From a Task-Centered Approach to Interdependent Activities: Revealing Gaps in Generative AI Research on Coordination and Cooperation

Generative AI is bound to have profound effects over work and how it is organized. However, while existing research has focused on uncovering the impact of Generative AI systems on the execution of individual tasks, it has significantly under-emphasized the interdependent and collaborative nature of work. To support filling this gap, this paper investigates the implications of generative AI on collaborative work activities, emphasizing the need for a shift from a taskcentric approach to a broader process-oriented perspective. By utilizing the 3C collaboration model—communication, coordination, and cooperation—this study employs a bibliometric-based analysis to map the current state of research in this domain, identifying gaps and opportunities for field development. Our analysis identifies significant disparities in academic focus on Generative AI in collaborative settings, highlighting under-researched areas such as cooperation and coordination. Moreover, research in the domain of collaboration is remarkably segregated, with few studies addressing multiple collaboration dimensions simultaneously. Lastly, while there is a strong emphasis on Human-AI interactions, the role of AI in mediating Human-Human interactions is less explored. Addressing these gaps could provide valuable insights into defining strategies to effectively integrate generative AI systems within complex organizational settings.