IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v127y2022i1d10.1007_s11192-021-04170-z.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04170-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-021-04170-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fujii, Hidemichi & Managi, Shunsuke, 2018. "Trends and priority shifts in artificial intelligence technology invention: A global patent analysis," Economic Analysis and Policy, Elsevier, vol. 58(C), pages 60-69.
    2. Jae Young Choi & Seongkyoon Jeong & Kyunam Kim, 2015. "A Study on Diffusion Pattern of Technology Convergence: Patent Analysis for Korea," Sustainability, MDPI, vol. 7(9), pages 1-24, August.
    3. Duk Hee Lee & Il Won Seo & Ho Chull Choe & Hee Dae Kim, 2012. "Collaboration network patterns and research performance: the case of Korean public research institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 925-942, June.
    4. Manuel Trajtenberg, 1990. "A Penny for Your Quotes: Patent Citations and the Value of Innovations," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 172-187, Spring.
    5. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    6. von Wartburg, Iwan & Teichert, Thorsten & Rost, Katja, 2005. "Inventive progress measured by multi-stage patent citation analysis," Research Policy, Elsevier, vol. 34(10), pages 1591-1607, December.
    7. Han, Eun Jin & Sohn, So Young, 2016. "Technological convergence in standards for information and communication technologies," Technological Forecasting and Social Change, Elsevier, vol. 106(C), pages 1-10.
    8. Dietmar Harhoff & Francis Narin & F. M. Scherer & Katrin Vopel, 1999. "Citation Frequency And The Value Of Patented Inventions," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 511-515, August.
    9. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    10. Hagedoorn, John & Cloodt, Myriam, 2003. "Measuring innovative performance: is there an advantage in using multiple indicators?," Research Policy, Elsevier, vol. 32(8), pages 1365-1379, September.
    11. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
    12. Euiseok Kim & Yongrae Cho & Wonjoon Kim, 2014. "Dynamic patterns of technological convergence in printed electronics technologies: patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 975-998, February.
    13. Kose, Toshihiro & Sakata, Ichiro, 2019. "Identifying technology convergence in the field of robotics research," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 751-766.
    14. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).
    2. Podrecca, Matteo & Culot, Giovanna & Tavassoli, Sam & Orzes, Guido, 2024. "Artificial intelligence for climate change: a patent analysis in the manufacturing sector," Papers in Innovation Studies 2024/12, Lund University, CIRCLE - Centre for Innovation Research.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ying Tang & Xuming Lou & Zifeng Chen & Chengjin Zhang, 2020. "A Study on Dynamic Patterns of Technology Convergence with IPC Co-Occurrence-Based Analysis: The Case of 3D Printing," Sustainability, MDPI, vol. 12(7), pages 1-26, March.
    2. Qian Xu & Yabin Yu & Xiao Yu, 2022. "Analysis of the Technological Convergence in Smart Textiles," Sustainability, MDPI, vol. 14(20), pages 1-13, October.
    3. 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).
    4. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
    5. Qian Xu & Hua Cheng, 2021. "Research on the Evolution of Textile Technological Convergence in China," Sustainability, MDPI, vol. 13(5), pages 1-13, February.
    6. Jong Wook Lee & So Young Sohn, 2021. "Patent data based search framework for IT R&D employees for convergence technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5687-5705, July.
    7. Guan-Can Yang & Gang Li & Chun-Ya Li & Yun-Hua Zhao & Jing Zhang & Tong Liu & Dar-Zen Chen & Mu-Hsuan Huang, 2015. "Using the comprehensive patent citation network (CPC) to evaluate patent value," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1319-1346, December.
    8. Christian Rammer & Gastón P Fernández & Dirk Czarnitzki, 2021. "Artificial Intelligence and Industrial Innovation: Evidence from Firm-Level Data," Working Papers of Department of Economics, Leuven 674605, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    9. ZHU Chen & MOTOHASHI Kazuyuki, 2022. "Government R&D spending as a driving force of technology convergence," Discussion papers 22030, Research Institute of Economy, Trade and Industry (RIETI).
    10. Blomkvist, Katarina & Kappen, Philip & Zander, Ivo, 2014. "Superstar inventors—Towards a people-centric perspective on the geography of technological renewal in the multinational corporation," Research Policy, Elsevier, vol. 43(4), pages 669-682.
    11. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    12. Jyun-Cheng Wang & Cheng-hsin Chiang & Shu-Wei Lin, 2010. "Network structure of innovation: can brokerage or closure predict patent quality?," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 735-748, September.
    13. Andreas Reinstaller & Peter Reschenhofer, 2017. "Using PageRank in the analysis of technological progress through patents: an illustration for biotechnological inventions," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1407-1438, December.
    14. Park, Mingyu & Geum, Youngjung, 2022. "Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    15. Nicolas van Zeebroeck, 2011. "The puzzle of patent value indicators," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 20(1), pages 33-62.
    16. Yu-Shan Chen & Chun-Yu Shih, 2011. "Re-examine the relationship between patents and Tobin’s q," Scientometrics, Springer;Akadémiai Kiadó, vol. 89(3), pages 781-794, December.
    17. Huo, Dong & Motohashi, Kazuyuki & Gong, Han, 2019. "Team diversity as dissimilarity and variety in organizational innovation," Research Policy, Elsevier, vol. 48(6), pages 1564-1572.
    18. Adam B. Jaffe & Gaétan de Rassenfosse, 2017. "Patent citation data in social science research: Overview and best practices," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1360-1374, June.
    19. Scott D. Bass & Lukasz A. Kurgan, 2010. "Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 217-241, February.
    20. Jakob Hoffmann & Johannes Glückler, 2023. "Technological Cohesion and Convergence: A Main Path Analysis of the Bioeconomy, 1900–2020," Sustainability, MDPI, vol. 15(16), pages 1-17, August.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:127:y:2022:i:1:d:10.1007_s11192-021-04170-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.