电商场景下的“二次激怒”效应：心理机制分析与人机协同客服优化策略

The “Secondary Aggravation” Effect in E-Commerce Scenarios: Psychological Mechanism Analysis and Human-AI Collaborative Customer Service Optimization Strategies

摘要: 本文聚焦于电商场景下，AI客服所带来的新型服务困境——“二次激怒”效应，即客户因客观问题产生初始不满后，在与AI客服进行低效、僵化的无效沟通中，负面情绪被进一步激化的现象。本文首先将“二次激怒”置于服务补救与客户愤怒管理的理论谱系中，明确了其作为一种由数智化服务前置环节诱发情绪升级的新机制的理论独特性。进而通过引入挫折–攻击假说、认知失调理论、心理抗拒理论及人本主义心理学的分析视角，揭示了该效应的本质其实是AI系统设计与人性的自主、共情及认知一致性等核心心理需求之间的冲突。基于这些理论机制以及不同种类AI客服在引发“二次激怒”路径上的差异，本文进一步从客户、客服、平台及行业四个层面，梳理了“二次激怒”所带来的多重负面影响。最后，本文构建了一套贯穿“事前预防–事中干预–事后完善–体系保障”全流程的、“针对不同技术路径AI”的人机协同系统化优化方案，为电商平台在智能客服体验的诊断与优化方面提供了兼具理论深度与技术实操性的决策参考。

Abstract: This article focuses on a novel service dilemma arising from AI customer service in e-commerce scenarios—the “secondary aggravation” effect. This phenomenon refers to the further escalation of customers’ negative emotions after initial dissatisfaction caused by objective issues, resulting from inefficient, rigid, and ineffective communication with AI agents. This study first situates “secondary aggravation” within the theoretical framework of service recovery and customer anger management, clarifying its uniqueness as a new mechanism for emotional escalation triggered by the pre-service phase in digitalized services. Furthermore, by drawing on analytical perspectives from the frustration-aggression hypothesis, cognitive dissonance theory, psychological reactance theory, and humanistic psychology, it reveals that the essence of this effect lies in the conflict between the design of AI systems and fundamental human psychological needs, such as autonomy, empathy, and cognitive consistency. Based on these theoretical mechanisms and the differentiated pathways through which various types of AI customer service provoke “secondary aggravation”, this article further delineates the multifaceted negative impacts of this effect across four levels: the customer, the customer service agent, the platform, and the industry. Finally, this article constructs a comprehensive, systemic “human-AI collaborative” optimization strategy that spans the entire customer service journey through four key phases: “prevention, intervention, resolution, and safeguard”, tailored to different technological pathways of AI. This provides e-commerce platforms with decision-making references that integrate theoretical depth and technical practicality for diagnosing and optimizing intelligent customer service experiences.