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1 – 2 of 2Qian Chen, Changqin Yin and Yeming Gong
This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.
Abstract
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.
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.
Details