Based on the task-technology fit theory, this study aims to examine how AI chatbot’s intelligence disclosure interacts with service temporal phase to influence customers’ interaction intention with AI chatbot during online hospitality service.
Two scenario-based experiments of between-subjects design were conducted to empirically examine the research model, with 175 valid responses collected from online customers in Study 1 and 193 valid responses collected from online customers in Study 2.
The findings of experiment studies showed that in the attraction phase, AI chatbot’s disclosure of thinking intelligence induces more favorable interaction intention through enhancing customers’ perceived performance expectancy. While during the retention phase, the disclosure of feeling intelligence exerts stronger impact on interaction intention through facilitating human-AI social attraction. Further, perceived common ground moderates the above congruity effects.
This paper serves as one of the pioneering works to investigate how and why the match between AI chatbot’s intelligence disclosure and service temporal phase enhances customers’ interaction intention.
