ISSN 1671-3710
CN 11-4766/R

心理科学进展 ›› 2024, Vol. 32 ›› Issue (1): 162-176.doi: 10.3724/SP.J.1042.2024.00162

• 研究前沿 • 上一篇    下一篇


尹萌(), 牛雄鹰   

  1. 对外经济贸易大学国际商学院, 北京 100029
  • 收稿日期:2023-03-28 出版日期:2024-01-15 发布日期:2023-10-25
  • 通讯作者: 尹萌
  • 基金资助:

Dancing with AI: AI-employee collaboration in the systemic view

YIN Meng(), NIU Xiongying   

  1. Business School, University of International Business and Economics, Beijing 100029, China
  • Received:2023-03-28 Online:2024-01-15 Published:2023-10-25
  • Contact: YIN Meng


AI−员工协作是一个以高效完成任务为目标, 由“AI−人−组织”构成的交互系统。促进AI−员工协作对于推动AI与实体经济的深度融合, 以及员工在数字化时代的心理健康与职业发展至关重要。AI与员工的交互关系错综复杂, 现有研究呈现碎片化特点, 缺乏对AI−员工协作的整体认识。因此, 有必要在厘清相关概念的基础上, 对AI−员工协作的相关研究进行系统地梳理。通过对相关研究的系统性回顾, 本文厘清了AI和AI−员工协作的内涵, 梳理了AI−员工协作系统的构成要素, 分析了构成要素的作用和影响, 并进一步基于系统化的视角构建了一个研究框架。最后, 基于AI−员工协作的研究框架提出未来研究展望。

关键词: 人工智能, AI-员工协作, 系统化视角, 研究框架, I-P-O


AI-employee collaboration is an interactive system composed of “AI-human-organization” with the goal of completing tasks efficiently. Promoting AI-employee collaboration is crucial for driving the deep integration of AI and the real economy, as well as the mental health and career development of employees in the digital era. However, the conceptual connotation of AI and AI-employee collaboration has not yet been systematically elaborated in the literature, which has led to ambiguity in the meaning of AI in organizations as well as confusion between concepts of different AI application. In addition, the research of AI-employee collaboration is fragmented and complex across disciplines, and the academic community lacks a comprehensive understanding of the current status and future direction of AI-employee collaboration research. Based on the above limitations, we conducted a comprehensive search of the literature related to AI-employee collaboration, coded the publication information, theoretical basis, core research conclusions and other contents of the literature, and organized the content of the paper based on a systemic review after reading the literature in depth. We first clarify the concept and dimensions of AI in the workplace, and then discuss the systemic view of AI-employee collaboration, and further clarify the conceptual connotation of AI-employee collaboration from the systemic view. This helps to unify the academic dialogue and lay the foundation for subsequent research on AI-employee collaboration. Then, based on the systemic view of AI-employee collaboration, the paper constructs a research framework of AI-employee collaboration using I-P-O paradigm, and describes AI-employee collaboration as input, process and output of a system in detail. At the input of the system, AI, employees, and organizations work together to drive the design, implementation, and use of AI. At the AI level, we review from three dimensions: physical attributes, mental attributes and ethical attributes. At the employee level, we review from four aspects: attitude, KSAs, personalities and demographic characteristics. At the organizational level, we review from three perspectives: organizational readiness, organizational support, organizational climate and culture. In the process of the system, actors operate around work tasks, and they influence the output by performing the tasks. Therefore, the process is a task configuration, including two aspects: task goal and interaction approaches. We further propose that optimizing AI-employee collaboration requires attention to the dynamic matching of interaction approaches and task goal. At the output of the system, we summarize the outcomes of three actors: employees, AI and organization. The research framework explicitly describes the components and internal mechanisms of AI-employee collaboration system, and provides a basic theoretical framework guide for further more in-depth research. Finally, based on the limitations of the research framework, we propose future research prospects from five perspectives. (1) Future research should emphasize the ethical issues in AI-employee collaboration system, providing more empirical and experimental evidence for the impact of ethical attributes on AI-employee collaboration. (2) Future research should develop objective measurements of the organizational consequences of AI-employee collaboration. (3) Future research should explore more individual factors that may influence AI-employee collaboration, such as prompt ability, cultural values, etc. (4) Future research should further elaborate the task configuration of AI-employee collaboration and examine the efficiency and effectiveness of AI-employee collaboration with different task configurations. (5) Future research should expand the research framework based on the new developments of I-P-O paradigm, such as feedback loops.

Key words: artificial intelligence, AI-employee collaboration, systemic view, research framework, I-P-O