Effect of AI Chatbot’s Interactivity on Consumers’ Negative Word-of-Mouth Intention: Mediating Role of Perceived Empathy and Anger

Abstract

Although much research has examined chatbot interactivity as one of the most human-like qualities, replicating a conversation norm in human-to-human dialogue, far less has examined the "native state" of two-way communication between humans and chatbots. This study fills the gap by substantiating a synchronous two-way conversation between users and chatbots and testing how chatbot interactivity shapes the users’ perceived empathy. Guided by the computers-are-social-actors (CASA) paradigm, like skilled customer service representatives interacting with customers during product failures, this study designs a between-subject experiment (N = 300) to embody the warm and competent types of human conversation and suggests two versions of chatbot interactivities: social interactivity (high/low) and message interactivity (high/low). We further investigate whether a chatbot’s warm or competent persona manifested in its interaction with consumers elicits empathic responses from consumers, such as calming down their anger in line with social response theory. As a result, both social and message interactivity in chatbots effectively elicit users’ perception of empathy, alleviating customer anger and reducing adverse word-of-mouth (WOM) intentions. The findings provide empirical evidence that supports the explanatory framework of social response theory. This research demonstrates how technological features, particularly interactivity, contribute to creating experiential value for customers. Specifically, it elucidates how customers perceive empathy displayed by chatbots, which subsequently influences their level of anger and inclination to engage in negative word-of-mouth communication. Notably, these results offer novel insights into the roles of chatbot interactivity and empathy as psychological mechanisms that enable consumers to express their dissatisfaction by interacting with chatbot service agents, particularly in situations involving product failures. The implications of this study extend to the practical realm, providing valuable guidance on the design and implementation of empathetic chatbots in frontline consumer service contexts.

Keywords:

• Chatbot
• interactivity
• empathy
• negative word-of-mouth (WOM)
• social response theory
• the computers-are-social-actors (CASA) paradigm

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Myungok Chris Yim

Myungok Chris Yim, is an associate professor of strategic communication at Loyola University Chicago. Her research interests include ethical AI usage in Public Relations and crisis communication. Her works appear in Public Relations Review, International Journal of Strategic Communication, Behaviour & Information Technology, and others.