Machine learning and external auditor perception: an analysis for UAE external auditors using technology acceptance model

Ahmad Faisal Hayek, Nora Azima Noordin, Khaled Hussainey

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
JournalJournal of Accounting and Management Information Systems
Publication statusAccepted for publication - 16 Nov 2022

Keywords

  • machine learning
  • auditing
  • external auditors
  • ease of use
  • usefulness
  • TAM

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