Collaboration Satisfaction in Human–AI Teams: The Dual Roles of Trust and Cognitive Load - Pan, Sheng; Xiong , Zhuang; Kong, Lingfu (Transformations In Business & Economics, 2025)
With the rapid development of artificial intelligence (AI), organizational collaboration structure and operational logic are being reshaped, and human–AI co-creation is gradually becoming the norm. However, the impact of AI embedding on team collaboration satisfaction remains unclear. To explore how AI embedded in teams affects team collaboration satisfaction, drawing on socio-technical systems theory and information processing theory, utilizing a between-subjects experimental design, 100 participants were recruited via an online platform in April 2025, how AI affects team collaboration satisfaction was investigated through the dual pathways of social and technical subsystems by manipulating team composition (human–human teams vs. human–AI teams). Results indicate that the introduction of AI significantly weakens human members’ team trust while effectively reducing their cognitive load. The opposite effects of these two pathways lead to a canceling indirect effect, which makes the overall impact of AI on collaboration satisfaction neutral. Further analysis reveals that team project experience significantly moderates the above mechanisms. Specifically, experienced members rely more on interpersonal cooperation and are more sensitive to AI’s emotionless collaboration style, thereby reducing trust. However, they perceive less value in AI’s assistance because they have internalized efficient cognitive strategies. The conclusions of this study deepen the present understanding of the human–AI collaborative mechanism in AI technology embedding, reveal the interaction between structural technological change and individual experience, and provide theoretical support for the design and management of future intelligent collaboration systems.