Social Network Analysis of the Irish Biotech Industry: Implications for Digital Ecosystems

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2. Proposed Structure Background - OPAALS Phase II Social Network Analysis Irish Biotech Interview Data (prelim. findings) Implications for Biotech DE

3. What is a Digital Ecosystem? Knowledge economies operating as ecosystems Ecosystems are assemblages of interdependent institutions in which the welfare of the component organisms is dependent on the interactions between them Local Ecosystems (clusters; regional systems of innovation) Ecosystems evolve and survive due to gradual adaptation Evolution is accelerated by the promotion of higher and more efficient levels of knowledge flow /sharing. Digital Ecosystems seek to exploit the benefits of new ICTs in terms of faster, better and higher capacity information and knowledge flow Our DE: an open source, peer-to-peer digital environment An integrated approach to development In simple terms: a territorial innovation model where the knowledge flow is facilitated/stimulated by peer-to-peer ICT.

4. OPAALS OPAALS (Open Philosophies for Associative Autopoietic Digital Ecosystems) (FP6) Aims to develop an open-source, peer-to-peer information technology system that can facilitate productive exchange among businesses and communities of interest, such as SME networks or academic research communities Theoretical foundation (social, natural and computer science)

5. Ph. II: Case Studies of Knowl. Flow �Socio-Spatial structures of collaboration and knowledge flow that underpin innovation among SMEs in biotech and digital media� In order to understand DEs and the contribution they could make to competitiveness of SMEs and regional development, we need to understand in detail the processes of knowledge flow and innovation. Objective: to gain a detailed understanding of the socio-spatial foundations of knowledge flow and innovation processes. A series of case-studies of innovation projects (innovation biographies) in two sectors in the Irish economy, biotechnology and digital media.

6. Ph II: Hypothesis and Findings Target hypothesis: activities in biotech (analytical knowledge base) tend to be relatively less sensitive to geographical proximity then in digital media (symbolic/synthetic). �know-how� and �know-who� knowledge flow is facilitated by proximity local �buzz� important, particularly for know who type knowledge In reality, in Biotech and DM nearly all partners, clients and knowledge sources are located overseas. no evidence that partner choice is influenced by distance decay in know-who-type knowledge global and virtual buzz more important than local buzz. Local communities do exist, but they are �buzzing globally� and virtually rather than locally

7. Implications for DE in Biotech In the Irish biotechnology industries, a DE is unlikely to play a significant role in promoting regional development, as a project management tool It is more likely to stimulate regional development by acting as a more general communication or knowledge sharing tool and knowledge resource, connecting all regional players, irrespective of whether they are in formal collaboration. History of local engagement might lead to the development of local communities and networks that can be important conduits for knowledge flow and innovation.

8. Phase IIISocial Network Analysis of the Irish Biotech Industry: Implications for Digital Ecosystems

9. Phase III Current territorial economic development concepts, e.g. Systems of Innovation, emphasise networks as important aspect of innovation and clustering processes. Network theory and analysis can lead to a better understanding of innovation and clustering processes Formal and Informal Networks Disagreement as to the salience and importance of informal networks Info of limited strategic value Knowledge may not flow freely throughout the network Aim of phase III: Get an insight into the quality of the networks, notably whether the structure is conducive to knowledge exchange Are the networks exploited How can DEs facilitate the exploitation

10. Methodology Get an insight into the quality of the networks, notably whether the structure is conducive to knowledge exchange Quantitative Social Network Analysis Are the networks exploited Interviews with actors (SME and Academic) to discuss sociograms Can DEs facilitate the exploitation Interviews with actors Focus Group Meeting: �Towards a DE for the Irish Biotech Industry� Regional Authorities Enterprise Ireland (potential catalyst) Biotech experts VCs SMEs Intel

11. Social Network Analysis Social network analysis is based on the assumption of the importance of relationships among interacting units or actors and that units don�t act independently but influence each other. Relational ties between actors are viewed as channels for transfer or flow of resources. SNA involves an analysis of extent and structure of social networks Views relationships as actors and ties Key Concepts: e.g. Connectedness, Centrality and Betweenness. UCINET

12. Irish Biotech Datasets Patents (companies and inventors) Directors, Joint directorship Founders / Serial Entrepreneurship Spin offs (company and universities) Datasets touch different types of networks (formal vs. informal)

13. Population for SNA:�Modern� Biotech Inventory

14. �Modern� Biotech Inventory

15. �Modern� Biotech Inventory

16. Patent Dataset:

17. Patent Search:

18. Directors and Founders

19. Origin and Spin off:

20. Inventors and Companies:

21. Directors and Companies:

22. �Small World� Network Phenomenon Milgram (1967) �everyone is connected by a chain of about 6 steps.� Most actors connected through small no. of intermediaries/links �it�s a small world!� Interesting networks: (i) large (ii)sparse (iii) decentralised (iv) clustered

23. Small World Network Structure (Kogut and Walker, 2001) �Small world� characteristics: 1. Observed ties represent only a small proportion of all possible ties (low network density) 2. But a number of dense clusters, giving the network structure and stability 3. Possible to get �from there to here� in few steps Permits actors to strategise: Conduits of control and information

24. Small world Characteristics (Watts, 1999) 1) The characteristic path length, L The average length of the shortest paths connecting any two actors. 2) The clustering coefficient, C the average local density. That is, Cv = ego-network density, and C = Cv/n [A small world graph is any graph with a relatively small L and a relatively large C.]

25. Irish Biotech Small World Analysis 1. Biotech Directors and Companies (via Directorships) no. of directors: 302; no. of firms: 86; no. of connected firms: 43 2. Biotech Researcher and Companies (via patents) no. of researcher: 315; connected researchers: 307; no. of firms (with registered patents): 40; connected firms: 23 [Can also compare results with random network with the same number of nodes and ties as the highly structured observed networks]

26. Biotech Network (via Directorships) Network Statistics

27. Biotech Network (via patents) Network Statistics

28. Comparison of Small World Network Statistics

29. Irish Biotech Small World findings Highly clustered, with short path lengths - small world network structure Directors and companies connected via directors relatively more clustered than researchers and companies via patents �informal network more clustered than formal The Directors network (informal) appears to be more conducive to knowledge flow than the �patent network� (formal) � (i) Less flow and (ii) different path structure

30. Are networks exploited? Interviews Preliminary findings What knowledge actually flows through the networks? Company network based on patent data Limited evidence that this currently functions as a pipeline for local knowledge flow Network of Directors Some, but still limited, evidence that this currently functions as an important pipeline for local knowledge flow Mostly single step flows (although difficult to assess) Network plays a role for �know-who� type knowledge flow at an international level (counter theoretical) Informal networks based on employment/work history appear to be more important � particularly for know who type knowledge

31. Implications for DE in Biotech In the Irish biotechnology industries, a DE is unlikely to play a significant role in promoting regional development, as a project management tool. It is more likely to stimulate regional development by acting as a more general communication or knowledge sharing tool and knowledge resource, connecting all regional players in a sector There is room for increasing the knowledge that flows through these networks with a role of DEs (better exploitation)

32. Applications With Greatest Potential A forum for regional actors (in universities; research institutions and private enterprise) to consult each other on a reciprocal basis about the location of (regional and extra-regional) actors (know who) and sources of knowledge. �A regionally-based science forum for biotechnology scientists and technicians. Here biotechnology scientists and technicians in companies and universities can ask for advice about, and interactively discuss, scientific and technical problems. � A central point for sharing knowledge about the supply and demand for intellectual property / expertise in specific areas of biotechnology in Ireland

33. Possible Applications

34. QUESTION / SUGGESTIONS?