, Volume 70, Issue 1, pp 27–39 | Cite as

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

  • Yong-Gil Lee
  • Jeong-Dong Lee
  • Yong-Il Song
  • Se-Jun Lee


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.


Citation Count Negative Binomial Patent Citation Knowledge Flow Team Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 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.CrossRefGoogle Scholar
  2. Barney, J. (2001), Comparative quality analysis — A statistical approach for rating and valuing patent assets. Mimeo, NACVA Valuation Examiner.Google Scholar
  3. Carpenter, M. P., F. Narin, P. Woolf (1981), Citation rates to technologically important patents. World Patent Information, 3(4): 160–163.CrossRefGoogle Scholar
  4. Chen, C., D. Hicks (2004), Tracing knowledge diffusion, Scientometrics, 59: 199–211.CrossRefGoogle Scholar
  5. 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
  6. Greene, W. H. (1997), Econometric Analysis the 3rd edition. Prentice-Hall, New Jersey.Google Scholar
  7. 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.Google Scholar
  8. 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.CrossRefGoogle Scholar
  9. Heilbron, D. (1994), Zero-altered and other regression models for count data with added zeros, Biometrical Journal, 36: 531–547.MATHGoogle Scholar
  10. Henderson, R., I. Cockburn (1996), Scale, scope, and spillovers: the determinants of research productivity in drug discovery, RAND Journal of Economics, 27: 32–59.CrossRefGoogle Scholar
  11. 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
  12. 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.CrossRefGoogle Scholar
  13. 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.CrossRefGoogle Scholar
  14. Jaffe, A. B., M. Trajtenberg (1999), International knowledge flows: Evidence from patent citations, Economics of Innovation and New Technology, 8: 105–136.Google Scholar
  15. Jaffe, A. B., M. Trajtenberg (Eds) (2002), Patents, Citations, and Innovations: A Window on the Knowledge Economy, MIT Press, Cambridge, MA.Google Scholar
  16. Lambert, D. (1992), Zero-inflated Poisson regression, with an application to defects in manufacturing, Technometrics, 34: 1–14.CrossRefMATHGoogle Scholar
  17. Meyer, M. (2000a), What is special about patent citations? Differences between scientific and patent citations, Scientometrics, 49(1): 93–123.CrossRefGoogle Scholar
  18. Meyer, M. (2000b), Does science push technology? Patents citing scientific literature, Research Policy, 29: 409–434.CrossRefGoogle Scholar
  19. Meyer, M. (2002), Tracing knowledge flows in innovation systems, Scientometrics, 54: 193–212.CrossRefGoogle Scholar
  20. Meyer, M., T. Goloubeva, J. T. Utecht (2003), A study of university-related patents and a survey of academic inventors, Scientometrics, 58: 321–350.CrossRefGoogle Scholar
  21. 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.CrossRefGoogle Scholar
  22. Narin, F., K. S. Hamilton, D. Olivastro (1997), The increasing linkage between U.S. technology and public science, Research Policy, 26: 317–330.CrossRefGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. Tijssen, R. J. W. (2002), Science dependence of technologies: evidence from inventions and their innovations, Research Policy, 31: 509–526.CrossRefGoogle Scholar
  25. Vuong, Q. (1989), Likelihood ratio tests for model selection and non-nested hypothesis, Econometrica, 57: 307–334.CrossRefMathSciNetMATHGoogle Scholar

Copyright information

© Akadémiai Kiadó 2007

Authors and Affiliations

  • Yong-Gil Lee
    • 1
  • Jeong-Dong Lee
    • 2
  • Yong-Il Song
    • 1
  • Se-Jun Lee
    • 3
  1. 1.Strategic Planning DivisionKorea Institute of Science and TechnologySeoulKorea
  2. 2.Techno-Economics and Policy ProgramSeoul National UniversitySeoulSouth Korea
  3. 3.Technology Innovation Evaluation BureauMinistry of Science and TechnologyAnyangSouth Korea

Personalised recommendations