DETERMINANTS OF ARTIFICIAL INTELLIGENCE ADOPTION IN ACCOUNTANCY FOR FRAUD PREVENTION AMONG SARAWAK SMES: A TOE-UTAUT FRAMEWORK PERSPECTIVE
Artificial Intelligence (AI) is increasingly positioned as a strategic technology for transforming accounting practice through automation, predictive analytics, continuous monitoring, and fraud detection (Rikhardsson et al., 2022; Hasan, 2022; Shiyyab et al., 2023). Although AI-enabled accounting systems may strengthen financial transparency and operational efficiency, adoption remains uneven among small and medium-sized enterprises (SMEs), particularly in regions with limited infrastructure, resource constraints, and lower digital maturity (Schönberger, 2023; Lutfi, Al-Debei, & Alshira’h, 2022; SME Corporation Malaysia, 2023). Within Sarawak, SMEs operate in a distinctive socio-technical environment characterized by geographical dispersion, uneven access to digital infrastructure, and limited exposure to advanced accounting technologies (Sarawak Digital Economy Corporation, 2021; Kamaruddin, Jamaludin, & Azmi, 2024). Accordingly, this manuscript develops a context-sensitive framework to examine the determinants of AI adoption in accountancy for fraud prevention among Sarawak SMEs. Drawing on the TechnologyOrganization-Environment (TOE) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study proposes that technological factors, organizational factors, environmental support, and individual-level perceptions influence AI adoption intention (Tornatzky & Fleischer, 1990; Venkatesh, Morris, Davis, & Davis, 2003). The model further incorporates trust and firm size as moderating variables to account for behavioral uncertainty and resource heterogeneity among SMEs (Badghish & Soomro, 2024; Tang, Lily, & Chew, 2024). Methodologically, the study is designed as a quantitative survey using structured questionnaires and Partial Least Squares Structural Equation Modelling (PLS-SEM), supported by SPSS for preliminary data screening and SmartPLS for measurement and structural model assessment (Hair et al., 2021; Sarstedt, Ringle, & Hair, 2022). The manuscript contributes to the technology adoption and accounting information systems literature by extending TOE-UTAUT integration to a digitally underserved regional context and by positioning AI adoption as a mechanism for strengthening fraud prevention in SME accounting practices.