The relevance of task technology fit and affective responses in predicting chatbot user’s sustained use: Moderating role of cognitive innovativeness

Business enterprises increasingly leverage artificial intelligence tools, particularly chatbots, to improve client relations and drive sustainable growth. While empirical research on post-adoption chatbot user behavior is in its nascent stage, the current study focuses on how Task-Technology Fit (TTF), Affective Response Model (ARM), and cognitive innovativeness drive post-adoption behavior, specifically continuance intention, extending beyond the confines of Expectation-Confirmation Model (ECM). Notably, previous studies had not collectively tested these predictors, marking the uniqueness of this study. Data from 401 travel chatbot users were empirically validated through structural equation modeling (SEM) and artificial neural network (ANN) analyses. Task-technology fit emerged as the potent predictor of satisfaction and sustained use. Contrary to expectations, anthropomorphism exhibited a detrimental effect on continuance intention. Additionally, this research offers intriguing perspectives into the catalytic role of users’ cognitive innovativeness, identifying two significant and non-significant moderations. The study offers implications for academia, travel management, and chatbot developers.