DETERMINANTS OF ARTIFICIAL INTELLIGENCE ADOPTION IN ACCOUNTANCY FOR FRAUD PREVENTION AMONG SARAWAK SMES: A TOE-UTAUT FRAMEWORK PERSPECTIVE
TL;DR
The study proposes that technological factors, organizational factors, environmental support, and individual-level perceptions influence AI adoption intention, and incorporates trust and firm size as moderating variables to account for behavioral uncertainty and resource heterogeneity among SMEs.
Abstract
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.