimation. The standardised estimates and SMCs for the five-factor model are presented in Figure 6.6. Most parameter estimates were ‘good’ or better according to the guidelines suggested by Comrey and Lee (1992). The items with lower loadings than this, geographical location, media, and political decisions, were nonetheless statistically significant at a minimum of p < .01. The factors were shown to be moderately correlated, as expected. 185
Figure 6.6
Standardised estimates and SMCs for five factor model of CDI: Arts students sample
(N = 218)
With adjustment for degrees of freedom, the chi-square statistic for the model indicated a fairly good fit, at χ2 / df (142) = 1.75. All of the indicators, apart from the
NNFI, provided support for the respecified model, as shown in Table 6.7. The NNFI 186 provided support for the respecification over the original hypothesised model, but the value fell just outside the criterion for good fit of .90. In all, the aggregated fit results give evidence for the construct validity of the scale, with a satisfactory model fit for the five-factor model across two samples.
Table 6.7
Fit Indices for Respecified Five Factor Model of CDI with Arts Students (N = 218)
NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Arts students .83 .90 .05 .90 .07
Internal consistency for the scales was once again measured by Cronbach’s alpha, and assessed against Nunnally’s (1978) statistical guideline of .70. The coefficients for the scales, as shown in Table 6.8, ranged from .68 (environmental / societal influences) to .85 (skills and abilities). The overall 19 item scale obtained a good alpha level of .79. 187
Table 6.8
Internal Consistency of the Respecified CDI Five Factor Model: Arts Students
Sample (N = 218)
Scale Name Cronbach’s α
Environmental / societal influences .68
Skills and abilities .85
Interpersonal influences .70
Physical characteristics .73
Interests and beliefs .80
Total CDI scale .79
Protean Career Success Orientation Scale
Exploratory Factor Analyses conducted with data obtained in the pilot study of
Education Faculty students (as discussed in Chapter 5 and Appendix D) yielded a one-factor solution, with the resultant factor named Protean Career Success
Orientation. The hypothesised single factor structure, based on 7 items, is presented in the path diagram shown in Figure 6.7. 188
Figure 6.7
Path diagram for the hypothesised single factor model of PCSO
Data Screening Procedures
The two data sets were screened for violations of statistical assumptions.
There were 4 multivariate outliers in the professional artists data set, and 5 in the arts student data set. In addition, all of the Protean Career Success Orientation items were strongly negatively skewed in both data sets, with items in the professional artists data set generally more strongly affected. According to Bradley (1982), taking the inverse is usually the best transformation in such circumstances; therefore inverse transformations plus reflection was conducted for each variable. After transformation the distributions were somewhat improved, but still exhibited consistent skewing. It was decided, in line with Tabachnik and Fidell’s (2001) advice regarding moderately and consistently skewed variables (p.81), to proceed with the analysis, although it was recognised that the continuing non-normality of the data might degrade the results. According to Micceri (1989), the typical impact of violations of the normality assumption in confirmatory factor analysis is to increase the value of the chi-square statistic and the standard errors associated with the parameter estimates, 189 thus decreasing the likelihood of achieving satisfactory model fit rather than erroneously increasing the chances of good model fit values. The item means and standard deviations, post-transformation and rescaled from 1-6, are presented in
Table 6.9.
Table 6.9
Item Means and Standard Deviations: PCSO in Professional Artists Sample and Arts
Students Sample
Professional artists sample (N =310) Arts students sample (N = 218) Mean SD Mean SD 1. Self directed, take personal responsibility 4.54 0.83 4.37 0.88 2. Proactive 4.66 0.65 4.53 0.96 3. Internally motivated 4.72 0.69 4.81 0.98 4. Positive interpersonal orientation 4.80 0.75 4.81 0.85 5. Resilient and adaptable 4.77 0.60 4.79 0.87 6. Open to opportunities 4.85 0.92 4.86 0.90 7. Positive self image 4.60 0.54 4.46 0.87 Note. Scale range 1-6. All items are inversely transformed and reflected.
Assumptions for linearity and homoscedasticity were examined in each cohort using bivariate scatterplots. Because of the continuing non-normality of the items even after transformation, moderately asymmetric distributions were observed in some cases, particularly in the professional artists data set between self directed, take personal responsibility (the most normally distributed item) and the other items.
Bivariate correlations and collinearity diagnostics did not reveal multicollinearity in either data set, with all items revealed to be moderately correlated. The bivariate item correlations are presented in Tables 6.10 and 6.11.
A small number of missing values were observed, representing 1.6% of the professional artists’ data set and 0.9% of the arts students’ data set. Given no 190 evidence of systematic missing data, the missing values were replaced with the series mean in both data sets.
Table 6.10
Inter-item Correlations: PCSO in Professional Artists Sample, N = 310
1 2 3 4 5 6 7 1. Self directed, take personal responsibility 1.00 2. Proactive 0.51 1.00 3. Internally motivated 0.49 0.50 1.00 4. Positive interpersonal orientation 037 0.50 0.43 1.00 5. Resilient and adaptable 0.41 0.57 0.40 0.64 1.00 6. Open to opportunities 0.35 0.46 0.40 0.57 0.50 1.00 7. Positive self image 0.69 0.50 0.65 0.37 0.39 0.34 1.00
Table 6.11
Inter-item Correlations: PCSO in Arts Students Sample, N = 218
1 2 3 4 5 6 7 1. Self directed, take personal responsibility 1.00 2. Proactive 0.37 1.00 3. Internally motivated 0.28 0.44 1.00 4. Positive interpersonal orientation 0.40 0.29 0.36 1.00 5. Resilient and adaptable 0.60 0.36 0.44 0.57 1.00 6. Open to opportunities 0.49 0.39 0.40 0.40 0.68 1.00 7. Positive self image 0.50 0.49 0.25 0.34 0.36 .42 1.00
191
Confirmatory Factor Analyses: Single Factor Structure
Model assessment of the PCSO began with examination of model parameter estimates. The standardised parameter estimates, SMCs and error values for the hypothesised single factor model are presented for the two data sets in Figures 6.8 and 6.9. All standardised regression weights for the items were statistically significant, and represented ‘fair’ or better loadings using Comrey and Lee’s (1992) guidelines. The SMCs indicated that Protean Career Success Orientation accounted for between 28% of the variance in some items (in the professional artists data set, internally motivated and proactive) and 71% of the variance in resilient and adaptable (also professional artists data set).
Figure 6.8
Standardised estimates and SMCs for single-factor model of PCSO: Professional artists sample (N = 310) 192
Figure 6.9
Standardised estimates and SMCs for single-factor model of PCSO: Arts students sample (N = 218)
Chi-square goodness-of-fit tests assessing overall model fit were conducted with the single factor PCSO model. The results indicated that the single-factor model was an unsatisfactory fit in both data sets, at χ2 (14) = 107.34, p < .0001 and χ2 / df =
7.67 for the professional artists’ sample, and χ2 (14) = 126.80, p < .0001, and χ2 / df
= 9.06 for the arts students’ sample.
The other fit indices for the hypothesised single factor model of PCSO are shown in Table 6.12. These indicators also suggested that the single factor model of
PCSO was not entirely adequate for the data, particularly for the arts students’ data. 193
Table 6.12
Fit Indices for Single Factor Model of PCSO
Data set NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Professional artists (N = 310) .87 .88 .15 .83 .09
Arts students (N = 218) .82 .83 .19 .68 .11
Model Respecification
When the analysis revealed that the model was inconsistent with the data, a respecification procedure was undertaken in order to identify sources of misfitting and determine a model which would provide a better fit with the data. The model specification search was conducted with one sample of participants, in this case the arts students, with the aim of then conducting a further confirmatory factor analysis with the professional artists’ data set once a satisfactory model had been generated.
The arts students’ sample was chosen as the initial single-factor model was a worse fit for this sample than for the professional artists’ sample.
An initial step in the specification search procedure was examination of the standardised covariance residuals. No standardised covariance residuals in excess of
2.58 were observed in the matrix. The closest value, 2.55, was found between self- directed, take personal responsibility and positive self image, indicating that a discrepancy between the model and data existed with the covariance between these two variables, and that this discrepancy approached statistical significance.
Modification indices for the variables and error terms were calculated, and relatively high modification index values (MI) were found between err7 and err1 (MI 194
= 42.70), err5 and err4 (MI = 27.61), and err7 and err 3 (MI = 23.67). These values represented correlated errors, which are systematic rather than random measurement error in item responses. These measurement errors can occur through participant characteristics, such as social desirability, or yea/nay-saying, or item characteristics, such as item redundancy (Aish & Joreskog, 1990; Byrne, 2001). In this case, it might be reflective of the non-normal distributions of the variables, with a large number of participants consistently giving ratings of “important”, or “very important” to most items. In addition, a moderately high modification index value of 20.01 was observed between the items self-directed, take personal responsibility and positive self image.
Based on these results, the positive self image item was removed from the model, and the fit indices recalculated. The respecified model is shown in Figure
6.10. The respecification did not result in a statistically significant good model fit according to the chi-square statistic, at χ2 (9) = 33.46, p < .0001 and χ2 / df = 3.72.
The remaining fit indices for the respecification are shown in Table 6.13. Three of the five indices showed acceptable model fit with the respecification (the NNFI, CFI, and SRMR). The respecified model modification indices and standardised residual covariance values were all within a satisfactory range, with no obvious options to further improve model fit.
The post-respecification single factor model is presented in Figure 6.10. The factor loadings for the respecified model continued to be satisfactory and statistically significant at p < .0001. 195
Figure 6.10
Path diagram for respecified single factor model of the PCSO
Table 6.13
Fit Indices for Single Factor Model of PCSO and Respecification: Arts Students
Sample (N = 218)
NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Original model .82 .83 .19 .68 .11
Respecified model: positive self .93 .95 .11 .87 .08
image removed
196
Figure 6.11
Standardised estimates and SMCs for respecified single-factor model of the PCSO:
Arts students sample (N = 218)
Confirmatory Factor Analysis: Respecified Single Factor Structure
The respecified model was then tested with the professional artists’ sample, using the same confirmatory factor analysis approach with maximum likelihood estimation. The standardised estimates and SMC for the respecified model are presented in Figure 6.12, and show ‘fair’ or better factor loadings and generally strong SMCs, ranging from .23 (proactive) to .79 (resilient and adaptable).
Figure 6.12
Standardised estimates and SMCs for respecified single-factor model of the PCSO: Professional artists sample (N = 310) 197
The respecification did not result in a statistically significant good model fit according to the chi-square statistic, at χ2 (9) = 44.89, p < .0001 and χ2 / df = 4.99.
However, three of the other indices (the NNFI, the CFI, and the SRMR) showed acceptable model fit with the respecified single factor model of PCSO and the professional artists’ sample (see Table 6.14).
Table 6.14
Fit Indices for Single Factor Model of PCSO and Respecification: Professional
Artists (N = 310)
NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Original model .87 .88 .15 .83 .09
Respecified model: positive self .93 .94 .11 .89 .07
image removed
The coefficient alpha measure of internal consistency was then calculated for the respecified model in each data set, and assessed against Nunnally’s (1978) statistical guideline of .70. The alpha coefficient for the professional artists’ sample was .82, and for the arts students’ sample was .85, indicating that the six items comprising the respecified single factor PCSO scale fit together well in the two data sets. The alpha coefficient for the original single factor model of PCSO with professional artists was .84 and for arts students was .87.
Career Management Competence Scale
A three factor solution for CMC was suggested by the Exploratory Factor
Analysis conducted with data obtained in the pilot study of Education Faculty 198 students (as summarised in Chapter 5 and Appendix D). The hypothesised three factor structure, based on eleven items, is depicted in the path diagram in Figure
6.13.
Figure 6.13
Path diagram for the hypothesised three factor model of CMC
Data Screening Procedures
A moderate degree of negative skewing and some kurtosis was evident in items from both data sets. The magnitude of skewing was fairly consistent between items (with the exception of securing or creating & maintaining work in the arts students’ sample, where the distribution approached normality), and therefore the untransformed variables were included in the analysis. Five univariate outliers were found in the professional artists data set, and three were found in the arts students data set; these, along with the records with the highest Mahalanobis distances (three 199 in the professional artists data set and four in the arts students data set), were removed from the analysis. Removal of the outliers made little difference to the results, and so the outliers were retained in the sample. The item means and standard deviations are presented in Table 6.15.
Table 6.15
Item Means and Standard Deviations: CMC in Professional Artists Sample and Arts
Students Sample
Professional artists Arts students sample sample (N = 218) (N = 310) Mean SD Mean SD 1. Building and maintaining a positive self image 4.45 1.16 4.43 1.06 2. Changing and growing throughout life 4.60 1.05 4.82 0.98 3. Participating in lifelong learning supportive of career goals 4.70 1.10 4.85 0.93 4. Maintaining balanced life and work roles 4.41 1.24 4.51 1.03 5. Understanding the changing nature of life and work roles 4.42 1.14 4.55 1.03 6. Locating and effectively using career information 4.38 1.22 4.22 1.01 7. Securing or creating and maintaining work 4.28 1.17 3.85 1.14 8. Making career enhancing decisions 4.44 1.14 4.36 1.05 9. Understand, engage in, and manage the career building process 4.39 1.22 4.21 1.05 10. Interacting positively and effectively with others 4.55 1.16 4.80 0.91 11. Understanding the relationship between work, society and the economy 4.29 1.20 4.35 1.00
Assumptions for linearity and homoscedasticity were examined in each cohort using a sample of bivariate scatterplots. The variables most expected to exhibit departure from linearity (the least normal variables, changing and growing throughout life and understanding life and work roles) were examined the most closely. Some minor heteroscedasticity and curvilinearity caused by variable non- 200 normality was found, but in the light of the suggestions by Tabachnik and Fidell
(2001) and McDonald and Ho (2002), that the analyses were likely to be robust to moderate and consistent item non-normality, it was decided to proceed with the analyses as planned.
Pearson correlations amongst observed variables, as shown in Tables 6.16 and 6.17, revealed that while many of the CMC variables in both data sets were strongly correlated, no correlation was higher than .90 (which might indicate redundancy). There was no evidence of multicollinearity when diagnostics were conducted. A small amount of missing data (1.8% of the professional artists’ data set and 1.3% of the arts students’ data set) was replaced by the series mean in both data sets.
Table 6.16
Inter-item Correlations: CMC in Professional Artists Sample, N = 310
1 2 3 4 5 6 7 8 9 10 11 1. Building and maintaining a positive self image 1.00 2. Changing and growing throughout life 0.45 1.00 3. Participating in lifelong learning supportive of career goals 0.38 0.74 1.00 4. Maintaining balanced life and work roles 0.39 0.52 0.53 1.00 5. Understanding the changing nature of life and work roles 0.45 0.59 0.65 0.64 1.00 6. Locating and effectively using career information 0.31 0.45 0.39 0.37 0.44 1.00 7. Securing or creating and maintaining work 0.40 0.36 0.36 0.39 0.37 0.44 1.00 8. Making career enhancing decisions 0.44 0.45 0.44 0.45 0.45 0.61 0.58 1.00 9. Understand, engage in, and manage the career building process 0.35 0.38 0.38 0.42 0.50 0.63 0.54 0.58 1.00 10. Interacting positively and effectively with others 0.64 0.51 0.50 0.40 0.55 0.40 0.34 0.45 0.37 1.00 11. Understanding the relationship between work, society and the economy 0.33 0.34 0.41 0.30 0.43 0.50 0.40 0.54 0.48 0.41 1.00
201
Table 6.17
Inter-item Correlations: CMC in Arts Students Sample, N = 218
1 2 3 4 5 6 7 8 9 10 11 1. Building and maintaining a positive self image 1.00 2. Changing and growing throughout life 0.52 1.00 3. Participating in lifelong learning supportive of career goals 0.47 0.62 1.00 4. Maintaining balanced life and work roles 0.48 0.36 0.42 1.00 5. Understanding the changing nature of life and work roles 0.49 0.45 0.58 0.58 1.00 6. Locating and effectively using career information 0.37 0.31 0.35 0.23 0.38 1.00 7. Securing or creating and maintaining work 0.28 0.26 0.34 0.24 0.40 0.59 1.00 8. Making career enhancing decisions 0.42 0.34 0.42 0.31 0.47 0.59 0.52 1.00 9. Understand, engage in, and manage the career building process 0.33 0.31 0.34 0.30 0.42 0.57 0.57 0.69 1.00 10. Interacting positively and effectively with others 0.48 0.44 0.42 0.37 0.47 0.30 0.24 0.42 0.26 1.00 11. Understanding the relationship between work, society and the economy 0.32 0.28 0.37 0.26 0.30 0.52 0.45 0.62 0.55 0.43 1.00
Confirmatory Factor Analyses: Three Factor Structure
Model parameter estimates were generated for the hypothesised three factor
model. The parameter estimates and SMC values for the model were encouraging.
SMCs for the items were quite high, ranging from .33 (building and maintaining a
positive self image in the professional artists’ sample) to .71 (make career enhancing
decisions in the arts students’ sample), indicating that a good proportion of the
variance in the items was explained by their respective factors. The standardised
factor loadings were all “good” or better, according to Comry and Lee’s (1992)
guidelines. However, some of the factor correlations were very high, indicating poor
discriminant validity between the factors, and the potential for problems associated
with multicollinearity if the unrespecified factors were used in other analysis
procedures. 202
The standardised parameter estimates, SMCs and error values are presented in Figures 6.14 and 6.15.
Figure 6.14
Standardised estimates and SMCs for three factor model of CMC: Professional artists sample (N = 310) 203
Figure 6.15
Standardised estimates and SMCs for three factor model of CMC: Arts students sample (N = 218).
Chi-square goodness-of-fit tests assessing overall model fit showed that the hypothesised 3 factor solution was not adequate for either data set, at χ2 (41) =
219.80, p < .0001 and χ2 / df = 5.36 for the professional artists’ sample and χ2 (41) =
127.19, p < .0001 and χ2 / df = 3.10 for the arts students’ sample. The majority of the other fit indices similarly showed borderline to inadequate model fit, particularly for the professional artists’ data set. The fit indices for the three factor model of CMC are presented in Table 6.18. 204
Table 6.18
Fit Indices for Three Factor Model of CMC
Data set NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Professional artists (N = 310) .88 .90 .12 .83 .08
Arts students (N = 218) .89 .92 .10 .85 .06
Model Respecification
A model respecification procedure, comprising a specification search with the professional artists’ sample followed by further confirmatory testing, was undertaken in order to improve model fit. The high factor correlations previously mentioned were one possible source of misspecification, with items loading onto relationships also loading strongly onto the other two factors. Further support for this idea was gained by examination of the standardised covariance residuals. One standardised covariance residual in excess of 2.56 was located in the matrices for the professional artists’ data set, between interacting positively and effectively with others, a relationships item, and building and maintaining a positive self image, a self management item.
Modification indices for the variables and error terms were calculated.
Relatively high MI values were found between interacting positively and effectively with others and building and maintaining a positive self image (MI = 39.74), and their respective errors terms, err1 and err10 (MI = 51.64).
Based on the above results and the bivariate correlations between items, it was decided that the factor relationships should be deleted, and the item interacting 205 positively and effectively with others be allowed to load onto the self management factor. The other relationships item, understanding the relationship between work, society and the economy, was added to the career building factor. In a second step, err1 and err10 were allowed to correlate. Some authors have argued that correlated errors in model specification can increase the likelihood of overfitting, be indicative of response bias, or even suggest an unincluded endogenous variable (factor) (James,
Mulaik, & Brett, 1982). However, Bentler and Chou (1987) suggested that model specification that forces all error terms to be uncorrelated is rarely appropriate with real data, and in practice models with correlated error terms are common (e.g.,
Salanova et al., 2005; Swisher, Beckstead, & Bebeau, 2004). The respecified model of CMC is shown in Figure 6.16.
Confirmatory Factor Analysis: Two Factor Structure
Figure 6.16
Path diagram for the respecified two factor model of CMC 206
Chi-square goodness-of-fit statistics were calculated, and for the first respecification where the relationships factor was deleted and the items reassigned, the model fit improved, at χ2 / df = 3.17, but fell just outside the usual overall criterion for good fit. The second respecification, in which the error terms were allowed to correlate, resulted in a well fitting model, at χ2 / df = 2.31. The successive respecification model fit indices are presented in Table 6.19.
Table 6.19
Fit Indices for Three Factor Model of CMC and Respecifications: Professional
Artists (N = 310)
NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Original model .88 .90 .12 .83 .08
Respecified model: relationships .93 .95 .08 .89 .06
factor deleted, two item loadings
changed
Respecified model: err1 and err10 .95 .97 .07 .91 .05
allowed to correlate
The final model, post-respecification, is presented in Figure 6.17, and shows consistently ‘good’ or better factor loadings using Comrey and Lee’s (1992) guidelines. Improved factor correlations were observed with the two factor model in the professional artists’ sample, with no evidence of multicollinearity between the factors. 207
Internal consistency for the scales was measured by Cronbach’s alpha. The coefficients for the scales, as shown in Table 6.20, showed excellent internal consistency, with no evidence of coefficient improvement should an item be removed from any of the scales. The total 19 item scale obtained an excellent alpha level of .90.
Figure 6.17
Standardised estimates and SMCs for respecified two factor model of the CMC:
Professional artists sample (N = 310) 208
Table 6.20
Internal Consistency of the Respecified CMC Two Factor Model: Professional
Artists Sample (N = 310)
Scale Name Cronbach’s α
Self Management .87
Career Building .85
Total CMC Scale .90
Confirmatory Factor Analysis: Respecified Two Factor Structure
The respecified model was then tested with the arts student sample, using confirmatory factor analysis with maximum likelihood estimation. All standardised regression weights were statistically significant and “good” or better (Comrey & Lee,
1992). The SMCs indicated that the specified factors accounted for a respectable proportion of the variance in the items. Interestingly, the err1 and err10 correlation was non-significant in the arts students’ sample, indicating that the correlated error terms were probably specific to the professional artists’ sample. The standardised estimates and SMCs for the two factor model with the arts students’ sample are presented in Figure 6.18. 209
Figure 6.18
Standardised estimates and SMCs for respecified two factor model of the CMC: Arts students sample (N = 218)
With adjustment for degrees of freedom, the chi-square statistic showed an excellent model fit for the 2 factor model with the arts students’ sample, at χ2 / df = 2.29. The other fit index values, as summarised in Table 6.21, also showed acceptable or borderline fit results for the respecified model. 210
Table 6.21
Fit Indices for Respecified Two Factor Model of CMC with Arts Students (N = 218)
NNFI CFI RMSEA AGFI SRMR
Criteria for good fit >=.90 >=.90 <=.05 >=.90 <=.08
Arts students .91 .95 .07 .89 .05
Internal consistency coefficients for the scales, as shown in Table 6.22, easily met the criterion of .70, with no evidence of coefficient improvement should an item be removed from any of the scales. The total scale obtained a very good alpha level of .89.
Table 6.22
Internal Consistency of the Respecified CMC Two Factor Model: Arts Students
Sample (N = 218)
Scale Name Cronbach’s α
Self Management .87
Career Building .85
Total CMC Scale .89
Discussion of Findings
The confirmatory factor analysis and respecification procedures undertaken for the brief scales in Study 1 resulted in the modification of all three scales from those originally piloted with Education students. The final scales used in Studies 4 and 5 in the predictive models of professional artists’ and arts graduates’ career 211 success are considered in this section, and the theoretical implications of the factorial structures revealed are discussed.
Career Development Influences Scale
The confirmatory factor analysis procedures showed that the hypothesised six-factor model of CDI was not an adequate fit for the samples examined. A five factor respecified model, in which the world of work knowledge item was removed and the highly correlated factors age and experience and gender and health were collapsed into the single factor physical characteristics, showed better fit according to most of the indices used. The five factor model, using 19 1-6 Likert scale items of the original 28, is employed in the remainder of the studies in this program of research. The final CDI items used in subsequent studies and the associated factors are shown in Table 6.23.
The Cronbach’s alpha internal consistency values were satisfactory for the respecified CDI, with the exception of environmental / societal influences, which yielded a borderline figure in both data sets. This internal consistency result, combined with the relatively low SMCs and high standard errors for this factor, indicates that the six items comprising environmental / societal influences are moderately heterogeneous. It is possible that with subsequent model testing and respecifications on other samples that the item with the lowest loading, geographical location, might be removed from the model, or that the existing single factor might be split into two or more. However, with the present data sets the model fit was acceptable without further modification and the factor loadings for all items were statistically significant, and so no further modifications were made. 212
Table 6.23
Final Career Development Influences Scale Items and Factors
How negative or positive an influence has each of the following had on your arts career development so far? This can include choices you have made about study, work, or any other aspects of your career development and career progression. Items Factor
a) Gender physical characteristics
b) Health - both physical and psychological physical characteristics
c) Ability – your potential to acquire the skills you need skills and abilities d) Aptitudes – the quickness or ease with which you can learn a skills and abilities skill you need
e) Personality interests and beliefs
f) Age physical characteristics
g) Physical attributes physical characteristics
h) Interests interests and beliefs
i) Skills skills and abilities
j) Beliefs – your ongoing ideas about yourself and the world interests and beliefs
k) Values – what is important to you e.g. prestige, risk, interests and beliefs autonomy, responsibility
l) Family interpersonal influences
m) Peers – friends and colleagues interpersonal influences
n) Media environmental / societal influences
o) Geographical location environmental / societal influences
p) Political decisions environmental / societal influences
q) Historical trends environmental / societal influences
r) Socioeconomic status environmental / societal influences
s) Globalisation – increased global connectivity, integration environmental / societal influences and interdependence
The present study provides some evidence for robustness of broad underlying dimensions to career development influences. These broad underlying dimensions are correlated, but fall within the systems suggested in the STF. Within the individual system, three underlying factors were identified: interests and beliefs, comprising the influences of interests, beliefs, values and personality; skills and abilities, comprising 213 abilities, aptitudes, and skills; and physical characteristics, comprising health, age, physical attributes and gender. One factor was identified in the social system: interpersonal influences, comprising family and peers; and one factor was identified in the environmental-societal system: environmental / societal influences, comprising geographical location, political decisions, historical trends, globalisation, socioeconomic status, and geographical location.
While the brief career development influences scale is intended to provide a broad context for quantitative research into career development, it is not an exhaustive instrument. There is strong evidence in the literature to suggest the importance of the nine influences which were not included in the final scale.
However, eight of these influences did not load significantly onto any of the five factors identified in the model with the present data sets and thus were removed from the scale. The item world of work knowledge, also removed from the final scale, loaded significantly onto four of the five factors in the main study, and was strongly correlated with many of the other items, thus providing support to the notion that world of work knowledge is a central and complex influence on career development.
Protean Career Success Orientation Scale
Unqualified support for the single factor model of the PCSO items was not achieved through the model fitting and respecification procedures. Although removing positive self image from the PCSO model resulted in some evidence of improved fit in both samples, with the majority of fit indices reaching adequate levels, the chi-square statistic continued to signal unacceptable fit after respecification. 214
The non-normality of the initial items was a cause for concern, particularly because the distributions remained skewed after transformation. As Curran, West and Finch (1996) noted, using the chi-square statistic as a measure of model fit under conditions of non-normality will lead to an inflated Type I error rate, and therefore “a researcher may be likely to mistakenly reject or modify a model because the distributions are not normal rather than because the model itself is not correct”
(p.16). In order to determine whether the continuing poor model fit was due to this phenomenon, further validation work with other samples is required. It is possible that the item non-normality is a function of the populations under investigation, and therefore it may be worthwhile to conduct further PCSO validation work with other populations of careerists.
For the purposes of the present investigation, the respecified single factor model of
PCSO comprising six items loading onto one factor is employed. Although model fit was not optimal with the data sets under investigation, the internal consistency of the scale was excellent, and the SMCs and factor loadings were satisfactory. The final
PCSO 1-6 Likert scale items for use in Studies 3 and 4 are presented in Table 6.24.
Table 6.24
Final Protean Career Success Orientation Scale Items and Factors
To what extent do you agree with the following with respect to your arts career? 1. I am self-directed and take personal responsibility 2. I am proactive 3. I am internally motivated 4. I have a positive interpersonal orientation 5. I am resilient and adaptable 6. I am open to opportunities
The PCSO scale, unlike the CDI and the CMC, did not have an underlying dimensional structure suggested previously by theory. The PCSO comprises items 215 relating to underlying dispositions and characteristics suggested by protean career literature (Briscoe & Hall, 2006; Hall & Chandler, 2005; Hall & Mirvis, 1996) to be important to career success in the protean career, and no studies have to date suggested how these dispositions might be related to one another. Future research studies may consider adding more items to the scale, with a view to enhancing its discriminability and validity. They may also consider the reinclusion of items relating to positive self image, for which there is a strong theoretical argument although it was removed from the final scale during respecification in this study.
Career Management Competence Scale
With some respecification, a reasonable model fit was obtained for the CMC in both samples. The respecified model, comprising two factors and all eleven items, demonstrated excellent internal consistency and good factor loadings and SMCs. In addition, the respecified model with a factor removed and two items reassigned was more parsimonious (Ferguson, 1954) than the original and arguably made better sense theoretically. In naming the originally hypothesised factors, it was difficult to identify the nature of a construct underlying only interacting positively with others and understanding the relationship between work, society and the economy. The reassignment of the items to self management and career building resulted in a simple two factor structure with strong factor interpretability. The final CMC 1-6
Likert scale items and their associated factors are presented in Table 6.25.
The ABCD classifies the 11 competencies included in the CMC into three categories: personal management, career building, and a further category, learning and work exploration. The pilot study with Education students revealed three factors, but the factors did not correspond to the categories suggested by theory. The model respecification and confirmatory factor analysis found only two factors, self 216 management, which comprised items from all three of the ABCD’s categories, and career building, which comprised items falling into the ABCD’s career building category and two items from learning and work exploration. In terms of deFillippi and Arthur’s (1994; 1996) ‘knowing’ classification of career self-management skills, items from the final self management subscale contained competencies roughly corresponding to knowing why, relating to self-knowledge, and knowing whom, relating to interaction with others. The final career building subscale contained competencies corresponding to knowing what, knowing where, and knowing when, all of which relate to being able to navigate one’s industry and the world of work.
However, the final respecified model emerging from the present study was not without potentially problematic idiosyncrasies. The error terms of interacting positively and building and maintaining a positive self image were strongly correlated in the professional artists’ sample, although were not significantly correlated in the arts students’ sample. Correlated error terms denote situations where the covariations between items are not sufficiently explained by factors in the model.
Influences underlying correlated error terms often include: participant response bias, including social desirability bias; item redundancy; or the presence of a factor not included in the model (Aish & Joreskog, 1990; Byrne, 2001). The successful confirmation of the respecified model with the arts students’ sample but without the correlated error terms suggests that in this instance the correlation is likely to be sample or population specific to the professional artists. 217
Table 6.25
Final Career Management Competence Scale Items and Factors
How confident are you in your abilities and skills in the below areas?
Items Factor
1. Building and maintaining a positive self-image self management Knowing who you are & what influences you, staying positive, understanding how self-image has an impact on goals and decisions
2. Interacting positively and effectively with others self management Understanding and maintaining positive relationships, being able to express yourself in an appropriate manner, knowing how to solve interpersonal problems
3. Changing and growing throughout your life self management Understanding that your motivations and aspirations change throughout our lives, that change and growth can impact on our physical and mental health and vice versa, knowing how to adapt to changes and manage stress
4. Participating in life-long learning supportive of your career goals self management Knowing what influences life and work successes, understanding how to improve skills and strengths, knowing about learning opportunities, behaving in ways that contribute to achieving your goals
5. Locating and effective use of career information career building Knowing where and how to access career information, and how to use it, knowing what working conditions you want, understanding the requirements of work settings
6. Understanding the relationship between work, society and the economy career building Understanding about how work contributes to our community, society, and ourselves; understanding how trends affect work, understanding how organizations operate
7. Securing or creating and then maintaining work career building Understanding the importance of personal qualities on getting/ keeping/ creating work, being able to articulate your skills, being able to transfer your skills between work settings, developing work search tools and skills
8. Make career enhancing decisions career building Understanding how choices are made, how personal beliefs and attitudes affect decision-making, knowing how to problem-solve, being able to explore alternatives, being able to evaluate the impact of decisions
218
Table 6.25 (continued)
Final Career Management Competence Scale Items and Factors
Items Factor
9. Maintain balanced life and work roles self management Being aware of the various roles we may have and the responsibilities linked to those roles, how these roles impact upon our lifestyles, determining the value of work, family and leisure activities and making choices about a balance of these
10. Understanding the changing nature of life and work roles self management Understanding the changing life roles of people in work and home settings, understanding how these roles are important to family and society, exploring and considering non-traditional life/ work scenarios, working to eliminate stereotypes, biases and discriminatory behaviours
11. Understand, engage in, and manage the career building process career building Being able to define your preferred future and create career scenarios in step with it, being able to set goals and short-term plans, and apply coping strategies and new career scenarios during transition periods e.g. starting a family, losing a job
As Gerbing and Anderson (1984) noted, allowing error terms to correlate in
structural equation models will nearly always improve model fit, although this may
be at the expense of interpretability. It can also result in a model which is overly
sample-specific (James et al., 1982), although in the present case the five factor
model was then confirmed with the arts students’ sample without difficulty. In
practice, correlated error terms are common in confirmatory factor models (e.g.,
Salanova et al., 2005; Swisher et al., 2004).
For the purposes of the present research, the primary potential ramification
of allowing correlated error terms in any of the models is the violation of an
important assumption in parametric multiple regression, that of independent errors
in prediction (Tabachnik & Fidell, 2001, p. 121). Other violations of linear
regression assumptions have also become evident during Study 1. For instance, the
non-normality of most of the item distributions in PCSO (and therefore the overall 219
scale) and some of the items in the CMC and CDI scales might be another cause
for concern if the scale scores were to be employed in a multiple regression model.
Studies 3 and 4, which involve prediction of several measures of career success
from the various instruments developed in Study 1, will therefore require the use of
a nonparametric, nonlinear regression method rather than traditional multiple
regression techniques.
Chapter Summary
Chapter 6 has documented an examination of the psychometric properties of the CDI, PCSO and CMC scales using the professional artists’ and arts students’ (at time 1) data sets. Confirmatory factor analysis procedures were undertaken for each scale, and respecifications were also made to the models where necessary. The final scales, post-respecification, are used in Studies 3 and 4, outlined in Chapters 8 and 9.
The next chapter of this document, Chapter 7, presents an investigation of the construct of career success in the two samples. Content analysis of the artists’ and arts graduates’ definitions of career success is undertaken, and the statistical relationships between various career success measures are explored.
220
CHAPTER 7
Study 2: Definition and Exploration of the Career Success Construct in Professional Artists and Arts Graduates
Study 2 addresses the second research question of the present program of inquiry: How can career success in the arts be defined? Two research subquestions are included within Study 2, namely: (i) what are the artists’ definitions of career success? and (ii) what are the statistical relationships between the measures of career success employed in the present study? These career success measures are then employed in the subsequent studies which aim to predict career success from career development influences and constructs validated in Study 1.
The measures of career success included in Study 2 were derived from the professional artists’ survey and the arts graduates’ survey at time 2 (one year after course completion). The survey asked participants to define career success in their field/s of arts, and then assign themselves a career success rating from 1 - 6 based on their own definition (where 1 was not at all successful and 6 was very successful).
Participants were also asked to rate their level of arts employability, where employability was described as the tendency to create or obtain arts employment
(where 1 was not at all employable and 6 was very employable), and employability overall. In addition, participants reported on their arts earnings and total earnings, thus providing objective indicators of their career success as well as multiple measures of subjective career success. The inclusion of multiple career success measures, including an open-ended question, allows for an exploration of the construct of career success in artists. An exhaustive literature search revealed no previous empirical work examining the construct of career success in artists, although some authors (e.g., Arthur et al., 2005; Smith et al., 2006) have suggested 221 that artists may define career success very differently from other workforce groupings with more ‘traditional’ career paths.
The present chapter is divided into two sections. The first section addresses research subquestion 1: What are the artists’ definitions of career success? A systematic content analysis of the professional artists’ and arts graduates’ definitions of career success is outlined and results of the content analysis are described. The second section of the chapter addresses research subquestion 2: What is the statistical relationship between the measures of career success employed in the present study?
Correlational analyses between the various ratings measures and earnings measures of career success are presented. The implications of the results outlined in the two sections are then discussed.
Content Analysis of Artists’ Definitions of Career Success
Content Analysis Procedure
Content analysis was used to code responses to the open-ended question using a systematic approach outlined by a number of theorists (Carey, Morgan, &
Oxtoby, 1996; Krippendorff, 1980; Neuendorf, 2002). Emergent coding was used with the professional artists’ data set because previous literature did not suggest a range of responses that the participants might give to the question presented. The codebook developed with the professional artists could then be used with the arts graduates’ responses in an a priori coding method.
Coding of Professional Artists’ Responses
The emergent coding procedure used with the professional artists’ responses is outlined in this section. Responses were read by a researcher and a codebook was compiled containing each unique idea or concept within the responses. Each 222 response might contain multiple concepts and therefore be assigned multiple codes.
Overarching theme categories that most aptly described distinct groupings of codes were then identified. Coded responses were assigned to each thematic category.
Theme subcategories were also created. Two coders then used the thematic subcategory codes to code the data. Cohen’s kappa was calculated as an indication of reliability (Lacy & Riffe, 1996; Litwin, 1995; Weber, 1990) and the two sets of codes were compared for discrepancies. Reasons for discrepancies can include redundant codes for the same concept, vague definitions, or lack of exclusivity between codes.
In this instance reliability coefficients were high enough not to warrant codebook refinement and recoding, using a κ level of .80 (Neuendorf, 2002).
However, the procedure to be used in the instance of insufficient intercoder reliability is as follows: based on the problems identified, the codebook is then refined and the data recoded by the two coders using the amended codebook. The level of agreement between the coders for the items is then tested for intercoder reliability once more. If .80 is not reached, further revisions to the codebook are made and the measure recoded again.
Themes in Professional Artists’ Responses
The 29 main themes identified in the professional artists’ definitions of career success are presented in Table 7.1. Although a range of themes were evident in the data, many commonalities were also revealed in the artists’ responses. A substantial proportion of the participants mentioned various aspects of intrinsic satisfaction associated with artistic practice – enjoyment, joy, feeling happy or satisfied, feeling challenged by projects, and personal development – of artistic skills or more broadly. 223
Artists also tended to mention a financial aspect to career success. This was most commonly couched in terms of having a regular or stable income from arts, making a minimum amount of money per week (e.g., $400) or month (e.g., $1500) from arts, meeting specific financial commitments, and not having to work outside the arts in order to live. Interestingly, a very small number of artists indicated that their definition of arts career success was becoming rich; a larger number of artists specifically mentioned that their definition of career success did not include being rich, and that being rich from artistic practice was ‘selling out’. A similar pattern occurred with the theme of fame / stardom: a few participants stated that they defined career success as becoming famous, and several expressly stated that they did not aspire to fame. However, recognition from peers and colleagues and from gatekeepers was mentioned by a large number of the participants, as was being requested to work for payment. Some participants indicated that contributing to their art form, the arts in general or society at large, was a criterion for career success.
Two participants indicated that continuing the attempt to work in arts despite adversity was their definition of career success. One participant said that career success in arts did not exist. 224
Table 7.1
Themes Identified in Professional Artists’ Definitions of Career Success
• Enjoyment / joy in artistic practice • Being able to support my family • Feeling happy / satisfied • People buying my work • Development of my artistic skills • Being rich • Personal development • Not being rich – selling out • Knowing I have done my best • Recognition from peers • Challenging myself • Recognition from gatekeepers e.g., • Finishing a particular project gallery owners • Creating something original • Recognition from a wider audience • Contributing to / developing art form • Recognition generally • Successfully communicating an idea / • Fame feeling through arts practice • Not fame – selling out • Contributing to society • Being requested to work • Not having to work outside arts • Working for a particular employer / • Regular / stable income client • Meeting financial commitments • No such thing, doesn’t exist • Earning a specific amount of money per • Still in arts / still trying week or month
Professional Artists’ Broad Career Success Definition Categories
Nearly all of the themes contained within the artists’ definitions of career success could be categorised into four main theme categories (see Table 7.2 for details). These four categories were: 1) internal definitions, which emphasises personal satisfaction or personal development; 2) contribution definitions, where the artist contributes to the development of the art form or society at large, 3) financial recognition definitions, involving remuneration for artistic practice, and 4) non- financial recognition definitions, pertaining to acknowledgement from others. Two of the identified themes were not able to be categorised into the four categories: ‘no such thing, doesn’t exist’, and ‘still in arts / still trying’. As these themes only occurred three times in the data set of 310 records, no further attempts were made to categorise them. The final categories of response and their associated themes are presented in Table 7.2.
225
Table 7.2
Categories of Professional Artists Definitions of Career Success Themes
Thematic category Definitional themes
• Enjoyment / joy in artistic practice Internal definitions • Feeling happy / satisfied • Development of my artistic skills Personal satisfaction, challenge or self • Personal development development associated with artistic practice • Knowing I have done my best • Challenging myself • Finishing a particular project • Successfully communicating an idea / Contribution definitions feeling through arts practice • Creating something original Contributing to: the art form, the development of • Contributing to / developing art form others, and / or society through artistic practice • Contributing to society • Not having to work outside arts Financial employability recognition definitions • Regular / stable income • Earning a certain amount of money per Receiving payment for artistic practice, most week or month often in terms of a regular or stable income and • Meeting financial commitments being able to meet financial commitments • Being able to support my family • People buying my work • Being rich • Recognition from peers / colleagues Non-financial recognition definitions • Recognition from gatekeepers e.g., gallery owners Recognition from others for artistic practice, • Recognition from a wider audience most frequently colleagues or gatekeepers • Recognition generally • Fame • Being requested to work • Working for a particular employer / client
The definitional themes were then collapsed further into subcategories. At this stage, frequencies of subcategories were tabulated, and inter-rater reliability was calculated. For the subcategories which appeared very infrequently (i.e., fewer than five times in the dataset), the subcategory was deemed to be an uncommon element of artists’ definitions of career success, and removed. The categories, subcategories, frequencies and Cohen’s kappa inter-rater reliability coefficients are presented in
Table 7.3. 226
The most common definitional category, at 36.76%, was internal definitions.
Financial recognition definitions were also common, at 32.42%. Non-financial recognition definitions accounted for 18.26% of the coded response themes.
Contribution definitions comprised 12.56% of the themes.
Table 7.3
Major Elements of Professional Artists’ Definitions of Career Success
f % κ Major elements of definitions of career success Internal definitions 161 36.76 0.82 Intrinsic satisfaction (e.g., joy, enjoyment, feeling happy) 73 16.67% Self development (e.g., being challenged, improving skills) 88 20.09% Contribution definitions 55 12.56 0.86 Contributing to the art form 36 8.22% Contributing to society 19 4.34% Financial recognition definitions 142 32.42 0.92 Meeting financial obligations through artistic income 113 25.80% Receiving payment for work 29 6.62% Non-financial recognition definitions 80 18.26 0.90 Recognition from peers and colleagues 44 10.05% Recognition from gatekeepers (e.g., being asked to exhibit 26 5.94% work) Recognition outside art form (wider audience) 10 2.28% Note. f = frequency of theme categories; % = percentage of themes; κ = Cohen’s kappa inter-rater reliability coefficient. However, the most common single subcategory within the responses was meeting financial obligations through artistic income, at 25.8% of the total coded themes. Other common subcategories were self development (20.09%), intrinsic satisfaction (16.67%), and recognition from peers and colleagues (10.05%). On average, 1.41 thematic subcategories were identified within each artist’s response.
Four of the artists’ responses (1.3%) were not able to be categorised at all using this classification system; at the other end of the distribution, one artist’s response contained five different thematic subcategories across four categories. 227
Description of Professional Artists’ Definitions of Career Success
In the section below, typical statements relating to the main thematic career success categories are presented and described.
Internal Definitions
The major category into which response themes were most commonly categorised was internal definitions, reflecting the prevalence and importance of intrinsic, psychological career motivations for professional artists. The two primary types of internal definitions were intrinsic satisfaction, relating to positive feelings associated with artistic practice, and self development, relating to the challenge of artistic practice, development of skills and other aspects of personal development.
One interesting pattern in the responses was the explicit rejection of financial and other recognition definitions of career success in favour of internal definitions.
This finding may well reflect a tension between a desire for a certain degree of commercial success in a highly competitive arts labour market, and strong personal motivations for artistic practice.
• Giving it your best and knowing you couldn't have done any better even if you don't get published or win a mass of awards or whatever.
• It's no good being represented in collections, having work purchased etc if I'm not feeling positive about how my work is progressing and developing. Usually when my work is going well the outside success happens too but my own judgment is critical.
• There are skilled photographers that are jobless and unknown who live off self- success and love what they create and there are skilled photographers that are big names in the industry and raking in millions of dollars who absolutely hate what they create. To me, career success in photography is loving what you do and how you do it. 228
Financial Recognition Definitions
Financial recognition definitions were also common amongst the professional artists. At the simplest level, participants talked about receiving payment for work, but most frequently financial recognition was discussed in terms of meeting financial obligations, having a regular arts income, and not needing to work outside arts in order to survive.
• Having a continuous supply of demand and customers.
• Getting by financially but not necessarily rolling in it.
• To be able to live (comfortably) off the money made from this field - this somehow makes the job "real" and tangible.
• Success to me as an actor or director means having a steady stream of work coming in that pays enough to support your lifestyle. It means finding enough work to support a lifestyle that normal people enjoy. If I have steady work but still have to live on instant noodles that ain't success. Work could be sporadic but jobs should pay enough to subsist you through down times. If you have to go find work in retail that ain't success.
• Being able to support my daughter without having to work endless stupid retail or hospitality jobs that I absolutely hate.
• All I need is $400 per week from photography or my own creative multimedia work and I’d be bloody ecstatic.
• Not having to rely on my partner’s day job to provide for the family through times when my work’s not selling or if I need to have a fallow period.
The concept of ‘selling out’, commercial success at the expense of artistic integrity, was also a key pattern within the financial recognition responses. Some professional artists stipulated that financial recognition comprised career success only if the work they were paid for met certain criteria – quality of work, type of work, purpose of work, or feelings of satisfaction.
• Maintaining adequate work to survive in the field without compromising on aesthetic quality. 229
• Getting regular pay for my work without selling out. Selling out is when you work for the money and your soul’s not in it.
Non-Financial Recognition Definitions
Recognition from others for artistic practice also accounted for a significant proportion of the career success definitional themes. Respect and recognition from peers / colleagues and also from ‘gatekeepers’ (such as gallery owners and agents) were the most frequent forms of non-financial recognition mentioned, and scenarios where the artist was sought out by others to contribute work were mentioned by several artists as being indicators of career success.
• You are successful when your work is sufficiently known that people seek you out at least some of the time.
• To be asked to contribute to a project by people I really respect.
• Career success in the creative / performing arts is when your creative vision skill and expertise are reflected in an artistic venture that engages the wider community and gains respect & recognition by peers.
Recognition from the wider community was also discussed by some of the participants, although as previously mentioned some artists explicitly indicated that fame was something they did not aspire to.
• To me though I suppose it would be just be having one stranger appreciate my work.
• Success as a performer in a band or solo is … getting people you don't know showing up to your shows, getting radio airplay.
• Not fame or stardom or anything like that. But to have a certain degree of general recognition for your work.
Contribution Definitions
A number of the professional artists responses included comments about contributing to the lives of others, the artist’s art form, or society at large. These 230 comments ranged from statements about specific ways the artistic work might assist an individual, to global statements about the betterment of society.
• If I've helped one person positively in any way then it's been worth it. That is success - 'my passion meeting someone else’s need.' And I'm already successful!
• Being able to create works that align with my desire to provoke and to better society.
• Influencing the way my discipline is developing, taking art in new and original directions.
Coding of Arts Graduates’ Responses
The codebook which was created for the professional artists’ responses was then employed with the arts graduates’ responses to the same open-ended question, in an a priori coding procedure. When dealing with a priori coding, the categories are established prior to the analysis. The coding is applied to the data, with revisions made to the codebook as necessary. The categories can be modified to a point which maximises mutual exclusivity and exhaustiveness (Weber, 1990).
Reliability of coding continues to be important in a priori coding. In the present procedure, two coders used the codebook and Cohen’s kappa was calculated as an indication of reliability of the coding (Lacy & Riffe, 1996; Litwin, 1995;
Weber, 1990).
Arts Graduates’ Response Theme Subcategories
The arts graduates’ responses from time 2 (one year after course completion) were coded using the codebook which was compiled from the professional artists’ data. Some themes emerged which were not as obvious in the professional artists’ data set. First, a moderate number of arts graduate respondents indicated that they did not believe career success in arts existed, and that they could not find work in their 231 chosen field / s. Second, some arts graduate respondents stated that they did not know what career success was, or were uncertain. Being rich was mentioned by a number of the participants, as was fame or stardom. These themes were added to the codebook. In addition, three participants discussed “being promoted”, “climbing the corporate ladder”, and “moving up in the field”. Because of the small number of times this promotion theme was mentioned in the overall dataset, it was not added to the theme category codebook. The frequencies and Cohen’s kappa inter-rater reliability coefficients for the arts graduates’ sample are presented in Table 7.4.
The most common definitional category for arts graduates, as with the professional artists, was internal definitions (34.02%). Financial recognition definitions accounted for 32.92% of response themes. Non-financial recognition definitions comprised 11.93% of the coded response themes, and contribution definitions encompassed 13.58% of the themes. Other definitions accounted for the remaining 7.41%.
The most common single subcategory within the arts graduate responses was self development, at 19.75% of the total coded themes. Other common subcategories were meeting financial obligations (14.40%) and intrinsic satisfaction (14.40%). On average, 1.99 thematic subcategories were identified within each arts graduate’s response. Aside from the three promotion comments, another three of the arts students’ responses were not able to be categorised. They appeared to interpret the question to be asking how successful they were at that point in time. 232
Table 7.4
Major Elements of Arts Graduates’ Definitions of Career Success
F % κ Major elements of definitions of career success Internal definitions 83 34.02 .86 Intrinsic satisfaction (e.g., joy, enjoyment, feeling happy) 35 14.40 Self development (e.g., being challenged, improving skills) 48 19.75 Contribution definitions 33 13.58 .89 Contributing to the art form 20 8.23 Contributing to society 13 5.35 Financial recognition definitions 80 32.92 .90 Meeting financial obligations through artistic income 35 14.40 Receiving payment for work 31 12.76 Being rich 14 5.76 Non-financial recognition definitions 29 11.93 .84 Recognition from peers and colleagues 9 3.70 Recognition from gatekeepers (e.g., being asked to exhibit 6 2.47 work) Recognition outside art form (wider audience) 8 3.19 Fame / stardom 6 2.47 Other definitions 18 7.41 .81 Doesn’t exist 10 4.12 Don’t know / uncertain 8 3.19 Note. f = frequency of theme categories; % = percentage of themes; κ = Cohen’s kappa inter-rater reliability coefficient.
Description of Arts Graduates’ Definitions of Career Success
In the below sections, examples of responses from arts graduates by thematic subcategory and descriptions of their response patterns are presented.
Internal Definitions
In a similar response pattern to that observed with professional artists, the most commonly coded definitional theme in the arts graduates’ data was internal definitions. Self development themes were rather more common than intrinsic 233 satisfaction themes. They represented the two most prevalent theme categories in the data set.
• Success in my mind is finding yourself doing the work you want to be doing and enjoying it.
• Being happy and having to pinch yourself on a regular basis that you do this for a living.
• Constantly improving the skills you have and acquiring new skills along the way so that you can move into different areas as a designer.
• Working in a career that makes use of my creative skills and evolves rapidly with new technology and methods.
Financial Recognition Definitions
Financial recognition definitions most frequently revolved around meeting financial obligations through artistic income, as with the professional artists’ data set. Receiving payment for work also continued to be a prevalent theme.
• I think anyone who can make a reasonable living from any CI [Creative Industries] field is doing well as they are not professions which are valued and are also very competitive.
• Being able to 'live off' the payment received from employment in Dance and not having to rely on Government welfare (Centrelink). Being in a company or receiving a number of paid contract work (e.g. cruise ships corporate events independent work etc)
• I’d like to sell some of my paintings at a real professional rate.
Tensions between financial success and artistic integrity were apparent in some of the arts graduates’ responses.
• Balance between sustainable income and artistic/aesthetic validity.
• Not being a sellout, just earning enough to get by, exploring what I need to explore.
However, a number of arts graduates did not seem as concerned about
‘selling out’ as others. A thematic subcategory which was not evident in the professional artists’ data set but which emerged in the arts graduates’ responses was
‘being rich’.
• I’d like to earn a bomb for what I do - live somewhere really nice, big house, car, the works. 234
• Success is when you’re filthy stinking rich!!
Non-Financial Recognition Definitions
Recognition from peers and gatekeepers continued to be of importance to many in the arts graduates’ sample. Some arts graduates also discussed the idea of recognition from an audience outside arts.
• The opportunity to create / manage something with a quality team (which results from earning respect and gaining experience).
• Have a name that is recognised in art schools a hundred years from now.
• Being acknowledged as a professional in the industry by peers, professionals and the public. Being acknowledged as a master of your area. Traveling nationally and internationally to display your work. Being in demand for what you do.
• It would be nice to feel like you are respected in what you do by your friends and family.
The tensions previously observed between commercial success and artistic integrity were also apparent with the recognition themes. A number of arts graduates commented on conflicts between peer / colleague recognition and financial success; and peer / colleague recognition and recognition from a wider audience.
• Often the respect of your peers comes at the expense of your financial life.
• Being recognised as a talented writer (which to me is being recognised for literary skills rather than just as someone who writes for the masses).
However, for some in the arts graduate sample, their definition of career success explicitly referred to fame or stardom without any reservations.
• Winning an Oscar and being in New Idea.
• Being famous! Having a thousand people a day coming to check out my work.
Contribution Definitions
Contributing to the lives of others, the art form, or wider society was also mentioned by a significant proportion of arts graduates. As with the professional 235 artists, the arts graduates’ comments ranged from changing one person’s life to changing the direction of an art form or a community.
• Changing one person’s life is enough.
• Creating innovative trend setting art (theatre) that takes Australian theatre to the next level and puts it on the international map as a leader of innovative new work.
• If I can get people thinking about some of the issues my art is about, my work is done.
Other Definitions
A category was added for the themes identified in the arts graduates’ responses that did not fall into the categories previously created. The responses categorised into other definitions included those that questioned the existence of career success in arts, or indications of definitional uncertainty. These 18 comments revealed that at least some arts graduates have somewhat underdeveloped ideas about their careers, and that there was a misalignment between arts student expectations of the workforce and their experiences of it.
• Does success even exist? I am currently between work and trying to decide on the next step in my career. At present I feel disheartened after losing out on a possible contract because of my age and apparent lack of experience.
• Feels like it’s not possible for the normal person. I truly tried very hard to gain as much experience as possible - but after 6 months of pursuing work without success I became disillusioned.
• I’m not quite sure yet. I thought it would be much easier than it is – dunno why though, everyone tells you it’s going to be hard but you don’t really think about it when you’re at uni.
Relationships Between Measures of Career Success
The exploration of professional artists’ and arts graduates’ definitions of career success revealed the importance of internal career success criteria such as intrinsic satisfaction and self development to artists. It also showed that both samples of artists regarded their ability to meet financial commitments through artistic practice 236 as key to career success. Recognition from others, including colleagues, gatekeepers and a wider audience, comprised another major category of career success criteria for both groups of artists. Contributing to the art form or society through artistic practice was a fourth major type of career success definition.
Once they had defined career success in their fields through the open-ended question, the participants rated their career success on a 1 - 6 Likert scale. Other measures of career success used in the present study were: self-rated employability in arts; self-rated employability overall (also on 1 - 6 Likert scales); earnings from arts; and, overall earnings.
In the second part of Study 2, the statistical relationships between the five measures of career success are considered for the professional artists and the arts graduates at time 2. The participants were grouped according to their major career success definition categories. Then Spearman’s ρ correlations for the career success measures were conducted for each definitional group. Participants’ ratings might be included in multiple groups, if their career success definition fell into multiple categories. Bonferroni adjustments to statistical significance levels were made because multiple statistical procedures were being undertaken with the same data, which may inflate the type 1 error rate (Tabachnik & Fidell, 2001).
Professional Artist Career Success Measure Correlations
Spearman's ρ, the statistical test employed to explore the statistical relationships between the career success measures in this study, is a nonparametric measure of correlation. Unlike the Pearson product-moment correlation coefficient, it does not require the assumption that the relationship between the variables is linear, nor does it require the variables to be measured on interval scales (Gibbons et al., 237
2003). Therefore, it can be used for variables measured at the ordinal level, as with the participant self-rating measures. It works by ranking the scores before calculating the coefficient, so outliers and non-normal item distributions also do not affect the results (Sheskin, 2003).
Descriptive statistics for the professional artists’ success measures are located in Table 7.5. The statistics are presented by career definition category, and overall.
On average, artists earned $34,477 annually, with a median annual income of
$30,000. Arts earnings represented 48.16% of overall earnings.
Table 7.5
Descriptive Statistics for Professional Artists’ Success Measures Total earnings Total Arts earnings Arts Career employability employability success rating rating rating Mean SD Mean SD Mean SD Mean SD Mean SD Median Median Median Median All artists $34,477 $15,330 3.76 1.30 $16,603 $9,603 3.05 1.37 3.78 1.41 (N = 310) $30,000 4.00 $15,000 3.00 4.00 Internal $33,530 $15,169 3.75 1.30 $16,873 $9,899 2.99 1.37 4.16 1.34 success definition $31,000 4.00 $15,000 3.00 4.00 category (n = 161)
Contribution $40,181 $18,401 4.23 1.38 $18,018 $10,186 3.16 1.45 3.64 1.37 success definition $35,000 4.00 $15,000 3.00 4.00 category (n = 55)
Financial $30,422 $12,742 3.46 1.27 $14,665 $8,454 2.95 1.32 3.50 1.36 recognition success $28,000 3.00 $14,000 3.00 4.00 definition category (n = 142)
Non- $36,287 $17,223 3.86 1.36 $18,356 $11,054 3.25 1.53 3.84 1.44 financial recognition $32,500 4.00 $16,000 3.00 4.00 success definition category (n = 80)
Note. The scale for all ratings is from 1 = not at all to 6 = very. 238
Total employability and arts career success ratings were, on average, higher than the arts employability ratings, at χ2(2) = 117.88, p < .0001, using Friedman’s nonparametric test for related groups. Differences between the success measures were also noticeable by success definition category. Broadly, the earnings and ratings of participants who were assigned to the financial recognition category appeared to be lower than the other three categories. In addition, the career success ratings of the participants who were assigned to the internal success definition category were much higher than the other categories. These and other apparent differences between the measures by definitional category were not able to be tested for statistical significance using nonparametric tests because the ratings of some participants were included in multiple definitional categories. The regression tree analysis approach employed in Study 3, outlined in Chapter 6, does not suffer from this limitation, and describes and quantifies these differences more effectively than is possible here.
The nonparametric correlations between the measures of career success by definitional category are presented in Table 7.6. To minimise the probability of a
Type 1 error being made (i.e., that a significant relationship would erroneously be found between measures because of multiple tests performed on the same data), a
Bonferroni adjustment was made to the significance level of the tests (Bland &
Altman, 1995). An overall α level of .05 was maintained by dividing this significance level by the number of tests performed, in this case 50 (10 correlations by 5 tables), resulting in a significance level of .001. 239
Table 7.6
Spearman’s ρ Correlation Coefficients for Professional Artists’ Success Measures
All Professional Artists (N = 310) Financial Recognition Success Definition Category (n = 142) 1 2 3 4 5 1 2 3 4 5 1.Total 1.Total earnings 1.00 earnings 1.00 2. Total 2. Total employability employability rating .66* 1.00 rating .66* 1.00 3. Arts 3. Arts earnings .24* .24* 1.00 earnings .23* .22* 1.00 4. Arts 4. Arts employability employability rating . 1 7 .23* .67* 1.00 rating .11 .13 .61* 1.00 5. Career 5. Career success success rating .23* .24* .56* .59* 1.00 rating .16* . 1 3 .45* .59* 1.00 Internal Success definition category (n = 161) Contribution Success Definition category (n = 55) 1 2 3 4 5 1 2 3 4 5 1.Total 1.Total earnings 1.00 earnings 1.00 2. Total 2. Total employability employability rating .66* 1.00 rating .69* 1.00 3. Arts 3. Arts earnings .17 .17 1.00 earnings .34 .27 1.00 4. Arts 4. Arts employability employability rating .14 .20 .70* 1.00 rating .25 .37 .62* 1.00 5. Career 5. Career success success rating .20* .23* .67* .58* 1.00 rating .31 .39 .41 .58* 1.00 Non-Financial Recognition Success Definition Category (n = 80) 1 2 3 4 5 1.Total earnings 1.00 2. Total employability rating .69* 1.00 3. Arts earnings .15 .26 1.00 4. Arts employability rating .24 .33 .72* 1.00 5. Career success rating .30 .35 .60* .52* 1.00 * statistically significant at Bonferroni adjusted level of p < .001
240
Looking at the whole sample of professional artists, all of the measures of career success were significantly correlated with one another except for arts employability rating and total earnings (ρ (308) = .17, p > .001). The strongest correlations were observed between the ‘total’ career success measures - total earnings and total employability rating (ρ (308) = .66, p < .001), and the ‘arts’ career success measures - arts earnings and arts employability rating (ρ (308) = .67, p <
.001). Career success rating was most strongly correlated with arts earnings and arts employability rating, at ρ (308) = .56, and ρ (308) = .59 respectively (both p < .001).
These were the strongest correlations for the all of the definitional categories.
Arts Graduate Career Success Measure Correlations
Spearman’s ρ nonparametric correlation coefficients were also calculated for the arts graduates’ career success measures, by definitional category and overall.
Preliminary descriptive statistics for the arts graduates success measures are presented in Table 7.7. These statistics show that arts graduates earned an average of
$25,549 in the year since course completion, with a median annual income of
$25,500. Arts earnings represented 67.37% of arts graduates’ total earnings, a higher percentage than previously reported for the professional artists’ group, reflecting the relatively smaller proportion of arts graduates who worked outside arts (36.07% arts graduates vs 80.00% professional artists).
The arts graduates’ total employability and self defined success ratings were, on average, higher than the arts employability ratings, at χ2 (2) = 33.66, p < .0001. As with the findings for the professional artists, differences between the success measures were noticeable by success definition category, but not able to be quantified because of the partially non-independent nature of the variables. There 241 were no large differences apparent between the earnings and employability ratings by career success definition category, but arts graduates with internal and contribution definitions of career success seemed to have higher career success ratings than those with financial or non-financial career recognition definitions.
Table 7.7
Descriptive Statistics for Arts Graduates’ Success Measures
Total earnings Total Arts earnings Arts Career employability employability success rating rating rating Mean SD Mean SD Mean SD Mean SD Mean SD Median Median Median Median All arts $26,549 $11,920 4.48 1.35 $17,885 $12,231 3.41 1.45 3.57 1.41 graduates (N = 122) $25,500 5.00 $15,000 3.00 4.00
Internal $26,910 $12,835 4.47 1.23 $18,557 $13,768 3.49 1.46 3.81 1.31 success definition $27,000 5.00 $16,000 3.00 4.00 category (n = 83) Contribution $26,576 $10,075 4.48 1.18 $16,667 $8,915 3.27 1.51 3.85 1.41 success definition $25,000 5.00 $14,000 3.00 4.00 category (n = 33)
Financial $26,125 $12,244 4.47 1.43 $17,637 $12,023 3.38 1.40 3.36 1.46 recognition success $25,000 5.00 $14,000 3.00 3.00 definition category (n = 80)
Non- $25,724 $11,600 4.45 1.53 $16,483 $12,070 3.03 1.24 2.93 1.41 financial recognition $24,000 5.00 $12,000 3.00 3.00 success definition category (n = 29)
Note. The scale for all ratings is from 1 = not at all to 6 = very.
The nonparametric correlations between the arts graduates’ measures of career success by definitional category are presented in Table 7.8. Once again, to 242 minimise the probability of a Type 1 error being made, a Bonferroni adjustment was employed, dividing the overall significance level of .05 by the number of tests performed, resulting in a testwise significance level of .001.
Using this adjusted significance level, most of the measures of career success were significantly correlated with one another. Exceptions to this were career success rating with total earnings, total employability rating, and arts employability rating (ρ (120) = .30, .10 and .28 respectively, p > .001). As with the professional artists’ data, the highest correlations were between the ‘total’ career success measures - total earnings and total employability rating (ρ (120) = .58, p < .001), the
‘arts’ career success measures - arts earnings and arts employability rating (ρ (120)
= .66, p < .001). Unlike the professional artists’ data, the correlation between the earnings measures – arts earnings and total earnings (ρ (120) = .62, p < .001) was equally as high. Career success rating was most strongly correlated with arts earnings in the arts graduates data set, at ρ (120) = .45 (p < .001). These were the strongest correlations for the all of the definitional categories, with no differences observed in the patterns of correlations where sample sizes were sufficient for a pattern to emerge (the sample sizes for non-financial recognition success definition category and contribution success definition category were low enough that quite high correlations were not statistically significant). 243
Table 7.8
Spearman’s ρ Correlation Coefficients for Arts Graduates’ Success Measures
All Arts Graduates Financial Recognition Success Definition (N = 122) Category (n = 80) 1 2 3 4 5 1 2 3 4 5 1.Total 1.Total earnings 1.00 earnings 1.00 2. Total 2. Total employability employability rating .58* 1.00 rating .60* 1.00 3. Arts 3. Arts earnings .62* .36* 1.00 earnings .65* .42* 1.00 4. Arts 4. Arts employability employability rating .46* .32* .66* 1.00 rating .48* .34 .67* 1.00 5. Career 5. Career success success rating .30 .10 .45* .28 1.00 rating .29 .14 .45* .28 1.00 Internal Success Definition Category Contribution Success Definition (n = 83) Category (n = 33) 1 2 3 4 5 1 2 3 4 5 1.Total 1.Total earnings 1.00 earnings 1.00 2. Total 2. Total employability employability rating .53* 1.00 rating .53 1.00 3. Arts 3. Arts earnings .62* .26 earnings .60* .28 1.00 4. Arts 4. Arts employability employability rating .42* .24 .63* 1.00 rating .47 .27 .64* 5. Career 5. Career success success rating .26 .05 .38* 0.26 1.00 rating .33 .47 .25 .24 1.00 Non-Financial Recognition Success Definition Category (n = 29) 1 2 3 4 5 1.Total earnings 1.00 2. Total employability rating .58* 1.00 3. Arts earnings .57* .24 1.00 4. Arts employability rating .31 .34 .54 1.00 5. Career success rating .24 .06 .49 -.08 1.00 * statistically significant at Bonferroni adjusted level of p < .001 244
Very high correlations (between .62 and .72) were observed between the earnings measures and their respective employability measures in both cohorts.
Because of this, total earnings and earnings from arts will be used in Studies 3 and 4
(in Chapters 8 and 9) as criterion (target) variables in the regression tree models of career success, along with self defined career success. Given the observed high correlations between the measures, it is unlikely that the predictors of employability are significantly different from the predictors of earnings in the present samples. The earnings variables, however, have continuous distributions with a good level of variability and are therefore preferred as decision tree criterion variables to the ordinal employability measures.
The self defined career success rating variable will be included in the decision tree analysis as a criterion variable, with the career success definition categories as predictor variables. The descriptive statistics outlined in Tables 7.5 and
7.7 of this chapter suggest that artists who have different criteria for career success have divergent salary levels and rate their levels of career success differently, and
Studies 3 and 4 will allow for further analysis of this phenomenon.
Discussion
Study 2 successfully addressed the second research question of the present program of inquiry: How can career success in the arts be defined? The professional artists and arts graduates provided definitions of career success which were able to be reliably content analysed to identify major themes, and the majority of the themes were then grouped into four main categories: internal definitions; financial recognition definitions; non-financial recognition definitions; and contribution definitions. The second section of the study involved the calculation of bivariate correlations between five different measures of career success for each career success 245 definitional category, and overall. Although all of the measures were correlated with one another, a particularly strong statistical relationship was identified between each employability measure and its corresponding earnings measure for each of the cohorts under investigation.
It is evident from the results of this study that the objective measures of total earnings and earnings from arts are insufficient indicators of career success in artists, when used alone. Only one third of the themes in the professional artists’ and arts graduates’ definitions of career success were categorised as relating to financial recognition. Further, many responses within the financial recognition category indicated that many of the artists only aspired to a subsistence level of arts income, instead prioritising intrinsic satisfaction, self development, contributing to the art form or society, or non-financial recognition. Indeed, some participants expressed ambivalence about earning too much, citing the concept of “selling out” and discussing the need to balance artistic integrity (including internally satisfying artistic practice and respect from peers) with financial success and recognition outside the arts.
The results of this chapter confirm previous suggestions (Arthur et al., 2005;
Hall & Mirvis, 1996) that many artists as protean careerists do not seem to use observable measures such as pay and employment status to demarcate their conceptions of career success. This finding is also compatible with previously published studies of artists’ working lives (Menger, 2001; Papandrea, 2004; Rengers
& Madden, 2000), which indicate that many artists persist in the arts despite consistently low levels of employment and remuneration. The career-related orientational categories suggested by Schein (1993), Derr (1986; Derr & Laurent,
1989), and Driver (1982) indicate that different careerists individually construct 246 personal meaning in their careers, and that there may be considerable divergence in criteria reported by different careerists. It follows that these differences will extend to notions of career success, and that careerists with particular ‘success orientations’ will be attracted to careers which are congruent with those orientations. Some artists may follow a career in arts even though they know can it be difficult to find consistent work, because they care more about other rewards that working in arts can bring them, such as personal development or contribution to their artistic field.
Artists who prioritise remuneration or employment status may turn to alternative careers once they realise that their priorities are not being met. Another possibility is that artists who initially value high incomes and employment status in their careers may revise their career success orientations when faced with the competitive reality of the world of arts work, and may learn to value other aspects of their career experience. The finding that a proportion of the arts graduates defined career success in terms of fame or riches, whereas the professional artists did not, lends support to the idea that emerging artists may aspire to high incomes and widespread recognition (although some do not), but more established or experienced artists tend to prioritise other career goals. It is unclear how malleable an individual’s career success orientation might be. Schein (1993) and Derr and Laurent (1989) both suggest that individuals’ beliefs about their careers might be long term and fundamental in nature. Derr and Laurent (1989) argue that a shift in internal career orientation is usually only preceded by a major transition in work or other aspects of life, such as retrenchment or divorce. Further investigation is required in order to understand the formation of artists’ career success orientations and how these career success orientations might relate to artists’ career development behaviours. 247
The present study provides some support to the notion that the arts graduates were somewhat under prepared for the world of arts work. About 15% of the arts graduates’ definitions of career success indicated a misalignment between arts student expectations of the world of arts work and their experiences of it upon course completion, even though the question did not directly pertain to their preparedness for the workforce. Some of these graduates commented that arts career success did not seem to exist, and expressed uncertainty and disillusionment about their career progress to date. Many of the surveyed graduates did not feel that they were particularly successful in their arts careers; on average, they assigned themselves neutral career success ratings. Arts graduates (and also professional artists) with career success definitions falling within the financial or non-financial recognition success definition categories generally assigned themselves lower career success ratings than the overall average.
A certain degree of mismatch between student expectations of the workforce and their actual experiences is probably inevitable, particularly amongst students who have enrolled in a bachelor’s level arts course immediately after secondary school and have little or no experience of the world of arts work. However, there may be factors which ease the transition process for arts graduates, and / or are predictive of better arts graduate outcomes. Study 4 of the present program of research, presented in Chapter 9, investigates some of these.
It certainly would seem to be in the interests of universities, arts employers and arts graduates alike for universities to assist students as far as they can to develop realistic appraisals of the world of work the graduates will emerge into at the completion of their courses. Graduates who have realistic expectations of the demands of the workforce are in a better position to make decisions regarding their 248 career development (Watts, 1999). For instance, a student who is aware of high demand in a particular field or location can adapt themselves to different career scenarios, develop specific skills, or move to a different geographical area to accommodate this demand (Mayston, 2002; Watts, 1999). This will result in better graduate employment outcomes.
The presence of strong non-traditional success orientations in arts graduates and professional artists might have an effect on the arts workforce participation rates in addition to the well-documented demand factors. If a proportion of artists have career motivations which do not provide a strong impetus to obtain and maintain continuous employment, or if they are primarily or exclusively motivated to perform certain types of work (e.g., work which contributes to the development of their art form, or work which might gain them recognition from peers), their employment outcomes may be affected. This finding will be of concern to universities, for whom public funding is now partially contingent on graduate employment outcomes
(Department of Education Science and Training, 2007b). In a higher education sector where there is increasing movement towards competitive performance-based funding rather than block grants, it may render the provision of arts programs less appealing to universities. However, demand for arts courses from potential students continues to increase, and thus the bulk of university teaching-related income – that relating to student fees, via the Commonwealth Grant Scheme (Department of Education
Science & Training, 2005), will in all probability be unchanged.
The fact that some artists appear possess non-traditional career priorities may have wider repercussions for the economy as well. Recognising the pervasive pattern of unemployment and underemployment in the arts labour market, the Australia
Council for the Arts and the ARC Centre for Excellent for Creative Industries and 249
Innovation (2007) have recently begun advocating for artists’ employability in fields outside the arts. They argued that artists as creative workers possess distinctive skills and abilities which would be desirable to employers in many sectors not allied with arts. In the interests of enhanced arts worker employment outcomes and economic productivity, it may be worthwhile for artists to consider working in different fields, and for diverse employers to consider the potential benefits of employing workers with formal creative training. However, results from the present study suggest that a significant proportion of artists and arts graduates, at least in the samples of artists studied, may not find work outside their artforms as fulfilling as artistic practice.
Chapter Summary
This chapter has explored definitions of career success given by professional artists and arts graduates. The study has identified a number of artistic career success orientations which fall outside the traditional objectively measured career success criteria of earnings and hierarchical job position.
The statistical relationships between the five measures of career success – total earnings, arts earnings, overall employability rating, arts employability rating, and self defined career success rating – were also explored in study. On the basis of this exploration it was decided to employ only the earnings measures and self defined career success rating in the subsequent two studies which will attempt to identify significant predictors of career success in the two artist cohorts. 250
CHAPTER 8
Study 3: Prediction of Professional Artists’ Career Success from Career Development Measures
Study 3 draws upon the career development measures validated in Study 1
(Chapter 6) and the career success constructs explored in Study 2 (Chapter 7). It employs a decision tree approach to identify salient predictors of the three chosen measures of career success in the professional artists’ cohort. The research question under investigation in this study is: Which of the measured career development influences and constructs predict career success in professional artists?
In answering this research question, Chapter 8 documents the generation of three decision trees which identify salient predictors of earnings in arts, total earnings, and self defined career success ratings in the professional artists’ data set.
First, the decision tree method for predictive modelling is outlined, and the tree algorithms chosen for the present study will be explained. Second, the attribute
(predictor) and target (criterion) variables used in the three trees are described. The trees are generated for these variables, and the output diagrams interpreted. The chapter concludes with a short discussion regarding the findings of the decision tree analyses.
Decision Tree Method
The first section of this chapter presents a summary of the decision tree method used in Studies 3 and 4 (Chapters 8 and 9). Although decision trees are well known in fields as diverse as biomedical engineering (Gibb et al., 1993), international relations (Furnkrantz et al., 1997), social welfare policy (Yohannes &
Webb, 1999) and plant ecology (Baker, Verbyla, Hodges, & Ross, 1993), decision tree approaches are not common in psychological or educational research, 251 presumably because of the dominance of parametric regression modelling in the social sciences. Consequently, this chapter commences with a thorough explanation of the decision tree technique used in the present investigation.
Overview of the Decision Tree Method
A decision tree can be used for classification, or, as in the present case, predictive modelling of data. Each tree depicts rules for dividing a data set into groups. The data is split into different segments, or nodes, according to the values of the single ‘best’ variable. Each of the nodes can then be further split according to a different rule applied to a different variable. This process continues in a hierarchical fashion until the tree algorithm specifies (via a ‘stopping rule’ e.g., if a split does not achieve a certain level of independence when measured by a chi-square measure test, as with the CHAID algorithm (Wilkinson, 1992)), or until no more splits can be made. In the latter case, the tree is then ‘pruned’ (Breiman et al., 1984) to prevent overfitting. Overfitting occurs when subtrees are overly specific, and do not contribute to generalisation accuracy. These trees may have high R2 values, but the predictive data splits they depict may not generalise well to other samples, particularly the splits located towards the bottom of the tree.
Decision trees aim to obtain the most accurate prediction of the target as possible, whilst maintaining maximal levels of: parsimony (i.e., representing and generalising the relationships succinctly); non-triviality (i.e., producing interesting results); and interpretability (Moore, Jesse, & Kittler, 2001). They have a number of advantages that make them the method of choice in the present investigation. Unlike traditional parametric regression techniques (Tabachnik & Fidell, 2001), decision trees do not rely on statistical assumptions of any kind (Murthy, 1998). This is 252 important with the present data set because there is some evidence of violations of statistical assumptions amongst the attribute variables, as outlined in Chapter 6.
As well as being a nonparametric approach, decision trees can easily accommodate a combination of continuous, categorical and ordinal variables and produces results that are easily interpretable (deVille, 2006). Interactions between the attribute variables are also well handled, unlike in parametric regression techniques
(Breiman et al., 1984). For instance, if a career development construct was a relevant and important predictor for artists within a certain discipline but not for another, the decision tree would depict a split for the career development construct only within the relevant discipline node. Finally, unlike parametric regression techniques, the decision tree approach can deal with collinearity and redundant variables, either by creating a surrogate or alternative split, or by simply not including the less useful of the variables in the tree (Murthy, 1998; Neville, 1999).
Choice of Algorithms: CART
To build a decision tree, it is necessary to decide on rules regarding issues such as the basis upon which attributes are selected for splitting, the number of splits for each variable, the determination of the best values for splits on a variable, and the number of branches in the tree overall. Many competing algorithms for decision trees are available.
Probably the best known and most used decision tree algorithm for prediction in the case of non-categorical target variables is the CART (Classification and
Regression Tree) algorithm (Breiman et al., 1984). CART uses binary recursive partitioning, that is, it asks successive questions with yes or no answers to split the data (Salford Systems, 2003). All possible splits for all variables included in the 253 analysis are considered, and ordered using a goodness-of-split criterion. The best split is the one that maximises the homogeneity or ‘pureness’ of the resulting two nodes; it maximises the between nodes sums of squares. CART then repeats the search process for each child node, continuing to make splits until further splitting is impossible or stopped. Splitting is impossible if a single case remains in a node, or if all the cases in a node are exact copies of each other on the attribute variables.
Once the maximal tree is grown, pruning occurs. A set of possible subtrees are derived from the maximal tree, and CART determines the optimal tree by testing for error rates or costs. Errors occur when cases are misclassified. The error rate is calculated for the largest tree and also for every subtree. The best subtree is the one with the lowest cost and highest explanatory power, which is often a fairly small tree
(Moore et al., 2001).
For relatively small samples, as in the present case, CART employs the technique of v-fold cross-validation (deVille, 2006; Salford Systems, 2003). In cross- validation, CART divides the data set into v groups. From this, it takes the first v-1 groups of the data, constructs the largest possible tree, and uses the remaining group of data to obtain initial estimates of the error rate of subtrees. The same process is then repeated on another v-1 groups of the data while using a different group to test the subtrees. The process continues until each data group has been used as a test sample at least once. The results of the v tests are then combined to form error rates for trees of each possible size. These error rates are applied to the tree which has been generated using the entire data set.
Before proceeding with the substantive decision tree analyses thus described, the next section of this chapter will present descriptive statistics and simple bivariate correlations for the variables to be included in the decision trees. Although the 254
CART method requires no initial data screening procedure, as it does not rely on any underlying assumptions regarding variable distributions or relationships, it is nonetheless worthwhile to make a preliminary examination of the variables to be included. The descriptive statistics provide a reference and context from which to observe the splits made in the decision trees, and an initial indication of the relationships between the variables.
Decision Tree Results
Descriptive Statistics for Variables Included in the Analyses
Key descriptive statistics for the professional artist target variables, career development measure attribute variables, and sociodemographic attribute variables are shown in Tables 8.1 to 8.3. First, the target variables of self defined career success rating, total earnings and earnings from arts are presented in Table 8.1. All three of the target variables exhibited a moderately normal distribution, although the earnings measures displayed a fairly long ‘tail’ to the upper end of the variable distributions (positive skewness).
Table 8.1
Descriptive Statistics for Target Variables Included in the Decision Tree Analysis
Variable Minimum Maximum Mean SD
Target Measures
Self defined career success rating 1.00 6.00 3.78 1.41
Earnings from arts $500 $55,000 $16,603 $9,603
Total earnings $4,000 $100,000 $34,477 $15,330
Note. N = 310
Table 8.2 shows descriptive statistics for the career development measures validated in Study 1. These measures include five Career Development Influences 255
(CDI) subscales, two Career Management Competencies (CMC) subscales and the single Protean Career Success Orientation (PCSO) scale, which were calculated by summing the ratings participants assigned to the items falling within each factor identified in Study 1. The minimum and maximum figures for the different subscales differ from one another because of the varying numbers of 1-6 Likert scale items corresponding to each factor. For instance, the CDI: Environmental / societal influences subscale includes six items, and therefore has a theoretical range between
6 and 36. However, the CMC: Career building subscale subsumes only five variables, and thus can range between 5 and 30. These disparities in scale make no difference to the outcomes of the decision tree analysis. In fact, retaining the measures as-is without transformation to a common scale increases the interpretability of the results.
The career development measure subscales summarised in Table 8.2 generally show a tendency towards negatively skewed distributions, particularly
CDI: Skills and abilities, and CDI: Interests and beliefs. These two subscales also exhibited a moderate degree of leptokurtosis (where there is a more acute ‘peak’ and
‘fatter’ tails in the distribution than normal). If parametric regression techniques were employed with these subscales, variable transformation would be necessary (Cohen
& Cohen, 1983; Pedhazur, 1997), but the decision tree approach is robust to these violations and thus no transformations were conducted.
In addition, the categorical variables relating to career success definition category, as outlined in Study 2, are described in Table 8.2. As discussed in Chapter
5, it was possible for professional artists’ definitions of career success to be coded into multiple categories. A total of 438 categorisations were made, translating to an average of 1.41 per participant. 256
Table 8.2
Descriptive Statistics for Career Development Measure Attribute Variables Included in the Decision Tree Analysis
Variable Minimum Maximum Mean SD N %
Career Development Measures
CDI: Environmental / societal influences 6.00 36.00 20.91 4.46
CDI: Interpersonal influences 2.00 12.00 9.29 2.11
CDI: Skills and abilities 3.00 18.00 15.39 2.39
CDI: Physical characteristics 4.00 24.00 16.18 3.70
CDI: Interests and beliefs 4.00 24.00 20.18 3.12
PCSO 14.00 42.00 32.77 5.39
CMC: Career building 5.00 30.00 21.77 4.71
CMC: Self management 9.00 36.00 27.23 5.33
Career success definition category
Internal definitions 161 51.93
Financial recognition definitions 142 45.81
Non-financial recognition definitions 80 25.81
Contribution definitions 55 17.74
Note. N = 310
Summary descriptive information for the sociodemographic variables included in the decision tree analysis is shown in Table 8.3. The continuous variable age was fairly normally distributed around a mean of 36.91, but length of time working in arts was slightly positively skewed, with a mean of 13.79 years.
The categorical sociodemographic variables included in the decision tree analysis were: gender; arts discipline category; career / work outside the arts; and formal arts education. Categorical variables with multiple categories (e.g., arts 257 discipline category), did not require dummy or effect coding for interpretability, as is needed in parametric regression (Cohen & Cohen, 1983; Pedhazur, 1997). Each category contained sufficient numbers of cases for splits to be made based upon the variable (at least 5 cases in each child node), and thus the variables were included without any recoding or transformation.
Table 8.3
Descriptive Statistics for Sociodemographic Attribute Variables Included in the
Decision Tree Analysis
Variable Minimum Maximum Mean SD N %
Sociodemographic Variables
Age 17.00 65.00 36.91 10.47
Gender
Male 167 53.87
Female 143 46.13
Arts discipline category
Creative artists 116 37.42
Performing artists 85 27.42
Technical / design artists 109 35.16
Length of time working in arts 0.00 45.00 13.79 9.75
Career / work outside arts
Has career outside arts 115 37.10%
Some work outside arts – not career 133 42.90%
Does not work outside arts 62 20.00%
Formal education
Has had formal arts education 246 79.35%
Has not had formal arts education 64 20.65%
Note. N = 310 258
Target variable means and standard deviations were calculated for each level of the categorical attribute variables, and preliminary nonparametric tests of difference were conducted. The tests were Mann-Whitney U for two independent groups and Kruskal-Wallis H test for more than two independent groups (Sheskin,
2003). These findings indicated significant differences in the career success measures between different levels of the categorical variables. All of the categorical variables showed potential to be important splitting criteria within at least one of the decision trees to be generated, except for non-financial recognition definitions vs other definitions, for which no significant differences were observed at p < .05. Further interpretation of the observed group differences summarised in Table 8.4, along with the results of the decision trees which provide an indication of the importance of the attribute variables in predicting career success, is made in the discussion section of this chapter.
For the continuous and ordinal variables, Spearman’s ρ nonparametric correlations were calculated to provide an initial indication of the bivariate statistical relationships between the variables. As outlined in Chapter 7, Spearman’s ρ can be employed when the statistical relationships between ordinal or non-normal data need to be explored. Spearman’s ρ is particularly useful when the relationship between the variables is non-linear (Gibbons et al., 2003), as with some of the present variables.
For instance, bivariate scatterplots revealed evidence of curvilinear relationships between CDI: Skills and abilities and the two earnings measures, and CDI: Interests and beliefs and all three career success measures. Once again, while this non- linearity represented a serious violation of the statistical assumptions of parametric regression, the decision tree analysis procedure makes no such assumptions and thus can be used with the original, untransformed variables. 259
Table 8.4
Target Variable Means, Standard Deviations and Nonparametric Tests of Difference by Categorical Variable Levels
Total earnings Earnings from arts Self defined career success rating Mean SD Mean SD Mean SD Gender Male (n = 167) $34,269 $15,464 $16,703 $9,586 3.40b 1.43 Female (n = 143) $34,720 $15,222 $16,486 $9,655 4.22a 1.25 Arts discipline category Creative artists (n = 116) $34,112 $15,562 $15,383b $10,007 3.66b 1.36 Performing artists (n = 85) $32,200 $10,996 $13,370b $6,269 3.60b 1.36 Technical / design artists (n $36,642 $17,649 $20,422a $8,016 4.05a 1.46 = 109) Career / work outside arts Has career outside arts (n = $45,626a $13,181 $15,643b $8,675 3.75b 1.45 115) Some work outside arts – $28,548b $10,273 $13,785b $6,692 3.58b 1.32 not career (n = 133) Does not work outside arts $26,516b $15,634 $24,427a $12,179 4.27a 1.42 (n = 62) Formal education in arts Has had formal arts $35,662a $15,969 $17,351a $10,143 3.86a 1.41 education (n = 246) Has not had formal arts $29,921b $11,589 $13,726b $8,451 3.47b 1.37 education (n = 64) Career success definition category Internal definitions (n = $35,354 $15,473 $16,872 $9,298 4.15a 1.34 161) Other definitions (n 149) $33,530 $15,169 $16,312 $9,899 3.38b 1.37
Financial recognition $30,422b $12,742 $14,665b $8,454 3.50b 1.36 definitions (n = 142) Other definitions (n = 168) $37,904a $16,491 $18,241a $10,218 4.02a 1.41
Non-financial recognition $36,287 $17,223 $18,356 $11,054 3.83 1.44 definitions (n = 80) Other definitions (n = 230) $33,847 $14,602 $15,993 $8,991 3.76 1.40
Contribution definitions $40,181a $18,041 $18,081 $10,186 3.64 1.36 (n = 55) Other definitions (n=255) $33,247b $14,427 $16,298 $9,466 3.81 1.41 Note. a significantly higher group mean, at least p < .05 b significantly lower group mean, at least p < .05 260
The Spearman’s ρ correlations presented in Table 8.5 show a moderate to high degree of correlation between many of the continuous and ordinal variables to be included in the analysis. The large number of statistically significant correlations between the attribute variables and the target career success variables indicates that many of the measured attributes might make good splitting criteria in the decision trees. Of particular note was the strong positive relationship (ρ = .83) between age and length of time working in arts. This correlation is theoretically unsurprising, but in parametric regression the high level of correlation between these two variables would probably lead to low tolerance values and one of the two variables would probably need to be dropped from the model. However, the decision tree approach can accommodate these strongly correlated variables and will simply choose the variable with the higher explanatory power to include in the tree. Table 8.5 Spearman’s Bivariate Correlation Coefficients for Continuous and Ordinal Variables Included in the Decision Tree Analysis 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 1. CDI: Environmental / societal influences 1.00 2. CDI: Interpersonal influences 0.15 1.00 3. CDI: Physical characteristics 0.19 0.26 1.00 4. CDI: Skills and abilities 0.11 0.24 0.28 1.00 5. CDI: Interests and beliefs 0.14 0.26 0.30 0.54 1.00 6. PCSO 0.22 0.30 0.22 0.33 0.40 1.00 7. CMC: Career building 0.12 0.12 0.05 0.33 0.16 0.16 1.00 8. CMC: Self management 0.20 0.15 0.04 0.25 0.22 0.25 0.62 1.00 9. Age 0.02 -0.03 0.04 0.18 0.07 0.06 0.43 0.31 1.00 10. Length of time working in arts 0.07 0.00 0.09 0.22 0.10 0.12 0.42 0.36 0.83 1.00 11. Total earnings 0.10 0.06 0.12 0.25 0.19 0.17 0.46 0.38 0.50 0.50 1.00 12. Arts earnings 0.17 0.18 0.19 0.24 0.18 0.22 0.29 0.29 0.34 0.39 0.23 1.00 13. Career success rating 0.10 0.13 0.24 0.21 0.23 0.22 0.32 0.22 0.31 0.30 0.23 0.56 1.00 Note. Bivariate correlations > = .11 are statistically significant at p < .05. N = 310.
Three decision trees were generated to predict professional artists total earnings, earnings from arts, and self-rated career success. The CART algorithm 261 previously outlined in this chapter was employed, using the decision tree tool in SAS
9.1 Enterprise Miner 4.3. All 18 of the attribute variables summarised in Tables 8.1 to 8.3 were included as potential splitting criteria. The variables and associated values are tabulated in Table 8.6.
Table 8.6
Attribute Variables Included in the Decision Trees
Attribute Variable Values Gender 1 = male, 2 = female Arts discipline category 1 = creative arts, 2 = performing arts, 3 = technical / design arts Career / work outside the arts 1 = no work outside arts, 2 = works outside arts but not career, 3 = has career outside arts Formal education in arts 1 = has formal education in arts, 0 = has not had formal education in arts Internal career success definitions 1 = internal career success definition , 0 = other definition Financial recognition career success 1 = financial recognition definition, 0 = other definitions definition Non-financial recognition definition 1 = non-financial recognition definition, 0 = other category definition Contribution definition category 1 = contribution definition, 0 = other definition CDI: Environmental / societal influences range 6 (low) – 36 (high) CDI: Interpersonal influences range 2 (low) – 12 (high) CDI: Physical characteristics range 3 (low) – 18 (high) CDI: Skills and abilities range 4 (low) – 24 (high) CDI: Interests and beliefs range 4 (low) – 24 (high) PCSO range 14 (low) – 42 (high) CMC: Career building range 5 (low) – 30 (high) CMC: Self management range 9 (low) – 36 (high) Age range 17 - 65 years Length of time working in arts range 0 – 45 years
Variance reduction, the default for CART approaches for non-categorical target variables, was used as the splitting criterion. In variance reduction, the algorithm exhaustively tests all of the possible splits by each attribute variable and 262 chooses the one with the most predictive power, defined as the difference between the variance in the parent node and mean variance in the successive child nodes
(Breiman et al., 1984). The search is performed on each child node until the data is completely split, and then v-fold cross-validation is used to prune the tree to select an optimal tree.
Total Earnings Decision Tree Results
The optimal tree predicting total earnings, selected via v-fold cross-validation as maximising prediction whilst minimising average squared error, accounted for
68.19% of the variance, and included 12 leaves (terminal nodes). This tree was also the maximal tree; no pruning was necessary. The 12-leaf model had an accuracy of
95.38% on the validation data set. The decision tree is presented in Figure 8.1.
Figure 8.1
Optimal decision tree for total earnings 263
The split which accounted for the highest proportion of the sums of squares was CMC: Career building (R2 = .24). Professional artists with a mean CMC:
Career building value of 24.5 or greater generally had higher average total earnings than those who had lower CMC: Career building scores, particularly if they had spent a long time working in arts (> = 32.5 years), or had worked in arts for 12.5 years or more and maintained a career outside arts. For professional artists with low
CMC: Career building scores (< 24.5), strong predictors of comparatively high total earnings were: having a career outside arts, and working in arts for more than 13.5 years. Artists who had low CMC: Career building scores (< 24.5) and did not have a career outside arts, or who had low CMC: Career building scores (< 24.5), did some work outside arts and had worked in arts for less than 6.5 years earned the least, on average. CDI: Interests and Beliefs was found to be a positive predictor of total earnings in artists (> = 19.5) who had low CMC: Career building scores (< 24.5), maintained a career outside arts, and who had worked in arts for less than 13.5 years.
SAS Enterprise Miner Tree module also calculates the relative importance of the input variables in growing the decision tree, in a similar way to least squares regression but based on changes in sums of absolute deviations rather than sums of squares (Breiman et al., 1984). The output importance values are presented in Table
8.7. The values show that many of the variables that were not included as the best criteria for splits in the final tree were nonetheless important. CMC: Career building, career outside arts, CMC: Self management, PCSO and age were all competing splits (alternative splits with almost as much predictive value as those actually used, according to Enterprise Miner) on several occasions in the tree in addition to where they were actually present. CMC: Career building and age were both competing splits for length of time working in arts to the top-right and middle of the tree, and in 264 addition age was a competing split for length of time working in arts where length of time working in arts appeared in all other splits as well. Length of time working in arts was a competing split for age. CMC: Self management was a competing split for
CMC: Career building. PCSO was also a competing split for CMC: Career building.
CDI: Skills and abilities was a competing split for CDI: Interests and beliefs.
Table 8.7
Relative Importance of Variables in Constructing the Optimal Total Earnings
Decision Tree
Attribute variable Importance Number of rules Length of time working in arts 1.00 5 Age .96 1 CMC: Career building .77 1 CMC: Self management .72 0 PCSO .54 0 Career / work outside arts .52 3 CDI: Skills and abilities .47 0 CDI: Interests and beliefs .45 1 CDI: Interpersonal influences .44 0 CDI: Environmental / societal influences .37 0 CDI: Physical characteristics .36 0 Financial recognition definitions of career .25 0 success Arts discipline category .23 0 Gender .21 0 Internal definitions of career success .18 0 Non-financial recognition definition of career .14 0 success Formal education in arts .13 0 Contribution definition of career success .02 0
Note. Variable importance values range from 0 (not at all important) to 1 (most important).
Arts Earnings Decision Tree Results
A maximal tree containing 37 leaves was generated, accounting for 75.43% of the variance. This tree was pruned back via cross-validation to an optimal tree of 265
10 leaves (R2 = .49). The 10-leaf model had an accuracy of 85.00% on the validation data sets. The optimal decision tree is presented in Figure 8.2.
Figure 8.2
Optimal decision tree for earnings from arts
Length of time in arts was the most important predictor of arts earnings, accounting for 21.36% of the total variance explained. Professional artists who had worked in arts for 19 years or more generally had higher mean arts earnings than less experienced artists, particularly if they did not work outside arts and had high CMC:
Career building scores (> = 24). Of the very experienced professional artists (> = 19 years in arts) who did work outside the arts, those who worked in technical / design fields had higher arts incomes than creative or performing artists. Of the less experienced artists (< 19 years in arts), slightly higher arts incomes were obtained by those who were older (> 22.5 years) and did not work outside arts. Very low arts 266 earnings were observed in professional artists who were relatively inexperienced (<
19 years in arts), young (< 22.5 years), and had low CMC: Self management scores
(< 23.5). Inexperienced artists who were 22.5 or older had almost as low mean arts earnings as this group if they were performing artists who worked outside arts, and had low CDI: Physical characteristics ratings (< 15).
The relative importance of the input variables in generating the decision tree is presented in Table 8.8. The most important variables for earnings in arts were age and length of time working in arts, with PCSO, CMC: Self management and CMC:
Career building noted as very important. Career outside arts was another important variable which was employed as a splitting criterion twice in the decision tree, as was arts discipline category. Variables which were not used as splitting criteria in the final tree but still represented strong competing splits included: CDI Interpersonal influences (for career outside arts on the left side of the tree); CDI: Skills and abilities and CDI: Interests and beliefs (for Discipline on the left side of the tree), and PCSO (for CMC: Self management and CMC: Career building). 267
Table 8.8
Relative Importance of Variables in Constructing the Optimal Earnings from Arts
Decision Tree
Attribute variable Importance Number of rules Age 1.00 1 Length of time working in arts .99 1 PCSO .88 0 CMC: Self management .85 1 CMC: Career building .83 1 Career outside arts .78 2 CDI: Interests and beliefs .74 0 CDI: Interpersonal influences .74 0 Arts discipline category .74 2 CDI: Skills and abilities .64 0 Internal definitions of career success .58 0 CDI: Physical characteristics .42 1 Gender .39 0 Financial recognition definitions of career success .22 0 Formal education in arts .20 0 CDI: Environmental / societal influences .03 0 Non-financial recognition definition of career .01 0 success Contribution definition of career success .00 0
Note. Variable importance values range from 0 (not at all important) to 1 (most important).
Self Defined Career Success Rating Decision Tree Results
A maximal tree containing 29 leaves was generated, accounting for 67% of the variance. This tree was pruned back via cross-validation to an optimal tree of 15 leaves (R2 = 0.49). The optimal 15-leaf model, shown in Figure 8.3, had an accuracy of 95.61% on the validation data sets. 268
Figure 8.3
Optimal decision tree for self defined career success rating
The primary split for the self-defined career success rating decision tree was
CMC: Career building. This split accounted for 11.89% of the total variance. The
189 participants who obtained a CMC: Career building score of 21.5 or more tended to assign themselves higher career success ratings than other participants, particularly if they were also highly experienced artists (> = 14.5 years in arts) aged 39 or over who obtained high PCSO scores (> = 29) and defined career success according to internal definitions rather than financial recognition definitions. Artists who were younger than 39 but nonetheless had spent 14.5 years or more in arts and obtained high PCSO and CMC: Career building scores (> = 29 and > = 21.5 respectively), 269 gave themselves very high career success ratings on average if they were in technical
/ design arts fields rather than creative or performing arts. Amongst artists with lower
CMC: Career building scores, females with higher CDI: Interests and beliefs scores
(> = 20.5) who also had high PCSO scores (> = 39) assigned themselves the highest mean success ratings.
The lowest mean career success ratings were associated with male artists with low CMC: Career building scores, particularly those with less than 21 years experience in arts, and without internally based definitions of career success.
Table 8.9 indicates the relative importance of the variables used in constructing the decision tree for self defined career success. The most important variables were CMC: Career building, followed by length of time in arts, gender,
PCSO and CDI: Interests and beliefs. In this decision tree, career success definition categories were more important than for the previous earnings trees, with financial recognition definitions and internal definitions both appearing as split criteria in the tree. Gender also played a far stronger role in the self defined career success rating tree than the previous two trees. By contrast, career / work outside arts played a far less important role in the rating tree than the two earnings trees, and most of the CDI subscales (aside from CDI: Interests and beliefs) did not appear important in the ratings tree construction.
Strong competing splits for the variables included in the optimal tree were:
CMC: Self management for PCSO in both places it appeared, age for financial recognition definitions, and length of time in arts for CMC: Career building in both instances. 270
Table 8.9
Relative Importance of Variables in Constructing the Optimal Self Defined Career
Success Rating Decision Tree
Attribute variable Importance Number of rules CMC: Career building 1.00 1 Length of time working in arts .88 2 Gender .85 2 PCSO .61 2 CDI: Interests and beliefs .55 1 CMC: Self management .53 1 Arts discipline category .50 1 Financial recognition definitions of career success .48 1 Age .43 1 Internal definitions of career success .41 2 Career outside arts .00 0 CDI: Interpersonal influences .00 0 CDI: Skills and abilities .00 0 CDI: Physical characteristics .00 0 Formal education in arts .00 0 CDI: Environmental / societal influences .00 0 Non-financial recognition definition of career .00 0 success Contribution definition of career success .00 0
Note. Variable importance values range from 0 (not at all important) to 1 (most important).
Discussion
The decision tree procedures employed to predict the three measures of career success in professional artists were successful in identifying salient predictors of total earnings, earnings in arts, and self-defined career success ratings. While some fairly complex decision rules were generated in the trees, including as many as six levels of nodes (splits in the data based on the values of a variable) in a tree, some clear findings emerged with respect to the predictive value of some of the attribute variables included. This discussion will discuss the key findings of each decision tree and compare and contrast the predictors of career success for each tree constructed. 271
A summary of the decision tree findings for each attribute variable is presented in
Table 8.10. The table summarises for the three trees whether each attribute variable was a significant positive or negative predictor, a competing split for a significant predictor, and / or was important (> .40) in the construction of the optimal decision tree. 272
Table 8.10
Summary of Attribute Variable Roles in the Decision Trees
Attribute variable Total earnings Earnings from Self defined career arts success rating Continuous variables CDI: Environmental / societal influences
CDI: Interpersonal influences o +o CDI: Physical characteristics +o CDI: Skills and abilities +o +o CDI: Interests and beliefs +o +o +o PCSO +o +o +o CMC: Career building ++o +o +o CMC: Self management +o +o ++o Age ++o ++o ++o Length of time working in arts ++o ++o ++o Categorical variables Male -o Female +o Creative artists + -o -o Performing artists -o -o Technical / design artists +o +o Has career outside arts +o -o Some work outside arts – not career +-o -o Does not work outside arts -o +o Has had formal arts education Has not had formal arts education Internal definitions o +o Financial recognition definitions -o Non-financial recognition definitions Contribution definitions
+ positive predictor + positive competing predictor - negative predictor - negative competing predictor o variable importance > 0.4 273
Total Earnings
The most important predictor of total earnings in the professional artists’ cohort was CMC: Career building, for which PCSO and CMC: Self management were strong alternative splits. This finding indicates the importance of the artists’ abilities to secure and maintain work and build a career in predicting their earning capacity. It also recognises the strong relationship (also as shown in correlations depicted in Table 8.5) between the artists’ abilities to build the external aspects of a career and their abilities to manage the internal, “self” aspects of career such as remaining positive and developing throughout life, being resilient, and interacting positively with others.
Other clear predictors of total earnings in the professional artists’ sample, whether or not they had high CMC: Career management scores, included the length of time they had worked in arts (for which age was a potential proxy) and whether the artist worked outside arts. Very experienced artists consistently reported higher total incomes than less experienced artists. Artists who maintained a career outside arts had higher total earnings than those who worked outside arts but did not have a career outside arts, who in turn earned more than those who did not work outside arts at all. The artists who earned the least overall had low CMC: Career building scores, were relatively young, inexperienced artists (< 6.5 years in arts) who did not work outside the arts at all.
CDI: Interests and beliefs, for which CDI: Skills and abilities represented a competing split, emerged as a potentially important predictor for professional artists under some circumstances, a finding which was also suggested by the simple correlations between the two predictors and total earnings shown in Table 8.4.
However, some variables which were suggested by the preliminary descriptive 274 statistics and nonparametric tests of difference as potentially important predictors of total earnings were not included in the optimal tree, and had fairly low relative importance. For instance, although artists with financial recognition definitions of career success had significantly lower total earnings than other artists, and artists with formal arts educational experiences had significantly higher total earnings than other artists, neither variable was a major contributor to the construction of the decision tree.
Arts Earnings
The decision tree for arts earnings revealed a somewhat similar pattern of predictors to the tree for total earnings, although a number of additional important predictors became apparent. The variable from which the primary data split was made was length of time in arts, with highly experienced artists generally earning more from arts. Age was a competing split variable for length of time in the arts.
Unlike the decision tree for total earnings, arts earnings were generally higher for artists who did not work outside the arts, although CMC: Self management and
CMC: Career building (for which PCSO was a competing split variable) continued to be important predictors of arts earnings as well as total earnings. Discipline appeared twice on the decision tree, and revealed a tendency for technical / design artists to earn more than artists from other disciplines. Performing artists’ earnings were higher if their CDI: physical characteristics scores were relatively high.
Three CDI variables were competing split variables on the arts earnings decision tree; these were CDI: Skills and abilities, CDI: Interpersonal influences, and CDI: Interests and beliefs. In similar results to the total earnings tree, artists who possessed a formal arts education generally earned more than other artists from arts, 275 but the variable was not included in the optimal decision tree. Likewise, artists with financial recognition definitions of career success generally earned less than other artists, but the variable was not very important in constructing the tree. However, internal definitions of career success was a relatively important variable in constructing the arts earnings tree but not the total earnings tree.
Self Defined Career Success Rating
Table 8.10 shows a substantially different pattern of predictors for self-rated career success compared with those for total earnings and earnings from arts.
Although CMC: Career building, CMC: Self management, PCSO and length of time in arts (for which age was a strong proxy) continued to be important in the career success rating decision tree, there was evidence of a gender difference in this tree that was not apparent in the earnings trees. Males tended to assign themselves lower career success scores than females, particularly amongst inexperienced artists with low CMC: Career building scores.
Technical / design artists had higher career success ratings than artists in other disciplines, which echoed the finding of the arts earnings decision tree.
However, career success definition category was important in the ratings decision tree, a finding that was not evident in the earnings trees. Artists with internal definitions of career success tended to assign themselves higher career success ratings than other artists, and artists with financial recognition definitions tended to assign themselves lower career success ratings than other artists.
Although CDI: Interests and beliefs was an important predictor in all three trees, CDI: Skills and abilities and CDI: Interpersonal influences were not important predictors in the ratings tree as they had been in previous trees. In addition, career / 276 work outside arts did not emerge as a predictor of self-rated career success, although there was a significant group difference observed in mean ratings by career / work outside arts (as shown in Table 8.4), and it was one of the most important variables in predicting total earnings and arts earnings.
Links Between Decision Tree Findings and Extant Literature
The decision trees depicted in this chapter clearly demonstrate the importance of (i) career self-management skills, and (ii) underlying dispositions as suggested by theory, to career success in the sample of professional artists, whichever measure of career success is used. These findings provide support to the arguments of Watts
(1999) and Mayston (2002), and initial empirical findings (Eby et al., 2003), specifically in the context of the protean career in arts. However, the findings run counter, in part, to the large scale study by Kuijpers and colleagues (Kuijpers &
Scheerens, 2006; Kuijpers et al., 2006), which found that, while career building skills were predictive of career success, competencies roughly corresponding to self- management skills were not. There are a number of possible explanations for this, including the differences between the populations studied (the participants in the
Dutch study had organisationally-based careers rather than protean careers), and differences in the operationalisation of ‘self-management skills’.
Although the current study lends support to the contention that career self- management skills and protean career success orientation predict career success in the professional artists studied, it is not clear from the research which of the competencies or dispositions are of the greatest importance, and what the relationships between these competencies and dispositions might be. Further targeted empirical work is required to explore these issues. 277
Existing literature also contains suggestions with respect to the predictive value of sociodemographic constructs to career success. A 2005 meta-analysis documented in Chapter 3 (Ng et al., 2005) suggested that males and older careerists
(who were implied to have greater experience in the workforce) had greater levels of objective career success than females and younger careerists, but no relationship was found for subjective career success. The meta-analysis also found that those with higher levels of education experienced greater levels of both subjective and objective career success.
The present study of professional artists confirmed some of these previous suggestions, but did not confirm others. The decision trees did show that older, more experienced artists tended to be more objectively successful. Unlike previous studies, however, the present research found that the older artists were also more subjectively successful than other artists. This disparity in findings is attributable to a difference in operationalisations of ‘subjective career success’. The present program of research did not use the usual measures of subjective career success (i.e., scales of career or job satisfaction), and thus the subjective career success findings were not directly comparable to previous studies. In addition, the studies included in the 2005 meta- analysis were mostly of success in traditional, organisationally-based careers, and predictors of protean or boundaryless career success may be quite different. For instance, it may be that only artists who perceive that they have achieved a certain level of success remain in the arts for long periods.
Also in contrast to the meta-analysis findings (Ng et al., 2005), the present study did not find that males were more successful than females when objective measures were used. Male and female artists earned similar amounts both overall and from arts, but male artists tended to assign themselves lower career success ratings 278 than female artists did. Findings suggest that males with less experience in the arts, low levels of career self-management competence, and financial recognition definitions of career success, were particularly likely to assign themselves low career success ratings.
The meta-analysis (Ng et al., 2005) also suggested that careerists with higher levels of education were both objectively and subjectively more successful. The decision trees, by contrast, did not conclusively show a predictive relationship between formal arts education and career success, although the preliminary nonparametric tests of difference did find a significant difference between the overall earnings, arts earnings, and self defined career success ratings of formally educated and non-formally educated artists. It is possible that this finding is reflective of working in the arts as opposed to career in other fields. Some writers have suggested that possession of a formal qualification in an art form is not necessarily valued by gatekeepers in the arts, and that self-taught, informally educated and ‘apprenticed’ artists can be highly successful (Caves, 2000; Inkson & Parker, 2005). However, in many other fields a degree or other formal qualification is seen as essential for employability and career progression.
Finally, personal support (e.g., from friends and family) and work peer support have previously been found to be predictors of both objective and subjective career success in various cohorts (Nabi, 2003; Peluchette, 1993). In the present study of professional artists, these career development influences were measured by CDI:
Interpersonal influences. CDI: Interpersonal influences was, as expected, a positive predictor in the regression trees in the present study, but only for objective career success. However, the simple correlations between CDI: Interpersonal influences and self defined career success rating were significant in this study. Thus, this study 279 provides support for the contention that strongly positive interpersonal influences on career development are predictive of career success in the sample studied.
Chapter Summary
Study 3 has documented the use of a CART decision tree approach to identify salient predictors of career success in the professional artists’ sample. Constructs which came forward as consistent and important predictors of all three measures of career success included the ability to build a career (in terms of both external and internal career management skills), and experience (length of time) working in the arts. The positive influences of the artists’ skills, abilities, interests and beliefs about their career development were also noted to be predictive of the various measures of career success.
There were some key differences in important predictors between the three decision trees. For instance, work outside the arts predicted higher total income, but it also predicted lower earnings from arts. Also, although there was no difference between the mean earnings of male and female artists, males assigned themselves much lower career success ratings. In terms of career success definitions, artists who gave financial recognition definitions of career success earned less, on average, than other artists, and also assigned themselves lower career success ratings. By contrast, artists with internal definitions of career success assigned themselves higher career success ratings but their levels of earnings were about the same as for other artists.
The theoretical and practical implications of the results of this study are discussed further in the final chapter of this document, Chapter 10. Chapter 9 will present the findings of a parallel study to the one documented in this chapter, using a decision tree approach to identify predictors of arts students’ successful transitions to 280 a career in arts, operationalised as total earnings, arts earnings and self defined career success. 281
281
CHAPTER 9
Study 4: Prediction of Arts Students’ Successful Transitions to Work from Career Development Influences and Constructs
The research question under investigation in Study 4 is: Which of the measured career development influences and constructs measured at undergraduate course completion predict successful transition to the world of arts work? Like Study
3, Study 4 makes use of the career development measures validated in Study 1 and the career success constructs explored in Study 2. Once again, a decision tree approach is used to identify major predictors of three measures of career success.
However, the design of Study 4 differs from that of Study 3 in several important respects. The sample under investigation in the present study is a group of
122 arts graduates who were tracked forward one year from the point of course completion as they moved into the world of work. A time 1, the arts students provided data for the career development measures and constructs, as well as the various sociodemographic variables. At time 2, one year after they had completed their courses, the arts students, now arts graduates, indicated how much they were earnings from arts, how much they were earning overall, and assigned themselves a 1
- 6 career success rating according to their own definitions of career success. These three career success measures were used as indicators of a successful transition to work from university. Thus, it was possible to build a model to examine the predictive value of the various career development constructs and influences at the point of course completion to a successful one year world-of-work transition.
Chapter 9 commences by describing the attribute (predictor) and target
(criterion) variables used in the three arts student decision trees. Although the career development measures of Career Management Competence (CMC), Career
282
Development Influences (CDI) and Protean Career Success Orientation (PCSO) and the career success definition categories are the same as those used in the professional artists’ decision trees, the sociodemographic variables used for the arts graduates’ trees are somewhat different, to make the predictors pertinent to a sample of arts students rather than a sample of professional artists. For instance, a four-level variable relating to the students’ level of arts work experience at course completion
(“none / some unpaid / some paid / extensive paid”) is included in the present study, but formal arts education is not included, as all of the participants in Study 4 had completed an undergraduate tertiary qualification in arts. A full list of the variables included in Study 4 is located in Table 9.6.
Once the attribute and criterion variables have been described, the chapter documents the generation of the decision trees, and presents the three optimal trees graphically. The final section of this chapter presents an interpretation and discussion of the arts graduates’ decision tree findings. In addition, a preliminary comparison of the decision tree findings for arts graduates and professional artists is conducted.
Decision Tree Method
The decision trees constructed in Study 4 are of the same type as those used for Study 3; namely, CART decision trees (Breiman et al., 1984) with variance reduction splitting criteria and v-fold cross tabulation, using the decision tree tool in
SAS 9.1 Enterprise Miner 4.3 (deVille, 2006; Neville, 1999). A comprehensive discussion of the analysis technique and justification for its use with the present measures and research questions is presented in Chapter 8. It was expected that the approach would also be effective with the arts graduates’ data set, although the decision trees might be smaller than for the professional artists’ data set because of the smaller sample size, and the R2 values might be lower because of the prospective
283 design of the study. Allowing a year to elapse between collection of the attribute
(predictor) data and the target (criterion) data permits the influence of many variables not included in the study on the target variables. A significantly higher degree of
“noise” or error variance (variance in the target variables not accounted for by the attribute variables) was expected to be present in the arts graduates’ results as opposed to the professional artists’ results; it might therefore be more difficult to identify important predictors of the career success measures.
Decision Tree Results
Descriptive Statistics for Variables Included in the Analyses
Tables 9.1 to 9.3 show descriptive statistics for the arts graduate target variables, career development measure attribute variables, and sociodemographic attribute variables. Means, standard deviations, and minimum / maximum values for the target variables of self defined career success rating, total earnings and earnings from arts are presented in Table 9.1. The total earnings variable exhibited a fairly normal data distribution around a modal figure of $27,000, but had a long ‘tail’ to the upper end of the distribution (positive skewness), with 6 outliers ranging from $39,000 to
$84,000. The arts earnings variable exhibited a strong ‘peak’ in the distribution at
$11,000 - $13,000, and as with total earnings had a long ‘tail’ running between
$23,000 and $84,000. The distribution of self defined career success rating was within the bounds of a normal distribution.
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Table 9.1
Descriptive Statistics for Target Variables Included in the Decision Tree Analysis
Variable Minimum Maximum Mean SD
Target Measures
Self defined career success rating 1.00 6.00 3.57 1.41
Earnings from arts $2,000 $84,000 $17,885 $12,231
Total earnings $6,000 $84,000 $26,549 $11,920
Note. N = 122
The career development measures included in Study 4 were the same as those used for Study 3. These measures were the five Career Development Influences
(CDI) subscales, two Career Management Competencies (CMC) subscales and the single Protean Career Success Orientation (PCSO) scale, which were calculated by summing the ratings participants at time 1 assigned to the items falling within each factor identified in Study 1. The descriptive statistics for these scales are presented in
Table 9.2.
A number of the career development measures were fairly normally distributed, including the two CMC scales, CDI: Environmental / societal influences, and CDI: Physical characteristics. For all of these scales, the mean scores were located above the absolute middle point of the possible score range. For the remainder of the career development measures, moderate negative skewing was evident.
The categorical variables relating to career success definition category, as outlined in Study 2, are also described in Table 9.2. The arts students’ definitions of career success (measured at time 2) were coded into categories. A total of 122 responses yielded 243 categorisations, of which 228 fell into four major categories.
285
Responses could be coded into multiple categories. The frequencies for the four major categories are presented in the table; each response contained an average of two thematic categories.
Table 9.2
Descriptive Statistics for Career Development Measure Attribute Variables Included in the Decision Tree Analysis
Variable Minimum Maximum Mean SD N %
Career Development Measures
CDI: Environmental / societal influences 12.00 31.00 22.05 3.61
CDI: Interpersonal influences 4.00 12.00 9.23 2.17
CDI: Skills and abilities 5.00 18.00 14.10 3.16
CDI: Physical characteristics 4.00 23.00 16.32 3.15
CDI: Interests and beliefs 9.00 24.00 18.82 3.45
PCSO: Protean Career Success Orientation 14.00 42.00 32.63 6.70
CMC: Career building 10.00 30.00 20.97 4.31
CMC: Self management 12.00 36.00 28.03 4.49
Career success definition category
Internal definitions 83 68.03
Financial recognition definitions 80 65.57
Non-financial recognition 29 23.77 definitions
Contribution definitions 33 27.05
Note. N = 122
Descriptive statistics for the sociodemographic variables employed in the decision tree analysis are shown in Table 9.3. The variable Age exhibited a strong
peak in its distribution, with 42.62% of the participants aged between 20 and 21, and
286
77.87% of the participants aged 25 or younger. Age exhibited a long distributional tail, ranging from 26 to 57 years.
The categorical sociodemographic variables included in the decision tree analysis were: gender; arts discipline category; and arts work experience. As with the professional artists’ decision trees, the categorical variables did not require dummy or effect coding for interpretability. All categories contained enough cases to allow a split to be made based upon them (10 cases, or 5 cases per child node), and so the variables were included without any recoding or transformation.
Table 9.3
Descriptive Statistics for Sociodemographic Attribute Variables Included in the
Decision Tree Analysis
Variable Minimum Maximum Mean SD N % Sociodemographic Variables Age 19.00 57.00 23.16 4.99 Gender Male 56 45.90 Female 66 54.10 Arts discipline category Creative artists 41 33.61 Performing artists 44 36.07 Technical / design artists 37 30.33 Arts work experience None 27 22.13 Some unpaid work experience 42 34.43 Some paid work experience 39 31.97 Extensive paid work 14 11.48 experience
Note. N = 122
Descriptive statistics were also calculated for each of the target variables as grouped by the categorical attribute variables. Nonparametric tests of difference were
287 conducted on the group mean ranks (Mann-Whitney U for two independent groups and Kruskal-Wallis H test for more than two independent groups (Sheskin, 2003)).
The tests indicate statistically significant differences on at least one target variable at p < .05 for all categorical variables except gender. The findings summarised in Table
9.4 are interpreted further in the discussion section of this chapter, in conjunction with the substantive decision tree results.
Spearman’s ρ nonparametric correlations were also calculated to provide an indication of the bivariate statistical relationships between the ordinal and continuous variables to be included in the decision trees (Gibbons et al., 2003). Although many of the correlation coefficients depicted in Table 9.5 were lower than the equivalent correlations for the professional artists’ cohort (shown in Table 8.5 in Chapter 8), many of the correlations were statistically significant, indicating many good potential criteria for splits in the decision trees. The highest correlation was between total earnings and arts earnings (.62), a finding that is congruent with the observation that relatively few of the arts graduates (36.07%) worked outside the arts compared with the professional artists’ cohort (80.00%). Other correlations of .50 or higher included: PCSO and CDI: Skills and abilities (.58); and CMC: Career management and CMC: Career building (.50).
288
Table 9.4
Target Variable Means, Standard Deviations and Nonparametric Tests of Difference by Categorical Variable Levels
Total earnings Earnings from arts Self defined career success rating Mean SD Mean SD Mean SD Gender Male (n = 46) $27,652 $13,375 $18,543 $13,304 3.44 1.28 Female (n = 76) $25,881 $10,986 $17,487 $11,606 3.66 1.49 Arts discipline category Creative artists (n = 41) $24,219c $7,824 $16,634c $7,327 3.78a 1.31 Performing artists (n = 44) $22,909c $10,101 $13,704cc $8,937 3.13c 1.37 Technical / design artists $33,459a $14,704 $24,243a $16,823 3.86a 1.48 (n = 37) Arts work experience None (n = 27) $19,222c $8,035 $12,852c $6,163 2.93c 1.29 Some unpaid work $25,619b $6,713 $15,881c $7,252 3.67b 1.48 experience (n = 42) Some paid work $26,974b $10,821 $18,385c $11,042 3.64b 1.29 experience (n = 39) Extensive paid work $42,286a $18,095 $32,214a $22,509 4.36a 1.39 experience (n = 14) Career success definition category Internal definitions (n = $26,910 $12,835 $18,557 $13,768 3.81a 1.31 83) Other definitions (n = 39) $25,780 $8,469 $16,455 $7,041 3.08c 1.51
Financial recognition $26,125 $12,244 $17,637 $12,023 3.36c 1.46 definitions (n = 42) Other definitions (n = 80) $27,357 $11,375 $18,357 $12,750 3.98a 1.24
Non-financial recognition $25,724 $11,600 $16,483 $12,070 2.93c 1.41 definitions (n = 29) Other definitions (n = 93) $26,806 $12,067 $18,323 $12,312 3.77a 1.36
Contribution definitions $26,576 $10,075 $16,667 $8,915 3.85 1.41 (n = 33) Other definitions (n = 89) $25,539 $12,588 $18,337 $13,267 3.47 1.41 Note. a highest group mean, significant difference at least p < .05; b middle group mean, significant difference at least p < .05; c lowest group mean, significant difference at least p < .05
289
Table 9.5
Spearman’s Bivariate Correlation Coefficients for Continuous and Ordinal
Variables Included in the Decision Tree Analysis
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. CDI: Environmental / societal influences 1.00
2. CDI: Interpersonal influences .21 1.00
3. CDI: Physical characteristics .23 .22 1.00
4. CDI: Skills and abilities .22 .37 .14 1.00
5. CDI: Interests and beliefs .16 .02 .13 .47 1.00
6. PCSO .18 .28 .13 .58 .21 1.00
7. CMC: Career building .05 .18 .05 .40 .27 .37 1.00
8. CMC: Self management .05 .06 .04 .32 .30 .37 .50 1.00
9. Age .03 .06 .14 .25 .22 .16 .30 .20 1.00
10. Total earnings .09 .18 .15 .18 .16 .26 .37 .34 .27 1.00
11. Arts earnings .13 .24 .05 .42 .15 .34 .39 .25 .23 .62 1.00
12. Career success rating .08 .17 .09 .18 .16 .17 .26 .19 .18 .30 .45 1.00 Note. Bivariate correlations > = .17 are statistically significant at p < .05. N = 122 Three decision trees were constructed in order to identify salient time 1 (i.e., at course completion) predictors of arts graduates’ total earnings; earnings from arts; and self-rated career success at time 2 (i.e., one year after course completion). The
CART procedure as outlined in Chapter 8 was followed using the decision tree tool in SAS 9.1 Enterprise Miner 4.3. All 16 of the attribute variables summarised in
Tables 9.1 to 9.3 were included as potential splitting criteria. Table 9.6 presents the attribute variable names, their associated values, and the time the variable measurement was taken (where time 1 was October 2005, at course completion, and time 2 was one year later, October 2006).
290
Table 9.6
Attribute Variables Included in the Decision Trees
Attribute Variable Data Collection Values Time Gender Time 1 1 = male, 2 = female Arts discipline category Time 1 1 = creative arts, 2 = performing arts, 3 = technical / design arts Arts work experience Time 1 1 = none , 2 = some unpaid work experience, 3 = some paid work experience, 4 = extensive paid work experience Internal career success definitions Time 2 1 = internal career success definition , 0 = other definition Financial recognition career success Time 2 1 = financial recognition definitions definition, 0 = other definition Non-financial recognition definition Time 2 1 = non-financial recognition category definition, 0 = other definition Contribution definition category Time 2 1 = contribution definition, 0 = other definition CDI: Environmental / societal influences Time 1 range 6 (low) – 36 (high) CDI: Interpersonal influences Time 1 range 2 (low) – 12 (high) CDI: Physical characteristics Time 1 range 3 (low) – 18 (high) CDI: Skills and abilities Time 1 range 4 (low) – 24 (high) CDI: Interests and beliefs Time 1 range 4 (low) – 24 (high) PCSO Time 1 range 14 (low) – 42 (high) CMC: Career building Time 1 range 5 (low) – 30 (high) CMC: Self management Time 1 range 9 (low) – 36 (high) Age Time 1 range 17 - 65 years
In an identical approach to that employed for the professional artists’ data set in Study 3, variance reduction was used as the splitting criterion in the construction of the arts graduates’ decision trees. The CART variance reduction algorithm tested all of the attributes variables and all of the possible splits. It chose the variable and bivariate split with the highest predictive power (Breiman et al., 1984). The procedure was then repeated for each of the two initial child nodes, and then successively with each child node until the data was completely split. V-fold cross- validation was then used to prune subtrees from the maximal tree until an optimal
291 tree (maximum predictive value with minimum error, using the 1 SE rule) was produced.
Total Earnings Decision Tree Results
A maximal tree of 13 leaves for total earnings was generated by the algorithm. This tree had an R2 value of 0.51. Through cross validation, the maximal tree was pruned to produce an optimal tree which maximised prediction whilst minimising average squared error, using the 1 SE rule (Breiman et al., 1984). This tree with 7 leaves accounted for 41% of the variance. The arts graduates’ total earnings decision tree is presented in Figure 9.1.
Figure 9.1
Optimal decision tree for total earnings
The most important split in the tree was arts discipline category (R2 = 0.19).
Technical / design arts graduates earned more, on average, than creative / performing
292 artists. The technical / design arts graduates with paid work experience earned more than the technical / design arts graduates without work experience or with unpaid work experience.
Amongst the creative and performing arts graduates, older participants (> =
25.5 years) with high CMC: Career building scores (> = 23.5) earned as much as many of the technical / design arts graduates. However, creative and performing arts graduates aged 25.5 years or older with lower CMC: Career building scores, and younger creative and performing arts graduates (< 25.5 years) earned less than this.
The participants with the lowest mean total earnings were creative and performing arts graduates who were under 25.5 years old and had low CMC: Self management scores (< 27.5), or had higher CMC: Self management scores but low CMC: Career building scores (< 19.5).
The tree module of Enterprise Miner calculated the relative importance of the input variables in growing the decision tree, based on changes in sums of absolute deviations in the variables (Breiman et al., 1984). The importance values for each of the input variables are presented in Table 9.7.
Another indicator of a variable’s importance in a decision tree is that it is a competing split for a variable present in the tree. A competing split occurs when a variable represents almost as good a split as the variable actually used in the tree
(i.e., it has almost as strong explanatory value as the used variable with minimal error). Four strong competing splits were observed in the tree: Age for work experience in arts; work experience in arts for age; CDI: Skills and abilities for
CMC: Career building; and PCSO for CMC: Self management.
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Table 9.7
Relative Importance of Variables in Constructing the Optimal Total Earnings
Decision Tree
Attribute variable Importance Number of rules CMC: Career building 1.00 2 Work experience in arts .95 1 CDI: Skills and abilities .84 0 CMC: Self management .83 1 Age .81 1 Arts discipline category .76 1 CDI: Interests and beliefs .54 0 PCSO .37 0 CDI: Environmental / societal influences .17 0 CDI: Physical characteristics .17 0 CDI: Interpersonal influences .15 0 Contribution definition of career success .13 0 Financial recognition definitions of career .00 0 success Internal definitions of career success .00 0 Non-financial recognition definition of career .00 0 success Gender .00 0 Note. Variable importance values range from 0 (not at all important) to 1 (most important).
Arts Earnings Decision Tree Results
A maximal tree was generated for the arts graduates’ arts earnings. This maximal tree contained only 6 leaves and accounted for 48.91% of the variance. This tree was pruned back via cross-validation to an optimal tree of 5 leaves (R2 = 0.46) which fell within the 1 SE rule (Breiman et al., 1984). The optimal decision tree for
arts earnings is presented in Figure 9.2.
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Figure 9.2
Optimal decision tree for earnings from arts
The primary split in the arts earnings tree was CMC: Career building, and this split obtained an R2 value of 0.13. The arts graduates who achieved a CMC:
Career building score of 27.5 or higher earned significantly more than the graduates who had a CMC: Career building score of less than 27.5. However, arts discipline category was also an important split variable for arts earnings. Technical / design arts graduates with relatively low CMC: Career building scores (< 27.5) earned as much from arts as the graduates with high CMC: Career building scores if they had some or extensive paid arts work experience at the point of course completion. The participants with the lowest arts earnings at time 2 were creative or performing arts graduates with low CMC: Career building scores. The decision tree suggested that for these graduates, very high CDI: Interpersonal influences scores (> = 11) predicted somewhat better arts earnings levels than those with lower CDI:
Interpersonal influences scores (< 11).
295
The relative importance of the input variables is generating the decision tree is presented in Table 9.8. The most important variables for earnings in arts were
CMC: Career building and work experience in arts. These two variables stood out in relative importance in constructing the tree, with a difference of .41 in importance between work experience in arts and the next most important variable, CDI:
Interpersonal influences. In this tree, there were no variables which were strong competing splits.
Table 9.8
Relative Importance of Variables in Constructing the Optimal Earnings from Arts
Decision Tree
Attribute variable Importance Number of rules CMC: Career building 1.00 1 Work experience in arts .95 1 CDI: Interpersonal influences .54 1 Age .53 0 CDI: Skills and abilities .53 0 PCSO .46 0 CDI: Interests and beliefs .42 0 Arts discipline category .39 1 CMC: Self management .35 0 Gender .00 0 Financial recognition definitions of career success .00 0 Internal definitions of career success .00 0 Non-financial recognition definition of career .00 0 success Contribution definition of career success .00 0 CDI: Physical characteristics .00 0
CDI: Environmental / societal influences .00 0 Note. Variable importance values range from 0 (not at all important) to 1 (most important).
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Self Defined Career Success Rating Decision Tree Results
For self defined career success rating, a maximal tree containing 13 leaves was generated, accounting for 56.65% of the variance. This tree was pruned back via cross-validation to an optimal tree of just 5 leaves (R2 = 0.33), once again using the 1
SE rule (Breiman et al., 1984).
Figure 9.3
Optimal decision tree for self defined career success rating
The primary split for the self-defined career success rating decision tree was
CMC: Career building. This split accounted for 17.60% of the total variance. The arts graduates who obtained a CMC: Career building score of 18.5 or more generally assigned themselves higher career success ratings than other arts graduates. This was particularly the case if they also obtained high CMC: Self management scores (> =
31.5), although arts graduates with lower CMC: Self management scores (< 31.5) but high CMC: Career building scores also tended to rate their level of career success
297 more highly than other arts graduates. Amongst the arts graduates with low CMC:
Career building scores (< 18.5), participants with internal success definitions of career success still rated their career success levels at about average levels.
Participants with low CMC: Career building scores (< 18.5) without internal definitions of career success assigned themselves the lowest mean career success ratings, although arts graduates in this group who had comparatively high CDI:
Interpersonal influences scores (> = 8) tended to rate their career success levels somewhat more highly than those with low CDI: Interpersonal influences scores (<
8).
Table 9.9 indicates the relative importance of the variables used in constructing the decision tree for self defined career success. The most important variables were CMC: Career building, followed by PCSO, CMC: Self management and CDI: Interpersonal influences. CDI: Physical characteristics and CDI: Skills and abilities also obtained relative importance figures of at least .90. Strong competing splits for the variables included in the optimal tree were: PCSO for CMC:
Self management, and arts discipline category for CDI: Interpersonal influences.
298
Table 9.9
Relative Importance of Variables in Constructing the Optimal Self Defined Career
Success Rating Decision Tree
Attribute variable Importance Number of rules CMC: Career building 1.00 1 PCSO .99 0 CMC: Self management .95 1 CDI: Interpersonal influences .95 1 CDI: Physical characteristics .91 0 CDI: Skills and abilities .90 0 Work experience in arts .68 0 Internal definitions of career success .49 1 Financial recognition definitions of career success .46 0 CDI: Interests and beliefs .43 0 CDI: Environmental / societal influences .24 0 Arts discipline category .22 0 Age .00 0 Contribution definition of career success .00 0 Non-financial recognition definition of career .00 0 success Gender .00 0 Note. Variable importance values range from 0 (not at all important) to 1 (most important).
Discussion
The CART procedures successfully generated predictive regression trees for total earnings, earnings in arts, and self defined career success for the arts graduates’ sample. The regression trees were simpler and had less predictive power than those grown for the professional artists’ data set (documented in Chapter 8), with 3 to 4 tree node levels, fewer splits contained within each tree, and lower R2 values.
However, the strong predictive worth of a number of the measured attributes for all three target variables was apparent. These variables included arts discipline category, arts work experience, and CMC: Career building. A summary of the
299 decision tree findings for each attribute variable is presented in Table 9.10. The table outlines whether each attribute variable was a significant positive or negative predictor, a competing split for a significant predictor, and / or was important (> .40) in the construction of the three optimal decision trees.
300
Table 9.10
Summary of Attribute Variable Roles in the Decision Trees
Attribute variable Total earnings Earnings from Self defined career arts success rating Continuous variables CDI: Environmental / societal influences
CDI: Interpersonal influences +o +o CDI: Physical characteristics o CDI: Skills and abilities +o o o CDI: Interests and beliefs o o o PCSO + o +o CMC: Career building +o +o +o CMC: Self management +o +o Age ++o Categorical variables Male Female Creative artists -o - - Performing artists -o - - Technical / design artists +o + + No arts work experience --o -o o Some unpaid arts work experience --o -o o Some paid arts work experience ++o +o o Extensive paid arts work experience ++o +o o Internal definitions +o Financial recognition definitions o Non-financial recognition definitions Contribution definitions + positive predictor + positive competing predictor - negative predictor - negative competing predictor o variable importance > 0.4
301
Total Earnings
The most important predictor of arts graduate earnings included in the present study was arts discipline category, with technical / design graduates earning an average of $33,459 during the previous year, and creative / performing arts graduates earning an average of only $23,541 overall. Level of arts work experience and participant age, which were competing split variables for one another, were also strong predictors of arts graduates’ total earnings in the year after course completion.
The initial nonparametric tests of group mean rank difference (in Table 9.4) indicated significant differences in total earnings between arts graduates with no work experience and some work experience, and again between arts graduates with some work experience and those with extensive work experience. However, the decision tree algorithm was only capable of a binary split, and found the best split for technical / design graduates (and as a competing split for age in creative / performing arts graduates) between no work experience / some unpaid work experience, and some / extensive paid work experience.
CMC: Career building and CMC: Self management (for which PCSO was a competing split) were also important predictors of participant total earnings at time 2, particularly for creative and performing arts graduates. As with the professional artists, high correlations were found between CMC: Career building and CMC: Self management (.50), CMC: Career building and PCSO (.37) and CMC: Self management and PCSO (.37) in the arts graduates’ data set. This finding provides further support to the contention that the abilities needed to build the various internal and external aspects of an arts career are strongly related to one another.
Two CDI subscales were also important in constructing the arts graduates’ total earnings decision tree, although they did not appear in the optimal decision tree.
302
The two variables were: CDI: Skills and abilities, and CDI: Interests and beliefs.
These variables were important in the generation of the optimal decision trees for arts earnings and self defined career success rating as well.
Arts Earnings
For the arts graduates cohort under study, there was a strong correspondence between their arts earnings and their total earnings, as only one third of the graduates worked outside the arts in addition to their arts work (in contrast with the professional artists’ sample, 80% of whom worked outside the arts in some capacity).
Nonetheless, a somewhat different optimal decision tree for arts earning than for total earnings was generated. The most important predictor of the graduates’ arts earnings amongst the variables studied was CMC: Career building, although discipline category continued to be a strong predictor of arts earnings, with technical / design artists earning more from arts, on average, than creative or performing artists. Arts work experience prior to course completion was also a factor in the arts earnings decision tree, with the best split for technical / design arts graduates found between no work experience / some unpaid work experience, and some work experience / extensive paid work experience. This split mimics the arts work experience split generated in the total earnings tree, and adds weight to the contention that at least for technical / design arts students, a considerable financial advantage may be gained through commercial (paid) experience in arts prior to course completion, although there may also be advantages conferred by unpaid work experience.
The variable relative importance statistics calculated by SAS (and shown in
Table 9.8) indicated that CMC: Career building and arts work experience were the most important variables in constructing the arts earning decision tree by a considerable margin, with no competing splits found for either variable. However,
303 strongly positive interpersonal career development influences at time 1 (CDI:
Interpersonal influences scores over 10, where 12 was the highest possible score) predicted slightly higher arts earnings amongst creative and performing arts graduates who had relatively poor CMC: Career building scores.
Self Defined Career Success Rating
The primary measured predictor of self defined career success rating in arts graduates was CMC: Career building. CMC: Self management (for which PCSO was a competing split) emerged as a strong predictor variable in the self defined career success rating tree. CMC: Self management was also a key variable in the total earnings tree, although it obtained a relative importance value of only .35 in the arts earnings tree; PCSO was an important variable in the generation of all three trees.
Despite the similarities between the three trees, there were also some marked differences between the patterns of predictors for earnings and those for self defined career success rating. Arts discipline category was less important in the self defined career success tree building process than for the earnings trees, and did not appear in the optimal self defined career success rating tree at all. Work experience also did not seem to be as important a predictor of career success ratings as for the earnings measures, although it obtained a relative importance score of .68 in the success ratings decision tree construction, and significant mean rank differences in were documented in the preliminary descriptive statistics (as shown in Table 9.4).
Internal career success definition emerged as a predictor of career success ratings even though no definitional variable was found to be important in the earnings trees. Amongst the graduates with the lowest CMC: Career building scores, an internal career success definition appeared to partially ‘immunise’ participants
304 against giving themselves very low career success ratings, although there was no difference found in earnings by graduates’ career success definition. Another variable which appeared to ‘protect against’ very low career success ratings was
CDI: Interpersonal influences. In a similar pattern to that observed in the arts earnings tree, high CDI: Interpersonal influences scores yielded somewhat higher career success ratings in the cases of some graduates with poor CMC: Career building scores.
Although CDI: Skills and abilities and CDI: Interests and beliefs did not appear in the optimal decision tree, both subscales were noted to be important in its construction, a similar finding to the total earnings and arts earnings trees. There was a positive relationship between scores on these two scales and all of the career success measures in the data set; the positive relationship was particularly evident for the CDI: Skills and abilities subscale.
Links Between Decision Tree Findings and Extant Literature
Several of the key findings from the decision trees documented in this chapter have implications for existing literature. First, arts discipline category was an important predictor of all three career success measures, with technical / design arts graduates earning more from arts and overall than other arts graduates. Technical / design arts graduates also assigned themselves higher career success ratings than performing arts graduates. The earnings disparity by arts discipline has previously been documented in studies of professional artists (Australian Bureau of Statistics,
2001, 2004, 2005; Throsby & Hollister, 2003), although to date there have been no studies published to suggest that the discipline-based earnings difference is also evident in tertiary arts graduates. The national Graduate Destination Survey
(Graduate Careers Council of Australia, 2006b) collapses income data for arts
305 students of all disciplines into only one category and thus does not differentiate between different arts graduate types, a significant shortcoming in its reporting which may mask important findings. For instance, the earnings of a small number of technical / design arts graduates who earn large salaries may artificially inflate the reported average earnings of arts graduates overall. Very recently, Queensland
University of Technology commenced ‘one year out’ phone survey research with graduates of selected courses, including several Faculty of Creative Industries courses (Queensland University of Technology Careers and Employment, 2007).
This survey collected some earnings information by discipline. More comprehensive longitudinal research of this nature will prove helpful in identifying courses which may require career education scaffolding or other interventions to maximise positive graduate outcomes.
The present discipline-based income findings suggest that some of the technical / design arts graduates seemed to have considerable arts earning power upon course completion, with 20% earning $30,000 or more in the preceding 12 months, and one graduate earning $84,000. Some of these graduates seemed to have a greater opportunity to engage in a ‘traditional’ career path, typified by more stable employment relationships than arts graduates in other disciplines. However, there was evidence to suggest that the technical / design arts students cohort were a heterogeneous group with respect to the target measures, with most graduates within the technical / design arts category obtaining comparable earnings to the creative and performing arts graduates. Four in ten technical / design arts graduates reported that they had earned less than $16,000 from arts in the previous year.
Career self management was also a major predictor of all three indicators of successful arts graduate transition from university to work. The CMC: Career
306 building subscale, which encapsulated external career building competencies relating to finding and using information relating to the world of work, securing or creating and then maintaining work, making decisions about work, and managing the career building process, was particularly important. As with the professional artists’ study, the CMC: Self management and PCSO scales were found in the present study to be strongly statistically related to the CMC: Career building scale. The high correlations between both of the CMC scales and the PCSO scale were unsurprising, as the three scales represent constructs which previous theoretical literature suggest to be highly interconnected (e.g., Quigley & Tymon, 2006; Saks & Ashforth, 1999).
As previously noted, the CMC: Self management scale pertained to the internal aspects of career management such as participating in lifelong learning, maintaining a work – life balance and interacting with others positively, and the PCSO scale encompassed many underlying dispositions suggested to be necessary to protean career self management, such as resilience, taking personal responsibility, and being open to opportunities.
Study 4 also showed that work experience prior to course completion was a strong predictor of career success in the arts students’ sample, particularly for the technical / design arts students. As documented in Chapter 3 of this document, previous longitudinal research also demonstrated links between student work experience or participation in work integrated learning programs and graduate employment outcomes (Cranmer, 2006; Harvey et al., 1997). It is somewhat unclear from the findings of the current study whether the value of arts work experience lies in factors associated with paid arts employment, in more extensive work experience, or a combination of the two. Targeted longitudinal research comparing the effects of
307 various models of work-integrated learning at university, prior work experience and commercial arts experience will be required to answer this question.
The arts graduates’ definitions of career success were important factors in predicting their self-defined career success rating, but were not significant predictors of earnings (cf. the professional artist’s results, where these variables were predictive of both career success ratings and earnings, as discussed in Chapter 8). The graduates who defined their career success in terms of internal criteria such as intrinsic enjoyment or personal development assigned themselves higher success ratings than others. Conversely, graduates who defined career success in terms of financial recognition, an externally focussed definition, tended to rate their career success levels comparatively poorly. This finding supports recent suggestions (Hall &
Chandler, 2005; Quigley & Tymon, 2006) that there is a connection between intrinsic career motivation and career success, although it is not obvious from previous research or the present findings to what extent this link is a direct one, is mediated by constructs such as career self management (e.g., intrinsic career motivation leads to proactive career behaviours, which in turn leads to enhanced career success), or is bi-directional.
The Career Development Influences subscale CDI: Interpersonal influences, which contained items relating to the influence of family and peers on career development, was also found to be a positive predictor of arts graduate career success. This variable was present in two of the decision trees, which showed that high CDI: Interpersonal influences scores predicted slightly better career success ratings and arts earnings amongst the graduates who earned the least and rated themselves as the least successful. The bivariate correlations between CDI:
308
Interpersonal influences and all three measures of career success were also statistically significant.
Previous research has shown that both personal and work peer support can be important predictors of both objective and subjective career success (Nabi, 2001;
Peluchette, 1993), and the findings of Study 3 supported this contention with the professional artist participants. However, remarkably little has been written about the predictive value of interpersonal career development influences on tertiary graduate outcomes, except for some studies discussing the positive role of mentoring (e.g.,
Theobold et al., 1999). However, there is a small body of literature regarding the positive effects of social context on individuals experiencing other types of career transitions (e.g., Greller & Richtermeyer, 2006; Higgins, 2001). It is of value to explore further the role of interpersonal influences in successful graduate transitions to the world of work. Future research is needed in order to investigate the relative importance of family support, personal friendships, colleagues and mentors with respect to graduate outcomes. Social network analysis (Wasserman & Faust, 1995) may be able to offer some interesting insights into links between the nature and extent of a student’s social ties, their career self-management competencies, and their graduate success levels.
The sociodemographic variable age was also a key predictor in the arts students’ decision trees. Although it only appeared in the tree for total earnings, the simple correlations between age and the three career success variables were all positive and significant, indicating that older graduates tended to report better outcomes at Time 2 than younger graduates did. In addition, these older students were likely to have higher CMC: Career building and CMC: Self management skills scores than younger graduates. Age was also a competing split for the amount of arts
309 work experience in predicting total earnings. The findings for the professional artists also revealed a positive relationship between age (with which length of time working in the arts was strongly correlated) and the career success measures, and there is some support for this in extant literature as well (Ng, Eby, Sorensen, & Feldman,
2005). However, studies of mature age tertiary students have tended to focus on their transitions to university (Reay, 2002), their learning styles and approaches
(Richardson, 1994), and their academic performance (Trueman & Hartley, 1996) rather than their world-of-work outcomes upon graduation. The present findings suggest that mature age tertiary arts graduates often have had more arts work experience at the point of course completion, report higher levels of career self- management competence, and have better graduate outcomes than younger graduates, making them attractive prospects for tertiary arts program recruitment.
The sociodemographic variable gender did not appear in the arts students’ decision trees, and no difference in career success was found for gender in the initial univariate nonparametric tests of difference. Previous suggestions in the literature on career success in general (Ng et al., 2005) have been that males experience higher levels of objective career success (e.g., earnings) than females do. The professional artists’ decision tree results in the present investigation suggested that this gender difference did not extend to the arts, with male and female artists earning similar amounts both overall and from arts, and with male artists also tending to assign themselves lower career success ratings than female artists did. In the present study the male arts graduates’ self defined career success ratings did appear slightly lower than the female arts graduates’ ratings, and their earnings levels were slightly higher, but these differences were not statistically significant in this fairly small sample of
310
122 graduates. Further large-scale longitudinal research into gender differences in graduate outcomes is needed to conclusively address this issue.
Chapter Summary
Study 4 employed a prospective CART decision tree approach to identify important pre-course completion predictors of successful transition to the world of work (using three career success measures) in arts graduates. The key predictors of career success were: arts discipline category (with technical / design arts graduates experiencing more positive outcomes than other arts graduates); level of work experience in arts; and career building abilities. The positive predictive value of interpersonal influences, skills and interests / beliefs was also apparent.
Some differences were noted in the patterns of predictors for the three decision trees. Internal career success definitions (as opposed to financial recognition definitions) were found to be positive predictors of self-defined career success ratings, but the variable did not appear in the earnings decision trees, a pattern that was also evident in the professional artists’ trees. Age was a strong positive predictor of total earnings, but although it was positively correlated with the other two career success measures, it did not appear in the final trees and was not an important variable in their construction.
There were also a number of differences in the patterns of predictors between the arts graduates trees in this chapter and the professional artists’ trees documented in Chapter 8. First, the decision trees presented in this chapter were smaller, with lower predictive value than the professional artists’ trees. This was probably related to the smaller sample size in Study 4, and also the extended length of time (one year) between the collection of the attribute variable data and the target variable data. In terms of differences in key findings between the studies, the positive role of internal
311 aspects of career self-management (CMC: Self management) and underlying protean career success dispositions (PCSO) was somewhat less clear in the trees depicted in this chapter, although the simple correlations were moderate to high in magnitude.
In summary, the present study has identified several key predictors of successful transition to the world of work in the tertiary arts graduates sample, including career self-management competence, arts discipline, positive interpersonal influences, and levels of work experience prior to graduation. The broader implications of these findings for career development theory and tertiary career education are discussed in the final chapter of this document, Chapter 10.
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CHAPTER 10
Discussion and Conclusion of the Research Program
Chapter 10 presents a discussion of the research program, including contributions to theory, methodology and practice, limitations of the research, and recommendations for future studies. First, an overview of the four studies is given, including a review of the main empirical findings of the research. The theoretical contributions of the studies are discussed, with an emphasis on implications for career development theory, career success theory, and graduate attributes theory.
This leads to a section outlining methodological contributions of the program of study, including the scale development undertaken in the program, and the innovative use of decision trees as an alternative to parametric regression techniques.
The practical implications of the findings for artists’ careers, arts education and employers / clients of artists are then considered. Recommendations are made regarding the enhancement of artistic careers for both individual artists and arts education providers. The final sections of this chapter outline some of the limitations identified in the present program of study. Options for future research to extend the present work are discussed, including ways that future research might redress some of the study limitations.
Overview of the Research Program
The research questions under examination in this program of research pertained to career success in the creative, performing and technical / design arts.
This topic was of particular interest because although the social and economic value of artists and the arts is widely acknowledged, Australian artists seem to experience very high levels of unemployment and underemployment, and earn comparatively little compared with workers in other sectors. However, the number of people 313 entering the arts workforce continues to increase, and there also continues to be strong demand for tertiary education programs in artistic fields.
In this research, graduating tertiary arts students and professional artists were surveyed in order to determine whether several career development constructs of interest were predictive of career success in artists at different stages of artistic career development. A sample of 310 professional artists with varying backgrounds and levels of experience were studied to provide insight into the careers of professional artists. Also, 122 graduating tertiary arts students were successfully tracked forward for one year to look at the predictive value of the chosen influences and constructs to career success as the students made transitions to the world of work. The study of tertiary arts graduates has particular implications for higher education institutions which are under increasing pressure to demonstrate that their graduates experience positive employment outcomes.
Before studying the predictive value of chosen career development constructs and influences to artistic career success in the two groups studied, it was important to explore the notion of career success in arts. Earnings and hierarchical job position / number of job promotions are often used as measures of career success, but may not be as relevant to artists as to other careerists. Thus, several measures of career success were investigated, including total earnings, earnings from arts, total employability, employability in arts, and self-defined career success ratings. The most useful of these measures were then used in the subsequent studies.
The career development constructs investigated as potential predictors of career success were chosen based on recent developments in career theory which document the rise of boundaryless and protean careers, characterised by ‘atypical’ working arrangements and non-linear, non-organisationally based career paths. The 314 theories suggest that there are certain groups of skills and dispositions which will enhance career outcomes in this new type of career. The most comprehensive conceptualisation of skills to manage life, learning and work in Australia is agreed to be the Australian Blueprint for Career Development (Haines et al., 2003). A number of underlying dispositions, known in this document as a ‘protean career success orientation’ have also been argued to be important to career self-management and career success in the non-traditional career (Briscoe & Hall, 2006; Briscoe et al.,
2006; Hall & Chandler, 2005; Hall & Mirvis, 1996). Brief measures of both career management competence based on the ABCD (Haines et al., 2003) and protean career success orientation were developed, validated, and used in the program of research. In addition to these targeted measures, a career development influences measure based on the Systems Theory Framework of career development (McMahon
& Patton, 1995; Patton & McMahon, 1997, 1999, 2006b) was developed, validated and then employed to provide a broad picture of the impact of a large number of individual, social and environmental influences on the artists’ career development and success.
Study 1
Study 1 addressed the first research question: Are the following researcher constructed measures sufficiently valid and reliable when used with the study samples? a) career development influences; b) protean career success orientation; c) career management competence?
A few quantitative studies of career management competence and various dispositions within protean career success orientation had been conducted prior to the present program of research (e.g., Briscoe et al., 2006; Eby et al., 2003; Ng et al.,
2005). However, there was no single measure available for protean career success 315 orientation, no scale relating to the preferred Australian Blueprint for Career
Development taxonomy, and research using the Systems Theory Framework of career development had primarily been qualitative in nature (e.g., McMahon et al.,
2005). Consequently, brief scales were constructed and went through initial validation through pilot testing and exploratory factor analysis procedures.
Confirmatory factor analyses were conducted in Study 1 with data collected from the
310 professional artists, and the 218 arts students who participated at time 1.
The final Career Development Influences (CDI) scale, based on the Systems
Theory Framework of career development (McMahon & Patton, 1995; Patton &
McMahon, 1997, 1999, 2006a), comprised 19 items. The confirmatory factor analyses refined a 6-factor solution based on 20 items suggested by the pilot study to a sufficiently reliable and valid five-factor solution, comprising: environmental / societal influences; skills and abilities; interpersonal influences; physical characteristics; and interests and beliefs.
The Protean Career Success Orientation (PCSO) scale briefly quantified the underlying dispositions and attributes suggested in protean career literature (Briscoe
& Hall, 2006; Briscoe et al., 2006; Hall & Chandler, 2005; Hall & Mirvis, 1996) as being necessary to success in the non-traditional career, and comprised 6 items. In pilot testing it had a single factor structure based on 7 items, which with modification and removal of one item, was confirmed in the main study with the artist groups.
The Career Management Competence (CMC) scale comprised 11 items, and was based on the Australian Blueprint for Career Development (Haines et al., 2003).
The pilot study revealed a three factor structure, but the confirmatory testing resulted in only two reliable factors – self management, and career building. 316
The three scales validated in Study 1 were then employed in Studies 3 and 4 as predictors of career success in the two groups under investigation, along with some sociodemographic variables of interest, and the participants’ career success definition categories.
Study 2
Study 2 addressed the research question: How can career success in the arts be defined? Two research subquestions were considered in turn in this study: (i)
What are the artists’ definitions of career success? and (ii) What is the statistical relationship between the measures of career success employed in the present study?
Study 2 aimed to identify the most appropriate career success measures to use in
Studies 3 and 4, and also to categorise the participants’ definitions of career success such that the definitions could be used as predictors of career success in the subsequent studies.
A number of measures of career success were included in the survey instruments given to the professional artists and arts students / graduates. These were: total earnings and arts earnings over the last 12 months; 1-6 rated total employability and arts employability; and 1-6 rated self-defined career success.
In addition, an open-ended question asked the participants to define career success. The responses were content-analysed with two coders. A codebook was created using the professional artists’ data set, and this codebook was later applied to the arts students’ data.
The majority of the themes could be grouped into four main categories: internal definitions (definitions relating to personal satisfaction, challenge or self development associated with artistic practice); financial recognition definitions 317
(definitions relating to receiving payment for artistic practice, most often in terms of a regular or stable income and being able to meet financial commitments); contribution definitions (contributing to the art form, the development of others, and
/ or society through artistic practice) and non-financial recognition definitions
(definitions relating to recognition from others for artistic practice, most frequently colleagues or gatekeepers).
The results of the first part of this study revealed that the generally accepted objective career success measures such as earnings are insufficient indicators of career success in artists when used by themselves. Only one third of the definition themes in the professional artists’ and arts graduates’ definitions of career success were categorised as relating to financial recognition. Responses within the financial recognition category also indicated that many of the artists aspired only to a regular subsistence level of arts income (although a small number of the arts graduates did aspire to fame and fortune).
The second section of the study explored the correlations between the five different measures of career success for each career success definitional category, and also overall. Nonparametric bivariate correlations showed that all of the measures were related to one another, but a very strong statistical relationship was identified between each employability measure and its corresponding earnings measure for both of the cohorts under investigation. On the basis of this finding, it was decided to include only the earnings measures (earnings from arts, and earnings overall) and the self-defined career success rating measure in the later studies. 318
Study 3
Study 3 addressed the third research question: Which of the measured career development influences and constructs predict career success in professional artists?
In this study, the career development constructs validated in Study 1, sociodemographic variables, and the career success measures from Study 2 were used in CART style decision trees to predict the various measure of career success from the career development constructs and influences of interest. Decision trees are a nonparametric analysis technique which can be used in the place of parametric regression techniques. The CMC, PCSO, CDI, career success definition category and sociodemographic variables for the 310 artists were the attribute (predictor) variables in the trees, and overall earnings, arts earnings, and 1-6 self defined career success ratings were entered as target (criterion) variables. Thus, there were three trees generated in Study 3.
The resulting optimal decision trees explained a large proportion of the variance in their respective target variables (R2 between 0.49 and 0.68). The Career building subscale of the CMC scale was the most consistent predictor of all three career success measures (and was the strongest predictor for two of the three trees), indicating the importance of the artists’ abilities to secure work and build the external aspects of a career. The PCSO and CMC: Self management scales often represented alternative split variables for CMC: Career building in the trees, and the simple correlations between these variables were high, denoting strong relationships between the artists’ capacities to build the external aspects of their career and to manage the internal aspects of career development, such as remaining positive, being resilient, and interacting well with others. 319
Another important predictor in all three professional artist trees was length of time the artist had worked in arts (for which age was a strong correlate, and a competing split variable). Very experienced artists consistently reported higher total earnings, higher earnings from arts, and assigned themselves higher career success ratings than other artists. The CDI subscale Interests and beliefs, comprising items relating to the influence of the individual’s values, personality, interests and beliefs, was also an important positive predictor in all three trees. The CDI: Skills and abilities subscale, comprising items relating to ability, aptitudes and skills, was closely related to CDI: Interests and beliefs in predicting total earnings and arts earnings.
There were a number of attribute variables which had dissimilar predictive value in the different trees. For instance, the role of the career / work outside arts variable was different in each of the decision trees. The 80% of artists who worked outside arts in addition to their arts practice tended to earn more overall than the remainder, but earned less from arts than the other artists. Work outside arts was not a significant attribute variable in the self defined career success rating tree.
Arts discipline was a factor in the earnings from arts and the self-defined career success rating decision trees. In both trees, technical / design artists reported better outcomes than creative and performing artists. In the arts earnings tree, some performing artists’ earnings seemed to be related to the influence of CDI: Physical characteristics, a subscale containing items relating to age, health, gender, and physical attributes.
Artist gender was a factor in only one tree, the tree for self-defined career success ratings. Although there was no gender difference in earnings, males assigned themselves much lower career success ratings than females did. Career success 320 definition was also a factor in the self-defined career success rating tree only. Some artists with internal definitions of career success gave themselves higher career success ratings than other artists, and artists with financial recognition definitions of career success appeared to assign themselves lower career success ratings than other artists. These findings may indicate that career success definition category is a mediating variable in the prediction of career success ratings from artist gender.
The variable CDI: Interpersonal influences showed potential as a positive predictor of professional artists’ career success, although the nature or strength of the relationship was ambiguous in the findings. The variable was noted as important in the construction of the decision trees for the two earnings measures, although it did not appear in the trees themselves. The bivariate correlations between CDI:
Interpersonal influences and the arts earnings and self-defined career success ratings measures were statistically significant, but not the correlation between CDI:
Interpersonal influences and total earnings. Further investigation will be required to clarify the role of positive interpersonal influences in predicting career success in the artistic career.
Two potential predictor variables did not appear anywhere in the three professional artists’ decision trees (whether as actual predictors, competing splits, or as flagged variables of importance). These were: formal education in the arts, and
CDI: Environmental / societal influences. However, there was a statistically significant difference in total earnings, arts earnings and self-defined career success ratings between artists who had completed formal arts education and those who had not, and a statistically significant bivariate correlation between CDI: Environmental / societal influences and arts earnings (although this did not extend to the total earnings and career success ratings measures). 321
Study 4
The fourth study answered the research question: Which of the measured career development influences and constructs measured at undergraduate course completion predict successful transition to the world of arts work? This study also used the career development measures validated in Study 1, sociodemographic variables, and the career success measures explored in Study 2 in CART style decision trees. However, in contrast to Study 3, where a cross-sectional design was used, Study 4 involved a prospective repeated measures design, whereby the data for the attribute variables was gathered from students at the point of undergraduate course completion and the target career success variables were measured one year later. A total of 122 arts graduates were successfully tracked forward using this procedure.
The resulting optimal decision trees had R2 values of between 0.33 and 0.46.
While the values were lower than those for the professional artists’ decision trees, and the trees themselves were smaller, the R2 values nonetheless indicated that the arts students’ trees possessed satisfactory explanatory power.
The arts graduates’ CMC: Career building scores at time 1 were strongly predictive of all three career success measures at time 2, a similar finding to the professional artists’ trees. A further similarity between the trees for the two samples was the strong statistical relationship between CMC: Career building, CMC: Self management, and PCSO. For the arts graduates, as for the professional artists, the ability to build the external aspects of a career, including the skills needed to locate career information, and obtain and maintain work, was a vital factor in predicting all three measures of career success. Internally-focussed career management skills and the underlying attributes and dispositions studied were strongly related to the 322 external career building capabilities, and also to the career success measures
(although PCSO and CMC: Self management did not appear as strongly in the decision trees as CMC: Career building did).
While CMC: Career building was a strong predictor in all three of the arts graduates’ decision trees, it was not the most important split variable in the decision tree which predicted total earnings. The most important variable in this tree was arts discipline category. Technical / design arts graduates consistently earned more overall than arts graduates from other disciplines. Arts discipline was an important variable in the arts earnings decision tree. However, the graduates’ arts discipline was a less important variable in the self-defined career success rating tree generation, and it did not appear in the final optimal tree at all, although the initial non- parametric tests showed discipline-based differences at the univariate level for all three career success measures. The arts discipline findings for the graduates were somewhat different to those for the professional artists’ trees, where a difference in career success levels could also be observed between technical / design artists and other artists for arts earnings and self-defined career success ratings, but not for total earnings (which, for most professional artists, included a significant proportion of earnings from work outside arts, a pattern that was less evident in the arts graduates’ work patterns).
Work experience in arts prior to course completion was another key predictor in the arts graduates’ decision trees. Although it was important in the construction of the self-defined career success rating tree, it did not appear in the optimal tree for this target variable. However, it did appear in both earnings trees (particularly for technical / design artists), and significant group differences were found in the initial non-parametric tests for arts work experience on all three outcome measures. It was 323 somewhat unclear from the findings whether the positive predictive value in arts work experience lay in the work experience itself, or experience in a paid arts work role. Graduate age was strongly correlated with arts work experience, and in addition was a split variable in the tree for total earnings. It did not appear elsewhere in the arts graduates’ decision trees.
In terms of the CDI measures, CDI: Skills and abilities and CDI: Interests and beliefs were important in the construction of all three arts graduate decision trees, as they had also been found to be in the professional artists’ trees. Also similar to the findings for the professional artists was the lack of evidence for predictive value in CDI: Environmental / societal influences in the arts students’ trees. The remaining two scales of the CDI, CDI: Interpersonal influences and CDI: Physical characteristics, showed different predictive patterns for the arts graduates compared with the professional artists. CDI: Interpersonal influences emerged as a positive predictor of earnings from arts and self-defined career success ratings amongst the arts graduates (particularly amongst arts graduates with low CMC: Career building scores). There was some evidence for the positive predictive value of CDI:
Interpersonal influences in the professional artists’ trees as well, but the results were less clear cut. CDI: Physical characteristics was an important variable in the arts students’ self-defined career success rating tree, although it did not appear in the optimal tree. However, it was found in the professional artists’ decision tree for arts earnings, in connection with performing artists.
The arts graduates’ career success definition categories were important predictors only in the self-defined career success ratings tree. In this tree, externally- focussed, financial recognition definitions of career success seemed to predict lower career success ratings. Internal definitions of career success, on the other hand, 324 seemed to ‘protect against’ very low career success ratings. This pattern of findings was also evident in the professional artists’ self-defined career success ratings tree.
However, gender did not appear to be an important predictor variable of career success in any of the arts graduates’ trees. The career success ratings provided by male graduates seemed slightly lower than those provided by female graduates, but the difference was not statistically significant, and there were no genders differences in terms of graduate earnings. In Study 3 as previously discussed, it was found that male professional artists tended to assign themselves lower career success ratings than female professional artists, although the earnings levels for male and female professional artists were similar.
Implications for Artists’ Careers
The findings of the current research have obvious implications for the career development and career success of professional artists and tertiary arts students.
From the results, artists and arts graduates who possess well developed career self- management skills (particularly career building skills such as locating and using career information, applying for or creating work and managing the career building process) experience better career outcomes than other artists, no matter which measure of career success is used. The mean ratings for the self-management skill items indicate that many artists and arts graduates may benefit from career education.
The findings regarding the benefits of arts work experience to the graduates suggest that work integrated learning may be an effective avenue to improve artists’ career outcomes.
Several other career development influences were found to be of relevance to artists’ career success. Artists who reported positive interpersonal influences (such as support from family and peers) had better career outcomes. In addition, artists who 325 reported that their interests, beliefs, skills and aptitudes had a strongly positive role in their career development experienced higher levels of career success. However, the findings of Study 3 showed that a formal arts education may not be as important as other factors to an artists’ career success. This finding is in contrast to previous studies of traditional, organisationally based careerists which show a clear positive relationship between formal qualifications and objective and subjective career success (Ng et al., 2005).
An artists’ work outside the arts also appears to have an impact on their career success, at least in terms of their earning capacity. Four in five of the professional artists worked outside their arts practice in some capacity, and 37% indicated that they maintained a career outside arts in addition to being a professional artist. Work outside arts was found be a positive predictor of total earnings, but was found to be a negative predictor of earnings from arts. This finding may be because the time an artist spends working outside arts will reduce the time they can spend working within arts and promoting their artistic work (Abbing, 2003; Throsby,
1994b).
In line with suggestions by Hall and Chandler (2005), artists’ motivations and definitions of career success may also be important to their career progression.
Intrinsic motivations for artistic practice, as opposed to financial recognition motivations, may protect artists against believing that they are unsuccessful, even though their earnings may be low. Conversely, if artists synonymise financial recognition with career success (and male artists appear more likely than female artists to do this), they are less likely to believe they are successful, which may have ramifications for their career-related behaviours and career outcomes. It is not clear 326 from the present research to what extent artists’ motivations and definitions of success extend to more objective measures of career success as well.
Implications for Artists’ Employers / Clients
The artists’ motivations and definitions of career success may also have implications for potential employers and clients. Employers both within and outside the arts who wish to take advantage of artists’ creative and other desirable skills
(ARC Centre of Excellence for Creative Industries and Innovation, 2007; Moga et al., 1999) will need to consider that a significant proportion of artists appear not to be motivated by traditional factors such as earnings. Once they have reached a regular, subsistence level of income, the artist may prioritise other career goals which may or may not be congruent with those of the employer or client. In light of this, careful job or role planning, taking into account the individual goals of the artist-careerist and the employer or client, is essential to a successful employment relationship.
It may be futile to attempt to lure some artists away from their arts practices into other fields, or to request them to produce arts work other than what they are already motivated to create. Evidence from Study 2 suggests that congruent with
Hall’s concept of the protean careerist (Hall, 2004; Hall & Mirvis, 1996; Hall &
Moss, 1998), a significant proportion of artists view artistic practice as a ‘calling’.
These artists have strong internal career motivations and criteria for success which may not correspond with what employers or clients want. The development of an artist’s career self-management skills may enhance their awareness of both their own career motivations and the priorities of employers and clients, thus permitting them to proactively navigate their career journeys. 327
Implications for Universities and Tertiary Students
Although a link between student career self management and graduate work outcomes is intuitively appealing and reflects a position that has been espoused by several authors in the career development field (Gillie & Gillie Isenhour, 2003;
Mayston, 2002; Watts, 1999, 2006), the present study is among the first to demonstrate empirically, via a longitudinal approach, that a group of final year undergraduate students who report having well-developed career self management skills experience enhanced levels of internal and external career success after graduation.
The implications of this finding for careers education services, arts education providers, arts students and employers are significant. Further research is required with final year students from multiple faculties and universities to indicate whether the predictive link between career self management and graduate outcomes can be generalised to other cohorts of tertiary students aside from arts students. Courses from which graduate career paths are often highly individual, such as liberal arts or science, as opposed to vocationally-based courses (such as nursing or education) are obvious choices for research of this nature.
Some of the potential repercussions of the link between career self- management skills and graduate career outcomes are discussed below.
Universities have several compelling reasons to show that their graduates are employable, and experience positive career outcomes. In line with a trend that is emerging in higher education sectors worldwide, the Australian government is moving from a block university funding model to one that is based on performance in areas such as graduate destinations and student satisfaction levels (Department of
Education Science and Training, 2005a, 2007b). If a university can engender well- 328 developed career management skills in its students, it is likely that the university’s ability to attract this kind of performance-based funding will be enhanced. In addition, universities which show strong performance in these areas will be more attractive to prospective students and therefore have the potential to attract higher levels of fee-based funding.
The potential appeal of university programs which can demonstrate enhancement of student employability and other post-graduation outcomes to students is clear. The present study has shown that arts students who possess strong career management skills tend to earn more than other graduates, and also have higher levels of self defined career success. Further, several cross-cultural studies of various careerist cohorts have shown that career self management skills, job and career satisfaction, and job prospects are all positively correlated with measures of psychological and physical well-being (Burke, 2001; Wiese, Freund, & Baltes, 2002) although the relationships between these variables seem to be complex and influenced by many other factors. One avenue for future research is to explore the link between career self management skills and broader measures of individual well- being, to discover whether it is exclusively mediated by career success, or whether the variables are otherwise related to one another, perhaps through the direct effect of self management competencies.
In order to take advantage of the rewards that a career self managing student body offers, universities will need to place a greater emphasis on their career education provision. All Australian universities maintain their own careers services, which offer career guidance and support to students. However, the resourcing of these services seems to vary considerably (Organisation for Economic Cooperation and Development, 2002), and the emphasis is therefore often on course choice rather 329 than career management competence and facilitation of graduates’ transitions to work (Watts, 2005). If universities are to engage with the career self management agenda, they will need to further develop and support their careers services. The development and implementation of student career management programs will involve considerable investment in partnerships between arts education faculties, careers services and employers.
Theoretical Contributions of the Program of Study
This section of Chapter 10 presents a discussion of the theoretical implications of the findings previously described in Studies 1, 2, 3, and 4. The studies’ methodological contributions and recommendations for future research are presented in later sections of this chapter.
The studies’ contributions to theory fall within four areas. First, the factorial structures of the three scales in Study 1 (CMC, PCSO, and CDI) can provide additional insights regarding the nature of the constructs and theories underlying the each of the measures. Second, the findings of Study 2 extend theory pertaining to internal and external career success in artists, and to career success in the protean career more broadly. Third, Studies 3 and 4 provide insights into links between career self-management skills, career motivations, and career success in the non- traditional career. Fourth, the findings of Study 4 have significant implications for theory relating to graduate attributes and graduate employability theory. Each of these four areas of theoretical contribution is considered in turn.
Factorial Structure of Scales
The confirmed factorial structure of the Career Management Competence scale suggested that there were two correlated robust factors underlying the scale. 330
These factors were CMC: Self management, comprising 6 items relating to life and work roles, self image, interacting with others, lifelong learning, and changing and growing throughout life; and CMC: Career building, comprising 5 items relating to securing or creating work, making career decisions, managing the career building process, finding and using career-related information, and knowledge about the world of work.
The study findings did not correspond to the factorial structure suggested by theory, nor to the factorial structure suggested the pilot study conducted with undergraduate Education students. The original categories suggested by the ABCD’s creators (Haines et al., 2003) classified the competencies into three categories: personal management, career building, and a further category, learning and work exploration. Exploratory factor analyses conducted on the pilot study data did reveal a three-factor solution, but the factors did not cohere with the structure suggested by theory.
It is recommended that a more comprehensive career management competence instrument be developed from the ABCD (Haines et al., 2003), perhaps drawing from the key performance indicators located within each competency. The development of such an instrument would allow for further investigation into the factorial structure and dimensionality of the competencies, as well as the relationships between competencies, underlying dispositions and characteristics, and career success.
The brief Protean Career Success Orientation scale, unlike the other two scales developed in this research, did not have an underlying dimensional structure suggested previously by theory. The PCSO comprises items relating to underlying dispositions and characteristics suggested by protean career literature (Briscoe & 331
Hall, 2006; Hall & Chandler, 2005; Hall & Mirvis, 1996) to be important to career success in the protean career. A single factor solution was settled upon for the purposes of the present research, indicating that the items in the scale shared an acceptable level of variance, but literature suggests that each individual construct included in the scale may be complex and multidimensional (e.g., Judge & Bono,
2001; Lounsbury et al., 2003). The brief scale used in the research program does not adequately address the complexity of the constructs used, reducing them to single items.
As with the Career Management Competence scale, it is suggested that a much longer instrument be developed to measure protean career success orientation, with a view to enhancing its discriminability and validity. Future researchers may also consider the reinclusion of items relating to positive self image, for which there is a strong theoretical argument (Day & Allen, 2004; Judge & Bono, 2001), although it was removed from the final Protean Career Success Orientation scale during the scale modification process in Study 1.
The first study provided some support for the idea of three broad correlated dimensions to career development influences, as proposed by the Systems Theory
Framework of career development (McMahon & Patton, 1995; Patton & McMahon,
1997, 1999, 2006a): the individual system, the social-contextual system, and the environmental-contextual system. Within the individual system, three underlying factors were identified: interests and beliefs, skills and abilities; and physical characteristics. One factor was identified in the social system: interpersonal influences; and one factor was identified in the environmental-societal system: environmental-societal influences. 332
Nine of the original influences were not included in the final instrument.
Eight of these did not load significantly onto any of the five factors identified in the model. The item world of work knowledge, also removed from the final scale, loaded significantly onto four of the five factors in the main study. It was also found to be strongly statistically related to many of the other items, suggesting that world of work knowledge is a central and complex influence on career development.
The findings with respect to world of work knowledge might be taken to suggest a second-order career development influences factor (Thomas, 1995) or at least that many of the modelled career development influences were strongly related to world of work knowledge in the tested data set of professional artists. The importance of world of work knowledge to career development has been acknowledged in extant theory since the work of Parsons (1909) and remains a key component of theories such as social learning theory (Krumboltz, 1979; Krumboltz
& Henderson, 2002) and cognitive information-processing models (Peterson et al.,
2002). Patton and McMahon (2006a) suggested that world of work knowledge, often contingent upon resource access, is particularly critical in an increasingly globalised working world. With the proliferation of non-linear careers characterised by an emphasis on employability security rather than employment security, the onus is on careerists to recognise and exploit career opportunities as they arise, constantly using and adding to their stock of world of work knowledge.
Career Success
The findings of Study 2 make a contribution to theory regarding subjective and objective career success in artists, which can perhaps also be extended to career success in other protean careerists. The results of the study demonstrated that the objective measures of career success commonly used in research, such as a 333 careerist’s hierarchical position and their salary levels, are not adequate. This means that many established measures of subjective career success which are based on objective measures (e.g., Greenhaus et al., 1990) are also not adequate to assess levels of career success in artists.
For artists, subjective career success appears to be a multifaceted construct which can encompasses several coexisting dimensions. For some artists, these dimensions may even be in tension with one another (the concept of ‘selling art’ vs
‘selling out’, i.e., making a living vs maintaining artistic integrity).
The idea that there are multiple coexisting subjective career success dimensions has some interesting parallels in work by Schein (1996) regarding career anchors, and by Derr and colleagues on career orientations (Derr, 1986; Derr &
Laurent, 1989). It is of interest to explore these parallels further in future studies. The present research into the nature of subjective and objective career success in artists also offers some potential for the development of a multidimensional career success orientation scale, which would include items relating to the careerist’s levels of identification with each of the categories of success definition found in the present research.
Predictors of Artists’ and Arts Graduates’ Career Success
The decision tree results in Studies 3 and 4 have theoretical implications regarding predictors of objective and subjective career success, and the relationship between objective subjective career success. A meta-analysis by Ng et al (2005) of
140 predictive studies found that subjective career success (career satisfaction) and objective career success (salary and promotions) were statistically related, but appeared to be associated with distinct patterns of predictors. 334
The present studies of professional artists and arts graduates supported the notion that the subjective and objective measures of career success were statistically related. The correlations between earnings and employability ratings were found to be high, and the correlations between these measures and self-defined career success ratings were moderate to high. Although there were some predictors which emerged as being important no matter which measure of career success was used (e.g., career self-management career building skills), the patterns of predictors for earnings overall, earnings from arts, and self defined career success ratings were somewhat different, particularly with respect to the artists’ definitions of career success, and the amount of work they did outside their artistic practice.
Graduate Attributes
The clear finding that career self-management skills predicted career success in tertiary arts graduates has repercussions for graduate attributes theory. Universities and higher education theorists have so far conceptualised graduate attributes as comprising only discipline-specific skills and generic / transferable skills which are developed during university courses (Australian Chamber of Commerce and
Industry, 2002; Queensland University of Technology, 2004). These skills have been argued to make graduates attractive to employers, and therefore enhance graduate employability and graduate outcomes (Bennett, 2002; Dahlgren et al., 2006; Garcia-
Aracil et al., 2004).
However, the present results show that other skills can be linked to positive graduate outcomes, as can other aspects of the graduate and their career development context. At a minimum, a university’s conception of graduate attributes for employability should also include career self management skills, comprising self- 335 management skills, and career building skills, relating to navigating the world of work.
Methodological Contributions of the Program of Study
The primary methodological contributions of the research relate to the scales developed in Study 1, and the use of decision tree analytical techniques to study predictors of career success in Studies 3 and 4. In addition, the 1-year prospective online survey approach employed to explore arts graduates’ transitions to the world of work can be regarded as a key strength of the research program.
Scale Development
Three brief scales were developed as part of the research. Because both professional artists and arts students were involved in the main studies, confirmatory factor analytical procedures were able to be conducted with two samples of participants. Respecifications to the scales based on testing with one sample were then able to be tested with the other sample, thus providing evidence for robustness of the factorial structures with two separate artist groups.
The scales refined and validated in Study 1, Career Management
Competence, Protean Career Success Orientation, and Career Development
Influences, were created because there were no existing quantitative measures relating to the constructs and influences of interest. These scales were not intended to be exhaustive measures of the constructs they quantified. It is vital for subsequent research to develop more in-depth, targeted measures relating to the constructs and influences of interest. It is also important to further examine the convergent and divergent validity of the brief scales with established career development measures, to validate the scales with other groups of careerists. 336
Decision Trees
The use of decision trees in Studies 3 and 4 also represents a methodological contribution of the research program. Decision trees are well known in fields outside the social sciences (Furnkrantz et al., 1997; Gibb et al., 1993; Yohannes & Webb,
1999), particularly as techniques for data mining. In the social sciences, parametric regression techniques such as ordinary least squares regression and hierarchical regression are predominant, and decision trees are virtually unheard of. However, appropriate use of parametric regression techniques depends on statistical assumptions, and there are few nonparametric alternatives utilised. In the case of serious violations of these assumptions, decision tree methodologies may be useful.
As well as being an easily interpretable nonparametric approach to predictive modelling, decision trees can also be used in practical and applied ways. For instance, using a decision tree as a profiling tool (Salford Systems, 2003), it is possible to identify individuals who possess a certain combination of characteristics
(e.g., non mature-age students in creative or performing arts programs with low
CMC scores and minimal support from peers and family) and therefore may be ‘at risk’ of poor outcomes. These individuals can then be provided with support and development tailored to their needs.
A Prospective Approach to the Study of Tertiary Graduate Outcomes
The online data collection procedure undertaken with the tertiary arts graduates’ sample involved two phases, one in October, 2005, when the students were at the point of course completion, and one in October, 2006, one year later. At present, national graduate outcome data is collected only four months after course completion, via the Graduate Destination Survey (Graduate Careers Council of 337
Australia, 2006b). There are significant shortcomings associated with this data collection, including the very short length of time between course completion and data collection, a lack of detailed data with respect to graduate career goals and outcomes, non-response biases (Bratti, McKnight, Naylor, & Smith, 2004; Coates,
2006), and the use of potentially misleading outcome indicators such as ‘percentage full-time employment’. The present online study, along with recent telephone-based prospective research conducted by QUT Careers and Employment (2007), shows that tracking graduates for periods of one year can provide more fine-grained and helpful information about the students’ transition experiences and graduate outcomes, and that using online or telephone methods to do this can yield much better response rates than those achieved by the Graduate Destination Survey.
Limitations of the Research Program
The current research faced a number of limitations, which primarily pertained to the methodological choices made. First, the method of sampling of the artists and arts graduates (self-selection with certain eligibility criteria), and the online survey method used, did not guarantee representativeness. The comparisons of key demographic characteristics between the present research samples and those from previous studies / labour force statistics outlined in Chapter 5 indicated that the samples were fairly representative with respect to these variables. However, it may be that the samples were unrepresentative in other ways. For instance, the participants may have systematically been more, or less, successful than typical artists and arts graduates. They may also have had generally higher levels of career self-management competence than the population from which they were drawn, which led them to want to participate in the research. There is no easy way to assess 338 the likelihood of this type of sample non-representativeness, short of conducting future studies using other ways of sampling.
In addition, the reliance on self-report data for all substantive measures was a shortcoming of the present research. An individual’s appraisal of their skills, abilities, and dispositions may not be 100% accurate, but more direct measures (e.g., tests) can be difficult to develop and administer, and artists may be less likely to participate as the result. A particular type of problem associated with self-report data is common method bias (Podsakoff et al., 2003), which was an acknowledged risk in the present study, particularly in the professional artists’ data. Attribute and target measures were kept as far apart as possible in the survey instruments to minimise this risk. In the arts graduates’ data, the prospective data collection method made common method bias less likely.
The measures developed as part of the research program were necessarily very brief because of space and survey instrument length constraints. These broad brush scales each subsumed a large number of constructs which were dealt with only in an exploratory, superficial way in the present research. It is difficult from the study results to understand the precise roles of the constituent constructs and how they might relate to one another, and also to career success. Also, many of the constructs for which there was only a single item in each survey instrument (e.g., resilience in the PCSO, or securing or creating and then maintaining work in the
CMC, or beliefs in the CDI) are complex, multidimensional concepts, and the research described in this document did not do justice to this. However, the present exploratory research does provide a solid foundation for future, targeted research using comprehensive measures of the constructs of interest. 339
The CART style decision trees employed in the research (Breiman et al.,
1984) also suffered a number of shortcomings. Unlike statistical techniques commonly used in the social sciences, decision trees are not based on probabilistic statistical models. There are no significance or confidence levels associated with the predictions made. In addition, at any node, the algorithm will make a bivariate split only, based on a single attribute variable. This became a particular issue in the present research with the arts graduates’ findings with respect to work experience.
Although there were differences in graduates’ levels of success between ‘no work experience’ and ‘some unpaid work experience’, and then between ‘some paid work experience’ and ‘extensive paid work experience’, the tree algorithm could only make a split based on one of these group differences at a time.
Another shortcoming of the decision tree approach used, as with parametric regression and correlation techniques, was that no causality can be attributed using decision tree methods. Although it was possible to indicate a predictive relationship between the attribute and target variables in the trees, it was not possible to indicate the nature of the predictive relationship. For instance, in the present research it was not able to be ascertained whether a longer length of time working in arts caused enhanced career success for the artists, whether more success led to artists staying in the arts, and to what extent other variables such as wisdom, self confidence, or professional networks were causative factors.
All of these study shortcomings can be addressed with clarifying research.
The next section of this chapter discusses further suggestions for future research.
Recommendations for Future Research
The findings of the present exploratory research program provide fertile ground for targeted research into the relationships between career self-management 340 competence, underlying dispositions and attributes such as openness to opportunities and self-directedness, careerists’ conceptions of career success, and levels of subjective and objective career success in boundaryless and protean careerists, and also traditional, organisationally-based careerists. Some theoretical models have previously been presented regarding the potential nature of some of these relationships (Hall & Chandler, 2005; King, 2004; Quigley & Tymon, 2006), and the present research supports the contention that at least some relationships do exist between these constructs in the samples of careerists studied. Further, it is of value to investigate the potential links between career self-management skills and behaviours, levels of career success, and broader physical and emotional well-being, which have previously been suggested by several theorists (Burke, 2001; Wiese et al., 2002), but hitherto have not been conclusively empirically demonstrated.
Another valuable extension to the present research would be to develop and evaluate pedagogical practices which might best engender important skills and abilities (of discipline-specific, generic / transferable, and career self-management types) throughout a tertiary program, with a view to creating undergraduate courses associated with enhanced graduate outcomes. The present findings suggest that work-integrated learning programs might show particular promise in this area.
It would also be a worthwhile endeavour to track tertiary graduates’ world of work experiences and outcomes for periods greater than one year after course completion, and to extend the research into work situations. Theoretical literature suggests that students who possess well-developed career self-management skills not only experience positive graduate outcomes, but are also likely to be a good ‘match’ for the work and employer/s, and will be highly motivated to perform well at work
(Mayston, 2002; Watts, 1999). Research with graduate employers into their 341 expectations and experiences of graduate employees will address this suggestion, and provide further ‘demand-side’ information about graduate outcomes.
In future studies, researchers may consider the inclusion of comparison groups of traditional, organisationally based careerists and tertiary graduates from these fields, in addition to the artists and arts graduates. This approach will provide interesting comparative insights into the nature of career success and its predictors, and yield information for universities regarding the career development needs of a range of tertiary students.
Finally, it is recommended that studies continue to provide insights into the important concept of subjective career success, and that appropriate, multidimensional measures of subjective career success be developed and used wherever possible in future predictive and explanatory studies. With respect to artist populations’ conceptions of subjective career success, it is of particular interest to further explore the apparent tensions in financial and non-financial recognition for artistic practice. Where does the notion of ‘selling out’ originate in artists, and to what extent are understandings of the notion consistent? How does the concept of
‘selling out’ affect artists’ career behaviours and their levels of career success?
Answers to these questions may provide important insights into the nature of artists as workers.
Conclusion
The program of study described in this document makes a substantial contribution to knowledge regarding artists’ career development and success in
Australia, and potentially to protean and boundaryless careerists more broadly. The findings first suggest that artists’ conceptions of career success differ from those assumed by previous research studies, such as hierarchical position in an 342 organisation or salary levels. Second, the present studies show that, although there are different patterns of predictors associated with the different measures of career success in the artists studied, there are certain predictors, such as career self- management competence, which appear to be important no matter which definition of career success is used.
Thus, the present research raises issues regarding the importance of career education, and the responsibilities of universities in preparing creative workers for the world of work. Based on these findings, it is clear that if arts education providers are to engage effectively with the graduate employability agenda, they will need to expand their conceptions of desirable graduate attributes, and change their curricula in line with these new empirically grounded conceptions.
343
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APPENDIX A: ONLINE SURVEY INSTRUMENTS
Career Development Influences Questionnaire : Graduating Creative Industries Students Students
Ruth Bridgstock Supervisor: Professor Wendy Patton Centre for Learning Innovation School of Learning and Faculty of Education, QUT Professional Studies Victoria Park Road Faculty of Education, QUT Kelvin Grove Qld 4059 Victoria Park Road Telephone: 3864 3466 Kelvin Grove Qld 4059 Email: [email protected] Email: [email protected]
Description and Eligibility This online survey study is being conducted to increase understanding of the various influences on career development and career success in the Creative Industries. It is hoped that the information collected will be used to assist Creative Industries students and graduates with their careers. You will be able to access the results of the survey in a few months by checking back at: www.australianartistssurvey.org
You are eligible to participate if you are a graduating (3 rd or 4 th year) student within the Creative Industries Faculty at QUT. You will be asked 6 sections of questions about your career decision-making, plans and educational background; the survey will take approximately 30 minutes to complete. This is a two-part survey, with the second part being conducted as a follow-up in one year’s time. The second part will also be online. By completing the online survey, you will be indicating consent to participate in the first part of the study.
Voluntary participation Your participation in this project is entirely voluntary. You may withdraw from the project at any time without comment or penalty. Your decision to participate will in no way impact upon your current or future relationship with QUT (eg your grades or future employment relationship).
Confidentiality Some identifying information will be collected so that we can follow up with you in a year’s time to find out how your career is progressing. This part is also fully voluntary, and all personal/ contact details provided will be completely confidential.
There is no link between the data you enter and the details provided in respect to the prize draw. Contact details to be put into the prize draw are for that purpose only.
Surveys and survey data are accessible to the research team only for research and educational purposes and will be downloaded and kept in a secure electronic database. All efforts will be made to ensure that spamming, phishing and other forms of electronic harassment will not occur when you provide your data to us.
Questions and further information For additional information or questions about the project, you are welcome to contact the project leader, Ruth Bridgstock, on 3864 3466 or email [email protected]
Concerns or complaints If you have any concerns or complaints about the ethical conduct of this research you are welcome to contact the Research Ethics Officer, on 3864 2340 or email [email protected] . Please quote the University Human Research Ethics Committee Reference Number 4173H.
If you experience any distress as the result of participating in this study, please contact QUT Counselling Services or Careers and Employment for a free confidential consultation on the following numbers: Counselling Careers and Employment Kelvin Grove: 3864 3488 Kelvin Grove, Gardens Point and Carseldine : 3864 2649 Gardens Point: 3864 2383 Carseldine : 3864 4539
Career Development Influences Survey: Graduating Creative Industries Students
Some questions in this survey may seem quite similar. Explanations of similar terms have been included to make everything as clear as possible. Please read the descriptions carefully!
Section 1. Your course and field/s in the Creative Industries
1. What ‘year’ are you in at QUT? 1 1st year (screen out) 2 2nd year (screen out) 3 3rd year 4 4th year / honours 5. Other (screen with message – “you may be eligible to participate in our professional artists’ survey” and provide URL for this)
2. Are you graduating at the end of this semester? 1. Yes 2. No (screen out)
3. What’s the name of your Creative Industries course? (please select from drop-down list) Bachelor of Creative Industries (Communication Design) (KI32) Bachelor of Creative Industries (Communication Design)/Bachelor of Information Technology (IF90) Bachelor of Creative Industries (Creative Writing) (KW32) Bachelor of Creative Industries (Creative Writing) / Bachelor of Laws (IF93) Bachelor of Creative Industries (Dance) (KD32) Bachelor of Creative Industries (Dance)/Bachelor of Education (Secondary) (IX05) Bachelor of Creative Industries (Drama) (KT32) Bachelor of Creative Industries (Drama)/Bachelor of Education (Secondary) (IX06) Bachelor of Creative Industries (Honours) (Communication Design) (KK52) Bachelor of Creative Industries (Honours) (Creative Writing) (KK52) Bachelor of Creative Industries (Honours) (Dance) (KK52) Bachelor of Creative Industries (Honours) (Drama) (KK52) Bachelor of Creative Industries (Honours) (Interdisciplinary) (KK52) Bachelor of Creative Industries (Honours) (Media and Communication) (KK52) Bachelor of Creative Industries (Honours) (Visual Arts) (KK52) Bachelor of Creative Industries (Interdisciplinary) (KK32) Bachelor of Creative Industries (Media and Communication) (KC32) Bachelor of Creative Industries (Media and Communication)/Bachelor of Laws (IF10) Bachelor of Creative Industries (Media and Communication)/Bachelor of Business (Advertising, International Business, Public Relations) (IF09) Bachelor of Creative Industries (Television) (KP32) Bachelor of Creative Industries (Visual Arts) (KV32) Bachelor of Creative Industries (Visual Arts)/Bachelor of Education (Secondary) (IX08) Bachelor of Fine Arts (Acting) (KS25) Bachelor of Fine Arts (Animation) (KI26) Bachelor of Fine Arts (Communication Design) - Sound Design (KI25) Bachelor of Fine Arts (Creative Writing Production) (KW25) Bachelor of Fine Arts (Dance) (KD25) Bachelor of Fine Arts (Fashion Design) (KF25) Bachelor of Fine Arts (Film and Television) (KP25) Bachelor of Fine Arts (Honours) (Communication Design) (KK53) Bachelor of Fine Arts (Honours) (Creative Writing) (KK53) Bachelor of Fine Arts (Honours) (Dance) (KK53)
Bachelor of Fine Arts (Honours) (Film & Television Production) (KK53) Bachelor of Fine Arts (Honours) (Visual Arts) (KK53) Bachelor of Fine Arts (Technical Production) (KS26) Bachelor of Fine Arts (Visual Arts) (KV25) Bachelor of Journalism (Honours) (KK54) Bachelor of Journalism (KJ32) Bachelor of Journalism/Bachelor of Business (Advertising, International Business, Public Relations) (IF05) Bachelor of Journalism/Bachelor of Laws (IF07) Bachelor of Mass Communication (IF27) Bachelor of Music (Honours) (KK55) Bachelor of Music (KM32) Bachelor of Music/Bachelor of Education (Secondary) (IX07) Another course (please specify)
4. With which of the following artistic occupational groups do you identify? (select as many as apply from the drop- down list)
1. Writer (other than journalist) 2. Craft practitioner/ artisan 3. Visual artist 4. Composer/song writer/ music arranger 5. Actor/performer/director 6. Dancer/choreographer 7. Musician/singer 8. Community cultural development worker 9. Fashion designer 10. Filmmaker (other than director) 11. Technical, stage or production manager 12. Interaction/ Information/ Multimedia Designer 13. Something else (please specify):
5. What are you planning on doing in your career over the next twelve months? (select as many as apply) 1. Working in the field/s I selected above 2. Working in other areas of the arts or Creative Industries 3. Working outside the arts or Creative Industries 4. Doing more study in the arts or Creative Industries 5. Doing more study outside the arts or Creative Industries 6. Something else (please tell us):
6. Have you already worked in your arts field/s? 1. Not yet 2. Yes, unpaid work experience 3. Yes, some paid work 4. Yes, significant paid work
7. How about outside your arts field/s? 1. Not yet 2. Yes, unpaid work experience 3. Yes, some paid work 4. Yes, significant paid work
Section 2: Influences on your career so far
1. How negative or positive an influence has each of the following had on the arts career development so far? This can include choices you have made about study, work, or any other aspects of your career development and career progression.
Degree of influence Strongly Negative Positive Strongly Negative - + Positive -- ++ Things about you a) Gender 1 2 3 4 5 6 b) Health - both physical 1 2 3 4 5 6 and mental c) Self-concept - your idea 1 2 3 4 5 6 of your important roles e.g. student, homemaker, community member, worker d) Ability – your potential to 1 2 3 4 5 6 acquire the skills you need e) Aptitudes – the quickness 1 2 3 4 5 6 or ease with which you can learn a skill you need f) Disability 1 2 3 4 5 6 g) Personality 1 2 3 4 5 6 h) Age 1 2 3 4 5 6 i) Ethnicity 1 2 3 4 5 6 j) Physical attributes 1 2 3 4 5 6 k) World-of-work knowledge 1 2 3 4 5 6 – what you know about work environments and how to find and secure work l) Interests 1 2 3 4 5 6 m) Skills 1 2 3 4 5 6 n) Beliefs – ongoing ideas 1 2 3 4 5 6 about yourself and the world o) Values – what is important 1 2 3 4 5 6 to you e.g. prestige, risk, autonomy, responsibility p) Sexual Orientation 1 2 3 4 5 6 Your social influences q) Family 1 2 3 4 5 6 r) Peers – friends and 1 2 3 4 5 6 colleagues s) Community Groups 1 2 3 4 5 6 t) Educational Institutions 1 2 3 4 5 6 u) Workplaces 1 2 3 4 5 6 v) Media 1 2 3 4 5 6 Your societal and environmental influences w) Geographical location 1 2 3 4 5 6 x) Political decisions 1 2 3 4 5 6 y) Employment Market 1 2 3 4 5 6 z) Historical trends 1 2 3 4 5 6 aa) Socioeconomic status 1 2 3 4 5 6 bb) Globalization 1 2 3 4 5 6
2. For up to three influences from the list in question 1 that you regard as being the strongest, please talk abut how they have affected your career development here. Influence 1 Influence name: (scroll for options) How has this influence affected you?
Influence 2 Influence name: (scroll for options) How has this influence affected you?
Influence 3 Influence name: (scroll for options) How has this influence affected you?
3. To what extent do you believe that luck, serendipitous events, or chance generally affect the career development of: (please circle) a) Someone pursuing a career in your field/s of arts Not at all 1 2 3 4 5 6 A lot b) Someone pursuing a career outside the arts Not at all 1 2 3 4 5 6 A lot
Please comment on your responses:
Section 3: Your beliefs about your career
1. To what extent do you agree with the following: (please circle) a) I am self-directed and take personal responsibility
Strongly 1 2 3 4 5 6 Strongly disagree agree b) I am proactive
Strongly 1 2 3 4 5 6 Strongly disagree agree c) I an internally motivated
Strongly 1 2 3 4 5 6 Strongly disagree agree
d) I have a positive interpersonal orientation
Strongly 1 2 3 4 5 6 Strongly disagree agree e) I am resilient and adaptable
Strongly 1 2 3 4 5 6 Strongly disagree agree f) I am open to opportunities
Strongly 1 2 3 4 5 6 Strongly disagree agree g) I have a positive self image
Strongly 1 2 3 4 5 6 Strongly disagree agree
Section 4.Your career skills and abilities
1. How important do you believe the following are to career success, and how confident are you in your abilities and skills in these areas? Please circle.
Importance to success Confidence Not at Very Not Very all at all 1. Building and maintaining a positive self- image 1 2 3 4 5 6 1 2 3 4 5 6 Knowing who you are & what influences you, staying positive, understanding how self-image has an impact on goals and decisions 2. Interacting positively and effectively with others 1 2 3 4 5 6 1 2 3 4 5 6 Understanding and maintaining positive relationships, being able to express yourself in an appropriate manner, knowing how to solve interpersonal problems 3. Changing and growing throughout your life 1 2 3 4 5 6 1 2 3 4 5 6 Understanding that your motivations and aspirations change throughout our lives, that change and growth can impact on our physical and mental health and vice versa, knowing how to adapt to changes and manage stress 4. Participating in life-long learning supportive of your career goals 1 2 3 4 5 6 1 2 3 4 5 6 Knowing what influences life and work successes, understanding how to improve skills and strengths, knowing about learning opportunities, behaving in ways that contribute to achieving your goals 5. Locating and effective use of career information 1 2 3 4 5 6 1 2 3 4 5 6 Knowing where and how to access career information, and how to use it, knowing what working conditions you want, understanding the requirements of work settings
6. Understanding the relationship between work, society and the economy 1 2 3 4 5 6 1 2 3 4 5 6 Understanding about how work contributes to our community, society, and ourselves; understanding how trends affect work, understanding how organizations operate
7. Securing or creating and then maintaining work 1 2 3 4 5 6 1 2 3 4 5 6 Understanding the importance of personal qualities on getting/ keeping/ creating work, being able to articulate your skills, being able to transfer your skills between work settings, developing work search tools and skills
8. Make career enhancing decisions 1 2 3 4 5 6 1 2 3 4 5 6 Understanding how choices are made, how personal beliefs and attitudes affect decision- making, knowing how to problem-solve, being able to explore alternatives, being able to evaluate the impact of decisions
9. Maintain balanced life and work roles 1 2 3 4 5 6 1 2 3 4 5 6 Being aware of the various roles we may have and the responsibilities linked to those roles, how these roles impact upon our lifestyles, determining the value of work, family and leisure activities and making choices about a balance of these
10. Understanding the changing nature of life and work roles 1 2 3 4 5 6 1 2 3 4 5 6 Understanding the changing life roles of people in work and home settings, understanding how these roles are important to family and society, exploring and considering non-traditional life/ work scenarios, working to eliminate stereotypes, biases and discriminatory behaviours
11. Understand, engage in, and manage the career building process 1 2 3 4 5 6 1 2 3 4 5 6 Being able to define your preferred future and create career scenarios in step with it, being able to set goals and short-term plans, and apply coping strategies and new career scenarios during transition periods e.g. starting a family, losing a job
9. Maintain balanced life and work roles 1 2 3 4 5 6 1 2 3 4 5 6 Being aware of the various roles we may have and the responsibilities linked to those roles, how these roles impact upon our lifestyles, determining the value of work, family and leisure activities and making choices about a balance of these 10. Understanding the changing nature of life 1 2 3 4 5 6 1 2 3 4 5 6 and work roles Understanding the changing life roles of people in work and home settings, understanding how these roles are important to family and society, exploring and considering non-traditional life/ work scenarios, working to eliminate stereotypes, biases and discriminatory behaviours 11. Understand, engage in, and manage the 1 2 3 4 5 6 1 2 3 4 5 6 career building process Being able to define your preferred future and create career scenarios in step with it, being able to set goals and short-term plans, and apply coping strategies and new career scenarios during transition periods e.g. starting a family, losing a job
Section 5. Your career goals and expectations
1. Please tell us about what you expect to be happening in your career in one year and in five years. If you are not certain, this is ok, just give us your ideas. a) In one year’s time, I expect to be
b) In five years’ time I expect to be
Section 6. Your Background
1. For how many years have you been studying your course? a) Less than one b) More than one but less than two c) More than two but less than three d) More than three but less than four e) More than four but less than five f) More than five but less than six g) More than six years
2. Are you studying: a) full-time b) part-time
3. Are you? a) Female b) Male
4. What is your age in years? ______years
5. Do you identify with any of the following groups: (circle as many as apply)
Aboriginal or Torres Strait Islander With a disability English as a second language
Thank you very much for taking the time to complete this questionnaire!
Next year at this time of year we will want to contact you again and follow up on your career moves (a shorter survey next time!). If this is ok -- you’ll have another opportunity to win a prize -- we will need some contact details so we can locate you.
We promise not to spam or phish you, pass on your contact details to anyone, or otherwise harass you. How best to contact me in September 2006 Try this method Name: first (please select) Email: If you’re no longer studying at QUT you won’t be able to use your student email address!
Phone 1: Mobile: A Snail Mail Address:
Career Development Influences Questionnaire : Creative Industries Graduates Students
Ruth Bridgstock Supervisor: Professor Wendy Patton Centre for Learning Innovation School of Learning and Faculty of Education, QUT Professional Studies Victoria Park Road Faculty of Education, QUT Kelvin Grove Qld 4059 Victoria Park Road Telephone: 3864 3466 Kelvin Grove Qld 4059 Email: [email protected] Email: [email protected]
Description and Eligibility This online survey study is being conducted to increase understanding of the various influences on career development and career success in the Creative Industries. It is hoped that the information collected will be used to assist Creative Industries students and graduates with their careers. You will be able to access the results of the survey in a few months by checking back at: www.australianartistssurvey.org
You are eligible to participate if you graduated from an undergraduate course in the Creative Industries Faculty at QUT in 2005. You will be asked 4 sections of questions about your career decision-making, plans and educational background; the survey will take approximately 20 minutes to complete.
By completing the online survey, you will be indicating consent to participate in the study.
One random participant will receive a $200 voucher to their choice of Amazon.com, HMV music, Borders Books, Allans Music, or Artmaterials.com.
Voluntary participation Your participation in this project is entirely voluntary. You may withdraw from the project at any time up until survey submission without comment or penalty. Once your data is submitted, your record is not identifiable as yours and withdrawal is therefore not possible.
Your decision to participate will in no way impact upon your current or future relationship with QUT (eg your grades or future employment relationship).
Confidentiality All details you provide in this survey will be confidential.
There is no link between the data you enter and the details provided in respect to the prize draw. Contact details to be put into the prize draw are for that purpose only.
Surveys and survey data are accessible to the research team only for research and educational purposes and will be downloaded and kept in a secure electronic database. All efforts will be made to ensure that spamming, phishing and other forms of electronic harassment will not occur when you provide your data to us.
Questions and further information For additional information or questions about the project, you are welcome to contact the project leader, Ruth Bridgstock, on 3864 3466 or email [email protected]
Concerns or complaints If you have any concerns or complaints about the ethical conduct of this research you are welcome to contact the Research Ethics Officer, on 3864 2340 or email [email protected] . Please quote the University Human Research Ethics Committee Reference Number 0600000558.
Career Development Influences Survey: Creative Industries Graduates
Some questions in this survey may seem quite similar. Explanations of similar terms have been included to make everything as clear as possible. Please read the descriptions carefully!
Section 1. Your course and field/s in the Creative Industries 1. Did you complete a 3 or 4 year undergraduate course in the Creative Industries in 2005? 1. Yes 2. No (screen with message – “you may be eligible to participate in our professional artists survey”)
2. What was the name of your Creative Industries course? (please select from drop-down list) Bachelor of Creative Industries (Communication Design) (KI32) Bachelor of Creative Industries (Communication Design)/Bachelor of Information Technology (IF90) Bachelor of Creative Industries (Creative Writing) (KW32) Bachelor of Creative Industries (Creative Writing) / Bachelor of Laws (IF93) Bachelor of Creative Industries (Dance) (KD32) Bachelor of Creative Industries (Dance)/Bachelor of Education (Secondary) (IX05) Bachelor of Creative Industries (Drama) (KT32) Bachelor of Creative Industries (Drama)/Bachelor of Education (Secondary) (IX06) Bachelor of Creative Industries (Honours) (Communication Design) (KK52) Bachelor of Creative Industries (Honours) (Creative Writing) (KK52) Bachelor of Creative Industries (Honours) (Dance) (KK52) Bachelor of Creative Industries (Honours) (Drama) (KK52) Bachelor of Creative Industries (Honours) (Interdisciplinary) (KK52) Bachelor of Creative Industries (Honours) (Media and Communication) (KK52) Bachelor of Creative Industries (Honours) (Visual Arts) (KK52) Bachelor of Creative Industries (Interdisciplinary) (KK32) Bachelor of Creative Industries (Media and Communication) (KC32) Bachelor of Creative Industries (Media and Communication)/Bachelor of Laws (IF10) Bachelor of Creative Industries (Media and Communication)/Bachelor of Business (Advertising, International Business, Public Relations) (IF09) Bachelor of Creative Industries (Television) (KP32) Bachelor of Creative Industries (Visual Arts) (KV32) Bachelor of Creative Industries (Visual Arts)/Bachelor of Education (Secondary) (IX08) Bachelor of Fine Arts (Acting) (KS25) Bachelor of Fine Arts (Animation) (KI26) Bachelor of Fine Arts (Communication Design) - Sound Design (KI25) Bachelor of Fine Arts (Creative Writing Production) (KW25) Bachelor of Fine Arts (Dance) (KD25) Bachelor of Fine Arts (Fashion Design) (KF25) Bachelor of Fine Arts (Film and Television) (KP25) Bachelor of Fine Arts (Honours) (Communication Design) (KK53) Bachelor of Fine Arts (Honours) (Creative Writing) (KK53) Bachelor of Fine Arts (Honours) (Dance) (KK53) Bachelor of Fine Arts (Honours) (Film & Television Production) (KK53) Bachelor of Fine Arts (Honours) (Visual Arts) (KK53) Bachelor of Fine Arts (Technical Production) (KS26) Bachelor of Fine Arts (Visual Arts) (KV25) Bachelor of Journalism (Honours) (KK54) Bachelor of Journalism (KJ32) Bachelor of Journalism/Bachelor of Business (Advertising, International Business, Public Relations) (IF05) Bachelor of Journalism/Bachelor of Laws (IF07) Bachelor of Mass Communication (IF27) Bachelor of Music (Honours) (KK55) Bachelor of Music (KM32) Bachelor of Music/Bachelor of Education (Secondary) (IX07) Another course (please specify)
3. With which of the following artistic occupational groups do you identify? (select as many as apply from the drop- down list)
1. Writer (other than journalist) 2. Craft practitioner/ artisan 3. Visual artist 4. Composer/song writer/music arranger 5. Actor/performer/director 6. Dancer/choreographer 7. Musician/singer 8. Community cultural development worker 9. Fashion designer 10. Filmmaker (other than director) 11. Technical, stage or production manager 12. Interaction/ Information/ Multimedia Designer 13. Something else within the arts (please specify):______
4. (for each occupational group selected) In the last five years, have you had paid employment in, sold, performed, exhibited, published, filmed, broadcast or otherwise produced a professional (paid) work in the field of
Yes No
5. (for each occupational group selected) During the last five years have you received a Government grant or similar to produce a professional work in the field of
Yes No
6. (if ‘yes’ to either 2 or 3 above for a field) How many different occupations do you currently have within the field of
___ occupations
Go back to Qs 2 & 3 for each occupational grouping selected in 1.
7. (if multiple occupational groups are selected) Which one of the occupational groupings you selected does your current main artistic occupation fall within? present the options selected at Q1
8. Do you also work outside the arts? Yes, I have a career in a different field as well Yes, I have another job or jobs but I wouldn’t call it/them a career No, I don’t work outside the arts
(if 1 or 2) My other work falls within the general grouping(s) of (select all that apply)
MANAGERS Generalist Managers Specialist Managers Farmers and Farm Managers PROFESSIONALS Science, Building and Engineering Professionals Business and Information Professionals Health Professionals Education Professionals Social, Arts and Miscellaneous Professionals ASSOCIATE PROFESSIONALS Science, Engineering and Related Associate Professionals Business and Administration Associate Professionals
Managing Supervisors (Sales and Service) Health and Welfare Associate Professionals Other Associate Professionals TRADESPERSONS AND RELATED WORKERS Mechanical and Fabrication Engineering Tradespersons Automotive Tradespersons Electrical and Electronics Tradespersons Construction Tradespersons Food Tradespersons Agricultural and Horticultural Workers Tradespersons and Related Workers CLERICAL AND SERVICE WORKERS Secretaries and Personal Assistants Clerical Workers Sales and Related Workers Service Workers PRODUCTION AND TRANSPORT WORKERS Plant Operators Machine Operators Road and Rail Transport Drivers Production and Transport Workers LABOURERS AND RELATED WORKERS Cleaners Factory Labourers Other Labourers and Related Workers Something else (please specify)______
9. At the point of course completion last November, what were you planning on doing over the next twelve months? (select as many as apply) 1. Working in the field/s I selected above 2. Working in other areas of the arts or Creative Industries 3. Working outside the arts or Creative Industries 4. Doing more study in the arts or Creative Industries 5. Doing more study outside the arts or Creative Industries 6. Something else (please tell us):
10 At that point, had you already worked in your arts field/s? 1. Not yet 2. Yes, unpaid work experience 3. Yes, some paid work 4. Yes, significant paid work
11. How about work outside your arts field/s? 1. Not yet 2. Yes, unpaid work experience 3. Yes, some paid work 4. Yes, significant paid work
12. Over the last year, did you attempt to work in your arts field/s? 1. Yes 2. No (screen if no)
13. What education other than your Creative Industries course have you completed in your art form/s? (select all that apply)
On-the-job training Private tuition Secondary school Self-taught Master classes and workshops
Mentoring Vocational training University – pre-bachelors course University – bachelor’s degree University – postgraduate degree Other – please specify ______
14a). How much money did you earn from your career in arts in the last 12 months? About ______weekly or ______monthly or ______yearly
14b. How much money did you earn from all sources in the last 12 months? About ______weekly or ______monthly or ______yearly (ensure that figure response to 2a does not exceed figure response to 2b)
16. In your artistic occupation/s, are you currently… (select as many as apply) Working full-time Working part-time Working casually Self employed A grant holder studying full-time studying part-time between jobs looking for work Not interested in arts work right now, doing something else
(if Q9 in Section 1 = “yes”) 17. In your occupation/s outside the arts, are you… (select as many as apply) Working full-time Working part-time Working casually Self employed I’ve got a grant studying full-time studying part-time between jobs looking for work
18. How would you currently describe your present arts career position? Select as many as you like. Recently started In training Exploring options Establishing myself Well-established Re-establishing myself Changing fields Taking a break right now Winding down I don’t have a career in the creative industries Something else (please specify) ______
Section 2. Your thinking about career success
1. How would you define career success in your field/s of arts?
2. Using your definition above, how successful do you believe you have been in your arts career so far? Not 1 2 3 4 5 6 Highly successful at successful all
Employability is defined as your ability to create or obtain work.
3. How employable within your artistic field/s do you believe you are right now? Not 1 2 3 4 5 6 Highly employable employable at all
4. How generally employable, in terms of work both inside and outside arts, do you believe you are right now? Not 1 2 3 4 5 6 Highly employable employable at all
6. Thinking of the skills you have learned during your course, how able were you to do the job in your chosen field/s at the point of graduation? e.g. edit a film, write an article, give a concert Not confident 1 2 3 4 5 6 Perfectly at all confident
7. And how about now, one year later? Not confident 1 2 3 4 5 6 Perfectly at all confident
8. At the point of graduation, how well-prepared are tertiary graduates from the Creative Industries to navigate the world of work?
Not well- 1 2 3 4 5 6 Very well prepared prepared
9. If you want to comment on your response in Q8, please do so here:
Section 3. Your career goals and expectations
1. Please tell us about what you expect to be happening in your career in one year and in five years. If you are not certain, this is ok, just give us your ideas. a) In one year’s time, I expect to be
b) In five years’ time I expect to be
b) At the point of course completion, I expected that in one years’ time I would be…
Section 4. Your background
1. For how many years did you study your Creative Industries course? a) Less than one b) More than one but less than two c) More than two but less than three d) More than three but less than four e) More than four but less than five f) More than five but less than six g) More than six years
2. Did you study: a) full-time b) part-time c) a mixture of both
3. Are you? a) Female b) Male
4. What is your age in years? ______years
5. Do you identify with any of the following groups: (circle as many as apply)
Aboriginal or Torres Strait Islander With a disability English as a second language
Thank you very much for taking the time to complete this questionnaire!
Career Development Influences Questionnaire: Professional Artists Students Ruth Bridgstock Supervisor: Professor Wendy Patton Centre for Learning Innovation School of Learning and Professional Faculty of Education, QUT Studies Victoria Park Road Faculty of Education, QUT Kelvin Grove Qld 4059 Victoria Park Road Telephone: 3864 3466 Kelvin Grove Qld 4059 Email: [email protected] Email: [email protected]
Description and Eligibility This online survey study is being conducted to increase understanding of the various influences on Australian artists’ career development and success. There is some evidence to suggest that artists face different working challenges to those working in other fields, and this survey is designed to explore those differences. It is hoped that the information collected will be used to assist Australian artists with their careers. You will be able to access the results of the survey in a few months by checking back at: www.australianartistssurvey.org
You are eligible to participate if you are a creative or performing artist, a technical/ design artist, or community cultural development worker. You will be asked 8 sections of questions about your career decision-making, plans and educational background; the survey will take approximately 30 minutes to complete.
By completing the online survey, you will be indicating consent to participate in the study.
Voluntary participation Your participation in this project is entirely voluntary. You may withdraw from the project at any time without comment or penalty. Your decision to participate will in no way impact upon your current or future relationship with QUT.
Anonymity and Confidentiality All surveys are anonymous and any information you provide cannot be linked to you personally.
Online surveys are accessible to the research team only for research and educational purposes. Data will be downloaded and kept in a secure electronic database
Some contact details are recorded for the purposes of awarding prizes. These contact details will be kept completely confidential and separate from the survey responses, and only used for the purpose for which they were collected. All efforts will be made to ensure that spamming, phishing and other forms of electronic harassment will not occur when you provide your data to us.
Questions and further information For additional information or questions about the project, you are welcome to contact the project leader, Ruth Bridgstock, on 3864 3466 or email [email protected]
Concerns or complaints If you have any concerns or complaints about the ethical conduct of this research you are welcome to contact the Research Ethics Officer on 3864 2340 or email [email protected] . Please quote the University Human Research Ethics Committee Reference Number 4174H.
Career Development Influences Survey: Professional Artists.
Section 1: Your field/s of arts
1. With which of the following artistic occupational groups do you identify professionally? (select as many as apply from the drop-down list) 1. Writer (other than journalist) 2. Craft practitioner/ artisan 3. Visual artist 4. Composer/song writer/music arranger 5. Actor/performer/director 6. Dancer/choreographer 7. Musician/singer 8. Community cultural development worker 9. Fashion designer 10. Filmmaker (other than director) 11. Technical, stage or production manager 12. Interaction/ Information/ Multimedia Designer 13. Something else within the arts (please specify):______
2. (for each occupational group selected) In the last five years, have you sold, performed, exhibited, published, filmed, broadcast or otherwise produced a professional (paid) work in the field of
Yes No
3. (for each occupational group selected) During the last five years have you received a Government grant or similar to produce a professional work in the field of
Yes No
4. Do you regard yourself as a professional artist in the field of
Yes No
5. (if ‘yes’ to either 2 or 3 above for a field) How many different occupations do you currently have within the field of
7. Do you also work outside the arts? Yes, I have a career in a different field as well Yes, I have another job or jobs but I wouldn’t call it/them a career No, I don’t work outside the arts
(if 1 or 2) My other work falls within the general grouping(s) of (select all that apply)
MANAGERS Generalist Managers Specialist Managers Farmers and Farm Managers PROFESSIONALS Science, Building and Engineering Professionals Business and Information Professionals Health Professionals Education Professionals Social, Arts and Miscellaneous Professionals ASSOCIATE PROFESSIONALS Science, Engineering and Related Associate Professionals Business and Administration Associate Professionals Managing Supervisors (Sales and Service) Health and Welfare Associate Professionals Other Associate Professionals TRADESPERSONS AND RELATED WORKERS Mechanical and Fabrication Engineering Tradespersons Automotive Tradespersons Electrical and Electronics Tradespersons Construction Tradespersons Food Tradespersons Agricultural and Horticultural Workers Tradespersons and Related Workers CLERICAL AND SERVICE WORKERS Secretaries and Personal Assistants Clerical Workers Sales and Related Workers Service Workers PRODUCTION AND TRANSPORT WORKERS Plant Operators Machine Operators Road and Rail Transport Drivers Production and Transport Workers LABOURERS AND RELATED WORKERS Cleaners Factory Labourers Other Labourers and Related Workers
In this survey you will be asked some questions about your career in the arts.Some questions in this survey may seem quite similar. Explanations of similar terms have been included to make everything as clear as possible. Please read the descriptions carefully!
Section 2: Influences on your career so far 1. How negative or positive an influence has each of the following had on the arts career development so far? This can include choices you have made about study, work, or any other aspects of your career development and career progression.
Degree of influence Strongly Negative Positive Strongly Negative - + Positive -- ++ Things about you a) Gender 1 2 3 4 5 6 b) Health - both physical 1 2 3 4 5 6 and mental c) Self-concept - your idea 1 2 3 4 5 6 of your important roles e.g. student, homemaker, community member, worker d) Ability – your potential to 1 2 3 4 5 6 acquire the skills you need e) Aptitudes – the quickness 1 2 3 4 5 6 or ease with which you can learn a skill you need f) Disability 1 2 3 4 5 6 g) Personality 1 2 3 4 5 6 h) Age 1 2 3 4 5 6 i) Ethnicity 1 2 3 4 5 6 j) Physical attributes 1 2 3 4 5 6 k) World-of-work knowledge 1 2 3 4 5 6 – what you know about work environments and how to find and secure work l) Interests 1 2 3 4 5 6 m) Skills 1 2 3 4 5 6 n) Beliefs – ongoing ideas 1 2 3 4 5 6 about yourself and the world o) Values – what is important 1 2 3 4 5 6 to you e.g. prestige, risk, autonomy, responsibility p) Sexual Orientation 1 2 3 4 5 6 Your social influences q) Family 1 2 3 4 5 6 r) Peers – friends and 1 2 3 4 5 6 colleagues s) Community Groups 1 2 3 4 5 6 t) Educational Institutions 1 2 3 4 5 6 u) Workplaces 1 2 3 4 5 6 v) Media 1 2 3 4 5 6 Your societal and environmental influences w) Geographical location 1 2 3 4 5 6 x) Political decisions 1 2 3 4 5 6 y) Employment Market 1 2 3 4 5 6 z) Historical trends 1 2 3 4 5 6 aa) Socioeconomic status 1 2 3 4 5 6 bb) Globalization 1 2 3 4 5 6
2. For up to three influences from the list in question 1 that you regard as being the strongest, please talk abut how they have affected your career development here. Influence 1 Influence name: (scroll for options) How has this influence affected you?
Influence 2 Influence name: (scroll for options) How has this influence affected you?
Influence 3 Influence name: (scroll for options) How has this influence affected you?
3. To what extent do you believe that luck, serendipitous events, or chance generally affect the career development of: (please circle) a) Someone pursuing a career in your field/s of arts Not at all 1 2 3 4 5 6 A lot b) Someone pursuing a career outside the arts Not at all 1 2 3 4 5 6 A lot
Please comment on your responses:
Section 3: Your beliefs about your career
1. To what extent do you agree with the following: (please circle) a) I am self-directed and take personal responsibility
Strongly 1 2 3 4 5 6 Strongly disagree agree b) I am proactive
Strongly 1 2 3 4 5 6 Strongly disagree agree c) I an internally motivated
Strongly 1 2 3 4 5 6 Strongly disagree agree
d) I have a positive interpersonal orientation
Strongly 1 2 3 4 5 6 Strongly disagree agree
e) I am resilient and adaptable
Strongly 1 2 3 4 5 6 Strongly disagree agree
f) I am open to opportunities
Strongly 1 2 3 4 5 6 Strongly disagree agree
g) I have a positive self image
Strongly 1 2 3 4 5 6 Strongly disagree agree
Section 4.Your career skills and abilities
1. How important do you believe the following are to career success in your field/s of the arts, and how confident are you in your abilities and skills in these areas? Please circle.
Importance to success Confidence Not at Very Not Very all at all 1. Building and maintaining a positive self- image 1 2 3 4 5 6 1 2 3 4 5 6 Knowing who you are & what influences you, staying positive, understanding how self-image has an impact on goals and decisions 2. Interacting positively and effectively with others 1 2 3 4 5 6 1 2 3 4 5 6 Understanding and maintaining positive relationships, being able to express yourself in an appropriate manner, knowing how to solve interpersonal problems 3. Changing and growing throughout your life 1 2 3 4 5 6 1 2 3 4 5 6 Understanding that your motivations and aspirations change throughout our lives, that change and growth can impact on our physical and mental health and vice versa, knowing how to adapt to changes and manage stress 4. Participating in life-long learning supportive of your career goals 1 2 3 4 5 6 1 2 3 4 5 6 Knowing what influences life and work successes, understanding how to improve skills and strengths, knowing about learning opportunities, behaving in ways that contribute to achieving your goals
5. Locating and effective use of career information 1 2 3 4 5 6 1 2 3 4 5 6 Knowing where and how to access career information, and how to use it, knowing what working conditions you want, understanding the requirements of work settings
6. Understanding the relationship between work, society and the economy 1 2 3 4 5 6 1 2 3 4 5 6 Understanding about how work contributes to our community, society, and ourselves; understanding how trends affect work, understanding how organizations operate
7. Securing or creating and then maintaining work 1 2 3 4 5 6 1 2 3 4 5 6 Understanding the importance of personal qualities on getting/ keeping/ creating work, being able to articulate your skills, being able to transfer your skills between work settings, developing work search tools and skills
8. Make career enhancing decisions 1 2 3 4 5 6 1 2 3 4 5 6 Understanding how choices are made, how personal beliefs and attitudes affect decision- making, knowing how to problem-solve, being able to explore alternatives, being able to evaluate the impact of decisions
9. Maintain balanced life and work roles 1 2 3 4 5 6 1 2 3 4 5 6 Being aware of the various roles we may have and the responsibilities linked to those roles, how these roles impact upon our lifestyles, determining the value of work, family and leisure activities and making choices about a balance of these
10. Understanding the changing nature of life and work roles 1 2 3 4 5 6 1 2 3 4 5 6 Understanding the changing life roles of people in work and home settings, understanding how these roles are important to family and society, exploring and considering non-traditional life/ work scenarios, working to eliminate stereotypes, biases and discriminatory behaviours
11. Understand, engage in, and manage the career building process 1 2 3 4 5 6 1 2 3 4 5 6 Being able to define your preferred future and create career scenarios in step with it, being able to set goals and short-term plans, and apply coping strategies and new career scenarios during transition periods e.g. starting a family, losing a job
Section 5. Your thinking about career success in the arts
1. How would you define career success in your field/s of arts?
2. Using your definition above, how successful do you believe you have been in your arts career so far? Not 1 2 3 4 5 6 Highly successful at successful all
Employability is defined as your ability to create or obtain work. 3. How employable within your artistic field/s do you believe you are right now? Not 1 2 3 4 5 6 Highly employable employable at all
4. How generally employable, in terms of work both inside and outside arts, do you believe you are right now? Not 1 2 3 4 5 6 Highly employable employable at all
Section 6. Your career history
1. What education have you completed in your art form/s? (select all that apply) On-the-job training Private tuition Secondary school Self-taught Master classes and workshops Mentoring Vocational training University – pre-bachelors course University – bachelor’s degree University – postgraduate degree Other – please specify ______
2a). How much money did you earn from your career in arts in the last 12 months? About ______weekly or ______monthly or ______yearly 2b). How much money did you earn from all sources in the last 12 months? About ______weekly or ______monthly or ______yearly (ensure that figure response to 2a does not exceed figure response to 2b)
3. In your artistic occupation/s, are you… (select as many as apply) Working full-time Working part-time Working casually Self employed A grant holder studying full-time studying part-time between jobs Not interested in arts work right now, doing something else
(if Q6 and 7 in Q1 = “yes”) 4. In your occupation/s outside arts, are you… (select as many as apply) Working full-time Working part-time Working casually Self employed I’ve got a grant studying full-time studying part-time between jobs
5. How would you currently describe your present arts career position? Select as many as you like. Winding down Re-establishing myself Taking a break right now Changing fields Well-established Establishing myself Exploring options Recently started In training Something else (please specify) ______
6. How long have you worked in the arts? ___ years
Section 7. Your arts career goals and expectations
1. Please tell us about what you expect to be happening in your creative/ performing arts career in one year and in five years. If you are not certain, this is ok, just give us your ideas. a) In one year’s time, I expect to be
b) In five years’ time I expect to be
Section 8. About you
1. Are you? a) Female b) Male
2. What is your age in years? ______years
3. Do you identify with any of the following groups: (circle as many as apply) Aboriginal or Torres Strait Islander With a disability English as a second language
4. Where do you live? Australia New Zealand UK United States of America Canada Africa Asia Central America Eastern Europe European Union (but not UK) Middle East North America (but not USA) Oceania (but not Australia/ New Zealand) South America The Caribbean
5. (if Australia) In which State/ territory of Australia do you live? NSW VIC QLD TAS ACT NT WA SA
6. (all respondents) And what type of area do you live in? Regional Remote Metropolitan
7. Marital Status Are you: Married Widowed Divorced Never Married Partnered Other (please specify)______
Do you want to tell us anything else about working in the arts?
Thank you very much for taking the time to complete this questionnaire!
APPENDIX B: ETHICAL CLEARANCES
Date: Mon, 29 Aug 2005 15:39:36 +1000 To: [email protected] From: Wendy Heffernan
Dear Ruth
I write further to the responses received regarding the ethical clearance provided for your projects, "Career Development Influences Survey: Phase 1 Graduating Creative Industries Students" (QUT Ref No 4173H) and "Career Development Influences Survey: Phase 2 Professional Artists" (QUT Ref No 4174H).
On behalf of the Chair, University Human Research Ethics Committee (UHREC), I wish to advise that the responses have addressed the additional information required by the Expedited Ethical Review Panel.
Consequently, you are authorised to immediately commence your projects on this basis. The decision is subject to ratification at the 20 September 2005 meeting of UHREC. I will only contact you again in relation to this matter if the Committee raises any additional questions or concerns in regard to the clearances.
The University requires its researchers to comply with:
• the Universitys research ethics arrangements and the QUT Code of Conduct for Research; • the standard conditions of ethical clearance; • any additional conditions prescribed by the UHREC; • any relevant State / Territory or Commonwealth legislation; • the policies and guidelines issued by the NHMRC and AVCC (including the National Statement on Ethical Conduct in Research Involving Humans).
Please do not hesitate to contact me further if you have any queries regarding this matter. Regards Wendy
X-EM-Version: 5, 0, 0, 4 X-EM-Registration: #0020530310231E005C00 From: "Research Ethics"
Date: Fri, 15 Sep 2006 16:29:01 +1000 MIME-Version: 1.0 Content-type: text/plain; charset=US-ASCII X-Junkmail-Status: score=10/50, host=mail-router02.qut.edu.au X-Junkmail-Whitelist: YES (by domain whitelist at mail-msgstore01.qut.edu.au)
Dear Ms Ruth Bridgstock
Re: Career development influences survey: Phase 3 Creative Industries graduates
This email is to advise that your application 0600000558 and subsequent response to queries raised, has been considered and approved. Consequently, you are authorised to immediately commence your project.
The decision is subject to ratification at the next available Committee meeting. You will only be contacted again in relation to this matter if the Committee raises any additional questions or concerns in regard to this clearance.
Please do not hesitate to contact me further if you have any queries regarding this matter.
Regards
David Wiseman Research Ethics Officer
APPENDIX C: PARTICIPATING ARTISTS’ PROFESSIONAL
ORGANISATIONS AND NETWORKS
Access Arts Deviant Art A-List Entertainment Experimental Art Foundation Artforum Australia Fuel4Arts Artists Foundation of Western Australia Judith Wright Centre of Contemporary Arts Artlink Artplace Kick Arts Arts Resource Collective National Association for the Visual Arts Arts@Work Oz Arts Online Artsconnect Pacific Film and Television Commission Artshub Perth Institute of Contemporary Art Artspace Sydney QPIX Artworkers Alliance Queensland Community Arts Network Ausdance Queensland Theatre Company Australian Centre for Photography Queensland Writers Centre Australian Dance Performance Institute Raw Metal Australian Film Commission Straight Out of Brisbane Australian Performing Arts Directory The Arts Centre Australian Society of Authors Theatre Arts Network Queensland Barkley Regional Arts This is Not Art Festival Brisbane Powerhouse Time Off Craft Queensland Youth Arts Qld
APPENDIX D: PILOT STUDY INFORMATION AND EXPLORATORY
FACTOR ANALYSES
Career Development Influences Questionnaire – Pilot
20 May, 2005 Ruth Bridgstock Supervisor: Professor Wendy Patton Centre for Learning Innovation School of Learning and Professional Studies Faculty of Education, QUT Victoria Park Road Faculty of Education, QUT Kelvin Grove Qld 4059 Victoria Park Road Telephone: 3864 3466 Kelvin Grove Qld 4059 Email: Email: [email protected] [email protected]
Description and Eligibility This study is being conducted to increase understanding of the various influences on career development and career success. It includes a new measure which is being evaluated for use in a later study, along with some established measures. You are eligible to participate if you are a student within the Faculty of Education. Voluntary participation Your participation in this project is entirely voluntary. You may withdraw from the project at any time without comment or penalty. Your decision to participate will in no way impact upon your current or future relationship with QUT (eg your grades). Anonymity and Confidentiality All surveys are anonymous and any information you provide cannot be linked to you personally. Surveys are accessible to the research team only for research and educational purposes and will be stored for five years in a lockable filing cabinet, then destroyed. Questions and further information For additional information or questions about the project, you are welcome to contact the project leader, Ruth Bridgstock, on 3864 3466 or email [email protected] Concerns or complaints If you have any concerns or complaints about the ethical conduct of this research you are welcome to contact the Research Ethics Officer, Mrs Wendy Heffernan on 3864 2340 or email [email protected]. Please quote the University Human Research Ethics Committee Reference Number 4058H.
If you experience any distress as the result of participating in this study, please contact QUT Counselling Services or Careers and Employment for a free confidential consultation on the following numbers:
Counselling Careers and Employment Kelvin Grove: 3864 3488 Kelvin Grove, Gardens Point and Carseldine : 3864 2649 Gardens Point: 3864 2383 Carseldine: 3864 4539 Career Development Influences Survey: Pilot Study 1
Some questions in this survey will ask about your opinions relating to the occupation or field you’re particularly interested in. If you’re not sure about a particular field or occupation or have a few options in mind, this is OK. Just answer as best you can given what you think and know right now. Some questions in this survey may seem quite similar. Explanations of similar terms have been included to make everything as clear as possible. Please read the descriptions carefully!
Section 1: Influences on your career so far
1. How negative or positive an influence has each of the following had on the arts career development so far? This can include choices you have made about study, work, or any other aspects of your career development and career progression.
Level of influence Strongly Negative Negative Positive Strongly Positive -- - + ++ Things about you a) Gender 1 2 3 4 b) Health - both physical and mental 1 2 3 4 c) Self-concept - your idea of your 1 2 3 4 important roles e.g. student, homemaker, community member, worker d) Ability – your potential to acquire 1 2 3 4 the skills you need e) Aptitudes – the quickness or ease 1 2 3 4 with which you can learn a skill you need f) Disability 1 2 3 4 g) Personality 1 2 3 4 h) Age 1 2 3 4 i) Ethnicity 1 2 3 4 j) Physical attributes 1 2 3 4 k) World-of-work knowledge – what 1 2 3 4 you know about work environments and how to find and secure work l) Interests 1 2 3 4 m) Skills 1 2 3 4 n) Beliefs – ongoing ideas about 1 2 3 4 yourself and the world o) Values – what is important to you 1 2 3 4 e.g. prestige, risk, autonomy, responsibility p) Sexual Orientation 1 2 3 4 Your social influences q) Family 1 2 3 4 r) Peers – friends and colleagues 1 2 3 4 s) Community Groups 1 2 3 4 t) Educational Institutions 1 2 3 4 u) Workplaces 1 2 3 4 v) Media 1 2 3 4 Your societal and environmental influences w) Geographical location 1 2 3 4 x) Political decisions 1 2 3 4 y) Employment Market 1 2 3 4 z) Historical trends 1 2 3 4 aa) Socioeconomic status 1 2 3 4 bb) Globalization 1 2 3 4
2. Name three influences which have changed for you over time. In what way have they changed? Influence 1 Influence name: How has this influence changed??
Influence 2 Influence name: How has this influence changed??
Influence 3 Influence name: How has this influence changed??
3. For the three influences from the list in question 1 that you regard as being the strongest (either positively or negatively), please talk abut how they have affected your career development here. They don’t have to be the same influences that you talked about in question 2. Influence 1 Influence name: How has this influence affected you?
Influence 2 Influence name: How has this influence affected you?
Influence 3 Influence name: How has this influence affected you?
4. To what extent do you believe that luck, serendipitous events, or chance generally affect the career development of: (please circle) a) Someone pursuing a career in the field you are interested in Not at all A lot 1 2 3 4 b) Someone pursuing a career outside the field you are interested in Not at all A lot 1 2 3 4 Please comment on your responses:
Section 2: Your beliefs about careers
1. To what extent do you agree with the following: (please circle) a) I am self-directed and take personal responsibility
Strongly 1 2 3 4 Strongly disagree agree b) I am proactive
Strongly 1 2 3 4 Strongly disagree agree c) I an internally motivated
Strongly 1 2 3 4 Strongly disagree agree d) I have a positive interpersonal orientation
Strongly 1 2 3 4 Strongly disagree agree e) I am resilient and adaptable
Strongly 1 2 3 4 Strongly disagree agree f) I am open to opportunities
Strongly 1 2 3 4 Strongly disagree agree g) I have a positive self image
Strongly 1 2 3 4 Strongly disagree agree
Section 3. Your career skills and abilities
1. How important do you believe the following are to career success, and how confident are you in your abilities and skills in these areas? Please circle. Importance to success Confidence Not Very Not Very at all at all Building and maintaining a positive self-image 1 2 3 4 1 2 3 4 Knowing who you are & what influences you, staying positive, understanding how self-image has an impact on goals and decisions Interacting positively and effectively with 1 2 3 4 1 2 3 4 others Understanding and maintaining positive relationships, being able to express yourself in an appropriate manner, knowing how to solve interpersonal problems 3. Changing and growing throughout your life 1 2 3 4 1 2 3 4 Understanding that your motivations and aspirations change throughout our lives, that change and growth can impact on our physical and mental health and vice versa, knowing how to adapt to changes and manage stress 4. Participating in life-long learning supportive 1 2 3 4 1 2 3 4 of your career goals Knowing what influences life and work successes, understanding how to improve skills and strengths, knowing about learning opportunities, behaving in ways that contribute to achieving your goals 5. Locating and effective use of career 1 2 3 4 1 2 3 4 information Knowing where and how to access career information, and how to use it, knowing what working conditions you want, understanding the requirements of work settings 6. Understanding the relationship between 1 2 3 4 1 2 3 4 work, society and the economy Understanding about how work contributes to our community, society, and ourselves; understanding how trends affect work, understanding how organizations operate 7. Securing or creating and then maintaining 1 2 3 4 1 2 3 4 work Understanding the importance of personal qualities on getting/ keeping/ creating work, being able to articulate your skills, being able to transfer your skills between work settings, developing work search tools and skills 8. Make career enhancing decisions 1 2 3 4 1 2 3 4 Understanding how choices are made, how personal beliefs and attitudes affect decision- making, knowing how to problem-solve, being able to explore alternatives, being able to evaluate the impact of decisions 9. Maintain balanced life and work roles 1 2 3 4 1 2 3 4 Being aware of the various roles we may have and the responsibilities linked to those roles, how these roles impact upon our lifestyles, determining the value of work, family and leisure activities and making choices about a balance of these 10. Understanding the changing nature of life 1 2 3 4 1 2 3 4 and work roles Understanding the changing life roles of people in work and home settings, understanding how these roles are important to family and society, exploring and considering non-traditional life/ work scenarios, working to eliminate stereotypes, biases and discriminatory behaviours 11. Understand, engage in, and manage the 1 2 3 4 1 2 3 4 career building process Being able to define your preferred future and create career scenarios in step with it, being able to set goals and short-term plans, and apply coping strategies and new career scenarios during transition periods e.g. starting a family, losing a job
Section 4. Your thinking about career success
1. 1. How would you define career success in your field?
2. Using your definition above, how successful are you in your career so far? Not 1 2 3 4 Highly successful successful at all
5. How employable within the field you’re interested in do you believe you are right now? Not 1 2 3 4 Highly employable employable at all
6. How about when you graduate? Not 1 2 3 4 Highly employable employable at all
7. How employable do you believe you are right now generally? Not 1 2 3 4 Highly employable employable at all
8. How about when you graduate? Not 1 2 3 4 Highly employable employable at all
Section 5. Your career goals and expectations
1. Please tell us about what you expect to be happening in your career in one year and in five years. If you are not certain, this is ok, just give us your ideas. a) In one year’s time, I expect to be
b) In five years’ time I expect to be
Section 6. Your Background
1. What is the name of the Education course that you are currently studying? a) B Ed (Early
Childhood) b) B Ed Is this part of a double, combined or Yes (Primary) parallel degree? e.g. B Ed/ BCI, B Ed/ No LLB c) B Ed (Secondary)
Or, are you studying another course? Please tell us its name: ______
2. What ‘year’ are you at University? First Second Third Fourth/ Honours
3. How many years have you been studying this course for so far? a) Less than one b) More than one but less than two c) More than two but less than three d) More than three but less than four e) More than four
4. Are you studying: a) full-time b) part-time
5. How many years have you got to go before you graduate? a) Less than one b) More than one but less than two c) More than two but less than three d) More than three but less than four e) More than four
6. Are you? a) Female b) Male
7. What is your age in years? ______years
8. Do you identify with any of the following groups: (tick as many as apply) a)Aboriginal or Torres Strait Islander b) Adult with a disability c) English as a second language d) International student
Thank you very much for taking the time to complete this questionnaire!
X-Mailer: QUALCOMM Windows Eudora Version 6.2.1.2 Date: Fri, 20 May 2005 14:13:28 +1000 To: [email protected] From: Wendy Heffernan
Dear Ruth
I write further to the application for ethical clearance requested for your project, "Career Development Influences Pilot Study 1: Education Students" (QUT Ref No 4058H).On behalf of the Chair, University Human Research Ethics Committee (UHREC), I wish to confirm that the project qualifies for Level 1 (Low Risk) ethical clearance.
This approval is subject to inclusion under Voluntary Participation in the questionnaire cover sheet of the wording "Your decision to participate will in no way impact upon your current or future relationship with QUT (eg your grades)".
However, you are authorised to immediately commence your project on this basis. The decision is subject to ratification at the 14 June 2005 meeting of UHREC. I will only contact you again in relation to this matter if the Committee raises any additional questions or concerns in regard to the clearance.
The University requires its researchers to comply with:
• the University’s research ethics arrangements and the QUT Code of Conduct for Research; • the standard conditions of ethical clearance; • any additional conditions prescribed by the UHREC; • any relevant State / Territory or Commonwealth legislation; • the policies and guidelines issued by the NHMRC and AVCC (including the National Statement on Ethical Conduct in Research Involving Humans).
Please do not hesitate to contact me further if you have any queries regarding this matter.
Regards Wendy Exploratory Factor Analysis: Career Development Influences Instrument (N=168)
Item Means and Standard Deviations
Item Mean SD Gender 2.72 .72 Health 2.82 .71 Self concept 2.99 .69 Ability 3.09 .69 Aptitudes 3.00 .69 Disability 2.20 .95 Personality 3.15 .60 Age 2.76 .76 Ethnicity 2.52 .95 Physical attributes 2.68 .86 World of work knowledge 2.99 .76 Interests 3.21 .69 Skills 3.11 .66 Beliefs 3.04 .75 Values 3.20 .64 Sexual orientation 2.45 .92 Family 3.15 .76 Peers 2.99 .79 Community groups 2.75 .82 Educational institutions 2.91 .74 Workplaces 2.63 .80 Media 2.42 .77 Geographical location 2.64 .88 Political decisions 2.37 .79 Employment market 2.72 .79 Historical trends 2.49 .77 Socioeconomic status 2.61 .84 Globalisation 2.46 .82
Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .856
Bartlett's Test of Approx. Chi-Square 2029.218 Sphericity df 325 Sig. .000
Item Communalities
Initial Extraction Gender .452 .419 Health .620 .664 Self concept .455 .423 Ability .544 .498 Aptitudes .544 .675 Personality .362 .347 Age .673 .633 Ethnicity .687 .597 Physicality .687 .675
World of work .529 .608 knowledge Interests .546 .658 Skills .546 .575 Beliefs .636 .622 Values .635 .588 Family .530 .520 Peers .614 .672 Community groups .620 .573 Educational institutions .601 .566 Workplaces .472 .292 Media .480 .463 Geographical location .515 .451 Political decisions .636 .638 Employment market .522 .434 Historical influences .732 .712 Socioeconomic status .706 .643 Globalisation .664 .615 Extraction Method: Principal Axis Factoring.
Total Variance Explained
Factor Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Cumulative % Total % of Variance Cumulative % Total Variance 1 9.341 35.966 35.966 8.930 34.347 34.347 4.685 2 2.963 10.972 46.938 2.426 9.331 43.679 2.738 3 1.556 5.599 52.537 1.032 3.968 47.647 2.607 4 1.262 4.622 57.159 .779 2.996 50.643 1.841 5 1.188 4.519 61.678 .747 2.874 53.516 1.643 6 1.114 4.168 65.846 .648 2.492 56.008 1.047 7 .893 3.433 69.279 8 .858 3.301 72.580 9 .707 2.718 75.298 10 .681 2.618 77.916 11 .618 2.377 80.293 12 .570 2.190 82.484 13 .550 2.114 84.598 14 .507 1.951 86.549 15 .484 1.862 88.410 16 .436 1.676 90.087 17 .409 1.574 91.661 18 .365 1.404 93.065 19 .353 1.358 94.424 20 .290 1.114 95.538 21 .267 1.026 96.564 22 .219 .843 97.407 23 .196 .752 98.160 24 .179 .689 98.849 25 .163 .628 99.477 26 .136 .523 100.000 Rotated Pattern Matrix (Oblique Rotation)
Factor 1 2 3 4 5 6 Political decisions .755 Historical trends .748 Globalisation .635 Socioeconomic .520 status Media .502 Geographical .468 location Ability .831 Aptitudes .814 Skills .741 Peers .811 Family .668 Age .792 World of work .573 knowledge Physical attributes .543 Beliefs .687 Interests .605 Values .547 Personality .497 Health -.496 Gender -.474 Community groups Disability Educational Institutions Employment market Ethnicity Self concept Sexual orientation Workplaces Note. N = 168. Loadings above .45 are included. Convergence in 8 iterations.
Factor Correlation Matrix
Factor 1 2 3 4 5 6 1 1.00 2 .20 1.00 3 .45 .17 1.00 4 .47 .24 .34 1.00 5 .16 .40 .29 .22 1.00 -.06 6 -.33 -.10 -.18 -.22 -.06 1.00
Exploratory Factor Analysis: Protean Career Success Orientation Instrument (N=168)
Item Means and Standard Deviations Item Mean SD Self-directed, personal 3.47 .63 responsibility Proactive 3.21 .69 Internally motivated 3.11 .73 Positive interpersonal 3.15 .78 Resilient and adaptable 3.41 .63 Open to opportunities 3.40 .65 Positive self image 3.48 .68
Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .877
Bartlett's Test of Approx. Chi-Square 369.743 Sphericity df 21 Sig. .000
Communalities
Initial Extraction Self-directed, personal 3.47 .63 responsibility Proactive 3.21 .69 Internally motivated 3.11 .73 Positive interpersonal 3.15 .78 Resilient and adaptable 3.41 .63 Open to opportunities 3.40 .65 Positive self image 3.48 .68 Extraction Method: Principal Axis Factoring.
Total Variance Explained
Factor Initial Eigenvalues Extraction Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative %
1 3.748 53.542 53.542 3.260 46.572 46.572 2 .848 12.110 65.652 3 .684 9.769 75.421 4 .602 8.593 84.014 5 .428 6.111 90.125 6 .389 5.554 95.679 7 .302 4.321 100.000
Factor Matrix Factor 1 Self-directed, personal .586 responsibility Proactive .586 Internally motivated .461 Positive interpersonal .707 Resilient and adaptable .785 Open to opportunities .754 Positive self image .822
Note. N = 168. Loadings above .45 are included.
Exploratory Factor Analysis: Career Management Competence Instrument (N=168)
Item Means and Standard Deviations
Item Mean SD Building and maintaining a positive self 3.72 .50 image Changing and growing throughout life 3.22 .75 Participating in lifelong learning 3.53 .69 supportive of career goals Maintaining balanced life and work 3.41 .66 roles Understanding the changing nature of 3.32 .64 life and work roles Locating and effectively using career 3.35 .69 information Securing or creating and maintaining 2.84 .81 work Making career enhancing decisions 3.11 .83 Understand, engage in, and manage the 3.05 .76 career building process Interacting positively and effectively 3.61 .66 with others Understanding the relationship between 3.22 .78 work, society and the economy
Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .782
Bartlett's Test of Approx. Chi-Square 383.604 Sphericity df 55 Sig. .000
Item Communalities
Initial Extraction Building and .467 .522 maintaining a positive self image Interacting positively .608 .815 and effectively with others Changing and growing .446 .324 throughout life Participating in lifelong .492 .500 learning supportive of career goals Locating and .501 .455 effectively using career information Understanding the .622 .810 relationship between work, society and the economy Securing or creating .564 .513 and maintaining work Making career .538 .583 enhancing decisions Maintaining balanced .400 .421 life and work roles Understanding the .393 .463 changing nature of life and work roles Understand, engage in, .387 .306 and manage the career building process Extraction Method: Principal Axis Factoring.
Total Variance Explained
Factor Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cumulative % Total % of Variance Cumulative Total % 1 4.270 38.821 38.821 3.815 34.683 34.683 2.804 2 1.641 14.917 53.737 1.177 10.700 45.383 2.804 3 1.075 9.774 63.512 .719 6.532 51.915 1.797 4 .847 7.704 71.216 5 .751 6.832 78.047 6 .697 6.335 84.382 7 .556 5.054 89.437 8 .384 3.490 92.927 9 .318 2.889 95.816 10 .271 2.466 98.282 11 .189 1.718 100.000
Rotated Pattern Matrix (Oblique Rotation)
Factor 1 2 3 Building and maintaining a positive .748 self image Understanding the changing nature of .689 life and work roles Maintaining balanced life and work .601 roles Participating in lifelong learning .558 supportive of career goals Changing and growing throughout life .546 Making career enhancing decisions .691 Securing or creating and maintaining .646 work Understand, engage in, and manage .537 the career building process Locating and effectively using career .476 information Understanding the relationship .713 between work, society and the economy Interacting positively and effectively .688 with others Note. N = 168. Loadings above .45 are included. Convergence in 12 iterations.
Factor Correlation Matrix
Factor 1 2 3 1 1.000 .369 .307 2 .369 1.000 .230 3 .307 .230 1.000