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Challenges Of Artificial Intelligence For Knowledge Management Systems: A Bibliometric Analysis Perspective

Author

Listed:
  • Constantin Bratianu

    (Bucharest University of Economic Studies, UNESCO Department for Business Administration, Bucharest, Romania; Academy of Romanian Scientists, Romania.)

  • Alexandru Ioan

    (National University of Political Studies and Public Administration, the Faculty of Management, Bucharest, Romania)

Abstract

This paper explores the opportunities and challenges associated with integrating artificial intelligence (AI) into knowledge management systems (KMS), by using a bibliometric analysis. The rapid advancement of AI technologies, particularly generative models, has opened new avenues for enhancing KMS theories and practices. The study examines publication trends, key contributors, predominant research themes, and the practical applications of AI in KMS, with a specific focus on how these technologies can transform knowledge creation, sharing, and dissemination. The study draws on data from the Scopus database, revealing the significant impact of AI on KMS practices, particularly its capacity to enhance knowledge transfer, support decision-making processes, and foster organizational learning. However, the study also identifies several challenges, including ethical concerns, the interpretability of AI-driven tools, and the scalability of AI methods. The analysis underscores the need for further research in addressing these challenges and exploring the full potential of AI to fill knowledge gaps and create new knowledge artefacts. This paper provides valuable insights for scholars, practitioners, and organizations looking to harness AI for improving KMS theories and practices, offering a systematic analysis for future research on the evolving intersection of AI and KMS.

Suggested Citation

  • Constantin Bratianu & Alexandru Ioan, 2025. "Challenges Of Artificial Intelligence For Knowledge Management Systems: A Bibliometric Analysis Perspective," Oradea Journal of Business and Economics, University of Oradea, Faculty of Economics, vol. 10(1), pages 108-120, March.
    • Handle: RePEc:ora:jrojbe:v:10:y:2025:i:1:p:108-120
    DOI: http://doi.org/10.47535/1991ojbe209
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    References listed on IDEAS

    as
    1. Bratianu, C. & Andrei ?tefan Nestian & Silviu Mihail Tita & Ana Iolanda Vodã & Alexandra Luciana Guta, 2020. "The Impact of Knowledge Risk on Sustainability of Firms," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 22(55), pages 639-639, August.
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    More about this item

    Keywords

    knowledge management systems; artificial intelligence; deep learning; large language models; bibliometric analysis;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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