Rethinking Conversation Styles of Chatbots from the Customer Perspective: Relationships between Conversation Styles of Chatbots, Chatbot Acceptance, and Perceived Tie Strength and Perceived Risk

Abstract

Grounded in the Stereotype Content Model, Risk Perception Theory, Technology Acceptance Model, and Relational Embeddedness Theory, this research delves into the relationship between chatbot conversation styles, customer risk, and the mediating role of chatbot acceptance and tie strength in online shopping. A 2 (warm vs. cold) * 2 (competent vs. incompetent) between-subjects experiment is conducted on 320 participants and the results obtained from two-way ANOVA and PROCESS macro revealed that: (a) customer-perceived risk decreases with conversation warmth rather than conversation competence; (b) customer acceptance of chatbots improves with conversation competence rather than conversation warmth, while not acting as an intermediary factor between the conversation styles and customer-perceived risk; (c) customer perceived tie strength increases with both conversation warmth and conversation competence. The findings contribute to the existing literature about the impact of chatbot anthropomorphism on customer cognitive processes and offer executives insights into the design of customer-friendly chatbots.

Keywords:

• Customer-chatbot interaction
• chatbot anthropomorphism
• customer risk perception
• chatbot acceptance
• perceived tie strength

Acknowledgments

None.

Disclosure statement

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

Additional information

Funding

None.

Notes on contributors

Ruilin Xiao

Ruilin Xiao is a research master's student at the University of Groningen. Her research is about discovering changes in consumers’ perception, psychology, and behavior triggered by human–computer interactions on new technologies (i.e., service robots) in marketing fields.

Mert Yazan

Mert Yazan is a PhD student at the Amsterdam University of Applied Sciences. His research focuses on adapting conversational agents to different sectors by taking the consumer profile and technical requirements of each domain into account while being as resource-efficient as possible.

Frederik Bungaran Ishak Situmeang

Frederik Bungaran Ishak Situmeang is an associate professor at the Amsterdam University of Applied Sciences with expertise in marketing, innovation, and data analytics. He has over 10 years of experience in academia and his main focuses are AI-driven marketing and consumer psychology of conversational agents.