Systematic Literature Review (SLR) on Accountants’ Behavioural Resistance Toward Machine Learning (ML) Adoption in Financial Auditing

As machine learning (ML) technologies increasingly permeate financial auditing practices, understanding the behavioural responses of accountants has become critical. This systematic literature review (SLR) investigates the psychological, professional, and organisational factors contributing to accountants’ resistance to adopting ML tools in auditing. Drawing on 44 peer-reviewed studies published between 2015 and 2025, the review synthesises findings from fields including accounting, organisational behaviour, and information systems. The review utilised databases such as ACM Digital Library, IEEE Explore, and Google Scholar to gather peer-reviewed articles and industry reports. The study also explores the moderating roles of leadership support, training initiatives, and change management strategies. Theoretical frameworks such as the Theory of Planned Behaviour and Resistance to Change theory are used to contextualise the findings. The study concludes by proposing a conceptual model to guide future empirical research and practical interventions aimed at reducing behavioural resistance and facilitating effective ML integration in auditing environments.