Skip to main content
Log in

An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Patent citation counts represent an aspect of patent quality and knowledge flow. Especially, citation data of US patents contain most valuable pieces of the information among other patents. This paper identifies the factors affecting patent citation counts using US patents belonging to Korea Institute of Science and Technology (KIST). For patent citation count model, zero-inflated models are announced to handle the excess zero data. For explanatory factors, research team characteristics, invention-specific characteristics, and geographical domain related characteristics are suggested. As results, the size of invention and the degree of dependence upon Japanese technological domain significantly affect patent citation counts of KIST.

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

Similar content being viewed by others

References

  • Adamsa, J. D., G. C. Blackb, J. R. Clemmonse, P. E. Stephand (2005), Scientific teams and institutional collaborations: Evidence from U.S. universities, 1981–1999, Research Policy, 34(3): 259–285.

    Article  Google Scholar 

  • Barney, J. (2001), Comparative quality analysis — A statistical approach for rating and valuing patent assets. Mimeo, NACVA Valuation Examiner.

  • Carpenter, M. P., F. Narin, P. Woolf (1981), Citation rates to technologically important patents. World Patent Information, 3(4): 160–163.

    Article  Google Scholar 

  • Chen, C., D. Hicks (2004), Tracing knowledge diffusion, Scientometrics, 59: 199–211.

    Article  Google Scholar 

  • Gay, C., C. Le Bas, P. Patel, K. Touach (2005), The determinants of patent citations: An empirical analysis of French and British patents in the US, Economics of Innovation and New Technology, 14(5): 339–350.

    Google Scholar 

  • Greene, W. H. (1997), Econometric Analysis the 3rd edition. Prentice-Hall, New Jersey.

    Google Scholar 

  • Harhoff, D., F. Narin, F. M. Scherer, K. Vopel (1997), Citation Frequency and the Value of Patented Innovation, Social Science Research Center, Berlin, Discussion Paper no 97-26.

  • Hausman, J. A., B. H. Hall, Z. Griliches (1984), Econometric models for count data with an application to the patents-R&D relationship, Econometrica, 52: 909–938.

    Article  Google Scholar 

  • Heilbron, D. (1994), Zero-altered and other regression models for count data with added zeros, Biometrical Journal, 36: 531–547.

    MATH  Google Scholar 

  • Henderson, R., I. Cockburn (1996), Scale, scope, and spillovers: the determinants of research productivity in drug discovery, RAND Journal of Economics, 27: 32–59.

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • Jaffe, A. B., M. S. Fogarty, B. A. Banks (1998), Evidence from patents and patent citations on the impact of NASA and other federal labs on commercial innovation, Journal of Industrial Economics, 46: 183–204.

    Article  Google Scholar 

  • Jaffe, A. B., M. Trajtenberg (1999), International knowledge flows: Evidence from patent citations, Economics of Innovation and New Technology, 8: 105–136.

    Google Scholar 

  • Jaffe, A. B., M. Trajtenberg (Eds) (2002), Patents, Citations, and Innovations: A Window on the Knowledge Economy, MIT Press, Cambridge, MA.

    Google Scholar 

  • Lambert, D. (1992), Zero-inflated Poisson regression, with an application to defects in manufacturing, Technometrics, 34: 1–14.

    Article  MATH  Google Scholar 

  • Meyer, M. (2000a), What is special about patent citations? Differences between scientific and patent citations, Scientometrics, 49(1): 93–123.

    Article  Google Scholar 

  • Meyer, M. (2000b), Does science push technology? Patents citing scientific literature, Research Policy, 29: 409–434.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Meyer, M., T. Goloubeva, J. T. Utecht (2003), A study of university-related patents and a survey of academic inventors, Scientometrics, 58: 321–350.

    Article  Google Scholar 

  • Michel, J., B. Bettels (2001), Patent citation analysis: A closer look at the basic input data from patent search reports, Scientometrics, 51(1): 185–201.

    Article  Google Scholar 

  • Narin, F., K. S. Hamilton, D. Olivastro (1997), The increasing linkage between U.S. technology and public science, Research Policy, 26: 317–330.

    Article  Google Scholar 

  • Tijssen, R. J. W. (2001), Global and domestic utilization of industrial relevant science: patent citation analysis of science-technology interactions and knowledge flows, Research Policy, 30: 35–54.

    Article  Google Scholar 

  • Tijssen, R. J. W. (2002), Science dependence of technologies: evidence from inventions and their innovations, Research Policy, 31: 509–526.

    Article  Google Scholar 

  • Vuong, Q. (1989), Likelihood ratio tests for model selection and non-nested hypothesis, Econometrica, 57: 307–334.

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yong-Gil Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, YG., Lee, JD., Song, YI. et al. An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics 70, 27–39 (2007). https://doi.org/10.1007/s11192-007-0102-z

Download citation

  • Received:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-007-0102-z

Keywords

Navigation