IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i17p7767-d1472807.html
   My bibliography  Save this article

The Sustainable Innovation of AI: Text Mining the Core Capabilities of Researchers in the Digital Age of Industry 4.0

Author

Listed:
  • Yajun Ji

    (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Shengtai Zhang

    (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Fang Han

    (National Science Library, Chinese Academy of Sciences, Beijing 100871, China)

  • Ran Cui

    (School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China)

  • Tao Jiang

    (School of Software, Beijing University of Aeronautics, Beijing 100876, China)

Abstract

Sustainable innovation in the field of artificial intelligence (AI) is essential for the development of Industry 4.0. Recognizing the innovation abilities of researchers is fundamental to achieving sustainable innovation within organizations. This study proposes a method for identifying the core innovative competency field of researchers through text mining, which involves the extraction of core competency tags, topic clustering, and calculating the relevance between researchers and topics. Using AI as a case study, the research identifies the core innovative competency field of researchers, uncovers opportunities for sustainable innovation, and highlights key innovators. This approach offers deeper insights for AI R&D activities, providing effective support for promoting sustainable innovation. Compared to traditional expertise identification methods, this approach provides a more in-depth and detailed portrayal of researchers’ expertise, particularly highlighting potential innovation domains with finer granularity. It is less influenced by subjective factors and can be conveniently applied to identify the core innovative competency field of researchers in any other research field, making it especially suitable for interdisciplinary areas. By offering a precise and comprehensive understanding of researchers’ capability fields, this method enhances the strategic planning and execution of innovative projects, ensuring that organizations can effectively leverage the expertise of their researchers to drive forward sustainable innovation.

Suggested Citation

  • Yajun Ji & Shengtai Zhang & Fang Han & Ran Cui & Tao Jiang, 2024. "The Sustainable Innovation of AI: Text Mining the Core Capabilities of Researchers in the Digital Age of Industry 4.0," Sustainability, MDPI, vol. 16(17), pages 1-17, September.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7767-:d:1472807
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/17/7767/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/17/7767/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    2. Philippe Baumard, 1999. "Tacit Knowledge in Organizations," Post-Print hal-03227234, HAL.
    3. Xiaoling Sun & Kun Ding, 2018. "Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1735-1748, September.
    4. Ruijie Wang & Yuhao Zhou & An Zeng, 2023. "Evaluating scientists by citation and disruption of their representative works," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1689-1710, March.
    5. Lucía Muñoz-Pascual & Jesús Galende, 2020. "Ambidextrous Knowledge and Learning Capability: The Magic Potion for Employee Creativity and Sustainable Innovation Performance," Sustainability, MDPI, vol. 12(10), pages 1-27, May.
    6. Felipe Viegas & Antônio Pereira & Pablo Cecílio & Elisa Tuler & Wagner Meira & Marcos Gonçalves & Leonardo Rocha, 2022. "Semantic Academic Profiler (SAP): a framework for researcher assessment based on semantic topic modeling," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 5005-5026, August.
    Full references (including those not matched with items on IDEAS)

    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. Zhang, Ming-Ze & Wang, Tang-Rong & Lyu, Peng-Hui & Chen, Qi-Mei & Li, Ze-Xia & Ngai, Eric W.T., 2024. "Impact of gender composition of academic teams on disruptive output," Journal of Informetrics, Elsevier, vol. 18(2).
    2. Gao, Qiang & Liang, Zhentao & Wang, Ping & Hou, Jingrui & Chen, Xiuxiu & Liu, Manman, 2021. "Potential index: Revealing the future impact of research topics based on current knowledge networks," Journal of Informetrics, Elsevier, vol. 15(3).
    3. Wenxuan Shi & Renli Wu, 2024. "Women’s strength in science: exploring the influence of female participation on research impact and innovation," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4529-4551, July.
    4. Hoekman, Jarno & Rake, Bastian, 2024. "Geography of authorship: How geography shapes authorship attribution in big team science," Research Policy, Elsevier, vol. 53(2).
    5. Guoqiang Liang & Ying Lou & Haiyan Hou, 2022. "Revisiting the disruptive index: evidence from the Nobel Prize-winning articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5721-5730, October.
    6. Yu, Shuo & Alqahtani, Fayez & Tolba, Amr & Lee, Ivan & Jia, Tao & Xia, Feng, 2022. "Collaborative Team Recognition: A Core Plus Extension Structure," Journal of Informetrics, Elsevier, vol. 16(4).
    7. Frédéric CREPLET, 2004. "Les Portails d’entreprise : une réponse aux dimensions de l’entreprise « processeur de connaissances »," Working Papers of BETA 2004-07, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    8. Starbuck, William H. & Barnett, Michael L. & Baumard, Philippe, 2008. "Payoffs and pitfalls of strategic learning," Journal of Economic Behavior & Organization, Elsevier, vol. 66(1), pages 7-21, April.
    9. Gallus, Jana & Bhatia, Sudeep, 2020. "Gender, power and emotions in the collaborative production of knowledge: A large-scale analysis of Wikipedia editor conversations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 160(C), pages 115-130.
    10. Peter Sjögårde & Fereshteh Didegah, 2022. "The association between topic growth and citation impact of research publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1903-1921, April.
    11. Shuo Xu & Liyuan Hao & Xin An & Hongshen Pang & Ting Li, 2020. "Review on emerging research topics with key-route main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 607-624, January.
    12. Michele Pezzoni & Fabiana Visentin, 2024. "Gender bias in team formation: the case of the European Science Foundation’s grants," Science and Public Policy, Oxford University Press, vol. 51(2), pages 247-260.
    13. You, Taekho & Park, Jinseo & Lee, June Young & Yun, Jinhyuk & Jung, Woo-Sung, 2022. "Disturbance of questionable publishing to academia," Journal of Informetrics, Elsevier, vol. 16(2).
    14. Tomi Rajala, 2019. "Mind the Information Expectation Gap," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(1), pages 104-125, March.
    15. Cinzia Daraio & Simone Di Leo & Loet Leydesdorff, 2022. "Using the Leiden Rankings as a Heuristics: Evidence from Italian universities in the European landscape," LEM Papers Series 2022/08, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    16. Wu, Lingfei & Kittur, Aniket & Youn, Hyejin & Milojević, Staša & Leahey, Erin & Fiore, Stephen M. & Ahn, Yong-Yeol, 2022. "Metrics and mechanisms: Measuring the unmeasurable in the science of science," Journal of Informetrics, Elsevier, vol. 16(2).
    17. Pierre Azoulay & Danielle Li, 2020. "Scientific Grant Funding," NBER Working Papers 26889, National Bureau of Economic Research, Inc.
    18. Li, Heyang & Wu, Meijun & Wang, Yougui & Zeng, An, 2022. "Bibliographic coupling networks reveal the advantage of diversification in scientific projects," Journal of Informetrics, Elsevier, vol. 16(3).
    19. Bellis, Paola & Cunial, Matteo & Trabucchi, Daniel, 2024. "Mastering hybrid worlds through digital leadership: The role of agility in fostering innovation," Business Horizons, Elsevier, vol. 67(4), pages 369-380.
    20. Sam Arts & Nicola Melluso & Reinhilde Veugelers, 2023. "Beyond Citations: Measuring Novel Scientific Ideas and their Impact in Publication Text," Papers 2309.16437, arXiv.org, revised Dec 2024.

    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:gam:jsusta:v:16:y:2024:i:17:p:7767-:d:1472807. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.