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A Method Using Two Dimensions of the Patent Classification for Measuring the Technological Proximity. An Application in Identifying a Potential R&D Partner in Biotechnology

Author

Listed:
  • Katia Angué

    (CEMOI - Centre d'Économie et de Management de l'Océan Indien - UR - Université de La Réunion)

  • Cécile Ayerbe

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique)

  • Liliana Mitkova

    (IRG - Institut de Recherche en Gestion - UPEM - Université Paris-Est Marne-la-Vallée - UPEC UP12 - Université Paris-Est Créteil Val-de-Marne - Paris 12)

Abstract

This paper aims to show how the information contained in patent documents can be used to identify basic and specific technological proximities between firms and therefore a potential research and development (R&D) partner. More generally, it looks at patents as a strategic tool that can be used for concluding cooperative R&D agreements (CRDA). The approach begins by looking at the state of the art on the role of technological proximity in CRDAs. This review clearly raises the problem of measuring technological proximity, which needs to be gauged at a two-fold level: general and specific. Then a dual method based on patent portfolios for analyzing the profiles of different potential partners is described along with an example of its application. Concretely, the exploratory study proposed here is based on an analysis of the patent portfolios of 14 French listed biotechnology companies and those of their main R&D partners. The analysis of 5,603 patents filed by the focal firms and their partners shows how the approach can be used to identify compatible partners that are more or less technologically matched.

Suggested Citation

  • Katia Angué & Cécile Ayerbe & Liliana Mitkova, 2014. "A Method Using Two Dimensions of the Patent Classification for Measuring the Technological Proximity. An Application in Identifying a Potential R&D Partner in Biotechnology," Post-Print hal-01133957, HAL.
    • Handle: RePEc:hal:journl:hal-01133957
    DOI: 10.1007/s10961-013-9325-8
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    Citations

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    Cited by:

    1. Ernest Miguelez & Andrea Morrison, 2023. "Migrant inventors as agents of technological change," The Journal of Technology Transfer, Springer, vol. 48(2), pages 669-692, April.
    2. Enrico Santarelli & Jacopo Staccioli & Marco Vivarelli, 2023. "Automation and related technologies: a mapping of the new knowledge base," The Journal of Technology Transfer, Springer, vol. 48(2), pages 779-813, April.
    3. Ma, Tingting & Zhang, Yi & Huang, Lu & Shang, Lining & Wang, Kangrui & Yu, Huizhu & Zhu, Donghua, 2017. "Text mining to gain technical intelligence for acquired target selection: A case study for China's computer numerical control machine tools industry," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 162-180.
    4. Katsuyuki Kaneko & Yuya Kajikawa, 2023. "Novelty Score and Technological Relatedness Measurement Using Patent Information in Mergers and Acquisitions: Case Study in the Japanese Electric Motor Industry," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(2), pages 163-177, June.
    5. Kuan, Chung-Huei & Chen, Dar-Zen & Huang, Mu-Hsuan, 2019. "Bibliographically coupled patents: Their temporal pattern and combined relevance," Journal of Informetrics, Elsevier, vol. 13(4).
    6. Alex J. Guerrero & Joost Heijs & Elena Huergo, 2023. "The effect of technological relatedness on firm sales evolution through external knowledge sourcing," The Journal of Technology Transfer, Springer, vol. 48(2), pages 476-514, April.

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