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Machine Learning and External Auditor Perception: An Analysis for UAE External Auditors Using Technology Acceptance Model

Author

Listed:
  • Ahmad Faisal Hayek
  • Nora Azima Noordin

    (Faculty of Business, Higher Colleges of Technology, Sharjah Women’s Campus, UAE)

  • Khaled Hussainey

    (Faculty of Business and Law, University of Portsmouth, UK)

Abstract

Research Question - Do external auditors in the United Arab Emirates (UAE) perceive the ease of use and usefulness of Machine Learning (ML)? Motivation - This study aims to investigate external auditors' perceptions of the ease of use and usefulness of Machine Learning in auditing in the UAE. In addition, the study intends to examine the difference in perceived ease of use of Machine Learning between local and international audit companies in the UAE. Data - Data for this study were gathered from 63 external auditors working for local and global audit firms in the UAE. The study's population comprises external auditors from national and international audit companies in UAE. Tool - The questionnaire was deployed through an online survey tool. Findings - The results have shown that the findings do not support the idea that there is a different perception of the Perceived Ease of Use of Machine Learning in auditing between local and international audit firms. According to the conclusions of this study, external auditors have a restricted perception of the simplicity of use and utility of Machine Learning. Practical implications - The importance of the findings of such research stems from the lack of research evidence on the perceived ease of use and usefulness of Machine Learning in external auditing in the UAE. As a result, this paper provides new empirical evidence by assessing external auditors' assessments of the usage of Machine Learning in the UAE.

Suggested Citation

  • Ahmad Faisal Hayek & Nora Azima Noordin & Khaled Hussainey, 2022. "Machine Learning and External Auditor Perception: An Analysis for UAE External Auditors Using Technology Acceptance Model," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(4), pages 475-500, December.
    • Handle: RePEc:ami:journl:v:21:y:2022:i:4:p:475-500
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    References listed on IDEAS

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    1. Marco Schreyer & Timur Sattarov & Christian Schulze & Bernd Reimer & Damian Borth, 2019. "Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks," Papers 1908.00734, arXiv.org.
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    4. repec:eme:maj000:maj-01-2018-1773 is not listed on IDEAS
    5. Chiu, Victoria & Liu, Qi & Vasarhelyi, Miklos A., 2014. "The development and intellectual structure of continuous auditing research," Journal of Accounting Literature, Elsevier, vol. 33(1), pages 37-57.
    6. Nora Azima Noordin & Khaled Hussainey & Ahmad Faisal Hayek, 2022. "The Use of Artificial Intelligence and Audit Quality: An Analysis from the Perspectives of External Auditors in the UAE," JRFM, MDPI, vol. 15(8), pages 1-14, July.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Machine Learning; Auditing; External auditors; Ease of use; Usefulness; TAM;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing
    • M48 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Government Policy and Regulation

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