IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i4d10.1007_s11192-021-03868-4.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-03868-4
    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/s11192-021-03868-4?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. Bornmann, Lutz, 2013. "The problem of citation impact assessments for recent publication years in institutional evaluations," Journal of Informetrics, Elsevier, vol. 7(3), pages 722-729.
    2. T. J. Phelan, 1999. "A compendium of issues for citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 45(1), pages 117-136, May.
    3. Jean-Francois Molinari & Alain Molinari, 2008. "A new methodology for ranking scientific institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(1), pages 163-174, April.
    4. Jue Wang & Philip Shapira, 2011. "Funding acknowledgement analysis: an enhanced tool to investigate research sponsorship impacts: the case of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 563-586, June.
    5. Philip Shapira & Seokbeom Kwon & Jan Youtie, 2017. "Tracking the emergence of synthetic biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1439-1469, September.
    6. Ying Huang & Jannik Schuehle & Alan L. Porter & Jan Youtie, 2015. "A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2005-2022, December.
    7. Sanjay K. Arora & Alan L. Porter & Jan Youtie & Philip Shapira, 2013. "Capturing new developments in an emerging technology: an updated search strategy for identifying nanotechnology research outputs," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 351-370, April.
    8. Xiao Zhou & Lu Huang & Yi Zhang & Miaomiao Yu, 2019. "A hybrid approach to detecting technological recombination based on text mining and patent network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 699-737, November.
    9. Philippe Mongeon & Adèle Paul-Hus, 2016. "The journal coverage of Web of Science and Scopus: a comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 213-228, January.
    10. Muñoz-Écija, Teresa & Vargas-Quesada, Benjamín & Chinchilla Rodríguez, Zaida, 2019. "Coping with methods for delineating emerging fields: Nanoscience and nanotechnology as a case study," Journal of Informetrics, Elsevier, vol. 13(4).
    11. Can Huang & Ad Notten & Nico Rasters, 2011. "Nanoscience and technology publications and patents: a review of social science studies and search strategies," The Journal of Technology Transfer, Springer, vol. 36(2), pages 145-172, April.
    12. Jonathan Adams, 2005. "Early citation counts correlate with accumulated impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 63(3), pages 567-581, June.
    13. Rotolo, Daniele & Hicks, Diana & Martin, Ben R., 2015. "What is an emerging technology?," Research Policy, Elsevier, vol. 44(10), pages 1827-1843.
    14. Jie Gao & Xinping Huang & Lili Zhang, 2019. "Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018," Sustainability, MDPI, vol. 11(23), pages 1-18, November.
    15. Mogoutov, Andrei & Kahane, Bernard, 2007. "Data search strategy for science and technology emergence: A scalable and evolutionary query for nanotechnology tracking," Research Policy, Elsevier, vol. 36(6), pages 893-903, July.
    16. Liu, Na & Guan, JianCheng, 2016. "Policy and innovation: Nanoenergy technology in the USA and China," Energy Policy, Elsevier, vol. 91(C), pages 220-232.
    17. Chen, Kaihua & Zhang, Yi & Fu, Xiaolan, 2019. "International research collaboration: An emerging domain of innovation studies?," Research Policy, Elsevier, vol. 48(1), pages 149-168.
    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. 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.
    2. 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.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. Beckert, Bernd & Kroll, Henning, 2024. "Definition of the research and innovation field of "Artificial Intelligence" and approaches to determining quality," Discussion Papers "Innovation Systems and Policy Analysis" 88, Fraunhofer Institute for Systems and Innovation Research (ISI).
    8. 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.
    9. Hongbo Fu & Rajah Rasiah, 2024. "Fostering Inclusive Green Growth in Chinese Cities: Investigating the Role of Artificial Intelligence," Sustainability, MDPI, vol. 16(22), pages 1-24, November.
    10. 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).
    11. Xiaoli Cao & Xiang Chen & Lu Huang & Lijie Deng & Yijie Cai & Hang Ren, 2024. "Detecting technological recombination using semantic analysis and dynamic network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7385-7416, November.

    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. Santiago Ruiz-Navas & Kumiko Miyazaki, 2018. "A complement to lexical query’s search-term selection for emerging technologies: the case of “big data”," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 141-162, October.
    2. Kwon, Seokbeom & Liu, Xiaoyu & Porter, Alan L. & Youtie, Jan, 2019. "Research addressing emerging technological ideas has greater scientific impact," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    3. Muñoz-Écija, Teresa & Vargas-Quesada, Benjamín & Chinchilla Rodríguez, Zaida, 2019. "Coping with methods for delineating emerging fields: Nanoscience and nanotechnology as a case study," Journal of Informetrics, Elsevier, vol. 13(4).
    4. Philip Shapira & Seokbeom Kwon & Jan Youtie, 2017. "Tracking the emergence of synthetic biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1439-1469, September.
    5. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    6. Sabatier, Mareva & Chollet, Barthélemy, 2017. "Is there a first mover advantage in science? Pioneering behavior and scientific production in nanotechnology," Research Policy, Elsevier, vol. 46(2), pages 522-533.
    7. Porter, Alan L. & Garner, Jon & Carley, Stephen F. & Newman, Nils C., 2019. "Emergence scoring to identify frontier R&D topics and key players," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 628-643.
    8. Li, Munan & Porter, Alan L. & Suominen, Arho & Burmaoglu, Serhat & Carley, Stephen, 2021. "An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Coccia, Mario & Wang, Lili, 2015. "Path-breaking directions of nanotechnology-based chemotherapy and molecular cancer therapy," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 155-169.
    10. 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).
    11. Kang, Inje & Yang, Jiseong & Lee, Wonjae & Seo, Eun-Yeong & Lee, Duk Hee, 2023. "Delineating development trends of nanotechnology in the semiconductor industry: Focusing on the relationship between science and technology by employing structural topic model," Technology in Society, Elsevier, vol. 74(C).
    12. T. Gorjiara & C. Baldock, 2014. "Nanoscience and nanotechnology research publications: a comparison between Australia and the rest of the world," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 121-148, July.
    13. Ying Huang & Jannik Schuehle & Alan L. Porter & Jan Youtie, 2015. "A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 2005-2022, December.
    14. Seokbeom Kwon & Jan Youtie & Alan Porter & Nils Newman, 2024. "How does regulatory uncertainty shape the innovation process? Evidence from the case of nanomedicine," The Journal of Technology Transfer, Springer, vol. 49(1), pages 262-302, February.
    15. Patrick Herron & Aashish Mehta & Cong Cao & Timothy Lenoir, 2016. "Research diversification and impact: the case of national nanoscience development," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 629-659, November.
    16. Parul Khurana & Kiran Sharma, 2022. "Impact of h-index on author’s rankings: an improvement to the h-index for lower-ranked authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4483-4498, August.
    17. Mingyang Wang & Zhenyu Wang & Guangsheng Chen, 2019. "Which can better predict the future success of articles? Bibliometric indices or alternative metrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1575-1595, June.
    18. Krzysztof Klincewicz, 2016. "The emergent dynamics of a technological research topic: the case of graphene," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(1), pages 319-345, January.
    19. Yun Liu & Zhe Yan & Yijie Cheng & Xuanting Ye, 2018. "Exploring the Technological Collaboration Characteristics of the Global Integrated Circuit Manufacturing Industry," Sustainability, MDPI, vol. 10(1), pages 1-23, January.
    20. Zhenyue Zhao & Xuelian Pan & Weina Hua, 2021. "Comparative analysis of the research productivity, publication quality, and collaboration patterns of top ranked library and information science schools in China and the United States," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 931-950, February.

    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

    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:126:y:2021:i:4:d:10.1007_s11192-021-03868-4. 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.