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Measuring knowledge persistence: a genetic approach to patent citation networks

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  • Arianna Martinelli
  • Önder Nomaler

Abstract

The aim of this paper is to propose a new empirical method for identifying technologically important patents within a patent citation network and to apply it to the telecommunication switching industry. The method proposed is labelled the genetic approach, as it is inspired by population genetics: as geneticists are interested in studying patterns of migration and therefore the common origins of people, in innovation studies we are interested in tracing the origin and the evolution of today knowledge. In the context of patent and citation networks, this is done by calculating the patent’s persistence index, i.e., decomposing patent’s knowledge applying the Mendelian law of gene inheritance. This draws on the idea that the more a patent is related (through citations) to “descendent” patents, the more it affects future technological development and therefore its contribution persists in the technology. Results show that the method proposed is successful in reducing the number of both nodes and links considered. Furthermore, our method is indeed successful in identifying technological discontinuities where previous knowledge is not relevant for current technological development. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Arianna Martinelli & Önder Nomaler, 2014. "Measuring knowledge persistence: a genetic approach to patent citation networks," Journal of Evolutionary Economics, Springer, vol. 24(3), pages 623-652, July.
    • Handle: RePEc:spr:joevec:v:24:y:2014:i:3:p:623-652
    DOI: 10.1007/s00191-014-0349-5
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    6. Hiran H. Lathabai & Susan George & Thara Prabhakaran & Manoj Changat, 2018. "An integrated approach to path analysis for weighted citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1871-1904, December.
    7. Kim, Erin H.J. & Jeong, Yoo Kyung & Kim, YongHwan & Song, Min, 2022. "Exploring scientific trajectories of a large-scale dataset using topic-integrated path extraction," Journal of Informetrics, Elsevier, vol. 16(1).
    8. Yoonki Rhee & Sejun Yoon & Hyunseok Park, 2022. "Exploring Knowledge Trajectories of Accounting Information Systems Using Business Method Patents and Knowledge Persistence-Based Main Path Analysis," Mathematics, MDPI, vol. 10(18), pages 1-22, September.
    9. Dieter F. Kogler & Jürgen Essletzbichler & David L. Rigby, 2017. "The evolution of specialization in the EU15 knowledge space," Journal of Economic Geography, Oxford University Press, vol. 17(2), pages 345-373.
    10. Kim, Dong-hyu, 2022. "Effects of catch-up and incumbent firms’ SEP strategic manoeuvres," Research Policy, Elsevier, vol. 51(5).
    11. Barbieri, Nicolò, 2016. "Fuel prices and the invention crowding out effect: Releasing the automotive industry from its dependence on fossil fuel," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 222-234.
    12. Ad van den Oord & Arjen van Witteloostuijn, 2018. "A multi-level model of emerging technology: An empirical study of the evolution of biotechnology from 1976 to 2003," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-27, May.
    13. Nomaler, Onder & Verspagen, Bart, 2016. "River deep, mountain high: Of long-run knowledge trajectories within and between innovation clusters," MERIT Working Papers 2016-048, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    14. Huenteler, Joern & Ossenbrink, Jan & Schmidt, Tobias S. & Hoffmann, Volker H., 2016. "How a product’s design hierarchy shapes the evolution of technological knowledge—Evidence from patent-citation networks in wind power," Research Policy, Elsevier, vol. 45(6), pages 1195-1217.
    15. Huenteler, Joern & Schmidt, Tobias S. & Ossenbrink, Jan & Hoffmann, Volker H., 2016. "Technology life-cycles in the energy sector — Technological characteristics and the role of deployment for innovation," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 102-121.
    16. Christopher R. Esposito, 2020. "The Emergence of Knowledge Production in New Places," Papers in Evolutionary Economic Geography (PEEG) 2046, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Sep 2020.
    17. Nicolò Barbieri & Claudia Ghisetti & Marianna Gilli & Giovanni Marin & Francesco Nicolli, 2016. "A Survey Of The Literature On Environmental Innovation Based On Main Path Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 30(3), pages 596-623, July.
    18. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    19. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    20. Donghyun You & Hyunseok Park, 2018. "Developmental Trajectories in Electrical Steel Technology Using Patent Information," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    21. Su Jung Jee & So Young Sohn, 2023. "Firms’ influence on the evolution of published knowledge when a science-related technology emerges: the case of artificial intelligence," Journal of Evolutionary Economics, Springer, vol. 33(1), pages 209-247, January.

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