AI助手驱动社媒电商绩效反馈优化：影响机制与边界条件——基于有调节的中介分析

AI Assistant-Driven Performance Feedback Optimization for Social Media E-Commerce: Impact Mechanism and Boundary Conditions—Based on Moderated Mediation Analysis

摘要: 数字化时代的到来为人力资源管理研究提出新命题，在绩效管理的诸多构成要素中，绩效反馈环节与人工智能的接洽与优化，是我们共同的重要议题。本文以人工智能绩效反馈助手为研究对象，通过对社媒电商行业员工开展问卷调查，对比分析了“专家”与“伙伴”两种社会角色在工作情景下，员工任务类型对人机信任感的调节效应。研究结论表明：(1)“专家”型智能反馈助手对于客观任务而言，增强人机信任感的作用更大，从而促进员工接受反馈意见。(2) “伙伴”型智能反馈助手对于主观任务而言，增强人机信任感的作用更大，从而促进员工接受反馈意见。基于上述结论，本文提出了企业接入AI绩效助手的相关建议。

Abstract: The advent of the digital era has posed new challenges for human resource management research. Among the various components of performance management, the integration and optimization of performance feedback with artificial intelligence (AI) is a crucial topic for our collective discussion. This paper takes AI-based performance feedback assistants as the research subject. Through a questionnaire survey conducted among employees in the social media e-commerce industry, it compares and analyzes the moderating effect of employee task types on human-machine trust in the context of work, focusing on two social roles: “expert” and “partner”. The research findings indicate that: (1) For objective tasks, “expert” type intelligent feedback assistants have a greater impact on enhancing human-machine trust, thereby facilitating employees’ acceptance of feedback. (2) For subjective tasks, “partner” type intelligent feedback assistants have a stronger effect on enhancing human-machine trust, thus promoting employees’ acceptance of feedback. Based on these conclusions, this paper proposes relevant suggestions for enterprises to integrate AI-based performance assistants.