Emotion-regulatory chatbots for enhancing consumer servicing: An interpersonal emotion management approach

Abstract

To reduce service staff's burden in customer servicing and improve the performance of automatic service recovery, we propose an emotion-regulatory chatbot for service recovery applications drawing on interpersonal emotion management (IEM) theory. In addition, we develop a model to examine the underlying mechanisms through which perceived IEM strategies influence consumers’ emotions and behavioral intentions. Our experimental results verify the effectiveness of IEM strategies. We find that appraisals and consumers’ post-recovery emotions sequentially mediate the relationship between perceived emotion regulatory strategies and positive word-of-mouth (PWOM).