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Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality

Author

Listed:
  • Myeongji Oh

    (Dongguk University)

  • Hyejin Jang

    (Dongguk University)

  • Sunhye Kim

    (Dongguk University)

  • Byungun Yoon

    (Dongguk University)

Abstract

Main path analysis (MPA) is a method for efficiently analyzing technological trends, which change rapidly in competitive environments. In general, MPA is based on citation networks, and it is used to derive the most key path in a complex network. However, the existing studies using MPA do not use important textual information of patents, except for citation data. In this paper, we suggest a new MPA based on patent documents to identify the main path of technological evolution. For this purpose, first, we used the subject-action-object structure to derive core keywords based on causal relationships in patent claims. Second, the DEcision-MAking Trial and Evaluation Laboratory (DEMATEL) technique was applied to draw link weights between patents where causal relationships of keywords were reflected. Finally, a main path in a patent network was identified using the global main path and key-route main path analysis methods. In this paper, we collected and analyzed patent data related to self-driving car technologies, and we verified the technical changes in the main path obtained based on the proposed approach. We found that the generic technologies of the self-driving operation had the strongest influence on the other self-driving car technologies in the sensing-planning-acting steps.

Suggested Citation

  • Myeongji Oh & Hyejin Jang & Sunhye Kim & Byungun Yoon, 2023. "Main path analysis for technological development using SAO structure and DEMATEL based on keyword causality," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2079-2104, April.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:4:d:10.1007_s11192-023-04652-2
    DOI: 10.1007/s11192-023-04652-2
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    References listed on IDEAS

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    1. Jang, Hyejin & Lee, Suyeong & Yoon, Byungun, 2023. "Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model," Technovation, Elsevier, vol. 126(C).

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