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    • Understanding Users' Switching Intention to AI-Powered Healthcare Chatbots.

    Yuanyuan Lai,Eleni Lioliou,Panos Panagiotopoulos

    ECIS（2021）

    Cited 0|Views0
    Abstract
    Chatbots are promising artificial intelligence applications with the potential to transform the healthcare industry. Research has gradually explored how users perceive the proposition of healthcare chatbots as an alternative route to medical advice. The paper presents a model of users’ switching intentions to healthcare chatbots by integrating the Push-Pull-Mooring (PPM) framework with the status quo bias theory. The model was validated empirically with 345 Chinese users who had previous experience with online healthcare services. Findings reveal that the perceived responsiveness of chatbots has a positive effect on switching intentions while AI resistance bias has a negative effect. In turn, AI resistance bias can be explained by expectations of chatbot performance, relationship with doctors, and can be mitigated by favorable chatbot use experience in other contexts. The study contributes to our knowledge of the emerging impacts of AI technologies in healthcare and extends previous IS research drawing on status quo bias theory and the PPM framework. The study can further assist developers of healthcare chatbots to understand users’ concerns and switching barriers.
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