社交电商情绪驱动互动策略推荐模型及实证研究——基于SOR理论模型

Emotion-Driven Interaction Strategy Recommendation Model and Empirical Research in Social Commerce—Based on the SOR Theoretical Model

摘要: 在数字经济背景下，情绪经济已成为消费市场增长的关键动力，以Z世代为代表的消费者正从“物质满足”转向“情绪悦己”。针对社交电商互动策略同质化、情绪适配性不足的问题，本研究基于SOR理论构建情绪驱动的互动策略推荐模型，利用10万条用户行为面板数据，验证了情绪–策略匹配效应、递进式互动路径及关键变量调节机制，并提出“用户细分–策略预测”两阶段技术架构，为社交电商精细化、情绪化运营提供可行方案。

Abstract: In the digital economy, the emotional economy is a key growth drive in consumer markets, particularly as Generation Z shifts from material satisfaction to emotional self-gratification. To overcome the homogeneity and low emotional adaptability of social e-commerce interaction strategies, this study builds an emotion-driven recommendation model based on SOR theory. Using panel data from 100,000 users, we validate the emotion-strategy matching effect, the progressive interaction pathway, and key moderating mechanisms. We then propose a two-stage “user segmentation-strategy prediction” framework, offering a practical solution for refined, emotion-aware social e-commerce operations.