Artificial Intelligence Adoption and Audit Quality: A Mediating Model of Performance Expectancy in East Java's Public Accounting Firms

Abstract

This study aim to investigates the impact of Artificial Intelligence (AI) adoption on audit quality, specifically examining the mediating role of performance expectancy within Public Accounting Firms in East Java. Utilizing a quantitative approach, data from 140 auditors were analyzed using Structural Equation Modeling-Partial Least Squares (SEM-PLS). Results indicate that AI adoption positively affects both audit quality and performance expectancy. Furthermore, performance expectancy significantly impacts audit quality and mediates the relationship between AI adoption and audit quality. This research highlights the critical role of auditors' performance expectations in realizing AI's benefits, offering insights for fostering effective AI integration to enhance audit practices. This research offers empirical evidence from the underrepresented Indonesian context, providing a nuanced understanding of how performance expectancy mediates the AI-audit quality link. It contributes valuable insights for strategic AI implementation in public accounting firms and lays groundwork for future behavioral studies in auditing.