{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T19:47:11Z","timestamp":1776368831497,"version":"3.51.2"},"reference-count":77,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2023,12,8]],"date-time":"2023-12-08T00:00:00Z","timestamp":1701993600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ITP"],"published-print":{"date-parts":[[2025,3,14]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research highlights<\/jats:title><jats:p><jats:list id=\"list1\" list-type=\"order\"><jats:list-item><jats:p>The study investigates customers' adoption of AI chatbots' recommendation.<\/jats:p><\/jats:list-item><jats:list-item><jats:p>The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.<\/jats:p><\/jats:list-item><jats:list-item><jats:p>The central and peripheral cues are generalized according to cooperative principle theory.<\/jats:p><\/jats:list-item><jats:list-item><jats:p>Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.<\/jats:p><\/jats:list-item><jats:list-item><jats:p>Central and peripheral cues affect customers' adoption to recommendation through trust in AI.<\/jats:p><\/jats:list-item><jats:list-item><jats:p>Customers' mind perception positively moderates the central and peripheral paths.<\/jats:p><\/jats:list-item><\/jats:list><\/jats:p><\/jats:sec>","DOI":"10.1108\/itp-10-2021-0764","type":"journal-article","created":{"date-parts":[[2023,12,6]],"date-time":"2023-12-06T01:15:07Z","timestamp":1701825307000},"page":"937-962","source":"Crossref","is-referenced-by-count":51,"title":["Would an AI chatbot persuade you: an empirical answer from the elaboration likelihood 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