The Impact of Socio-Technical Determinants and Mediating Mechanisms on AI Adoption in Internal Auditing - Sunanta Supapon; Kalyaporn Pan-Ma-Rerng (Emerging Science Journal, 2026)
This study examines how socio-technical factors shape the adoption of artificial intelligence (AI) in internal auditing. A theory-driven model links organisational readiness, management support, auditors’ perceptions, and attitudes to AI adoption through direct and mediated pathways. Survey data from 340 listed firms were analysed using covariance-based structural equation modelling (CB-SEM) in AMOS, employing bias-corrected bootstrapping with 5,000 resamples to assess construct validity, model fit, and mediation effects. The results indicate that management support is the strongest driver, enhancing auditors’ perceptions and attitudes. Attitude emerges as the most potent predictor of adoption, whereas perception affects adoption only indirectly through attitude, confirming indirect-only mediation. Organisational readiness is not statistically significant, implying that infrastructure alone does not ensure adoption without leadership commitment and behavioural alignment. By integrating the Resource-Based View and the Technology Acceptance Model with institutional insights, the study advances understanding of how organisational resources, behavioural mechanisms, and institutional pressures jointly influence sustainable AI adoption in internal auditing. The findings emphasise the importance of executive sponsorship, role-specific AI literacy, and participatory system design while informing policy on competency and governance frameworks for effective AI integration.