Skip to main content
Log in

A bibliometric tool to assess the regional dimension of university–industry research collaborations

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

The present study proposes a bibliometric methodology for measuring the grade of correspondence between regional industry’s demand for research collaboration and supply from public laboratories. The methodology also permits measurement of the intensity and direction of the regional flows of knowledge in public–private collaborations. The aim is to provide a diagnostic instrument for regional and national policy makers, which could add to existing ones to plan interventions for re-balancing sectorial public supply of knowledge with industrial absorptive capacity, and maximizing appropriability of knowledge spillovers. The methodology is applied to university–industry collaborations in the hard sciences in all Italian administrative regions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

Notes

  1. It should be noted that in addition to universities, research institutes also contribute to the production of new knowledge, but are not fully considered in this work. The current work is primarily intended to describe a measurement system and provide an example of its application to the Italian case: the results should be interpreted in this sense.

  2. Legislation in 2001 introduced the so called “academic privilege”, presumably resulting in additional patents filed by university researchers, but relevant data are not readily available, making the identification of joint patents very difficult.

  3. Civil Engineering was not considered because the relevant publications are poorly represented in the SCI.

  4. For further information see Abramo et al., 2011.

  5. A number of publications evidently are co-authored by more than one university/company, and by researchers from different SDSs.

  6. From Azagra-Caro (2007): “We follow Cohen and Levinthal’s (1990) definition of absorptive capacity: ‘‘a limit to the rate or quantity of scientific or technological information that a firm can absorb’’. To justify the extension of the concept of absorptive capacity from firms to regions see Niosi and Bellon (2002)”.

  7. The assumption can easily be modified to adapt the analysis to the characteristics of different SDSs, or in light of the personnel resources that universities might assign to respond to industrial demand for collaboration.

  8. This interpretation is not intended as a superficial suggestion that universities should resize their research capacity in the SDS examined. Capacity must also be planned in relation to the other two primary roles of the university: higher education and research.

  9. However, over time, there have been significant delegations of central authority to the regions (Title V of the constitution), and these are tending to increase, resulting in a current gradual development of regional federalism. For example, the regions have recently obtained the power to enact incentive measures for research in specific sectors, with appropriately targeted financing.

  10. See Abramo et al. (2008) for further information on potential distortions in aggregate bibliometric analyses that do not consider sectorial specificity.

References

  • Abramo, G., D’Angelo, C. A., & Di Costa, F. (2008). Assessment of sectorial aggregation distortion in research productivity measurements. Research Evaluation, 17(2), 111–121.

    Article  Google Scholar 

  • Abramo, G., D’Angelo, C. A., Di Costa, F., & Solazzi, M. (2011). The role of information asymmetry in the market for university–industry research collaboration. The Journal of Technology Transfer, 36(1), 84–100.

    Article  Google Scholar 

  • Abramo, G., & Pugini, F. (2005). L’attività di licensing delle università italiane: un’indagine empirica. Economia e Politica Industriale XXXII, 3, 43–60.

    Google Scholar 

  • Acs, Z. J., Anselin, L., & Varga, A. (2002). Patents and innovation counts as measures of regional production of new knowledge. Research Policy, 31, 1069–1085.

    Article  Google Scholar 

  • Anselin, L. (1998). Spatial econometrics: Methods and models. Dordrecht: Kluwer.

    Google Scholar 

  • Anselin, L., Varga, A., & Acs, Z. J. (1997). Local geographic spillovers between university research and high technology innovations. Journal of Urban Economics, 42, 422–448.

    Article  Google Scholar 

  • Anselin, L., Varga, A., & Acs, Z. J. (2000). Geographic and sectoral characteristics of academic knowledge externalities. Papers in Regional Science, 79, 435–443.

    Article  Google Scholar 

  • Arundel, A., & Geuna, A. (2004). Proximity and use of public science by innovative European firms. Economics of Innovation and New Technology, 13(6), 559–580.

    Article  Google Scholar 

  • Audretsch, D. B., & Feldman, M. P. (1996). R&D spillovers and the geography of innovation and production. American Economic Review, 86(3), 630–640.

    Google Scholar 

  • Audretsch, D. B., & Keilbach, M. (2004). Entrepreneurship and regional growth: an evolutionary interpretation. Journal of Evolutionary Economics, 14, 605–616.

    Article  Google Scholar 

  • Audretsch, D. B., & Lehmann, E. E. (2005). Does the knowledge spillover theory of entrepreneurship hold for regions? Research Policy, 34, 1191–1202.

    Article  Google Scholar 

  • Autant-Bernard, C. (2001). Science and knowledge flows: Evidence from the French case. Research Policy, 30(7), 1069–1078.

    Article  Google Scholar 

  • Azagra-Caro, J. M. (2007). What type of faculty member interacts with what type of firm? Some reasons for the delocalisation of university–industry interaction. Technovation, 27, 704–715.

    Article  Google Scholar 

  • Barca, F. (2009). An agenda for a reformed cohesion policy, Report to the EU. Accessed November 11, 2011, from http://ec.europa.eu/regional_policy/policy/future/pdf/report_barca_v0306.pdf.

  • Boschma, R. (2005). Proximity and innovation: A critical assessment. Regional Studies, 39(1), 61–74.

    Article  Google Scholar 

  • Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351–374.

    Article  Google Scholar 

  • Breschi, S., & Lissoni, F. (2001). Knowledge spillovers and local innovation systems: A critical survey. Industrial and Corporate Change, 10(4), 975–1005.

    Article  Google Scholar 

  • Cineca (2008). Accessed November 11, 2011 http://cercauniversita.cineca.it/php5/docenti/cerca.php.

  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

    Article  Google Scholar 

  • D’Angelo, C. A., Abramo, G., & Giuffrida, C. (2011). A heuristic approach to author name disambiguation in large-scale bibliometric databases. Journal of the American Society for Information Science and Technology, 62(2), 257–269.

    Article  Google Scholar 

  • Etzkowitz, H., & Leydesdorff, L. (1998). The endless transition: A “triple helix” of university–industry–government relations. Minerva, 36, 203–208.

    Article  Google Scholar 

  • Feldman, M. P. (1994). The geography of innovation. Dordrecht: Kluwer.

    Google Scholar 

  • Fingleton, B., & López-Bazo, E. (2006). Empirical growth models with spatial effects. Papers in Regional Science, 85(2), 171–198.

    Article  Google Scholar 

  • Gerstlberger, W. (2004). Regional innovation systems and sustainability-selected examples of international discussion. Technovation, 24, 749–758.

    Article  Google Scholar 

  • Greunz, L. (2003). Geographically and technologically mediated knowledge spillovers between European regions. The Annals of Regional Science, 37, 657–680.

    Article  Google Scholar 

  • Jaffe, A. B. (1989). Real effects of academic research. American Economic Review, 79(5), 957–970.

    Google Scholar 

  • Jaffe, A. B., Henderson, R., & Trajtenberg, M. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. The Quarterly Journal of Economics, 108, 577–598.

    Article  Google Scholar 

  • Katz, J. S. (1994). Geographic proximity and scientific collaboration. Scientometrics, 31(1), 31–43.

    Article  Google Scholar 

  • Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.

    Article  Google Scholar 

  • Laudel, G. (2001). What do we measure by co-authorships? Proceedings of the 8th International Conference on Scientometrics and Informetrics, Sydney (pp. 369–384). Sydney: BIRG (UNSW).

  • LeSage, J. P., Fischer, M. M., & Scherngell, T. (2007). Knowledge spillovers across Europe: Evidence from a poisson spatial interaction model with spatial effects. Papers in Regional Science, 86(3), 393–421.

    Article  Google Scholar 

  • Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60(2), 348–362.

    Article  Google Scholar 

  • Maggioni, M.A., Uberti, T.E. (2005) Knowledge flows and regional disparities in Europe: Geographic, functional and sectoral distance. Paper prepared for the Conference on Agglomeration Economies and Regional Growth, Cagliari (pp. 20–21). Cagliari: University of Cagliari.

  • McCann, P., Ortega-Argilés, R. (2011). Smart specialisation, regional growth and applications to EU, Working paper. Accessed November 11, 2011, from http://www.rug.nl/staff/p.mccann/McCannSmartSpecialisationAndEUCohesionPolicy.pdf.

  • Melin, G., & Persson, O. (1996). Studying research collaboration using co-authorships. Scientometrics, 36(3), 363–367.

    Article  Google Scholar 

  • Metcalfe, J. S. (2002). Knowledge of growth and the growth of knowledge. Journal of Evolutionary Economics, 12, 3–15.

    Article  Google Scholar 

  • Moreno, R., Paci, R., & Usai, S. (2005). Spatial spillovers and innovation activity in European regions. Environment and Planning A, 37(10), 1793–1812.

    Article  Google Scholar 

  • Mueller, P. (2006). Exploring the knowledge filter: How entrepreneurship and university–industry relationships drive economic growth. Research Policy, 35, 1499–1508.

    Article  Google Scholar 

  • Niosi, J., Bellon, B. (2002). The absorptive capacity of regions, Colloque Economie Mediterranee Monde Arabe, Sousse, 20–21 September.

  • Noyons, E. C. M., & Calero-Medina, C. (2009). Applying bibliometric mapping in a high level science policy context. Scientometrics, 79(2), 261–275.

    Article  Google Scholar 

  • OECD, (2007). OECD Science, Technology and Industry Scoreboard 2007, ISBN 978-92-64-03788-5.

  • Parente, R., Petrone, M. (2006). Distretti tecnologici ed efficacia delle strategie pubbliche nella mobilitazione del venture capital, Conference AIDEA 06Finanza e Industria in Italia, Roma, 28/29 settembre 2006.

  • Polanyi, M. (1985). Implicit knowledge, Frankfurt/Main.

  • Rodrìguez-Pose, A., & Crescenzi, R. (2008). Research and development, spillovers, innovation systems, and the genesis of regional growth in Europe. Regional Studies, 42(1), 51–67.

    Article  Google Scholar 

  • Rondé, P., & Hussler, C. (2005). Innovation in regions: What does really matter? Research Policy, 34, 1150–1172.

    Article  Google Scholar 

  • van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538.

    Article  Google Scholar 

  • Van Looy, B., Debackere, K., & Andries, P. (2003). Policies to stimulate regional innovation capabilities via university–industry collaboration: An analysis and an assessment. R&D Management, 33(2), 209–229.

    Article  Google Scholar 

  • Varga, A., & Schalk, J. (2004). Knowledge spillovers, agglomeration and macroeconomic growth: An empirical approach. Regional Studies, 38, 977–989.

    Article  Google Scholar 

  • Waltman, L., van Eck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635.

    Article  Google Scholar 

Download references

Acknowledgments

The authors express their sincere thanks to Flavia Di Costa, for her invaluable contribution to the data analysis. Any possible inaccuracies or other errors remain as the complete responsibility of the authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Giovanni Abramo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Abramo, G., D’Angelo, C.A. & Solazzi, M. A bibliometric tool to assess the regional dimension of university–industry research collaborations. Scientometrics 91, 955–975 (2012). https://doi.org/10.1007/s11192-011-0577-5

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-011-0577-5

Keywords

Navigation