Would an AI chatbot persuade you: an empirical answer from the elaboration likelihood model

Qian Chen, Changqin Yin, Yeming Gong
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Abstract

PurposeThis study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.Design/methodology/approachDrawing 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.FindingsThe 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/valueThis 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 highlightsThe 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.
人工智能聊天机器人会说服你吗:阐述可能性模型的经验答案
本研究探讨人工智能(AI)聊天机器人如何在网上购物环境中说服客户接受他们的推荐。设计/方法/途径本研究通过细化似然模型建立了一个研究模型,揭示了客户接受AI聊天机器人推荐的前因和内在机制。作者用530名人工智能聊天机器人用户的调查数据测试了该模型。研究结果表明,在人工智能聊天机器人推荐采纳过程中,中心线索和外围线索显著影响顾客采纳人工智能聊天机器人推荐的意愿,而顾客对人工智能聊天机器人的认知信任和情感信任在两者之间起中介作用。此外,客户对AI聊天机器人的心智感知,包括感知代理和感知经验,分别调节了中心路径和外围路径。原创性/价值本研究对人工智能聊天机器人的设计者具有理论和实践意义,并为从业者提供管理见解,以增强客户采用人工智能聊天机器人推荐的意愿。该研究调查了客户对人工智能聊天机器人推荐的接受程度。作者建立了基于ELM理论的研究模型,揭示了中枢和外围线索和路径。根据合作原则理论对中心线索和外围线索进行了概括。中心线索包括推荐的可靠性和准确性,外围线索包括类似人类的同理心和推荐选择。中心和外围线索通过对人工智能的信任影响客户对推荐的采用。顾客的心智知觉正向调节中心路径和外围路径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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