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

1. The study investigates customers' adoption of AI chatbots' recommendation.

2. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

3. The central and peripheral cues are generalized according to cooperative principle theory.

4. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

5. Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

6. Customers' mind perception positively moderates the central and peripheral paths.

中文翻译：

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人工智能聊天机器人会说服你吗：来自精细化可能性模型的实证答案

目的

本研究调查了人工智能 (AI) 聊天机器人如何说服客户在在线购物环境中接受他们的建议。

设计/方法论/途径

本研究利用精细化可能性模型，建立了一个研究模型来揭示客户采用人工智能聊天机器人推荐的前因和内部机制。作者使用 530 名 AI 聊天机器人用户的调查数据测试了该模型。

发现

结果表明，在人工智能聊天机器人推荐采用过程中，中心和外围线索显着影响客户采用人工智能聊天机器人推荐的意图，而客户对人工智能聊天机器人的认知和情感信任调节了这种关系。此外，客户对人工智能聊天机器人的心理感知，包括感知代理和感知体验，分别调节中心路径和外围路径。

原创性/价值

这项研究对人工智能聊天机器人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。

研究亮点

1. 该研究调查了客户对人工智能聊天机器人推荐的采用情况。

2. 作者基于 ELM 理论开发了研究模型，以揭示中心和外围的线索和路径。

3. 根据合作原理理论概括了中心线索和外围线索。

4. 中心线索包括推荐的可靠性和准确性，外围线索包括类人的同理心和推荐选择。

5. 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。

6. 顾客的心理感知对中心路径和外围路径有积极的调节作用。