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Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: the case of Japanese patents

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Although many studies have been conducted to clarify the factors that affect the citation frequency of “academic papers,” there are few studies where the citation frequency of “patents” has been predicted on the basis of statistical analysis, such as regression analysis. Assuming that a patent based on a variety of technological bases tends to be an important patent that is cited more often, this study examines the influence of the number of cited patents’ classifications and compares it with other factors, such as the numbers of inventors, classifications, pages, and claims. Multiple linear, logistic, and zero-inflated negative binomial regression analyses using these factors are performed. Significant positive correlations between the number of classifications of cited patents and the citation frequency are observed for all the models. Moreover, the multiple regression analyses demonstrate that the number of classifications of cited patents contributes more to the regression than do other factors. This implies that, if confounding between factors is taken into account, it is the diversity of classifications assigned to backward citations that more largely influences the number of forward citations.

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  1. 1.

    Bornmann and Daniel (2008) introduced and reviewed citation categorization regarding academic papers from various viewpoints.


  1. Bornmann, L., & Daniel, H.-D. (2007). Multiple publication on a single research study: Does it pay? The influence of number of research articles on total citation counts in biomedicine. Journal of the American Society for Information Science and Technology, 58(8), 1100–1107.

  2. Bornmann, L., & Daniel, H.-D. (2008). What do citation counts measure?: A review of studies on citing behavior. Journal of Documentation, 64(1), 45–80.

  3. Foltz, J., Barham, B., & Kim, K. (2000). Universities and agricultural biotechnology patent production. Agribusiness, 16(1), 82–95.

  4. Fujii, A., Utiyama, M., Yamamoto, M., Utsuro, T., Ehara, T., Echizen-ya, H., & Shimohata, S. (2010). Overview of the patent translation task at the NTCIR-8 workshop. In Proceedings of NTCIR-8 workshop meeting (pp. 371–376). Tokyo: National Institute of Informatics.

  5. Glänzel, W. (2002). Co-authorship patterns and trends in the sciences (1980–1998): A bibliometric study with implications for database indexing and search strategies. Library Trends, 50(3), 461–473.

  6. Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. The Review of Economics and Statistics, 81(3), 511–515.

  7. Inuzuka, A. (2011). Factors facilitating technology reuse: An estimation from patent citation data. Okayama Economic Review, 43(3), 15–28.

  8. Kostoff, R. N. (2007). The difference between highly and poorly cited medical articles in the journal Lancet. Scientometrics, 72(3), 513–520.

  9. 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.

  10. Nanba, H., Fujii, A., Iwayama, M., & Hashimoto, T. (2008). Overview of the patent mining task at the NTCIR-7 workshop. In Proceedings of NTCIR-7 workshop meeting (pp. 325–332). Tokyo: National Institute of Informatics.

  11. Narin, F. (1995). Patents as indicators for the evaluation of industrial research output. Scientometrics, 34(3), 489–496.

  12. Odagiri, H., Koga, T., & Nakamura, K. (2002). R&D boundaries of the firm and the intellectual property system (Discussion Paper, 24). Tokyo: National Institute of Science and Technology Policy.

  13. Peters, H. P. F., & van Raan, A. F. J. (1994). On determinants of citation scores: A case study in chemical engineering. Journal of the American Society for Information Science, 45(1), 39–49.

  14. Sato, Y., & Iwayama, M. (2006). A study of patent document score based on citation analysis. Information Processing Society of Japan SIG Technical Report, 2006(59), 9–16.

  15. Snizek, W. E., Oehler, K., & Mullins, N. C. (1991). Textual and nontextual characteristics of scientific papers: Neglected science indicators. Scientometrics, 20(1), 25–35.

  16. Tang, L., & Shapira, P. (2012). Effects of international collaboration and knowledge moderation on China’s nanotechnology research impacts. Journal of Technology Management in China, 7(1), 94–110.

  17. WIPO (World Intellectual Property Organization) (2010). International Patent Classification (IPC). http://www.wipo.int/classifications/ipc/en/. Accessed 14 Sep 2012.

  18. Yoshikane, F., Suzuki, Y., & Tsuji, K. (2012). Analysis of the relationship between citation frequency of patents and diversity of their backward citations for Japanese patents. Scientometrics, 92(3), 721–733.

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This work was partially supported by Grant-in-Aid for Scientific Research (C) 23500294 (2012) from the Ministry of Education, Culture, Sports, Science and Technology, Japan, and I would like to express my gratitude to the support. I also acknowledge the anonymous reviewers for their helpful comments.

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Correspondence to Fuyuki Yoshikane.

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Yoshikane, F. Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: the case of Japanese patents. Scientometrics 96, 365–379 (2013). https://doi.org/10.1007/s11192-013-0953-4

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  • Patent citation
  • Citation frequency
  • Regression analysis
  • Japan