Would an AI chatbot persuade you

An Empirical Answer From the Elaboration Likelihood Model

Bibliographic Data

ID7155708
AuthorsQian Chen (0000-0002-5249-7421, Huazhong Agricultural University), Changqin Yin (0000-0001-8017-5705, Wuhan Polytechnic University), Yeming Gong (0000-0001-9270-5507, École de management de Lyon)
Year2025
Volume38
Issue2
Pages937-962
Publication date2025-03-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueInformation Technology and People (JOURNAL)
Journal identifiersISSN: 0959-3845 • E-ISSN: 1758-5813
PublisherEmerald (PUBLISHER)
DOI10.1108/itp-10-2021-0764
OpenAlexW4389419985
LanguageEN
Citations received13
References cited52

Purpose This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context. Design/methodology/approach Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users. Findings The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively. Originality/value This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation. Research highlights The study investigates customers' adoption of AI chatbots' recommendation. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths. The central and peripheral cues are generalized according to cooperative principle theory. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice. Central and peripheral cues affect customers' adoption to recommendation through trust in AI. Customers' mind perception positively moderates the central and peripheral paths

Chatbot · Elaboration likelihood model · Knowledge management · Persuasion · World Wide Web · AI in Service Interactions · Computer Science · Death Anxiety and Social Exclusion · Psychology · Psychology of Moral and Emotional Judgment · Social Psychology

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