Science Technology Links

1. Science Technology Links Aldo Geuna SPRU-University of Sussex & Politecnico di Torino DIMETIC, PECS, July,2007

2. Structure of the Lecture • Research collaborations:The broader framework JVs/CA/STA. • University-industry relationships: Historical background. • University-industry relationships: Typologies and Motivations. • Characteristics of Univ-ind relationships (focus on firms) • University-industry relationships: The importance of searching, screening and signalling

3. Research collaborations

4. JVs & CAs • Joint Venture (JV) = new organisational entity jointly owned and controlled by the parents organisations. • Cooperative agreement (CA) = non equity based agreement, can include organisational mechanisms for oversight and management. • JVs and CAs are interorganisational linkages that enable the organisation to manage some of its environmental constraints (quasi-markets, quasi-hierarchies).

5. Strategic Technology Alliance • Strategic Technology Alliance (STA) = form of cooperation and agreement for which a combined innovative activity or an exchange of technology is at least part of the agreement. Research Joint Venture (RJV). • Prior to 1975 STA were or little or no importance.

6. Types of STAs • Simple unilateral contracts ("technology for cash") -e.g. technology licensing, • Multilateral contracts -e.g. Cross-licensing & Technology sharing, • Customer-supplier (user-producer) partnership, • Joint development agreement (which often includes organisational mechanisms for oversight and management), • ”Pure" equity joint venture.

7. Theoretical approaches to STA • Mainstream IO analysis of R&D cooperation based on game-theoretic approaches: analysis of strategic behaviour of firms and societal/competitive consequences of R&D cooperation (d’Aspremont and Jacquemin, 1988). • Transaction Costs and incomplete contracts : R&D cooperation as an intermediary organisational form (Williamson, 1996) • Resource based view of the firm /organisational learning: collaboration is seen as a response by organisations to environmental changes demanding improvements in their know-how and/or technological capabilities (Hagedoorn, 1993; Mowery, Oxley & Silverman, 1996).

8. Main motives for STAs I • Present rapid changes in technological development ICTs/Biotech/Nano • Acquisition of new technical skills and technological capabilities, • Necessity of monitoring a wide spectrum of technologies.

9. Main motives for STAs II • Necessity of quick preemption strategies, • Complexity and uncertainty surrounding technological development: • Need of spreading costs and risks, • Coordinating and formulating technical standards (user-producers, producers in telcom),

10. University-industry relationships:Historical background

11. Historical trends • 1945-1980s • industry relied on universities mainly for supply of QSEs (e.g. for own R&D labs) – exercised some influence on curricula in e.g. engineering, chemistry • knowledge often flowed first through public sector labs before taken up by industrial labs • industrial support for universities often took form of endowments and gifts (rather than specific project contracts) i.e. no ‘strings’ attached • responsibility of university was to publish results of research so that available to all (see Fig. 1 in STI Review, No. 23)

12. Historical trends (continued) • 1980s onwards – various changes driven by • increased globalisation, competition and emphasis on innovation, so firms need to get closer to sources of knowledge creation • increased speed of knowledge exploitation • budgetary constraints faced by governments and universities, so latter sought new funding sources • government policies encouraging technology transfer, collaborative research in key areas, commercialisation of research, U-I links

13. Historical trends • 1980s onwards (continued) • declining profits and/or increasing costs of research encouraged many firms to outsource more basic research (outsource not only to U but to also others) • examples/‘heroic myths’ of MIT & Route 128, Stanford and Silicon Valley, ‘the Cambridge Phenomenon’ • Industry increasingly interested in university research as well as QSEs – seen as offering specific opportunities for cooperation • In some fields knowledge may flow directly from U to I • Resource flow to U from I no longer limited to endowments etc.

14. Historical trends (continued) • US • NSF established University-Industry Cooperative Research Centers (UICRCs) 1975. • e.g. Center for Integrated Systems (Stanford) • Required changes to regulations on cartels to allow establishment of industrial consortia • NSF subsequently launched Engineering Research Centers, and Science and Technology Centers • Later, individual firms signed multi-M$ partnership deals with academic departments (e.g. Monsanto & Washington U, Hoechst and Harvard Med School)  concerns re (foreign) firms ‘buying up’ U departments

15. Historical trends (continued) • UK • establishment of Alvey Programme in early ’80s to foster collaboration between I and U in IT • followed by variety of other schemes to foster U-I collaboration • Other OECD countries. • Industry still only accounts for under 6% of university research funding (up just 0.4% since 1991)

16. Industrial Support for HERD Some OECD Countries (log scale)

17. % of HERD financed by industry

18. University-industry relationships: Typologies and Motivations

19. University-Industry-Government Relationships

20. Typologies of U-I partnerships • STI Review (1999) – typology based on • relative control over outputs – spectrum from full U control to I control • degree of I involvement – zero for endowments, reaches peak in research consortia and cooperative centres • industry expectations re outcomes – range from very few for endowments to much greater when I involved in technology/knowledge transfer from U

21. Types of U-I partnerships

22. Universities’ motivations for research partnerships • Obtain financial support for its missions. • Broaden experience of students and faculty. • Identify significant and interesting research problems. • to enhance regional economic development. • to increase employment opportunities.

23. Industry’s motivations for research partnerships • to access research infrastructure. • to access expertise. • to aid renewal of company’s technology. • to gain access to potential employees. • to increase pre-competitive research. • to be plugged in the open science network.

24. Conditions for U-I collab’s • More likely to occur in some universities than in others due to differences in: • disciplines emphasised by the HEI • e.g. technological universities (MIT, RPI, Chalmers etc.) • academic culture of the HEI • i.e. different weight given to the various goals of the institution (cf. B. Clark on ‘entrepreneurial universities’)

25. Conditions for U-I collab’s • development strategy of the HEI • e.g. new universities in Finland where research oriented around regional needs (Oulu, Joensuu) • environment of the HEI • e.g. a thriving industrial sector in the region, a science park (e.g. Research Triangle Park, Cambridge)

26. Characteristics of successful U-I partnerships • Well-defined objectives, roles and expectations; • Identification of key personnel, duties and restrictions; • Clear funding arrangements; • Stable support and flexibility provided by U for the researcher; • IP and publication issues resolved early on (or ex-ante);

27. Characteristics of successful U-I partnerships • Relation based on mutual trust, respect (for other partner’s values etc.) and flexibility; • Projects run professionally – deliverables, timelines, financial management; • Continuous communication between principal players for U & I; • Inclusion of dispute resolution methods. (Source: STI Review, No. 23)

28. (FIRM) CHARACTERISTICS OF UNIIVERSITY-INDUSTRY RELATIONSHIPS

29. Firm Characteristics I • Quantitative analysis based on surveys: Yale, Carnegie Mellon, PACE, CIS II-III, KNOW, National surveys: • Klevorick et al., 1995 US • Meyer-Krahmer and Schmoch (1998) and Beise and Stahl (1999) national survey Germany; • Arundel and Geuna (2004) PACE EU countries; • Mohnen and Hoareau (2002) CIS II EU countries; • Cohen, Nelson and Walsh (2002) CM USA; • Swann (2002) and Laursen and Salter (2003) CIS III UK.

30. Firm Characteristics II • Product versus process innovation: • Mixed results. • Openness of the firm (+): • Searching, screening and signalling • The role of demand !!! • Independent (+) versus subsidiaries: • The role of the headquarter.

31. Firm Characteristics III • The size of the firm affect collaboration: • The larger the more collaboration. but • Small biotech firms and spin-offs. • The R&D investment and/or R&D intensity: • Absorptive capacity. • Technological sector.

32. Differences Between Technologies Faulkner, Senker and Velho (1995) • Biotechnology • Most formal links • PSR provides help with new experimental techniques and new recruits • Ceramics • Other companies important source of STI • Government programmes important • PSR gives access to instrumentation/expertise • Parallel Computing • Least formal links; most links with PSR users

33. Differences Between Technologies(Fontana et al. 2006) • BUT not only high tech & not only manufacturing: • Food industry • Comp. Services

34. University-industry relationships: The importance of searching, screening and signalling

35. Fontana, Geuna and Matt 2006 • We want to explain why certain firms do cooperate with universities while other don’t (probability of cooperation yes/no); • For the sample of firms that cooperated with university, we want to explain the number of R&D JV that firms had (intensity of cooperation – how many times.

36. Literature and hypotheses (1) • The degree of openness: import external knowledge and knowledge disclosure on a voluntary basis • Search strategy: firms look for sources of knowledge (number of external knowledge channels)(Laursen & Salter 2003) • Screening activity: selection of a specific relevant source (journals = source of open science, but also of info about scientists) • Signalling activity: voluntary disclosure (Pénin 2004) – trigger reciprocity, gain feedbacks, network, reputation, higher order knowledge, attract potential partners. H1: Openness should affect positively the probability and the intensity(different effects).

37. Literature and hypotheses (2) • The size: • Absolute - (Arundel & Geuna 2004, Mohnen & Hoareau 2003, Cohen et al 2002, etc.); • Relative to R&D. H2.1 Larger firms should have a higher probability to cooperate. H2.2. Firms with larger R&D investment should be involved in a greater # of R&D projects.

38. Literature and hypotheses (3) • R&D intensity • Active at the technological frontier more reliant on science (Arundel & Geuna 2004, Schartinger et al. 2001); • High R&D investment => high absorptive capacity (Cohen & Levinthal, 1990). H3. The higher the R&D intensity, the higher the probability of cooperating and the greater the number of projects.

39. Literature and hypotheses (4) • The legal status of the firm: • R&D activities concentrated at a firm’s headquarter; • Independent firms cooperate more with PROs than firms belonging to a large group (Mohnen & Hoareau 2003). H4. Within multi-plan firms, headquarters mediate collaboration.

40. Literature and hypotheses (5) • Type of innovative activities: contrasted results: • Positive relation between radical product innovation and cooperation with PROs (Mohnen & Hoareau, 2003); • Companies involved in process innovation are more likely to cooperate with PRO’s than those engaged in product innovation (Swann, 2002).

41. Data sources • KNOW survey – 2000 • 7 EU countries: Denmark, France, Germany, Greece, Italy, Netherlands, UK • 5 sectors: food and beverages, chemicals excluding pharma, communications equipment, telecom services and computer services • 2 size classes: (10-249 employees, 250-999 employees) • Average response rate: 33% (minus UK) • 50% of innovative firms (222) signed R&D cooperation with PROs in the 3 years before the survey.

42. The variables (1) • Openness of the firm : • Number of external sources (fairs and conferences, searching patent db, reverse engineering, internet) - SEARCH • Mean % of new innovations introduced in collaboration with partners - ExtCOLL • Screening publications – PUBLICATIONS • Government R&D projects – SUBSIDIES • Patents - PATENTS SEARCHING SCREENING SIGNALLING

43. The variables (2) • Firm size: • Number of employees - Employees • R&D employment – R&D • Firm R&D Activity: • R&D intensity –R&DINT • Outsourcing R&D expenditures – ExtR&D • Headquarter - HEADQ

44. The variables (3) • Firm innovative activity • Process innovation –PROCINN • Product innovation – PRODINN • Country and sector fixed effects – • COUNTRY, • SECTOR.

45. Estimation: models & results (1) • Negative Binomial Models. • Zero Inflated Negative Binomial • Number of R&D Projects = extent of collaboration; • Propensity for firms to engage in R&D Project = existence of a relationship (Logit Selection)

48. Estimation: models & results (2) • Propensity for firms to engage in R&D Projects with PROs: • Absolute Size (+) • Openness (+): screening (publications + subsidies) • Absorptive capacity (+) • Headquarter (+)

49. Estimation: models & results (3) • Number of collaborations : • Relative Size: R&D employment (+) • Openness (+): signalling (patents), outsourcing • Absorptive capacity (+)

50. Estimation: models & results (4) • As in previous literature, the type of innovative activity (process versus product) does not provide any definitive result may be also due to the fact that the large majority of respondents do both. • Country dummies are significant to explain the number of collaborations, not so much the selection. • Sector dummies are not significant except in the case of food and chemicals in the selection model.