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Tracking developments in artificial intelligence research: constructing and applying a new search strategy

Author

Listed:
  • Na Liu

    (Shandong Technology and Business University)

  • Philip Shapira

    (University of Manchester
    Georgia Institute of Technology)

  • Xiaoxu Yue

    (Tsinghua University)

Abstract

Artificial intelligence, as an emerging and multidisciplinary domain of research and innovation, has attracted growing attention in recent years. Delineating the domain composition of artificial intelligence is central to profiling and tracking its development and trajectories. This paper puts forward a bibliometric definition for artificial intelligence which can be readily applied, including by researchers, managers, and policy analysts. Our approach starts with benchmark records of artificial intelligence captured by using a core keyword and specialized journal search. We then extract candidate terms from high frequency keywords of benchmark records, refine keywords and complement with the subject category “artificial intelligence”. We assess our search approach by comparing it with other three recent search strategies of artificial intelligence, using a common source of articles from the Web of Science. Using this source, we then profile patterns of growth and international diffusion of scientific research in artificial intelligence in recent years, identify top research sponsors in funding artificial intelligence and demonstrate how diverse disciplines contribute to the multidisciplinary development of artificial intelligence. We conclude with implications for search strategy development and suggestions of lines for further research.

Suggested Citation

  • Na Liu & Philip Shapira & Xiaoxu Yue, 2021. "Tracking developments in artificial intelligence research: constructing and applying a new search strategy," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3153-3192, April.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:4:d:10.1007_s11192-021-03868-4
    DOI: 10.1007/s11192-021-03868-4
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    References listed on IDEAS

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    Cited by:

    1. Ana Suárez & Alberto Adanero & Víctor Díaz-Flores García & Yolanda Freire & Juan Algar, 2022. "Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students’ Diagnostic Skills," IJERPH, MDPI, vol. 19(14), pages 1-14, July.
    2. Yitong Chen & Keye Wu & Yue Li & Jianjun Sun, 2023. "Impacts of inter-institutional mobility on scientific performance from research capital and social capital perspectives," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3473-3506, June.
    3. Hajkowicz, Stefan & Sanderson, Conrad & Karimi, Sarvnaz & Bratanova, Alexandra & Naughtin, Claire, 2023. "Artificial intelligence adoption in the physical sciences, natural sciences, life sciences, social sciences and the arts and humanities: A bibliometric analysis of research publications from 1960-2021," Technology in Society, Elsevier, vol. 74(C).
    4. Farhat Chowdhury & Albert N. Link & Martijn Hasselt, 2022. "Public support for research in artificial intelligence: a descriptive study of U.S. Department of Defense SBIR Projects," The Journal of Technology Transfer, Springer, vol. 47(3), pages 762-774, June.
    5. Steve J. Bickley & Ho Fai Chan & Benno Torgler, 2022. "Artificial intelligence in the field of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2055-2084, April.
    6. Bordoloi, Tausif & Shapira, Philip & Mativenga, Paul, 2022. "Policy interactions with research trajectories: The case of cyber-physical convergence in manufacturing and industrials," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. Ana Teresa Santos & Sandro Mendonça, 2022. "Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7449-7470, December.
    8. Hajkowicz, Stefan & Naughtin, Claire & Sanderson, Conrad & Schleiger, Emma & Karimi, Sarvnaz & Bratanova, Alexandra & Bednarz, Tomasz, 2022. "Artificial intelligence for science – adoption trends and future development pathways," MPRA Paper 115464, University Library of Munich, Germany.

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    More about this item

    Keywords

    Emerging technology; Artificial intelligence; Bibliometric analysis; Search strategy; Research trends;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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