Effect of AI Language Factors on Consumer Acceptance of Product Recommendations

Workshop’s Topic: With advancements in artificial intelligence (AI) technologies, AI conversation agents (i.e., chatbots) are increasingly prevalent in marketing contexts such as product recommendations and customer services. In efforts to make AI agents simulate human conversations as much as possible, companies and organizations have widely used language factors such as conversational fillers in their chatbots’ customer interactions. This research proposes that, in the product recommendation context, persuasion knowledge can come into work when consumers encounter certain language factors such as conversational fillers from AI agents, thereby decreasing their acceptance of product recommendations. The authors conducted field experiment and five experiments, to provide empirical support for the hypotheses.

Time and Location: 10:30-12:00 AM (GMT+8), Room A523 (School of Management)

Language: Bilingual (Chinese and English)

Introduction of Speakers

LIU Wenjing

Tsinghua University, School of Economics and Management

LIU Wenjing is an Associate Professor (with tenure) and Doctoral student advisor in the Marketing Department, the School of Economics and Management (SEM), Tsinghua University. She serves as Associate Director of Computational and Behavioral Science Lab of Tsinghua SEM, Associate Director of China Retail Research Center of Tsinghua SEM, and Chair of Academic Seminar Committee of Tsinghua Marketing Department. Her research interests include consumer behaviors, product and service experience, decision science, and pricing. She has published multiple papers in leading academic journals such as Production and Operations Management (UTD), International Journal of Research in Marketing, INFORMS Journal on Computing (UTD), Journal of Consumer Psychology (FT50), Journal of International Marketing, Journal of Business Research, Marketing Letters, Journal of Service Theory and Practice, and Journal of Economic Psychology.