Recognizing the growing potential of AI-generated chatbots in healthcare and healthcare marketing, this study investigates how different types of chatbot-expressed empathy influence user responses in stigmatized versus non-stigmatized health contexts. Perceived authenticity is introduced as a key mediator. Drawing on Mind Perception Theory, a 3 (empathy: none, cognitive, affective) × 2 (illness type: stigmatized, non-stigmatized) online experiment was conducted. The findings show that affective empathy reduced perceived authenticity, which subsequently decreased user satisfaction, perceived understanding, reuse intention, and follow intention. These indirect effects varied by illness type: in stigmatized contexts, non-empathetic communication was viewed most favorably, whereas in non-stigmatized contexts, cognitive empathy was more effective. The results suggest that chatbot-delivered empathy is not uniformly beneficial and must be carefully aligned with user expectations and psychological needs. This research provides both theoretical insight and practical recommendations for designing authentic, context-sensitive chatbot communication in healthcare and healthcare marketing settings.
Kim et al. (Tue,) studied this question.