What type of faculty member interacts with what type of firm? Some reasons for the delocalisation of university–industry interaction
Introduction
There is abundant theoretical argumentation and empirical evidence of the influence of technological innovation on economic development. It is therefore consequent that economic science opened a body of analysis on technological innovation, studying its determining factors. Initially focused within the industrial world (Griliches, 1958; Scherer, 1965; Mansfield, 1968), it has given rise to much empirical work on the quantification of the contribution of university R&D. Studies have found a relevant link between science and industry (Narin and Noma, 1985; Narin et al., 1997) and between academic R&D and patents or other measures of innovation (Jaffe, 1989; Acs et al., 1991; Mansfield, 1991).
However, these approaches scarcely deal with the way in which university R&D reaches the industrial world. They rather suggest that it takes place spontaneously, beyond control of the individual who intervene, maybe inspired by the linear model of technological innovation (often attributed to Bush, 1945). This line of thought underestimates the idea that some contexts favour the contribution of university R&D to innovation more than others, as well as the limited use of science by firms from an interactive understanding of the link between knowledge production and innovation (Kline and Rosenberg, 1986).
Several approaches that incorporate ideas from sociology to economics have come to justify that increasing the contribution of university R&D requires fostering university–industry interaction (UII), Freeman (1987) and Lundvall (1988) under the perspective of national systems of innovation, Gibbons et al. (1994) with their detection of new Mode 2 of knowledge production, Etzkowitz and Leydesdorff (1996) with their ideas about the Triple Helix model. These approaches differ in the importance granted to universities in the innovation process, but do not question that some degree of interaction with firms should exist.
On the other hand, some studies have insisted on the idea that the traditional view of the contribution of university basic research refers to its direct effects, i.e. the provision of explicit knowledge in the form of tangible results for firms. A more modern view should also take into account the existence of indirect benefits, e.g. increasing useful (mainly tacit) knowledge, training skilled graduates, creating new scientific instrumentation and methodology, forming networks and social interaction, increasing the capacity for scientific and technological problem solving, creating new firms (Salter and Martin, 2001), providing social knowledge and access to unique facilities (Scott et al., 2002). Even if it is arguable that these benefits are necessarily the outcome of university basic research, it is reasonable to assume that the contribution of universities to innovation does not come only from basic research but also from other activities that define the so-called third mission, in addition to teaching and research (Molas-Gallart et al., 2002).
The convenience of the ongoing changes in the institutional settings to maximise UII is therefore put into question, since they do not ensure the maintenance of a broader set of benefits from academic activities. Despite these concerns, the academic reflection privileges some aspects of UII on the university side that leave some gaps in the literature.
First, there are copious studies at institutional level (e.g. Vedovello, 1997; Mora-Valentin et al., 2004; Marques et al., 2006; Rasmussen et al., 2006) and the scarce studies at individual level have paid more attention to the institutional and input factors than to the characteristics of the personnel involved (Lee, 1996; D’Este and Patel, 2005), in spite of their great autonomy at public universities to decide whether to engage on interactive activities or not. The literature on scientific production can still provide some insights into the influence of personal characteristics on UII.
Second, most analysis focus on case studies of managerial actions leading to increase UII (Marques et al., 2006; Vedovello, 1997; Rasmussen et al., 2006), sometimes measuring and explaining the degree of interaction (Lee, 1996; Mora-Valentin et al., 2004; D’Este and Patel, 2005; Schartinger et al., 2002). They do not specify the kind of firms, assuming that the incentives and possibilities for all faculty members to interact with publicly targeted firms are homogeneous, e.g. with firms matching the average profile in the region. Nevertheless, there is no reason, a priori, why that interaction should not take place with non-targeted firms. We have to take resource to the literature on UII from the side of firms to derive ideas about the preference of faculty members for some firms to interact with.
Third, research is usually conducted at country level, for high-tech countries like the US (Lee, 1996), Canada (Hanel and St-Pierre, 2006), the UK (Vedovello, 1997; D’Este and Patel, 2005), Germany (Schartinger et al., 2002) or Scandinavia (Rasmussen et al., 2006), and occasionally for low-tech countries like Spain (Mora-Valentin et al., 2004) or Portugal (Marques et al., 2006). However, the analysis at regional level is as important, since UII may not play the same role in all of them (Buesa et al., 2006), but it has not merited so much interest, especially in low-tech regions, with pioneering exceptions like the case of Aragon in Spain (Martinez-Sanchez and Pastor-Tejedor). The call for attention in this kind of regions is appealing, as their firms may show undesirable properties for interaction, e.g. small size, traditional orientation, low engagement in R&D activities or scarce human capital.
This contribution will try to bridge the existing gap in the literature by providing some theoretical reflection on UII from the academic side at individual level, and by focusing not only on the degree of interaction of faculty members but also on the type of firms they are more eager to interact with. It will also propose a two-step method to test the hypothesis that only selected faculty members interact with selected firms. First, we will identify the type of faculty member who interacts with firms. Second, we examine whether this type of faculty member interacts with every type of firm.
Section 2 reviews the literature to this end. In Section 3, we explain the methodology and data used to test the hypothesis, based on the case of the Valencian Community of Spain, which we define as a region with low absorptive capacity, where there is evidence that faculty members will engage into UII provided that they exchange relevant scientific knowledge (Azagra-Caro et al., 2006), and for this reason they may not restrict interaction to firms in the region. Now we explore in some more detail on the characteristics of firms with which faculty members interact in order to better understand why they may not find them in the region. Section 4 shows the results and Section 5 offers the conclusions.
Section snippets
Review of the literature
This section explores the existing literature on the determinants of UII according to our two-step method to formulate the hypothesis that only selected faculty members interact with selected firms. First, we will identify the type of faculty member who interacts with firms. Second, we examine whether this type of faculty member interacts with every type of firm.
Data and methodology
The aim of this section is to explain the methodology followed to test the hypothesis. We have data from the Valencian Community, a Spanish region with a per capita GDP around the national average. Its manufacturing structure is based on microfirms in traditional, low-tech sectors such as toys, textile, shoes, furniture or ceramic tiles. This pattern of specialisation is one of the reasons why the region has several technological weaknesses, as for example a low level of expenditure on R&D
Results
Table 5 shows the results of models reduced after a selection strategy based on minimising the Bayesian Information Criterion (BIC). BIC tends to penalise the entrance of new observations. Hence, final reduced models admit some non-significant variables that, if deleted, incorporate a large number of “don’t knows”. We divide the results according to our two main questions.
Conclusions, limitations and future research lines
We have tried to contribute to the literature on UII from the side of university by addressing three questions: Do personal characteristics of faculty members matter more than institutional and input factors to engage into university–industry interaction? Do faculty members who interact most with industry do so mainly with a specific type of firm? Is the region a relevant unit of observation for the analysis of university–industry interaction?
Regarding the first question, it may be the case
Acknowledgements
I wish to express my gratitude to the Valencian High Consultancy Council in R&D, for providing the funds to carry out this research. To Ignacio Fernandez de Lucio for entrusting the author with the elaboration of the report on which this work is based. I am also grateful to him and to Antonio Gutiérrez Gracia and Fragiskos Archontakis for working with me in previous analyses of the aforementioned survey. Also to Fernando Jiménez Sáez, for the application of techniques in preliminary analyses
References (50)
- et al.
Biotechnology R&D partnership for industrial innovation in Nigeria
Technovation
(2005) - et al.
Faculty support for the objectives of university–industry relations versus degree of R&D cooperation: the importance of regional absorptive capacity
Research Policy
(2006) - et al.
Firm's motivation for cooperative R&D: an empirical analysis of Spanish firms
Research Policy
(2001) - et al.
Public research and industrial innovations in Germany
Research Policy
(1999) - et al.
Regional systems of innovation and the knowledge production function: the Spanish case
Technovation
(2006) Technology transfer and the research university: a search for the boundaries of university–industry collaboration
Research Policy
(1996)Academic research and industrial innovation
Research Policy
(1991)- et al.
How can university–industry–government interactions change the innovation scenario in Portugal?—the case of the University of Coimbra
Technovation
(2006) - et al.
University–industry relationships in peripheral regions: the case of Aragon in Spain
Technovation
(1995) - et al.
Patterns of technological change among Spanish innovative firms: the case of the Madrid region
Research Policy
(1996)
Determining factors in the success of R&D cooperative agreements between firms and research organizations
Research Policy
The increasing linkage between US technology and public science
Research Policy
Initiatives to promote commercialization of university knowledge
Technovation
The economic benefits of publicly funded basic research: a critical review
Research Policy
Knowledge interactions between universities and industry in Austria: sectoral patterns and determinants
Research Policy
Public financing of cooperative R&D projects in Spain: the Concerted Projects under the National R&D Plan
Research Policy
Real effects of academic research: comment
American Economic Review
Proximity and the use of public science by innovative European firms
Economics of Innovation and New Technology
The regional dimension of university–industry interaction
How do female and male faculty members construct job satisfaction?
Journal of Technology Transfer
Science, the Endless Frontier: A Report to the President
Absorptive capacity: a new perspective on learning and innovation
Administrative Science Quarterly
Cited by (0)
- 1
The research was mostly performed when the author worked at INGENIO, but since the 1st of December 2006 he works at the JRC IPTS. The views expressed in this article are the authors' and do not necessarily reflect those of the European Commission. Neither the European Commission nor any person acting on behalf of the Commission is responsible for the use which might be made of the following information.