What are university-industry research links about?

1. What are university-industry research links about?

2. Structure of the Lecture • The university-industry complex: A yet poorly understood system. • University-industry relationships: The importance of searching, screening and signalling • The Governance of University-Industry Knowledge Transfer: Are Different Models Coexisting?

3. The university-industry complex

4. What do we know? • 30 years after the start of the institutionalisation (with policy support) of uni-ind relationships we know something but not yet enough to have a consolidated understanding (conflicting results): • Field/sector effect • Researcher characteristics • University characteristics • Firm characteristics

5. Field/sector • Most of the evidence is based on hightech industries and especially biomedical; in most recent years also other fields (engineering) have been increasingly studied; Fields with most intense collaborations. • We still fail to recognize the importance of non hightech fields: see for example Food; • We know very little of the interactions in services (important in the UK case);

6. Field/sector • Across fields/sectors there are extremely important differences in: • type of knowledge, • the codification of knowledge, • incentives and reward system, • supply or demand led, etc…

7. Researcher Characteristics • Recent wave of studies at the individual level: • Previous experience; • Entrepreneurial capacity in raising funding (public and private); • Seniority and tenure ~ • Male ~ • Teaching ?

8. University characteristics • More likely to occur in some universities than in others due to differences in: • Type (disciplinary orientation, local development focus) of the UNI; • Environment of the UNI; • Culture (more is done in the centre/department and more is accepted and more will be done …. B. Clark entrepreneurial UNI);

9. University characteristics • Quality of the centre/department +/- • Existence of formal infrastructure of KT ? • Size ?

10. Firm Characteristics I • Quantitative analysis based on surveys: Yale, Carnegie Mellon, PACE, CIS II-III-IV, 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.

11. Firm Characteristics II • 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.

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

13. Firm Characteristics IV • Countries differences. • Technological sector. • Distance matters (but not always and not for all).

14. University-industry relationships: The importance of searching, screening and signalling Roberto Fontana, Aldo Geuna, Mireille Matt

15. Contribution of the paper • 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. • We want to test if “openess” of the firm plays a role – e.g. the role of demand

16. 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).

17. 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 (internalisation of spillovers). H2.2. Firms with larger R&D investment should be involved in a greater # of R&D projects (spare resources).

18. 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.

19. 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.

20. 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).

21. DATA SOURCES

22. 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.

23. 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

24. 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

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

26. ECONOMETRIC RESULTS

27. 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)

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

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

32. 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.

33. Conclusion (1) • The role of size and R&D activity: • Larger firms have a higher probability to engage in formal agreements with PROs but the number of R&D project signed depends on the size of the R&D department (do I have enough R&D people). • Firms with important absorptive capacity (being near the technological frontier) have a higher chance to cooperate and conclude more R&D projects with PROs.

34. Conclusion (2) • The role of openness of firms: • Acquiring external knowledge via the screening of publications and the involvement in public policies affects the probability to cooperate with PROs. • Signalling competencies via patenting and R&D outsourcing affects the level of collaboration. • Policy implication: • Demand pool policies informed by the idea of firm openness (in its various specific aspects) as a major driving force.

35. The Governance of University-Industry Knowledge Transfer: Are Different Models Coexisting? Isabel Bodas Freitas Aldo Geuna Federica Rossi

36. Research questions • What is the relative importance of the two governance models? • Do firm differ according to the governance model they choose? • Do proximity and collaboration objective explain the importance of institutional collaborations? • None of these questions have yet been addressed by the literature in an exhaustive way

37. Data & Methodology

38. Data • The questionnaire was circulated in October/November 2008 • 1052 valid responses (representative sample validated by the local Chamber of the Commerce) • Survey asked about • whether firms engaged in institutional or personal collaborations in the last three years • for non-collaborators: reasons for not collaborating • for institutional collaborators: which universities they collaborated with, objectives of the collaboration, amount of money spent

39. Institutional collaborations

40. Personal contractual collaborations Overall 17.5% of the sample has had a collaboration with at least one univ

41. Methodology: Models 1&2 • A firm does not decide to collaborate and then select the “best” governance structure to collaborate, institutional or personal. • A firm may not collaborate (either it has internal competences to solve the technological problem or does collaborate with other partners); • Collaborate with a personal contract with a researcher; • Develop an institutional collaboration. • We start by running a series of Logit models (to exploit the larger number of observation) then we check our results with a Multinomial Logit model.

42. Methodology: Model 3 For those firms that engaged in institutional collaborations • factors that explain the financial investment in institutional collaborations • Tobin • on the logarithm of one plus the total amount spent in the collaboration • Regressors as in Model 2 with the addition of: • The objective of the collaboration (R&D, testing, organisation, marketing, etc…) • The location of the university (in the region, in neighbouring regions, in Italy abroad)

43. Results

44. Methodology: Model 1 For those firms that did not engage in institutional collaborations with universities in the last three years: • the choice of establishing personal collaborations vs. not collaborating • Logit model • dependent variable: personal collaboration vs. no collaboration at all • Size, Innovative effort, Sourcing knowledge outside, organizational characteristics (outsource, multinational, expert)

45. Table 5. Reasons for not collaborating with universities: distribution of answers

46. Table 6. Rotated Loading factors of reasons for not having participated in institutional collaborations with universities in the previous 3 years

47. Table 6. Logit Model Estimation of Probability of non-institutional collaborators to engage in personal collaborations with Universities

48. Methodology: Model 2 • Are firms engaging in institutional collaborations with universities significantly different from those that either do not cooperate or cooperate with university researchers through personal contract? • 2 Logit model • Dependent variable: institutional collaboration vs. no institutional collaboration, • Dependent variable: institutional collaborations vs personal collaboration.

49. Table 7. Logit Model Institutional Collaboration with Universities