信息加工下高卷入度产品中消费者评论与网红测评的影响比较研究
A Comparative Study of the Effects of Consumer Reviews and Influencer Evaluations on High-Involvement Products from an Information Processing Perspective

本研究立足信息加工理论与信任理论，构建了高卷入度产品(以新能源汽车为例)的用户生成内容(UGC)影响框架，采用问卷调查与实验设计相结合并辅以购买评消费者购买评论与抖音网红测评爬取数据，通过多元回归与中介效应分析发现：详实且情感一致的消费者评论可直接增强消费者信任与购买意向，优质且高传播力的网红测评则通过提升购买意向进一步促进销量，而消费者购买意向在评论/测评与实际销售之间发挥了显著中介作用。研究为高卷入度产品的线上口碑管理与数字营销策略提供了实证依据，建议企业精细化评论引导、深化专业网红合作，并结合差异化传播策略与实时舆情监测构建精准营销闭环。
This study, grounded in information processing theory and trust theory, constructs a user-generated content (UGC) impact framework for highinvolvement products (using new energy vehicles as an example). By combining survey questionnaires and experimental design with crawled consumer purchase reviews and influencer evaluations and applying multiple regression and mediation analyses, we find that detailed, emotionally consistent consumer reviews directly enhance consumer trust and purchase intentions, while high-quality, widely disseminated influencer evaluations further drive sales by boosting purchase intentions; moreover, consumer purchase intention serves as a significant mediator between reviews/evaluations and actual sales. The findings offer empirical support for online word-of-mouth management and digital marketing strategies for high-involvement products, recommending that firms refine review guidance, deepen collaborations with professional influencers, and integrate differentiated dissemination strategies with real-time sentiment monitoring to establish a precise marketing closed-loop.