Talk to Me Like a Human: How AI Chatbot Message Tone, Recommendation Type, and Personalization Drive Consumers' Continuance Intention
Year: 2026
Type: article
Abstract: ABSTRACT This study investigates how artificial intelligence (AI) chatbot message tone, fashion product recommendation type, and personalization level interact to shape consumers' continuance intention in online fashion shopping. Building on Construal Level Theory and the Stereotype Content Model, we treat warmth and competence cues as psychological distance signals and define construal congruence as their fit with the abstraction level of recommendation content. Two experiments provide converging evidence. In Study 1 ( n = 225), competent (vs. warm) tones are more effective when paired with benefit‐focused (vs. attribute‐focused) recommendations. Study 2 ( n = 357) shows that personalization conditions these effects through perceived task‐solving competence and psychological reactance. Together, the results suggest that continuance intention improves when message tone, recommendation abstraction, and personalization convey a consistent level of psychological distance in fashion e‐commerce.
Source: Journal of Consumer Behaviour
DOI: 10.1002/cb.70213
Authors: Jung Mee Kim, Seeun Kim
Institution: Sungkyunkwan University
Language: English
Topic: AI in Service Interactions
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,350
OpenAlex ID: W7170038488