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

This research examines the factors influencing users' intention to adopt and continue using AI-based accounting systems, extending the Technology Acceptance Model (TAM) with Perceived Risk and Trust. Using Structural Equation Modeling (SEM) with SmartPLS, data were collected in January 2026 through a Google Forms survey targeting respondents in the Jabodetabek area who are familiar with artificial intelligence in accounting, using purposive sampling to ensure relevant participants. The study analyzes seven constructs: Perceived Ease of Use, Perceived Usefulness, Perceived Risk, Trust, Attitude Toward Use, Behavioral Intention, and Continuance Intention. The results show that all proposed hypotheses are significantly supported, with Perceived Usefulness emerging as the most dominant predictor of adoption and continuance, while Perceived Risk significantly hinders adoption intention.