University-Industry Collaboration : a firm perspective Prof. Dr. R. Veugelers KULeuven, EC-BEPA and CEPR

EU-25 Innovation Gap with US : improving Source: EIS 2006 EIS uses a composite indicator to assess Innovation (input&output)

Decomposing the EU- US Innovation Gap Source: EIS 2006

…no significant change in business R&D expenditures

Europe’s performance in R&D and innovation continues to be a disappointing story Capabilities failure (supply)  R&D investments (public, esp private) Incentives/rewards – framework conditions failure  Fragmented product markets, capital markets, labour markets IPR, standards&regulations, competition, lead markets, public procurement, … Systems failure  Client/supplier networks  Public Private networks Industry Science Links

Importance of Indicators and Analysis Currently no ISL indicators used in EU policy monitoring (EIS, Structural Indicators) No robust research findings on effects of ISL on performance (do they matter?) Which policy levers to use? (business sector, science sector, intermediaries)

GROWING SCIENCE-INDUSTRY CONNECTIONS  University-industry collaboration (Darby and Zucker; 2001; Zucker et al, 2001; 2002);  Start-ups and university spin-offs and licensing (Jensen and Thursby, 2001; Thursby and Thursby, 2002);  Science parks (Siegel 2003)  Increased linkage to science in patents (Narin et al (1997); Branstetter and Ogura, 2005):  “Open Innovation” literature reflects an increasing attention to the phenomenon of industry science links But are industry science links indeed important for (innovative) performance?

Frequency Innovation Strategy % Sales from New Products NoMake&Buy16 (6%)14.9% MakeOnly59 (22%)13.5% BuyOnly16 (6%)9.7% Make&Buy178 (66%)20.5% TOTAL269 (100%)18.0% Internal and External Activities performed by the same firm …improve innovation performance! Source: Cassiman and Veugelers (Management Science, 2006)

IMPORTANCE OF SCIENCE FOR TECHNOLOGICAL PROGRESS At the macro level:  Positive impact of public research on industrial innovation and economic growth; and implied geographical effects (Jaffe (1989) and Adams,1990; Acs, et al (1992) ). Rates of return to publicly funded research estimated between 20% and 60%. At the micro level:  Mansfield (1998): 15% of new products, 11% of new processes representing about 5% of total sales in a sample of major firms in US could not have been developed in the absence of academic research.  Yale Survey (1983) and Carnegie Mellon Survey (1994) confirm relevance of university research for innovation for R&D active firms. But  In Eurostat Community Innovation Survey ( ) only 4.5% of all innovation active firms rate universities as an important source of information. 68% indicate universities as not important at all.  European Paradox? Science but no Link to Industry

Measuring Firm Level Links to Science While at the industry level ISL is correlated with R&D intensity of the industry, a lot of variation at the firm level exists, i.e. there is not that much overlap between different types of ISL at the firm level Source: Cassiman, Veugelers and Zuniga (2006) Having a link to science is correlated with the innovation performance of firms, in particular products new to market, but no one indicator seems to dominate this effect. Firms with scientific NPR Use of public sources of information Cooperation public institutions Firms “without” Science Link 649 Firms with Science Link 193

 Co-authorship with university employees increases R&D productivity by pharmaceutical firms as measured by international patents (Henderson and Cockburn,1998).  Star scientists associated to firm entry, new product development in biotech and nanotech (Zucker et al. 1998, Zucker et al., 2005, Stephan et al 2006)  Recruitment of university scientists increases research productivity (Kim et al., 2005)  Cooperation with Universities results in a higher percent of innovations new to the market (Monjon & Waelbroeck 2003) SCIENCE MATTERS FOR INNOVATION AT FIRM LEVEL?

WHY WOULD SCIENCE MATTER FOR INNOVATION at the firm level? – By providing a map for research and codified forms of problem solving science helps firms Increase the productivity of applied research (Nelson; 1959; Evenson and Kislev, 1976) Avoid wasteful experimentation when working with highly coupled (complex) technologies (Fleming and Sorenson (2004) Better identification, absorption and integration of external knowledge, e.g. what is cutting edge; identifing most promising technological opportunities (Cohen and Levinthal, 1989; Gambardella, 1995; Henderson and Cockburn, 1998). Internal Spillovers; cross-projects fertilization of basic knowledge (Cockburn and Henderson, 1994)

Research Questions when studying cooperative agreements with science (CPscience) from industry perspective Which firms choose CPscience? What do firms do within CPscience ? What are the effect of CPscience on (innovative) performance of these firms? What are the effects of CPscience on social (innovative performance)? Taking into account: CPscience within Industry – Science link portfolio CPscience within Innovation strategy

Literature Why do firms cooperate?  Transaction Cost Economics: balancing access to new knowledge and exposure to opportunism (Pisano, 1990; Oxley, 1997)  Industrial Organization: Appropriation conditions can increase the incentive to internalize the positive externality (d’Aspremont and Jacquemin, 1988; De Bondt, 1997). Appropriation conditions can increase the incentive to free ride on the cooperative agreement; Kesteloot and Veugelers, 1995; Greenlee and Cassiman, 1999).  Management: Complementarity with own R&D activities (Cohen and Levinthal, 1989; Kamien and Zang, 2000) Complementarity with internal and external innovation activities (Arora and Gambardella, 1990; Cassiman and Veugelers, 2003)

Literature Why do firms cooperate with science?  Empirical Studies: firm size, own R&D, organization, government support, uncertainty, spillovers, scientific status of industry,... Hall, Link and Scott, 2001; Capron and Cincera, 2002; Tether 2002, Mohnen and Hoareau, 2006; Miotti and Sachwald, 2003) Performance effects of CPscience  Innovative performance (sales of new products, especially more radically new products) (CIS-data) Crusciola & Haskel (2003), Janz et al (2003), van Leeuwen (2002), Tether (2002) Monjon & Waelbroeck (2003)  Productivity growth Belderbos et al (2006)  No evidence of positive performance effects of single use of CPscience  Complementarity with CPcustomers for small firms and with CPsuppliers for large firms  Subadditivitiy between CPscience and CPcompetitors

Our research questions when studying cooperative agreements with science (CPscience) from industry perspective: Which firms choose CPscience? Taking into account  Size, Organization, Technology Characteristics  Appropriation (Spillovers)  Complementary innovation activities Veugelers & Cassiman, 2005, IJIO, R&D cooperation between firms and universities: some empirical evidence from Belgium,

Hypotheses Cooperation with Universities  Appropriation conditions might increase the likelihood of R&D cooperation (internalization), decrease the likelihood of R&D cooperation (free riding) or be irrelevant given the early stage of the innovation process.  R&D Cooperation with Universities increases the firm’s basic R&D capability. Increases in the basic R&D capability of the firm increases the marginal benefit of engaging in other (applied) R&D activities: Complementarity with other innovation activities. Basic R&D Capability University R&D Cooperation Own (Applied) R&D Public Information Vertical Cooperation

Hypotheses Cooperation with Universities  Larger firms have the necessary in-house capabilities to interact with universities, but small firms might be more agile.  Facing a more competitive environment increases innovation, but this might pressure towards more short-term applied innovations.  At multinationals, research linking with universities is done at headquarters.  At early stage technology development of the type developed in university-industry R&D cooperation, financial barriers might be high.  The high uncertainty involved in university-industry cooperation might lead to risk sharing. At the same time, it leads to higher transaction costs, making contracting on output more difficult.

Data DATA: CIS I ( ) for Belgium 1335 questionnaires sent 748 usable questionnaires returned (53%) 439 innovators between 1990 and 1992 (59%) SAMPLE: Innovation Active Firms in observations 89 cooperate with universities quantitative and qualitative information

Empirical Model Dependent Variable: Cooperation with Universities (0/1) Appropriation  Legal: effectiveness of patents at industry level  Strategic: effectiveness of complexity, secrecy, or, lead time Complementary Innovation Activities  Own R&D: absorption, integration and application (Cohen and Levinthal, 1989)  Cooperation with customers and suppliers (Arora and Gambardella, 1990)  Publicly available information (Cassiman and Veugelers, 2002) Control Variables  Size (number of employees)  Foreign ownership (foreign headquarter)  Costs (importance of innovation costs as barrier to innovation)  Risk (importance of risk as barrier to innovation)  Industry Cooperation

Empirical Model: Instrumental variables estimation We regress the complementary strategies on a set of specific assumed exogenous variables and instruments in a first step. In the second step, we use the predicted values of the complementary strategy variables as independent variables in the probit estimation of the cooperation with universities decision Instruments for INTSourcing =  {OBSTEXTERNAL, OBSTRESOURCE, IndINTsourcing, X} Instruments for PUBSourcing=  {BASICRD, IndPUBsourcing, X} Instruments for CPvert=  {TECH, IndCPvert,X} With the set of exogeneous variables X  X= {SIZE, FOR, COST, RISK, PROTstrat, IndPROTleg, IndCPuniv} Heckman correction for innovation  {SIZE, EXP, FOR, COST, OBSTEXTERNAL, OBSTRESOURCE, OBSTMARKET, OBSTTECHNOLOGY, Industry dummies}

Summary findings on who cooperates with universities? Cooperation is more likely in chemical and pharmaceutical industries Larger firms are more likely to cooperate Sharing costs seems an important driver of cooperation. Cooperation happens when risk is not an important obstacle to innovation Appropriation is not an important factor in cooperation with universities (but for cooperation with customers and suppliers it is) Cooperation with universities complementary to other forms of cooperation and innovation activities Source: Veugelers and Cassiman (IJIO,2005)

What happens inside the Firm? Of the 50+ projects examined, about 40% include cooperation and/or contracting with universities. Cooperation with universities is more likely in more basic projects that develop new knowledge. In strategically important projects, cooperation with other firms is less likely, but cooperation with universities in initial activities is more likely. There are distinct measures of success of a project:  Efficiency (on-time, within budget,...)  Learning over time of new applications, new capabilities... As well as knowledge for future projects, but this is difficult to measure and account for, i.e. SPILLOVERS Source: Cassiman, Di Guardo y Valentini (2006)

Conclusions Still far from helping policy design for improving ISL Nevertheless, helpful insights: Large firms are more likely involved in Industry – Science links, but subsidiaries of multinationals less likely to cooperate with local universities An important characteristic to Industry – Science links is the fact that firms engaged in cooperating with universities are at the same time involved in many complementary innovation activities Cooperation with universities is characterized by an open, non- exclusive exchange between researchers