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Comparing technology convergence of artificial intelligence on the industrial sectors: two-way approaches on network analysis and clustering analysis

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  • Soyea Lee

    (Seoul National University)

  • Junseok Hwang

    (Seoul National University)

  • Eunsang Cho

    (Seoul National University)

Abstract

This study investigates technology convergence of AI considering both industrial sectors and technological characteristics with patent data in terms of two-way approaches: IPC-based network analyses and text-based clustering analysis. The IPC-based network analyses, which indicate a top-down approach in this study, focuses on influential technology area with hub nodes and their tie nodes in an IPC-based convergence network. A network centrality analysis is applied to determine the hub nodes which identify notable industrial sectors and influential technology. In addition, an ego-network analysis is conducted to examine the strongly related technology on the hub nodes. Meanwhile, from a bottom-up approach, a text-based clustering analysis is performed and the result shows an applied target of the technology and an integrated form of various technology which are not found from the top-down approach. Consequently, this study suggests new research framework to understand technology convergence based on the industrial sector, influential technology category, and technology application aspects. In line with the findings, this study analyzes technology convergence of AI by the notable industrial sectors: finance/management, medical, transport, semiconductor, game, and biotechnology sector. The results of this study suggest practical implications for AI technology and related industries.

Suggested Citation

  • Soyea Lee & Junseok Hwang & Eunsang Cho, 2022. "Comparing technology convergence of artificial intelligence on the industrial sectors: two-way approaches on network analysis and clustering analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 407-452, January.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:1:d:10.1007_s11192-021-04170-z
    DOI: 10.1007/s11192-021-04170-z
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    Cited by:

    1. Seo, Wonchul & Afifuddin, Mokh, 2024. "Developing a supervised learning model for anticipating potential technology convergence between technology topics," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

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    More about this item

    Keywords

    Technology convergence; Artificial intelligence; Patent analysis; Network analysis; Clustering analysis;
    All these keywords.

    JEL classification:

    • C00 - Mathematical and Quantitative Methods - - General - - - General
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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