AI Adoption and accounting efficiency in Indonesia and Malaysia - Rima Sundari; Muhammad Rizal Satria; Mubassiran Mubassiran (Institute of Accounting Research and Novation (IARN), 2026)
This study extends the artificial intelligence (AI) accounting literature by examining how AI adoption translates into measurable accounting efficiency rather than focusing solely on technology adoption intention. It also reconceptualizes organizational readiness as a moderating mechanism influencing post-adoption performance. A quantitative research design was employed using survey data collected from 500 accounting professionals in Indonesia and Malaysia. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), including moderation and multi-group analyses. The findings indicate that AI adoption significantly improves accounting efficiency, with employee competence emerging as the strongest predictor, followed by data quality. Organizational readiness significantly strengthens the positive relationship between AI adoption and accounting efficiency, demonstrating that infrastructure, leadership commitment, and innovation-oriented culture are essential for maximizing AI benefits. The structural model explains 37.2% of the variance in accounting efficiency (R² = 0.372), while multi-group analysis reveals no significant structural differences between Indonesia and Malaysia. By integrating the Technology Acceptance Model, Resource-Based View, and Contingency Theory into a unified framework, this study advances a capability-performance perspective of AI-enabled accounting transformation. The findings suggest that sustainable accounting digitalization requires not only AI investment but also workforce upskilling, robust data governance, and strong organizational readiness.