Modeling Organizational Effectiveness of Human–Robot Collaboration in Construction

Abstract The increasing deployment of robotics in construction is reshaping project organization and management by redefining how authority, coordination, and performance are structured within hybrid human–robot teams. However, current research remains fragmented, lacking a unified analytical model to examine how organizational structures and collaborative processes influence organizational effectiveness in human–robot collaboration (HRC) under uncertainty. This study develops a human–robot collaboration interdependence model (HRCIM) that integrates structural, process, and performance criteria to evaluate organizational effectiveness in construction management. By integrating a fuzzy cognitive map with cloud modeling, the proposed model captures the interdependencies and cognitive uncertainty inherent in managerial reasoning. The model was applied to robot-assisted construction environments, modeling interdependencies among factors and simulating influence propagation. The findings reveal that structural clarity and process coordination drive organizational effectiveness, while adaptive performance fosters organizational learning and resilience. Building on these insights, the study establishes a structural–dynamic dual regulation mechanism that elucidates how stability and adaptability coexist within organizational management in HRC. The HRCIM provides a quantifiable basis for optimizing management structures, enhancing coordination, and improving the organizational effectiveness of HRC.