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论文编号：	16114
作者编号：	1120201077
上传时间：	2026/6/8 9:37:11
中文题目：	人工智能评论对消费者决策的影响机制研究
英文题目：	Research on the Influence Mechanisms of AI Reviews on Consumer Decision-Making
指导老师：	李凯
中文关键字：	AI评论；电子商务；人工智能生成内容；在线评论；消费者决策
英文关键字：	AI reviews; e-commerce; AI-generated content (AIGC); online reviews; consumer decision-making
中文摘要：	随着人工智能（AI）技术的快速发展，AI评论作为一种新兴的商品信息展示形式，逐渐被应用于电子商务平台。相较于传统消费者评论，AI评论能够高效整合多源信息并生成结构化内容，从而在降低消费者认知负担的同时提升决策效率，并对购买意愿与满意度产生重要影响。然而，现有研究多聚焦于AI评论与消费者评论效果的对比，缺乏对AI评论在多源信息环境中的系统性分析。基于此，本文从多源评论视角出发，系统探讨AI评论对消费者感知与决策的影响机制，重点围绕AI评论的作用效果、与消费者评论的交互关系、评论一致性问题以及评论表达方式四个方面展开研究。 首先，本文基于不同产品类型情境，考察AI评论对消费者购买意愿的影响及其作用边界。研究发现，AI评论在搜索型产品中能够显著提升消费者购买意愿，而在体验型产品中其作用相对较弱。这一差异源于消费者在不同产品情境下对信息类型的需求不同，从而揭示了产品类型在AI评论作用中的关键调节作用。 其次，本文突破以往将AI评论与消费者评论视为独立信息源的研究视角，系统分析了二者的交互作用。研究结果表明，在搜索型产品情境下，AI评论在消费者评论存在的条件下能够发挥信息补充作用，从而提升购买意愿；而在体验型产品情境下，AI评论的影响不再显著，其作用受到消费者评论存在与数量的调节，体现出显著的情境依赖性与边界条件。 再次，本文从话题一致性与效价一致性两个维度出发，探讨AI评论与消费者评论之间的一致性如何影响消费者行为。研究发现，当不同来源评论在话题与效价上保持一致时，消费者更容易整合信息，从而提升评论采纳与在线参与；相反，不一致信息则可能引发认知冲突，降低评论可信度并抑制参与行为。该发现将一致性问题从单一评论来源拓展至多源信息环境。 最后，本文进一步考察了AI评论表达方式的影响机制，重点分析评论聚焦对象与效价结构的作用。研究表明，聚焦于商品的评论更有助于理性决策，而聚焦于消费者的评论则通过增强情感共鸣影响决策过程。结合自我参照与社会临场感理论，本文揭示了不同表达方式对消费者心理加工路径的差异化影响。 基于上述研究，本文构建了一个系统性的多维分析框架，从评论来源、产品类型、信息一致性与内容表达等多个维度，全面揭示了AI评论对消费者决策的作用机制。研究表明，AI评论的影响并非单一主效应，而是受到情境因素与心理加工过程的共同作用，呈现出显著的情境依赖性与结构依赖性。本文不仅拓展了在线评论与生成式人工智能领域的相关研究，也为电商平台优化AI评论设计与信息呈现策略提供了重要的理论依据与实践启示。
英文摘要：	With the rapid development of artificial intelligence (AI) technologies, AI reviews have emerged as a novel form of product information presentation and have been increasingly adopted by e-commerce platforms. Compared with traditional consumer reviews, AI reviews can efficiently integrate multi-source information and generate structured content, thereby reducing consumers’ cognitive load, improving decision-making efficiency, and ultimately influencing purchase intention and satisfaction. However, existing studies mainly focus on comparing the effects of AI reviews and consumer reviews, while lacking a systematic investigation of AI reviews in multi-source information environments. To address this gap, this study adopts a multi-source review perspective and systematically examines the mechanisms through which AI reviews affect consumer perceptions and decision-making. Specifically, the study focuses on four aspects: the effects of AI reviews, their interaction with consumer reviews, the role of review consistency, and the impact of review expression. First, this study investigates the impact of AI reviews on consumers’ purchase intention across different product types and explores its boundary conditions. The results show that AI reviews significantly enhance purchase intention for search products, whereas their effect is relatively weaker for experience products. This difference stems from consumers’ varying information needs across product contexts, highlighting the moderating role of product type in shaping the effectiveness of AI reviews. Second, moving beyond prior research that treats AI reviews and consumer reviews as independent information sources, this study systematically examines their interaction effects. The findings indicate that, in the context of search products, AI reviews can complement consumer reviews when the latter are present, thereby enhancing purchase intention. In contrast, for experience products, the effect of AI reviews becomes insignificant and is contingent upon the presence and volume of consumer reviews, reflecting strong contextual dependence and boundary conditions. Third, this study explores how consistency between AI reviews and consumer reviews influences consumer behavior from two dimensions: topic consistency and valence consistency. The results reveal that when reviews from different sources are consistent in both topic and valence, consumers can more easily integrate information, leading to higher review adoption and online engagement. Conversely, inconsistency may induce cognitive conflict, reduce perceived credibility, and inhibit engagement. These findings extend the concept of consistency from single-source reviews to multi-source information environments. Finally, this study examines the impact of AI review expression by focusing on review focus (product-focused vs. consumer-focused) and valence structure. The results suggest that product-focused reviews facilitate rational decision-making, whereas consumer-focused reviews influence decisions by enhancing emotional resonance. Drawing on self-referencing theory and social presence theory, this study further reveals the distinct psychological processing pathways triggered by different expression styles. Based on these findings, this study develops a systematic multidimensional analytical framework that comprehensively explains how AI reviews influence consumer decision-making from multiple perspectives, including review source, product type, information consistency, and content expression. The results demonstrate that the effects of AI reviews are not driven by a single main effect but are jointly shaped by contextual factors and psychological processes, exhibiting strong contextual and structural dependence. This study contributes to the literature on online reviews and generative AI, and provides important theoretical and practical implications for optimizing AI review design and information presentation strategies on e-commerce platforms.
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