Abstract

Generative artificial intelligence (AI) tools such as ChatGPT are transforming how individuals access information and accomplish tasks, raising critical questions about why users continue to use these technologies and are willing to pay for them. This study proposes an integrated framework that combines task–technology fit (TTF) with the information systems success model to examine the determinants of continuance intention and willingness to pay. Survey data from 563 users were analyzed using structural equation modeling (SEM). Findings show that task expectations, task feedback, system quality, and information quality enhance TTF, whereas service quality has no significant effect. TTF increases user satisfaction, which drives both continuance intention and willingness to pay. Continuance intention also emerges as a strong predictor of payment intention. The results provide practical insights for AI providers seeking to design tools that foster sustained user engagement and willingness to pay.

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