Understanding Continuance Intention in Generative AI Image Platform: The Sequential Mediating Roles of AI Self-Efficacy and Consumer Satisfaction—Evidence from Midjourney

Abstract

This study examines the determinants of continuance intention among Midjourney users within the information systems continuance research tradition, viewed through a human–AI interaction lens. Drawing on Expectation-Confirmation Theory and Self-Efficacy Theory, we develop a sequential dual mediation model in which AI self-efficacy and consumer satisfaction jointly mediate the effects of platform service characteristics (creativity, accuracy, reliability, accessibility, and personalization) and individual characteristics (personal innovativeness, AI privacy concerns, and AI digital literacy) on continuance intention. Survey data from 400 adult users were analyzed using structural equation modeling with bootstrapped confidence intervals. With the exception of AI privacy concerns—whose influence on continuance intention operated only through the sequential pathway via AI self-efficacy and consumer satisfaction, while its single mediation path through satisfaction alone was not significant—all antecedents exerted significant indirect effects on continuance intention. Reliability exerted the strongest effect on AI self-efficacy, whereas accessibility and personalization exerted the strongest effects on consumer satisfaction. AI self-efficacy functions as a cognitive antecedent of satisfaction rather than a direct predictor of continuance intention. These findings contribute to information systems continuance research by empirically validating a sequential cognitive–affective mechanism underlying sustained human–AI engagement in generative AI environments.