The Effect of Critical Factors on Team Performance of Human–Robot Collaboration in Construction Projects: A PLS-SEM Approach

Human–Robot Collaboration (HRC) in construction projects promises enhanced productivity, safety, and quality, yet realizing these benefits requires understanding the multifaceted human and robotic factors that influence team performance. This study develops and validates a multidimensional framework that links key human abilities (operational skill, decision-making ability, and learning ability) and robot capacities (functionality and operability) to HRC team performance, with task complexity considered as contextual influence. A field survey of construction practitioners (n = 548) was analyzed using partial least squares structural equation modeling (PLS-SEM) to test direct effects and human–robot synergies. Results reveal that all five main effects are positive and significant, indicating that both human abilities and robot capacities have significant contribution. Moreover, every hypothesized two-way interaction is supported, evidencing strong interaction effects. Three-way moderation analyses further reveal that task complexity significantly strengthened the interactions of human abilities with robot functionality, whereas its interactions with robot operability were not significant. The study contributes an integrated and theory-driven model of HRC team performance that accounts for human abilities and robot capacities under varying task complexity, and validated constructs that can be used to diagnose and predict performance. The findings offer actionable guidance for project managers by recommending that they prioritize user-friendly robot operability to translate worker expertise into performance across a wide range of tasks, invest in training to strengthen operators’ skills and decision-making, and, for complex tasks, pair highly skilled workers with high-functionality robots to maximize performance gains.