AI-Enabled Fraud Detection Readiness in Sarawak SMEs: A Quantitative
TOE-UTAUT Model for Accounting Transformation

Asri Firdaus De Rozario; Dr. Rahmat Aidil Djubair

doi:10.46609/IJSSER.2026.v11i07.011

Title: AI-Enabled Fraud Detection Readiness in Sarawak SMEs: A Quantitative TOE-UTAUT Model for Accounting Transformation
Authors: Asri Firdaus De Rozario and Dr. Rahmat Aidil Djubair
Journal: International Journal of Social Science and Economic Research (IJSSER) ISSN: 2455-8834 Volume: 11 Issue: 7 Publication Date: July 2026 DOI: 10.46609/IJSSER.2026.v11i07.011
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Asri Firdaus De Rozario1 and Dr. Rahmat Aidil Djubair2 1. Student of School of Business and Management, University of Technology Sarawak, No. 1, Jalan Universiti, 96000 Sibu 2. School of Business and Management, University of Technology Sarawak, No. 1, Jalan Universiti, 96000 Sibu
MLA 8 Rozario, Asri Firdaus De, and Dr. Rahmat Aidil Djubair. "AI-Enabled Fraud Detection Readiness in Sarawak SMEs: A Quantitative TOE-UTAUT Model for Accounting Transformation." Int. j. of Social Science and Economic Research, vol. 11, no. 7, July 2026, pp. 3019-3033, doi.org/10.46609/IJSSER.2026.v11i07.011. Accessed July 2026. APA 6 Rozario, A., & Djubair, D. (2026, July). AI-Enabled Fraud Detection Readiness in Sarawak SMEs: A Quantitative TOE-UTAUT Model for Accounting Transformation. Int. j. of Social Science and Economic Research, 11(7), 3019-3033. Retrieved from https://doi.org/10.46609/IJSSER.2026.v11i07.011 Chicago Rozario, Asri Firdaus De, and Dr. Rahmat Aidil Djubair. "AI-Enabled Fraud Detection Readiness in Sarawak SMEs: A Quantitative TOE-UTAUT Model for Accounting Transformation." Int. j. of Social Science and Economic Research 11, no. 7 (July 2026), 3019-3033. Accessed July, 2026. https://doi.org/10.46609/IJSSER.2026.v11i07.011.
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ABSTRACT: This manuscript develops a quantitative research model for examining artificial intelligence (AI)-enabled fraud detection readiness among small and medium-sized enterprises (SMEs) in Sarawak, Malaysia. Rather than treating AI adoption solely as a general technology acceptance issue, the paper positions adoption readiness as a multi-layered condition shaped by technological fit, organizational mobilisation, institutional facilitation, and user-level acceptance. Drawing on the Technology-Organization-Environment (TOE) framework and the Unified Theory of Acceptance and Use of Technology (UTAUT), the proposed model explains how relative advantage, compatibility, cost, security, task-technology fit, business innovativeness, top management support, government support, effort expectancy, and social influence may affect SMEs' intention to adopt AI in accounting. Trust and firm size are incorporated as boundary conditions to capture behavioral uncertainty and resource asymmetry in regional SME settings. The manuscript further specifies a survey-based methodological protocol involving purposive sampling, a seven-point Likert-scale questionnaire, pilot testing, SPSS-based data screening, and Partial Least Squares Structural Equation Modelling (PLSSEM) using SmartPLS. By emphasizing readiness, measurement logic, and empirical testability, the paper offers a methodological contribution to AI accounting research and provides a replicable model for studying digitally underserved regional economies.

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