Algorithmic management and employee innovation: unveiling the mediating mechanisms of perceived fit, perceived control and job crafting

With the rapid development of digital technologies, algorithmic management has become an important management model in modern organizations. Drawing on the Job Demands-Resources theory and Self-Determination Theory, this study constructs a chain mediating model to explore how algorithmic management influences employee innovation performance through perceived fit, perceived control, and job crafting. Based on a survey and empirical analysis of 353 employees in high-technology enterprises, the results show that algorithmic management has positive effects on both perceived fit and perceived control, but the effect on perceived fit is significantly stronger than that on perceived control. Moreover, perceived fit and perceived control mediate the relationship between algorithmic management and innovation performance, with job crafting serving as a chain mediator in both pathways. Notably, the mediating effect of perceived control is very weak. This study reveals that the resource-driven mechanism of algorithmic management is significantly stronger than its demand-constraint mechanism. These findings provide theoretical insights and practical implications for organizations to better leverage algorithmic management to foster employee innovation.