Defining the Determinants of AI-Enhanced Modern Audit Capabilities and Work Performance in an Emerging Market

Abstract

This study examines the determinants of AI-enhanced modern audit capabilities and their effects on audit quality and work performance in an emerging market context, using evidence from certified public accountants in Thailand. AI-enhanced modern auditing is conceptualized as intelligent planning, AI-assisted procedures, and adaptive audit responses. Comprehensive audit expertise reflects technical proficiency, professional experience, and rigorous risk assessment, while audit quality management captures adherence to professional standards and ethical discipline. Grounded in Dynamic Capability Theory and the Resource-Based View, audit quality is specified as a mediating mechanism linking these capability dimensions to work performance. Survey data from 208 certified public accountants were analyzed using covariance-based structural equation modeling. The findings indicate that AI-enhanced modern auditing, audit expertise, and audit quality management have statistically significant positive effects on audit quality and work performance. Audit quality also positively contributes to work performance and partially mediates the relationships among the capability constructs. However, the results exhibit limited explanatory power, suggesting that these capabilities function as necessary but modest drivers of audit outcomes. The study integrates technological, human capital, and governance-based perspectives and provides emerging market evidence on the constrained yet meaningful role of AI-enabled audit capabilities in supporting audit quality and auditor performance.