Research on the Interactive Effects of Product Recommendation Approaches and Product Types on Consumers’ Purchase Intention–Regulation of Product Involvement

The main purpose is to analyze how different product recommendation approaches and product types influence consumers’ purchasing decisions, elucidate the interactive effects between these variables, and develop platform-specific operational strategies to optimize recommendation systems. Based on the Stimulus-Organism-Response (SOR) theory and the Elaboration Likelihood Model (ELM), this study employs a 2 (product recommendation type: AI vs. human) × 2 (product type: functional vs. hedonic) between-subjects experimental design to examine how recommendation systems interact with product characteristics to influence consumers’ purchase intentions. Specifically, it examines the mediating role of perceived value and the moderating effect of product involvement. An inter-group experimental design was employed to gather data via online questionnaires. ANOVA and other statistical methods were used to analyze the interaction, mediation, and moderation effects among the variables. The findings indicate a significant interaction between recommendation mode and product type. AI recommendations are more effective for functional products, whereas human recommendations are more advantageous for hedonic products. Perceived value mediates this interaction, with quality and price value being predominant in the AI recommendation pathway, while emotional, quality, and price value drive the human recommendation pathway. Moreover, the interaction effect is moderated by product involvement, with the matching effect being significantly enhanced under high involvement conditions, while the difference diminishes under low involvement conditions. This research offers new insights into consumer behavior theory and provides a practical foundation for optimizing recommendation strategies on e-commerce platforms.

本研究核心目标为剖析不同产品推荐范式与产品类型对消费者购买决策的影响机制，阐明两类变量间的交互效应，并针对电商平台制定适配性运营策略以优化推荐系统。本研究基于刺激-机体-反应（Stimulus-Organism-Response, SOR）理论与精细加工可能性（Elaboration Likelihood Model, ELM）模型，采用2（产品推荐类型：人工智能（AI）vs. 人工）×2（产品类型：功能型vs. 享乐型）的被试间实验设计，探究推荐系统与产品特征如何交互作用进而影响消费者购买意向。具体而言，本研究同时检验感知价值的中介作用与产品卷入度的调节效应。本研究采用组间实验设计，通过线上问卷收集研究数据，并运用方差分析（ANOVA）及其他统计方法对变量间的交互效应、中介效应与调节效应进行检验分析。研究结果显示，推荐模式与产品类型间存在显著交互效应：人工智能推荐对功能型产品的推广效果更优，而人工推荐则更适配享乐型产品。感知价值在该交互效应中发挥中介作用：在人工智能推荐路径中，质量感知价值与价格感知价值占据主导驱动地位；而在人工推荐路径中，情感感知价值、质量感知价值与价格感知价值共同构成核心驱动因素。此外，产品卷入度对该交互效应存在显著调节作用：当消费者产品卷入度较高时，推荐模式与产品类型的匹配效应会显著增强；而当卷入度较低时，二者的差异则会弱化。本研究不仅为消费者行为理论提供了全新的研究视角，同时也为电商平台优化推荐策略提供了实践依据。