IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-698-7_17.html

The Effect of Professional Training, Computer Self-Efficacy, and Technology Acceptance Model on Digital Fraud Detection with Audit Digitalization as A Moderating Variable

In: Proceedings of the 3rd International Conference on Management and Business (ICOMB 2024)

Author

Listed:
  • Betri Betri

    (Universitas Muhammadiyah Palembang)

  • S. M. Gumulya

    (Universitas Muhammadiyah Palembang)

  • Najmi Najmi

    (Universitas Muhammadiyah Palembang)

  • Lana Lutviyah

    (Universitas Muhammadiyah Palembang)

Abstract

This research examines the influence of professional training, computer self-efficacy, and technology acceptance model on digital fraud detection with audit digitalization as a moderation variable. Sampling was done using saturated sampling to select the respondents. The respondents in this research were 79 internal Auditors at a conventional state-owned bank in Sumatra Selatan—method analysis using regression analysis. The F-test result shows that professional training, computer self-efficacy, and technology acceptance models significantly affect digital fraud detection. T-test results show that professional training does not substantially affect digital fraud detection. In contrast, computer self-efficacy and technology acceptance models have a significant effect on digital fraud detection partially. MRA test results, audit digitalization, is a predictor moderation for the influence of professional training, computer self-efficacy, and technology acceptance model on digital fraud detection.

Suggested Citation

  • Betri Betri & S. M. Gumulya & Najmi Najmi & Lana Lutviyah, 2025. "The Effect of Professional Training, Computer Self-Efficacy, and Technology Acceptance Model on Digital Fraud Detection with Audit Digitalization as A Moderating Variable," Advances in Economics, Business and Management Research, in: Alfiatul Maulida & Md. Mahmudul Alam & ⁠⁠Mark Gabriel Wagan Aguilar (ed.), Proceedings of the 3rd International Conference on Management and Business (ICOMB 2024), pages 155-163, Springer.
  • Handle: RePEc:spr:advbcp:978-94-6463-698-7_17
    DOI: 10.2991/978-94-6463-698-7_17
    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:978-94-6463-698-7_17. 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.