人工智能警觉对员工工作倦怠的影响研究

——基于科技公司的调查数据

孟祥婧　徐 鹏

（山东财经大学）

内容提要：随着人工智能技术发展及与传统经营模式和业务流程的实质性融合，员工在经历传统雇佣模式的动荡后可能会产生一系列反应。本文以科技公司为样本，基于情感事件理论框架，实证检验人工智能警觉对员工工作倦怠的作用机理，并考察员工消极情绪的中介作用与企业组织公平的调节作用。研究结果显示，人工智能警觉对员工工作倦怠具有促进作用。作用机制分析结果显示，消极情绪在人工智能警觉与员工工作倦怠的关系中具有中介作用，即人工智能警觉通过激发员工消极情绪影响工作倦怠。调节作用检验结果显示，组织公平程度越高，人工智能警觉对员工工作倦怠的促进作用越弱。有调节的中介作用表明，组织公平程度越高，人工智能警觉通过员工消极情绪对工作倦怠的正向影响越弱。该结论揭示了人工智能警觉对员工工作倦怠的过程机制和情境约束，对企业优化人工智能管理、缓和劳资冲突以及促进就业稳定等具有重要的借鉴价值。

关键词：人工智能警觉；工作倦怠；消极情绪；组织公平；情感事件理论；资源保存理论

作者简介：孟祥婧，山东财经大学工商管理学院博士研究生，济南，250014；徐鹏，山东财经大学工商管理学院教授、博士生导师，通信作者。

基金项目：国家自然科学基金面上项目“母子公司高管协同配置驱动机制及效应研究——基于公司治理视角的探索”（71972117）；泰山学者工程专项“金字塔型企业集团框架内上市公司治理质量提升机制研究”（tsqn202103095）

引用格式：孟祥婧,徐鹏.人工智能警觉对员工工作倦怠的影响研究——基于科技公司的调查数据[J].经济与管理研究,2024,45(10):99-110.DOI:10.13502/j.cnki.issn1000-7636.2024.10.006.

Impact of AI Awareness on Job Burnout

—Based on Survey Data from Technology Companies

MENG Xiangjing, XU Peng

(Shandong University of Finance and Economics, Jinan 250014)

Abstract: Artificial intelligence (AI) emerges as a transformative force, unlocking substantial potential and exerting profound effects on the labor market. The application of AI exhibits a dual-edged sword effect on employees. On the one hand, it enhances productivity, safety, and efficiency, creating new job opportunities and improving working conditions. On the other hand, it displaces some jobs and challenges the protection of labor rights. From a micro-level perspective, AI awareness is becoming a critical issue in the application of workers, reflecting their sensitivity to AI applications and potential impacts, particularly concerns about AI undermining their career prospects. This paper proposes that as companies extensively adopt AI and increase relevant investment, the relationship between AI and employees is likely to become confrontational, ultimately manifesting in heightened AI awareness. This phenomenon not only influences employees' emotional experiences and objective evaluations of AI but also exacerbates the risk of job burnout. Consequently, this paper employs technology companies as a sample to empirically investigate the mechanisms by which AI awareness impacts job burnout within the framework of affective events theory.

Through a survey administered to employees working in technology companies in China, this paper employs structural equation modeling to analyze the data. The findings indicate that AI awareness has a significant impact on job burnout. Mechanism analysis reveals that AI awareness positively influences job burnout via the mediating variable of negative emotions. Furthermore, moderation analysis demonstrates that higher levels of organizational justice attenuate the effect of AI awareness on job burnout. The moderated mediation effect shows that as organizational justice increases, the positive impact of AI awareness on job burnout through negative emotions is mitigated. Therefore, enterprises need to take positive measures to alleviate employees' negative emotions toward AI applications and pay more attention to improving organizational fairness to reduce job burnout caused by AI awareness.

This paper makes several significant contributions. Firstly, it explores the impact of AI awareness on job burnout within technology enterprises, providing empirical evidence across diverse industry contexts. Secondly, from the perspective of employees' negative emotions, it elucidates the mechanism by which AI awareness influences job burnout, thereby supplementing and enriching existing micro-level research. Thirdly, it proposes feasible operational solutions using organizational justice to mitigate the relationship between AI awareness and employees’ negative emotions, offering new perspectives and theoretical foundations for managing job burnout in practice. Lastly, it aids in understanding the impact of new technologies, identifying the current state of human-machine compatibility, providing scientific support for building harmonious labor relations and achieving employment stability under the new development paradigm.

Keywords: AI awareness; job burnout; negative emotion; organizational justice; affective events theory; conservation of resources theory

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