When Trust Sharpens Skepticism: Large Language Models in Indonesian Audit Practice

Abstract

This study examines the effects of perceived transparency, explainability, and social influence on auditors' trust in Large Language Models, and the effect of that trust on their professional skepticism at Indonesian public accounting firms. The gap between global acceptance and trust levels toward artificial intelligence systems suggests that technology adoption is not always matched by adequate evaluation, a condition relevant to auditors, who must remain critical toward Large Language Models given their tendency to produce inaccurate answers. This study used a quantitative approach with Partial Least Squares Structural Equation Modeling, involving 102 auditors selected through purposive sampling. Results show that all three antecedent variables positively and significantly affect auditors' trust in Large Language Models, with perceived transparency contributing most, while trust in Large Language Models also positively and significantly affects professional skepticism, a direction opposite to the reliance pattern reported in prior audit automation literature. These findings suggest that trust in artificial intelligence based technology can form in a calibrated manner, coexisting with auditors' awareness of system limitations rather than diminishing their professional skepticism.