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  1. Driving JRVs:The Role of Universities in Vienna’s Regional Innovation System • Innovation System Nodes (paper) • -Concepts invoked: RIS and Joint Research Ventures • -Using Framework Program and JRV Data • -Geography and Features of JRV Actions • -JRV participation dynamics • -Major findings for Vienna‘s RIS • II. Innovation System Networks (in progress) • -Defining networks: basic architecture and geography • -Emerging network components • -Organizational dominance • -“Key player“ organizations • -Provisional findings Edward Bergman

  2. Innovation System Frameworks: EU, Austria, Vienna

  3. The share of sectors (U, N, I) in total publication output is represented by circles: areas are calculated as proportional to sector share, while areas of overlap between sectors indicate relative shares of co-publications. BALÁZS SCHLEMMER and WOLFGANG GLÄNZEL National Research Profiles in the Changing Europe (1983-2003). An exploratory study on sectoral characteristics in the Triple Helix

  4. University Interactions with Industry P. D’Este and P. Patel (2005), „University - Industry linkages in the UK: what are the factors determining the variety of interactions with industry?“

  5. Joint Research Ventures • OECD (1986) identify “research and development operations” engaged in by 2 or more firms, which extend considerably typical joint business activities such as buying and selling, developing resource or production operations, and engineering/construction operations. • Caloghirou, et al (2004) examine “…a certain kind of cooperative R&D agreement, those involving the generation/adaption (but not simple exchange) of new technological advances, broadly defined to include both pre-competitive (generic) and development (close to market) knowledge as well as the definition of standards”. …and Framework Programs Such agreements “…underlined the establishment of a formal science and technology policy in the European Union during the 1980s, very much based on the support of collaborative R&D through the Framework Programmes for Research and Technological Development.” (Caloghirou, et al, 2004, p. 8).

  6. JRV NODES AND „INNOVATION SYSTEM“

  7. Geography of JRV Actions in Austria

  8. JRV Actions: Organizations, Programs and Funding Success Subsequent Network Focus

  9. Time-Series of Mark I/II Share and Postal Code Concentration Time (call date rank)

  10. Major JRV Node Findings • -3,885 unique Austrian participants active in 10,589 projects (1994-2002) • -Average project involved 2.72 Austrian participants • -7,334 project participations in Vienna’s RIS: universities 3,010 + research institutes 1,559 + SMEs 945 + larger firms 877 projects • -Universities received Vienna’s EU funding most frequently, large firms least, with small firms in 2nd and research institutes in 3rd place • -Of 33 specific FP programs, relatively more funds were captured by Vienna in 15 FP programs; Austria captured more in 18 FP programs: division of labor. • -Every type organization in Vienna captured more coordinator positions relative to Austria: research centers (24.2 v. 22.6), SMEs (22.4 v. 20.9), universities (19.6 v. 18.2), big industries (16.0 v. 11.8) • -Industrial JRV participants show tendencies toward Mark I (SME)innovation regimes in Austria and Vienna. • -Environment, ICT and General Research topics account for the largest number of participations in Vienna and Austria, while Vienna shows comparative strength in Bio-technology and Traffic projects • -JRVs highly centralized: the 30+ most concentrated postal code addresses account for half of all JRVs, while the remaining half are scattered over an additional 520+ Austrian postal codes. Slight tendency to disperse over time. • -Within the Vienna RIS, SMEs are most peripheralized nodes, but show no further dispersion; funded SMEs actually show greater concentration over time. Large Vienna firm nodes (>250 workers) becoming somewhat more peripheral.

  11. Managed Sectors vs. Self-Organizing Networks Henry Etzkowitz*  and Loet Leydesdorff, „THE DYNAMICS OF INNOVATION: FROM NATIONAL SYSTEMS AND "MODE 2" TO A TRIPLE HELIX OF UNIVERSITY‑INDUSTRY‑GOVERNMENT RELATIONS“

  12. Network Connections: Nodes* and Components * *

  13. Biotech Austria Connected nodes 86 (Isolates 112, Duals 19) Components 10 BASIC NETWORK ELEMENTS [Non-Austian nodes] „cliques“ (JRVs) B2 B3 „components“ (JRV networks) B1

  14. ILLUSTRATING NETWORK GEOGRAPHY AND ARCHITECTURE B1 network geography B1 network architecture

  15. Biotech Austria: Emerging 1995 Total Connected 53 (Isolates 112, Duals 19)

  16. Biotech Austria: Emerging1996 Total Connected 76 (Isolates 112, Duals 19)

  17. Biotech Austria: Emerging1997 Total Connected 86 (Isolates 112, Duals 19)

  18. Universities Research Centers Big Industry SMEs A V B1 9 2 2 2 4* 12* B2 9 6 0 1 10 6 B3 11 1 1 3 5 11 B2 B3 B1* * Geographic plot on earlier slide

  19. Active JRV nodes in research fields (e.g., Environment) either link only internationally (isolates) or connect to form 81 network „components“ of varying size in Austria and its regions (e.g., Vienna). All Vienna nodes: Total Connected 496 (Isolates 229, Duals 32) All Austrian Nodes Total Connected 617 (Isolates 373, Duals 46)

  20. JRV cliques that arise during a long series of FP/other projects may share overlapping nodes, eventually producing super-components of networked nodes (e.g., Environment). These are the network structures most closely related to RIS activities. Such components occur at the country and in reduced form as geographic sub-components, e.g. Vienna. Vienna‘s sub-component Software UCINET PAJEK Austria‘s (super) Environment Component

  21. Emergence of Austria‘s (super) Environment Component Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250

  22. Emergence of Austria‘s (super) Environment Component Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250

  23. Emergence of Austria‘s (super) Environment Component Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250

  24. Emergence of Austria‘s (super) Environment Component Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250

  25. Emergence of Austria‘s (super) Environment Component Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250

  26. Emergence of Austria‘s (super) Environment Component Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250

  27. Emergence of Austria‘s (super) Environment Component Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250

  28. Emergence of Austria‘s (super) Environment Component Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250

  29. Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250 JRV Organizational Dominance Environment ICT

  30. Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250 Absent Universities and Research Environment ICT

  31. Universities Research Freelancer Foreign org Gov I Gov II SME>250<500 Big industry Misc Unknown SME<50 SME>50<250 Absent SMEs-Big Industry Environment ICT

  32. Principal „Tie Partners“ of JRV Organizations

  33. „Key Players“ in Environment and ICT Austrian Super Components

  34. Environment and ICT „Key Players“

  35. Provisional Network Findings • Division of Labor: Specific JRV networks focus on selected organizations: research- vs. commercially-dominated. • Concentration of Labor: Individual JRVs represent major „points of leverage“ within networks. Their presence is critical. Clear tendency for geographic concentration. • Continuity of Labor: Complex networks may emerge early but remain fixed at initial size/complexity; others continue adding nodes, forming ‚super-component‘ networks over time. • Partner Labor: University JRVs often work together vs. Large-Firm JRVs typically work with orther organizations. Various partner models.

  36. Standard Metrics and Statistics General usefulness: designed originally to measure macro features of social populations and directed ties (hierarchies, kinship, etc); of limited immediate relevance to analysis of reciprocal JRV networks Specific relationships based on time, growth, space, nodal variables are more difficult to implement within standard SNA software presently in use