Determinants of Effective Human-AI Teaming in Work Contexts: A Meta-Synthesis

To construct a coherent theoretical model from the fragmented literature on human-AI teaming, this study moves beyond summarization to present a new, integrated synthesis. Through a meta-synthesis of 28 peer-reviewed articles (2015–2024), we applied thematic analysis and Shannon entropy to establish an empirical hierarchy of determinants. The principal contribution is a three-tiered conceptual framework structuring factors into: (1) Foundational Inputs (Human and Technological Dimensions), (2) Mediating Processes (Interaction Dynamics), and (3) Overarching Context (Strategic Requirements). A key theoretical finding is the confirmation of a socio-technical equilibrium, evidenced by the symmetrical importance of human-cognitive factors and AI-design characteristics. We conclude that effectiveness is not an outcome of discrete components but an emergent property of their systemic interplay. This work transforms multidisciplinary findings into an empirically prioritized, testable model, offering a novel conceptual foundation and practical roadmap for designing and managing these complex hybrid systems.