FACTORS AND BARRIERS INFLUENCING THE INTENTION TO ADOPT ARTIFICIAL INTELLIGENCE (AI) AMONG ACCOUNTING PERSONNEL IN MALAYSIA
DOI:
https://doi.org/10.35631/AIJBES.829008Keywords:
Artificial Intelligence, Accounting Personnel, Technology Theory of Acceptance Model, Innovation Resistance Theory, PLS-SEMAbstract
Artificial Intelligence (AI) has been increasingly transforming accounting practices. Cloud accounting, predictive analytics, and intelligent auditing systems have been adopted by accounting industries to enhance the quality of financial reporting. Despite technological advancements and government efforts to accelerate the use of AI, AI adoption among Malaysian accounting personnel remains modest. Thus, this study aims to understand the factors that drive intention to adopt AI among accounting personnel in Malaysia. The study integrates the Technology Acceptance Model (TAM) and Innovation Resistance Theory (IRT) to investigate the effects of perceived usefulness, perceived ease of use, health crisis, attitude, value barrier, risk barrier, tradition barrier, and image barrier on the intention to adopt AI. The study adopted quantitative research design through a cross-sectional survey. Data were gathered from a total of 192 accounting personnel in Malaysia using purposive sampling technique Data analysis was performed using partial least squares structural equation modelling (PLS-SEM). The findings showed that perceived usefulness significantly influenced attitude and intention to adopt AI, while perceived ease of use significantly influenced attitude. Health crisis positively affected attitude toward AI adoption but insignificantly influenced intention to adopt AI. Attitude was found to significantly affect intention to adopt AI. However, value barrier, risk barrier, tradition barrier, and image barrier did not significantly influence intention to adopt AI. The findings suggest that accounting personnel in Malaysia are willing to accept AI adoption despite concerns regarding technological barriers. This study contributes to literature by developing a conceptual framework that integrates TAM and IRT in the context of AI adoption among accounting personnel. Furthermore, the findings will be useful to policymakers, professional bodies, and accounting firms in formulating strategies to increase AI adoption.
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