Predicting User Satisfaction with Artificial Intelligence: A Study of ChatGPT

Abstract

This study investigates the factors influencing users’ behavioral intention to use ChatGPT by applying the Unified Theory of Acceptance and Use of Technology (UTAUT), and further examines the extent to which behavioral intention contributes to user satisfaction with ChatGPT. The analysis of survey data revealed that performance expectancy, effort expectancy, social influence, and facilitating conditions were all positively correlated with behavioral intention to use ChatGPT. Regression analysis identified performance expectancy, social influence, and facilitating conditions as statistically significant predictors, highlighting their role in shaping users’ willingness to adopt the platform. Although effort expectancy showed a positive correlation, it did not emerge as a significant predictor in the regression model. Furthermore, behavioral intention was found to have a strong and significant effect on user satisfaction with ChatGPT, indicating its central role in shaping users’ evaluative responses to generative AI tools. These findings offer empirical support for the applicability of UTAUT in the context of ChatGPT and suggest practical implications for improving user adoption and satisfaction through design, support infrastructure, and social engagement strategies.

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