Factors Affecting the Adoption of Artificial Intelligence in Auditing in Vietnam

Abstract

The advent of artificial intelligence (AI) has presented more opportunities for audit firms to improve their productivity and audit quality. Currently, all big 4 audit firms have implemented their AI-powered audit systems, however the acceptance and use of AI technologies in auditing are still limited, especially in Vietnam. Furthermore, few studies have explored the predictors of AI adoption in auditing in Vietnam. To address this research gap, this study proposes a conceptual research model developed by incorporating individual-level predictors (derived from the UTAUT model and status quo bias theory) and firm-level predictors (derived from the TOE framework) of technology acceptance and use. The factors affecting auditors’ intention to adopt AI include technological compatibility, training, competitive pressure, performance expectancy, effort expectancy, switching costs, colleague opinion, and facilitating conditions.

An online survey was conducted at audit firms based in Vietnam, including Big 4, non-Big, and local firms. 150 valid responses were collected and used to test all hypotheses. Results of the PLS-SEM analysis show that competitive pressure, performance expectancy, effort expectancy, colleague opinion, and facilitating conditions positively influence auditors’ AI adoption intention. On the other hand, switching costs are found to discourage AI adoption intention. It is also confirmed that technological compatibility and training both have positive effects on performance expectancy and effort expectancy. The data also supports the indirect positive effects of technological compatibility and training on AI adoption intention, which are mediated by performance expectancy and effort expectancy.