Understanding users’ online health information seeking switching intention: from traditional health information platforms to GAI
Junping Qiu, Tingyong Zhang, Zhongyang Xu, Yutian Fu, Qinze Mi, Songtao Li
Abstract
Purpose Online health information-seeking behavior is undergoing a transformation worldwide. The development of generative artificial intelligence (GAI) has prompted users to switch from traditional health information platforms (THIPs) to GAI. In this study, we examine factors influencing users’ switching intentions so that THIPs can optimize services, enhance user experience and improve retention. Design/methodology/approach Using the push–pull–mooring (PPM) model, we analyzed data from 834 valid samples through a mixed-method approach combining covariance-based structural equation modeling (CB-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). Findings We found that push factors (health anxiety, usage fatigue and privacy concerns), pull factors (perceived convenience, perceived mind and information quality) and the mooring factor (social influence) significantly and positively influenced switching intention. Further, social influence was found to moderate the effects of health anxiety, usage fatigue, perceived convenience and information quality on switching intention. Three paths to switching intention were identified through fsQCA. Practical implications From the study findings, it is recommended that THIPs can optimize services, boost user experience and improve retention by enhancing information filtering, strengthening privacy protection, integrating GAI technology and leveraging social influence and reputation management. Originality/value The PPM model has been used for the first time to explore health information switching intentions in the context of GAI. The mixed-method approach combining CB-SEM and fsQCA ensures robust findings, contributing to research on health information behaviors.