IDEAS home Printed from https://ideas.repec.org/a/ids/ijbsre/v19y2025i3p271-294.html
   My bibliography  Save this article

AI-enabled HR analytics: effective HR practices and organisational sustainability using predictive decision making

Author

Listed:
  • Om Prakash Yadav
  • Dharm Bir Singh
  • Richa Srivastava
  • Priyadarshani Singh
  • Ruchi Rayat
  • Shubhash Kumar Verma
  • Akash Kumar Srivastava

Abstract

This research investigates the influence of AI-driven HR analytics on predictive decision making and its consequential implications for organisational sustainability and the efficacy of HR practices within the service industry. The methodology employed is both descriptive and cross-sectional, leveraging a survey instrument to gather data from 476 HR managers and executives affiliated with service sector organisations in India. The survey comprised 39 scaled items designed to evaluate nine variables pertinent to AI-enabled HR analytics and anticipatory HRM decision-making. The results indicate that virtual interviews, onboarding automation, goal setting and tracking, adaptive learning, and retention prediction positively and statistically significantly impact AI-enabled HR analytics in the service sector. Furthermore, it has been demonstrated that AI-enabled HR analytics possesses a considerable positive effect on anticipatory HRM decision making. In addition, anticipatory HRM decision making is shown to significantly bolster organisational sustainability and the efficiency of HR practices in the service sector.

Suggested Citation

  • Om Prakash Yadav & Dharm Bir Singh & Richa Srivastava & Priyadarshani Singh & Ruchi Rayat & Shubhash Kumar Verma & Akash Kumar Srivastava, 2025. "AI-enabled HR analytics: effective HR practices and organisational sustainability using predictive decision making," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 19(3), pages 271-294.
    • Handle: RePEc:ids:ijbsre:v:19:y:2025:i:3:p:271-294
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=147563
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    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:ids:ijbsre:v:19:y:2025:i:3:p:271-294. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=206 .

    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.