ISSN 1671-3710
CN 11-4766/R
主办：中国科学院心理研究所
出版：科学出版社

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当员工遇见AI：员工-AI协作的构念测量、前因组态与影响机制

陈慧, 丰超   

  1. 南京师范大学金陵女子学院/社会学院/会计学院, 江苏 210024 中国
    南京航空航天大学经济与管理学院, 江苏 211106 中国
  • 收稿日期:2025-10-29 修回日期:2026-02-05 接受日期:2026-03-03
  • 基金资助:
    国家自然科学基金青年项目“数智时代的员工-AI协作：构念测量、前因组态与影响机制研究”(72502116); 国家自然科学基金面上项目“数智赋能的渠道治理：治理效果、机制协同与负面效应干预”(72572085); 国家自然科学基金青年项目“企业社交媒体行为如何影响跨组织治理？基于多层次匹配视角”(72102107); 江苏省社会科学基金青年项目“数智赋能江苏低空经济产业链供应链韧性提升的机制及对策研究(25GLC019)

When employee meets AI: Research on employee-AI collaboration’s construct measurement, antecedent configuration and influence mechanism

CHEN Hui, FENG Chao   

  1. , Ginling College, Nanjing Normal University 210024, China
    , College of Economics and Management, Nanjing University of Aeronautics and Astronautics 211106, China
  • Received:2025-10-29 Revised:2026-02-05 Accepted:2026-03-03

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摘要： 数智时代，员工-AI协作成为重要的工作模式。在此背景下，探究员工-AI如何协作，为何会采取不同协作模式，不同协作模式会带来何种影响以及如何进行干预成为重要研究问题。为此，本研究针对员工-AI协作的类型、前因和后果开展了一系列探索。首先，基于主导权和交互度双重维度将员工-AI协作模式划分为增强型、共生型、辅助型和替代型四类，并开发相应量表。其次，基于社会技术系统理论，从“员工-AI-任务-组织”四方面识别员工-AI协作的影响因素，并从组态研究视角探究四方面因素的协同效应。最后，基于认知-情感系统理论，引入认知和情感双重机制，并提出四种针对性干预措施，以揭示不同员工-AI协作模式影响员工工作绩效和工作幸福感的作用机理。本研究将拓展员工-AI协作研究，为实现员工与AI高效协同提供重要参考。

关键词: 人机协作, 人工智能, 工作绩效, 工作幸福感

Abstract: In the digital-intelligence era, employee-AI collaboration has become an important work pattern. Against this backdrop, it has significance to explore how employees collaborate with AI, why different collaboration patterns emerge, what the impacts of distinct collaboration patterns are, and how to intervene in them. To address these questions, this study conducts a series of explorations focusing on the typologies, antecedents, and consequences of employee-AI collaboration. Firstly, based on the dual dimensions of dominance and interaction, the employee-AI collaboration is divided into four typologies: Augmentation, Symbiosis, Assistance, and Substitution, while developing corresponding measurement scales. Secondly, grounded in sociotechnical systems theory, this study identifies influencing factors across four dimensions (employee, AI, task, and organization) and investigates their synergistic effects through a configurational approach. Finally, based on the cognitive-affective processing system theory, this study introduces cognitive-affective dual mechanisms and explores the moderating role of four management interventions to reveal how different types of employee-AI collaboration shape employee performance and well-being. This study theoretically expands the frameworks for employee-AI collaboration research, and practically it provides critical insights for achieving high efficiency of employee-AI collaboration in organizations.

Key words: Human-AI collaboration, AI, job performance, job well-being