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Patent citation indicators: One size fits all?

Author

Listed:
  • Jurriën Bakker
  • Dennis Verhoeven
  • Lin Zhang
  • Bart Van Looy

Abstract

The number of citations that a patent receives is considered an important indicator of the quality and impact of the patent. However, a variety of methods and data sources can be used to calculate this measure. This paper evaluates similarities between citation indicators that differ in terms of (a) the patent office where the focal patent application is filed; (b) whether citations from offices other than that of the application office are considered; and (c) whether the presence of patent families is taken into account. We analyze the correlations between these different indicators and the overlap between patents identified as highly cited by the various measures. Our findings reveal that the citation indicators obtained differ substantially. Favoring one way of calculating a citation indicator over another has non-trivial consequences and, hence, should be given explicit consideration. Correcting for patent families, especially when using a broader definition (INPADOC), provides the most uniform results.

Suggested Citation

  • Jurriën Bakker & Dennis Verhoeven & Lin Zhang & Bart Van Looy, 2016. "Patent citation indicators: One size fits all?," Working Papers of Department of Management, Strategy and Innovation, Leuven 545092, KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven.
    • Handle: RePEc:ete:msiper:545092
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    2. Jurriën Bakker, 2017. "The log-linear relation between patent citations and patent value," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 879-892, February.
    3. Martin Kalthaus, 2020. "Knowledge recombination along the technology life cycle," Journal of Evolutionary Economics, Springer, vol. 30(3), pages 643-704, July.
    4. Kok, Holmer & Monroe, Joseph & Kappen, Philip, 2025. "Trajectory integration and the impact of inventions," Journal of Business Research, Elsevier, vol. 191(C).
    5. Manuel Acosta & Daniel Coronado & Esther Ferrándiz & Manuel Jiménez, 2022. "Effects of knowledge spillovers between competitors on patent quality: what patent citations reveal about a global duopoly," The Journal of Technology Transfer, Springer, vol. 47(5), pages 1451-1487, October.
    6. Zhao Qu & Shanshan Zhang & Chunbo Zhang, 2017. "Patent research in the field of library and information science: Less useful or difficult to explore?," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 205-217, April.
    7. Plantec, Quentin & Cabanes, Benjamin & le Masson, Pascal & Weil, Benoit, 2023. "Early-career academic engagement in university–industry collaborative PhDs: Research orientation and project performance," Research Policy, Elsevier, vol. 52(9).
    8. Manuel Acosta & Daniel Coronado & Jennifer Medina, 2024. "Effects of co-patenting across national boundaries on patent quality. An exploration in pharmaceuticals," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 33(2), pages 248-281, February.
    9. Zwick, Thomas & Frosch, Katharina & Hoisl, Karin & Harhoff, Dietmar, 2017. "The power of individual-level drivers of inventive performance," Research Policy, Elsevier, vol. 46(1), pages 121-137.
    10. Xun Zhang & Biao Xu, 2019. "R&D Internationalization and Green Innovation? Evidence from Chinese Resource Enterprises and Environmental Enterprises," Sustainability, MDPI, vol. 11(24), pages 1-18, December.
    11. Francesca Michelino & Antonello Cammarano & Andrea Celone & Mauro Caputo, 2019. "The Linkage between Sustainability and Innovation Performance in IT Hardware Sector," Sustainability, MDPI, vol. 11(16), pages 1-15, August.
    12. Mafini Dosso & Didier Lebert, 2019. "A geography of corporate knowledge flows across world regions: evidence from patent citations of top R&D-investing firms," JRC Working Papers on Corporate R&D and Innovation 2019-03, Joint Research Centre.
    13. Gui, Liang & Wu, Jie & Liu, Peng & Ma, Tieju, 2025. "Recognition of promising technologies considering inventor and assignee's historic performance: A machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 214(C).
    14. Fernández, Ana María & Ferrándiz, Esther & Medina, Jennifer, 2022. "The diffusion of energy technologies. Evidence from renewable, fossil, and nuclear energy patents," MPRA Paper 123361, University Library of Munich, Germany.
    15. Wang, Xiaoli & Daim, Tugrul & Huang, Lucheng & Li, Zhiqiang & Shaikh, Ruqia & Kassi, Diby Francois, 2022. "Monitoring the development trend and competition status of high technologies using patent analysis and bibliographic coupling: The case of electronic design automation technology," Technology in Society, Elsevier, vol. 71(C).
    16. Zhao Qu & Shanshan Zhang, 2020. "References to literature from the business sector in patent documents: a case study of charging technologies for electric vehicles," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 867-886, August.
    17. Cammarano, Antonello & Michelino, Francesca & Lamberti, Emilia & Caputo, Mauro, 2017. "Accumulated stock of knowledge and current search practices: The impact on patent quality," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 204-222.
    18. Isabel Cavalli & Charlie Joyez, 2021. "The Dynamics of French Universities in Patent Collaboration Networks," GREDEG Working Papers 2021-38, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    19. Higham, Kyle & Contisciani, Martina & De Bacco, Caterina, 2022. "Multilayer patent citation networks: A comprehensive analytical framework for studying explicit technological relationships," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    20. Fernández, Ana María & Ferrándiz, Esther & Medina, Jennifer, 2022. "The diffusion of energy technologies. Evidence from renewable, fossil, and nuclear energy patents," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    21. Kok, Holmer & Faems, Dries & de Faria, Pedro, 2020. "Ties that matter: The impact of alliance partner knowledge recombination novelty on knowledge utilization in R&D alliances," Research Policy, Elsevier, vol. 49(7).

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    JEL classification:

    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital

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