System transparency, artificial intelligence (AI) self-efficacy, job satisfaction and resistance in AI-enabled accounting systems

Purpose Artificial intelligence (AI)-enabled accounting information systems (AISs) are reshaping the accounting profession, requiring professionals to adapt to emerging technologies. In this context, understanding how perceptions of system transparency and accounting professionals' AI self-efficacy influence their psychological states, job satisfaction and resistance to adoption is crucial. Despite the relevance of these issues, research examining these relationships remains limited. To address this gap, this study emphasizes three core psychological conditions–meaningfulness, safety and availability–and explores their roles in linking system transparency and AI self-efficacy to job satisfaction and resistance. Design/methodology/approach This study is based on a dataset of 675 valid survey responses, which were analyzed via the partial least squares (PLS) method to evaluate the proposed research model. Findings The results show that for users of AI-enabled AISs, higher levels of system transparency significantly increase AI self-efficacy, which in turn increases their sense of psychological meaningfulness, psychological safety and psychological availability during system use. These psychological conditions subsequently lead to higher job satisfaction and lower resistance to AI-enabled AISs. Furthermore, AI self-efficacy, along with psychological meaningfulness, safety and availability, fully mediates the relationship between system transparency and job satisfaction and partially mediates the relationship between system transparency and resistance to AI-enabled AISs. Originality/value This study clarifies how perceived system transparency and AI self-efficacy influence accounting professionals' psychological conditions, which in turn affect job satisfaction and resistance to AI-enabled AISs. The findings underscore transparent design and AI self-efficacy as key drivers of psychological readiness and successful AI-enabled AIS implementation.