Skip to main content
SpringerLink
Log in

Effect of collaboration network structure on knowledge creation and technological performance: the case of biotechnology in Canada

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Many of the novel ideas that lead to scientific publications or yield technological advances are the result of collaborations among scientists or inventors. Although various aspects of collaboration networks have been examined, the impact of many network characteristics on knowledge creation and innovation production remains unclear due to the inconsistency of the conclusions from various research studies. One such network structure, called small world, has recently attracted much theoretical attention as it has been suggested that it can enhance the information transmission efficiency among the network actors. However, the existing empirical studies have failed to provide consistent results regarding the effect of small-world network properties on network performance in terms of its scientific and technological productivity. In this paper, using the data on 29 years of journal publications and patents in the field of biotechnology in Canada, the network of scientists’ collaboration activities has been constructed based on their co-authorships in scientific articles. Various structural properties of this network have been measured and the relationships between the network structure and knowledge creation, and quantity and quality of technological performance have been examined. We found that the structure of the co-authorship network of Canadian biotechnology scientists has a significant effect on the knowledge and innovation production, but it produced no impact on the quality of patents generated by these scientists.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. The OECD definition of biotechnology patents covers the following IPC classes: A01H1/00, A01H4/00, A61K38/00, A61K39/00, A61K48/00, C02F3/34, C07G(11/00, 13/00, 15/00), C07K(4/00, 14/00, 16/00, 17/00, 19/00), C12M, C12N, C12P, C12Q, C12S, G01N27/327, G01N33/(53*, 54*, 55*, 57*, 68, 74, 76, 78, 88, 92).

  2. The first period consists of the scientists who published in 1973–1977, and the last one includes the ones who published in 2001–2005. Therefore, we have a total of 29 networks (Table 1).

  3. Patent claims are a series of numbered expressions describing the invention in technical terms and defining the extent of the protection conferred by a patent (the legal scope of the patent). A high number of patent claims is an indication that an innovation is broader and has a greater potential profitability. It has been frequently suggested and empirically demonstrated (see for example Tong and Frame 1994) that the number of claims is significantly and consistently indicative of higher value patents. The conclusions of most of the papers on patent value reviewed by van Zeebroeck and van Pottelsberghe de la Potterie (2006) are supportive of positive association of the number of claims with patent value. Lanjouw and Schankerman (2004) have suggested that specifically in the biotechnology field the number of claims is the most important indicator of patent quality. Apart from patent claims, patent citations have been also considered as another quality index of patents (e.g. Jang et al. 2011; Fontana et al. 2009). However, one of the major limitations of patent citation data is that more citations could be added by the examiner without even informing the inventor. Alcácer and Gittelman (2006) found a very high magnitude for the examiners’ citation effect where two-thirds of citations on an average patent are being inserted by examiners. This is being widely seen in the USPTO (Lukatch and Plasmans 2002). Hence, we used patent claims as the quality proxy of the patent in this paper.

References

  • Abrahamson, E., & Rosenkopf, L. (1997). Social network effects on the extent of innovation diffusion: A computer simulation. Organization Science, 8, 289–309.

    Article  Google Scholar 

  • Aharonson, B., Baum, J., & Feldman, M. (2004). Industrial clustering and the returns to inventive activity: Canadian biotechnology firms, 1991–2000.

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

    Article  Google Scholar 

  • Albert, R., & Barabási, A. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47.

    Article  MathSciNet  MATH  Google Scholar 

  • Alcácer, J., & Gittelman, M. (2006). Patent citations as a measure of knowledge flows: The influence of examiner citations. Review of Economics and Statistics, 88(4), 774–779.

    Article  Google Scholar 

  • Allen, T. (1983). Collective invention. Journal of Economic Behavior and Organization, 4, 1–24.

    Article  Google Scholar 

  • Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614.

    Google Scholar 

  • Baum, J., Shipilov, A., & Rowley, T. (2003). Where do small worlds come from? Industrial and Corporate Change, 12(4), 697.

    Article  Google Scholar 

  • Bavelas, A. (1950). Communication patterns in task oriented groups. Journal of the Acoustical Society of America, 22, 271–282.

    Article  Google Scholar 

  • Beaudry, C., & Schiffauerova, A. (2011). Impacts of collaboration and network indicators on patent quality: The case of Canadian nanotechnology innovation. European Management Journal, 29(5), 362–376.

    Article  Google Scholar 

  • Bland, C. J., & Ruffin, M. T. (1992). Characteristics of a productive research environment: Literature review. Academic Medicine, 67(6), 385–397.

    Article  Google Scholar 

  • Burt, R. (2001). Structural holes versus network closure as social capital. Social capital: theory and research. Amsterdam: Kluwer Academic Publishers.

    Google Scholar 

  • Butts, C. T. (2006). Exact bounds for degree centralization. Social Networks, 28(4), 283–296.

    Google Scholar 

  • Centra, J. A. (1983). Research productivity and teaching effectiveness. Research in Higher Education, 18(4), 379–389.

    Article  Google Scholar 

  • Choi, H., Kim, S. H., & Lee, J. (2010). Role of network structure and network effects in diffusion of innovations. Industrial Marketing Management, 39(1), 170–177.

    Article  Google Scholar 

  • Chung, K., & Hossain, L. (2009). Measuring performance of knowledge-intensive workgroups through social networks. Project Management Journal, 40(2), 34–58.

    Article  Google Scholar 

  • Clements, M. (2008). Patenting at universities in the United States: A network analysis of the complexities of domestic and international university patenting activities.

  • Coleman, J. (1981). Longitudinal data analysis. New York: Basic Books. 102-081-920.

    Google Scholar 

  • Cowan, R., & Jonard, N. (2001). Knowledge creation, knowledge diffusion and network structure (pp. 327–343). Berlin: Springer.

    Google Scholar 

  • Cowan, R., & Jonard, N. (2003). The dynamics of collective invention. Journal of Economic Behavior & Organization, 52(4), 513–532.

    Google Scholar 

  • Cowan, R., & Jonard, N. (2004). Network structure and the diffusion of knowledge. Journal of Economic Dynamics and Control, 28(8), 1557–1575.

    Article  MathSciNet  MATH  Google Scholar 

  • Dahl, M., & Pedersen, C. (2004). Knowledge flows through informal contacts in industrial clusters: myth or reality? Research Policy, 33, 1673–1686.

    Article  Google Scholar 

  • Davis, G., Yoo, M., & Baker, W. (2003). The small world of the American corporate elite, 1982–2001. Strategic Organ, 1(3), 301–326.

    Article  Google Scholar 

  • de Nooy, W., Mrvar, A., & Batagelj, V. (2005). Exploratory social network analysis with Pajek. New York: Cambridge University Press.

    Book  Google Scholar 

  • Feldman, M., & Audretsch, D. (1999). Innovation in cities: Science-based diversity, specialization and localized competition. European Economic Review, 43, 409–429.

    Article  Google Scholar 

  • Fell, D., & Wagner, A. (2000). The small world of metabolism. Nature Biotechnology, 18, 1121–1122.

    Article  Google Scholar 

  • Fleming, L., King, C., & Juda, A. (2007). Small worlds and regional innovation. Organization Science, 18(6), 938–954.

    Article  Google Scholar 

  • Fontana, R., Nuvolari, A., & Verspagen, B. (2009). Mapping technological trajectories as patent citation networks. An application to data communication standards. Network Strategy, 18(4), 311–336.

    Google Scholar 

  • Freeman, L. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.

    Article  Google Scholar 

  • Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G., & van den Oord, A. (2008). Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density. Research Policy, 37, 1717–1731.

    Article  Google Scholar 

  • Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380.

    Article  Google Scholar 

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis (5th ed.). New Jersey: Prentice Hall.

    Google Scholar 

  • Hargadon, A. (2003). How breakthroughs happen: the surprising truth about how companies innovate. Boston: Harvard Business School Press.

    Google Scholar 

  • Harris, R. J. (1985). A primer of multivariate statistics (2nd ed.). New York: Academic Press.

    Google Scholar 

  • Hausman, J., Hall, B., & Griliches, Z. (1984). Econometric models for count data with an application to the patents-R&D relationship. Econometrica, 52, 909–937.

    Article  Google Scholar 

  • He, J., & Fallah, M. H. (2009). Is inventor network structure a predictor of cluster evolution? Technological Forecasting and Social Change, 76(1), 91–106.

    Article  Google Scholar 

  • Hu, A., Adam, G. Z., & Jaffe, B. (2003). Patent citations and international knowledge flow: The cases of Korea and Taiwan. International Journal of Industrial Organization, 21(6), 849–880.

    Article  Google Scholar 

  • Jaffe, A., Trajtenberg, M., & Fogarty, M. (2000). Knowledge spillovers and patent citations: Evidence from a survey of inventors. American Economic Review, 90(2), 215–218.

    Article  Google Scholar 

  • Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577–598.

    Article  Google Scholar 

  • Jang, S. L., Yu, Y. C., & Wang, T. Y. (2011). Emerging firms in an emerging field: an analysis of patent citations in electronic-paper display technology. Scientometrics, 89(1), 259–272.

    Article  Google Scholar 

  • Knofczynski, G. T., & Mundfrom, D. (2008). Sample sizes when using multiple linear regression for prediction. Educational and Psychological Measurement, 68(3), 431–442.

    Article  MathSciNet  Google Scholar 

  • Kogut, B., & Walker, G. (2001). The small world of Germany and the durability of national networks. American Sociological Review, 66, 317–335.

    Article  Google Scholar 

  • Kuzhabekova, A. (2011). Impact of co-authorship strategies on research productivity: A social-network analysis of publications in Russian cardiology (Doctoral dissertation, University of Minnesota, 2011).

  • Lamoreaux, N., & Sokoloff, K. (1997). Location and technological change in the American glass industry during the late nineteenth and early twentieth century. 5938.

  • Lanjouw, J., & Schankerman, M. (2004). Patent quality and research productivity: Measuring innovation with multiple indicators. Economic Journal, 114, 441–465.

    Article  Google Scholar 

  • Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.

    Article  Google Scholar 

  • Leavitt, H. (1951). Some effects of certain communication patterns on group performance. The Journal of Abnormal and Social Psychology, 46(1), 38–50.

    Article  MathSciNet  Google Scholar 

  • Liebeskind, J., Oliver, A., Zucker, L., & Brewer, M. (1996). Social networks, learning, and flexibility: Sourcing scientific knowledge in new Biotechnology firms. Organization Science, 7(4), 428–443.

    Article  Google Scholar 

  • Lukatch, R., & Plasmans, J. (2002). Measuring knowledge spillovers using patent citations: Evidence from Belgian firms’ data.

  • Maxwell, S. E. (2000). Sample size and multiple regression analysis. Psychological Methods, 5(4), 434–458.

    Article  MathSciNet  Google Scholar 

  • McFadyen, M. A., Semadeni, M., & Cannella, A. A. (2009). Value of strong ties to disconnected others: Examining knowledge creation in biomedicine. Organization Science, 20(3), 552–564.

    Article  Google Scholar 

  • Milgram, S. (1967). The small world problem. Psychology Today, 2(1), 60–67.

    MathSciNet  Google Scholar 

  • Munn-Venn, T., & Mitchell, P. (2005). Biotechnology in Canada: A technology platform for growth. Conference Board of Canada.

  • Montoya, J. M., & Sole, R. V. (2002). Small world patterns in food webs. Journal of Theoretical Biology, 214(3), 405–412.

    Google Scholar 

  • Newman, M. (2001a). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review, 64(1), 016131.

    Google Scholar 

  • Newman, M. (2001b). The structure of scientific collaboration networks: Proceedings of the National Academy of Sciences, 98(2), 404.

  • Newman, M. E. (2001c). Clustering and preferential attachment in growing networks. Physical Review E, 64(2), 025102.

    Article  Google Scholar 

  • Newman, M. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1), 5200.

  • Niosi, J. (2005). Canada’s regional innovation systems. The science-based industries. Montreal: McGill-Queen’s University Press.

    Google Scholar 

  • Powell, W. (1990). Neither market nor hierarchy: Network forms of organization. Research in Organizational Behavior, 12, 295–336.

    Google Scholar 

  • Rumsey-Wairepo, A. (2006). The association between co-authorship network structures and successful academic publishing among higher education scholars.

  • Saxenian, A. (1994). Regional advantage: Culture and competition in Silicon Valley and Route 128. Cambridge: Harvard University Press.

    Google Scholar 

  • Schiffauerova, A., & Beaudry, C. (2012). Who owns the intellectual property and where? The case of Canadian biotechnology. International Journal of Biotechnology, 12(3), 147–169.

    Google Scholar 

  • Schilling, M. A., & Phelps, C. C. (2007). Interfirm collaboration networks: The impact of large-scale network structure on firm innovation. Management Science, 53(7), 1113–1126.

    Article  MATH  Google Scholar 

  • Schrader, S. (1991). Informal technology transfer between firms: Cooperation through information trading. Research Policy, 20, 153–170.

    Article  Google Scholar 

  • Taylor, M. S., Locke, E. A., Lee, C., & Gist, M. E. (1984). Type A behavior and faculty research productivity: What are the mechanisms? Organizational Behavior and Human Performance, 34(3), 402–418.

    Article  Google Scholar 

  • Tong, X., & Frame, J. (1994). Measuring national technological performance with patent claims data. Research Policy, 23, 133–141.

    Article  Google Scholar 

  • Toutkoushian, R. K., Porter, S. R., Danielson, C., & Hollis, P. R. (2003). Using publications counts to measure an institution’s research productivity. Research in Higher Education, 44(2), 121–148.

    Article  Google Scholar 

  • Travers, J., & Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 32, 425–443.

    Article  Google Scholar 

  • Uzzi, B., & Spiro, J. (2005). Collaboration and creativity: The small world problem! American Journal of Sociology, 111(2), 447–504.

    Article  Google Scholar 

  • van Zeebroeck, N., & van Pottelsberghe de la Potterie, B. (2006). Filing strategies and patent value.

  • von Hippel, E. (1987). Cooperation between rivals: Informal know-how trading. Research Policy, 16, 291–302.

    Article  Google Scholar 

  • Wampold, B. E., & Freund, R. D. (1987). Use of multiple regression in counseling psychology research: A rexible data-analytic strategy. Journal of Counseling Psychology, 34(4), 372–382.

    Article  Google Scholar 

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

    Book  Google Scholar 

  • Watts, D. (1999). Networks, dynamics, and the small-world phenomenon 1. The American Journal of Sociology, 105(2), 493–527.

    Article  Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of “small-world” networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  • Yook, S. H., Jeong, H., Barabási, A. L., & Tu, Y. (2001). Weighted evolving networks. Physical Review Letters, 86(25), 5835–5838.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, EV7.647, 1515 Ste-Catherine Street West, Montreal, QC, H3G 2W1, Canada

    Hamidreza Eslami, Ashkan Ebadi & Andrea Schiffauerova

Authors
  1. Hamidreza Eslami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ashkan Ebadi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Schiffauerova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Schiffauerova.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Eslami, H., Ebadi, A. & Schiffauerova, A. Effect of collaboration network structure on knowledge creation and technological performance: the case of biotechnology in Canada. Scientometrics 97, 99–119 (2013). https://doi.org/10.1007/s11192-013-1069-6

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-013-1069-6

Keywords

Search

Navigation