Understanding the acceptance and resistance of Explainable AI in financial auditing and accounting
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2025-07-08
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en
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This study explores the psychological and organizational factors that influence the acceptance and resistance of Explainable Artificial Intelligence (XAI) in a financial auditing and accounting context. A mixed-methods approach combined semi-structured interviews with a quantitative survey. The survey was based on established models such as TAM, UTAUT, Status Quo Bias, Psychological Reactance and Algorithm Aversion. Most hypothesized predictors were not statistically significant after correction for multiple comparisons. However, trust in the system and prior experience with AI emerged as key factors that influence both acceptance and resistance. Interview insights helped to explain these quantitative results. They also revealed additional factors not captured in the research models that could influence user attitudes towards XAI such as privacy, legal and ethical risks, and environmental impact. These findings suggest that traditional technology acceptance frameworks may need to be extended when they are applied to newer systems like XAI. Practically, organizations should not only focus on the technical functionality of the system but also on building user trust, offering learning opportunities in practice and address broader professional concerns. This study contributes to the growing body of knowledge on human-centred AI adoption and also gives concrete suggestions for future research and organizational implementation.
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Faculteit der Managementwetenschappen