Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfilment of the requirement for the degree of Doctor of Philosophy. MODEL OF BIOTECHNOLOGY INNOVATION ADOPTION AS PERCEIVED BY THE RESEARCHERS By HADI FARID September 2010 Chairman: Professor Abu Daud Silong, PhD Faculty: Educational Studies Malaysia s biotechnology industry has progressed well with the launching of the National Biotechnology Policy in April 2005. However there is still a need to intensify efforts to achieve its milestones. The main objective of this research was to determine whether the quantum of funding, level of knowledge, acceptance and receptiveness, cooperation, transfer of technology and personal characteristics could explain the variations in the level of adoption of biotechnology innovations among academic researchers and managing directors of biotechnology companies. Another objective was to provide and optimize a model for transfer and adoption of biotechnology innovations by Malaysian companies. The research was conducted qualitatively through a co-relational design, the data of which was collected through a validated questionnaire. The target population, all of whom responded to the questionnaire, included 98 academic biotechnology researchers i
and 51 biotechnology company managers, based on the lists obtained from the universities and MOSTI. The data collected was analysed using correlation analysis to determine the significant relationships between the dependent and independent variables. The logistic regression was used to determine the level of influence of the correlates on the dependant variable, i.e the level of adoption of biotechnology innovations. The results were used to develop a model for more effective adoption of biotechnology innovations in the Malaysian context. The findings, from the perspective of biotechnology academic researchers and company managers, revealed that the level of adoption of innovations is low. This is in line with the MOSTI official documents which state that the targets set by the Malaysian government have yet to be realised. The results of this research also affirmed this, as observed in the Rogers normal S-curve which does not show an increase in the adoption level at the 16% cut off point. This indicates a low rate of adoption of innovation in the target population. The most significant correlate was found to be the quantum of funding, followed by the level of knowledge, acceptance, transfer of technology and cooperation, In addition, the data suggested that academic researchers and biotechnology company managers have different perspectives on the issue of level of adoption and its predictors. The university researchers believed in the effect of the level of knowledge, acceptance and transfer of technology while the managers emphasized the importance of funding as the most effective variable. Using all the data above, an optimized model was developed to ii
identify and propose the predictors of adoption of biotechnology innovations in Malaysian biotechnology companies. iii
Abstrak tesis untuk dibentangkan kepada Senat Universiti Putra Malaysia bagi memenuhi syarat ijazah Doktor Falsafah. PERSEPSI PENYELIDIK DAN PENGURUS SYARIKAT TERHADAP MODEL INOVASI BIOTEKNOLOGI Oleh HADI FARID September 2010 Pengerusi: Professor Abu Daud Silong, PhD Fakulti: Pengajian Pendidikan Industri bioteknologi di Malaysia telah berkembang maju sejak pelancaran Dasar Bioteknologi Nasional pada April 2005. Walau bagaimanapun usaha-usaha perlu di pergiatkan lagi bagi mencapai tanda aras yang ditetapkan. Tujuan utama kajian ini adalah untuk mengenalpasti samada jumlah pembiayaan, tahap pengetahuan, penerimaan-guna, kerjasama, pemindahan teknologi dan ciri-ciri peribadi, mempengaruhi tahap penerimaan innovasi bioteknologi dari sudut pandangan penyelidik akademik dan pengurus syarikat. Ia juga bertujuan untuk mengemukan dan mengoptimumkan satu model berkaitan dengan pemindahan dan penerimaan-guna innovasi bioteknologi oleh syarikat Malaysia. Kajian ini dijalankan secara kuantitatif melalui rekabentuk korelasi, Data dikumpulkan melalui soal selidik yang telah sahkan. Sasaran populasi bagi kajian ini termasuk 98 penyelidik akademik bioteknologi dan 51 pengurus syarikat bioteknologi, berasaskan iv
senarai yang diperolehi dari universiti dan MOSTI. Kesemua sasaran populasi menyertai dalam proses pengumpulan data. Data yang dikutip dikaji dengan mengunakan analisis perkaitan bagi menentukan hubungkait yang penting di antara angkubah bergantung dan tidak bergantung. Regresi logistic digunakan untuk mengenalpasti tahap pengaruhan faktor ke atas angkubah yang bergantung,iaitu tahap penerimaan-guna inovasi bioteknologi. Penemuan kajian ini kemudiannya digunakan untuk membentuk model bagi penerimaan-guna inovasi secara berkesan dalam konteks Malaysia. Penemuan dari sudut pandangan penyelidik akademik bioteknologi dan pengurus syarikat menunjukan bahawa tahap penerimaan-guna inovasi adalah rendah. Ini adalah selaras dengan dokumen rasmi MOSTI yang menyatakan bahawa sasaran yang ditetapkan oleh Kerajaan Malaysia masih belum dicapai. Penemuan kajian ini juga mengesahkan kenyataan ini, seperti diperhatikan dalam Rogers S-curve yang biasa, yang tidak mencatatkan peningkatan dalam tahap penerimaan-guna inovasi dikalangan sasaran populasi. Faktor yang paling berkesan adalah jumlah pembiayaan diikuti dengan tahap pengetahuan, penerimaan-guna, pemindahan teknologi dan kerjasama. Disamping itu, data juga menunjukan bahawa penyelidik akademik dan pengurus syarikat bioteknologi mempunyai pandangan yang berbeza dalam isu penerimaan-guna dan juga faktor ramalan. Penyelidik kebanyakkannya menyokong kesan tahap pengetahuan, penerimaan dan pemindahan teknologi manakala pengurus memberi penekanan kepada kepentingan jumlah pembiayaan sebagai angkubah yang paling berkesan. Satu model v
yang optimum dibentuk dengan mengunakan semua data di atas bagi mengenalpasti dan mencadangkan faktor ramalan penerimaan-guna inovasi di syarikat bioteknologi Malaysia. vi
ACKNOWLEDGEMENTS First and foremost I offer my gratitude to my supervisor, Professor Dr. Abu Daud Silong who has supported me throughout my thesis. I would like to express my sincere gratitude to my co-supervisor, Professor Dr. Azimi Hamzah. His encouraging, detailed and constructive comments have enabled me to develop an understanding of the subject. I am also grateful to Associate Professor Dr. Bahaman Abu Samah as a member of the supervisory committee. In particular I would like to acknowledge the help of Associate Professor Dr. Saroje Kumar Sarkar for his tremendous support, numerous stimulating discussions and general advice. My most special gratitude goes to my parents who have supported me throughout this research. Without their encouragement, understanding and support it would have been impossible for me to finish this work. My special gratitude is due to my sister, for her support in different ways during the whole process. Finally, I offer my regards and blessings to all of those who supported me in any respect during the completion of the project. vii
I certify that a Thesis Examination Committee has met on date of viva voce to conduct the final examination of Hadi Farid on his Degree of Doctor of Philosophy thesis entitled Model of Biotechnology Innovation Adoption as Perceived by the Researchers in accordance with the Universities and University Colleges Act 1971 and the Constitution of the Universiti Putra Malaysia [P.U. (A) 106] 15 March 1998. The Committee recommends that the student be awarded the Degree of Doctor of Philosophy. Member of the Examination committee were as follows Azahari Ismail, PhD Associate Professor Dr. Faculty of Educational Studies Universiti Putra Malaysia (Chairman) Jegak Uli, PhD Associate Professor Dr. Faculty of Educational Studies Universiti Putra Malaysia (Internal Examiner) Azizan Asmuni, PhD Associate Professor Dr. Faculty of Educational Studies Universiti Putra Malaysia (Internal Examiner) Gary J. Confessore, Professor Dr. Faculty of Higher Education George Washington University, United States (External Examiner) BUJANG KIM HUAT, PhD Professor and Deputy Dean School of Graduate Studies Universiti Putra Malaysia Date: viii
This thesis was submitted to the Senate of Universiti Putra Malaysia and has been accepted as fulfilment of the requirement for the degree of Doctor of Philosophy. The members of Supervisory committee were as follows: Abu Daud Silong, PhD Professor Faculty of Educational Studies Universiti Putra Malaysia (Chairman) Azimi Hj Hamzah, PhD Professor Faculty of Educational Studies Universiti Putra Malaysia (Member) Bahaman Abu Samah, PhD Associate Professor Faculty of Educational Studies Universiti Putra Malaysia (Member) HASANAH MOHD GHAZALI, PhD Professor and Dean School of Graduate Studies Universiti Putra Malaysia Date: ix
DECLARATION I hereby declare that the thesis is based on my original work except for quotations and citations which have been duly acknowledged. I also declare that it has not been previously, and it is not concurrently, submitted for any other degree at Universiti Putra Malaysia or at any institutions. HADI FARID Date: 30 / Sep / 2010 x
TABLE OF CONTENTS Page ABSTRACT ABSTRAK ACKNOWLEDGEMENTS APPROVAL DECLARATION LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS i iv vii viii x xiv xvii xix CHAPTER 1 INTRODUCTION 1.1 Background of the Study 1 1.2 Problem Statement 7 1.3 Research Objectives 11 1.3.1 General Objective 11 1.3.2 Specific Objectives 11 1.4 Research Hypothesis 12 1.5 Significance of the Study 13 1.6 Scope of the Study 15 1.7 Limitations of the Study 16 1.8 Operational Definitions 17 1.8.1 Adoption of Innovation 17 1.8.2 The level of Adoption 17 1.8.3 Predictors of Adoption of Biotechnology Innovations 18 1.8.4 Biotechnology Company 20 2 LITERATURE REVIEW 2.1. Introduction 21 2.2 Agricultural Extension 21 2.3 Biotechnology Innovation and Biotechnology Transfer 23 2.4 Level of Adoption of Biotechnology Innovations and its Effecting Factors 25 2.4.1 Level of Adoption 27 2.4.2 Level of Knowledge 29 2.4.3 Amount of Fund 30 2.4.4 Level of Acceptance & Receptiveness 31 2.4.5 Level of Cooperation 31 2.4.6 Level of Transfer of Technology 32 2.5 Diffusion of Innovation Theory 33 2.5.1 Rogers Innovation Diffusion Theory 35 2.5.2 Adoption Analyses 39 2.5.3 Models Analyzing Adoption of Innovations 43 2.5.4 Organizational Adoption 44 2.5.5 Change Agents Role in Diffusion and Adoption of Innovation 46 xi
2.5.6 Innovation Attributes 47 2.6 Agricultural Extension 48 2.7 History of Extension Education in Malaysia 51 2.8 Agricultural Biotechnology Innovation Adoption 54 2.9 Adoption of GM Technologies 55 2.10 International and National Development of Biotechnology Innovation Systems 56 2.10.1 Biotechnology Innovation System in Selected Countries 57 2.10.2 Malaysia Biotechnology Innovation System and Bio Industry 62 2.11 Public Acceptance of Biotechnology in Malaysia 69 2.12 Related Research on Level of Adoption 70 2.13 Theoretical Framework 74 3 METHODOLOGY 3.1 Introduction 76 3.2 Research Design 76 3.3 Research Process 80 3.4 Research Framework 83 3.5 Population and Sampling 84 3.6 Data Collection 87 3.6.1 The Research Instrument 87 3.7 Validity 94 3.7.1 Internal Validity 94 3.7.2 External Validity 96 3.8 Reliability 97 3.9 Data Analysis 99 3.9.1 Descriptive Statistics 100 3.9.2 Logistic Regression 100 3.9.3 Pearson Correlation 102 3.9.4 The Level of Significance 103 3.10 Ethical Considerations 104 4 RESULTS AND DISCUSSION 4.1 Introduction 105 4.2 Demographic Characteristics 105 4.2.1 Demographic Characteristics of Academic Researchers 106 4.2.2 Demographic Characteristics of Biotechnology Company Managers 109 4.2.3 Level of Independent Variable 115 4.2.4 Level of Dependent Variable 117 4.3 Model of Biotechnology Innovation Adopted 118 4.3.1 Model of Biotechnology Innovation Adopted for Academic Researchers 120 4.3.2 Model of Biotechnology Innovation Adopted for Company Managers 137 4.4 Correlation Analysis 156 4.4.1 Data for Academic Researchers 157 4.4.2 Data for Biotechnology Company Managers 162 xii
4.5 Cumulative Results 167 4.5.1 Objective 1 167 4.5.2 Objective 2 168 4.5.3 Objective 3 170 4.5.4 Objectives 4 and 5 170 5 SUMMARY, CONCLUSION AND RECOMMENDATIONS FOR FUTURE RESEARCH 5.1 Summary 171 5.2 Discussion 174 5. 2.1 Demographic Data 174 5.3 Probability of Adoption Based on Logistic Regression 179 5.3.1 Interpretation of University Data for Level of Adoption 179 5.3.2 Interpretation of Biotechnology Company Managers Data for Level of Adoption 187 5.4 Interpretation of Relationship among Factors through Correlation 192 5.4.1 Interpretation of Correlation Results as Perceived by Academic Researchers 192 5.4.2 Interpretation of Correlation Results as Perceived by Biotechnology Company Managers 193 5.5 Factors Affecting on Level of Adoption 193 5.6 Current Situation for Level of Adoption Biotechnology in Malaysia 195 5.7 Proposed Model on Adoption of Innovation 196 5.8 Conclusion 198 5.9 Recommendations 203 5.10 Recommendations for Future Research 205 REFERENCES 207 APPENDICES 221 BODATA OF STUDENT 241 LIST OF PUBLICATIONS 242 xiii
LIST OF TABLES Table Page 2.1 Factors Affecting Biotechnology Adoption 26 2.2 Summery of Biotechnology Innovation Systems in Selected Countries 57 3.1 Specific Variables: Measure of Independent Variables 82 3.2 Justification & Measurement for Questionnaire 90 3.3 Reliability Statistics 98 4.2.1 Demographic Characteristics of Academic Researchers 107 4.2.1.1 Innovations Presented Annually (2005-2008) 108 4.2.2 Demographic Characteristics of Biotechnology Company Managers 109 4.2.2.1 Characteristics of Companies Biotechnology Activities 111 4.2.2.2 Kind of Innovation by Biotechnology Companies in Malaysia 113 4.2.2.3 Innovations Adopted by Companies (2005-2008) 113 4.2.2.4 Annual Innovations Commercialized by Companies (2005-2008) 114 4.2.3 Factors Related to Level of Adoption 115 4.2.4 Level of Adoption 117 4.3.1.1 Case Processing Summary 120 4.3.1.2 Dependent Variable Encoding 121 4.3.1.3 Classification Table a,b 121 4.3.1.4 Variables in the Equation 122 4.3.1.5 Variables not in the Equation 122 4.3.1.6 Omnibus Tests of Model Coefficients 122 4.3.1.7 Model Summary 123 4.3.1.8 Classification Table a 123 xiv
4.3.1.9 Analysis of Maximum Likelihood Estimates 124 4.3.1.10 Analysis of Maximum Likelihood Estimates (G.1) 127 4.3.1.11 Analysis of Maximum Likelihood Estimates (G.2) 128 4.3.1.12 Analysis of Maximum Likelihood Estimates (G.3) 129 4.3.1.13 Analysis of Maximum Likelihood Estimates (G.4) 130 4.3.1.14 Analysis of Maximum Likelihood Estimates (G.5) 131 4.3.1.15 Analysis of Maximum Likelihood Estimates (G.6) 132 4.3.1.16 Analysis of Maximum Likelihood Estimates (G.7) 133 4.3.1.17 Analysis of Maximum Likelihood Estimates (G.8) 134 4.3.1.18 Analysis of Maximum Likelihood Estimates (G.9) 135 4.3.1.19 Analysis of Maximum Likelihood Estimates (G.10) 136 4.3.2.1 Case Processing Summary 137 4.3.2.2 Dependent Variable Encoding 137 4.3.2.3 Classification Table a,b 138 4.3.2.4 Variables in the Equation 138 4.3.2.5 Variables not in the Equation 138 4.3.2.6 Omnibus Tests of Model Coefficients 139 4.3.2.7 Model Summary 139 4.3.2.8 Classification Table a 140 4.3.2.9 Analysis of Maximum Likelihood Estimates 141 4.3.2.10 Analysis of Maximum Likelihood Estimates (G.1) 143 4.3.2.11 Analysis of Maximum Likelihood Estimates (G.2) 144 4.3.2.12 Analysis of Maximum Likelihood Estimates (G.3) 145 4.3.2.13 Analysis of Maximum Likelihood Estimates (G.4) 146 4.3.2.14 Analysis of Maximum Likelihood Estimates (G.5) 147 xv
4.3.2.15 Analysis of Maximum Likelihood Estimates (G.6) 148 4.3.2.16 Analysis of Maximum Likelihood Estimates (G.7) 149 4.3.2.17 Analysis of Maximum Likelihood Estimates (G.8) 150 4.3.2.18 Analysis of Maximum Likelihood Estimates (G.9) 151 4.3.2.19 Analysis of Maximum Likelihood Estimates (G.10) 152 4.3.2.20 Analysis of Maximum Likelihood Estimates (G.11) 153 4.3.2.21 Analysis of Maximum Likelihood Estimates (G.12) 154 4.3.2.22 Analysis of Maximum Likelihood Estimates (G.13) 155 4.4.1 Criteria for Strength of the Relationship 156 4.4.1.1 Correlation Analysis for Academic Researchers Data 157 4.4.2.1 Correlation Analysis for Biotechnology Company Managers Data 163 4.5.1 Predictors of Level of Adoption as Perceived 169 xvi
LIST OF FIGURES Figure Page 1.1 The Relationship between the Policy Makers & the Factors Affecting the Implementation of the Innovation Adoption Policy 2 1.2 A model for Technology Transfer in Malaysia 6 2.1 Rogers Innovation Diffusion Graph 36 2.2 Innovation Decision Process 37 2.3 The Relationship between Perceived Attributes of Innovation and Level of Adoption of Innovation 38 2.4 Adoptions of Innovation Factors 55 2.5 Theoretical Framework 75 3.1 Research Process 81 3.2 Research Framework 84 3.3 Sequences for Questionnaire / Instrument Development 88 4.1 Comparison of Logistic Regression Model and Linear Regression Model 119 4.2 Academic Researchers Scatter plot 162 4.3 Biotechnology Company Researchers Scatter plot 167 5.1 Chances of Adoption of Biotechnology Innovations Based on Academic Researchers Data 184 5.2 Reject Probability of Adoption of Biotechnology Innovation by Using Academic Researchers Data 185 5.3 Chance of Adoption Biotechnology Innovation by Using Biotechnology Company Managers Data 189 5.4 Reject Probability of Adoption of Biotechnology Innovation by Using Biotechnology Company Managers Data 190 5.5 Factors Currently Affecting the Level of Adoption 194 5.6 Diffusion Curve for the Level of Adoption Biotechnology in Malaysia 195 xvii
5.7 The Present Scenario of Adoption of Biotechnology Innovations in Malaysia 196 5.8 Suggested Model For Adotion of Biotechnology Innovation 197 xviii
LIST OF ABBREVIATIONS ABS: Access and Benefit Sharing CAM: College of Agriculture, Malaya CECE: Center for Extension and Continuing Education CRDF: Commercialization of R&D Fund DOA: Department of Agriculture FAO: Food and Agricultural Organization GM: Genetically Modified GMP: Good Manufacturing Practice IDT: Innovation Diffusion Theory IT: Information Technology LMOs: Living Modified Organisms MAHA: Malaysian Agriculture, Horticulture and Agro-tourism MARDI: Agricultural Research and Development Institute MOSTI: Ministry of Science, Technology and Innovation MTDC: Malaysian Technology Development Corporation NBBNet: National Biotechnology and Bioinformatics Network NBP: National Biotechnology Policy NPCB: National Pharmaceutical Control Bureau xix
SEARCA: Southeast Asian Regional Center for Graduate Study & Research in Agriculture ToT: Transfer of technology UM: University Malaya UKM: University Kebangsaan Malaysia UPM: University Putra Malaysia USM: University Sains Malaysia WHO: World Health Organization xx