Understanding Adoption and Continuance Intention Toward AI-Based Accounting Systems Among Accounting Students and Professionals: An Extended TAM Approach

Abstract

The aim of this study is to investigate the adoption and continuance intention of accounting systems based on AI from the extended Technology Acceptance Model (TAM) by measuring the factors that determine attitude toward use, behavioral intention and continuance intention, namely Perceived Ease of Use, Perceived Usefulness, Perceived Risk, and Trust. It should be noted that a majority of the sample are accounting students (62.2%) and that 62.4% of the sample had no work experience (this would be most relevant to educational and longer term workforce readiness contexts). All hypotheses were confirmed by PLS-SEM and Perceived Usefulness was found as the most consistent predictor for all the outcome variables, and Trust was the strongest predictor for Continuance Intention. The MGA results indicate that the structural relationships were not statistically different between student and professional respondents, although the findings should be interpreted primarily within educational and workforce readiness contexts.