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Artificial Intelligence in Business Management: A Bibliometric Study (2015–2025)

Author

Listed:
  • Ahmad Ismail Mohd Anuar

    (Faculty of Management and Business, Universiti Teknologi MARA (UiTM) Terengganu Branch, Dungun Campus, 23000 Sura Hujung Dungun, Terengganu)

  • Wan Maziah Wan Ab. Razak

    (Faculty of Management and Business, Universiti Teknologi MARA (UiTM) Terengganu Branch, Dungun Campus, 23000 Sura Hujung Dungun, Terengganu)

  • Afif Zuhri Muhammad Khodri Harahap

    (Faculty of Management and Business, Universiti Teknologi MARA (UiTM) Terengganu Branch, Dungun Campus, 23000 Sura Hujung Dungun, Terengganu)

  • Ahmad Suffian bin Mohd Zahari

    (Faculty of Management and Business, Universiti Teknologi MARA (UiTM) Terengganu Branch, Dungun Campus, 23000 Sura Hujung Dungun, Terengganu)

  • Rabiatul Adawiyah Ma'arof

    (Faculty of Management and Business, Universiti Teknologi MARA (UiTM) Terengganu Branch, Dungun Campus, 23000 Sura Hujung Dungun, Terengganu)

Abstract

Artificial Intelligence (AI) has become a transformative force in business management, reshaping organizational strategies, decision-making processes, and competitive advantage. This study presents a bibliometric analysis of AI-related research in business management, based on 2,999 documents indexed in Scopus between 2015 and 2025. Using the Bibliometrix R package and its Biblioshiny interface, the analysis maps publication trends, influential authors, institutions, journals, and countries, as well as the conceptual structure of the field. The results reveal a geometric growth in scholarly output, with an annual increase of 47.09%, reflecting the accelerating adoption of AI in organizational contexts. The findings highlight the dominance of journal articles and reviews, strong collaboration patterns with 29.41% international co-authorship, and concentration of publications in a limited number of multidisciplinary and management-focused journals. Citation analysis identifies highly influential works within the field, while keyword co-occurrence analysis reveals clusters around technical methods (machine learning, deep learning, robotics), applications (natural language processing, data mining), and managerial themes (decision-making, organizational performance, digital transformation). This study provides a comprehensive overview of the intellectual landscape of AI in Business Management, identifies emerging themes and research gaps, and offers valuable insights for academics, practitioners, and policymakers seeking to harness AI for organizational transformation and sustainable competitiveness.

Suggested Citation

  • Ahmad Ismail Mohd Anuar & Wan Maziah Wan Ab. Razak & Afif Zuhri Muhammad Khodri Harahap & Ahmad Suffian bin Mohd Zahari & Rabiatul Adawiyah Ma'arof, 2026. "Artificial Intelligence in Business Management: A Bibliometric Study (2015–2025)," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 10(2), pages 2287-2298, February.
    • Handle: RePEc:bcp:journl:v:10:y:2026:i:2:p:2287-2298
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    References listed on IDEAS

    as
    1. Ruifeng Gong & Jian Xue & Laijun Zhao & Oleksandra Zolotova & Xiaoqing Ji & Yan Xu, 2019. "A Bibliometric Analysis of Green Supply Chain Management Based on the Web of Science (WOS) Platform," Sustainability, MDPI, vol. 11(12), pages 1-18, June.
    2. Donthu, Naveen & Kumar, Satish & Mukherjee, Debmalya & Pandey, Nitesh & Lim, Weng Marc, 2021. "How to conduct a bibliometric analysis: An overview and guidelines," Journal of Business Research, Elsevier, vol. 133(C), pages 285-296.
    3. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    4. Rabiatul Adawiyah Ma’arof & Afif Zuhri Muhammad Khodri Harahap & Umi Kartini Rashid & Juzaimi Nasuredin & Farah Liyana Kadar, 2025. "A Bibliometric Analysis on Green Human Resource Management Using Scopus Database," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(6), pages 2246-2257, June.
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