Gender Bias in Virtual Doctor Interactions: Gender Matching Effects of Chatbots and Users on Communication Satisfactions and Future Intentions to Use the Chatbot
Eunjoo Jin, Matthew S. Eastin
Abstract
A healthcare provider’s gender not only influences patients’ health-related behaviors but also the reliability of health information. Grounded in the Computers Are Social Actors (CASA) theory and prior health literature, the current study examines how healthcare chatbots’ gender cues and user gender affect intentions to use the chatbot and chatbot expertise perceptions. Using a 3 (Chatbot Gender Cues: Chatbot vs. Male Doctor vs. Female Doctor) X 2 (User Gender: Male vs. Female) between-subjects experiment, this study indicates that the female-doctor design cues led to significantly higher perceived warmth and communication satisfaction, which subsequently increased social presence and future intentions to use the chatbot. Results also indicated a significant gender congruence effect between female users and the female-doctor design cue chatbot to yield greater communication satisfaction. This study, however, did not find a significant difference in perceived expertise between male versus female doctor design cues. Theoretical and practical implications are discussed.