Abstract:

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