Emotional expressions of care and concern by customer service chatbots: Improved customer attitudes despite perceived inauthenticity

In customer service, emotional expressions by chatbots are considered a promising direction to improve customer experience. However, there is a lack of comprehensive understanding of how and when chatbots' emotional expressions improve customer attitudes. Although chatbots' emotional expressions of care and concern may feel inauthentic to customers in the inferential path, which can negatively affects customer attitudes, we propose that the positive effect of the affective reactions path can result in a positive effect on customer attitude based on the dual-path view of Emotions as Social Information (EASI). The relative strengths of the two EASI paths can be moderated, and we explored the moderating effects of rational thinking styles (information processing in EASI) and beliefs in computer emotion (perceived appropriateness in EASI). According to EASI, situation can affect the meaning of emotions, so we conducted experiments in two situations. With chatbot identity disclosure, we found that the chatbot's emotional expressions reduce customers' perceived authenticity (reflecting the inferential path in EASI) but ultimately improve customer attitudes. Belief in computer emotions and rational thinking style moderated the negative relationship between emotional expressions and authenticity. With chatbot identity non-disclosure, the chatbot's emotional expressions still improve customer attitudes but with no effect on authenticity. Because there is high likelihood of chatbot identities being discovered by customers, this finding of the moderating effect of perceived humanness on authenticity is highly relevant. Our findings make important contributions to research on computer emotion and service authenticity.