Commercializing academic research: a social network approach exploring the role of regions and distance
Relationships between firms and universities have been centre stage for some time. However, empirical studies on firms contracting research to universities remains limited. The likelihood of engaging in contract research depends on the characteristics of the firm and the university. Because existing literature further suggests that location is a key facilitator for knowledge transfer activities, the paper investigates the role played by regions and geographical distance between firms and universities when engaging in contract research. Hence, the analysis combines characteristics from both organisations and adds relationship-specific features with respect to the distance between them and the region they are located in. It also looks at the role played by cognitive distance. The paper contributes to the understanding of how academic research, commissioned by firms, is influenced by locational features: the ability to engage in contract research and the regional context, the regional embeddedness of research contract partners, and the geographical distance between these partners. It builds on an original dataset with information on contract research at firm. Based on a panel of three consecutive waves of R&D surveys in Belgium conducted in 2006, 2008 and 2010, the linkages of universities with R&D active firms are examined by linking a database on universities with one on firm R&D investments. Using the most recent insights in the social network approach, highlights the variables that impact the likelihood of firms engaging in research contracted to a university. Descriptive measurements are calculated from social network analysis to capture the basic structure of the firm-university network and construct an Exponential Random Graph model to predict firm-university relationships based on network characteristics and node attributes. Four main conclusions are drawn. First, more innovative regions do not show a higher likelihood of firms to engage in contract research with universities. Second, the likelihood for contract research is higher, if firms and universities are located in the same region. Third, geographical distance shows a negative relation to the likelihood of contract research suggesting cluster formation. Fourth, in the case of contract research cognitive distance complements geographic distance.
KeywordsFirm-university relationships Contract research Geographical distance Cognitive distance Regional embeddedness Social network analysis
JEL ClassificationsI23 L24 O32 O33 R12
- Agneessens, F., Roose, H., & Waege, H. (2004). Choices of theatre events: p* models for affiliation networks with attributes. Metodoloski Zvezki, 1(2), 419–439.Google Scholar
- Belgian Science Policy Office. (2016). Last Accessed October 8, 2016. http://www.stis.belspo.be/en/statisticsRD.asp.
- Cairncross, F. (2001). The death of distance 2.0. London: Texere Publishing Limited.Google Scholar
- Caniëls, M. C. J., Kronenberg, K., & Werker, C. (2014). Conceptualizing proximity in research collaborations. In R. Rutten, P. Benneworth, D. Irawati, & F. Boekema (Eds.), The social dynamics of innovation networks (pp. 221–238). London: Routledge.Google Scholar
- Charles, D. (2006). Universities as key knowledge infrastructures in regional innovation systems. Innovation: The European Journal of Social Science Research, 19(1), 117–130.Google Scholar
- Dahl, M. S., & Sorenson, O. (2012). Home sweet home: Entrepreneurs’ location choices and the performance of their ventures. Management Studies, 58(6), 1059–1071.Google Scholar
- European Commission. (2017). Regional innovation scoreboard. Luxembourg, European Commission. http://ec.europa.eu/growth/industry/innovation/facts-figures/regional_en.
- Garcia, R., Araújo, V., Mascarini, S., Santos, E. G. D., & Costa, A. R. (2018). An analysis of the relation between geographical and cognitive proximity in university-industry linkages. Proceedings of the 44th Brazilian Economics Meeting, no. 132.Google Scholar
- Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., & Morris, M. (2003). Statnet: Software tools for the statistical modeling of network data. http://statnetproject.org.
- Lusher, D., Koskinen, J., & Robins, G. (Eds.). (2013). Exponential random graph models for social networks: Theory, methods, and applications. Cambridge University Press.Google Scholar
- Marsan, G. A., & Maguire, K. (2011). Categorisation of OECD regions using innovation-related variables. OECD Regional Development Working Papers, 2011/03. Paris: OECD Publishing.Google Scholar
- OECD. (2013). Regions at a Glance. Paris: OECD.Google Scholar
- OECD. (2015). Frascati manual. Proposed standard practice for surveys on research and experimental development. Paris: OECD.Google Scholar
- Office, Belgian Science Policy. (2010). Belgian report on science and technology indicators. Brussels: BELSPO.Google Scholar
- Ohmae, K. (1995). The borderless world: Power and strategy in an interdependent economy. New York: Harper Business.Google Scholar
- Porter, M. E. (1998). Clusters and the new economics of competition. Harvard Business Review, 76(6), 77–90.Google Scholar
- Snijders, T. (2002). Markov Chain Monte Carlo estimation of exponential random graph models. Journal of Social Structure, 3, 1–40.Google Scholar
- Subramani, M. R., & Venkatraman, N. (2003). Safeguarding investments in asymmetric interorganizational relationships: Theory and Evidence. Academy of Management Journal, 46(1), 46–62.Google Scholar
- Trippl, M., Grillitsch, M., Isaksen, A. (2017). Exogenous sources of regional industrial change: Attraction and absorption of non-local knowledge for new path development. Progress in Human Geography (forthcoming).Google Scholar