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Driving Diffusion of Scientific Innovation

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Driving Diffusion of Scientific Innovation Driving Diffusion of Scientific Innovation - The Role of Institutional Entrepreneurship and Open Science in Synthetic Biology Vom Promotionsausschuss der Technischen Universit¨at Hamburg-Harburg zur Erlangung des akademischen Grades Doktor der Wirtschafts- und Sozialwissenschaften (Dr. rer. pol.) genehmigte Dissertation von Giulio Barth aus M¨unster 2018 Advisors: Prof. Dr. C. Ihl, Prof. Dr. M. G. M¨ohrle Institute of Entrepreneurship, TUHH i Gutachter: Prof. Dr. C. Ihl Prof. Dr. M. G. Mohrle¨ Vorsitz: Prof. Dr. C. Luthje¨ Tag der mundlichen¨ Prufung:¨ 14. September 2018 ”Our victory: inevitable; our timing: uncertain.” (Drew Endy, Assistant Professor Stanford University) i Abstract Scientific innovations need to widely diffuse to fully exploit their potential. Prior research investigated levers on the diffusion of scientific innovation with particular interest on institutions, e.g., settings of property rights. As institutional theory lacks in explaining emergence and shaping of institu- tions, the institutional entrepreneur approach faces these limitations. Key actors combine logics from multiple fields and convince their social context of their ideas to legitimate the creation of new institutions and shape an emerging field. This thesis validates theories on institutional entrepreneurs and investigates the end-to process from diffusing a logic to the impact of an established insti- tution on scientific innovations in context of the emerging synthetic biology. The field is expected to introduce the 5th revolution and characterized by the central logic of making biology an engineering discipline. In chapter 4 theories on institutional entrepreneurs driving diffusion of in- stitutional logics to shape an emerging field are validated. To measure the social influence mechanisms, the heterogeneous diffusion model is adapted to the institutional logic. Predictions are tested using 8.3 million article- author pairs from Scopus.com. Authors who adopt the institutional logic are identified with topic modeling methods. To control for competition effects through similar prior research, a new measure called knowledge equivalence is introduced. This study makes two contributions to the inves- tigation of how institutional logics diffuse to shape emerging fields. Firstly, the study highlights the role of institutional entrepreneurs in diffusing in- stitutional logics in context of scientific innovation. Secondly, to track the process of social influence the heterogeneous diffusion model is applied to the diffusion of an institutional logic. Based on the diffused logic, property rights are shaped in a field. In context of synthetic biology, open science initiatives are motivated by the engineer- ing approach. To assess status quo, an impact assessment of open science and its success factors is performed in chapter 5. Here a query list of 478 open science parts is matched to 104 million sequences in patent applica- tions. This new methodology yields eleven times more hits in comparison to the count of references. Results show a moderate diffusion of open sci- ence parts and cannot validate higher characterization quality as success factor. Potential success factors are discussed in the influence model frame- work and recommendations for practice elaborated. To analyze the right side of the end-to-end process, the impact of selecting between open science and commercial research tools on knowledge diffu- sion is examined in chapter 6. Data from research articles using the com- mercial Zinc Finger research tool or an open science alternative are used for analysis. Exclusion restrictions are created to correct for the endogeneity of research tool selection. Results predict lower citation rates for an explicit selection of the commercial research tool. Also, no negative effect on the commercial research tool diffusion could be observed due to co-existence of an open science alternative. iii Acknowledgements Firstly, I would like to express my sincere gratitude to my advisor Prof. Christoph Ihl for the continuous support of my Ph.D study and related research, for his collegiality, patience, motivation, and immense knowledge. I could not have imagined having a better advisor and mentor for my Ph.D study. Besides my advisor, I would like to thank my co-advisor Prof. Martin G. Mohrle¨ for his insightful comments and encouragement. I am also hugely appreciative of Linda Kahl from the BioBricks Foundation for her frequent inspirational discussions, coaching and the introduction to the synthetic biology community. Without her precious dedication it would not be possible for me to conduct this research. Special mention goes to my fellow mates at the TIE for the stimulating discussions, uncomplaining support on issues with R and for all the fun we have had in the last years. My sincere thanks also go to Drew Endy, the Endy lab and especially Conary Meyer at Stanford University, who provided me an opportunity to join their amazing team, gave me countless lessons on genetic engineering and expert input for my analyses. I thank my forever interested, encouraging and always enthusiastic grand- mother: she was always keen to know what I was doing and how I was proceeding, although it is likely that she has never grasped what it was all about. Last but not the least, I would like to thank my beloved ones: my parents, my sister, my godfather, my aunt and uncle, Fritz, and to my girlfriend for supporting me spiritually throughout writing this thesis and my life in general. Thanks for all your encouragement! iv Contents Contents v List of Tables viii List of Figures ix List of Abbreviations x List of Symbols xii 1 Introduction 1 2 Theoretical foundations: Institutional Entrepreneurship and Institu- tions 11 2.1 Organizational Field and Institutions . 11 2.2 Institutional Logics . 14 2.2.1 Definition of Institutional Logics . 14 2.2.2 Five Principles of Institutional Logics Meta-Theory . 15 2.2.3 Institutional Logics Shape Behavior . 19 2.3 Institutional Entrepreneurship . 21 2.3.1 Determinants for Inducing Institutional Change . 23 2.3.2 Strategies to Drive Change . 26 2.3.3 Institutional Entrepreneurship in Institutional Economics . 28 2.4 Impact of Institutions on Innovation Diffusion . 29 3 The Emerging Field of Synthetic Biology 35 3.1 Structure, Challenges and Potential of the Field . 35 3.2 Organizations and Initiatives to Establish Open Science . 39 3.3 Property Rights in Synthetic Biology . 55 3.3.1 History and Current Situation . 55 3.3.2 Debate on Property Rights in Synthetic Biology . 57 3.4 Foundational Mechanisms in Genetics . 58 v Contents 4 Study I: How Institutional Entrepreneurs Drive the Diffusion of In- stitutional Logics 61 4.1 Introduction . 61 4.2 Theoretical Background . 64 4.2.1 The Institutional Entrepreneurship Approach . 64 4.2.2 The Heterogeneous Diffusion Model . 71 4.3 Data and Methods . 77 4.3.1 Sample Construction . 77 4.3.2 Measurement . 81 4.3.3 Econometric Model . 87 4.4 Results . 91 4.4.1 Hypotheses Tests . 92 4.4.2 Model Validation and Robustness Check . 95 4.5 Discussion and Implications . 96 4.6 Limitations . 97 5 Study II: Assessing the Impact of Open Science in Synthetic Biology 99 5.1 Introduction . 99 5.2 Organizations and Initiatives Establishing Open Science . 101 5.3 The Basic Local Alignment Search Tool (BLAST) . 103 5.4 Prior Research on Open Science in Synthetic Biology . 104 5.5 Data and Methods . 105 5.5.1 Build Databases . 105 5.5.2 BLAST Search and Validation Steps . 106 5.6 Results . 109 5.7 Discussion . 114 5.7.1 Implications for Research . 114 5.7.2 Implications for Practice . 115 5.8 Limitations . 117 6 Study III: Impact of Commercial vs Open Science Research Tools on Knowledge Diffusion 119 6.1 Introduction . 119 6.2 Theoretical Background . 122 6.3 The Case of Zinc Finger Nucleases . 126 6.4 Data and Methods . 130 6.4.1 Sample Construction . 130 6.4.2 Measurement . 133 6.4.3 Econometric Model . 137 6.5 Results . 140 6.5.1 Prediction Test . 142 6.5.2 Model Validation and Robustness Check . 144 6.6 Discussion . 148 6.6.1 Implications for Research . 148 6.6.2 Implications for Practice . 149 6.7 Limitations . 151 vi Contents 7 Conclusion 153 7.1 Summary of findings and practical implications . 154 7.2 Summary of limitations and future research opportunities . 157 A Appendix 161 A.1 Validation Models for Chapter 4 . 161 A.2 Code to run BLAST query for BIOFAB parts matching . 164 A.3 Variation in citation rates per author per technology . 177 A.4 Validation of instrument variables for multinomial treatment re- gression . 177 Bibliography 183 vii List of Tables 3.1 List of Frameworks, Legal Entities and Collaborative Projects . 40 4.1 Descriptive statistics for Synthetic Biology community database . 91 4.2 6 models using cox regression for infectiousness and susceptibility . 93 6.1 Citations of research articles using ZFN categorized by research tool . 133 6.2 Descriptive statistics for ZFN analysis database . 140 6.3 Regression estimates for 1st step of multinomial treatment regression 141 6.4 Regression table for 2nd step of multinomial treatment regression . 142 6.5 OLS Regression table for ZF Research Tool Selection . 145 6.6 Model statistics for multinomial treatment regression . 146 A.1 Regression table with 55% threshold . 162 A.2 Regression table for logit and parametric models . 163 A.3 Overview BIOFAB parts . 165 A.4 Regression table for 2nd step robustness check with SJR . 182 viii List of Figures 3.1 Four research areas in Synthetic Biology . 36 4.1 Amount of total research articles in corpus, flagged with keyword search and identified with topic modeling . 79 4.2 Development of co-author networks in the emerging field of synthetic biology . 81 4.3 Marginal effects for infectiousness predictors with 95% confidence interval . 94 4.4 Marginal effects for reputation on susceptibility with 95% confidence interval . 95 5.1 Re-usage of BIOFAB sequences in patent applications .
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