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Do main paths reflect technological trajectories? Applying main path analysis to the semiconductor manufacturing industry

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  • Flavia Filippin

    (UNU-MERIT and Maastricht University)

Abstract

It has been proposed that main path analysis can be used to identify technological trajectories in patent-citation networks. In this paper, the method is applied to a network composed of one million US patents and eight million citations in order to trace the backbone of the technological trajectory of the semiconductor manufacturing industry. An in depth discussion of the method is presented, focusing on the many parameters that can be adjusted while applying it and on the consequences of adjusting any of them. Moreover, and differently from other papers on the subject, the result of the algorithm is analysed to determine if it indeed represents the most important technological contributions to the trajectory or if it is merely a collection of relevant and connected patents. This is made easier by the fact that the semiconductor industry has a clear and widely known technological trajectory that spans more than 50 years, Moore's law.

Suggested Citation

  • Flavia Filippin, 2021. "Do main paths reflect technological trajectories? Applying main path analysis to the semiconductor manufacturing industry," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6443-6477, August.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04023-9
    DOI: 10.1007/s11192-021-04023-9
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    References listed on IDEAS

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    Cited by:

    1. Alessandri, Enrico, 2021. "Identifying technological trajectories in the mining sector using patent citation networks," MERIT Working Papers 2021-048, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    2. Fang Han & Sejun Yoon & Nagarajan Raghavan & Hyunseok Park, 2022. "Investigating Company’s Technical Development Directions Based on Internal Knowledge Inheritance and Inventor Capabilities: The Case of Samsung Electronics," Sustainability, MDPI, vol. 14(5), pages 1-19, March.
    3. Martin Ho & Henry CW Price & Tim S Evans & Eoin O'Sullivan, 2023. "Order in Innovation," Papers 2302.13076, arXiv.org.
    4. Kuan, Chung-Huei, 2024. "Integrating prior field knowledge as key documents with main path analysis utilizing key-node path search," Journal of Informetrics, Elsevier, vol. 18(3).
    5. Xiaorui Jiang & Junjun Liu, 2023. "Extracting the evolutionary backbone of scientific domains: The semantic main path network analysis approach based on citation context analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(5), pages 546-569, May.
    6. Alessandri, Enrico, 2023. "Identifying technological trajectories in the mining sector using patent citation networks," Resources Policy, Elsevier, vol. 80(C).

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    More about this item

    Keywords

    Social networks; Patents; Technological trajectories; Semiconductor manufacturing industry; Moore's law;
    All these keywords.

    JEL classification:

    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • L63 - Industrial Organization - - Industry Studies: Manufacturing - - - Microelectronics; Computers; Communications Equipment
    • N7 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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