Entrepreneurial Pursuit in Academic-Industry Collaboration: an Exploratory Study of Factors Influencing Financial Success in Private Universities in Malaysia

ENTREPRENEURIAL PURSUIT IN ACADEMIC-INDUSTRY COLLABORATION: AN EXPLORATORY STUDY OF FACTORS INFLUENCING FINANCIAL SUCCESS IN PRIVATE UNIVERSITIES IN MALAYSIA

KIZITO EMMANUEL NYEKO

A thesis submitted in fulfilment of the requirements for the degree of

Masters of Commerce (by Research)

Performed at

Swinburne University of Technology

February 2016

Abstract

Entrepreneurship has attracted much attention in the academic circles. Many universities are offering entrepreneurship education courses with the aim of equipping students with entrepreneurial values and skills. Entrepreneurship is also accepted as a legitimate domain of research with many scholarly journals dedicated to advancing knowledge in entrepreneurship theory and practice.

More recently, entrepreneurship has been approached as an academic practice that extends beyond the traditional mandates of teaching and research. This is because as competition for student enrolment drives private universities to improve their academic standing and reputations, the academicians are pressured to undertake scholarly activities beyond conventional mandates of teaching and research. The present study is a pioneering attempt to explore multi-level factors that influence the performance of industry engagements by academicians in private universities in Malaysia.

In the study, the research hypotheses were structured around three specific research objectives, namely, measure academics’ engagement in entrepreneurial collaborations with industry, investigate the influence of personal variables, and examine multi-level factors on the academics’ engagement in entrepreneurial collaborations.

Specifically, selected social-psychological, organisational and inter-organisational factors were analysed. This was accomplished after a concise review of relevant literature which resulted in research objectives and a general conceptual framework with hypotheses to guide the study.

To collect data, a cross-sectional design was adopted, where data were collected from a sample of 510 full-time academicians in private universities and foreign branch campus universities in Malaysia. A survey questionnaire was utilised and hypotheses were tested using multiple regression analysis modelling.

The study measured respondents’ involvement in 17 academic-industry collaboration activities, related to teaching, research and company-creation. More than 40 percent i of the respondents indicated active and sustained involvement in at least one of six teaching-related collaborations, namely external teaching, development of new degree programmes, placing students as trainees in industry, conducting industry seminars and training, teaching a subject that involves significant interactions with industry and sitting on the committee of industry/ trade bodies.

At least 37 percent of the respondents’ indicated active and sustained involvement in at least one of seven research-related collaborations, namely, research-based consultancy through the university, research-based consultancy privately, acquiring external funding, joint-research projects, new product development; providing research-related assistance to small business owners; and secondment to the university.

Only, 14 percent of the respondents indicated active and sustained involvement in at least one of five company-creation activities, namely, forming university centres for commercialisation activities, forming spin-off company owned by the university, establishing university incubators and/or science parks; forming joint-venture privately and forming own company. Overall, the study findings show less than half of the respondents were involved in active and sustained collaborations with industry.

The results of regression analyses indicate that a number of factors are related to academicians’ industry engagements. First, the academicians’ proactiveness is positively related to breadth of teaching collaborations. Second, readiness to collaborate is positively related to breadth of teaching, research, company-creation, and cross-functional collaborations. Third, universities’ learning orientation is positively related to research and company-creation collaborations. Fourth, strong collaborative purpose is positively related to research collaborations. Fifth, strong collaborative environment is positively related to company-creation engagements. The study also found significant relations between collaborations and selected performance measures. Research collaborations is positively related to performance variable enhanced reputations and resources, while research and teaching engagements are positively related to performance variable effective knowledge transfer.

The study has three practical implications for policy makers from government agencies, industry and universities who are now able to design more effective policies and management practices by proposing a set of guidelines that universities should follow to get the most value out of their academic industry engagements with industry. Firstly, define the academic-industry collaboration project’s strategic context as part of the selection process. Secondly, universities and their industry partners must share a project’s collaborative purpose with academicians and provide a strong collaborative environment. Lastly, universities should invest in long-term relationships.

In addition theoretically, these results fill four important gaps in existing literature. Firstly, the study provides empirical evidence establishing the entrepreneurial industry engagement activities that characterise the academicians’ engagement with industry. Secondly, it establishes the antecedents and consequences of academic-industry collaborations. Thirdly, the study establishes the multi-factors that influence the outcomes of academic-industry collaborations in the Malaysian context. Lastly, this study sought to introduce, and achieved, several methodology improvements. Unlike previous studies that were conducted in one or two universities, the present study involves all private universities in Malaysia. In contrast to previous research with findings based on a limited sample size, which attracted criticism on their validity, this study provided evidence of validity with high number of samples in a real life work scenario.

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Acknowledgment

Thank you Almighty God for standing by me and strengthening me throughout my life. He has always been my light, guide and protector by blessing me with mentors, family and friends who have lighted my path and shouldered my burdens. Great are You Lord, and greatly to be praised. Lord you reign.

I would like to express my deepest gratitude to my principal supervisor, Dr Ngui Kwang Sing and associate supervisor, Dr Voon Mung Ling for their excellent guidance, patience, support structure, detailed feedback and overwhelming devotion to supervising this thesis. I feel greatly honoured and privileged to have the opportunity to work under your tutelage. I remain ever grateful and indebted to both of you.

I am thankful to Chair, Human Research Ethics Committee at Swinburne University of Technology, Professor Dr Ken Heskin, Associate Professor Dr Lo May Chiun, Dr Ho Chye Kok and Dr Amer Khan for their guidance, direction, and assistance. They provided me with a detailed feedback and advice on my thesis, which has been instrumental in my completing the thesis. I would like to express sincere appreciation to them for supporting me in many ways throughout the elaboration of this thesis.

I would also like to express my appreciation to the Director, Research and Consultancy, Associate Professor Wallace Wong Shung Hui, all the administrative staff in the Research & Consultancy Office and all the administrative staff in the Faculty of Business and Design for their support and corporation toward my postgraduate affairs.

To my parents, thank you for constantly tolerating my mood swings and indulging my eccentricities. My deepest gratitude goes to you for having continued faith in me, for not giving up on me, and for being proud of me.

To my siblings (Paula, Greyc, Andrew, Simon, Patricia and Jeje), nieces (Natalie, Maria- Luci and Amy), nephew (Samuel) and the most diligent family guard dogs, pets and comrade ones would ever wish for (Boris Nikolayevich Yeltsin and Hernia), thank you iv for being the fun in my life. You have always helped me fight for the things I want and need in life and to make me realise that life is worth living and dreams can come true. I’m going to keep fighting, keep living, and keep dreaming.

To my friends near and far, thank you for all of the unforgettable memories, advice, companionship, jokes, and one-liners. You all were so supportive of my decision to move to Borneo and constantly push me to be a better person.

To my dearest confidant Jemimah, even though we were separated in space and time, you always patiently stand by me, through thick and thin while I pursue my professional career.

To Jane Gray and Darren John Angking for your valuable help editing and proofreading.

I would like to thank New Zealand Rugby (NZR) teams, namely, All Blacks, Maori All Blacks, All Blacks Sevens, Black Ferns, NZ Women’s Sevens, Provincal Unions, Junior All Blacks, New Zealand Schools, New Zealand Under 20 and Heartland XV. Your dedication and commitment to the craft results in swashbuckling rugby scoring points from seemingly impossible scenarios. This has been responsible for about 20% of my ecstasy and happiness over the past two decades.

Last, but certainly not least, to my therapists. Thank you for helping me get to where I am right now. Therapy saved my life and I promise to write that book one day.

You all have been wonderful! I dedicate this work to all the great people mentioned above.

Declarations

This contains no material that has been presented or accepted for the award of another degree, diploma or award at any university or educational institution. To the best of my knowledge and belief, it contains no material previously published or written by another person or persons, except where due reference has been made.

______

Kizito Emmanuel Nyeko, February 2016

Table of Contents

Abstract i Acknowledgments iv Declarations vi Table of Contents vii List of Tables xii List of Figures xiii CHAPTER ONE INTRODUCTION 14 1.0 Introduction 14 1.1 Background of the Study 15 1.2 Problem Statement 16 1.3 Research Questions 16 1.4 Research Objectives 17 1.5 General Conceptual Framework 17 1.6 Scope of the Study 19 1.7 Definition of Terms 20 1.8 Organisation of Chapters 22 1.9 Summary 23 CHAPTER TWO LITERATURE REVIEW 24 2.0 Introduction 24 2.1 Universities 24 2.1.1 Nature of Academic Work 25 2.1.2 Academic Disciplines 25 2.1.3 The Core missions—Teaching and Research 26 2.1.4 Economic Development 27 2.1.5 The First Wave of Commercialisation: Collaboration 28 2.1.6 The Second Wave of Commercialisation: Academic Entrepreneurship 29 2.2 The Evolution of the University 29 2.2.1 Mediaeval Universities 30 2.2.2 Classical Universities to Ivory Towers 31 2.2.3 Engaged Universities 33 2.2.4 Entrepreneurial University 33 2.3 The Definitions of Entrepreneurship 35 vii

2.3.1 Socio-Psychological Approach to Entrepreneurship 37 2.3.2 Behavioural Approach to Entrepreneurship 38 2.3.3 Resource-Based Approach to Entrepreneurship 39 2.3.4 Organisational Learning 40 2.3.4.1 Exploring the Concept of Organisational Learning 42 2.3.5 Organisational Learning Approach to Entrepreneurship 43 2.4 Definitions of Academic Entrepreneurship 44 2.5 Collaboration 49 2.6 Academic-Industry Collaboration 52 2.6.1 Commercialisation 54 2.6.2 Learning 54 2.6.3 Resource access 55 2.7 Synergistic Effects of Diversifying Academician Entrepreneurial Activities 55 2.8 Summary 58 CHAPTER THREE METHODOLOGY 59 3.0 Introduction 59 3.1 Research Design 59 3.1.1 Population and Sample 60 3.1.1.1 Sample 63 3.1.1.2 Sampling Method 63 3.1.2 Sampling Rationale 64 3.1.3 Sample Size 65 3.2 Research Instruments 66 3.2.1 Demographic Characteristics 68 3.2.2 Social-Psychological Factors 69 3.2.3 Organisational-Level Factors 70 3.2.4 Inter-Organisational Factors 72 3.2.5 Academic-Industry Collaborations Activities 72 3.2.6 Performance of Academic-Industry Collaborations 73 3.3 Data Collection Procedures 73 3.4 Overview of Statistical Analysis Techniques 74 3.4.1 Data Screening 74 3.4.2 Normality Assessment 75 3.4.3 Descriptive Analysis 75 viii

3.4.4 Factor Analysis 75 3.4.5 Analysis of Variance and Multiple Regression Analysis 77 3.5 Summary 78 CHAPTER FOUR FINDINGS 79 4.0 Introduction 79 4.1 General Characteristics of the Respondents 79 4.1.1 Response Rate 79 4.1.2 Respondent Profile 81 4.2 Exploratory Factor Analysis 82 4.3 Academic Entrepreneurial Collaboration 87 4.4 New Conceptual Framework 91 4.4.1 Descriptive Analyses 92 4.5 Statistical Test of Hypotheses 95 4.5.1 Analysis of Variance of Collaborative Engagements Based on Personal Factors 97 4.5.2 Regression Analysis of the Relationship Between Social-Psychological and Organisational Factors with Breath of Teaching-Related Engagement 102 4.5.3 Regression Analysis of the Relationship Between Social-Psychological and Organisational Factors with Breadth of Research-Related Engagement 104 4.5.4 Regression Analysis of the Relationship between Social-Psychological and Organisational Factors with Breadth of Company-Creation Engagement 105 4.5.5 Regression Analysis of the Relationship between Social-Psychological and Organisational Factors with Breadth of Cross-Functional Engagement 106 4.5.6 Regression Analysis of the Relationship between Multi-Level Factors and Collaborative Engagements with Organisational Performance 107 4.5.7 Regression Analysis of Industry Engagement as Mediator of the Relationship between Socio-Psychological and Organisational Factors, with Organisational performance 110 4.5.7.1 Testing Breadth of Teaching-Related Engagement as a Mediator 111 4.5.7.2 Testing Breadth of Research-Related Engagement as a Mediator 113 4.5.7.3 Testing Breadth of Company Creation-Related Engagement as a Mediator115 4.5.7.4 Testing Cross-Functional Engagement as a Mediator 117 4.6 Findings of Hypotheses Testing 119 4.7 Summary 126

CHAPTER FIVE DISCUSSION OF FINDINGS 127 5.0 Introduction 127 5.1 Background 127 5.2 Discussion of Findings 129 5.2.1 There are Significant Differences in Academicians’ Engagement in Entrepreneurial Collaborations, with Respect to Age 129 5.2.2 There are Significant Differences in Academicians’ Engagement in Entrepreneurial Collaborations, with Respect to Seniority 132 5.2.3 There are Significant Differences in Academicians’ Engagement in Entrepreneurial Collaborations, with Respect to Academic Qualifications 133 5.2.4 Academicians’ Proactiveness is Positively Related to Engagement in Entrepreneurial Collaborations 136 5.2.5 Academicians’ Readiness to Collaborate is Positively Related to Engagement in Entrepreneurial Collaborations 137 5.2.6 Universities’ Learning Orientation is Positively Related to Academicians’ Engagement in Entrepreneurial Collaborations 138 5.2.7 Strong Collaborative Purpose is Positively Related to Academicians’ Engagement in Entrepreneurial Collaborations 140 5.2.8 Strong Collaborative Environment is Positively Related to Academicians’ Engagement in Entrepreneurial Collaborations 141 5.2.9 Academicians’ Engagement in Entrepreneurial Collaborations is Positively Related to Performance Variable Enhanced Reputations and Resources 143 5.2.10 Academicians’ Engagement in Entrepreneurial Collaborations is Positively Related to Performance Variable Effective Knowledge Transfer 144 5.3 Summary 146 CHAPTER SIX CONCLUSIONS 147 6.0 Introduction 147 6.1 Implications 147 6.1.1 Theoretical Implications 148 6.1.2 Policy and Managerial Implications 150 6.2 Limitations of the Research 154 6.3 Recommendations for Future Research 155 6.4 Summary 158

REFERENCES 159 APPENDICES 187 APPENDIX A: LIST OF PUBLICATION 188 APPENDIX B: ETHICS CLEARANCE 189 APPENDIX C: CONSENT INFORMATION STATEMENT 191 APPENDIX D: LETTER TO DEAN OF FACULTY 194 APPENDIX C: STUDY QUESTIONNAIRE 195

List of Tables

Table 1.1: The summary of hypotheses ...... 18 Table 2.1: Categories of academic disciplines in the study ...... 26 Table 2.2: The phases of the historical development of universities ...... 29 Table 2.3: The engaged universities...... 33 Table 2.4: Definitions of entrepreneurial university ...... 34 Table 2.5: Definitions of entrepreneurship and descriptions of entrepreneurs ...... 36 Table 2.6: Various definitions of organisational learning ...... 42 Table 2.7: Categorises of commercial-oriented academic entrepreneurship activities ...... 46 Table 2.8: Academics categorised according to their commercial ambitions and attitudes ...... 48 Table 2.9: Motives to enter strategic alliances ...... 50 Table 2.10: The five levels of collaboration ...... 51 Table 2.11: Types of inter-organisational relationships ...... 52 Table 2.12: Types of academic-industry collaborations ...... 53 Table 2.13: Incentives for academic-industry engagements ...... 53 Table 3.1: List of private and foreign branch campuses universities in Malaysia ...... 62 Table 3.2: Theoretical constructs and the number of questionnaire items ...... 68 Table 4.1: The questionnaire response rate ...... 79 Table 4.2: List of private universities surveyed ...... 80 Table 4.3: The demographic characteristics of the study population (N=510) ...... 81 Table 4.4: Results of factor analysis ...... 83 Table 4.5: Descriptive statistics of composite variables measuring antecedents and outcomes of entrepreneurial collaborations ...... 86 Table 4.6: Activities associated with teaching, research and company-creation related collaborations ...... 87 Table 4.7: Respondents’ involvement in teaching-related collaboration ...... 88 Table 4.8: Respondents’ Involvement in research-Related Collaborations ...... 89 Table 4.9: Respondents’ involvement in company creation-related collaboration ...... 90 Table 4.10: Diversity of collaboration based on breadth of activities ...... 91 Table 4.11:Descriptive statistics of the variables under study (n=510) ...... 93 Table 4.12: Correlation matrix for study variables ...... 94 Table 4.13: Research objectives and hypotheses ...... 96 Table 4.14: Results of analysis of variance of collaborative engagements based on age ...... 98 Table 4.15: Results of analysis of variance of collaborative engagements based on gender ...... 99 Table 4.16: Results of analysis of variance of collaborative engagements based on position ...... 100 Table 4.17: Results of analysis of variance of collaborative engagements based on academic attainment ...... 102 Table 4.18: Summary of regression analysis with breadth of teaching engagement as a dependent variable ...... 103 Table 4.19: Summary of regression analysis with breadth of research-related engagement as a dependent variable ...... 104 Table 4.20: Summary of regression analysis with breadth of company-creation engagement as a dependent variable ...... 105

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Table 4.21: Summary of regression analysis with breadth of cross-functional engagement as a dependent variable ...... 107 Table 4.22: Summary of regression analysis with enhanced reputation and resources as a dependent variable ...... 108 Table 4.23: Summary of regression analysis with effective knowledge transfer as a dependent variable...... 109 Table 4.24: The results summary of mediated regression testing of breadth of teaching-related engagement as a mediator between multi-level factors and enhanced reputation and resources ...... 111 Table 4.25: The results summary of mediated regression testing of breadth of teaching-related engagement as a mediator between multi-level factors and effective knowledge transfer ...... 112 Table 4.26: The results summary of mediated regression testing of breadth of research-related engagement as a mediator between multi-level factors and enhanced reputation and resources ...... 113 Table 4.27: The results summary of mediated regression testing of breadth of research-related engagement as a mediator between multi-level factors and effective knowledge transfer ...... 114 Table 4.28: The results summary of mediated regression testing of breadth of company creation-related engagement as a mediator between multi-level factors and enhanced reputation and resources...... 115 Table 4.29: T The results summary of mediated regression testing of breadth of company creation-related engagement as a mediator between multi-level factors and effective knowledge transfer ...... 116 Table 4.30: The results summary of mediated regression testing of cross-functional engagement as a mediator between multi-level factors and enhanced reputation and resources ...... 117 Table 4.31: The results summary of mediated regression testing of cross-functional engagement as a mediator between multi-level factors and effective knowledge transfer ...... 118 Table 4.32: The summary of the hypotheses and test results ...... 125

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List of Figures

Figure 1.1: General conceptual framework of the study ...... 19 Figure 4.1: The new conceptulaised relationship between independent and dependent variables with the presence of a mediator ...... 91

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CHAPTER ONE

INTRODUCTION

1.0 Introduction

Academic entrepreneurship literature reveals that academics are increasingly expected to contribute to entrepreneurial activities in addition to teaching and research roles (Laukkanen, 2003; Venkataraman, MacMillan & McGrath, 1992). For example, at a government policy level, the commercialisation of university-generated knowledge is often considered a way of achieving national competitiveness (Henderson, Jaffe, & Trajtenberg, 1998; McMullan & Vesper, 1987; Mowery, Sampat & Ziedonis, 2002) and innovation (Lam, 2005). Additionally, commercialising academic intellectual property is often considered a means of achieving economic growth and development (Henderson, Jaffe & Trajtenberg, 1998; McMullan & Vesper, 1987; Mowery et al., 2002). This has escalated pressure on universities to generate additional economic returns (Shane & Stuart, 2002; Storey & Tether, 1998) through bridging the gaps between industry and the universities through a wide array of projects (Mowery & Shane, 2002).

In response to these pressures, universities have developed a wide range of academic-industry entrepreneurial engagements under three board categories; namely, teaching, research and company-creation (Phan & Siegel, 2006; Wright, Lockett, Clarysse & Binks, 2006).

Although universities have a long tradition of engagement with industry partners across the globe, studies have highlighted the persistent difficulties in fostering and managing commercial-oriented collaborations (Ancona, Bresman & Kaeufer, 2002; Griffith, Miller & O’Connell, 2015; Okamuro & Nishimura, 2013; Reagans & McEvily, 2003).

To examine the issues and identify areas for improvement, the present study focuses on an important aspect of academic entrepreneurship in universities—

14 academic-industry collaboration. Collaborating with industry partners facilitates knowledge sharing, provides access to complementary resources such as financial capital and commercial know-how and helps universities build commercial legitimacy (D’Este & Patel, 2007; D’Este & Perkmann, 2011; Ramli, 2013).

1.1 Background of the Study

The present study advances our knowledge on academic-industry collaborations by exploring the social psychological (Azjen, 1988; Bolton & Lane, 2012; Covin & Slevin, 1989; Covin & Covin, 1990), organisational (Chiva, Alegre & Lapiedra, 2007; Gomez, Lorente & Cabrera, 2004) and inter-organisational (Bryan, Krusich, Collins -Camargo & Allen, 2006; Garstka, Collin-Camargo, Hall, Neal & Ensign, 2012; Mattessich, Murray-Close & Monsey, 2004) factors that influence academics’ engagement and performance in entrepreneurial collaborations (Calvert & Patel, 2003; D’Este & Patel, 2007; Glassman, Moore & Rossy, 2003).

The rationale for concentrating on entrepreneurial collaboration with industry partners is as follows. First, collaboration between academicians and industry partners underpins more formal and complex forms of university-sanctioned entrepreneurial initiatives. It builds, but is also moderated by, the reputation of academicians and their social capital, who serve as a safety valve mechanism for inter-organisational relations.

Second, appropriate entrepreneurial collaborations help academicians overcome the challenges they confront in teaching, research and entrepreneurship obligations, and enhance the long-term development of universities. This is important because universities are increasingly expected to engage the industry beyond its traditional mandates of education and research.

1.2 Problem Statement

Academic entrepreneurship has been described as the third mandate of academia, following teaching and research. Its importance in driving innovation and as a vital source of revenue for universities is well-established (Di Gregorio & Shane, 2003; Jones-Evans & Klosten, 2000; Iversen, Gulbrandesen & Klitkou, 2007).

Academic entrepreneurship refers to the commercialisation of academic intellectual property, where the outcomes of academic activities are transferred to the public domain for monetary gains. These have usually involved collaborating with industry partners (Fogelberg & Lundqvist, 2013; Groen & Walsh, 2013; Norain, Mohd Jailani & Safiah, 2015).

However, the recent successes in entrepreneurial collaborations have usually involved public universities (Thursby, Fuller & Thursby, 2010; Wennberg, Hellerstedt, Wiklund & Nordqvist, 2011; Yusof, Mohammad & Mohd Nor 2012). Given that public and private universities are different in many respects, and because entrepreneurship is heavily context-dependent, there is a need for more in-depth knowledge on private universities.

In this context, the present study attempts to explore the factors that influence the performance of entrepreneurial collaborations by academics in private universities. Specifically, selected social-psychological, organisational and inter-organisational factors were analysed.

1.3 Research Questions

In the context of Malaysian private universities, this study established the following research questions to investigate:

1) What are the entrepreneurial collaborative activities that characterise academicians’ engagement with industry? 2) What are the antecedents and consequences of academicians’ entrepreneurial collaborations with industry?

1.4 Research Objectives

The present study aims to meet the following objectives.

General objective: To describe academicians’ engagement in entrepreneurial collaborations with industry, in the context of Malaysian private universities.

Specifically, the present study aims to:

i. Measure academicians’ engagement in entrepreneurial collaborations with industry

ii. Investigate the influence of personal and multi-level factors on the academicians’ engagement in entrepreneurial collaborations.

iii. Examine the factors that influence the outcomes of entrepreneurial collaborations.

1.5 General Conceptual Framework

Based on the research objectives, the following general conceptual framework was developed to guide the study. As illustrated in Figure 1.1, it offers an overview of the hypotheses that concern the relationships between the constructs under study.

Personal and multi-level factors are modelled as antecedents of entrepreneurial collaboration. The multi-level factors represent the academicians’ entrepreneurial orientation and readiness to collaborate, as well as the perception of the extent the academic environment is supportive of collaborations. These constructs are modelled to measure their influence on the performance of entrepreneurial collaboration. Given the above discussions, the following hypotheses are therefore proposed in Table 1.1.

Table 1.1: The summary of hypotheses.

Hypothesis

H1 There are significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to age.

H2 There are significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to gender.

H3 There are significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to seniority.

H4 There are significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to qualifications.

H5 Academicians’ innovativeness and propensity to take risk are positvely related to academicians’ engagement in entrepreneurial collaborations.

H6 Academicians’ proactiveness is positively related to engagement in entrepreneurial collaborations.

H7 Academicians’ readiness to collaborate is positively related to engagement in entrepreneurial collaborations.

H8 University learning orientation is positively related to academicians’ engagement in entrepreneurial collaborations.

H9 Strong collaborative purpose is positively related to academicians’ engagement in entrepreneurial collaborations.

H10 Supportive collaborative environment is positively related to academicians’ engagement in entrepreneurial collaborations.

H11 Academicians’ engagement in entrepreneurial collaboration is positively related to enhanced reputation and resources.

H12 Academicians’ engagement in entrepreneurial collaboration is positively related to effective knowledge transfer

Figure 1.1: General Conceptual Framework of the Study.

1.6 Scope of the Study

The present study is a pioneering attempt to identify key determinants of academicians’ involvement and performance in entrepreneurial collaborations. The study analysed selected social psychological, organisational and institutional factors, drawn from a sample of 13,737 academicians from private universities and foreign branch campuses in Malaysia (MoHE, 2012). Given the general low response rate (3 to 5 percent) from questionnaire surveys in developing countries (Casely & Krishna, 1988), the 510 returned useable questionnaires from the 5000 sets distributed resulted in a resulted in a final response rate of 10.2 percent. The survey yielded up-to-date information on the key determinants of academicians’ involvement and performance in entrepreneurial collaborations. These add to the limited empirical literature concerning academic entrepreneurial collaborations at a time when universities are increasingly expected to engage the industry beyond their traditional mandates of education and research.

The application of an initial exploratory study has enabled the researcher to adapt the extant survey instruments to the context of private universities and foreign branch campus universities in Malaysia and to record the various theoretical constructs with a higher level of accuracy.

Overall, the present study contributes valuable information to academic, industry and university administrators on how to support and manage commercially- oriented collaborations that sustain long-term competitiveness in an increasingly turbulent environment.

1.7 Definition of Terms

The following were the key terms used in present study and were operationalised in the following manner.

Academician—an individual who works within a university, juggling the roles of generating new knowledge (research) and transmitting knowledge (teaching) with administrative duties (Coaldrake & Stedman, 1999).

Academic entrepreneurship—the engagement of universities with outside stakeholders in activities involving the creation, transfer and distribution of academic intellectual property for commercial gains (Shane, 2004).

Collaboration—a process where two or more parties work closely with each other to achieve mutually beneficial outcomes (Miles, Miles & Snow, 2006).

Foreign branch campus university—a type of educational institution that has been established in a country other than the one where the home (primary) campus exists (MoHE, 2012).

Individual entrepreneurial orientation—academicians’ intentions and inclination to engage in entrepreneurial collaborations (Bolton & Lane, 2012; Covin & Slevin, 1989; Rauch, Wiklund, Lumpkin & Frese, 2009; Stam & Elfring, 2008; Wiklund & Shepherd, 2005).

Organisational capability—the ability to build, integrate and configure resources, using organisational processes to affect a desired end (Eisenhardt & Martin, 2000; Teece, Pisano & Shuen, 1997). The present study argues that organisational learning capability is a key strategic capability with the potential to enhance the competitiveness of universities.

Organisational learning capability—refers to organisational and managerial characteristics that facilitate effective organisational learning processes (Chiva, Alegre & Lapiedra, 2007; Garvin, 1993; Gomez, Cespedes-Lorente & Valle-Cabrera, 2005; Marquardt, 1996). This process is grounded in the structural and social contexts in which universities exist.

Organisational-level entrepreneurial orientation—refers to universities’ intentions and inclinations to engage in academic entrepreneurial collaborations (Chiva, Alegre, & Lapiedra, 2007; Covin & Slevin, 1989; Covin & Covin, 1990; Gomez, Lorente & Cabrera, 2004).

Private universities— refer to institutions of higher learning not operated by governments, although many receive tax breaks, public student loans, and grants. Some universities are non-profit and some are for-profit. They are under the purview of the Ministry of Higher Education and governed by the Universities and University Colleges (Amendment) Act 1996, and ITM Act 1976 (Amendment) (MoHE, 2012).

Readiness to collaborate with industry—refers to academicians’ intentions and inclination to engage with the industry (Ajzen, 1985; Ajzen & Fishbein, 2005; Armitage & Conner, 2001; Bolton & Lane, 2012; Miller, 2005; Sheppard, Hartwick & Warshaw, 1988).

University—refers to an institution of higher (or tertiary) education and research that grants academic degrees in a variety of subjects and provides both undergraduate education and postgraduate education. Some universities are non-profit and some are for-profit. They are under the purview of the Ministry of Higher Education, and governed by the Universities and University Colleges (Amendment) Act 1996, and ITM Act 1976 (Amendment) (MoHE, 2012).

1.8 Organisation of Chapters

This thesis consists of six chapters. This introductory chapter is followed by the second chapter which reviews the relevant academic entrepreneurship literature. The third chapter exemplifies the research methodology. The fourth chapter reports the findings from the analysis of data, while the fifth chapter presents implications of this study. Finally, the sixth chapter draws the conclusions of the study.

The following sections of this chapter briefly outline the content of each of the above mentioned chapters.

Chapter 2 presents a concise review of the literature that is relevant for understanding and exploring academic entrepreneurship. This is organised around three themes: academics as entrepreneurs, theoretical foundation of academic entrepreneurship and academic-industry engagement.

Chapter 3 discusses the quantitative research methodology adopted in this study. It initially justifies the choice of the research philosophy, and subsequently, discusses sampling, data collection, and data analysis, together with methodological and philosophical justifications.

Chapter 4 reports the findings from the analysis of data, using multiple regression analysis method. Hypotheses concerning the relationships between the constructs under study are tested and implications of the outcomes are discussed, with reference to the extant literature reviewed in Chapter 2.

Chapter 5 discusses the findings of the study in relation to the research objectives and hypotheses.

Chapter 6 presents the implications of the study in terms of the theoretical and practical aspects, its strengths and potential limitations, and directions for future research.

1.9 Summary

This study sets out to enhance knowledge and management practices required for supporting and improving the performance of academicians involved in commercially-oriented entrepreneurial collaborations, with a focus on private universities in Malaysia. These findings could create the evidence base to inform and influence programmes and strategies to engage and facilitate academic entrepreneurship in Malaysia.

This chapter has set the preliminaries for the remainder of the thesis. The chapter provided an introduction and background to the study, problem statement, research questions, research objectives, general conceptual framework, definition of terms, significance of study, scope of the study, organisation of chapters and conclusion. On these foundations, the thesis progresses with a comprehensive review of the extant literature and research on academic-entrepreneurship.

CHAPTER TWO

LITERATURE REVIEW

2.0 Introduction

This chapter presents a concise review of the existing literature on academic entrepreneurship. Starting with the nature of academic work, classifications of academic disciplines and core missions of universities, it then explores the evolution of the university. Next, the chapter introduces the early theoretical and contemporary approaches to entrepreneurship, namely, the socio-psychological approach, the behavioural approach, and the resource-based approach, the organisational learning approach and academic entrepreneurship. This is then followed by discussions on collaboration, academic-industry collaborations and the synergistic effects of diversifying academic entrepreneurial activities.

2.1 Universities

Universities have always been involved in activities that benefit their surrounding economies, but the set of outputs through which research universities enhance economic development have become much broader over time (Varga, 2009). Universities now are multiproduct organisations, (Luger & Goldstein, 1997) with activities ranging from the creation of knowledge, building human capital and conducting research. They are also involved in the transfer of know-how and technological application of knowledge to create and commercialise new products. Their involvement in the economy may also extend to capital investment, leadership in addressing social problems, co-production of knowledge-based infrastructure and the creation of a favourable social welfare programs (Goldstein, 2009; Luger & Goldstein, 1997).

2.1.1 Nature of Academic Work

The literature describes an academician as a university employee responsible for generating new knowledge (research), transmitting knowledge (teaching) and administrative duties (Coaldrake & Stedman, 1999). These roles have been described as the traditional mandates of academia as these stem from the historical missions of universities.

The roles of creating and institutionalising knowledge, together with its dissemination through teaching and research can be traced to the 13th century in France and Italy, where universities were established under the patronage of the Roman Catholic Church and the French monarchy (Malagola, 1888; Marcia, 1997; Richie, 1978; Ruegg, 2004). Initially, universities focused on intellectual pursuits, for example, educating men in the legal, medical and religious professions (Grendler, 2004). Over the centuries, universities have become a global phenomenon, as they became pathways for addressing the various needs of society. This expansion resulted in the gradual transformation of institutional identity and the nature of academic work (Akerlind, 2008a, 2008b; Baldwin, 1990; Barnett, 2009; Baruch & Quick, 2007; Buchanan, Gordon & Schuck, 2008; Kavanagh, 2009). This phenomenon, described as the academic revolution (Etzkowitz, 1990, 1997, 1998), has resulted in the emergence of various types of universities: research universities, technology universities, teaching universities, hybrid universities and, of late, entrepreneurial universities.

2.1.2 Academic Disciplines

An academic discipline or field of study is a branch of knowledge taught and researched as part of higher education (Abbott, 2001). A wide range of discipline categories exist, which define university faculties, learned societies and academic journals (Oleson & Voss, 1979). However, no formal criteria for defining an academic discipline exist. A range of categories of the academic disciplines in the study are listed in Table 2.1.

Table 2.1 Categories of academic disciplines in the study.

Humanities Human history, Linguistics, Literature, Arts, Philosophy, and Religion Social Sciences Anthropology, Archaeology, Area Studies, Cultural and ethnic studies, Economics, Gender and Sexuality studies, Geography, Political science, Psychology, and Sociology Natural Sciences Biology, Chemistry, Earth Sciences, Physics, and Space Sciences Formal Sciences Mathematics, Applied Mathematics, Pure Mathematics, Computer Sciences, Logic, Statistics, and Systems Science Professions Agriculture, Architecture and Design, Business, Divinity, Education, Engineering, Engineering, Environmental Studies and Forestry, Family and Consumer Science, Human Physical Performance and Recreation, Journalism, Media Studies and Communication, Law, Library and Museum Studies, Medicine, Military Sciences, Public Administration/ Public Policy, Social Work, and Transportation Source: Abbott (2001) and Oleson & Voss (1979)

2.1.3 The Core Missions – Teaching and Research

Teaching is the oldest form of universities’ contribution to the wealth of the society, and this was their primary role from an industrial perspective (Etzkowitz, 1998).

Goldstein (2002) argued that the creation of human capital is still the major contribution of universities to society, and teaching, stated Guena (1999), prepares students for education, ecclesiastical, government and professional careers.

Early universities offered a curriculum starting with the seven liberal arts, divided into apprentice (grammar, logic and rhetoric) and bachelor (arithmetic, geometry, astronomy and music). The next stage included the advanced professional courses with postgraduate faculties, theology, law and medicine (Geuna, 1999).

In the last half century, two significant changes have occurred in the teaching environments and mechanisms of delivery to students. Firstly, higher education, originally accessible only to the elite, faced a dramatic increase in the number of students after the Second World War that led to “massification” of teaching (Geuna, 1999). Secondly, the traditional classroom was complemented and partly shifted by distant education (Goldstein, 2009).

The first academic revolution, which occurred in the 19th century, incorporated research into the core missions of universities (Etzkowitz & Leydesdorff, 2000). Two distinct types of research formed, namely, basic and applied research. Basic research primarily aims to extend the area of fundamental understanding, whereas applied research develops practical applicability, to improve individual, group or societal need (Stokes, 1997). Typically, basic research produces “in the public good” type of knowledge (Goldstein, 2009), characterised by non-rivalry, non-excludability, very low marginal cost of duplication (Geuna, 2001) and wide geographic impact (Luger & Goldstein, 1997). Applied research is a form of systematic inquiry involving the pratical application of science to solve problems (Soeters et al., 2014). On the otherhand, applied research “the goal is to predict a specific behaviour in a very specific setting” Stanovich (Stanovich 2007, p. 106). It accesses and uses some part of the research communities' (the academia’s) accumulated theories, knowledge, methods, and techniques, for a specific, often state-driven, business-driven or client-driven purpose. Owing to these peculiarities, governments are more likely than industry to fund basic research (Luger & Goldstein, 1997; Goldstein, 2009).

For modern universities, the creation of knowledge and teaching, and the dissemination of new knowledge are integral components. This has led to a narrowing and deepening academic discipline for many university professors, who have become “single-discipline professors focused on the advancement and transmission of a specific, well-defined portion of knowledge” (Geuna 1999, p. 45).

2.1.4 Economic Development

The second academic revolution has added regional development to the accepted core missions of teaching and research (Etzkowitz, 1998). There are various types of economic contributions a university can make (Goldstein & Glaser, 2012) to economic development, for example, establishing knowledge infrastructure and creating a favourable social environment (Perry & Wiewel, 2005). While these impacts are both related to the teaching and research functions of universities, since they provide the necessary frameworks, the knowledge available from researchers and

27 facilitation of knowledge flows within the scientific and business world can enhance economic growth and development (Goldstein, 2009).

From a financial perspective, university capital investment activities are in classrooms, laboratories, administrative offices, but they also serve the interest of the broader region with roads, power stations, recreational facilities (Goldstein, 2009). Direct business-related capital investments include the establishment of research and advanced technology parks (Luger & Goldstein, 1997).

From a leadership and governance perspective, by taking up elected or appointed seats on commissions addressing social, environmental and economic issues, and participation in policy and strategy creation, universities actively contribute to the quality of societal welfare (Luger & Goldstein, 1997). Additionally, universities play a double role in regional leadership—providing both technical expertise and moral authority (Goldstein, 2009). Their participation has a symbolic importance to stakeholders, but at the same time it serves the university’s institutional goals through greater control of their resource flows (Goldstein & Glaser, 2012).

2.1.5 The First Wave of Commercialisation: Collaboration

In academic commercialisation literature, the researchers use different labels to refer to the same group of activities with common features, for example, transfer of existing know-how (Luger & Goldstein, 1997), commercialisation (Jacob, Lundqvist & Hellsmark, 2003), user-directed commercialisation (Gulbrandsen & Slipersaeter, 2007), or technical assistance (Goldstein, 2009). Contributions to stake holders are described as consultancy, expert advice or custom-made education services (Golstein, 2010).

Consultation dates back to the 19th century, and is often considered a traditional mechanism linking universities and private firms (Etzkowitz & Peters, 1991). Extension services of universities, provided through faculty consultation, can be paid or pro bono engagements (Luger & Goldstein, 1997).

Also commencing in the 19th century, external teaching (Bok, 2003), in the form of “institutionalized public service”, aims to disseminate knowledge and best practices to society (Goldstein, 2010). 28

2.1.6 The Second Wave of Commercialisation: Academic Entrepreneurship

This wave of commercialisation, gaining pace during the second half of the 20th century, continues to draw the attention of scientists and policy makers. Similar to the first wave of commercialisation, these activities were labelled in the recent academic entrepreneurship literature, such as science-directed commercialisation (Gulbrandsen & Slipersaeter, 2007), commodification (Jacob, Lundqvist & Hellsmark, 2003) or technological innovation (Goldstein, 2009). Usually the aim is to produce inventions with commercial potential (Goldstein, 2009), so the application of knowledge targets the creation and commercialisation of a new product or service, which requires the resources of corporations and university laboratories (Luger & Goldstein, 1997).

2.2 The Evolution of the University

A number of transitions have taken place in universities. Firstly, the traditional mission of universities, teaching, was complemented with the research function. This process took place in the 19th century during the first academic revolution. The second academic revolution, in the 20th century, added commercialisation to the explicit missions of universities. Neither of the revolutions has been without controversy (Gulbrandsen & Slipersaeter, 2007), but as Geuna (1999) argued, universities have always evolved through incremental innovations to adapt to a rapidly changing environment. This continuous innovation has led to the evolution of the modern university. Geuna (1999) cited four phases of university development, which along with an additional phase, are listed in Table 2.2.

Table 2.2: The phases of the historical development of universities.

Historical Development of Universities i. Birth of the university between the late 12th and early 16th century. ii. Period of decline from the second part of the 16th century until the end of the 18th century. iii. Recovery and transformation from the 19th century until the Second World War. iv. Expansion and diversification from the end of the Second World War until the end of the 1970s. v. Institutional reconfiguration Source: Geuna (1999) 29

Geuna (1999) argues these phases 1, 3, 4 and 5 are of crucial importance. The first phase is about the mediaeval university; the second can be considered as a forerunner to the first academic revolution, which resulted in the evolution of the ‘classical’ universities in the third phase. This latter period also includes the evolution of “engaged universities”, a phrase coined by Martin and Etzkowitz (2000). The fourth phase is the era of the linear innovation model, described by Bush (1945), which can be considered as a forerunner to the entrepreneurial university. This phase evolved in the second half of the 20th century during institutional reconfiguration.

2.2.1 Mediaeval Universities

Geuna (1999) argued that universities are European creations, and the predecessor of the modern university, the studium generale, evolved in the Middle Ages between the 12th and 13th centuries. The most prominent representatives of this type were in Paris (theology and philosophy) and Bologna (law), but as the centre of mathematics and natural sciences, Oxford can be mentioned here (Ferencz, 2001).

Early centres of higher learning, like the philosophy school of Athens (4th century before Christ), the school of Beirut (which flourished between the 3rd and 6th centuries) or the University of Byzantine (425–1453), cannot be comprehended as predecessors of the mediaeval universities. They lacked the emphasised organisational/corporate features of the latter, and there was no institutional/organisational continuity between them (Ferencz, 2001).

Geuna (1999) described how the studium generale incorporated three important rights: (i) the jus ubique docendi, which meant awarding of masters or doctoral degrees that were generally acknowledged in the Christendom, (ii) papal or imperial protection from local, religious and lay authority, and (3) clergy studying entitlement to receive the benefits of their benefices.

Martin and Etzkowitz (2000) pointed out two important functions of mediaeval universities: providing teaching for public servants and priests, and offering scholarship in various disciplines (medical, classical). They argued that teaching diversified, and aimed to develop the full potential of the individual student or to train people based

30 on the societies’ needs. The relationship of universities with the key external actors, church, monarch or government and industry, was fundamental in shaping the evolution of universities (Martin & Etzkowitz, 2000).

Regardless of their aims, universities at that time “were all members of a ‘super-national’ intellectual unity devoted to the cultivation of knowledge, enjoying a certain degree of independence from the papacy, the empire and the municipal authority” (Geuna 1999, p. 42). However, the legal, political and theoretical independence that universities enjoyed had disappeared by the 15th century and universities became political and social economic battlefields (Borbély, 2001).

2.2.2 ‘Classical’ Universities to ‘Ivory Towers’

During the 17th and 18th centuries, scientific research was carried out in scientific societies and academies whose members started to develop an international scientific community along the norms of open science (Geuna, 1999; Tóth, 2001). However, research was not yet part of professors’ duties (Johnsson, 2006), as university education and scientific research were practically independent (Békés, 2001). Békés argued that although university professors were required to be experts in their subject areas, personal scientific results were neither expected nor supported by the institutions; university professors conducted research only in their spare time if at all.

However, as scientific societies and academies became unable to adjust to the specialisation of knowledge required by professionalisation and the emergence of new scientific fields, the world of research opened up for universities that had earlier played only a peripheral role in knowledge generation (Geuna, 1999).

With these antecedents, the first academic revolution in the 19th century added research to the already existing teaching mission of universities (Etzkowitz & Leydesdorff, 2000; Frängsmyr, 2006). Mediaeval universities transformed into classical universities, characterised with a pure or immaculate ethos that desired teaching and research for its own sake (Martin & Etzkowitz, 2000).

However, at this point we also have to note that the permanent modernisation of the European universities–tracing back to the comprehensive (but precisely opposite) reform measures of Napoleon in France (1808) and Humboldt in Germany (1810)–lasted until the 1960s. The resulting university models strongly influenced the reform of other European university systems as well (Tóth, 2001). Even though each reformer held different views, Tóth (2001) argued that the envisioned university of both reformers was a modern institution developing and disseminating knowledge of direct or indirect societal benefit and one that educates the legal, political, engineering and military elite.

Geuna (1999) described modern research universities to be national institutions that prepare citizens for professional careers and create knowledge for the benefit of the nation-state. While retaining some elements of the mediaeval university, they have added scientific research by merging the methodologies and social organisation of academies and societies.

Against the shared features, there were some national differences among universities (Geuna, 1999). In the Cardinal Newman University in Britain, independent professors pursued knowledge for its own sake and taught it to students. Whereas in the German version, the Humboldt model, this was complemented with the unity of teaching and research (Martin & Etzkowitz, 2000) and, thus, teaching and research had to be conducted within the same institution (Martin, 2003).

The European classical university models were transferred to the USA and Japan, resulting in the ‘Ivy League’ university and imperial (subsequently national) university, respectively (Martin & Etzkowitz, 2000; Martin, 2003). The first research university in the USA, built on the Humboldtian concept, was the Johns Hopkins Research University established in 1876 (Békés, 2001), which was later followed by more private and wealthy universities (Goldstein, 2010).

2.2.3 ‘Engaged’ Universities

Table 2.3: The engaged universities.

Type Definition Examples Technical Designation employed for a wide range of French Ecole Polytechnique; universities learning institutions awarding different German and Swiss ‘high types of degrees and operating often at schools’; British institutes of variable levels of the educational system. science and technology (like the Imperial College in London); the American MIT and Caltech; the Italian polytechnics in Milan and Turin; and the Japanese Tokyo Institute of Technology Regional Provide a full range of undergraduate The European regional universities programs and some master-level colleges and American land- programs. The aim is to contribute to the grant universities. economic, industrial or cultural development of a region. Land universities Aim to meet local and regional needs. For The Massachusetts Institute example, to support agricultural of Technology (MIT). development, the Morrill Act, passed in 1862, granted government owned land to universities. Adopted: Martin & Etzkowitz (2000)

2.2.4 Entrepreneurial University

The term entrepreneurial university was coined by Etzokowitz (1998) to illustrate occasions in which universities have proven themselves as stakeholders critical to regional development (O’Shea, Allen, O'Gorman & Roche, 2004). Several definitions of entrepreneurial university were submitted by scholars, such as Etzkowitz (1983), Clark (1998), Röpke (1998), Subotzky (1999), Kirby (2006), Etzkowitz (2003a) and Jacob, Lundqvist and Hellsmark (2003). A review of literature reveals no consensus on the definition, however, there exist common characteristics that reveal that factors that can influence and support entrepreneurial activities in a university setting.

Table 2.4: Definitions of entrepreneurial university.

Definitions of Entrepreneurial University Publication These are universities that are considering new sources of funds like Etzkowitz (1983) patents, research funded by contracts and entry into a partnership with a private enterprises. The entrepreneurial university involves the creation of new business Chrisman, Hynes & ventures by university professors, technicians, or students. Fraser (1995) Entrepreneurial universities engage in formal efforts to capitalise Dill (1995) upon university research by bringing research outcomes to fruition as commercial ventures. The formal efforts are in turn defined as organisational units with explicit responsibility for promoting technology transfer. An entrepreneurial university seek to innovate in how it goes to Clark (1998) business, to work out a substantial shift in organisational character, to become standup in entrepreneurial activities, universities that are significant actors in their own terms. The term entrepreneurial university mean three things: the university Röpke (1998) itself, the members of the University faculty and the interaction of the university with the environment. An entrepreneurial university is an institution characterised by closer Subotzky (1999) university business partnerships, by greater faculty responsibility for accessing external sources of funding and by a managerial ethos in institutional governance, leadership and planning. Entrepreneurial University is defined as a university that has Kirby (2002) the ability to innovate, recognise and create opportunities, work in teams, take risks and respond to challenges An entrepreneurial university is a natural incubator, providing Etzkowitz (2003) support structures for teachers and students to initiate new ventures: intellectual, commercial and conjoin. An entrepreneurial university is nothing more than a seller of services Williams (2003) to the knowledge industry. An entrepreneurial university Is based both on commercialisation Jacob, Lundqvist & (custom made further education courses, consultancy services and Hellsmark (2003) extension activities) and commoditisation (patents, licensing or student owned start-ups). Adopted: OCED (2005)

Some of these definitions (Clark, 1998; Röpke, 1998; Etzkowitz, 2003) have expressed the impact of the institutional and organisational context on university-level entrepreneurship. Some definitions have implicitly or explicitly described the intrapreneurial process and actions that go on within the university setting (Röpke, 1998; Kirby, 2006; Etzkowitz, 2003) that leads not only to new venture creation (Chrisman et al., 1995; Etzkowitz, 2003) and other sources of income (Etzkowitz, 1983), but also to commercialisation and commoditisation technology transfer activities

(Jacob et al., 2003) through university–government–industry collaboration (Röpke, 1998; Subotzky, 1999) supported by external funding (Subotzky, 1999).

2.3 The Definitions of Entrepreneurship

Entrepreneurship theories provide the theoretical foundation for academic entrepreneurship. While there is no single entrepreneurship theory per se, researchers have relied on a number of theoretical approaches to understand and explore entrepreneurship.

Research on entrepreneurship can be traced back to the 19th century, with theoretical insights from a broad range of disciplines. These include anthropology (De Montoya, 2000; Firth, 1967; Fraser, 1937), economics (Casson, 2003; Cantillon, 1734; Kirzner, 1973; Penrose 1957; Say, 1730; Schumpeter, 1934; Shane, 2003; Von Hayek, 1948; Von Mises, 1949, 1996), management (Drucker, 1985, 1999; Ghoshal & Bartlett, 1995) and social sciences (Swedberg, 1993; Waldringer, Aldrich, & Ward, 1990; Weber, 1898, 1990). Over the years, advances in theory and practice have led to the emergence of entrepreneurship as a legitimate field of knowledge (Andres & Debackere, 2007; Moroz & Hindle, 2011; Rauch, Wiklund & Lumpkin, 2009).

Shane and Venkataraman (2000) noted that the field of entrepreneurship involves the study of sources of opportunities, which includes the processes of discovery, evaluation, and exploitation of opportunities, and the set of individuals who discover, evaluate, and exploit them. Recent literature has indicated that scholars have yet to converge on a common theory of entrepreneurship. A summary of commonly cited definitions of entrepreneurship and descriptions of entrepreneurs is presented in Table 2.5.

Closer examination of these definitions revealed the underlying theoretical approaches have informed the development of three major theoretical approaches, introduced in the following paragraphs, namely, the socio-psychological approach, the behavioural approach, and the resource-based approach.

Table 2.5: Definitions of entrepreneurship and descriptions of entrepreneurs.

Definitions of Entrepreneurship and Descriptions of Entrepreneurs Publication Self-employment of any sort. Entrepreneur is a person who pays a certain Cantillon (circa 1730) price to a product to resell it at an uncertain price, thereby making decisions about obtaining and using resources while consequently admitting the risk of enterprise. “…a systematic innovation which consists in the purposeful and organised Smith (1776, pp. 48-49) search for changes and in a systemic analysis of the opportunities such changes might offer for economic and social innovation”. The entrepreneur is “…an individual, who undertakes the formation of an organisation for commercial purposes by recognising the potential demand for goods and services, and thereby acts as an economic agent and transforms demand into supply”. Entrepreneurship is the driving force that coordinates the use of factors of Marshall (1890) production (e.g. land, labour, capital and organisation) in an innovative manner. The entrepreneur creates new commodities or improves on how existing products are produced by creatively organising factors of production. The entrepreneur is a risk-bearer who attempts to predict and act upon Knight (1921) change within markets, in the face of uncertainty and imperfect information. The entrepreneur is the innovator who implements change within markets Schumpeter (1934) through the carrying out of new combinations. These can take several forms:  the introduction of a new good or quality,  the introduction of a new method of production,  the opening of a new market,  the conquest of a new source of supply of new materials or parts, and  the carrying out of the new organisation of any industry. The entrepreneur is the person who maintains immunity from control of Weber (1947) rational bureaucratic knowledge. The entrepreneur is co-ordinator and arbitrageur. Penrose (1959) The entrepreneur recognises and acts upon profit opportunities, essentially Kirzner (1973) an arbitrageur. “…the process where individuals and teams create value by bringing Morris & Sexton (1996, together unique packages of resource inputs to exploit opportunities in the p. 6) environment” The essential act of entrepreneurship is new entry. New entry can be Lumpkin & Dess (1996) accomplished by entering new or established markets with new or existing goods or services. New entry is the act of launching a new venture, either by a start-up firm, through an existing firm, or via internal corporate venturing. “…the creation of a new organisation” and “…the act of innovation involving Drucker (1985, p. 35) endowing existing resources with new wealth-producing capacity”. “…a context dependent social process through which individuals and teams Ireland, Hitt, & Simon create wealth by bringing together unique packages of resources to exploit (2003, p. 965) marketplace opportunities”. Entrepreneurs do not engage in entrepreneurship by accident; they do it Krueger (2007) intentionally as a result of choice. Source: Developed for this study.

2.3.1 Socio-psychological Approach to Entrepreneurship

As a subset of the field of psychology, the study of the human mind and behaviour (Baker, 2012; Brook, 2006), social-psychological is the study of how an individual’s thoughts, feelings and behaviours are influenced by the actual, imagined or implied presence of others (Baron, Byrne & Suls, 1989). Derived from the overlapping foci of psychological and sociology, two academic emphases of social- psychological have emerged, namely, (1) psychology-focused social psychologists (Ajzen, 1985; Ajzen & Fishbein, 2005; Sheppard, Hartwick & Warshaw, 1988) and (2) sociology-focused social psychologists (Bolton & Lane, 2012; Covin & Slevin, 1989; Rauch et al., 2009).

Psychology-focused social psychologists concentrate on an individual’s mental processes, dispositions, experiences and immediate social situation. Sociology-focused social psychologists are more concerned with external factors surrounding the individual, mental processes, depositions, experiences and immediate social situation.

Socio-psychology is concerned with the study of human perceptions, cognitions, emotions, motivations and behaviours (Aldrich & Widenmayer, 1993; Gartner, 1989; McClelland, 1961; McClelland & Winter, 1971). Extending this approach to entrepreneurship, an entrepreneur’s social actions are examined through personality, values, social structures and culture. The socio-psychological view is to look upon entrepreneurial activity as the manifestation of personality and behavioural characteristics of the individual, influenced by environmental structural conditions and social factors (McClelland, 1961; Kets de Vries, 1977; Reynolds, 1991; Shapero & Sokol, 1982).

The approach has informed previous studies on entrepreneurial traits, such as decisiveness, risk-taking, innovativeness, tolerance to ambiguity and proactivity in exploiting entrepreneurial opportunities. Researchers have examined the association between these traits and psychological factors, such as personality, need for achievement and locus of control (Bonnet & Furnham, 1991; Coon, 2004; Cromie, 2000; Ho & Koh, 1992; Koh, 1996; Robinson et al., 1991b; Rotter, 1966). These researchers noted that linking the relationship between psychological traits and entrepreneurial 37 postures is imperative for theoretical and empirical reasons. Entrepreneurs with certain psychological traits may exhibit certain degrees of entrepreneurial behaviours, which in turn provide benefits to the organisation (Okhomina, 2012).

Socio-psychological studies on entrepreneurs have asked whether an entrepreneur’s psychological behaviour and personality traits are generic to all individuals or a subjective according to the individual in question. According to Gartner (1989), the entrepreneurial individual is more appropriately viewed as a continual process rather than a singular state. He argued that measures must be used to assess the entrepreneur’s attitudes and propensity for sustained behaviour.

Upper echelon theory is an important theory often used in socio-psychological studies of the entrepreneur (Hambrick & Mason, 1984). The theory argues that an entrepreneurial firm is an extension of the entrepreneur. Specifically, the firm’s strategic choices, behaviours, and performance are influenced by the characteristics of the entrepreneur (Smith, Smith, Olian & Sims, 1996), his or her social connections (Geletkanycz & Hambrick, 1997), perceptions of the environment (Kiesler & Sproull, 1982), and decision-making style (Eisenhardt, 1999).

2.3.2 Behavioural Approach to Entrepreneurship

The behavioural approach to entrepreneurship concentrates on the functions, activities and actions associated with new venture creation (Byrgave & Hofer, 1991). Stevenson and Sahlman (1989, p. 104) described entrepreneurship as “most fruitfully defined as the relentless pursuit of opportunity without regard to resources currently controlled… there is an underlying process in entrepreneurship that starts with the identification of opportunity and ends with harvesting the fruits of one’s labours”.

The behavioural approach views the creation of an organisation as a contextual event, the outcome of many influences. Behavioural studies on entrepreneurship aimed to describe the behaviours of entrepreneurs (what they do instead of who they are) and to determine the range of situational factors or conditions that may moderate the effects of entrepreneurial behaviours on performance (Gartner, 1988). Gartner (1988) categorised the variables in new venture creation into four categories, namely,

38 the characteristics of the entrepreneur, the characteristics of the entrepreneurial organisation, the entrepreneurial process and the external environment.

In line with these, Bygrave and Hofer (1991) offered four key research questions for studying entrepreneurship, based on the behavioural approach. These are:

i. What is involved in perceiving opportunities effectively and efficiently? ii. What are the key tasks in successfully establishing new organisations? iii. How are these tasks different from those involved in successfully managing on- going organisations? iv. What are the entrepreneur’s unique contributions to this process?

2.3.3 Resource-Based Approach to Entrepreneurship

Proponents of the resource-based view of the firm (RBV) perspective argued that an organisation’s competitive advantage emerges as the result of interactions among its unique pool of resources and capabilities (Barney, 1991; Amit & Shoemaker, 1993). An organisation’s resources encompass all assets, processes, firm attributes, information, knowledge, skills, and the like, controlled by the organisation, which enable it to conceive of and implement strategies that are efficient and effective. These can be categorised into those which are tangible (financial or physical) and intangible (knowledge, experiences, skills, reputation, processes).

Amit and Shoemaker (1993) distinguish between resources and capabilities by stating that resources are tradable and non-specific to the firm, while capabilities are firm-specific and are used to engage the resources within the firm. In other words, capabilities are abilities to deploy and coordinate different resources, using internal processes to affect a desired end. In citing Amit, Brigham, and Markman (2000), Ottaviano (2004, p. 15) defined capabilities as “firm-specific, information-based processes that are developed over time through complex interactions amongst the organisation’s resources”. These typically consist of combinations of distinctive knowledge, experiences and skills that flourish in a particular organisational context.

Although an organisation may possess many different capabilities, Galbreath (2004) points out that not all capabilities are sources of competitive advantage, as an organisation may possess strength in only a few key areas. The capabilities which the organisation is able to perform with superiority, giving it an edge over the competitors, are known as strategic capabilities (Collis, 1994; Day, 1994). Chaston, Badger and Sadler-Smith (1999) found that the success of high growth small-and-medium-sized enterprises is often a reflection of their internal capabilities in the areas of innovation, entrepreneurialism, human resource management (HRM) and development, and organisational learning.

Within the study of entrepreneurship, the resource-based view emphasises the crucial role of the entrepreneur in managing the organisation’s resources and his or her impact on the new venture (Coase, 1937; Chandler, 1962; Penrose, 1959; Mahoney & Pandian 1992; Rugman & Verbeke, 2002; Stigler, 1961).

2.3.4 Organisational Learning

Organisational learning (OL) researchers have stressed that the structural and procedural arrangements within organisations, in addition to their culture, need to be configured in a manner that foster and support effective learning process. Where these are present, it is argued that they would significantly enhance an organisation’s learning capability. Subsequently, a high learning capability is postulated to positively contribute to effective performance and, eventually, higher levels of competitiveness.

In recent years, the learning imperative in the concepts of OL have emerged as important research topics within the domain of organisational studies (Cangelosi & Dill, 1965; Cyert & March, 1963; Daft & Weick, 1984; Huber, 1991; Senge, 1990). In particular, a high level of OL capability has been described as a strategic imperative for dealing with an increasingly dynamic environment, characterised by increased competition, rapid technological advances and the shift towards knowledge as the main source of competitive advantage (Kiernan, 1993; Nonaka, 1994; Simonin, 1997).

Garavan, Ross, Li and Stein (2000) stressed that organisations need to develop a capacity to learn faster than their competitors, to find solutions to novel and

40 complex problems and to enhance the quality of what they do through effective leadership and learning. In Darwin’s evolutionary term (Van de Ven & Poole, 1995), organisations have to learn at a rate that outpaces the rate of environmental change in order to survive and flourish in a dynamic environment (Ellinger, Watkins, & Bostrom, 1999).

Despite the importance ascribed to OL, a significant feature of the OL domain has been its level of fragmentation. Smith (2004, p. 371) stated that the literature on the concept of OL is diffuse as “there is no clear agreement on either the differences or the commonalities between the different characterisations of the process of organisational learning”. Spicer and Sadler-Smith (2003) believe that clearer distinction needs to be drawn between the concept of OL, which tends to be descriptive and analytical (Argyris & Schon, 1978; Dodgson, 1993; Fiol & Lyles, 1985; Huber, 1991; Kim, 1993), and that of the learning organisation literature, which is more prescriptive and action-oriented (McGill, Slocum, & Lei, 1993; Senge, 1990).

Armstrong and Foley (2003) clarified that learning organisations can be conceived as those firms which are adept at engaging in effective learning processes. Such capability is fostered through implementation of coordination systems and practices designed to support the process of learning. While similar views were conveyed by Denton (1998), Schwandt and Marquardt (2000) pointed out that there has been a lack of consistent empirical knowledge concerning how organisations can become learning organisations. Likewise, few practical guidelines are available for organisations that wish to develop their learning capabilities (Easterby-Smith, 1997; Goh & Ryan, 2002; Simonin, 1997; Thomsen & Hoerst, 2001).

The mainstream literature also assumes an unproblematic link between enhanced learning capability and improved firm performance (Palmer & Hardy, 2000). However, both Chaston, Badger & Sadler-Smith (1999) and Goh and Ryan (2002) highlighted the paucity of large-scale empirical work that validated such a link, or identified the particular path through which OL capability affects performance. Consequently, King and Anderson (2002) cautioned against the optimistic overtone of

41 research claims about the strategic value of OL, while calling for more empirical evidence to support its hypothesised roles.

2.3.4.1 Exploring the Concept of Organisational Learning

Bell et al. (2000) noted that researchers have yet to converge on a consistent definition of OL. Table 2.6: illustrates a list of prominent definitions proposed by OL theorists.

Table 2.6: Various definitions of organisational learning.

Definitions of Organisational Learning Author “the detection and correction of error” Argyris & Schon (1978, pp. 2-3) the process of improving actions through better knowledge and Fiol & Lyles (1985, understanding” p. 803) “the encoding of lessons learned through history into routines that Levitt & March guide behaviour” (1988, p. 319) “the continual expansion of the organisation’s capacity to create its Senge (1990, p. 3) future” “an entity learns if, through its processing of information, the range Huber (1991, p. 89) of its potential behaviours is changed” “a dynamic process of cognitive and behavioural changes involving Crossan, Lane, individual, group and organisational-level actions and interactions” White & Djurfeldt. (1999, p. 522) “the capacity or processes within an organisation to maintain or DiBella et al. (1996, improve performance based on experience” p. 363) “organisational learning is a three stage process that includes Sinkula (1994, p. information acquisition, information dissemination and shared 36); interpretation” Slater & Narver (1995, p. 64) Source: Ngui (2008)

Dodgson (1993) stated that the divergence among existing definitions can be traced to the multidisciplinary nature of OL theories. For instance, researchers have noted the relevance of psychology, organisational theory, innovation management, strategic management, economics, organisational behaviour, sociology, political science, anthropology and so forth (Argyris & Schon, 1978; Dodgson, 1993; Fiol & Lyles, 1985; Leibenstein & Maital, 1994; Shrivastava, 1983) in the OL domain.

In view of such conceptual diversity, Bell et al. (2000) advised against seeking a unanimous concept of OL. Instead, Wittgenstein (1953, p. 233) advised that researchers should “…look at the use of the concept in the discourse of those who

42 employ it” or adopt a problem-centred approach (Bigley & Pearce, 1998), where differences in theoretical views and associated research frameworks would only require attention to the extent that they address the same fundamental problems (Bell et al., 2000). According to Bell et al. (2000, p. 5), doing so would shift attention from the perennial question of “what is organisational learning?” to “which learning and when?”

2.3.5 Organisational Learning Approach to Entrepreneurship

The organisational learning (OL) approach focuses on the ways individuals or organisations process and use knowledge. It suggests that the performance variance among entrepreneurial firms can be traced to differences in their knowledge base as well as their capabilities to develop and exploit knowledge. In line with this, Huber (1991) defined OL as the process of firm-wide information processing, involving the acquisition, dissemination, interpretation and institutionalisation of knowledge. It is a dynamic process, involving individual, group and organisational-level actions and interactions (Crossan, Lane & White, 1999) that are shaped by the structural and social context of the organisation.

Drawing on the resource-based view (RBV), and its offshoot, the knowledge- based view (KBV), of the firm, OL scholars stress the importance for organisations to develop a strong capability in effective OL. Senge (1990) described organisations with strong OL capability as learning organisations, defining it as “...organisations where people continually expand their capacities to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning how to learn together” (p. 3). Similarly, Goh (2003) defined OL capability as “the ability of the organisation to implement the appropriate management practices, structures and procedures that facilitate and encourage learning” (p. 217). Together, these definitions of OL capability underscore two key propositions. First, managerial interventions are necessary to support effective OL. Second, OL needs to be directed at the development and exploitation of knowledge that would contribute towards the pursuit of desired organisational goals.

The notion of OL capability as an integral dimension of entrepreneurship is not new. Drucker (1993) described innovation as the primary activity of entrepreneurship, while Nonaka (1994) stated that the effectiveness of organisations to act in radically innovated ways is critically dependent upon the way they acquire and utilize new sources of information. Likewise, the Organisation for Economic Co-operation and Development (1996) described innovative (i.e. entrepreneurial) organisations as efficient learning organisations that seize technological and market opportunities creatively in order to expand production frontiers.

Three key knowledge processes are consistently referred to in the OL and entrepreneurship literature. These are knowledge creation, knowledge sharing and knowledge exploitation. The process of knowledge creation has been referred to as exploration (March, 1991), combination (Nonaka & Takeuchi, 1995) and sense making (Weick, 1993). Knowledge sharing involves the transfer of knowledge within firms and between firms. According to Nonaka & Takeuchi (1995), knowledge sharing takes place through socialisation and externalisation processes. Knowledge exploitation involves the process of converting knowledge into valuable products or firm actions. The exploitation process is facilitated through dynamic, flexible systems that allow shared ideas to be converted into products.

2.4 Definitions of Academic Entrepreneurship

In general, academic entrepreneurship is considered as the engagement of academicians in commercially-oriented activities. While scholars concur on the commercial motive of entrepreneurial engagements, they differ on the nature of engagements that qualify as academic entrepreneurship. Cantaragiu (2012) classified the definitions of academic entrepreneurship into three broad categories, namely, commercial definitions, knowledge transfer definitions and value-based definitions.

Commercial definitions of academic entrepreneurship focus on for-profit business creations (Shane, 2004; Wright, Piva, Mosey & Lockett, 2009). These cover businesses that are created by academicians on the basis of their own research or through other academic activities (i.e. university spin-offs, venture capital) (Goel & Grimpe, 2011). Some commercial definitions also include businesses that are created 44 on the basis of intellectual property generated inside universities, irrespective of whether the entrepreneur was involved or not (Hayter, 2011). De Silva (2012) further categorised commercially-oriented academic entrepreneurship activities into three categories, namely company creation, research-related and teaching-related. These are presented in Table 2.7.

Knowledge transfer definitions cover any entrepreneurial initiatives that facilitate the transfer of academic knowledge to industry, government or the general public. In fact, Jones-Evans and Klofsten (2000) declared that any activities outside the normally accepted duties of academicians (teaching and personal research) could be regarded as entrepreneurial as these require innovativeness and proactivity on the part of the academician. The commercial motive is regarded as secondary as not all knowledge transfer activities are profit-oriented.

Value-based definitions of academic entrepreneurship stem from the view of entrepreneurship as the creation of new value, either commercial or social. According to Santos (2009), academic entrepreneurs engage external partners in dialogues that involve mutual exchanges of knowledge. The outcome of the dialogues would be the creation of new economic and/or social value.

Table 2.7: Categorises of commercial-oriented academic entrepreneurship activities.

Academic Academic Entrepreneurship Publication Contribution Activities Company creation i. Contributing to the formation of joint ventures in which university and industry are the joint partners ii. The formation of joint venture/(s) privately through collaborating with industry iii. Contributing to the formation of D’Este & Patel, (2007); one or more new spin-off Jones-Evans & Klofsten, companies (2000); Jones-Evans, (1997); iv. Contributing to the Schmoch, (1997) establishment of university incubators and/or science parks v. Contributing to the formation of university centres designed to carry out commercialisation activities vi. The formation of your own company/(s) Research related i. Working in the industry academic (research based) entrepreneurial ii. Research based consultancy for activities industry through the university iii. Research based consultancy privately (but without forming a company) Calvert & Patel, (2003); iv. Developing products or services Glassman, Moore, Rossy, with potential for (2003); Goldfarb & commercialisation Henrekson, (2003); John- v. Acquiring research funding from Evans, (1997); Louis, government, non-governmental Blumenthal, Gluck & Stoto, or international bodies (those (1989); Siegel, Thrusby, without collaborations with Thrusby, Ziedonis, (2001) industry) vi. Collaborating with industry through joint research projects vii. Research related assistance to small business owners Teaching related i. External teaching Louis, Blumenthal, Gluck & academic ii. Initiating the development of Stoto, (1989) entrepreneurial new degree programmes activities iii. Placing students as trainees in industry Conducting seminars and training sessions for industry Adapted: De Silva (2012)

Some scholars have attempted to define academic entrepreneurship by classifying academics based on the types of entrepreneurial activities that they are engaged in. Three general classifications of academicians are the academic entrepreneur, the entrepreneurial academic and the academic-entrepreneur.

The academic entrepreneur is one who engages in the commercialisation of academic intellectual property (Radosevich, 1995). The types of activities that characterise an academic entrepreneur correspond to De Silva (2012)’s categorisation of commercial-oriented academic entrepreneurship activities. Most definitions of academic entrepreneurship are aligned with the identity of the academic entrepreneur. This is due to their emphasis on the commercial motive of entrepreneurial initiatives.

The second classification, the entrepreneurial academic, engages in any forms of entrepreneurial behaviours in academia. These may relate to teaching, research, administration or consulting (Dickson, Coles & Smith, 1998). An example of an entrepreneurial academic is one who identifies research opportunities and lobbies for funding from interested parties. Perlman, Gueths and Weber’s (1988) definition of the academic intrapreneur is synonymous with the entrepreneurial academic. They described the academic intrapreneur as a managerial change agent in university, who builds research and teaching enterprises outside the conventional walls of the university. In most cases, the entrepreneurial academic relies almost entirely on external funding sources, gathers their own research teams, competes for research grants, inks contracts with industry and draws graduates into research teams.

The third category, the academic-entrepreneur, is one who engages in commercial activities outside academia and without the involvement of the university. According to Gintaras (2012), the academic-entrepreneur has two career options, namely, to quit academia and start a business or to start a business while employed in academia.

Table 2.8: Academics categorised according to their commercial ambitions and attitudes.

Entrepreneurial Intentions in an Academic Surrounding Academicians with… 1. 2. Balanced 3. Non- 4. Consciously 5. “Lazy bone” Disproportionate attitude to commercial non-commercial attitude attitude to commercialisation orientation and altruistic commercialisation intentions Characterisation Little emotional Applied research Focus on Focus on Not interested attachment with fundamental applied and in research or university and Clear perception research, fundamental excellence in being a professor of research - teaching, and research, teaching and teacher commercialisation PhD education teaching and interface PhD education Often Misuses the Intrinsic frustrated by university’s Intrapreneurial motivation to Intrinsic some incident resources to professors perform good motivation to throughout benefit own as a professor perform good career or by company Legally and as a professor family issues ethically balanced Knowledge Title of between job at generation is an New knowledge Main interests “Professor” university end in itself, not can be both, have shifted utilised as part of (Professor) and just a means ends and means from work to business model entrepreneurial leisure career Money is less Money is less Often in conflict important than important than Don’t generate with current Think win-win on overall reputation or even adopt contract by the returns from reputation within a new knowledge capitalising on new knowledge, community new knowledge which are utilised New knowledge to benefit own in both is used New knowledge company commercial and strategically to is shared academic pursue willingly (open application advances in source) and academic regarded as a careers, journal public good, publications are patenting is more important immoral and than patents publishing is good Adapted from Braukmann, Böth & Ahrens, (2010)

2.5 Collaboration

Entrepreneurship literature traces the term collaboration to the 19th century era of industrialisation, an era characterised by the emergence employee specialisation and inter-organisational relationships in the workplace (Aldrich & Zimmer, 1986; Berger & Luckman, 1967).

The literature suggests that organisations have the capacity to form collaborative relationships, where each party accepts responsibility for its own inputs as well as for the equitable sharing of returns on outputs (Agranoff, 2006; Huxham & Vangen, 2000; Simonin, 1997). Miles, Miles and Snow (2007) defined collaboration as a process where two or more parties work closely with each other to achieve mutually beneficial outcomes. Likewise, Bardach (1998) referred to it as an activity by two or more agencies to increase their value by working together. Lawrence, Phillips and Hardy (1999) described the political aspect of collaboration by defining it as a cooperative, inter-organisational relationship that relies on negotiation and communicative processes. In a similar vein, Gary (1989) defined collaboration as a mechanism by which a new negotiated order emerges among a set of stakeholders. Most researchers would contend that collaboration is voluntary, interactive, ongoing, and inclusive and requires commitment to a common goal.

The ability to collaborate with internal and external parties is critical for successful entrepreneurship, according to Miles et al. (2007). Collaboration, in the form of an inter-firm alliance for instance, provides a means to overcome resource constraints and to access additional competencies necessary for the success of a venture. Webster (1999) pointed out that firms collaborate for many reasons: to enhance their productive capacities, to reduce uncertainties in their internal structures and external environments, to acquire competitive advantages that enable them to increase profits, or to gain future business opportunities that allow them to command higher market values for their outputs. Todeva and Knoke (2005) introduced four broad categories of motives and drivers for collaboration: economic, organisational, strategic and political. The motives associated with each category are presented in Table 2.9.

Table 2.9: Motives to enter strategic alliances.

Category Motives Organisational Learning and internalising tacit knowledge; collective and embedded skills; restructuring; improving performance; acquiring distribution; extending supply links to adjust to environmental changes; complementing goods and services to markets; legitimisation Economic Market seeking; cost sharing and pooling of resources; reducing or diversifying risk; obtaining economies of scale; co-specialisation Strategic Achieving vertical integration; achieving competitive advantage; diversifying into now business; gaining access to new technology; converging technology; R&D; developing new products and technology; co-operation with potential rivals for pre-emptying competitors; bandwagon effect and following industry trends Political Developing technical standards; overcoming legal or regulatory barriers Adapted: Todeva and Knoke (2005)

Previous work on collaboration has also advanced the discussion on the stages and levels of collaboration. Researchers typically classify collaboration levels based on the intensity and complexity of engagement. For instance, Todeva and Knoke (2005) defined the levels of collaboration based on the degree of integration of activities and the formalisation of collaborative agreements. Hogue (1993) has developed a five- stage model comprising networking, cooperation, coordination, coalition and collaboration. The key characteristics of each stage, which differs in its purpose, structure and processes, is summarised in Table 2.10.

Table 2.10: The five levels of collaboration.

Level Purpose Structure Process Networking Dialog and common Non–hierarchical Low key leadership understanding Loose/flexible link Minimal decision making Clearinghouse for Roles loosely defined Little conflict information Community action is Informal communication Create base of support primary link among members Cooperation Match needs and Central body of people Facilitative leaders provide coordination as communication hub Complex decision making Limit duplication of Semi–formal links Some conflict services Roles are somewhat Formal communications Ensure tasks are done defined within the central group Links are advisory Group leverages/ raises money Coordination Share resources to Central body of people Autonomous leadership address common consists of decision but focus in on issue issues makers Group decision making in Merge resource base Roles defined central and subgroups to create something Links formalised Communication is frequent new and clear Group develops new resources and joint budget Collaboration Accomplish shared Consensus used in Leadership high, trust level vision and impact shared decision- high, productivity high benchmarks making Ideas and decisions equally Build interdependent Roles, time and shared system to address evaluation formalised Highly developed issues and Links are formal and communication opportunities written in work assignments Source: Hogue (1993)

Consistent with the stage-models of collaboration, Todeva and Knoke (2005) introduced a hierarchy of inter-organisational relations, based on the motives for entering strategic alliances. The types of relations together with their descriptions are presented in Table 2.11.

Table 2.11: Types of Inter-organisational relationships.

Types Description Hierarchical relations Through acquisition or merger, one firm takes full control of another’s assets and coordinates actions by the ownership rights mechanism Joint ventures Two or more firms create a jointly owned legal organisation that serves as limited purpose for its patents, such as R&D or marketing Equity investments A major or minority equity holding by one firm through a direct stock purchase of shares in another firm Cooperatives A coalition of small enterprises that combine, coordinate and manage their collective resources Research and Inter-firm agreements for research and development collaboration Development consortia typically formed in fast-changing technological fields Strategic cooperative Contractual business networks based on joint multi-party strategic agreements control with the partners collaborating over key strategic decisions and sharing responsibilities and performance outcomes Cartels Large corporations collude to constrain competition by cooperatively controlling production and/or prices within a specific industry Franchising A franchiser grants a franchisee the use of a brand-name identity within a geographic area, but retains control over pricing, marketing and standardised service forms Subcontractor networks Inter-linked firms where a subcontractor negotiates its suppliers’ long-term prices, production runs and delivery schedules Industry standards Committees that seek the member organisations’ agreements on groups the adoption of technical standards for manufacturing and trade Action sets Short-lived organisational coalitions whose members coordinate their lobbying efforts to influence public policy making Market relations Arm’s-length transactions between organisations coordinated only through the price mechanism Source: Todeva & Knoke (2005)

2.6 Academic-industry collaboration

Academicians have had a long tradition of collaborating and engaging with industry. Wilson (2012) presented the findings of a government-sponsored review on the state of academic-business collaborations in the UK. The researcher identified 12 main types of academic-industry collaborations prevalent in the UK. These are listed in Table 2.12.

Table 2.12: Types of academic-Industry collaborations.

Types of academic-industry collaboration 1. Future-oriented research in advanced technologies 2. Up skilling of employees 3. University science park developments 4. Support for entrepreneurial research students finding their way in the business world 5. Providing progression routes to higher-level apprenticeships 6. Enhancing the skills of post-doctoral staff for their transition into the business world 7. Improving enterprise skills amongst our undergraduates 8. Enabling small companies to recognise the value of employing a first graduate 9. Providing support for new start-ups and SMEs 10. Supporting spin-out companies from research teams 11. Providing support for graduate entrepreneurs 12. Helping government agencies attract major employers to invest in the state Source: Wilson (2012)

Studies on academic-industry collaborations revealed that academicians engage with industry for a broad range of reasons. Common motivators include testing practical applications of theories, collecting data for scholarly research, raising funds and network-building. Past studies revealed that the primary motive of most academic partners is to further their research rather than to generate commercial profit. This is not surprising as it conforms to the traditional mandates of academia, namely, to create and disseminate new knowledge. Some scholars argue there are three broad reasons for academics to collaborate with industry (D’Este & Patel, 2007; D’Este & Perkmann, 2011). These are to commercialise academic knowledge, to engage in learning, and to access financial and in-kind resources. The incentives for each reason, together with the relevant theories for explaining them are summarised in Table 2.13.

Table 2.13: Incentives for academic-Industry engagements.

Incentives Rationale Theories Explaining Behaviour Commercialisation Personal income, entrepreneurial Agency, life-cycle model, life-time success income maximization Learning Interactive learning / knowledge Affinity between science and exchange technology - network learning Access to funding Attract financial resources to Complementary assets and Resources further academic research reciprocity among different organisations Access to in-kind Access resources (materials, Complementary assets and resources equipment, data) to further reciprocity among different academic research organisations Adopted from D’Este & Patel (2007) and D’Este & Perkmann (2011).

2.6.1 Commercialisation

Academic researchers driven by commercialisation motives of intellectual property assume direct responsibility for technical development of their research activities, and undertake the commercial exploitation of their technology or knowledge (Etzkowitz, 1983). According to agency theory, the commercialisation logic assumes that collaborating with industry is driven by the desire for personal monetary gain leading to the emergence of the entrepreneurial academic (Clark, 1998; Etzkowitz, 2003; Shane, 2004). Academicians are seen as economic actors who capitalise on technology or expertise they generate while working at the university.

The commercialisation argument requires academicians to secure formal rights on their intellectual property (Etzkowitz, Webster, Gebhardt & Terra, 2000). Royalty sharing policies at universities are generally designed to provide incentives for the disclosure of inventions to the university administration (Bercovitz & Feldman, 2003) and the subsequent participation of the inventor in product development efforts, regardless of whether this is pursued via spin-off companies or licenses to third parties (Lowe, 2006). Alternatively, academicians can leverage their expertise via consulting engagements for external clients. Many universities have formal policies encouraging their academic staff to pursue this type of activity for a specified share of their time (Perkmann & Walsh, 2006).

2.6.2 Learning

Learning is an important rationale for academicians to engage with industry in the attempt to further their research agenda. A growing amount of research undertaken at universities, particularly in disciplines that grow out of professional practice is applied, in that it addresses technical problems (Niiniluoto, 1993; Allan, 1988). Problems arising in technology development often provide challenges for follow-on research activities, and inform academic research agendas (Mansfield, 1995; Rosenberg, 1992). For instance, Mansfield (1995) observed that the problems many academicians worked on often stemmed from ideas and problems they encountered in

54 industrial consulting. In view of this, researchers have called for more effective interactions between the two institutional realms.

2.6.3 Resource Access

The third main incentive for academicians to engage industry is to access resources they require for pursuing research. The logic of resource access differs from those considered above. While resource access logic is inspired by research-oriented goals, these goals can be accomplished without close task-integration with industrial researchers. Instead, interactions are characterised by resource-exchange and reciprocity (Oliver, 1990). An interaction allows each party to share resources to pursue its own objectives as opposed to drawing benefits from the outcome of joint efforts. The background to this type of arrangement is provided by approaches that conceptualize inter-organizational engagement as reciprocity-induced and as a way of accessing resources and skills that the partners decide not to produce internally (Powell, Koput & Smith-Doerr, 1996).

There are two types of resources that academicians can access by working with industry. These are monetary funds and in-kind resources. Funding can be appropriated by offering consulting and contract research to industrial partners, who usually pay for the services. In addition, the involvement of industrial partners can also increase the amount of funding that academicians can obtain from government research funding sources. In some cases, the academic exploitation of the results might be limited due to the lack of scientific novelty or non-disclosure agreement. The second type of resources is contribution in-kind, such as access to artefacts, data, equipment and materials.

2.7 Synergistic Effects of Diversifying Academic Entrepreneurial Activities

A review of diversification and portfolio literature reveals that academic engagement in multiple entrepreneurial activities provides additional benefits due to the synergies that can develop between activities (Alsos, Ljunggren, & Pettersen, 2003; Westhead, Ucbasaran & Wright, M 2005). 55

Additionally, regarding dynamic capabilities of firms, Chandler (1990) argued that when firms grow, employees develop capabilities that provide firms with a competitive advantage. Based on this, it may be implied that academicians, during the diversification process, may develop additional capabilities due to interactions between different entrepreneurial activities. This phenomenon of generating synergistic effects is defined in the literature on systems theory as the whole being better than the sum of its parts (Von Bertalanffy, 1972).

Adapting this definition for the purpose of this study, synergistic effects are defined as additional benefits generated by active academician entrepreneurs as a result of interactions between entrepreneurial activities.

Some of these synergistic effects, identified in the literature, are social networking (Mayer & Schooman, 1993; Westhead et al., 2005), knowledge and skills (Alsos et al., 2003; Shane, 2000; Westhead et al., 2005), input-output flows and physical resources (Alsos et al., 2003; Westhead et al., 2005) Hence, these are regarded as relevant to the breadth and depth of academic entrepreneurial commercial-oriented engagements with industry considered in the present study.

The above discussions have indicated that the social network of an academician is very important to entrepreneurial engagements. Social networks have been recognised as important when obtaining resources (Birley, 1985; Mayer & Schooman, 1993), identifying opportunities, and acquiring legitimacy (Aldrich & Fiol, 1994). Besides reaping benefits from contacts with industrial partners (Krabel & Mueller, 2009), networking with peers with commercialization experience has also been found to have positive impacts on entrepreneurial endeavours (Azoulay, Ding & Stuart, 2007).

The portfolio entrepreneurship literature suggested that those who carry out multiple entrepreneurial engagements are capable of forming, and working in, productive teams, owing to their extensive network of contacts (Westhead et al., 2005). Drawing on this work, this study argues that when an academician is engaged in multiple entrepreneurial engagements, they could capitalise on a network of contacts

56 developed from one engagement activity type to carry out another engagement activity type, which generates synergistic effects.

Empirical evidence from recent studies has suggested that when entrepreneurs are engaged in multiple entrepreneurial activities, they make use of knowledge and skills developed from one activity to engage in another activity (De Silva, 2012). As a result, the literature has stated that portfolio entrepreneurs have a higher ability to identify and capitalise on opportunities than other entrepreneurs (Alsos et al., 2003; Westhead et al., 2005). On the other hand, a lack of knowledge and skills on business management, entrepreneurship, and the application of theory have been identified recognised as barriers to achieving success during academic entrepreneurial engagements (Dickson, Coles & Smith, 1998; Fowler, 1984; Franklin, Wright & Lockett, 2001; Monck & Segal, 1983). Consequently, the present study argues that knowledge and skills developed by engaging in entrepreneurial engagement activities might be used by academic entrepreneurs to further diversify their entrepreneurial engagement activities, which generate knowledge and skill synergies.

The academic entrepreneurship literature has argued that one reason why academics form spinoff companies is to commercialise their knowledge. In such an instance a patent, which is an output of one academic entrepreneurial activity (i.e. applied research or joint research projects with industry), might be used as an input to a spinoff company (Eun, Lee & Wu, 2006). Furthermore, it may also be the case that the output of one consultancy assignment or joint research project might be used as an input to another project. Hence, input-output flow is considered another type of synergistic effect.

The literature suggested that engagement in multiple income generation activities enable resources acquired from one academic entrepreneurial activity to be used in another academic entrepreneurial activity (De Silva & Kodithuwakku, 2011). The efficacy of this phenomenon might be high in resource constrained environments, since it is necessary to utilise resources efficiently and effectively in order to go beyond resource limitations and be successful through academic entrepreneurial engagements (Vyankarnam, 1990). Hence, it could be argued that the carrying out of a combination

57 of academic entrepreneurial activities may result in synergistic effects in terms of physical resources.

2.8 Summary

In this chapter, relevant literature has been reviewed in order to provide an overview of the literature on academician entrepreneurship and a theoretical underpinning for this study. Initially, the chapter highlighted the lack of consensus on the definition of entrepreneurship and academic entrepreneurship.

The chapter has also highlighted recent evidence that suggests that academicians may engage in three forms of entrepreneurial engagements, namely company-related, research-related, and teaching-related activities. Based on this body of work, the current study argues that academicians operating in private universities in Malaysia may also engage in several academic entrepreneurial engagement activities.

CHAPTER THREE METHODOLOGY

3.0 Introduction

This chapter describes the methodology for the present study on entrepreneurial academic-industry collaboration: an exploratory study of factors influencing commercial success in private universities in Malaysia.

Covered in this chapter are the research design, sample and population, descriptions of research instruments and data collection procedures, and the techniques employed for statistical analysis in the study.

3.1 Research Design

The first considerations in research design are the selection of a topic and a paradigm (Creswell 1994; Trochim, 2002). Once these are determined, the design maps the basic steps for data collection methods, measurement procedures, questionnaire design, sampling and data analysis (Churchill, 1995; Hair, Celsi, Money, Samuel & Page, 2011).

A common data collection method is the survey, which in this instance, measures the perception of academicians on a range of variables associated with academic-industry collaboration. These include academicicans’ entrepreneurial orientation and readiness to collaborate, organisational learning capability of universities, the strength of inter-organisational ties and the performance of collaborative activies. A field survey method was utilised because survey research is usually associated with the deductive approach and is among the most widely used research methodology based on the positivist paradigm (Neuman, 2003; Saunders, Lewis & Thornbill, 2003). A series of theoretically justified research hypotheses, based on various moderating and mediating models, was tested.

The field survey adopted a cross-sectional design, in which data was collected from a sample drawn from a specified population at a given point in time and methodically summarised statistically (Hair et al., 2011). The main study was preceded by an initial quantitative pilot stage, which served to facilitate the development of the survey instrument.

3.1.1 Population and Sample

The population of the present study consisted of 13,737 (based on Ministry of Higher Education, 2010 estimates) full-time academics at various position rankings in 31 Malaysian private universities and 5 branch campuses of foreign universities. The job roles comprised associate lecturers, lecturers, senior lecturers, associate professors and professors.

In Malaysia, the Federal Constitution of Malaysia legislates private universities and university colleges using two laws, the Private Higher Educational Institutions Act 1996 and the Universities and University Colleges (Amendment) Act 1996 enforced by the Ministry of Higher Education (MoHE) and the Malaysian Qualification Agency (MQA) (MoHE, 2012).

The Private Higher Educational Institutions Act 1996 allows private universities, foreign branch campuses and university colleges provide tertiary education and to confer their own degrees (MoHE, 2012).

The Universities and University Colleges (Amendment) Act 1996 allows institutional autonomy within parameters stipulated in the Act from the Government and all other forces of society to make decisions regarding its internal government, finances, administration, and to establish its policies of education, research, extension work, and other related activities (MoHE, 2012).

Universities in Malaysia are categorised into two groups, namely, private universities and foreign branch campus universities. In the context of this study, private universities are not operated by the government and are under the purview of the Ministry of Higher Education, and governed by the Universities and University

Colleges (Amendment) Act 1996, and ITM Act 1976 (Amendment). The foreign branch campus is a type of foreign educational institution which has been established in a country other than the one where the home (primary) campus exists (MoHE, 2012). The contact details of the university academic staff that were to from the study population were obtained from their respective website. The list of private and foreign branch campuses in Malaysia is available in Table 3.1.

Table 3.1: List of private and foreign branch campuses universities in Malaysia.

Universities Type of University JOHOR STATE 1. Raffles University Iskandar (RUI). private university MELAKA 2. Multimedia University, Melaka Campus. private university PERAK 3. Quest International University, Perak. private university 4. Petronas University of Technology (UTP). private university 5. University Tunku Abdul Rahman (UTAR). private university SELANGOR 6. Al-Madinah International University (MEDIU). private university 7. Binary University OF Management and Entrepreneurship. private university 8. Malaysia University of Science and Technology. private university 9. Management and Science University (MSU). private university 10. Manipal International University (MIU). private university 11. Perdana University. private university 12. UNITAR International University. private university 13. Asia Metropolitan Asia. private university 14. University of Selangor (UNISEL). private university 15. University Tenaga Nasional (UNITEN). private university 16. Sunway University. private university 17. Taylor’s University. private university 18. Limkokwing University of Creative Technology. private university 19. Tunku Abdul Rahman University (UTAR). private university KEDAH 20. AIMST University. private university 21. Universiti Antarabangsa AlBukhary. private university NEGERI SEMBILAN 22. INTI International University. private university 23. Nilai University. private university PULUA PINANG 24. Wawasan Open University private university KUALA LUMPUR 25. Asia e University (AeU). private university 26. HELP University. private university 27. Infrastructure University, Kuala Lumpur. private university 28. International Medical University. private university 29. Open University Malaysia (OUM). private university 30. UCSI University. private university 31. University Tun Abdul Razak (UNIRAZAK). private university SARAWAK 32. Curtin University, Sarawak, Malaysia. foreign branch campus 33. Swinburne University of Technology Sarawak Campus. foreign branch campus SELANGOR 34. Monash University Sunway Campus Malaysia. foreign branch campus 35. The University of Nottingham Malaysia Campus. foreign branch campus 36. Newcastle University Medicine Malaysia. foreign branch campus Source: Ministry of Higher Education (2012) (Buku informasi IPTS: Bertaraf Universiti, Kolej Universiti dan Kampus/ Kementerian Pengajian Tinggi)

3.1.1.1 Sample

For very large survey populations, making a census or a completed enumeration of all the values in the population is impractical or impossible due to financial and time constraints. Researchers, therefore, select a sample of target respondents who represent the whole research population in a study (Cooper & Schindler, 2008). A sample is a subset of measurements selected from the population by a defined procedure to participate in a research study (Mendenhall, Beaver & Beaver, 2009). Polit and Hungler (1999) advised using the largest possible sample for quantitative research design. In this study, to obtain a good sample, purposive sampling method was utilised to select potential participants. The details of the sampling method are discussed in the following sections.

3.1.1.2 Sampling Method

Two major approaches to sampling exist, probability and non-probability sampling methods. The main difference is that non-probability sampling does not involve random selection, whereas probability sampling does (Trochim, 2002). In probability sampling, every individual of a target population has an equal opportunity to be included in a study; however in non-probability sampling, a researcher purposefully selects a respondent over another, based on stipulated criteria (Hair et al, 2010).

The present study employed non-probability purposive sampling technique. Only academicians that fulfilled a set of sampling criteria (Cooper & Schindler, 2008; Hair et al., 2011) received the suvey instrument. The criteria were developed because the researchers anticipated different academicians may exhibit different patterns of academic-industry engagement, based on their demographic characteristics, academic discipline and academic position. The criteria for selecting target respondents in the present study are:

 She or he is currently employed as a full-time academician in a private university or foreign branch campus in Malaysia and holds an academic position as a professor, associate professor, senior lecturer, lecturer, associate lecturer or tutor.  She or he is attached to a specific School or Faculty in a private university or foreign branch campus in Malaysia.  She or he consents to be a participant in the proposed study.

Literature on research design highlights advantages in using the purposive sampling technique. Firstly, it is less time-consuming for the researcher (Smith & Albaum, 2005). In the present study, the researcher selected target respondents based on the stipulated criteria. Secondly, it allows the researcher to use his or her research skill and prior knowledge in selecting target respondents as informants for studying a phenomenon (Bailey, 1994; Brink, Walt & Ransburg, 2006). Thirdly, the data collected from the target respondents can be very informative and useful for demonstrating that a particular trait exists in the population (Brink, 1996).

3.1.2 Sampling Rationale

The present study used private universities and foreign branch campuses in Malaysia as the research context to study the social-psychological, organisational and inter-organisational factors that influence the performance of academic-industry entrepreneurial collaborations.

The study sample comprised full-time academicians holding all academic positions, professors, associate professors, senior lecturers, lecturers and associate lecturer or tutor. The rationale for this sample was that they form part of a statistical population of 13,737 (MoHE, 2012) full-time academicians in private universities in Malaysia who may be involved in academic-industry entrepreneurial collaborations.

We expected that the study findings would shed light on the types of academic- industry entrepreneurial collaborations and the factors that influence their performance. The researchers believe that the findings of this research project contribute new knowledge on academic-industry collaborations in Malaysia. These findings may form the basis for promoting dialogue among academicians, industry and

64 university administrators on how to foster and manage commercially-oriented collaborations.

3.1.3 Sample Size

Determining the final sample size is a fundamental step in the research methodology planning. Literature revealed that the final sample size (i.e. the actual number of academicians who complete the survey instrument) needs to meet the criteria for multiple regression analysis. This analysis requires five factors to be considered when determining the sample size, namely, the variety of elements in the target population, the type of sample required, time availability, financial budget and required estimation precision (Hair et al., 2003; Van Dalen, 1979). Previous research by Roscoe (1975) stated the ideal sample size for the application of multiple regression analysis should be a few times (preferably a minimum of 10 times or more) larger than the number of variables in a study. Krejcie and Morgan (1970) developed a formula, which was used in the present study to calculate the minimum required sample size from the general population of 13,373 (MoHE, 2012) academicians.

X 2 NP  P)1( s  2 2  PPXNd )1()1(

Where

s = required sample size.

X 2 = the table value of chi-square for 1 degree of freedom at the desired confidence level (3.841).

N = the population size.

P = the population proportion (assumed to be 0.50 since this would provide the maximum sample size).

d = the degree of accuracy expressed as a proportion (0.05).

.3 841(13737)( .0 50)(  )5.01 s  .0 052 (13,737  .3) 841 5.0( )(  )5.01  370.

Based on Krejcie and Morgan’s (1970) formula, the minimum sample size for a population of 13,737 is 370 respondents. It is noted that a size of 370 meets the sample size requirement of multiple regression analysis, as prescribed by Roscoe (1975).

3.2 Research Instruments

The primary instrument in the current study was a survey questionnaire that measures the perception of academicians on six variables associated with academic- industry collaborations. These include; academics readiness to collaborate with industry, individual entrepreneurial orientation, organisational learning capability, organisational-level entrepreneurial orientation, the strength of inter-organisational ties, and the performance of collaborations. The survey was considered to be the most efficient means of collecting data compared with other methods, such as conducting an interview or case study.

Based on an extensive review of relevant literature on the social-psychological, organisational and inter-organisational factors that influence the performance of academic entrepreneurial collaborations, a preliminary questionnaire was developed. This was tested in a pilot study involving a sample of 10 academicians that satisfied the three sample criteria. They were selected based on their experience and expertise in academic-industry entrepreneurial engagements. This information was available on the staff profiles from university web sites. The pilot study ensured that all variables were unambiguous and captured the major theoretical constructs of interest (Fischer, 2010). The pilot study findings also underwent validity and reliability testing (i.e. exploratory factor analysis and Cronbach’s alpha) to improve content and construct validity of the main survey.

The main survey instrument consisted of sixty two items, measuring six theoretical constructs discussed in Chapter 2. These are (1) demographic characteristics; (2) social-psychological factors; (3) organisational-level factors; (4) inter-organisational factors; (5) academic-industry collaborations; and (6) performance of academic-industry collaborations.

The measurement scales were adapted from instruments validated in previous studies. The number of questionnaire items in each scale ranged from six to eleven. Responses to social- psychological, organisational-level and inter-organisational factors scales were recorded using a five-point Likert rating scale with (1) being Strongly disagree; (2) being Disagree; (3) being Neither agree nor disagree; (4) being Agree; and (5) being Strongly Agree. Responses for scales measuring academic-industry collaboration was recorded using a four-point level of participation Likert rating, with (1) being, No, never; (2) Yes, engaged in the last 12 months; (3) being Yes, engaged in the last 3 years; and (4) Yes, engaged in both the last 12 months and 3 years. Response for scales measuring performance of academic-industry collaborations were recorded using a five-point scale with the level of significance Likert rating, with (1) being Significant decline; (2) being Significant slightly; (3) being Significant somewhat; (4) being Significant; and (5) being Significant improvement. Each of the theoretical constructs highlighted above are discussed in detail in the following sections of this chapter. Table 3.2 illustrates the number of items measuring each construct.

Table 3.2: Theoretical constructs and the number of questionnaire items. Section Constructs Dimensions Sources No. of Rating Scale Items 1. Demographic 5 characteristics 2. Social- Readiness to Azjen (1988); Bolton & Lane 6 1 = Strongly psychological collaborate with (2012); Covin & Covin (1990); Disagree, factors industry Covin & Slevin (1986; 1989). 5 = Strongly Individual Azjen (1988); Bolton & Lane 6 Agree entrepreneurial (2012); Covin & Covin (1990); orientation Covin & Slevin (1986; 1989) 3. Organisational- Organisational Chiva, Alegre & Lapiedra 5 1 = Strongly level factors learning (2007); Gomez, Lorente & Disagree, capability Cabrera (2004); Jerez-Gomez 5 = Strongly Cespedes-Lorente & Valle- Agree Cabrera (2005) Organisational- Alegre & Lapiedra (2007); 4 1 = Strongly level Chiva, Gomez, Lorente & Disagree, entrepreneurial Cabrera (2004); Covin & Covin 5 = Strongly orientation (1990); Covin & Slevin (1986, Agree 1989) 4. Inter- Inter- Bryan, Krusich, Collins-Camargo 7 1 = Strongly organisational organisational & Allen (2006); Garstka Disagree, factors collaborations Collin-Camargo, Hall, Neal & 5 = Strongly Ensign (2012); Murray-Close & Agree Monsey (2004) 5. Academic-industry Company- D’Este & Patel (2007); Jones- 6 1 = No, never, collaborations creation related Evans & Klofsten (2000) Jones- 4 = Yes activities activities Evans (1997); Schmoch (1997) engaged in Research related Calvert & Patel (2003); 7 both the last activities Glassman, Moore, Rossy 12 months (2003); Goldfarb & Henerkson and 3 years (2003); John-Evans (1989); Louis, Blumenthal, Gluck & Stoto (1989) Teaching related Louis, Blumenthal, Gluck & 5 activities Stoto (1989) 6. Performance of Collaboration Bryan et al., 2006; Garstka 11 1 = Significant academic-industry dimensions Collin-Camargo, Hall, Neal & decline, collaborations Ensign (2012); Murray-Close & Monsey 2004 5 = Significant improvement Total number of measurement scale items 62

3.2.1 Demographic Characteristics

Five items were developed to collect respondent profile and demographic information. These were related to (1) gender, (2) age, (3) educational level, (4)

68 respondent’s current position at the university and (5) respondent’s academic discipline.

3.2.2 Social-Psychological Factors

The survey instrument included 12 items to gather and measure academicians’ perceptions of social-psychological factors that influence their performance in academic-industry entrepreneurial collaborations. These were adapted and adopted from relevant previous research to suit the present study. The items covered two factors, namely, (1) readiness to collaborate with industry and (2) individual entrepreneurial orientation.

Of these 12 items, six measured the academician’s readiness to collaborate with industry. These items measured 3 theoretical dimensions of academicians’ intentions and inclination to engage with industry, namely, attitudes, subjective norms and behavioural intention (Ajzen, 1985; Ajzen & Fishbein, 2005; Armitage & Conner, 2001; Bolton & Lane, 2012; Miller, 2005; Sheppard, Hartwick & Warshaw, 1988; Stern, 2005).

In the context of the present study, attitudes refer to the degree to which performance of academic engagement with industry is positively or negatively valued by academicians and to which this influences their readiness to participate in entrepreneurial collaborations. Subjective norms refers to an academician's perception about involvement in academic-industry entrepreneurial collaborations, which is influenced by the judgment of significant others (for example, colleagues, industry players and top leaders of the university). Perceived behavioural control refers to academicians’ perceptions of their ability to perform in entrepreneurial collaborations.

Respondents indicated the accuracy of the individual statements using a 5- point Likert scale (1 = strongly disagree, to 5 = strongly agree). Examples of the items measuring readiness to collaborate are (1) industry engagement will enrich my academic career and (2) industry engagement is useful for acquiring knowledge and skills that are useful for teaching and research.

The remaining six items measured individual entrepreneurial orientation of academicians. In the study, individual entrepreneurial orientation represented 3 theoretical dimensions of the academicians’ intentions and inclination to engage in entrepreneurial collaborations, namely, risk-taking, innovativeness and proactiveness (Bolton & Lane, 2012; Covin & Slevin, 1989; Rauch et al., 2009; Stam & Elfring, 2008; Wiklund & Shepherd, 2005).

In this study, risk-taking examines academicians perceptions of themselves to engage in bold, rather than cautious, actions in academic-industry entrepreneurial engagement activities. Innovativeness considers whether academicians pursue creativity and experimentation in academic-industry entrepreneurial engagement activities, and proactiveness measures academicians perceptions of their own tendency to anticipate and act on the future needs of academic-industry entrepreneurial engagement activities, rather than react to events after they unfold.

These items also used a 5-point Likert scale (1 = strongly disagree, to 5 = strongly agree). Two example items are (1) I tend to plan ahead of projects and (2) I like to take bold action by venturing into the unknown.

3.2.3 Organisational-Level Factors

Organisational-level factors were measured using nine items that covered (1) organisational learning capability and (2) organisational-level entrepreneurial orientation. These were adapted from previous studies to suit the Malaysian context (Chiva et al., 2007; Gomez et al., 2004).

In the survey, five items measured organisational learning capability, which refers to organisational and managerial characteristics that facilitate effective organisational learning processes. The four dimensions of organisational learning capability measured were (1) managerial commitment to learning, (2) presence of system perspective, (3) openness and readiness to experiment and (4) effective knowledge transfer and integration (Chiva et al., 2007; Gomez et al., 2004; Jerez- Gomez et al., 2005).

Managerial commitment to learning refers to academicians perceptions of university top management and institutional managerial characteristics creating and reinforcing a culture supporting academicians involved in academic-industry collaborations. Presence of system perspective refers to academicians perceptions of university and managerial practices are well-ordered, repeatable and use data and information so learning in academic-industry collaborations is possible for academicians involved and universities so as to optimise their performance. Openness and readiness to experiment measures academicians perceptions of the degree of freedom the university leadership allows those involved in academic-industry collaborations to systematically exploit new ways of doing the job, take risks and suggest new ideas. Effective knowledge transfer and integration measures academicians perceptions of the extent university organisational and managerial characterisitcs facilitate the creation a culture that reinforces the acquisition, creation, and transfer of knowledge in academic-industry collaborations.

Respondents indicated their perceptions using a 5-point Likert scale (1 = strongly disagree, to 5 = strongly agree). The examples items measuring organisational learning capability are, (1) the top leaders of my university frequently involve academic staff in important decision-making processes; and (2) in this university, innovative ideas that work are rewarded.

The survey instrument also included four items that measured organisational- level entrepreneurial orientation, the organisational and managerial characteristics that promote academic entrepreneurial collaborations. Four theoretical dimensions were analysed, namely, proactiveness, innovativeness, risk-taking and competitive aggressiveness (Alegre & Lapiedra, 2007; Chiva et al., 2007; Covin & Slevin, 1986, 1989;

Covin & Covin, 1990; Gomez et al., 2004).

In this study, proactiveness is the tendency of universities to anticipate and act on future needs in academic-industry entrepreneurial engagement activities, rather than react to events after they unfold. Innovativeness considers whether universities pursue creativity and experimentation in academic-industry entrepreneurial engagement activities, and risk-taking examines whether universities engage in bold,

71 rather than cautious, actions in academic-industry entrepreneurial engagement activities. Competitive aggressiveness refers to the tendency of universities to intensely and consistently seek potential and external partners for collaborations in academic-industry entrepreneurial engagement activities.

Respondents indicated their perceptions using a 5-point Likert scale (1 = strongly disagree, to 5 = strongly agree). Examples of items measuring organisational learning capability are (1) in this university, academic staff are encouraged to interact with the environment: competitors, customers, technological institutes, universities, suppliers; and (2) in my university, there is free and open communication within academic work groups.

3.2.4 Inter-Organisational Factors

Seven items measured inter-organisation factors that influence the performance of academic-industry entrepreneurial collaborations. The items covered three theoretical dimensions, (1) collaborative environment, (2) collaborative communication and (3) collaborative purpose. These were adapted from previous studies (Bryan et al., 2006; Garstka et al., 2012; Murray-Close & Monsey, 2004). Three examples of items are (1) top university leaders are supportive of industry engagements; (2) communication between collaborative groups (that you are involved in), university top leaders and industry partners is effective; and (3) my ideas about what we want to accomplish with this collaboration seem to be the same as the ideas of others.

3.2.5 Academic-Industry Collaborations Activities

Academic entrepreneurship is conceptualised as universities engaging with external stakeholders to create, transfer and distribute academic intellectual property for commercial gain (Shane, 2004).

In the present study, academic-industry entrepreneurial engagement activities were categorised as (1) teaching-related activities, (2) research-related activities and (3) company -creation related activities. These eighteen items were adapted from relevant previous research to suit the Malaysian context. Five items measured teaching-related 72 activities (Louis et al., 1989; Blumenthal, 1989), seven items measured research- related activities (Calvert & Patel, 2003; Glassman et al, 2003; Goldfarb & Henrekson, 2003; John-Evans, 1997; Louis et al., 1989; Siegel et al., 2001) and six items measured company- creation related activities (D’Este & Patel, 2007; Jones-Evans & Klofsten, 2000; Jones-Evans, 1997; Schmoch, 1997).

3.2.6 Performance of Academic-Industry Collaborations

Respondent’s involvement in academic-industry entrepreneurial collaborations, in the last three years, was measured by eleven items. Respondents indicated the accuracy of the individual statements using a 7-point Likert scale (1 = significant decline, to 7 = significant improvement). Example items are (1) achieving positive financial returns, (2) securing access to facilities/resources in the industry and (3) maintaining favourable relationship with industry partners.

3.3 Data Collection Procedures

According to the statistics compiled by the Malaysian Ministry of Higher Education (2010), there are approximately 13,737 full-time academicians employed in various position rankings at 31 Malaysian private universities and five foreign branch campuses. Using Krejcie and Morgan’s (1970) formula for calculating the minimum sample size, the number of respondents required to form a sample in a population of 13,737 is at least 370. A minimum sample size of 370 meets the requirement for undertaking multiple regression analyses (Roscoe, 1975).

Before directly contacting the potential respondents, the researchers sought institutional approval from all the deans of faculty or heads of schools of the 36 universities. Electronic mail (email) was used to distribute a survey questionnaire package, comprising a permission letter, a consent information statement and survey questionnaire to the senior management.

Once institutional approval was granted, the researcher was able to distribute the 5000 questionnaire packages to potential respondents via postal mail. Each package comprised three printed documents, a permission letter, consent information

73 statement and survey questionnaire in addition to a self-addressed stamped envelope to be used to return completed survey questionnaire.

By completing and returning the survey questionnaire, participants informed consent to participate in the present study is implied. The researcher used the consent information statement to assure respondents of the purpose of the study, their rights to anonymity, interests, benefits, consent to participate and right to withdraw. Each participant was given one to five weeks to complete the questionnaire, after which time the researcher sent follow up e-mails requesting them to return copies of completed questionnaires. At the end of the 12 week distribution period, a total of 538 completed questionnaires were collected out of the 5000.

3.4 Overview of Statistical Analysis Techniques

This section describes the data analysis. Collected datasets were initially screened for missing values and outliers, using the Statistical Package for Social Sciences (SPSS) 19.0 software. This was followed by assessment of the normality of data distribution. The data were then analysed in the following order. First, the demographic characteristics of respondents were analysed using descriptive statistics. Next, factor analysis and reliability testing were conducted to determine the goodness of fit measures. Lastly, analysis of variance and multiple regression analyses were applied to examine the hypothesised relationships among the variables.

3.4.1 Data Screening

Overall, a total of 538 questionnaires were returned. These were checked for completeness, revealing that 11 sets were incomplete, which represented two percent of all returned questionnaires. These were subsequently discarded.

The datasets were then screened for univariate and multivariate outliers. Outliers are cases with extreme values that are unusually high or low, making the cases distinctly different from other cases (Hair et al., 2010). A univariate outlier can be identified by computing the standardised values (i.e. Z scores) for all cases at once and then examining each variable in order to spot individual case(s) with extreme values

(Hair et al., 2006; Tabachnick, & Fidell, 2001). Each univariate outlier was treated by increasing or decreasing its case value by one unit (Tabachnick & Fidell, 2001).

Multivariate outliers refer to cases with an unusual combination of values on more than two variables (Tabachnick & Fidell, 2001). These outliers were identified by computing the Mahalanobis distance with a p < 0.01 criterion for all cases. Next, the critical Chi-square value at the alpha level 0.001 was obtained by using the number of independent variables as the degrees of freedom (Tabachnick & Fidell, 2001). Cases with Mahalanobis distance greater than the critical Chi-square value (Tabachnick & Fidell, 2001) were regarded as multivariate outliers and were deleted. A total of 17 cases were deleted.

3.4.2 Normality Assessment

Distribution normality of each independent variable was assessed numerically by computing the skewness (symmetry) and kurtosis (peakedness) values. Skewness and kurtosis values for a variable with normal distribution are zero (Weiner, Freedheim, Schinka & Velicer, 2003). Hair et al. (2006) suggested that as a rule of thumb, both skewness and kurtosis should not exceed the absolute value of 1. Distribution normality was further assessed through visual inspection of the normal probability plot, which compares the standardised residuals with the normal distribution. Analysis of skewness and kurtosis values for each independent variable revealed that all were below one. Visual inspections of the probability plots further indicate that the distribution of data for each independent variable was normal.

3.4.3 Descriptive Analysis

The background information of respondents was presented through descriptive statistics such as mean, standard deviations, frequencies and percentages. These statistics provide the means for organising, summarising, simplifying and describing important characteristics of a set of data.

3.4.4 Factor Analysis

Factor analysis was undertaken to reveal the underlying structure of the data, and construct the summated scales that represent the antecedents and outcomes of 75 entrepreneurial collaboration. DeCoster (1998) advised that factor analysis is the best approach to identify the underlying factors or constructs that influence a set of observed variables. By examining the pattern of correlations between observed variables, a group of highly correlated variables are assumed to be influenced by the same construct or factor, while those that are relatively uncorrelated, are likely to be influenced by different factors.

The survey questionnaire comprised of 12 items measuring two social- psychological constructs, entrepreneurial orientation (Bolton & Lane, 2012; Covin & Slevin, 1989) and readiness to collaborate (Ajzen & Fishbein, 2005). In addition, nine items measured the organisational-learning capability of universities (Gomez et al. 2005; Chiva, Alegre & Lapiedra, 2007) and seven items measured the effectiveness of inter-organisation collaborations (Bryan et al., 2006; Garstka et al., 2012). These theoretical constructs were conceptualised as the antecedents of entrepreneurial collaborations.

The outcomes of entrepreneurial collaborations were measured using 11 items, which include meeting project timelines and budgets, achieving positive financial returns, strengthening reputation within industry, securing industry input for teaching and research and enhancing career mobility between academia and industry.

Exploratory factor analysis (EFA) can be distinguished from confirmatory factor analysis (CFA) through the latter’s ability to determine whether a specified set of underlying constructs is influencing the observed variables in a predicted direction. In contrast, EFA explores the nature of underlying constructs that influence the observed variables (Saggino et al., 2006).

Factor analysis is also commonly for data reduction (Saggino et al., 2006). It reduces a large number of items to a smaller group of underlying factors, which summarises the most essential information. According to Hair et al. (2010), the sample size for conducting factor analysis should be more than 100 or at least five times as many observations as there are variables. The present study has met the criteria of using factor analysis as the sample size of 510 was considered sufficient.

3.4.5 Analysis of Variance and Multiple Regression Analysis

Analysis of variance (ANOVA) was computed to determine if significant differences exist among respondents’ collaborative engagements, based on key demographic characteristics. Where significant differences were detected, results from post-hoc Tukey tests were referred to in order to determine the nature of the differences.

Cronbach’s alpha values were computed for the scales measuring the factors to check reliability. Nunally (1970) advised that a value of at least 0.70 would indicate that a scale is reliable. Factors that fail to meet the benchmarked value were dropped from further analysis. Correlation analyses were undertaken to assess the relationship between the factors.

Lastly, multiple regression analyses were employed to test the hypothesised relationships. The procedure allows simultaneous testing of the effects of two or more independent variables on single, interval-scaled dependent variables (Zikmund & Babin, 2007).

Four regression models were analysed in order to determine the factors that influenced teaching, research, company-creation and cross-functional engagements. In each model, four demographic characteristics and six social-psychological and organisational factors were entered in the regression equation as the independent variables.

Two regression models were tested with all six social-psychological and organisational factors, four demographic variables and four collaborative engagement variables as the independent variables. In the first model, performance variable enhanced reputation and resources were tested as the dependent variable, while in the second model, performance variable effective knowledge transfer were tested.

3.5 Summary

This chapter presented the methodology of the research and the process of data collection and analysis. Information regarding the sample size, sampling method, survey instrument construction, data collection procedures and data collection procedures and data analysis techniques were also discussed. The next chapter presents the data analysis results and attempts to test the research hypotheses.

CHAPTER FOUR

FINDINGS

4.0 Introduction

The results analysis described in chapter three is summarised in this chapter. This chapter begins with a discussion of the general characteristics of the respondents, the exploratory factor analysis and operationalisation of factors for all the variables. This is followed by a presentation of the statistical tests and results used to examine the relationships and variables between demographics, social-psychological factors, organisational-level factors, inter-organisational factors, academic-industry collaborations, and performance in academic-industry collaborations. Finally, the chapter presents a summary of data analysis.

4.1 General Characteristics of the Respondents

4.1.1 Response Rate

Table 4.1: The questionnaire response rate.

Description Number/percentage Number of survey questionnaire sent 5000 Number of questionnaires returned 538 Number of discarded questionnaires 28 Number of usable questionnaires 510 Rate of response (510/5000) 10.2 %

The final response rate of respondents from private universities in Malaysia is presented in Table 4.1. Overall, a total of 5,000 sets of questionnaires were mailed to academic staff in 31 private universities and 5 foreign branch campus universities. The names and types of institution are listed in Table 4.2. The rationale for selecting a large sample was to ensure an adequate number of returned responses. Of the 5,000 questionnaires sent, 538 sets were returned; 11 were discarded as the respondents did not complete major portions of the questionnaires, leaving 527 sets of completed questionnaires. An additional 17 questionnaires were discarded as these were

79 identified as multivariate outliers. With 510 returned and useable questionnaires, the final response rate was 10.2 percent.

Table 4.2: List of private universities surveyed.

Universities Type of University 1. Raffles University Iskandar (RUI). private university 2. Multimedia University, Melaka Campus. private university 3. Quest International University, Perak. private university 4. Petronas University of Technology (UTP). private university 5. University Tunku Abdul Rahman (UTAR). private university 6. Al-Madinah International University (MEDIU). private university 7. Binary University OF Management and Entrepreneurship. private university 8. Malaysia University of Science and Technology. private university 9. Management and Science University (MSU). private university 10. Manipal International University (MIU). private university 11. Perdana University. private university 12. UNITAR International University. private university 13. Asia Metropolitan Asia. private university 14. University of Selangor (UNISEL). private university 15. University Tenaga Nasional (UNITEN). private university 16. Sunway University. private university 17. Taylor’s University. private university 18. Limkokwing University of Creative Technology. private university 19. Tunku Abdul Rahman University (UTAR). private university 20. AIMST University. private university 21. Universiti Antarabangsa AlBukhary. private university 22. INTI International University. private university 23. Nilai University. private university 24. Wawasan Open University private university 25. Asia e University (AeU). private university 26. HELP University. private university 27. Infrastructure University, Kuala Lumpur. private university 28. International Medical University. private university 29. Open University Malaysia (OUM). private university 30. UCSI University. private university 31. University Tun Abdul Razak (UNIRAZAK) private university 32. Curtin University, Sarawak, Malaysia. foreign branch campus 33. Swinburne University of Technology Sarawak Campus. foreign branch campus 34. Monash University Sunway Campus Malaysia. foreign branch campus 35. The University of Nottingham Malaysia Campus. foreign branch campus 36. Newcastle University Medicine Malaysia foreign branch campus

4.1.2 Respondent Profile

The demographic characteristics of the study population are presented in Table 4.3. There are slightly more male than female respondents, and their ages varied. The majority of respondents are within the 31-40 years age bracket (44.9%), followed by 41-50 years (31.8%), 51-60 years (12.4%), below 30 years (9.0%), and above 60 years (2.0%). In terms of educational attainments, a majority possess a doctorate (46.3%), followed by masters (43.5%), and the remainder a bachelor’s degree (10.2%). Regarding current positions in universities, the majority were lecturers (53.3%), followed by senior lecturers (25.7%), associate professors (14.9%), and the least were professors (4.9%). For academic discipline specialisation, the majority were Social sciences (27.8%), followed by Professions (20.2%), Formal sciences (20.0%), Natural sciences (17.3%), and Humanities (14.7%).

Table 4.3: The demographic characteristics of the study population (N=510).

Demographic Category Frequency Percentage Variable Gender Male 279 54.7 Female 231 45.3 Age Below 30 years 46 9.0 31-40 years 229 44.9 41-50 years 162 31.8 51-60 years 63 12.4 Above 60 years 10 2.0 Educational Level Bachelor 52 10.2 Master 222 43.5 Doctorate 236 46.3 Current Position Associate lecturer 6 1.2 Lecturer 272 53.3 Senior Lecturer 131 25.7 Associate Professor 76 14.9 Professor 25 4.9 Academic Discipline Humanities 75 14.7 Social sciences 142 27.8 Natural sciences 88 17.3 Formal sciences 102 20.0 Professions 103 20.2

4.2 Exploratory Factor Analysis

A factor analysis was undertaken with the goal of revealing the underlying structure of the data, and constructing summated scales that represent the antecedents and outcomes of entrepreneurial collaboration. The survey questionnaire comprised of 12 items measuring two social-psychological constructs, namely, entrepreneurial orientation (Bolton & Lane, 2012; Covin & Slevin, 1989) and readiness to collaborate (Ajzen & Fishbein, 2005). In addition, nine items measure the organisational-learning capability of universities (Gomez et al. 2005; Chiva, Alegre & Lapiedra, 2007) and seven items measure the effectiveness of inter-organisation collaborations (Bryan et al., 2006; Garstka et al., 2012). These theoretical constructs were conceptualised as the antecedents of entrepreneurial collaborations.

The outcomes of entrepreneurial collaborations were measured using 11 items. The measured outcomes include meeting project timelines and budgets, achieving positive financial returns, strengthening reputation within industry, securing industry input for teaching and research, and enhancing career mobility between academia and industry.

A total of 39 items was factor analysed using the principal components method with varimax rotation. The result was the identification of nine factors with Eigenvalue higher than one, accounting for 73 percent of variance. As shown in table 4.4, the number of items that loaded on each factor range from seven to three (all above .40), with the exception of one factor that has only a single item loading. The factor was subsequently dropped from further analyses.

Table 4.4: Results of factor analysis.

Component 1 2 3 4 5 6 7 8 9 Eigen- 5.79 5.241 3.547 3.543 2.862 2.75 2.04 1.615 1.215 values 6 Var % 14.8 28.29 37.395 46.478 53.816 60.867 66.096 70.236 73.351 6 9 Cronbach’s 0.94 0.894 0.854 0.871 0.845 .838 0.716 0.791 n/a Alpha 4 Factor 1: Reputation & Resources PEF53 .906 PEF54 .904 PEF56 .894 PEF55 .887 PEF52 .882 PEF57 .712 PEF61 .651 .403 Factor 2: Learning capability OG20 .790 OG22 .785 OG23 .783 OG21 .772 OG26 .711 OG25 .699 OG19 .698 OG24 .637 Factor 3: Innovativeness & Risk-taking SP14 .844 SP15 .830 SP13 .802 SP12 .730 SP11 .574 Factor 4: Readiness to collaborate SP7 .835 SP8 .834 SP6 .818 SP9 .770 SP10 .518 Factor 5: Knowledge transfer PEF59 .823 PEF58 .803 PEF60 .782 PEF62 .462 .632

Table 4.4: Results of factor analysis continued.

Component 1 2 3 4 5 6 7 8 9 Eigen- 5.796 5.241 3.547 3.543 2.862 2.75 2.04 1.615 1.215 values Var % 14.86 28.29 37.395 46.478 53.816 60.867 66.096 70.236 73.351 9 Cronbach’s 0.944 0.894 0.854 0.871 0.845 .838 0.716 0.791 n/a Alpha Factor 6: Collaborative purpose IO32 .811 IO31 .775 IO33 .748 Factor 7: Collaborative environment IO29 .721 IO28 .720 OG18 .513 .539 Factor 8: Proactiveness SP17 .754 SP16 .615 IO27 -.404 Factor 9: n/a IO30 .433 .572 KMO 0.84 9 Bartlett 0.00 TOS 0 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalisation. a. Rotation converged in 8 iterations.

The remaining eight factors, which account for 70 percent of the variance extracted, were interpreted based on their respective items. All seven items that loaded on Factor 1 measure specific outcomes of entrepreneurial collaboration relating to the acquisition or strengthening of the university’s resource pool (factor loadings range from 0.904 to 0.651). The factor was deemed conceptually valid as all seven items measure outcomes and are loaded on single factor as expected. A composite score for the factor was created using the average score of the seven items and operationalised as reputation and resources.

Factor 2 comprises of eight items that measure different dimensions of an organisation’s learning capability (factor loadings range from 0.790 to 0.637). Consistent with the theoretical underpinnings of the items, a composite score for the factor was created and operationalised as learning capability.

Five items loaded on Factor 3, measuring entrepreneurial orientation dimensions of innovativeness and risk-taking. A composite score was therefore created and operationalised as innovativeness and risk-taking.

Factor 4 comprises of five items that measure respondents’ perception on the usefulness of industry engagement, the extent academic peers would regard industry engagement favourably and the extent they are capable of undertaking such engagements (factor loadings range from 0.835 to 0.518). A composite score for the factor was created and operationalised as readiness to collaborate.

Factor 5 comprises of four items that measure the extent existing collaborations have enabled the respondents to secure industry input for teaching, transfer industry best practices to the university, secure new collaborative opportunities, and enhance career mobility between the two sectors (factor loadings range from 0.823 to 0.632). A common theme that underlies the four items would be effective transfer and integration of knowledge between academia and industry. Therefore, the factor was operationalised as knowledge transfer.

Three items loaded on Factor 6, measuring the extent the respondents have a clear understanding of collaborative purposes and shared common belief with collaborative partners (factor loadings from 0.811 to 0.748). The factor was operationalised as collaborative purpose.

Factor 7 also comprises of three items that measure perceptions on the extent that the organisational climate is conducive for collaboration, the communication along the organisational hierarchy is effective and the extent of academics are involved in key decision-making (factor loadings from 0.721 to 0.539). The factor was operationalised by collaborative environment.

Factor 8 comprises of two items that measure the extent respondents would act in anticipation of future events and plan ahead on projects (factor loadings from 0.715 to 0.615). These attributes reflect a key dimension of entrepreneurial orientation, namely, proactivity. The factor was therefore operationalised as proactiveness.

A composite score for each factor was created using the average score of the items that loaded onto the factor. Descriptive statistics of the composite variables are presented in Table 4.5. Reliability tests for each of the composite scores all exceeded the threshold of 0.70 for acceptance (Cronbach’s alpha value ranges from 0.944 to 0.791). In summary, results from the factor analysis supported the proposed conceptual framework as most of the items that measure a common theoretical construct have loaded onto a common factor.

Table 4.5: Descriptive statistics of composite variables measuring antecedents and outcomes of entrepreneurial collaborations.

Composite variables Mean Std. Deviation Variance Innovativeness & Risk-taking 3.416 0.740 .547 Readiness to Collaborate 3.628 0.740 .547 Proactiveness 3.713 0.717 .514 Learning Orientation 3.405 0.638 .407 Collaborative Purpose 3.593 0.643 .414 Collaborative Environment 3.491 0.634 .402 Reputation & Resources 3.259 0.710 .503 Knowledge Transfer 3.492 0.556 .309

4.3 Academic Entrepreneurial Collaboration

The survey measured respondents’ involvement in 17 academic-industry collaboration activities, related to teaching, research and company-creation. Responses were recorded using an ordinal scale where 1 = never; 2 = involved in the last 3 years; 3 = involved in the last 12 months; and 4 = involved in both the last 12 months and 3 years. The activities associated with each form of collaboration are listed in Table 4.6.

Table 4.6: Activities associated with teaching, research and company-creation related collaborations.

Form of collaboration Activities Teaching-related 1. External teaching for which additional salary is paid 2. Initiating the development of new degree programs with advise from industry 3. Placing students as trainees in the industry 4. Conducting seminars and training sessions for industry 5. Teaching a subject that involves significant interactions with industry (e.g. capstone/ final year projects, guest lectures) 6. Sitting on the committee of industry/ trade bodies. Research-related 7. Research-based consultancy for industry through the university 8. Research-based consultancy privately (but without forming a company) 9. Joint-research projects with industry 10. Developing products/services with the potential for commercialisation 11. Providing research-related assistance to small business owners 12. Working in the industry while being attached to the university 13. Acquiring funding from government, non-governmental or international bodies, through collaborations with industry partners Company-creation 14. Contributing to the formation of university centres designed related to carry out commercialisation activities 15. Contributing to the formation of spin-off company/(s) (university is the owner) 16. Contributing to the establishment of university incubators and/or science parks 17. Forming joint-venture/(s) privately through collaboration with industry 18. Forming own company/(s)

The respondents’ involvement in six teaching-related collaborations is summarised in Table 4.7. More than 40 percent of the respondents have indicated active and sustained involvement (i.e. last 12 months and 3 years) in external teaching, new program development, student industry placement and conducting seminars and training for industry. Likewise, 37 percent of respondents are teaching subjects that involve significant interactions with industry. Their active involvement in teaching- related collaborations indicates that there are many opportunities for industry engagement, even for a teaching-centric academic role. Lastly, 12 percent of the respondents are actively involved in industry/trade bodies. The low percentage is most likely due to limited opportunities as committees usually comprise of industry practitioners.

Table 4.7: Respondents’ involvement in teaching-related collaboration.

Collaborative activity Never Last 3 Last 12 Last 12 years months months & 3 years 1. External teaching for which you are paid in 21.4 15.1 21.6 42.0 addition to your basic salary 2. Initiating the development of new degree 32.0 14.9 10.8 42.4 program/(s) with advise from industry 3. Placing students as trainees in the industry 21.0 16.5 15.7 46.9 4. Conducting seminars and training sessions for 14.5 21.4 16.3 47.8 industry 5. Teaching a subject that involves significant 27.6 13.3 22.0 37.1 interactions with industry (e.g. capstone/ final year projects, guest lectures) 6. Sitting on the committee of industry/ trade 65.1 14.7 8.0 12.2 bodies

The findings on the respondents’ involvement in research-related collaborations are summarised in Table 4.8 below. With the exception of industry secondment (i.e. working in the industry while being attached to the university), at least 30 percent of the respondents have indicated active and sustained involvement (i.e. involved in the last 12 months and 3 years) in research-related collaborations. Two research-related collaborations with the highest percentages of active and sustained involvement are ‘joint-application for funding’ (42.4 percent) and ‘providing research-

88 related assistance to small business owners’ (41.6 percent). However, it is noted that the percentages of respondents who indicated that they were never involved in a particular research-related collaboration are fairly high, as these range from 25.7 percent to 51.4 percent.

Table 4.8: Respondents’ involvement in research-related collaborations.

Collaborative activity Never Last 3 Last 12 Last 12 years months months & 3 years 1. Research-based consultancy for industry through 27.3 27.8 12.5 32.4 the university 2. Research-based consultancy privately (but without 28.6 18.0 16.3 37.1 forming a company) 3. Acquiring funding from government, non- 25.7 17.6 14.3 42.4 governmental or international bodies, through collaborations with industry partners 4. Joint-research projects with industry 32.5 19.6 13.7 34.1 5. Developing products/services with the potential for 40.4 20.2 7.3 32.2 commercialisation 6. Providing research-related assistance to small 38.8 10.4 9.2 41.6 business owners 7. Working in the industry while being attached to the 51.4 22.4 6.3 20.0 university

The respondents’ involvement in five company creation-related collaborations is summarised in Table 4.9. The findings indicate that majority of the respondents have never been involved in company-creation activities. This may be due to the lack of opportunities because Malaysian private universities are generally smaller than public universities, thus lacking the resources and expertise for commercialising academic intellectual properties.

Table 4.9: Respondents’ involvement in company creation-related collaboration.

Collaborative activity Never Last 3 Last 12 Last 12 years months months & 3 years 1. Contributing to the formation of university 42.7 24.9 10.2 22.2 centres designed to carry out commercialisation activities 2. Contributing to the formation of spin-off 72.4 14.1 2.9 10.6 company/(s) (university is the owner) 3. Contributing to the establishment of university 83.7 9.6 1.0 5.7 incubators and/or science parks 4. Forming joint-venture/(s) privately through 46.3 17.6 8.8 27.3 collaboration with industry 5. Forming your own company/(s) 72.2 12.5 6.5 8.8

Three variables were constructed to measure the breadth of teaching, research and company-creation-related forms of collaboration. To measure each variable, a binary code for each collaborative activity was used, which takes the value of 1 if an academic reports having been engaged in the activity in the last 36 months, or 0 otherwise. The sum of the values of all the relevant activities represents the breadth of an academics’ engagement in a particular form of collaboration. A higher value reflects a more diverse engagement, while a lower value implies a more narrow focus.

The breadth of teaching collaborations was determined by measuring involvement in external teaching, development of new academic programmes, student industry placement, conducting seminars or training for industry, and teaching a subject that involves significant interaction with industry. The breadth of research collaborations was determined by measuring involvement in research-based consultancy, joint-research projects, new product development, providing research- related assistance to small businesses, secondment to industry and joint-application for research funding. The breadth of company-creation collaborations was determined by measuring contribution to the formation of commercialisation centres, spin-off companies, business incubators or science parks, and lastly, setting-up business joint- ventures as well as forming own companies. The breadth of cross-functional collabortions was determined by measuring the simultaneous involvement in the breadth of teaching, company-creation and cross-functional collaborations. As

90 presented in Table 4.10, the mean values of engagement variables indicate the diversity of engagement.

Table 4.10: Diversity of collaboration based on breadth of activities.

Collaborative activity Minimum Maximum Mean Std. Deviation Breadth of teaching engagement 0.00 5.00 3.8353 1.5188 Breadth of research engagement 0.00 7.00 4.5529 2.4025 Breadth of company-creation engagement 0.00 5.00 1.8275 1.6145 Breadth of cross-functional engagement 0.00 3.00 2.6275 0.7692

4.4 New Conceptual Framework

The new conceptualised relationships among the variables in this study are shown in Figure 4.1 below.

Figure 4.1: The New Conceptualised Relationship Between Independent and Dependent Variables with the Presence of a Mediator.

The similarity between the general conceptual model in Figure 1.1 and the new conceptual framework in Figure 4.1 is they were both developed to guide the study by offering an overview of the hypotheses that concern the relationships between the

91 constructs modelled to measure their influence on the performance of entrepreneurial collaboration.

However, the difference between the general conceptual model in Figure 1.1 and the new conceptual framework in Figure 4.1 is that the results of the factor analysis revealed eight factors that measure the antecedents and outcomes of entrepreneurial collaborations. As many items did not load on the intended factors, the new factors have been renamed based on the characteristics of the items illustrated in Figure 4.1.

Multi-level factors, comprising individual, organisational and inter- organisational factors are modelled as independent variables. Individual factors are measured by six dimensions, namely ‘attitudes’, ‘subject norms’, ‘behavioural intention’, ‘risk-taking’, ’innovativeness’ and ‘proactiveness’. Organisational factors are measured by eight dimensions, namely ‘managerial commitment’, ‘presence of systems perspective’, ‘behavioural intention’, ‘effective knowledge transfer and integration’, ‘proactiveness’, ‘innovativeness’, ‘risk-taking’, and ‘competitive aggressiveness’. Inter-organisational factors are measured by ‘collaborative environment’, ‘collaborative communications’, and ‘collaborative purpose’.

In this conceptual framework, the dependent variables are ‘breadth of teaching-related engagement’, ‘breadth of research-related engagements’, ‘breadth of company- creation engagements’ and ‘breadth of cross functional engagements’.

4.4.1 Descriptive Analyses

The mean values and standard deviation of all the descriptive statistics are presented in Table 4.11, and the bivariate-correlation coefficients of the variables are presented in Table 4.12. The Pearson product-moment correlation test is utilised to examine the presence of multi-collinearity, where two or more variables in a linear regression model are highly related (r=.9 or above). This may inflate either the p-value of a variable or the confidence intervals on the regression coefficients. In both cases, statistical results may become misleading. In the current study, however, all

92 correlation coefficients are below the threshold level as suggested by Pallant (2007) (shown in Table 4.12).

Table 4.11: Descriptive statistics of the variables under study (n=510).

Variables Mean Std. Deviation Gender Male 2.645 .781 Female 2.606 .755 Age Below 30 years 2.304 1.152 31-40 years 2.624 .736 41-50 years 2.703 .648 51-60 years 2.761 .587 Above 60 years 2.100 1.449 Academic Bachelor 1.461 1.259 Master 2.675 .6946 Doctorate 2.839 .3683 Position Associate lecturer 2.333 1.211 Lecturer 2.573 .834 Senior Lecturer 2.603 .8103 Associate Professor 2.776 .4194 Professor 2.960 .2000 Innovation and Risk-taking 3.416 0.740 Readiness to Collaborate 3.628 0.740 Proactiveness 3.713 0.717 Learning Orientation 3.405 0.638 Collaborative Purpose 3.593 0.643 Collaborative Environment 3.491 0.634 Breadth of teaching engagement 3.835 1.518 Breadth of research engagement 4.552 2.402 Breadth of company-creation engagement 1.827 1.614 Breadth of cross-functional engagement 2.627 0.769 Reputation & Resources 3.259 0.710 Knowledge Transfer 3.492 0.556

Table 4.12: Correlation matrix for study variables.

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) Gender 1 Age -.089* 1 Academic -.026 .280** 1 Position -.075 .556** .480** 1 Innovativeness & Risk-taking -.053 .181** .172** .204** 1 Readiness to Collaborate -.058 .186** .196** .246** .399** 1 Proactiveness -.059 .137** .257** .190** .454** .433** 1 Learning Orientation -.101* .069 .061 .080 .200** .248** .293** 1 Collaborative Purpose -.019 .113* .169** .208** .284** .374** .255** .205** 1 Collaborative Environment -.036 -.038 .142** .061 .249** .296** .272** .556** .349** 1 Reputation & Resources .015 .258** -.022 .180** .051 .214** .014 -.213** .297** -.139** 1 Knowledge Transfer -.044 .188** .140** .191** .205** .294** .183** .069 .375** .157** .465** 1 Breadth of research engagement .025 .240** .403** .300** .223** .308** .266** .227** .353** .226** .369** .351** 1 Breadth of company-creation engagement -.039 .232** .437** .263** .107* .257** .254** .260** .181** .250** .165** .201** .612** 1 Breadth of teaching engagement -.018 .094* .412** .153** .157** .226** .261** .120** .182** .130** .114** .312** .617** .576** 1 Breadth of cross-functional engagement -.025 .081 .428** .129** .129** .097* .164** .124** .142** .193** .094* .162** .663** .548** .674** 1 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).

4.5 Statistical Test of Hypotheses

This study seeks to identify the key determinants of academics’ involvement and performance in entrepreneurial collaboration by analysing selected factors, namely social psychological, organisational and institutional factors.

The research hypotheses are structured around three specific research objectives of this study, which are to measure academics’ engagement in entrepreneurial collaborations with industry, to investigate the influence of personal variables, and to examine multi-level factors on the academics’ engagement in entrepreneurial collaborations. The study uses regression analysis technique to test six regression models while investigating the three objectives of the study.

In the study, personal and multi-level factors are modelled as antecedents of entrepreneurial collaboration. The multi-level factors represent the academics’ entrepreneurial orientation and readiness to collaborate, as well as perception of the extent that the academic environment is supportive of collaborations. These constructs are modelled to measure their influence on the performance of entrepreneurial collaboration. The Table 4.13 summarises the hypotheses of this research.

Table 4.13: Research objectives and hypotheses.

Objective 1 Measure academicians’ engagement in entrepreneurial collaborations with industry

H1 There are significant differences in academicians’ engagement in entrepreneurial collaborations with respect to age.

H2 There are significant differences in academicians’ engagement in entrepreneurial collaborations with respect to gender.

H3 There are significant differences in academicians’ engagement in entrepreneurial collaborations with respect to seniority.

H4 There are significant differences in academicians’ engagement in entrepreneurial collaborations with respect to academic qualifications. Objective 2 Investigate the influence of personal and multi-level factors on the academics’ engagement in entrepreneurial collaborations

H5 Academicians’ innovativeness and propensity to take risk are positively related to academics’ engagement in entrepreneurial collaborations.

H6 Academicians’ proactiveness is positively related to engagement in entrepreneurial collaborations.

H7 Academicians’ readiness to collaborate is positively related to engagement in entrepreneurial collaborations.

H8 Universities’ learning orientation is positively related to academicians’ engagement in entrepreneurial collaborations.

H9 Strong collaborative purpose is positively related to academicians’ engagement in entrepreneurial collaborations.

H10 Supportive collaborative environment is positively related to academicians’ engagement in entrepreneurial collaborations. Objective 3 Examine the factors that influence the outcomes of entrepreneurial collaborations

H11 Academicians’ engagement in entrepreneurial collaborations is positively related to enhanced reputations and resources

H12 Academicians’ engagement in entrepreneurial collaborations is positively related to effective knowledge transfer

4.5.1 Analysis of Variance of Collaborative Engagements Based on Personal Factors

The mean and standard deviation of all four forms of collaborative engagements for different age groups are presented in Table 4.14. An analysis of variance of collaborative engagements based on age group, revealed significant differences between the age groups, for all four forms of collaborative engagements. There were significant differences in teaching, F (4, 505) = 2.311, p < .05; research, F (4, 505) = 9.545, p < .00; company-creation, F (4, 505) = 8.648, p < .00; and cross- functional engagement, F (4, 505) = 4.186, p < .00. Significant differences in teaching can be traced to a higher mean score for respondents between the ages 31 to 60 years as they engaged in more diverse forms of teaching-related activities than their counterparts from other age groups. For research, respondents between the ages of 41 and 60 years scored higher than their counterparts in terms of the breadth of research-related engagements. Comparison of the mean score for breadth of company-creation engagement revealed that respondents from ages 51 to above 60 years scored higher than their younger counterparts. Lastly, respondents between the ages of 31 and 60 years scored higher than their counterparts in terms of breadth of cross-functional engagement.

Table 4.14: Results of analysis of variance of collaborative engagements based on age.

N Mean Std. Std. Levene F Deviation Error Statistic Breadth of Below 30 46 3.3478 1.99129 .29360 7.236*** 2.311* research years engagement 31-40 years 229 3.7773 1.40744 .09301 41-50 years 162 3.9877 1.47015 .11551 51-60 years 63 4.0794 1.41765 .17861 Above 60 10 3.4000 2.36643 .74833 years Total 510 3.8353 1.51882 .06725 Breadth of Below 30 46 3.4783 2.59691 .38289 6.308*** 9.545*** company- years creation 31-40 years 229 4.1004 2.41586 .15964 engagement 41-50 years 162 5.1420 2.16082 .16977 51-60 years 63 5.4444 2.00626 .25277 Above 60 10 4.7000 3.26769 1.03333 years Total 510 4.5529 2.40255 .10639 Breadth of Below 30 46 1.5870 1.51402 .22323 10.208*** 8.648*** teaching years engagement 31-40 years 229 1.4847 1.34946 .08917 41-50 years 162 2.0062 1.69213 .13295 51-60 years 63 2.6508 1.92738 .24283 Above 60 10 2.7000 1.88856 .59722 years Total 510 1.8275 1.61453 .07149 Breadth of Below 30 46 2.3043 1.15219 .16988 17.375*** 4.186** cross- years functional 31-40 years 229 2.6245 .73658 .04867 engagement 41-50 years 162 2.7037 .64883 .05098 51-60 years 63 2.7619 .58790 .07407 Above 60 10 2.1000 1.44914 .45826 years Total 510 2.6275 .76927 .03406 *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Academician’s Age

The mean and standard deviation of all four forms of collaborative engagements for male and female respondents are presented in Table 4.15. The analysis of variance revealed no significant difference for teaching, F (1,508) = .319, p < .572; research F (1,508) = .791, p < .374; company-creation, F (1,508) = .166, p < .684; and cross-functional engagement, F (1,508) = .326, p < .568.

Table 4.15: Results of analysis of variance of collaborative engagements based on gender.

N Mean Std. Std. Levene F Deviation Error Statistic Breadth of Male 279 3.8602 1.56346 .09360 .189 .166 teaching Female 231 3.8052 1.46589 .09645 engagement Total 510 3.8353 1.51882 .06725 Breadth of Male 279 4.4982 2.39735 .14353 .221 .319 research Female 231 4.6190 2.41236 .15872 engagement Total 510 4.5529 2.40255 .10639 Breadth of Male 279 1.8853 1.61193 .09650 1.017 .791 company- Female 231 1.7576 1.61841 .10648 creation Total 510 1.8275 1.61453 .07149 engagement Breadth of Male 279 2.6452 .78169 .04680 .387 .326 cross- Female 231 2.6061 .75513 .04968 functional Total 510 2.6275 .76927 .03406 engagement *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Academician’s Gender

The mean and standard deviation of all four forms of collaborative engagements for different levels of academic position are presented in Table 4.16. The results of the analysis of variance indicate significant differences among academic positions in terms of teaching, F(4, 505) = 3.110, p < .05; research, F(4, 505) = 13.266, p < .01; company-creation, F(4, 505) = 11.443, p < .01; and cross-functional engagement, F(4, 505) = 2.495, p < .05. Across all forms of collaborative engagements, it was observed that respondents with more senior positions, namely, associate professors and professors, score higher than their counterparts in lower-level positions.

Table 4.16: Results of analysis of variance of collaborative engagements based on position.

N Mean Std. Std. Levene F Deviation Error Statistic Breadth of Associate 6 4.3333 2.42212 .98883 23.900*** 13.266*** research lecturer engagement Lecturer 272 4.0294 2.52644 .15319 Senior 131 4.5954 2.29976 .20093 lecturer Associate 76 5.6974 1.60848 .18450 Professor Professor 25 6.6000 .86603 .17321 Total 510 4.5529 2.40255 .10639 Breadth of Associate 6 1.8333 1.83485 .74907 6.323*** 11.443*** company- lecturer creation Lecturer 272 1.5625 1.43871 .08723 engagement Senior 131 1.7252 1.57896 .13795 lecturer Associate 76 2.4342 1.89269 .21711 Professor Professor 25 3.4000 1.38444 .27689 Total 510 1.8275 1.61453 .07149 Breadth of Associate 6 3.3333 1.86190 .76012 4.555** 3.110** teaching lecturer engagement Lecturer 272 3.6728 1.57691 .09561 Senior 131 3.8626 1.55298 .13568 lecturer Associate 76 4.2237 1.13840 .13058 Professor Professor 25 4.4000 1.35401 .27080 Total 510 3.8353 1.51882 .06725 Breadth of Associate 6 2.3333 1.21106 .49441 12.036*** 2.495** cross- lecturer functional Lecturer 272 2.5735 .83407 .05057 engagement Senior 131 2.6031 .81031 .07080 lecturer Associate 76 2.7763 .41948 .04812 Professor Professor 25 2.9600 .20000 .04000 Total 510 2.6275 .76927 .03406 *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Academic position

100

The mean and standard deviation of all four forms of collaborative engagements for different levels of academic attainment are presented in Table 4.17. The results of the analysis of variance of collaborative engagements based on academic attainment revealed significant differences for teaching, F(2, 507) = 60.456, p < .01; research, F(2, 507) = 55.159, p < .01); company-creation, F(2, 507) = 59.732, p < .01; and cross-functional engagement, F(2, 507) = 94.448, p < .01. When comparing the mean scores for the three levels of academic attainment, it was observed that the mean scores increase as the level of academic attainment increases from Bachelor to Doctoral, across all forms of collaborative engagement.

101

Table 4.17: Results of analysis of variance of collaborative engagements based on academic attainment.

N Mean Std. Std. Error Levene F Deviation Statistic Breadth of Bachelor 52 2.0577 1.84086 .25528 45.353*** 60.456*** teaching Master 222 3.7207 1.54082 .10341 engagement Doctoral 236 4.3347 1.03257 .06721 Total 510 3.8353 1.51882 .06725 Breadth of Bachelor 52 1.9038 2.26014 .31342 3.443** 55.159*** research Master 222 4.3288 2.26821 .15223 engagement Doctoral 236 5.3475 2.07878 .13532 Total 510 4.5529 2.40255 .10639 Breadth of Bachelor 52 .4038 .66449 .09215 56.628*** 59.732*** company- Master 222 1.4279 1.23367 .08280 creation Doctoral 236 2.5169 1.74421 .11354 engagement Total 510 1.8275 1.61453 .07149 Breadth of Bachelor 52 1.4615 1.25965 .17468 115.358*** 94.448*** cross- Master 222 2.6757 .69467 .04662 functional Doctoral 236 2.8390 .36833 .02398 engagement Total 510 2.6275 .76927 .03406 *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Academic attainment

4.5.2 Regression Analysis of the Relationship between Social- Psychological and Organisational Factors with Breath of Teaching-Related Engagement

A regression model with breadth of teaching-related engagement as the dependent variable was tested. In the model, four demographic characteristics and six social-psychological and organisational factors were entered in the regression equation as independent variables. The statistical results are presented in Table 4.18.

The value of R2 indicates that demographic, multi-level factors and engagement variables accounted for 21.6 percent of the variance in breadth of teaching engagements. Analysis of variance indicates that the model as a whole was significant (F = 13.764; p <.000). Only three items, namely, academic attainment (β = .404; ρ <.000), readiness to collaborate (β = .394; ρ <.049), and proactiveness (β = .114; ρ <.0.19) had positive and significant relationships with breadth of teaching engagement.

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Table 4.18: Summary of regression analysis with breadth of teaching engagement as a dependent variable.

Collinearity R2 F Statistics Model B Std. Beta t Sig. Tolerance VIF Error (Constant) -.212 .548 -.388 .698 .216 13.764*** Gender .005 .122 .002 .039 .969 .981 1.020 Age -.020 .083 -.011 -.237 .813 .668 1.496 Academic .929 .107 .404 8.698 .000 .730 1.371 Position -.159 .088 -.095 -1.804 .072 .561 1.783 Innovative & -.006 .096 -.003 -.061 .951 .718 1.393 Risk-taking Readiness to .194 .098 .394 1.971 .049 .683 1.463 Collaborate Proactive .240 .102 .114 2.347 .019 .672 1.489 Learning .123 .117 .052 1.053 .293 .653 1.531 Orientation Collaborative .173 .107 .073 1.623 .105 .772 1.296 Purpose Collaborative -.083 .123 -.035 -.679 .497 .599 1.669 Environment *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Breadth of teaching-related engagement

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4.5.3 Regression Analysis of the Relationship between Social- Psychological and Organisational Factors with Breadth of Research- Related Engagement

A regression model with breadth of research-related engagement as the dependent variable was tested. In the model, four demographic characteristics and six social-psychological and organisational factors were entered in the regression equation as independent variables. The statistical results are presented in Table 4.19.

The value of R2 indicates that demographic, multi-level factors and engagement variables accounted for 29.6 percent of the variance in breadth of research engagements. Analysis of variance indicated that the model as a whole was significant (F = 20.996; p <.000). Four items namely, academic attainment (β = .292; ρ <.000), readiness to collaborate (β = .299; ρ <.000), university’s learning orientation (β = .125; ρ <.01) and collaborative purpose (β = .212; ρ <.000) had positive and significant relationships with breadth of research-related engagement.

Table 4.19: Summary of regression analysis with breadth of research-related engagement as a dependent variable.

Collinearity R2 F Statistics Model B Std. Beta t Sig Tol VIF Error (Constant) -5.500 .821 -6.700 .000 .296 20.996*** Gender .329 .183 .068 1.797 .073 .981 1.020 Age .247 .124 .092 2.000 .046 .668 1.496 Academic 1.062 .160 .292 6.633 .000 .730 1.371 Position .068 .132 .026 .511 .610 .561 1.783 Innovative & .038 .144 .012 .267 .790 .718 1.393 Risk-taking Readiness to .322 .148 .299 2.185 .029 .683 1.463 Collaborate Proactive .127 .154 .038 .830 .407 .672 1.489 Learning .469 .175 .125 2.680 .008 .653 1.531 Orientation Collaborative .792 .160 .212 4.963 .000 .772 1.296 Purpose Collaborative .012 .184 .003 .065 .948 .599 1.669 Environment *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Breath of research-related engagement

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4.5.4 Regression Analysis of the Relationship between Social- Psychological and Organisational Factors with Breadth of Company- Creation Engagement

A regression model with breadth of company-creation engagement as the dependent variable was tested. In the model, four demographic characteristics and six social-psychological and organisational factors were entered into the regression equation as independent variables. The statistical results are presented in Table 4.20.

The value of R2 indicates that demographic, multi-level factors and engagement variables accounted for 28.2 percent of the variance in breadth of company-creation engagements. Analysis of variance indicates that the model as a whole was significant (F = 19.639; p <.000). Five items, namely, age (β = .121; ρ <.010), academic attainment (β = .365; ρ <.000), readiness to collaborate (β = .107; ρ <.05), university’s learning orientation (β = .152; ρ <.01), and collaborative environment (β = .220; ρ <.10) had positive and significant relationships with breadth of research-related engagement.

Table 4.20: Summary of regression analysis with breadth of company-creation engagement as a dependent variable.

Collinearity R2 F Statistics Model B Std. Beta t Sig. Tolerance VIF Error (Constant) -3.697 .557 -6.637 .000 .282 19.639*** Gender .010 .124 .003 .083 .934 .981 1.020 Age .218 .084 .121 2.602 .010 .668 1.496 Academic .894 .109 .365 8.229 .000 .730 1.371 Position -.036 .090 -.020 -.396 .692 .561 1.783 Innovative & -.237 .098 -.108 -2.424 .016 .718 1.393 Risk-taking Readiness to .234 .100 .107 2.335 .020 .683 1.463 Collaborate Proactive .175 .104 .078 1.676 .094 .672 1.489 Learning .385 .119 .152 3.240 .001 .653 1.531 Orientation Collaborative .049 .108 .019 .449 .653 .772 1.296 Purpose Collaborative .220 .125 .220 1.767 .078 .599 1.669 Environment *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Breadth of company-creation engagement

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4.5.5 Regression Analysis of the Relationship between Social- Psychological and Organisational Factors with Breadth of Cross- Functional Engagement

A regression model with breadth of cross-functional engagement as the dependent variable was tested. In the model, four demographic characteristics and six social-psychological and organisational factors were entered into the regression equation as independent variables. The statistical results are presented in Table 4.21.

The value of R2 indicates that demographic, multi-level factors and engagement variables accounted for 21.4 percent of the variance in breadth of company-creation engagements. Analysis of variance indicates that the model as a whole was significant (F = 13.574; p <. 000). Four items, namely, academic attainment (β = .453; ρ <.000), position (β = -.114; ρ <.05), readiness to collaborate (β = .146; ρ <.05), and collaborative environment (β = .217; ρ <.10) had significant relationships with breadth of cross-functional engagement.

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Table 4.21: Summary of regression analysis with breadth of cross-functional engagement as a dependent variable.

Collinearity R2 F Statistics Model B Std. Beta t Sig. Tolerance VIF Error (Constant) .837 .278 3.012 .003 .214 13.574*** Gender -.019 .062 -.013 -.312 .755 .981 1.020 Age .010 .042 .012 .244 .807 .668 1.496 Academic .528 .054 .453 9.738 .000 .730 1.371 Position -.096 .045 -.114 -2.155 .032 .561 1.783 Innovative & .036 .049 .035 .747 .455 .718 1.393 Risk-taking Readiness to -.048 .050 .146 -.964 .006 .683 1.463 Collaborate Proactive .023 .052 .021 .433 .665 .672 1.489 Learning .046 .059 .038 .782 .435 .653 1.531 Orientation Collaborative .058 .054 .048 1.066 .287 .772 1.296 Purpose Collaborative .117 .062 .217 1.883 .060 .599 1.669 Environment *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Breadth of cross-functional engagement

4.5.6 Regression Analysis of the Relationship between Multi-Level Factors and Collaborative Engagements with Organisational Performance

Two regression models were tested with all six social-psychological and organisational factors, four demographic variables and four collaborative engagement variables as the independent variables. In the first model, the performance variable ‘enhanced reputation and resources’ was tested as the dependent variable, while in the second model, the performance variable ‘effective knowledge transfer’ was tested. The results of the regression analysis are presented in Table 4.22 and 4.23 respectively.

As shown in Table 4.22, the value of R2 indicates that the independent variables accounted for 40.9 percent of the variance in enhanced reputation and resources. Analysis of variance indicates that the model as a whole was significant (F = 24.462; p <.000). Seven variables were significantly related to the performance variable ‘enhanced reputation and resources’. These were age (β = .168; ρ <.000), academic 107 attainment (β = .251; ρ <.000), readiness to collaborate (β = .145; ρ = .001), learning orientation (β = .315; ρ <.000), collaborative purpose (β = .237; ρ <.000), collaborative environment (β = .453; ρ <.000) and breadth of research-related engagement (β = .453; ρ <.000).

Table 4.22: Summary of regression analysis with enhanced reputation and resources as a dependent variable.

2 Collinearity R F Model B Std. Beta t Sig. Statistics Error Tolerance VIF (Constant) 3.732 .258 14.490 .000 .409 24.462*** Gender -.021 .050 -.015 -.424 .672 .967 1.034 Age .133 .034 .168 3.914 .000 .651 1.535 Academic -.270 .048 -.251 -5.565 .000 .588 1.701 Position .024 .036 .030 .647 .518 .548 1.825 Innovativeness & -.045 .040 -.046 -1.127 .260 .703 1.423 Risk-taking Readiness to .139 .041 .145 3.402 .001 .654 1.528 Collaborate Proactiveness -.036 .042 -.036 -.853 .394 .661 1.513 Learning Orientation -.350 .048 -.315 -7.248 .000 .634 1.577 Collaborative .261 .045 .237 5.822 .000 .721 1.386 Purpose Collaborative -.144 .051 -.129 -2.851 .005 .583 1.715 Environment Research .141 .017 .478 8.479 .000 .375 2.664 engagement Company-creation .037 .022 .084 1.705 .089 .493 2.030 engagement Teaching -.042 .024 -.090 -1.723 .086 .440 2.273 engagement Cross-functional -.084 .051 -.091 -1.651 .099 .392 2.552 engagement *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Enhanced reputation and resources

As shown in Table 4.23, the value of R2 indicates that demographic, multi-level factors and engagement variables accounted for 26.2 percent of the variance in effective knowledge transfer. Analysis of variance indicates that the model as a whole was significant (F = 12.528; p <.000). Four variables were significantly related to ‘effective knowledge transfer’. These were collaborative purpose (β = .233; ρ <.000), breadth of research-related engagement (β = .251; ρ = .000), breadth of teaching- 108 related engagement (β = .274; ρ < .000) and breadth of cross-functional engagement (β = .161; ρ = .009).

Table 4.23: Summary of regression analysis with effective knowledge transfer as a dependent variable.

Collinearity R2 F Statistics Model B Std. Beta t Sig. Error Tolerance VIF (Constant) 2.277 .226 10.095 .000 .262 12.528*** Gender -.043 .044 -.038 -.976 .330 .967 1.034 Age .064 .030 .103 2.146 .032 .651 1.535 Academic -.058 .042 -.069 -1.369 .172 .588 1.701 Position .013 .032 .022 .423 .672 .548 1.825 Innovativeness & .024 .035 .032 .696 .487 .703 1.423 Risk-taking Readiness to .069 .036 .092 1.926 .055 .654 1.528 Collaborate Proactiveness -.009 .037 -.012 -.248 .804 .661 1.513 Learning -.086 .042 -.098 -2.028 .043 .634 1.577 Orientation Collaborative .201 .039 .233 5.115 .000 .721 1.386 Purpose Collaborative .062 .044 .071 1.409 .159 .583 1.715 Environment Research .050 .015 .215 3.405 .001 .375 2.664 engagement Company-creation -.020 .019 -.059 -1.069 .286 .493 2.030 engagement Teaching .100 .021 .274 4.711 .000 .440 2.273 engagement Cross-functional -.117 .045 -.161 -2.616 .009 .392 2.552 engagement *p <0.100; **p<0.05; ***p<0.01 Dependent Variable: Effective knowledge transfer

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4.5.7 Regression Analysis of Industry Engagement as Mediator of the Relationship between Socio-Psychological and Organisational Factors, with Organisational Performance

This study utilised Baron and Kenny’s (1986) mediated regression approach to test the hypothesised mediation effects of industry engagements (i.e. mediator) on the relationship between multi-level factors (i.e. independent variables) and organisational performance (i.e. dependent variables). Two dimensions of performance were measured, namely, enhanced reputation and resources, and effective knowledge transfer.

The authors prescribed four conditions for full mediation. First, the independent variables must affect the dependent variables in the predicted direction. Second, these factors must have effects on the mediator. Third, the mediator must also have a relationship with the dependent variables. And fourth, when both the independent variables and the mediator are concurrently tested on their relationships with the dependent variables, the effects of the independent factors are reduced as a result of the inclusion of the mediator.

Separate regression equations were estimated for each of the four forms of industry engagement (i.e. mediators), and the regression equations correspond to the four conditions for full mediation (Baron & Kenny, 1986). The Sobel test was used to validate mediation effects (Baron & Kenny, 1986; Wood, et al., 2008). The results for each form of industry engagement are reported as follows.

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4.5.7.1 Testing Breadth of Teaching-Related Engagement as a Mediator

The results of the Sobel test indicate that teaching-related engagement significantly mediates the relationship between two multi-level factors and enhanced reputation and resources. The two factors were ‘academic attainment’ (z’ = 3.100, p<0.000) and ‘readiness to collaborate’ (z’ = 3.231, p<0.000). Together, these results suggest that ‘academic attainment’ and ‘readiness to collaborate’ predict ‘reputation and resource outcomes’, and that they do so by enhancing the diversity of teaching- related engagement between academics and industry partners. The results are presented in Table 4.24.

Table 4.24: The results summary of mediated regression testing of breadth of teaching- related engagement as a mediator between multi-level factors and enhanced reputation and resources.

Model 1 Model 3 Sobel Test of Reputation & Reputation & Significance Model 2 Resources Resources Teaching (Without (With Mediator) Mediator) (Constant) Gender .055 .002 .019 Age .037*** -.011 .223*** Academic .048*** .404***/.107 -.213*** 3.100*** Position .039 -.095 .073 Innovative & Risk- .043 -.003 -.052 taking Readiness to .044*** .394**/.098 .185*** 3.231*** Collaborate Proactive .046 .114**/.102 -.039 Learning Orientation .052*** .052 -.257*** Collaborative .048*** .073 .319*** Purpose Collaborative .055** -.035 -.121** Environment Breadth of teaching .135** Note: *** represents significant level at 0.01 or below; ** represents significant level at 0.05 or below; * represents significant level at 0.1 or below

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When testing the mediated regressions with the second performance variable, namely, ‘effective knowledge transfer’, it was found that ‘breadth of teaching-related engagement’ also mediated the effect of ‘academic attainment’ (z’ = 3.715, p<0.000) and ‘readiness to collaborate’ (z’ = 3.947, p<0.000). Consistent with the earlier findings, higher ‘academic attainment’ and ‘readiness to collaborate’ facilitate ‘effective knowledge transfer’, by enhancing academic and industry collaboration in teaching- related activities. The results of mediated regression testing are presented in Table 4.25.

Table 4.25: The results summary of mediated regression testing of breadth of teaching- related engagement as a mediator between multi-level factors and effective knowledge transfer.

Model 1 Model 3 Sobel Test of Knowledge Knowledge Significance Model 2 Transfer Transfer Teaching (Without (With Mediator) Mediator) (Constant) Gender -.021 .002 -.022 Age .110** -.011 .113* Academic .010 .404***/.107 -.094* 3.715*** Position .021 -.095 .046 Innovative & Risk- .035 -.003 .035 taking Readiness to .140*** .394**/.098 .116** 3.947*** Collaborate Proactive .020 .114**/.102 -.010 Learning Orientation -.073 .052 -.086* Collaborative .289*** .073 .270*** Purpose Collaborative .042 -.035 .051 Environment Breadth of teaching .258*** Note: *** represents significant level at 0.01 or below; ** represents significant level at 0.05 or below; * represents significant level at 0.1 or below

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4.5.7.2 Testing Breadth of Research-Related Engagement as a Mediator

The Results of the Sobel test of significance indicate that breadth of research- related engagement was a significant mediator between two multi-level factors and performance variable ‘enhanced reputation and resources’. The factors were ‘academic attainment’ (z’ = 1.822, p<0.05) and ‘readiness to collaborate’ (z’ = 2.016, p<0.05). The mediated regression results are presented in Table 4.26.

Table 4.26: The results summary of mediated regression testing of breadth of research-related engagement as a mediator between multi-level factors and enhanced reputation and resources.

Model 1 Model 3 Sobel Test of Reputation & Reputation & Significance Model 2 Resources Resources Research (Without (With Mediator) Mediator) (Constant) Gender .055 .068 .009 Age .037*** .092 .184*** Academic .048*** .292***/.160 -.277*** 1.822** Position .039 .026 .049 Innovative & Risk- .012 .043 -.058 taking Readiness to .299**/.148 .044*** .157*** 2.016** Collaborate Proactive .046 .038 -.039 Learning Orientation .052*** .125***/.175 -.301*** Collaborative .212***/.160 .048*** .242*** Purpose Collaborative .003 .055** -.127*** Environment Breadth of research .409*** Note: *** represents significant level at 0.01 or below; ** represents significant level at 0.05 or below; * represents significant level at 0.1 or below

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When testing the mediating effect using the performance variable ‘effective knowledge transfer’, it was found that ‘research engagement’ was a significant mediator for two multi-level factors, namely, ‘readiness to collaborate’ (z’ = 2.017, p<0.05) and ‘collaborative purpose’ (z’ = 1.324, p<0.01). The regression results are presented in Table 4.27. Together, the findings suggest that higher ‘readiness to collaborate’ and the establishment of a clear ‘collaborative purpose’ would facilitate academic-industry research collaboration, leading to effective knowledge transfer.

Table 4.27: The results summary of mediated regression testing of breadth of research-related engagement as a mediator between multi-level factors and effective knowledge transfer.

Model 1 Model 3 Sobel Test of Knowledge Knowledge Significance Model 2 Transfer Transfer Research (Without (With Mediator) Mediator) (Constant) Gender -.021 .068 -.037 Age .110** .092 .089* Academic .010 .292***/.160 -.058 Position .021 .026 .015 Innovative & Risk- .012 .035 .032 taking Readiness to .299**/.148 .140*** .117** 2.017** Collaborate Proactive .020 .038 .011 Learning Orientation -.073 .125***/.175 -.102** Collaborative .212***/.160 .289*** .240*** 1.324* Purpose Collaborative .003 .042 .041 Environment Breadth of research .233*** Note: *** represents significant level at 0.01 or below; ** represents significant level at 0.05 or below; * represents significant level at 0.1 or below

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4.5.7.3 Testing Breadth of Company Creation-Related Engagement as a Mediator

The results of the Sobel test of significance indicate that breadth of company creation-related engagement was a significant mediator between two multi-level factors and the performance variable ‘enhanced reputation and resources’. The factors were ‘age’ (z’ = 1.421, p<0.1) and ‘academic attainment’ (z’ = 3.124, p<0.01). The mediated regression results are presented in Table 4.28. The results suggest that ‘age’ and ‘academic attainment’ influence the ‘reputation and resource’ outcomes of the university, through their respective effect on academic-industry engagement in company creation activities.

Table 4.28: The results summary of mediated regression testing of breadth of company creation-related engagement as a mediator between multi-level factors and enhanced reputation and resources.

Model 1 Model 3 Sobel Test of Reputation & Model 2 Reputation & Significance Resources Company- Resources (Without creation (With Mediator) Mediator) (Constant) Gender .055 .003 .018 Age .037*** .121**/.084 .194*** 1.421* Academic .048*** .365***/.109 -.242*** 3.124*** Position .039 -.020 .064 Innovative & Risk- -.108 .043 -.028 taking Readiness to .107**/.100 .044*** .173*** Collaborate Proactive .046 .078 -.042 Learning Orientation .052*** .152***/.119 -.285*** Collaborative .019 .048*** .325*** Purpose Collaborative .220*/.125 .055** -.146*** Environment Breadth of company .231*** creation Note: *** represents significant level at 0.01 or below; ** represents significant level at 0.05 or below; * represents significant level at 0.1 or below

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When testing the mediating effect of company creation engagement between multi-level factors and performance variable knowledge transfer, the Sobel test was significant for two factors, namely, ‘age’ (z’ = 1.432, p<0.1) and ‘readiness to collaborate’ (z’ = 1.067, p<0.05). The results are presented in Table 4.29.

Table 4.29: The results summary of mediated regression testing of breadth of company creation-related engagement as a mediator between multi-level factors and effective knowledge transfer.

Model 1 Model 3 Sobel Test of Knowledge Model 2 Knowledge Significance Transfer Company- Transfer (Without creation (With Mediator) Mediator) (Constant) Gender -.021 .003 -.022 Age .110** .121**/.084 .097* 1.432* Academic .010 .365***/.109 -.029 Position .021 -.020 .023 Innovative & Risk- -.108 .035 .046 taking Readiness to .107**/.100 .140*** .129*** 1.067** Collaborate Proactive .020 .078 .011 Learning Orientation -.073 .152***/.119 -.089* Collaborative .019 .289*** .287*** Purpose Collaborative .220*/.125 .042 .033 Environment Breadth of .107** company-creation Note: *** represents significant level at 0.01 or below; ** represents significant level at 0.05 or below; * represents significant level at 0.1 or below

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4.5.7.4 Testing Cross-Functional Engagement as a Mediator

The results of the Sobel test of significance indicate that cross-functional engagement was a significant mediator between three multi-level factors and the performance variable ‘enhanced reputation and resources’. The factors were ‘academic attainment’ (z’ = 2.626, p<0.01), ‘readiness to collaborate’ (z’ = 2.008, p<0.05), and ‘collaborative environment’ (z’ = 2.170, p<0.05). The mediated regression results are presented in Table 4.30.

Table 4.30: The results summary of mediated regression testing of cross-functional engagement as a mediator between multi-Level factors and enhanced reputation and resources.

Model 1 Model 3 Sobel Test of Reputation & Model 2 Reputation & Significance Resources Cross- Resources (Without functional (With Mediator) Mediator) (Constant) Gender .055 -.013 .021 Age .037*** .012/.042 .220*** Academic .048*** .453***/.054 -.237*** 2.626*** Position .039 -.114**/.045 .080 Innovative & Risk- .035 .043 -.059 taking Readiness to .146***/.050 .044*** .206*** 2.008** Collaborate Proactive .046 .021 -.027 Learning Orientation .052*** .038 -.257*** Collaborative .048 .048*** .321*** Purpose Collaborative .217*/.062 .055** -.143*** 2.170** Environment Breadth of cross- .174*** functional Note: *** represents significant level at 0.01 or below; ** represents significant level at 0.05 or below; * represents significant level at 0.1 or below

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When testing the mediated regressions with the performance variable ‘effective knowledge transfer’, it was found that cross-functional engagement mediated the effect of ‘readiness to collaborate’ (z’ = 2.529, p<0.01) but not other multi-level factors. The results of mediated regression testing are presented in Table 4.31.

Table 4.31: The results summary of mediated regression testing of cross-functional engagement as a mediator between multi-level factors and effective knowledge transfer.

Model 1 Model 3 Sobel Test of Knowledge Model 2 Knowledge Significance Transfer Cross- Transfer (Without functional (With Mediator) Mediator) (Constant) Gender -.021 -.013 -.020 Age .110** .012/.042 .109** Academic .010 .453***/.054 -.039 Position .021 -.114**/.045 .033 Innovative & Risk- .035 .035 .031 taking Readiness to .146***/.050 .140*** .145*** 2.529*** Collaborate Proactive .020 .021 .017 Learning Orientation -.073 .038 -.077 Collaborative .048 .289*** .284*** Purpose Collaborative .217*/.062 .042 .031 Environment Breadth of cross- .107** functional Note: *** represents significant level at 0.01 or below; ** represents significant level at 0.05 or below; * represents significant level at 0.1 or below

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4.6 Findings of Hypotheses Testing

This chapter so far has presented the results of multiple regression analysis conducted to test the direct relationships between social-psychological, organisational, inter-organisational factors, and a breadth of entrepreneurial activities. The results of the hypotheses testing are presented and discussed below.

H1 There are significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to age.

Hypothesis H1 argues that there are significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to age. Findings from analyses of variance provide empirical support for the hypothesis. Specifically, the findings indicated significant differences in teaching, F(4, 505) = 2.311, p < .05; research, F (4, 505) = 9.545, p <.00; company-creation, F (4, 505) = 8.648, p <.00; and cross-functional engagement, F (4, 505) = 4.186, p <.00.

Significant differences in teaching can be traced to a higher mean score for respondents between the ages of 31 to 60 years as they engage in more diverse forms of teaching-related activities compared to their counterparts from other age groups. For research, respondents between the ages of 41 to 60 years scored higher than their counterparts in terms of the breadth of research-related engagements. A comparison of the mean scores for breath of company-creation engagement revealed that respondents aged 51 to above 60 years scored higher than their younger counterparts. Lastly, respondents between the ages of 31 and 60 years have scored higher than their counterparts in terms of breadth of cross-functional engagement.

H2 There are significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to gender.

Hypothesis H2 argues that there are significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to gender. However, the findings from analysis of variance revealed no significant difference for teaching, F (1,508) =.319, p <.572; research F (1,508) =.791, p <.374; company-creation, F (1,508)

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=.166, p <.684; and cross-functional engagement, F (1,508) =.326, p < .568). Therefore, hypothesis H2 cannot be supported.

H3 There are significant differences in academicians’ engagement, with respect to seniority.

Hypothesis H3 proposes that there are significant differences in academicians’ engagement, with respect to seniority. The results of the analysis of variance indicated significant differences among academic positions in terms of teaching, F (4, 505) = 3.110, p <.05; research, F (4, 505) = 13.266, p <.01; company-creation, F (4, 505) = 11.443, p <.01; and cross-functional engagement, F (4, 505) = 2.495, p <.05. Across all forms of collaborative engagements, it was observed that respondents with more senior positions, namely, associate professors and professors, score higher than their counterparts in lower-level positions. Therefore, the statistical findings provide support for hypothesis H3.

H4 There are significant differences in academicians’ engagement, with respect to academic attainment.

Hypothesis H4 proposes that there are significant differences in academicians’ engagement, with respect to academic attainment. The results of the analysis of variance revealed significant differences in teaching, F (2, 507) = 60.456, p <.01; research, F (2, 507) = 55.159, p <.01); company-creation, F (2, 507) = 59.732, p <.01; and cross-functional engagement, F (2, 507) = 94.448, p <.01). When comparing the mean scores for the three levels of academic attainment, it was observed that the mean scores increased together with the level of academic attainment from Bachelor to Doctoral, across all forms of collaborative engagement. Therefore, the statistical results provide support for hypothesis H4.

H5 Academicians’ innovativeness and propensity to take risk are positively related to academics’ engagement in entrepreneurial collaborations.

Hypothesis H5 states that academicians’ innovativeness and propensity to take risk are positively related to academics’ engagement in entrepreneurial collaborations. However, the multiple regression analysis results indicated that innovativeness and 120 propensity to take risk, was not significantly related to breadth of teaching (β = .003; ρ = .951), research (β = .012; ρ = .790), company-creation (β = -.108; ρ = .316), or cross- functional (β = .035; ρ = .455) entrepreneurial collaborations. This clearly indicates that hypothesis H5 is not supported.

H6 Academicians’ proactiveness is positively related to engagement in entrepreneurial collaborations.

Hypothesis H6 argues that academicians’ proactiveness is positively related to engagement in entrepreneurial collaborations. However, the results of multiple regression analysis revealed that proactiveness had a significant relationship with breadth of teaching-related activities (β = .114; ρ = .019) only. Its relationship with research, company-creation and cross-functional engagement are not significant.

Therefore, hypothesis H6 is partially supported.

H7 Academicians’ readiness to collaborate is positively related to engagement in entrepreneurial collaborations.

Hypothesis H7 predicted that academicians’ readiness to collaborate is positively related to engagement in entrepreneurial collaborations. The results of multiple regression analysis result indicated that readiness to collaborate was significantly related to breadth of teaching (β = .394; ρ = .049), research (β = .299; ρ = .000), company- creation (β = .107; ρ = .05), and cross-functional (β = .146; ρ = .05) entrepreneurial collaborations. Accordingly, the statistical findings clearly provide support for hypothesis H7.

H8 Universities’ learning orientation is positively related to academicians’ engagement in entrepreneurial collaborations.

Hypothesis H8 proposed universities’ learning orientation is positively related to academicians’ engagement in entrepreneurial collaborations. The results of multiple regression analyses revealed that universities’ learning orientation had a significant relationship with breadth of research (β = .125; ρ = .01) and company-creation (β = .152; ρ = .01) entrepreneurial collaborations only. Its relationship with teaching and

121 cross-functional engagements was not significant. Consequently, hypothesis H8 is partially supported.

H9 Strong collaborative purpose is positively related to academicians’ engagement in entrepreneurial collaborations.

Hypothesis H9 stated that strong collaborative purpose is positively related to academicians’ engagement in entrepreneurial collaborations. However, the results of multiple regression analyses revealed that strong collaborative purpose had a positive and significant relationship with breadth of research-related activities (β = .212; ρ =.000) only. Its relationship with teaching, company-creation and cross-functional engagements were not significant. Thus, hypothesis H9 is partially supported.

H10 Supportive collaborative environment is positively related to academicians’ engagement in entrepreneurial collaborations.

Hypothesis H10 argues that supportive collaborative environment is positively related to academicians’ engagement in entrepreneurial collaborations. Results of the multiple regression analysis indicated that supportive collaborative environment had a positive and significant relationship with breadth of company-creation (β = .220; ρ = .10) and cross-functional engagement (β = .217; ρ = .10) only. On the other hand, breadth of teaching and research engagements had no significant relation with supportive collaborative environment. Overall, hypothesis H10 is partially supported.

H11 Academicians’ engagement in entrepreneurial collaborations is positively related to performance variable enhanced reputations and resources.

Hypothesis H11 argues that academicians’ engagement in entrepreneurial collaborations is positively related to performance variable enhanced reputations and resources. Results of the multiple regression analyses indicated that only breadth of research-related engagement had a significant positive relation with the performance variable (β = .478; ρ = .000). In contrast, teaching, company-creation and cross- functional engagement were not positively related to enhanced reputation and resources. Therefore, Hypothesis H11 is partially supported.

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H12 Academicians’ engagement in entrepreneurial collaborations is positively related to performance variable effective knowledge transfer.

Hypothesis H12 argues that academicians’ engagement in entrepreneurial collaborations is positively related to performance variable effective knowledge transfer. Results of the multiple regression analysis indicated that breadth of research (β = .215; ρ = .001) and teaching-related engagements (β = .274; ρ = .000) had significant positive relation with the performance variable. In contrast, breadth of cross-functional engagements had a significant negative relation, while company- creation had no significant relation with effective knowledge transfer. Thus, hypothesis

H12 partially supported.

H13 Academicians’ engagement in entrepreneurial collaborations mediates the relations between multi-level factors and enhanced reputations and resources.

Hypothesis H13 argues that academicians’ engagement in entrepreneurial collaboration mediates the relations between multi-level factors and performance variable enhanced reputations and resources. The results of the Sobel test indicated that teaching-related engagement significantly mediated the relationship between two multi-level factors and enhanced reputation and resources. The two factors were academic attainment (z’ = 3.100, p<0.000) and readiness to collaborate (z’ = 3.231, p<0.000). Likewise, breadth of research-related engagement was a significant mediator between academic attainment (z’ = 1.822, p<0.05) and readiness to collaborate (z’ = 2.016, p<0.05) with enhanced reputation and resources.

The breadth of company creation-related engagement was a significant mediator between two multi-level factors and performance variable enhanced reputation and resources. The factors were age (z’ = 1.421, p<0.1) and academic attainment (z’ = 3.124, p<0.01). Lastly, cross-functional engagement was a significant mediator between three multi-level factors and performance variable enhanced reputation and resources. The factors were academic attainment (z’ = 2.626, p<0.01), readiness to collaborate (z’ = 2.008, p<0.05), and collaborative environment (z’ = 2.170, p<0.05). Overall, the results of Sobel test partially support the hypothesis H13 as each form of academic engagement is a significant mediator for two or three multi-level factors. 123

H14 Academicians’ engagement in entrepreneurial collaborations mediates the relations between multi-level factors and effective knowledge transfer.

Hypothesis H14 argues that academicians’ engagement in entrepreneurial collaboration mediates the relations between multi-level factors and performance variable effective knowledge transfer. Results of Sobel tests indicated that breadth of teaching-related engagement mediated the effect of academic attainment (z’ = 3.715, p<0.000) and readiness to collaborate (z’ = 3.947, p<0.000). Research-related engagement was a significant mediator for readiness to collaborate (z’ = 2.017, p<0.05) and collaborative purpose (z’ = 1.324, p<0.01), while company-creation engagement significantly mediate age (z’ = 1.432, p<0.1) and readiness to collaborate (z’ = 1.067, p<0.05). Lastly, cross-functional engagement mediated the effect of one variable, namely, readiness to collaborate (z’ = 2.529, p<0.01). Overall, the results provide partial support for the hypothesis H14 as each form of academic engagement is a significant mediator for either one or two multi-level factors. The summarised results of the hypotheses testing are presented in Table 4.32 below.

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Table 4.32: The summary of the hypotheses and test results.

Hypothesis Results

H1 There are significant differences in academicians’ engagement in Supported entrepreneurial collaborations, with respect to age.

H2 There are significant differences in academicians’ engagement in Not supported entrepreneurial collaborations, with respect to gender.

H3 There are significant differences in academicians’ engagement in Supported entrepreneurial collaborations, with respect to seniority.

H4 There are significant differences in academicians’ engagement in Supported entrepreneurial collaborations, with respect to qualifications.

H5 Academicians’ innovativeness and propensity to take risk are positively Not supported related to academicians’ engagement in entrepreneurial collaborations

H6 Academicians’ proactiveness is positively related to engagement in Partially supported entrepreneurial collaborations.

H7 Academicians’ readiness to collaborate is positively related to Supported engagement in entrepreneurial collaborations.

H8 Universities’ learning orientation is positively related to academicians’ Partially supported engagement in entrepreneurial collaborations.

H9 Strong collaborative purpose is positively related to academicians’ Partially supported engagement in entrepreneurial collaborations.

H10 Supportive collaborative environment is positively related to Partially supported academicians’ engagement in entrepreneurial collaborations.

H11 Academicians’ engagement in entrepreneurial collaborations is Partially supported positively related to performance variable enhanced reputations and resources.

H12 Academicians’ engagement in entrepreneurial collaborations is Partially supported positively related to performance variable effective knowledge transfer.

H13 Academicians’ engagement in entrepreneurial collaboration mediates Partially supported the relations between multi-level factors and performance variable enhanced reputations and resources.

H14 Academicians’ engagement in entrepreneurial collaboration mediates Partially supported the relations between multi-level factors and performance variable effective knowledge transfer.

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4.7 Summary

This chapter presents the results of the various statistical analyses carried out to test the hypotheses. Multiple regression analyses were rigorously conducted according to published procedures and the results presented in table format. The major findings with regards to the hypotheses have been highlighted in this chapter. Generally, the proposed hypotheses received moderate support from the collected and analysed data. The next chapter focuses on the discussions regarding the tested hypotheses.

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CHAPTER FIVE

DISCUSSION OF FINDINGS

5.0 Introduction

This chapter provides an overview of the research questions and questions, followed by a discussion on the significance of the research findings, together with its justification.

5.1 Background

The present study has three objectives. The first objective measures academicians’ engagement in entrepreneurial collaborations with industry. The second objective investigates the influence of personal and multi-level factors on the academicians’ engagement in entrepreneurial collaborations. The last objective examines the factors that influence the outcomes of entrepreneurial collaborations.

i. Measure academicians’ engagement in entrepreneurial collaborations with industry. ii. Investigate the influence of personal and multi-level factors on the academicians’ engagement in entrepreneurial collaborations. iii. Examine the factors that influence the outcomes of entrepreneurial collaborations

The current study seeks to identify the key determinants of academicians’ involvement and performance in entrepreneurial collaborations by analysing selected factors, namely, social psychological, organisational and institutional factors. The effects of selected social-psychological, organisational and inter-organisational factors are examined, with the aim of identifying the key determinants of collaborative performance.

Social-psychological is the scientific study of how people's thoughts, feelings, and behaviours are influenced by the actual, imagined, or implied presence of others. Social-psychological factors were operationalised to measure academicians’ intentions

127 and inclination to engage with the industry. It comprises two dimensions, namely, readiness to collaborate with industry and individual entrepreneurial orientation. The three variables of readiness to collaborate with industry analysed include attitudes, subjective norms and behavioural intention (Ajzen, 1985; Ajzen & Fishbein, 2005; Bolton & Lane, 2012; Miller, 2005). The variables of individual entrepreneurial orientation analysed include risk-taking, innovativeness and proactiveness (Bolton & Lane, 2012; Covin & Slevin, 1989).

Organisational factors were operationalised as the university and managerial characteristics that facilitate effective organisational learning processes and promote academic entrepreneurial collaborations. It consists of two dimensions, namely, organisational learning capability and organisational-level entrepreneurial orientation. The four characteristics of organisational learning capability analysed were managerial commitment to learning, presence of a system perspective, openness and readiness to experiment and effective knowledge transfer and integration (Gomez et al., 2005; Garvin, 1993; Chiva, Alegre & Lapiedra, 2007). Additionally, the four characteristics of organisational-level entrepreneurial orientation analysed, which were proactiveness, innovativeness, risk-taking and competitive aggressiveness (Covin & Slevin, 1986, 1989; Covin & Covin, 1990; Chiva, Alegre & Lapiedra, 2007).

Inter-organisational factors were operationalised as the ability of universities and industry to enter commercial-oriented academic-industry entrepreneurial collaborations where resources, power, and authority are shared and where people are brought together to achieve common goals. It comprises of three dimensions, namely the collaborative environment, collaborative communication, and collaborative purpose (Bryan, Collins-Carmago & Allen, 2006; Garstka et al., 2012; Mattessich et al., 2004).

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5.2 Discussion of Findings

The following sections discuss the findings of the study in relation to the research objectives and hypotheses discussed about in earlier chapters of this thesis.

Research objective 1: Measure academicians’ engagement in entrepreneurial collaborations with industry

5.2.1 There are Significant Differences in Academicians’ Engagement in Entrepreneurial Collaborations, with Respect to Age

Academic literature describes an academic as someone who works within a university, juggling the roles of generating new knowledge (research) and transmitting knowledge (teaching) with administrative duties (Coaldrake & Stedman, 1999).

Despite previous studies identifying age as a factor affecting academician’s involvement in academic entrepreneurial activities, there is no consensus on its effect on academics (Ambos et al., 2008; De Silva, 2012; Lam, 2005). An analysis of variance (ANOVA) of collaborative engagements based on age group presented in Table 4.15 revealed significant differences between the groups, for all four forms of collaborative engagement. There were significant differences in teaching, F (4, 505) = 2.311, p <. 05; research, F (4, 505) = 9.545, p <. 00; company-creation, F (4, 505) = 8.648, p <. 00; and cross-functional engagement, F (4, 505) = 4.186, p <. 00.

Significant differences in teaching can be traced to higher mean scores for respondents between the ages of 31 to 60 years as they engaged in more diverse forms of teaching-related activities compared to their counterparts from other age groups. For research, respondents between the ages of 41 to 60 years scored higher than their counterparts in terms of the breadth of research-related engagements. Comparisons of the means score for breath of company-creation engagement revealed that respondents from the ages 51 to above 60 years scored higher than their younger counterparts. Lastly, respondents between the ages of 31 and 60 years have scored higher than their counterparts in terms of breadth of cross-functional engagement.

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There are Significant Differences in Academicians’ Teaching-Related Engagements, with Respect to Age

The results for academics’ teaching engagements are consistent with previous studies, which found significant differences in academicians’ engagement in entrepreneurial collaborations, with respect to age and attributed the differences to various reasons.

In the case of younger academicians, some scholars attributed this finding to younger academics being regarded as inexperienced and unable to engage in diverse forms of teaching-related engagements by peers and are often assigned as teaching assistant or tutor roles in introductory courses to gain experience (Baldwin & Blackburn, 1981; Neumann & Neumann, 1985).

In the case of older academicians, some scholars (Horner et al., 1986; Shock, 1985) have attributed this finding to academics’ age and the attainment of senior positions in the faculty. They eventually spend more time serving in managerial or administrative activities and committees or as consultants. Thus, they have little or no time to engage in teaching related engagements.

Therefore, in the context of the current study, it may be argued that the experience gained in teaching related activities below the age of 30 years makes academics seasoned enough to engage in more diverse teaching activities between the ages of 31 to 60 years, before withdrawing from teaching related engagements after the age of 61 years due to commitments to administrative roles. However, it must be noted that the perceived decline in teaching engagements is subjective. It may manifest itself at different rates in different individuals

There are Significant Differences in Academicians’ Research-Related Engagements, with Respect to Age

The results of research engagement are in line with prior studies (Lehman 1953, 1960); Pelz and Andrew (1966) that found that academicians involved in academic research engagements peak during their 40’s (depending on their academic disciplines)

130 followed by a period of 10 to 20 years of consistent research related engagements before stagnation, declining and ceasing all together.

The scholars attribute the results to the fact that at a certain stage in their academic careers academicians are drawn off into other work in academic teaching, university administration roles or other work outside academia, for example, in politics and business. Alternatively, as the teachers’ age, they become members of a group generally believed to be less competent in a variety of physical and intellectual functions thus going into retirement (Schaie, 1988).

There are Significant Differences in Academicians’ Company-Creation Engagements, with Respect to Age

The results for company-creation engagements are consistent with previous studies which suggest that older academicians have a higher tendency to engage in entrepreneurial activities as they grow older, attain higher academic qualifications and exposure to industry engagement over time (Audretsch, 2000; Levin & Stephan, 1991). Levin and Stephan explain this by stating that experienced and renowned professors, who do not have much pressure for publications, capitalise on their experience, credential, and stronger social network by engaging in entrepreneurial activities. In contrast, young researchers, who have not yet developed their reputation, are more focused on publications.

There are Significant Differences in Academicians’ Cross Functional Engagements, with Respect to Age

The results for cross functional engagements are consistent with prior studies that found academic entrepreneurial engagements begin at a relatively low rate in the late 20s, peaking around 40, and declining thereafter (Cole, 1979; Dennis, 1956; Homer et al., 1986; Lehman, 1953).

In the context of this study, it may be argued this finding is due to the effect of academic training on academicians: individuals who are trained in earlier periods in their careers are more inclined and better equipped to engage in cross functional engagement. This is because young academicians below 30 years who are in the early 131 stages of their academic careers explorer diverse forms of teaching related engagements gaining experience and expertise (Shane, 2000; Westhead et al., 2005). Once the academicians are in between the ages of 31 and 60 years they attain training via higher education qualifications and senior positions in administrative positions due to research related and company creation-related engagements gaining reputation and clout before eventually retiring above 61 years.

5.2.2 There are Significant Differences in Academicians’ Engagement in Entrepreneurial Collaborations, with Respect to Seniority

In the study, the position of the academician was a categorical questionnaire survey item with seven hierarchical levels, namely, tutor, associate lecturer, lecturer, senior lecturer, associate professor and professor. The results of an analysis of variance (ANOVA) of academics’ engagement in entrepreneurial collaborations, with respect to seniority presented in Table 4.17 revealed significant differences among academic positions in terms of teaching, F (4, 505) = 3.110, p < .05; research, F (4, 505) = 13.266, p < .01; company-creation, F (4, 505) = 11.443, p < .01; and cross-functional engagement, F (4, 505) = 2.495, p < .05. Across all forms of collaborative engagement, it was observed that respondents with more senior positions, namely, associate professors and professors, score higher than their counterparts in lower-level positions.

This finding is consistent with previous studies (Agarwal et al., 2004; Birley, 1985) which found there are significant differences in academics’ entrepreneurial engagements with respect to seniority.

The scholars explain this finding by arguing that the senior academics score higher than their junior counterparts due to the fact that academic entrepreneurial engagements often are seeded by personal contacts, the strength of social networks, and autonomously driven by academics. These enable the senior academics to routinely experience interaction with various people during teaching and research engagements enabling them to develop larger and stronger social networks and social capital necessary to obtain potential partners in the industry for the company-creation and cross-functional engagements (D’Este & Patel, 2007; Haeussler, 2011).

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5.2.3 There are Significant Differences in Academicians’ Engagement in Entrepreneurial Collaborations, with Respect to Academic Qualifications

There are Significant Differences in Academicians’ Teaching-Related Engagements, with Respect to Academic Qualifications

Engaging in teaching (transmitting knowledge) is recognised as a fundamental traditional mandate of an academician’s career and can be traced to the historical missions of universities (Coaldrake & Stedman, 1998). The current study findings revealed when comparing the mean score results (See Table 4.18) for the three levels of academic attainment for academics involved in diverse forms of teaching related engagement, it was observed that the mean scores increase as the level of academic qualifications increases from Bachelor to Doctoral, across all forms of teaching-related engagement. The finding is consistent with prior studies (Ajayi, 2009; Betts, Zau, & Rice, 2003; Goldhaber & Brewer, 2000) which also found that there were significant differences in academics’ engagement in teaching activities, with respect to academic qualifications.

The scholars explain this by arguing that academicians with higher academic qualifications engage in more diverse forms of teaching activities compared those with lower qualifications due to professional qualities acquired during certification requirement training prior to obtaining an academic qualification. This is because qualifications of teachers play an important role in teaching but professional education or training is more important in teaching, because a trained teacher can teach better than an untrained teacher. Generally, it is claimed that a trained teacher knows how to teach effectively.

According to their point of view, the professional qualities of a teacher have to do with the following: mastery of the subject matter, a sense of organisation, ability to clarify ideas, ability to motivate students, good imagination, ability to involve the students in meaningful activities throughout the period of teaching, management of the details of learning, frequent monitoring of students’ progress through tests and written and oral quizzes. No wonder Fajonyomi (2007) in his study remarked that the 133 success of any educational teaching-related undertaking depends largely on the availability of professional teachers with relevant higher academic qualifications. This is possible because the trained teachers with higher academic qualifications have been taught the technical knowhow for effective learning to take place in their target audiences.

There are Significant Differences in Academicians’ Research-Related Engagements, with Respect to Academic Qualifications

A comparative analysis of the current study involved mean scores results for the three levels of academic qualifications for academics involved in diverse forms of research related engagement, it was observed that the mean scores increase as the level of academic attainment increases from Bachelor to Doctoral, in all forms of research-related engagements (See Table 4.18).

The finding is in line with previous studies (Van Looy et al., 2006; Lowe & Gonzalez-Brambila, 2007) that found positive and significant relationships between a person’s postgraduate academic qualifications and the number of academic publications with involvement in a greater variety of entrepreneurial activities.

Based on the above empirical evidence it may be argued that post graduate academic qualifications give academicians various research-related skills, namely scientific expertise, project and team management, personal aptitudes/ interpersonal skills, and networking.

This is because such skills are deemed essential to academic researchers and are expected over and above their research skills and scientific expertise to apply for research grants from their own institutions and external organisations in the industry and public sector.

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There are Significant Differences in Academicians’ Company-Creation Engagements, With Respect to Academic Qualifications

Company-creation engagements offer academic institutions and academics a means to commercialise academic knowledge and intellectual property that may have otherwise gone undeveloped within the university. Significant differences in company- creation can be traced to increasing mean scores (See Table 4.18) for the level of academic qualifications.

The results are in line with previous studies which found that academics with post graduate academic qualifications are more likely to engage in company-creation engagements compared to workmates with lower levels of educational qualifications (Dickson et al., 1998; Franklin et al., 2001).

Scholars attribute this finding to post-graduate qualifications giving academics enhanced transferable company creation skills within the entrepreneurial process, namely communication, critical thinking, interpersonal and networking skills, uncertainty management skills, product development skills, and procurement and allocation of critical resources skills necessary to engage in company-creation unlike those with lower academic qualifications.

This is because post-graduate education qualifications equip individuals with the additional knowledge, attributes and capabilities required to apply these abilities in the context of setting up a new venture or business creation (McMullan & Vesper, 1987; Henderson et al., 1998; Mowery et al., 2002).

There are Significant Differences in Academicians’ Cross-Functional Engagements, with Respect to Academic Qualifications

The results of the analysis of variance revealed significant differences in cross- functional engagement, with respect to qualifications F (2, 507) = 94.448, p < .01) summarised in Table 4.18. In the current study, the significant differences in cross- functional engagements can be traced to a higher mean score for respondents with post-graduate academic qualifications as they engage in more diverse forms of cross- functional engagements than their counterparts without post-graduate qualifications.

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The finding is consistent with previous studies (Alsos et al., 2003; Mayer & Schooman, 1993; Shane, 2000; Westhead et al., 2005) that attributed it to post- graduate qualifcations giving academics the ability of academics to engage in teaching, research and company-creation entrepreneurial activities due to the social networks and knowledge and skills training related to scientific/technological aspects, potential applications, and relevant business/market received and nurtured during training to obtain a post-graduate qualifcation.

Research objective 2: Investigate the influence of personal and multi-level factors on the academics’ engagement in entrepreneurial collaborations

5.2.4 Academicians’ Proactiveness is Positively Related to Engagement in Entrepreneurial Collaborations

Academicians’ Proactiveness is Positively Related to the Breadth of Teaching-Related Engagements

Proactiveness measures respondents’ perception on the extent they would act in anticipation of future events and plan ahead during academic-entrepreneurial projects. Hypothesis H6 argues that academicians’ proactiveness is positively related to engagement in entrepreneurial collaborations. However, the results of multiple regression analyses revealed that proactiveness has a significant relationship with breadth of teaching-related activities (β = .114; ρ = .019) only. Its relationship with research, company-creation and cross-functional engagement are not significant.

The findings are consistent with previous studies (Bolton & Lane, 2012; Covin & Slevin, 1989; Rauch et al., 2009) that found positive and significant relationships between individuals proactivity personality scale and engagement entrepreneurial activities and attribute this to people influencing their environments as well as vice versa. This is because individuals seek, select, interpret, and alter situations in environments that offer opportunities to capitalise on individual strengths and needs. This implies individuals with high proactive personalities are more likely to get involved in teaching-related activities compared to academics with low proactive personalities.

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Therefore, in the context of the present study the findings imply academicians are more involved in teaching-related activities due to their academic qualifications and teaching-related professional training which gives them the expertise, confidence, and capabilities to participate in teaching-related activities.

5.2.5 Academicians’ Readiness to Collaborate is Positively Related to Engagement in Entrepreneurial Collaborations

In the present study, readiness to collaborate represents the academicians’ intentions and inclination to engage in collaborative engagement with the industry. The results of multiple regression analysis indicated that readiness to collaborate is positively related to breadth of teaching (β = .394; ρ = .049), research (β = .299; ρ = .000), company-creation (β = .107; ρ = .05), and cross-functional (β = .146; ρ = .05) entrepreneurial collaborations

In order to explain this finding, the study draws upon the Theory of Planned Behaviour (Azjen 1975; 1985) which states that an individual’s attitude towards behaviour, subjective norms, and perceived behavioural control, together shape an individual's behavioural intentions and behaviours.

In the context of academic engagement, personal attitude relates to the degree to which performance of academic engagement with the industry is positively or negatively valued by academicians, influencing their readiness to participate in entrepreneurial collaborations. Subjective norm refers to an academician's perception about involvement in teaching-related activities, which is influenced by the judgment of significant others (for example, colleagues, industry players and top leaders of the university). Perceived behavioural control refers to the academician’s perceptions of their ability to perform in entrepreneurial collaborations.

An academician’s attitude influences academic engagement because the Theory of Planned Behaviour specifies the nature of relationships between beliefs and attitudes. This is because people's evaluations of, or attitudes toward academic engagement are determined by their accessible beliefs about the academic engagement, where a belief is defined as the subjective probability that the behaviour 137 will produce outcomes of either participation or non-participation in academic engagements.

Therefore, it may be argued that the positive relationship finding indicates a consequence of respondents in the present study having a favourable attitude towards engagement in diverse forms of entrepreneurial collaborations, their perceptions of support and encouragement from university leadership and fellow workmates, and self-belief in own skills and abilities.

5.2.6 Universities’ Learning Orientation is Positively Related to Academicians’ Engagement in Entrepreneurial Collaborations

Universities’ learning orientation measures respondents’ perception of the presence of organisational and managerial characteristics that facilitate effective organisational learning processes. These characteristics encompass the management’s strong commitment to learning, the presence of a shared identity and clear vision of the organisation’s objectives, and high levels of openness and willingness to experiment with new ideas.

The results of multiple regression analyses revealed that universities’ learning orientation has a positive and significant relationship with breadth of research (β = .125; ρ = .01) and company-creation (β = .152; ρ = .01) entrepreneurial collaborations only.

Universities’ Learning Orientation is Positively Related to Academicians’ Breadth of Research-Related Engagements

The finding is consistent with related studies (Amit & Schoemaker, 1993; Gomez et al., 2005) which suggest that the application of human-capital enhancing practices contributes to the development of employees’ absorptive capacity, thereby improving their capability to acquire, transfer, and integrate knowledge from various sources during the breadth of research-related engagements with industry.

According to Yahya and Goh (2002), strategic training practices are valuable tools for reinforcing the organisational values and skills deemed essential for sustaining effective organisational learning processes. This is because the positive

138 outcomes from investments in such practices strengthen the motivations and commitments of both employees and top-level management towards the continuous development of a strong organisational learning orientation (Gomez et al., 2005).

Universities’ Learning Orientation is Positively Related to Academicians’ Breadth of Company-Creation Related Engagements

Company-creation related engagements constitute a complex phenomenon that involves the transfer of technological knowledge from universities (or higher education institutions, in general) to new companies (Nicolaou & Birley, 2003; Powers & McDougall, 2005; Wright et al., 2004).

The current study finding is consisitent with prior studies (Argote & Ingram, 2000; Huber, 1991) that attributed it to organisational memory being embedded in organisational members, tools and tasks and the networks formed by crossing members, tools and tasks.

Therefore in the context of the present study its vital that the university’s learning orientation supports academicians’ involvement in company creation engagements through various means, namely, the management’s strong commitment to learning, the presence of a shared identity and clear vision of the organisation’s objectives, and high levels of openness and willingness to experiment with new ideas. This is because in the case of academicians engaging in company-creation activities, it is necessary to connect the academics and institutional intellectual property to the specific demands of stakeholders (Bond & Houston, 2003).

This is an argument further supported by the proponets of the knowledge based view that the firm consider knowledge as the most strategically significant resource of a firm (Penrose 1959; Nickerson & Zenger, 2004; Kogut & Udo, 2000).

Based on various scholars’ arguments above, it’s only logical to assume it is most likely that in a learning oriented university, its academicians will perform better than those in non-learning oriented university.

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5.2.7 Strong Collaborative Purpose is Positively Related to Academicians’ Engagement in Entrepreneurial Collaborations

Strong Collaborative Purpose is Positively Related to Academicians’ Breadth of Research-Related Engagements

The variable collaborative purpose measures to what extent the respondents and their collaborative partners have a clear understanding, and share a common belief of the goals of collaboration. The results of multiple regression analyses revealed that a strong collaborative purpose had a positive and significant relationship with breadth of research-related engagement (β = .212; ρ =.000) only. The relationship with teaching, company-creation and cross-functional engagements are not significant.

According to previous studies, the positive relationship can be attributed to the process of establishing a mutual collaborative purpose (Bryan, Collins-Carmago & Allen, 2006; Garstka et al., 2012; Mattessich et al., 2004; Gupta, Acharya & Gupta, 2015). The process involves, among others, developing rules and procedures for partners to communicate, identification of roles and responsibilities of parties, clearly setting out objectives, goals and milestones for the collaboration, and fostering mutual trust between the parties.

This process is necessary because not all university and industry stakeholders are aware of the possibilities and pitfalls of collaboration. Therefore establishing a collaborative purpose enables all stakeholders to mutually recognise and accommodate their differences.

This is because failure to accommodate their different objectives, at best, causes friction and wasted time. At worst, it may result in a complete failure to meet objectives and withdrawal from further collaboration in research-related engagements. Therefore, it may be argued that collaborative purpose facilitates research-related engagements by setting the terms and conditions of engagement.

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5.2.8 Strong Collaborative Environment is Positively Related to Academicians’ Engagement in Entrepreneurial Collaborations

Strong Collaborative Environment is Positively Related to Academicians’ Engagement in Company-Creation Engagements

Collaborative environment measures perceptions on the extent that the organisational climate is conducive for collaboration, the communication along the organisational hierarchy is effective, and the extent that academics are involved in key decision-making. The collaborative environment consists of the university, which comprises the internal environment, and actors in the wider economic and social environment, especially the government and industry (Etzkowitz & Leydesdorff, 2000; Siegel et al., 2004).

The results of multiple regression analyses revealed that strong collaborative environment had a positive and significant relationship with company-creation (β = .220; ρ <.10) and cross-functional (β = .217; ρ <.10) entrepreneurial engagements only.

In order to explain this finding, the present study draws upon the theory of the resource-based view of the firm (Barney, 1991; Amit & Shoemaker, 1993) which argues that a firm’s competitive advantage emerges as the result of interactions among its unique pool of resources (tangible and intangible) and capabilities being managed properly.

The present study is consistent with prior studies (Mark et al, 2005; Shane et al, 2004; Siegel et al., 2004) which found that a collaborative environment is positively related to academics entrepreneurial engagements. The scholars attributed their findings to the role of the technology transfer offices (TTOs) to facilitate academics in spin-off creation activities with external stakeholders from firms, entrepreneurs, and venture capitalists.

This is because the primary goal of academic spin-offs is to commercialise scientific knowledge and technological know-how accumulated within the university (Shane, 2004). Besides exploitation of academic knowledge, an academic start-up is

141 also responsible for employment creation, economic growth, and the overall level of innovation within a region (Gerry et al., 2009; Mets, 2009).

This is due to all academic entrepreneurial collaborations starting in a fast changing environment facing lots of competitors of different sizes with their own sets of unique resources, knowing that it is important to manage their own limited set of resources adequately (Gras et al., 2008). A review of literature on academic entrepreneurship reveals that collaborative engagements evolve and mature through several development phases, being: 1) research, 2) opportunity framing, 3) pre- organisation, and 4) re-orientation and sustainability (O’Shea et al., 2008; Vahora et al., 2004). The first phase mainly entails the development phase of an idea through research. Consequently, in phase two, this idea/invention is investigated for further commercialisation; the potential market needs to be examined to identify possible business opportunities. Phase three consists of the marketing of a product, the construction of a distribution system, (large scale) production, etc. In this phase, the organisation is described and built, which is needed for market penetration. This entails the final phase, which explains the reaction of the firm to the reactions of the environment to the service/product launch.

In order to attain long-term durability, re-orientation of the market and adaptation to the changed business environment is essential. Service/product alterations could be made and novel business opportunities may arise that demand organisational change. (Vahora et al., 2004) These different phases in the development of a spin-off ask for an adjusted set of resources. External acquisition and internal management of these resources are essential. When these resources cannot be generated internally, external opportunities need to be embraced/ exploited to complete the set of resources.

The discussion highlights where the technology transfer offices (TTOs) prove their value providing an adequate strong collaborative environment to academics involved in academic spin-offs. This is because they provide academics access to the university’s unique pool of resources (tangible and intangible) and capabilities

142 highlighted in the theory of the resource-based view of the firm. These particular resources and capabilities include organisational / human capital, technological capital, financial capital and social capital.

Research objective 3: Examine the factors that influence the outcomes of entrepreneurial collaborations

5.2.9 Academicians’ Engagement in Entrepreneurial Collaborations is Positively Related to Performance Variable Enhanced Reputations and Resources

In the current study, reputation and resources measures specific outcomes of entrepreneurial collaboration relating to the acquisition or strengthening of the university’s resource pool. Hypothesis H11 argues that academics’ engagement in entrepreneurial collaborations is positively related to performance variable enhanced reputations and resources. However, the results of the multiple regression analyses indicate that only breadth of research-related engagement had a significant positive relation with the performance variable (β = .478; ρ = .000).

The finding is consistent with previous studies (Mark et al., 2005; Bercovitz et al., 2001; Mansfield, 1998; Richard & Phillip, 2006; Thursby et al., 2001; Wales, Gupta & Mousa, 2013) whom found successful academic-industry university research engagements resulting in commercial and non-commercial benefits to industry thereby enhancing universities reputation and resources. The scholars attributed this finding to well documented empirical evidence that companies gain competitive advantage from university research through various ways, namely, access to academic research expertise, access to existing university resources, access to academic interdisciplinary knowledge and expertise, and access to university cutting-edge facilities.

The above findings and arguments are further reinforced by Marginson’s (2005) statement that all over the world research engagements enhance university reputation and resources. This scholar argues that research is the driver of the university mission and status, and for good reason. All else equal, strong research universities attract

143 more resources and better academics; have a more advanced capacity in teaching; and offer more to governments, professions, industry, foreign universities, and the brighter students from home and abroad.

5.2.10 Academicians’ Engagement in Entrepreneurial Collaborations is Positively Related to Performance Variable Effective Knowledge Transfer

According to Nonaka & Takeuchi (1995) and Jerez-Gomez (2005) the transfer of proprietary knowledge from individuals and groups to the organisation is achieved through managerial practices that promote knowledge sharing and application of new knowledge to create new or evolve existing products and organisational systems. In the present study, knowledge transfer measures the extent existing collaborations have enabled the respondents to secure industry input for teaching, transfer industry best practices to the university, secure new collaborative opportunities, and enhance career mobility between the two sectors of academia and industry. The results of the multiple regression analysis indicate that breadth of research (β = .215; ρ = .001) and teaching-related engagements (β = .274; ρ = .000) have significant positive relation with the performance variable.

Academicians’ Research Related Engagements is Positively Related to Performance Variable Effective Knowledge Transfer

The current study identified the research related academic entrepreneurial activities undertaken by academicians as working in the industry (research based), research based consultancy for industry through the university, research based consultancy privately (but without forming a company), developing products or services with potential for commercialisation, acquiring research funding from government, non-governmental or international bodies (those without collaborations with industry), collaborating with industry through joint research projects and research related assistance to small business owners (Calvert & Patel, 2003; Glassman, Moore, Rossy, 2003; Clausen & Korneliussen, 2012; Goldfarb & Henrekson, 2003; John-Evans, 1997; Louis et al., 1989; Siegel et al., 2001).

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The positive finding implies research collaboration would support effective knowledge transfer from industry to academia, and vice-versa. The result is consistent with previous studies (HEFCE, 2009; Lach & Schankerman, 2004; Lockett & Wright, 2005) which found that academic research is positively related to the performance variable effective knowledge transfer. The scholars attributed this finding to universities and businesses working together on research in many varied channels, for example, via the co-design, funding and production of basic or more applied research, joint sponsorship and training of doctoral students, networking activities with supply chain partners, consultancy, or continuing professional development, as well as licensing and patenting and the formation of spin-out companies.

A study conducted on academic research and industrial innovation in the UK by Mansfield (1998) found that 10% of all new products and services developed by companies surveyed could not have been developed, without significant delay, without university research. The empirical evidence that these channels promote effective knowledge transfer is the new knowledge from academic research related engagements being utilised by companies in various ways. For example, informing and improving their own research and development (R&D) projects, the development of new goods or services, improvement of business productivity through advances in technologies and manufacturing processes, and management or organisational change.

Academicians’ Teaching Related Engagements is Positively Related to Performance Variable Effective Knowledge Transfer

This finding suggests that teaching engagement portfolio, namely, external teaching, initiating the development of new degree programmes, placing students as trainees in industry, and conducting seminars and training sessions for industry would support effective knowledge transfer from industry to academia, and vice-versa. Teaching takes place not only classroom, laboratory, and office hour commitments but also mentoring, advising, supporting, and recommending students at the undergraduate, graduate, and post‐doctoral levels.

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The finding is consistent with previous studies (Argote et al., 2000; Piccoli et al., 2001; Steyn, 2004; Szulanski, 1996) which found that academics’ teaching related engagements is positively related to performance variable effective knowledge transfer.

The scholars attribute this finding to teaching engagements promoting knowledge transfer via training in professional and non-professional skills, techniques and ideas to learners from academia and industry at the undergraduate, graduate, and post‐doctoral levels. This includes those who will go on to be managers and business leaders, as well as researchers, engineers, technical and other specialists. This is because teaching engagements enable knowledge transfer to learners by actively enagaging students in their learning experience, encouraging them to pursue new knowledge and to develop independence of thought, critical thinking, entrepreneurial skills and the ability to handle a wide range of challenges in today’s globalised economy (Salter & Martin, 2001).

5.3 Summary

This chapter discussed the findings based on the research objectives. The key observation from the research findings is that there are significant differences in academics’ engagement in commercial-oriented entrepreneurial collaborations with respect to demographic characteristics and academic engagement in entrepreneurial collaborations is positively related to outcome. The next chapter focuses on the theoretical and managerial implications, the limitations of the study that affect its generalisability, and the recommendations for future research.

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CHAPTER SIX

CONCLUSIONS

6.0 Introduction

In recent years, entrepreneurship has increasingly been considered as an academic practice, which extends beyond the traditional mandates of teaching and research. The study of this phenomenon, now widely discussed in academic entrepreneurship literature, has centred on public universities in developed and developing countries (Thursby, Fuller & Thursby, 2009; Wennberg, Wiklund & Wright, 2011; Yusof, Saeed Siddiq & Nor, 2012).

Entrepreneurial practices in private universities have largely been ignored. Hence, the current study attempts to identify the key determinants of academicians’ involvement and performance in entrepreneurial collaborations by analysing selected factors, namely, social psychological, organisational and institutional factors, in private universities in Malaysia. The study explored the breadth and depth of academic entrepreneurial engagement activities, such as teaching, research, company-creation and cross fu nctional, undertaken in academia-industry collaborations in the last three years.

The findings from the present study have been discussed at length in the previous chapter. This chapter concludes the discussion, and highlights the main theoretical, policy and managerial implications of the study. It further outlines the major limitations of the study and indicates the direction for future research in academic entrepreneurial engagement activities.

6.1 Implications

The present study has a number of theoretical and policy /managerial implications for researchers, practitioners audiences and policymakers.

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6.1.1 Theoretical Implications

The present study focused on exploring the factors that influence the performance of entrepreneurial collaborations by academicians in private universities in Malaysia—a phenomenon that is relatively untouched in academic entrepreneurship literature in Malaysia. Specifically, selected social-psychological, organisational and inter-organisational factors were analysed.

It is believed that this study has added to the existing literature, filling four important gaps. Firstly, the study provides empirical evidence establishing the entrepreneurial collaborative activities that characterise the academicians’ engagement with industry. Secondly, it establishes the antecedents and consequences of academic entrepreneurial collaborations with industry. Lastly, the study establishes the factors that influence the outcomes of entrepreneurial collaborations in the Malaysian context.

Other contributions include identifying and measuring the dimensions of organisational learning capability and organisational-level entrepreneurial orientation, which help establish the importance of a strong university management team in academic-industry entrepreneurial collaborations. These dimensions comprise managerial commitment to learning, presence of a system perspective, openness and readiness to experiment, effective knowledge transfer and integration, proactiveness, innovativeness, risk-taking and competitive aggressiveness. The findings confirmed that university management play a crucial role in managing organisational resources, which directly impacts academicians’ involvement and performance in commercially- oriented academic-industry engagements.

The study also contributed to socio-psychological approach to entrepreneurship, which looks upon entrepreneurial activity as the manifestation of personality and behavioural characteristics of the individual that are influenced by environmental structural conditions and social factors (Kets de Vries, 1977; McClelland, 1961; Reynolds, 1991; Shapero & Sokol, 1982). Social-psychological factors were assessed using twelve items, adapted from existing scales, that measure two dimensions (individual entrepreneurial orientation and readiness to collaborate with 148 industry) commonly associated with individual level studies (Azjen, 1988; Bolton & Lane, 2012; Covin & Covin, 1990; Covin & Slevin, 1986, 1989). The research findings revealed mixed results. Hypothesis H6, academicians’ entrepreneurial orientation was positively related to engagement in entrepreneurial collaborations, was partially supported; however, hypothesis H7, academicians’ readiness to collaborate is significantly related to engagement in entrepreneurial collaborations, was fully supported.

This study adds empirical evidence to understanding the importance of individual academicians’ personality and behavioural characteristics in the Malaysian context. This includes the dimensions of readiness to collaborate with industry, individual entrepreneurial orientation being influenced by environmental structural conditions and social factors shaping their perceptions towards involvement and eventual performance in commercial- oriented academic-industry engagements.

Additionally, the study’s investigation of combined academic entrepreneurial activities revealed that academic entrepreneurial diversification generates synergistic effects in terms of knowledge and skills, social networking, input-output flows and physical resources. The research findings are consistent with the diversification and portfolio entrepreneurship literature, which has argued that the carrying out of several entrepreneurial activities provides additional benefits, due to the synergistic effects that can develop between activities (Alsos et al., 2003; Westhead et al., 2005). Hence the thesis provides empirical evidence that the existence of teaching, research and company-creation related collaborations activities (see Table 4.6) extend the theoretical concepts of diversification portfolio entrepreneurship to academic entrepreneurship, which is particularly focused on private universities in Malaysia, a developing country.

The extent of these synergistic effects, social networking (Westhead et al., 2005), knowledge and skills (Alsos et al., 2003; Shane, 2000; Westhead et al., 2005), input-output flows, and physical resources (Alsos et al., 2003; Westhead et al., 2005), varied depending on the complexity and the breadth of academic entrepreneurial activities (teaching, research and company-creation) and cross-functional engagement.

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Contrary to expectation, the research findings do not agree with the arguments from existing literature that diversifying into similar activities (for example, diversifying only into teaching related activities) generated more synergistic effects (since similar activities allows sharing common resources and competencies) (Markides & Williamson, 1996).

Lastly, this study sought to introduce, and achieved, several methodology improvements. Unlike previous studies that were conducted in one or two universities, the present study involves all private universities in Malaysia. In contrast to previous research with findings based on a limited sample size, which attracted criticism on their validity, this study provided evidence of validity with high number of samples in a real life work scenario.

6.1.2 Policy and Managerial Implications

Across the globe, government agencies, the private sector and universities have independently or collaboratively launched initiatives to increase academic entrepreneurial engagements. The reasons range from commercialisation, generating societal legitimacy for publicly subsidised social and scientific research, knowledge generation and dissemination, stimulating economic activity, and access to artefacts, data, equipment and materials for raising revenue for universities.

This research is able to draw a number of policy and managerial implications from the empirical evidence, which is discussed below.

In the following sections, four policies and implications are discussed. The insights inform policy makers on the effectiveness of human resource (HR) policies to influence academic performance and involvement in commercially-oriented academic- industry collaborations. The policy makers must understand the outcomes from different types of entrepreneurial activities before forming policy, the importance of different support structures, incentive mechanisms, and building trust among external industry partners.

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Firstly, policy makers play an important role in developing effective HR policies that influence academic performance and involvement in commercially-oriented academic-industry collaborations. Levin and Stephan (1991) stated that senior academicians have a higher propensity to engage in academic entrepreneurship compared with those who are at the initial stages of their career. Levin and Stephan explain this by stating that renowned scientists or professors, who have less pressure to publish, capitalise on their experience, credentials, and strong social network to engage in entrepreneurial activities. In contrast, young researchers are building their reputation through publishing papers.

While the research findings in the present study are consistent with their arguments, this does not suggest there is one approach to HR policies for academicians in Malaysian private universities. When developing HR polices that support academicians, university policy makers should take into account academicians’ different levels of seniority and career phases.

Secondly, policy makers should understand the range of outcomes generated from different types of entrepreneurial activities. For example, funding bodies across the globe call on academicians to provide evidence of societal impact; hence how these engagements result in benefits and how academicians maintain scientific quality are relevant issues for policy makers.

The study data show respondents’ most popular teaching related collaborations, summarised in Table 4.7, were conducting seminars and training sessions for industry (47.8 percent) followed by placing students as trainees in the industry (46.9 percent). Respondents’ indicated the research-related collaborations with the highest percentages of active and sustained involvement were joint- application for funding (42.4 percent) and providing research-related assistance to small business owners (41.6 percent) (see Table 4.8). For company creation-related collaboration, summarised in Table 4.9, the two main activities were forming joint- ventures privately through collaboration with industry (27.3 percent) and contributing to the formation of university centres designed to carry out commercialisation activities (22.2 percent).

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From these findings, academicians clearly engage in an array of academic entrepreneurial activities that result in various teaching, research, company-creation and cross-functional outcomes. However, evidence of the impact of these collaborations on other university activities, for example, teaching and research is scant so should not be taken for granted.

Additionally, specific research on the consequences of commercially-oriented academic-industry engagements will inform policy makers’ decisions on the behaviours and organisational forms to promote, and under which conditions they are likely to further scientific, social, knowledge and economic objectives. For example, a better grasp of the causal relationship between engagement and research performance is crucial for designing policy interventions. If engagement spurs research performance, then policy makers should promote engagement. Nonetheless, if the opposite is true, research performance drives engagement, then other interventions are required to boost research excellence. This issue is particularly important in the context of increasing pressures exerted by democratic constituencies to evaluate the impact of academic research.

Thirdly, because previous studies on university-industry engagements have emphasised the role of technology transfer offices or engagement liaison offices, policy makers have established engagement liaison offices at universities. However, from a policy perspective, it is crucial to recognise that academicians may require different support structure for external collaboration activities, namely, teaching, research, company and cross-functional related activities. Examples of support structures include technology transfer offices or engagement liaison offices to improve networking with industry, flexible work hours and training program. Examples of incentives for academics include access to funding (financial) resources and access to in-kind resources (materials, equipment, data).

Additionally, given individual discretion seems the main determinant of academic engagement with industry, policy measures should address individuals, as well as influencing university practices and structures. For example, fostering individual-level engagement skills via entrepreneurship training, workshops and

152 apprenticeship schemes would appear to be a potentially powerful lever, not only for increasing the volume of university–industry relations but also their quality. In this respect, policy should not implicitly assume that ‘more is better’ but seek to differentiate the conditions under which engagement generates both academic and industrial benefits, so minimise the risk of failure.

Another area for consideration is in external partner skills. Considering the high volume of other entrepreneurial engagement activities compared with patents in academic-industry collaborations, it is essential that universities be well-equipped to effectively participate in collaboration (Perkmann & Salter, 2012). However, these external partners also need to be skilled in initiating and managing such collaborations. It is crucial they recognise and acknowledge that collaborating with academia presents distinct challenges, separate to those of customers or suppliers.

Drawing on these research findings, we proposed a set of guidelines that universities should follow to get the most value out of their academic entrepreneurial engagements with industry.

Firstly, define the academic-industry collaboration project’s strategic context as part of the selection process. Academic-industry collaborations must be aligned with the university’s research and development strategy and address the needs of the industry partner. If not, there is a high risk, and no point of investing, in projects that have little or no impact. The point is that there should be a vision and objectives that define what the university entrepreneurial engagements with industry will provide each partner. University engagements that lack a link to the needs of an industry partner are unlikely to be given enough attention.

Secondly, universities and their industry partners must share a project’s collaborative purpose with academicians and provide a strong collaborative environment. The research findings showed that knowledge of the collaborative purpose and the provision of a strong collaborative environment has a significant and positive impact on academicians’ engagement in entrepreneurial collaborations. This may be attributed to academicians’ having insights on the skills sets and strategy necessary to accomplish the projects. 153

Lastly, universities should invest in long-term relationships. The research findings revealed that there significant differences in academicians’ engagements in entrepreneurial collaborations, with respect to age, seniority and qualifications. This confirms that over time academicians gain experience and a better understanding of industry partner need to develop social networks and access more resources to improve their performance.

6.2 Limitations of the Research

This present study has several limitations. Firstly, the study was conducted only in Malaysia and the respondents in this study were academicians from private universities and foreign-branch campuses. Therefore, this single country focus offers limited possibilities for theory development as the findings may not be generalised to other types of universities in different countries. In order to develop a more extensive understanding of academicians’ perceptions towards social-psychological, organisational and inter-organisational factors on the nature of commercially-oriented academic entrepreneurial collaborations, the study could be extended to other countries.

The second limitation is that the current study is not a longitudinal study and, like any other cross-sectional study, it can only provide a static perspective on fit. As the data were collected from academicians at a fixed period of time, the direction of causality cannot be determined. It must be noted that a longitudinal approach would have placed the researchers in a better position to draw causal conclusions. Therefore, only conclusions or discussions of the general relationships between the variables of the interest could be drawn.

Thirdly, due to cost, time, and data concerns, the results of this study depended on the extent to which the respondents understood the questions and responded according to their genuine perceptions. Data for all variables were academicians’ perceptions, so the results of the study reflect only their perceptions. Some of the respondents may not have fully considered their answer in completing the questionnaire. This unfavourable behavioural attribute might distort the reliability and validity of the instrument. Furthermore, respondents were told that the 154 questionnaires were collected for research purposes, which lessens the benefits they may receive compared with data collected for administrative purposes, and this may limit their effort in answering the questions (Farh & Werbel, 1986; Korsgaard, Schweger & Sapienza, 2004).

Lastly, to the knowledge of the researchers, academic-industry collaborations have not been widely explored in the academic entrepreneurship literature in either developed or developing countries. Therefore, this single country focus offers limited possibilities for theory development. Hence, future research might be carried out in multi-national research in both developed and developing countries for a better understanding of academic entrepreneurship in differing national contexts.

6.3 Recommendations for Future Research

The analysis of academic entrepreneurship literature and the analysis of the research findings generated novel insights into the perceptions of academicians. Firstly, given that public and private universities differ in many dimensions, and because entrepreneurship is heavily context-dependent, differences in dimensions may be found, such as in the mission or purpose, ownership, sources of revenue, government controls and management norms of a university (Altbach, 1999; Geiger, 1986; Williams, 1996). Hence, there is a need for more in-depth knowledge on private universities.

Secondly, should a similar study be carried in public universities in Malaysia, it will hopefully determine whether any differences exist compared to private universities and foreign-branch campuses in Malaysia. Clearly, this is an area that calls for further investigation. In addition, this study could be replicated with industry practitioners as respondents to gauge their perceptions towards engaging academicians in academic-industry collaborations. It may result in a comparative study of industry practitioners’ and academicians’ perceptions of selected factors that influence their engagement and performance in academic-industry collaborations. However, this may require the application of alternative forms of research methodology to minimise self-bias. Simple steps researchers may take to minimise self-bias include avoiding loaded terms or phrases, avoiding silly questions, reversing wording several agree/disagree format questions when they occur in groups, avoiding 155 touchy questions, using questions that have been well-tested by other researchers and using filter questions or built-in filters to eliminate door-step opinions (Jacobs, Hartog & Vijverberg, 2009).

Moreover, the quantitative methods used in this study may limit the amount of data that can be gathered from academic staff on the constructs under study. Hence, it recommended the future study adopt more comprehensive qualitative methods that include interviews in order that respondents express their views in accordance to the interview questions. For example, researchers may interview academicians on their perceived self-identity as an academic, whether industry engagement is an important aspect of their professional identity, and whether experiencing industry engagement changes their self-identity. These measures will enhance the quality of information on the perceptions of academicians on the effects of selected social-psychological, organisational and inter-organisational factors on their engagement and involvement in academic-industry collaborations.

Thirdly, existing studies (De Silva, 2012; Jain & Yusof, 2007; Yusof, Mohammad & Leilanie, 2012) have not considered the impact of entrepreneurial collaborations on educational output. Therefore, the consequences and impacts of academic entrepreneurial activities, namely, teaching, research and company-creation related activities with the academic engagement need to be further explored. Extant analyses have neglected to consider its impact on educational outputs, such as time devoted to teaching, curriculum and course development, and teaching quality. Insights into this aspect of engagement would be valuable in extending our knowledge of the benefits or costs of ‘third stream’ activities within the context of universities’ other missions.

Furthermore, future research on the consequences of academic entrepreneurial engagements will provide empirical evidence allowing policy makers to derive considered judgements as to the behaviours and organisational forms to promote, and under what conditions they are likely to further teaching, company- creation and research outcomes that contribute to further social, scientific, economic and knowledge objectives.

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Fourthly, existing research on academic engagement has been criticised by several scholars (Hambrick, 2007; Helfat, 2007; Miller, 2007) as overly devoted to theory. They argue that despite the strengths it is likely to prevent the reporting of rich details about interesting phenomena for which no theory exists. Given this background, future research should deploy data on this phenomenon to build and test theory. For instance, studies may view academic engagement as pro-active behaviour in knowledge-intensive organisations (Crant, 2000). Academia is an ideal context to study this kind of individual behaviour, which is likely to be beneficial to overall organisational performance, because academicians enjoy a large degree of professional autonomy, and hence their individual performance as well as their contribution to organisational goods is driven largely by self-motivation rather than command and control. Moreover, relative to professional services organisations, academic settings are richer in publicly available data on individual characteristics such as performance and career histories, enabling more detailed studies. Another opportunity for contributing to theory may arise from studying how individuals respond to local norms, such as those prevailing in their immediate, departmental work contexts (Bercovitz & Feldman, 2008), and how these relate to global norms, for instance at the level of scientific disciplines (Fini & Lacetera, 2010). This may also provide clues for determining the locus of possible policy interventions aimed at supporting engagement or mitigating adverse effects.

One specific recommendation on the use of institutional theory is that academic engagement may offer insights into how individuals within organisations manage exposure to different logics (Friedland & Alford, 1991); that of academic science and of commercial R&D (Murray, 2010). Working with industry is likely to generate conflicting pressures, including whether research results should be public or private, and whether research should be oriented towards publication or technical application (David, 2004). Although we know that ambidextrous management of both logics is commonplace, the study of academic engagement is likely to expand our understanding of how individuals accomplish this and what factors enable these efforts.

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Lastly, future research should explore the relationship between academic engagement and commercialisation. While our literature review in Chapter 2 (Table 2.11) highlighted commercialisation as one of the four incentives for academic- industry engagement, we cannot infer possible complementarities or contradictions between them. Research here should address two critical issues. On one hand, there may be a temporal relationship between engagement and commercialisation, in the sense that prior involvement in collaboration with industry may lead to commercial output later in time, either individually or within research groups. On the other hand, researchers should investigate the possibility that some types of collaboration are complementary with commercialisation outputs while others may be neutral or even compete with them. Knowing more about the relationship between academic engagement and commercialisation would also benefit policy debates by clarifying whether the policies designed to stimulate entrepreneurship also stimulate academic engagement, or whether more focused approaches are needed.

6.4 Summary

The present study measured the perceptions of academics on a range of variables associated with commercially-oriented academic-industry collaborations at private universities in Malaysia. These are the entrepreneurial orientation of academics, readiness to collaborate with industry, organisational learning capability of universities, the strength of inter-organisational ties, and the performance of collaborations.

This study contributes to the existing knowledge on the types of entrepreneurial collaborations that academicians in private universities are engaged in, and the factors that influence their performance. It achieves this by firstly providing empirical evidence on the impact of the academician’s demographic characteristics of gender, age, education level, existing position and discipline on involvement and performance on the breadth and depth of academic entrepreneurial activities in commercially-oriented academic-industry collaborations.

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APPENDICES

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APPENDIX A: LIST OF PUBLICATION

Nyeko, KE & Ngui, KS 2015 ‘Academic Entrepreneurs and Entrepreneurial Academics: Are They the Same?’, International Journal of Social Science and Humanity, vol. 5, no. 1, pp. 1050-1055.

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APPENDIX B: ETHICS CLEARANCE

From: Astrid Nordmann [[email protected]] Sent: Monday, 2 March, 2015 11:37 AM To: George Kwang Sing Cc: Kizito Emmanuel Nyeko; Mung Ling Voon; RES Ethics Subject: SUHREC Project 2013/166 Ethics Clearance

To: Dr Ngui Kwang Swing, Sarawak

SUHREC 2015/028 – Entrepreneurial pursuit in academic-industry collaboration: an exploratory study of factors influencing financial success in private universities in Malaysia

Dr Ngui Kwang Sing, Kizito Emmanuel Nyeko (Student), Voon Mung Ling – Faculty of Business and Design, Sarawak. Approved duration: 03-03-2015 to 28-02-2016 [adjusted]

I refer to the ethical review of the above project protocol by a Subcommittee (SHESC3) of Swinburne’s Human Research Ethics Committee (SUHREC). Your responses to the review, as per the email sent on 02 March 2015, were put to the Subcommittee delegate for consideration.

I am pleased to advise that, as submitted to date, the project may proceed in line with standard on-going ethics clearance conditions here outlined.

- All human research activity undertaken under Swinburne auspices must conform to Swinburne and external regulatory standards, including the current National Statement on Ethical Conduct in Human Research and with respect to secure data use, retention and disposal.

- The named Swinburne Chief Investigator/Supervisor remains responsible for any personnel appointed to or associated with the project being made aware of ethics clearance conditions, including research and consent procedures or instruments approved. Any change in chief investigator/supervisor requires timely notification and SUHREC endorsement.

- The above project has been approved as submitted for ethical review by or on behalf of SUHREC. Amendments to approved procedures or instruments ordinarily require prior ethical appraisal/clearance. SUHREC must be notified immediately or as soon as possible thereafter of (a) any serious or unexpected adverse effects on participants any redress measures; (b) proposed changes in protocols; and (c) unforeseen events which might affect continued ethical acceptability of the project.

- At a minimum, an annual report on the progress of the project is required as well as at the conclusion (or abandonment) of the project. Information on project monitoring, self-audits and progress reports can be found at: http://www.research.swinburne.edu.au/ethics/human/monitoringReportingChanges/

- A duly authorised external or internal audit of the project may be undertaken at any time.

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Please contact the Research Ethics Office if you have any queries about on-going ethics clearance. The SHR project number should be quoted in communication. Chief Investigators/Supervisors should retain a copy of this e-mail as part of project recordkeeping.

Best wishes for the project.

Kind regards,

Astrid Nordman SHESC3, Secretary

______

Dr Astrid Nordmann Research Ethics Officer Swinburne Research (H68) Swinburne University of Technology PO Box 218, Hawthorn, VIC 3122 Tel: +613 9214 3845 Fax: +613 9214 5267 Email: [email protected]

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APPENDIX C: CONSENT INFORMATION STATEMENT

Consent Information Statement

Project Title: Entrepreneurial Pursuit in Academic-Industry Collaboration: An Exploratory Study of Factors Influencing Financial Success in Private Universities in Malaysia.

Investigators

 Dr Kwang Sing NGUI (First Investigator)  Dr Mung Ling VOON (Associate Investigator)  Mr Nyeko KIZITO EMMANUEL (Main Student Investigator)

Faculty of Business and Design, Swinburne University of Technology, Sarawak Campus.

Introduction to the Research Project and Invitation to Participate

This Consent Information Statement contains detailed information about the research project, in which we would like you to participate. Its purpose is to explain to you as openly and clearly as possible all the procedures involved in this project before you decide whether or not to take part in it.

We are undertaking a research project on academic entrepreneurship in the context of private universities in Malaysia. Specifically, we focus on the factors that may influence the performance of academic-industry collaborations.

We have obtained institutional agreement for participants’ involvement in this project from various faculty/school Deans, including yours. In this project, your personal consent to participate is implied by your returning the completed survey questionnaire to the investigators in the self-addressed and stamped envelope provided.

You will be given a copy of this Consent information Statement to keep as a record.

What this project is about and why it is being undertaken

The purpose of the survey is to measure the perception of academics on a range of variables associated with academic-industry collaborations. These are the entrepreneurial orientation of academics, readiness to collaborate with industry, organisational learning capability of universities, the strength of inter-organisational ties, and the performance of collaborations. The gender, age, education level, existing position and discipline will also be measured.

It is anticipated that the findings from the survey shall shed light on the types of entrepreneurial collaborations that academics in private universities are engaged in, and the factors that influence their performance.

We believe that the findings of this research project will contribute new knowledge on academic- industry collaborations in Malaysia. These findings will form a basis for promoting dialogue 191 among academics, industry and university administrators on how to foster and manage commercial-oriented collaborations.

What Participation Will Involve

Your participation in this research project will involve filling out a survey questionnaire which will take approximately 20 minutes to complete. Please complete all the sections and return the questionnaire before 30 March, 2015, using the self-addressed and stamped envelopes. By completing and returning the survey questionnaire, your free and informed consent is implied.

Participant rights and interests- Risk & Benefits

In this project, questionnaires will be used to seek the views of participants. The topic is not a sensitive one and participants will not be identifiable. It is anticipated that completing these questionnaires will pose no greater risk to participants than they encounter in everyday life.

We do hope that you will also derive some enjoyment and benefit from participating in this research. Your contribution will potentially improve academics involvement in academic-industry collaboration by encouraging among academics critical reflection on (i) existing approaches to academic-industry collaboration, (ii) the changing nature of a University as a context of work, (iii) academics perception of self and social identity and (iv) the integration of academic-industry collaborations as a new mandate of academics in addition to the traditional mandates of teaching, research and administrative duties.

Consent to Participate & Right to Withdraw

It is important that you understand that your participation in this study must be voluntary. If you do not wish to take part in the study, you are under no obligation to do so.

Participant Anonymity

In this survey, the researchers will not be tracking any identifying information of individual participants and their respective universities. Your responses are completely anonymous.

Privacy & Confidentiality

The project data will be stored securely within the premises of the University’s Faculty of Business and Design at the Swinburne, Sarawak campus.

Your privacy and confidentiality will be protected at all times, subject to legal limitations as follows:

 Research findings will be reported as aggregated results in any future publications so as to protect the identity of the respondents.  All data pertaining to the research will be converted to electronic form and kept on password-protected hard-drives.  Back-up disks will be kept in a locked cabinet in a steel metal, fire proof, locked cabinet.  Only the three people listed above who are involved with this research will have access to these records.  Following completion of the study, the data will be kept for a minimum of 5 years and maximum of 7 years. After this time all data will be destroyed ((See Swinburne’s Policy on Conduct of Research http://www.research.swinburne.edu.au/induction/code-of- conduct.html).

Research output

This research project is being undertaken by Kizito Emmanuel Nyeko to fulfil the requirements of a Masters of Commerce (by research) degree programme at Swinburne University of 192

Technology, Sarawak Campus.

It is anticipated that work related to this research will be published in peer-reviewed journals and presented at national or international conferences. Individual participants will not be identified and only aggregated results will be reported. You may wish to obtain copies of written reports based on these research findings. If so, please notify the researcher in writing using the details below (no additional costs will be involved).

Further information about the project- who to Contact

If you would like further information about the project, please do not hesitate to contact:

OR Kizito Emmanuel Nyeko Faculty of Business and Design, Swinburne University of Technology, Sarawak Campus Jalan Simpang Tiga 93350 Kuching, Sarawak, Malaysia Tel +6012 8926958 (Mobile) Fax +60 82260815 Email: [email protected]

Ethical Concerns/ complaints about the project-who to contact

This project has been approved by or on behalf of Swinburne’s Human Research Ethics Committee (SUHREC) in line with the Australian National Statement on Ethical Conduct in Research Involving Humans. If you have any concerns or complaints about the conduct of this project, you can contact:

Research Ethics Officer Office of Research & Graduate Studies (H68) Swinburne University of Technology P O Box 218, Hawthorn VIC 3122, AUSTRALIA

Tel +61 3 9214 5218; E-mail: [email protected]

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APPENDIX D: LETTER TO DEAN OF FACULTY

Letter to Dean of Faculty

Dear Sir/ Madam,

RE: SURVEY ON FACTORS AFFECTING ACADEMIC-INDUSTRY COLLABORATIONS IN MALAYSIAN PRIVATE UNIVERSITIES

With reference to the above, we would like to seek your permission to distribute survey questionnaires to the academic staff in your faculty. The data gathered from the survey shall be used for a post graduate degree (by research) project entitled “Entrepreneurial pursuit in academic-industry collaboration: an exploratory study of factors influencing financial success in private universities in Malaysia”.

The purpose of the survey is to measure the perception of academics on a range of variables associated with academic-industry collaborations. These include readiness to collaborate and entrepreneurial orientation of academics, organisational learning capability of universities, the strength of inter-organisational ties, and the performance of collaborations.

It is to our best knowledge that no previous studies have explored academic-industry collaborations in the context of Malaysian private universities. Hence, this study is novel and timely, as private universities have contributed significantly to the country’s human capital development.

Please refer to the Consent Information Statement. It contains detailed information about the research project, in which we would like your faculty members to participate. Its purpose is to explain to you as openly and clearly as possible all the procedures involved in this project.

We assure you that all responses will be treated in the strictest anonymity, privacy, confidentiality and only aggregated data will be reported. We would greatly appreciate if you can reply to us as soon as possible. Thank you for your time and kind attention to this matter.

Thank you.

Dr Ngui Kwang Sing, Dr Voon Mung Ling & Kizito Emmanuel Nyeko Faculty of Business & Design Swinburne University of Technology, Sarawak Campus Jalan Simpang Tiga, 93350 Kuching Sarawak, Malaysia Tel: +60 82 260702/ 416353 ext 7702 Fax: +60 82 260815 194

APPENDIX E: STUDY QUESTIONNAIRE SURVEY ON ENTREPRENEURIAL PURSUIT IN ACADEMIC-INDUSTRY COLLABORATION: AN EXPLORATORY STUDY OF FACTORS INFLUENCING FINANCIAL SUCCESS IN PRIVATE UNIVERSITIES IN MALAYSIA

INTRODUCTION The purpose of the survey is to measure the perception of academics on a range of factors related to academic-industry collaboration. Findings from the survey shall inform our research on academics’ involvement in entrepreneurial collaborations with the industry, and the determinants of financial success.

The research is unique as it is among the first to focus on private universities in Malaysia. It is hoped that the research outcomes shall enhance the knowledge and management practices required for supporting academic entrepreneurship and strengthening linkages with the industry.

We encourage you to contribute to its success by completing this survey questionnaire and return it to us in a timely manner. Rest assured that all responses shall be treated in strict confidence and only aggregated data shall be reported.

The survey is undertaken by a research team from Swinburne University of Technology, Sarawak Campus Malaysia, comprising of Dr Ngui Kwang Sing, Dr Voon Mung Ling and Kizito Emmanuel Nyeko.

INSTRUCTION

The time required to complete this survey is 20 minutes. Please read each question and the relevant items carefully.

In response to each item, please circle a number, which represents the answer, you deem most appropriate, relative to the question.

For example; (2)Disagree

ORGANISATIONAL FACTORS Please tick/ circle one number which represent your answer for each item: (1) Strongly Disagree (2) Disagree (3) Undecided (4) Agree (5) Strongly Agree On a scale of 1 to 5, please rate the extent you agree with Strongly Strongly each statement. Disagree Agree

1. The top leaders of my university frequently involve (1) (2)(2) (3) (4) (5) academic staff in important decision-making processes.

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BACKGROUND INFORMATION

1. Gender: Female Male

2. Age: Years

3. Your highest educational level: Bachelor

Master

Doctoral

4. Your current position at this university: Tutor

Associate Lecturer

Lecturer

Senior Lecturer

Associate Professor

Professor

5. Your academic discipline: Humanities

Social Sciences

Natural Sciences

Formal Sciences

Professions

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SOCIAL-PSYCHOLOGICAL FACTORS The following statements concern your readiness to engage with industry and your orientation towards entrepreneurship. On a scale of 1 to 5, please rate the extent you agree with Strongly Strongly each statement. Disagree Agree 6. The top leaders of my university frequently involve (1) (2) (3) (4) (5) academic staff in important decision-making processes. 7. Industry engagement is useful for acquiring (1) (2) (3) (4) (5) knowledge and skills that are useful for teaching and research. 8. Most academics whom I respect would regard (1) (2) (3) (4) (5) industry engagement as an important academic activity. 9. Most academics whom I respect would regard (1) (2) (3) (4) (5) industry engagement as an important academic activity. 10. I have significant control over the direction and (1) (2) (3) (4) (5) progress of collaborative projects. 11. I am able to juggle between my academic duties (1) (2) (3) (4) (5) and collaborative engagements. 12. I like to take bold action by venturing into the (1) (2) (3) (4) (5) unknown. 13. I tend to act “boldly” in situations where risk is (1) (2) (3) (4) (5) involved. 14. I prefer to try my own unique way when learning (1) (2) (3) (4) (5) new things rather than doing it like everyone else does. 15. I favour experimentation and original approaches (1) (2) (3) (4) (5) to problem solving rather than using methods other academics generally use for solving their problems. 16. I usually act in anticipation of future problems, (1) (2) (3) (4) (5) needs or changes. 17. I tend to plan ahead on projects. (1) (2) (3) (4) (5)

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ORGANISATIONAL-LEVEL FACTORS The following statements concern organisational learning capability and organisational-level entrepreneurial orientation On a scale of 1 to 5, please rate the extent you agree with Strongly Strongly each statement. Disagree Agree 18. The top leaders of my university frequently involve (1) (2) (3) (4) (5) academic staff in important decision-making processes. 19. In this university, innovative ideas that work are (1) (2) (3) (4) (5) rewarded. 20. All facilities, schools, departments and staff can (1) (2) (3) (4) (5) express their opinions and make suggestions regarding the procedures and methods in place for carrying out tasks. 21. All parts that make up this university are (1) (2) (3) (4) (5) interconnected working together in a coordinated fashion. 22. Part of my university’s culture is that academic (1) (2) (3) (4) (5) staff can express their opinions and make suggestions regarding the procedures and methods in place for carrying out tasks. 23. In this university, academic staff are encouraged to (1) (2) (3) (4) (5) interact with the environment: competitors, customers, technological institutes, universities, suppliers etc. 24. Errors and failures are always discussed and (1) (2) (3) (4) (5) analysed in this university, on all levels. 25. In my university there exist systems and (1) (2) (3) (4) (5) procedures for receiving, collating and sharing information from outside. 26. In my university, there is a free and open (1) (2) (3) (4) (5) communication within academics work groups.

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INTER-ORGANISATIONAL-LEVEL FACTORS The following statements concern organisational learning capability and organisational-level entrepreneurial orientation On a scale of 1 to 5, please rate the extent you agree with Strongly Strongly each statement. Disagree Agree 27. Top university leaders are supportive of industry (1) (2) (3) (4) (5) engagements. 28. The political and social climate seems to be “right” (1) (2) (3) (4) (5) for starting collaborative projects or programs. 29. Communication between collaborative groups (that (1) (2) (3) (4) (5) you are involved in), university top leaders and industry partners is effective. 30. Communication among the people in my (1) (2) (3) (4) (5) collaborative group happens both at formal meetings and in informal ways. 31. As an academic involved in collaboration programs (1) (2) (3) (4) (5) and projects, I have a clear understanding of what our collaboration is supposed to accomplish. 32. Academic staff involved in collaborative projects (1) (2) (3) (4) (5) and programs are dedicated to the idea that we can make this project work. 33. My ideas about what we want to accomplish with (1) (2) (3) (4) (5) this collaboration seem to be the same as the ideas of others.

ACADEMIC-INDUSTRY COLLABORATIONS The following table illustrate different activities that involve collaborations between academics and industry partners Please state if you have engaged in any of these No, Yes, in Yes, in Yes, in activities never the last the last both the 12 3 years last 12 months months

and 3 years 34. Research-based consultancy for industry (1) (2) (3) (4) through the university.

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35. Research-based consultancy privately (but (1) (2) (3) (4) without forming a company). 36. Contributing to the formation of university (1) (2) (3) (4) centres designed to carry out commercialisation activities. 37. Contributing to the formation of spin-off (1) (2) (3) (4) company/(s) (university is the owner). 38. Contributing to the establishment of (1) (2) (3) (4) university incubators and/or science parks. 39. Forming joint-venture/(s) privately through (1) (2) (3) (4) collaboration with industry. 40. Forming your own company/(s). (1) (2) (3) (4)

41. Joint-research projects with industry. (1) (2) (3) (4)

42. Developing products/services with the (1) (2) (3) (4) potential for commercialisation. 43. Providing research-related assistance to small (1) (2) (3) (4) business owners. 44. Working in the industry while being attached (1) (2) (3) (4) to the university. 45. External teaching for which you are paid in (1) (2) (3) (4) addition to your basic salary. 46. Initiating the development of new degree (1) (2) (3) (4) programme/(s) with advise from industry. 47. Acquiring funding from government, non- (1) (2) (3) (4) governmental or international bodies, through collaborations with industry partners. 48. Placing students as trainees in the industry. (1) (2) (3) (4)

49. Conducting seminars and training sessions for (1) (2) (3) (4) industry. 50. Teaching a subject that involves significant (1) (2) (3) (4) interactions with industry (e.g. capstone/ final year projects, guest lectures). 51. Sitting on the committee of industry/ trade (1) (2) (3) (4) bodies.

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PERFORMANCE OF ACADEMIC-INDUSTRY COLLABORATION The following statements concern the performance of academic-industry collaborations that you are involved with, in the last 3 years. On a scale of 1 to 5, please rate your performance in the Significant Significant following areas in the last 3 years. decline improvement 52. Meet project timelines and budgets. (1) (2) (3) (4) (5)

53. Achieve positive financial returns. (1) (2) (3) (4) (5)

54. Maintain favourable relationship with industry (1) (2) (3) (4) (5) partners. 55. Develop reputation as the go-to person to initiate (1) (2) (3) (4) (5) new industry collaborative projects. 56. Strengthen reputation within the industry. (1) (2) (3) (4) (5)

57. Secure industry input for basic research projects. (1) (2) (3) (4) (5)

58. Secure industry input for teaching. (1) (2) (3) (4) (5)

59. Adopt industry best practices to the university. (1) (2) (3) (4) (5)

60. Secure new opportunities for industry (1) (2) (3) (4) (5) collaboration. 61. Secure access to facilities/resources in the industry (1) (2) (3) (4) (5)

62. Enhance career mobility between academia and (1) (2) (3) (4) (5) industry

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Thank you for your time and cooperation!

If you would like further information about the project, please do not hesitate to contact:

Dr Ngui Kwang Sing Faculty of Business and Design Swinburne University of Technology, Sarawak Campus Jalan Simpang Tiga 93350 Kuching, Sarawak, Malaysia Tel +60 (82) 260702 (Work) Fax +60 82260815 Email: [email protected] OR Dr Voon Mung Ling Faculty of Business and Design Swinburne University of Technology, Sarawak Campus Jalan Simpang Tiga 93350 Kuching, Sarawak, Malaysia Tel +60 (82) 260707 (Work) Fax +60 82260815 Email: [email protected] OR Kizito Emmanuel Nyeko Faculty of Business and Design, Swinburne University of Technology, Sarawak Campus Jalan Simpang Tiga 93350 Kuching, Sarawak, Malaysia Tel +6012 8926958 (Mobile) Fax +60 82260815 Email: [email protected]

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