THE EFFECTS OF PERCEIVED USER EXPERIENCE WITH GENERATIVE ARTIFICIAL INTELLIGENCE APPLICATIONS ON SATISFACTION AND CONTINUANCE INTENTION

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This study examines the effects of perceived user experience—conceptualized as a multidimensional construct in the context of generative artificial intelligence (AI) applications—on user satisfaction and continuance intention. Grounded in Expectation–Confirmation Theory and the Technology Acceptance Model, and informed by prior empirical and conceptual research, the study proposes a comprehensive conceptual framework in which user experience positively influences user satisfaction, and satisfaction plays a decisive role in shaping users’ intention to continue using generative AI technologies. In addition, user satisfaction is hypothesized to function as a mediating mechanism in the relationship between user experience and continuance intention. The research sample consists of individuals with prior experience using generative AI applications. Data were collected through an online survey administered to 625 participants. Respondents without generative AI usage experience and outliers identified through statistical screening were excluded from the dataset, resulting in 548 valid observations for analysis. Data analysis was conducted using IBM SPSS 20 and SmartPLS 4. The validity and reliability of the measurement model were assessed through confirmatory factor analysis, while the proposed hypotheses were tested using partial least squares structural equation modeling (PLS-SEM). The findings indicate that a favorable generative AI user experience significantly and positively enhances user satisfaction, which, in turn, exerts a strong and positive effect on continuance intention. Furthermore, the effect of user experience on continuance intention is found to occur indirectly through user satisfaction. These results highlight the critical role of user experience and satisfaction in fostering the long-term adoption and sustainable use of generative AI applications. The study offers important theoretical and practical contributions to the user-centered design and sustainable development of generative AI technologies.