University of Bath PHD Tacit Knowledge Transfer in Inter-Organisational Networks A social network analysis of Formula 1 Mishra, Danish Award date: 2018 Awarding institution: University of Bath Link to publication Alternative formats If you require this document in an alternative format, please contact: [email protected] General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Download date: 06. Oct. 2021 Tacit Knowledge Transfer in Inter-Organisational Networks: A social network analysis of Formula 1 Danish Mishra A thesis submitted for the degree of Doctor of Philosophy Department of Mechanical Engineering University of Bath September 2017 Copyright Attention is drawn to the fact that copyright of this thesis rests with its author. A copy of this thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and they must not copy it or use material from it except as permitted by law or with the consent of the author. This thesis may be made available for consultation within the University Library and may be photocopied or lent to other libraries for the purposes of consultation. Danish Mishra i Acknowledgement I wish to convey my thanks to various people and institutions that helped me on my course to completion and, my gratitude to academics who participated in interviews and shared their valuable insights. I also want to acknowledge Dr Paolo Aversa (Cass Business School, City University of London) for his support and guidance. I have received advice and encouragement from friends, colleagues, and fellow researchers in the department and I thank them all very much. You know who you are. I want to make a special mention and covey my thanks to Tony Locke for tolerating my irksome presence in his house over the years. I want to particularly acknowledge the support, guidance, and encouragement of my supervisors, Dr Steve Cayzer and Prof Andrew Graves. I have learnt a great deal from their experience, benefited from their feedback, and perhaps most importantly, I have always enjoyed their company! I want to thank my parents, my brother and Bhabhi, little Nitya, and Amit. They have been a source of inspiration for me throughout my research. Last but not the least, I want to thank Didem for her continuous love and support. Her unwavering belief sustained me through the PhD process and I cannot imagine this project succeeding without her. ii Abstract Inter-organisational networks of businesses such as Formula 1, information management systems, pharmaceutical industries, and aerospace manufacturers face challenges in for technological development, competition, and logistics. In such businesses firms form alliances with their competitors to tackle specific projects, expand their resource base, and overcome regulatory changes. Researchers have identified these conditions as ideal for fostering explicit and tacit knowledge transfer and the network as a source of competitive advantage. This study finds that it is networked individuals with high tacit knowledge content that are source of competitive advantage in such inter-organisational networks. These networked individuals impact organisational performance and alter the competitive balance within the network. The research is situated within the context of Formula 1. The business model of Formula 1 involves trading and selling both technology and human resource with the competitors and reflects the important role played by tacit knowledge in this process. Formula 1 is an ideal context for this research as the grand prix industry is a fast clockspeed and small world ecosystem with organisations producing the same product i.e. the grand prix car. Formula 1 teams innovate and respond to external challenges such as regulation changes via movement of networked individuals. These individuals introduce novel knowledge within the organisation, and play different managerial and technical roles. This research establishes other contextual factors that affect tacit knowledge transfer in Formula 1. The role of an individual in an organisation is salient when considering the effect of that individual on team performance. Individuals in technical and managerial roles affect performance to a greater degree than drivers. Regulation changes are key drivers of technological discontinuities in Formula 1, and the movement of networked individuals plays an important role in teams’ ability to respond to these regulation changes. The research findings are particularly relevant for industries that share Formula 1’s technological and competitive context such as pharmaceutical, aerospace, and information management industries. iii Table of Contents Acknowledgement ii Abstract iii Table of Contents iv List of Figures vi List of Tables vii List of Abbreviations viii Chapter 1. Introduction 1 1.1. The Focus of the Investigation 3 1.2. Background to the Research 4 1.3. The Contribution to the Research: Gatekeepers and Small World Networks 5 1.4. The Structure of the Thesis 7 Chapter 2: Literature Review: Knowledge Transfer and Social Network Analysis 10 2.1. Tacit Knowledge Transfer 14 2.2. Inter-organisational Relationships 18 2.3. Network Theories 25 2.4. Studying Inter-Organisational Relationships as Social Networks 28 2.5. Social Networks and Knowledge Transfer 29 2.7. Research Question 31 Chapter 3. Methodology 33 3.1. Triangulation of Methods and Research Strategy 35 3.2. Interviews 36 3.3. Case Studies 38 3.4. Social Network Analysis 40 3.5. Small World 49 3.6. Social Network Analysis Data 50 3.7. Literature Survey: Databases and Keywords 51 3.8. Ethics Statement 52 Chapter 4: Context: Formula 1 - A Small World 53 4.1. Technological Overview of Formula 1 53 4.2. Early Days: 1950-1970 54 4.3. Innovation in Action 1970-1989 61 4.4. Evolving for 21st Century 1990-2010 66 4.5. Why Formula 1? 69 4.6. Small World 70 4.7. Innovation and Small World Networks 72 Chapter 5: Interviews and Case Studies: Qualitative Analysis 75 5.1. Interviews 75 5.2. Case Studies 85 Chapter 6. Social Network Analysis: Quantitative Analysis 103 6.1. Small World 106 6.2. Top and Bottom Nodes 108 Chapter 7. Discussion of Research Findings 117 iv 7.1. Movement of Individuals, Tacit Knowledge, and Competitive Advantage 117 7.2. Nature of Formula 1 124 7.3. Formula 1: A Small World Network 126 7.4. Way of Doing Things 129 7.5. Regulations and Geographical Proximity 130 Chapter 8. Conclusion and Limitations 132 8.1. Effect of Movement of Tacit Knowledge on Organisation Performance 133 8.2. Small World Network of Formula 1 134 8.3. Movement of Tacit Knowledge in Inter-organisational Network 135 8.4. Regulations 135 8.4. Implications for teaching and research, and motor sport policy 136 8.5. Limitations and Future Work 137 Appendix 1 Python Syntax 140 Appendix 2 Performance of top thirty nodes 142 Appendix 3 Performance of bottom thirty nodes 150 Appendix 4 Performance of top thirty nodes in technical roles 154 Appendix 5 Performance of top thirty drivers 159 Appendix 6 Interview Questionnaire 166 Appendix 7 Interview Transcripts 167 Appendix 8 Journal Publication Based on the Thesis 189 REFERENCE 203 v List of Figures Figure 1 Research contribution 6 Figure 2 Thesis outline 9 Figure 3 Framework for Tacit Knowledge Transfer 17 Figure 4 Research Onion (adopted from Saunders et al. 2007) 33 Figure 5 Research strategy diagram 36 Figure 6 Network graph for test case 1 42 Figure 7 Network graph for test case 2 45 Figure 8 Regular, Small World, and Random Networks (adopted from Watts and Strogatz, 1998) 49 Figure 9 Formula 1 Network Graph 1992 – 2010 104 Figure 10 Team Performance 109 Figure 11 Effect of movement of highly connected nodes on team performance 112 Figure 12 Performance of top and bottom placed 30 nodes on team performances 114 Figure 13 Effect of technical members and drivers on team performance 115 vi List of Tables Table 1 Dimensions of knowledge 12 Table 2 Factors affecting tacit knowledge transfer 17 Table 3 inter-organisational relationship theories (adopted from Rossignoli and Riccardi, 2015) 19 Table 4 Assumptions of the main paradigms (adopted from Creswell, 2007 and Johns, 2010; 54) 34 Table 5 Metaphysics of alternative paradigms (adopted from Guba and Lincoln, 1994 and Johns, 2010; 55) 35 Table 6 Tests for establishing quality of the case study research (adopted from Yin. 2003; 34) 39 Table 7 Network Glossary 41 Table 8 Test Case 41 Table 9 Network level metrics for Test Case 1 42 Table 10 Node level metrics for Test Case 1 42 Table 11 Network level metrics for Test Case 2 45 Table 12 Test Case 2 Node level metric for Test Case 2 46 Table 13 Technological discontinuities and regulation changes (adapted from Jenkins, 2010: 888) 53 Table 14 Impact of individuals on organisational performance 1950 -1970 60 Table 15 Impact of individuals on organisational performance 1970 - 1990 65 Table 16 Impact of individuals on organisational performance 1990 - 2010 68 Table 17 Labels for analysing interviews 75 Table 18 Interview findings 81 Table 19 Case Studies 85 Table 20 Case Study and Interview Findings (Source: Interviewees and case studies) 102 Table 21 Metric Table 1992- 2010 106 Table 22 Comparison of Formula 1 network to random graph; demonstrating small world nature.