IDEAS home Printed from https://ideas.repec.org/a/ids/ijbexc/v39y2026i4p511-533.html

Customers' acceptance of artificially intelligent robots in the insurance industry: evidence from an emerging market

Author

Listed:
  • Sumathi Kumaraswamy
  • Yomna Abdulla
  • M. Suresh

Abstract

There is a growing consensus among Indian insurance firms, which have been increasingly attempting to adopt and integrate artificial intelligence (AI) and robotic process automation in the recent past. This calls for a heated debate on how far the machines that read algorithms will replace human intelligence and satisfy the customers who always expect error-free and faster processing of insurance services. This research paper is the first of its kind in analysing the customers' acceptance of adopting artificial intelligent robots (ART) in the insurance sector for its sustainable growth methodology. The interrelationships among the factors were analysed using the total interpretive structural modelling (TISM) approach. The results suggest that individuals' knowledge about artificial intelligent robots and their performance expectancy from AIR, in terms of accuracy and consistency, are the main triggering factors for customer acceptance of AI robots in the Indian insurance industry.

Suggested Citation

  • Sumathi Kumaraswamy & Yomna Abdulla & M. Suresh, 2026. "Customers' acceptance of artificially intelligent robots in the insurance industry: evidence from an emerging market," International Journal of Business Excellence, Inderscience Enterprises Ltd, vol. 39(4), pages 511-533.
  • Handle: RePEc:ids:ijbexc:v:39:y:2026:i:4:p:511-533
    as

    Download full text from publisher

    File URL: https://www.inderscience.com/link.php?id=155645
    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:ijbexc:v:39:y:2026:i:4:p:511-533. 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=291 .

    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.