Modeling Chatgpt Continuance Intention: The Role of Expectancy, Satisfaction, and Trust
DOI:
https://doi.org/10.55606/jurimbik.v5i3.1164Keywords:
Continuance Intention, Effort Expectancy, Performance Expectancy, Satisfaction, TrustAbstract
The growth of digital technology has increased the use of online services, with AI tools like ChatGPT becoming widely used. This study examines the impact of user perceptions on their intention to continue using ChatGPT, emphasising performance expectancy, effort expectancy, satisfaction, and trust as moderating variables. Data were gathered from 200 ChatGPT users using an organised survey and analysed via Partial Least Squares, Structural Equation Modelling (PLS, SEM). Findings indicate that performance expectancy as well as effort expectancy significantly enhance satisfaction and directly affect continuance intention. Satisfaction serves as a mediating factor between the anticipation variables and the intention to continue use. Nonetheless, trust does not substantially influence the correlation between performance or effort expectancy and satisfaction. The findings indicate that users' perception of ChatGPT as beneficial and user-friendly enhances their pleasure, hence reinforcing their intention to continue utilising it. This emphasises the significance of utility and user-friendliness in fostering sustained engagement with AI services.
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