From human-machine collaboration to deviation: Understanding the catastrophe and resilience mechanisms of human-machine behaviors in intelligent environments

Abstract

Due to environmental factors, work behaviors may suddenly change from human-machine collaboration to deviant behavior in intelligent environments, which can negatively impact human-machine efficiency. Thus, the factors that influence labor-intensive human-machine behaviors and their catastrophe and resilience mechanisms are explored in this paper. First, a conceptual model outlining the factors influencing human-machine behaviors is proposed in a qualitative analysis of city smart sanitation. Next, the threshold conditions for catastrophe in human-machine behaviors are analyzed using a stochastic catastrophe model. A resilience computational model is proposed, in which the resilience index accurately reflects the ability of human-machine behaviors to maintain stability in the face of random disturbances. Finally, numerical analysis reveals that digital supervision reduces the resilience of collaborative behavior and increases the resilience of deviant behavior and that humanitarian management mitigates both effects. The increase in humanitarian management decreases the catastrophe threshold of collaborative behavior but has no significant role in the catastrophe threshold of deviant behavior. From the perspective of behavioral resilience computation, this paper discusses two key arguments regarding the influences of humanitarian management and punishment on human-machine behaviors. This paper makes methodological and theoretical contributions to the understanding of human behavior in intelligent environments and provides theoretical insights for organizations managing employee behavior in digital transformation.