Skip to main content
Log in

The nonlinear nature of the relationships between the patent traits and corporate performance

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

This study utilizes neural network to explore the nonlinear relationships between corporate performance and the patent traits measured from Herfindahl-Hirschman Index of patents (HHI of patents), patent citations, and relative patent position in the most important technological field (RPPMIT) in the US pharmaceutical industry. The results show that HHI of patents and RPPMIT have nonlinearly and monotonically positive influences upon corporate performance, while the influence of patent citations is nonlinearly U-shaped. Therefore, pharmaceutical companies should raise the degrees of the leading position in their most important technological fields and the centralization of their technological capabilities to enhance corporate performance.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Bettis, R. A., & Hitt, M. A. (1995). The new competitive landscape. Strategic Management Journal, 16(Special Summer Issue), 7–19.

    Google Scholar 

  • Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Math Control Signal System, 2, 303–314.

    Google Scholar 

  • Ernst, H. (1998). Patent portfolios for strategic R&D planning. Journal of Engineering and Technology Management, 15(4), 279–308.

    Article  MathSciNet  Google Scholar 

  • Grabowski, H., & Vernon, J. (1990). A new look at the returns and risks to pharmaceutical R&D. Management Science, 36, 804–821.

    Article  Google Scholar 

  • Hall, B. H. (2002). A note on the bias in the Herfindahl based on count data. In A. Jaffe & M. Trajtenberg (Eds.), Patents, citations, and innovations. Cambridge, MA: MIT Press.

    Google Scholar 

  • Hall, B. H., Jaffe, A., & Trajtenberg, M. (2005). Market value and patent citations. Rand Journal of Economics, 36(1), 16–38.

    Google Scholar 

  • Hall, M., & Tideman, N. (1967). Measures of concentration. Journal of the American Statistical Association, 62, 162–168.

    Article  Google Scholar 

  • Haykin, S. (1994). Neural networks. New York: Macmillan College Company.

    MATH  Google Scholar 

  • Hirschey, M., & Richardson, V. J. (2004). Are scientific indicators of patent quality useful to investors? Journal of Empirical Finance, 11(1), 91–107.

    Article  Google Scholar 

  • Hornik, K., Stinchcombe, M., & White, H. (1989), Multilayer feedforward networks are universal approximations. Neural Networks, 2, 336–359.

    Google Scholar 

  • Lee, Y. G., Lee, J. D., Song, Y. I., & Lee, S. J. (2007). An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics, 70(1), 27–39.

    Article  Google Scholar 

  • Lin, B., & Chen, J. (2005). Corporate technology portfolios and R&D performance measures: A study of technology intensive firms. R&D Management, 35(2), 157–170.

    Article  Google Scholar 

  • Markides, C. C., & Williamson, P. J. (1994). Related diversification, core competences and corporate performance. Strategic Management Journal, 15(5), 149–165.

    Google Scholar 

  • McMillan, E. (2004). Complexity, organizations and change. London: Routledge.

    Google Scholar 

  • Nagaoka, S. (2005). Determinants of high-royalty contracts and the impact of stronger protection of intellectual property rights in Japan. Journal of the Japanese & International Economies, 19(2), 233–254.

    Article  Google Scholar 

  • Narin, F., Noma, E., & Perry, R. (1987). Patents as indicators of corporate technological strength. Research Policy, 16(2–4), 143–155.

    Article  Google Scholar 

  • Nayak, P. C., Sudheer, K. P., Rangan, D. M., & Ramasastri, K. S. (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology, 291, 52–66.

    Article  Google Scholar 

  • Polanco, X., François, C., & Keim, J.-P. (1998). Artificial neural network technology for the classification and cartography of scientific and technical information. Scientometrics, 41(1–2), 69–82.

    Article  Google Scholar 

  • Polanco, X., François, C., & Lamirel, J.-C. (2001). Using artificial neural networks for mapping of science and technology: A multi-self-organizing-maps approach. Scientometrics, 51(1), 267–292.

    Article  Google Scholar 

  • Prahalad, C. K., & Hamel, G. (1990). The core competence of the corporation. Harvard Business Review, 68(3), 79–91.

    Google Scholar 

  • Scherer, F. M., & Ross, D. (1990). Industrial market structure and economic performance (pp. 73–79). Dallas, TX: Houghton Mifflin.

  • Soete, L., & Wyatt, S. (1983). The use of foreign patenting as an internationally comparable science and technology output indicator. Scientometrics, 5(1), 31–54.

    Article  Google Scholar 

  • Stacey, R. D. (1996). Complexity and creativity in organizations. San Francisco: Berrett-Koehler Publishers.

    Google Scholar 

  • Teece, D. J. (1986). Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, 15(6), 285–305.

    Article  Google Scholar 

  • Wasserman, P. D. (1994). Advanced methods in neural computing. New York: Van Nostrand Reinhold.

    Google Scholar 

  • White, H. (1989). Some asymptotic results for learning in single hidden layer feedforward network models. Journal of the American Statistical Association, 84, 1003–1013.

    Article  MATH  MathSciNet  Google Scholar 

  • Wray, B., Palmer, A., & Bejou, D. (1994). Using neural network analysis to evaluate buyer-seller relationships. European Journal of Marketing, 28(10), 32–48.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu-Shan Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, YS., Chang, KC. The nonlinear nature of the relationships between the patent traits and corporate performance. Scientometrics 82, 201–210 (2010). https://doi.org/10.1007/s11192-009-0101-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-009-0101-3

Keywords

Navigation