Switching intention toward conversational AI for health information seeking: a push-pull-mooring analysis

With the rapid development of artificial intelligence, users are increasingly turning to conversational AI for health information seeking. This study applies the Push-Pull-Mooring (PPM) framework to explain users' switching intention from social media to conversational AI for comparable health-information-seeking tasks.

Survey data were collected from 2,876 Chinese respondents. Structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) were used to examine the associations of push, pull, and mooring factors with switching intention.

Health information overload and cyberchondria were positively associated with dissatisfaction with social media, which was positively associated with switching intention. Perceived personalization and perceived anonymity were positively associated with flow experience during conversational AI use, which was also positively associated with switching intention. In contrast, medical AI resistance bias and switching costs were negatively associated with switching intention. The fsQCA results further showed that high switching intention was more likely to emerge under configurations characterized by strong pull and weak mooring conditions, whereas low switching intention was typically associated with high mooring conditions.

The findings highlight the joint roles of push, pull, and mooring factors in shaping users' switching intention and demonstrate that switching intention can arise through multiple asymmetric configurations.