IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6239-660-9_24.html

Artificial Intelligence in HR: A Bibliometric Analysis Through VOS Viewer

In: Proceedings of the 5th International Conference on Management Research (ICMR 2025)

Author

Listed:
  • Sushil Minz

    (KIIT KSOM, Ph.D. Scholar)

Abstract

Introduction: The emergence of artificial intelligence (AI) has changed several domains, Human Resources (HR), included. The aims of this study are to provide a comprehensive bibliometric analysis of AI applications in HR, highlighting the transformation of HR functions done traditionally and identifying emerging trends. By addressing a research gap in the literature, this study identifies, the extent and impact of integration of AI in HR practices. Objective: To explore its evolving landscape of through a detailed bibliometric analysis. And to investigate key areas of management such as recruitment, performance, and engagement of employee, aiming to enhance both academic understanding it’s practical implementation. Methodology: Relevant literature was collected from SCOPUS using specific search criteria. The 934 selected articles from 1984 to 2024 were analysed through VOS viewer to identify prevailing themes and trends. Advanced bibliometric tools and techniques, such as co citation analysis and keyword mapping, were employed for a thorough examination. Results: Application of AI in HR is steadily growing, with significant research focusing on predictive analytics, automated recruitment processes, and AI driven employee performance assessments. There is an increasing volume of publications in recent years, indicating heightened scholarly and practical interest. These findings illustrate the dynamic nature of adoption of AI in HR practices. Conclusion: The bibliometric analysis highlights worthy insights into the present state and its future prospects. It also highlights key areas requiring study in the near future, emphasizing the need for interdisciplinary approaches to leverage AI’s capabilities, completely. For HR professionals, the findings emphasise the importance of embracing AI technologies to enhance efficiency and effectiveness in HR management.

Suggested Citation

  • Sushil Minz, 2026. "Artificial Intelligence in HR: A Bibliometric Analysis Through VOS Viewer," Advances in Economics, Business and Management Research, in: Arvind Tripathy & Kumar Mohanty (ed.), Proceedings of the 5th International Conference on Management Research (ICMR 2025), pages 497-516, Springer.
  • Handle: RePEc:spr:advbcp:978-94-6239-660-9_24
    DOI: 10.2991/978-94-6239-660-9_24
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:advbcp:978-94-6239-660-9_24. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.