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Sectoral collaboration in biomedical research and development

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Abstract

This paper explores the role of sectors in scientific research and development networks by drawing on bibliometric analyses and innovation systems and triple helix literatures. I conducted a bibliometric study of Vancouver Canada’s worldwide infection and immunity network and examined network structure through sociograms, social network metrics, as well as relational contingency table and ANOVA network analyses. Universities are the key network sector followed by hospitals and government organisations. The private sector plays a weak role. Most sectors show a preference for collaborating within, as opposed to across, sectors. This trend is most pronounced in hospitals and least pronounced among firms. Hospitals and universities collaborate well above statistical expectations. I discuss the implications of these findings for future science policy and studies of research and development networks.

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Notes

  1. Canada’s health and education systems are almost exclusively publicly funded.

  2. See http://www.statcan.gc.ca/pub/92f0138m/2003002/4225101-eng.pdf (Accessed September 28, 2010).

  3. See McGinn et al. (2004) for guidelines on how to interpret the κ statistic.

  4. See Borgatti and Everett (1997) and Wasserman and Faust (1994) for further discussions of two-mode matrices and their conversion to one-mode matrices.

  5. See Borgatti and Everett (1997), p. 254 for a further explanation of the calculation and interpretation of two-mode degree statistics.

  6. For examples of other analyses using this approach see Conti and Dorein (2010) and Cross et al. (2001).

References

  • Ahuja, G. (2000). Collaboration networks, structural holes and innovation: A longitudinal study. Administrative Science Quarterly, 45(3), 425–455.

    Article  MathSciNet  Google Scholar 

  • Atkinson-Grosjean, J., & Douglas, C. (2010). The third mission and the laboratory: How translational science engages and serves the community. In P. Inman & H. G. Schuetze (Eds.), The community engagement and service mission of universities (pp. 309–323). Leicester: Naice.

    Google Scholar 

  • Borgatti, S. P. (2002). NetDraw: Graph visualization software. Harvard: Analytic Technologies.

    Google Scholar 

  • Borgatti, S. P., & Everett, M. G. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.

    Article  MathSciNet  Google Scholar 

  • Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). UCINET 6 for windows: Software for social network analysis. Harvard: Analytic Technologies.

    Google Scholar 

  • Bourgeosi, F. T., Murthy, S., & Mandl, K. (2010). Outcome reporting among drug trials registered in ClinicalTrials.gov. Annals of Internal Medicine, 153(3), 158–166.

    Google Scholar 

  • Burt, R. (1992). The social structure of competition. In N. Nohria & R. G. Eccles (Eds.), Networks and organization: Structure, form and action (pp. 57–91). Boston: Harvard Business School Press.

    Google Scholar 

  • Cliff, A. D., & Ord, J. K. (1973). Spatial autocorrelation. London: Pion.

    Google Scholar 

  • Conti, N., & Doreian, P. (2010). Social network engineering and race in a police academy: A longitudinal analysis. Social Networks, 32, 30–43.

    Article  Google Scholar 

  • Cooke, P. (1998). Introduction: Origins of the concept. In H. Braczyk, P. Cooke, & M. Heidenreich (Eds.), Regional innovation systems (pp. 2–27). Reading: UCL Press.

    Google Scholar 

  • Cross, R., Borgatti, S. P., & Parker, A. (2001). Beyond answers: Dimensions of the advice network. Social Networks, 23, 215–235.

    Article  Google Scholar 

  • Djellal, F., & Gallouj, F. (2005). Mapping innovation dynamics in hospitals. Research Policy, 34(6), 817–835.

    Article  Google Scholar 

  • Dosi, G. (1982). Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Research Policy, 11(3), 147–162.

    Article  Google Scholar 

  • Edquist, C. (2005). Systems of innovation: Perspectives and challenges. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), The Oxford handbook of innovation (pp. 181–208). Oxford: Oxford University Press.

    Google Scholar 

  • Edquist, C., & Johnson, B. (1997). Institutions and organizations in systems of innovation. In C. Edquist (Ed.), Systems of innovations: Technologies, institutions and organizations (pp. 41–63). London: Pinter Publishers.

    Google Scholar 

  • Etzkowitz, H., & Leydesdorff, L. (1999). The future location of research and technology transfer. Journal of Technology Transfer, 24, 111–123.

    Article  Google Scholar 

  • Etzkowitz, H., & Leydesdorff, L. (2000). The dynamics of innovation: From national systems and ‘mode 2’ to a triple helix of university-industry-government relations. Research Policy, 29, 109–123.

    Article  Google Scholar 

  • Etzkowitz, H., Webster, A., Gebhardt, C., & Terra, B. R. C. (2000). The future of the university and the university of the future: Evolution of ivory tower to entrepreneurial paradigm. Research Policy, 29, 313–330.

    Article  Google Scholar 

  • Gelijns, A. C., Zivin, J. G., & Nelson, R. R. (2001). Uncertainty and technological change in medicine. Journal of Health Politics, Policy and Law, 25(5), 913–924.

    Article  Google Scholar 

  • Gilding, M. (2008). The tyranny of distance: Biotechnology networks and clusters in the antipodes. Research Policy, 37, 1132–1144.

    Article  Google Scholar 

  • Godin, B. (1996). Research and the practice of publication in industries. Research Policy, 25(4), 587–606.

    Article  Google Scholar 

  • Goes, J. B., & Park, S. H. (1997). Interorganizational links and innovation: The case of hospital services. The Academy of Management Journal, 40(3), 673–696.

    Article  Google Scholar 

  • Gregersen, B. (1992). The public sector as a pacer in national systems of innovation. In B. Lundvall (Ed.), National systems of innovation: Towards a theory of innovation and interactive learning (pp. 129–145). London: Pinter Publishers.

    Google Scholar 

  • Hanneman, R. A., & Riddle, M. (2005). Introduction to social network methods. Riverside: University of California (published in digital form at http://faculty.ucr.edu/hanneman/).

  • Hansen, M. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organizational subunits. Administrative Science Quarterly, 44(1), 82–111.

    Article  Google Scholar 

  • Hicks, D., & Katz, J. S. (1996). Hospitals: The hidden research system. Science and Public Policy, 23(5), 297–304.

    Google Scholar 

  • Holbrook, J. A., & Clayman, B. P. (2006). Research funding by city: An indicator of regional technological competitiveness? Research Evaluation, 15(3), 221–231.

    Article  Google Scholar 

  • Hopkins, M. (2006). The hidden research system: The evolution of cytogenetic testing in the National Health Service. Science as Culture, 15(3), 253–276.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Lander, B., & Atkinson-Grosjean, J. (2011). Translational science and the hidden research system in universities and academic hospitals: A case study. Social Science and Medicine, 72, 537–544.

    Article  Google Scholar 

  • Lewison, G. (1999). The definition and calibration of biomedical subfields. Scientometrics, 46(3), 529–537.

    Article  Google Scholar 

  • Lundvall, B. (1992). Introduction. In B. Lundvall (Ed.), National systems of innovation: Towards a theory of innovation and interactive learning (pp. 1–16). London: Pinter.

    Google Scholar 

  • Malerba, F. (2004). Sectoral systems of innovation: Basic concepts. In F. Malerba (Ed.), Sectoral systems of innovation: Concepts, issues and analyses of six major sectors in Europe (pp. 9–41). Cambridge: Cambridge University Press.

    Chapter  Google Scholar 

  • Malerba, F. (2005). Sectoral systems: How and why innovation differs across sectors. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), The oxford handbook of innovation (pp. 381–405). Oxford: Oxford University Press.

  • McFetridge, D. G. (1993). The Canadian system of industrial innovation. In R. R. Nelson (Ed.), National innovation systems: A comparative analysis (pp. 299–323). Oxford: Oxford University Press.

    Google Scholar 

  • McGinn, T., Wyer, P. C., Newman, T. B., Keitz, S., Leipzig, R., & Guyatt, G. (2004). Tips for learners of evidence-based medicine: Measures of observer variability (kappa statistic). CAMJ, 23(11), 1369–1373.

    Article  Google Scholar 

  • McKelvey, M., Alm, H., & Riccaboni, M. (2003). Does co-location matter for formal knowledge collaboration in the Swedish biotechnology-pharmaceutical sector? Research Policy, 32, 483–501.

    Article  Google Scholar 

  • Meyer, M. (2002). Tracing knowledge flows in innovation systems. Scientometrics, 54(2), 193–212.

    Article  Google Scholar 

  • Montori, V. M., Wilcynski, N. L., Morgan, D., & Haynes, R. B. (2005). Optimal search strategies for retrieving systematic reviews from Medline: Analytical survey. British Medical Journal, 330(7482), 68–71.

    Article  Google Scholar 

  • Morlacchi, P., Wilkinson, I. F., & Young, L. C. (2005). Social networks of researchers in B2B marketing: A case study of the IMP group 1984–1999. Journal of Business-to-Business Marketing, 12(1), 3–34.

    Article  Google Scholar 

  • Murphy, K., Travers, P., & Walport, M. (2008). Janeway’s immunobiology (7th ed.). New York: Garland Science, Taylor and Francis Group.

    Google Scholar 

  • Murray, F. (2002). Innovation as co-evolution of scientific and technological networks: Exploring tissue engineering. Research Policy, 31, 1389–1403.

    Article  Google Scholar 

  • Nelson, R. R., & Rosenberg, N. (1993). Technical innovation and national systems. In R. R. Nelson (Ed.), National innovation systems: A comparative analysis (pp. 3–22). Oxford: Oxford University Press.

    Google Scholar 

  • Newman, M. E. J. (2001). The structure of scientific collaboration networks. PNAS, 2, 404–409.

    Article  Google Scholar 

  • OECD. (1997). National innovation systems. Paris: Organisation for Economic Co-operation and Development.

    Google Scholar 

  • OECD. (1999). Managing national innovation systems. Paris: Organisation for Economic Co-operation and Development.

    Google Scholar 

  • Owen-Smith, J., & Powell, W. W. (2004). Knowledge networks as channels and conduits: The effects of spillovers in the Boston biotechnology community. Organization Science, 15(1), 5–21.

    Article  Google Scholar 

  • Rees, K. (2005). Interregional collaboration and innovation in Vancouver’s emerging high tech cluster. Tijdschrift Voor Economische En Sociale Geografie, 96(3), 298–312.

    Article  Google Scholar 

  • Salazar, M., Bliemel, M. J., & Holbrook, J. A. (2008). A comparison of R&D indicators for the Vancouver biotechnology cluster. Journal of Commercial Biotechnology, 14(3), 233–246.

    Article  Google Scholar 

  • Sandstrom, A., Pettersson, I., & Nilsson, A. (2000). Knowledge production and knowledge flows in the Swedish biotechnology innovation system. Scientometrics, 48(2), 179–201.

    Article  Google Scholar 

  • Sharif, N. (2006). Emergence and development of the national innovation systems concept. Research Policy, 35, 745–766.

    Article  Google Scholar 

  • Snijders, T. A. B., & Borgatti, S. P. (1999). Non-parametric standard errors and tests for network statistics. Connections, 22(2), 161–170.

    Google Scholar 

  • Sun, Y., & Negishi, M. (2010). Measuring the relationships among university, industry and other sectors in Japan’s national innovation system: A comparison of new approaches with mutual information indicators. Scientometrics, 82(3), 677–685.

    Article  Google Scholar 

  • Swan, J., Goussevskaia, A., Newell, S., Robertson, M., Bresnen, M., & Obembe, A. (2007). Modes of organizing biomedical innovation in the UK and US and the role of integrative and relational capabilities. Research Policy, 36, 529–547.

    Article  Google Scholar 

  • Thune, T. (2007). University-industry collaboration: The network embeddedness approach. Science and Public Policy, 34(3), 158–168.

    Article  Google Scholar 

  • Uzzi, B. (1997). The social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42, 35–67.

    Article  Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and application. New York: Cambridge University Press.

    Book  Google Scholar 

  • Windrum, P., & Garcia-Goni, M. (2008). A neo-Schumpeterian model of health services innovation. Research Policy, 37, 649–672.

    Article  Google Scholar 

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  1. Centre for Health Services Research, School of Population and Public Health, University of British Columbia, 201-2206 East Mall, Vancouver, BC, V6T 1Z3, Canada

    Bryn Lander

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Lander, B. Sectoral collaboration in biomedical research and development. Scientometrics 94, 343–357 (2013). https://doi.org/10.1007/s11192-012-0776-8

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