AI-Enabled Algorithmic Management and Frontline Employee Job Crafting: The Roles of Control, Support, and Managerial Orientation

Algorithmic management combines functions that regulate work with those that provide information and guidance, yet their distinct psychological and behavioral associations require further examination. Drawing on the Job Demands–Resources framework and a sociotechnical perspective, this study examines algorithmic control and support in relation to frontline employees’ job crafting, distinguishing system functions from perceived managerial enactment. Three-wave time-lagged survey data from 547 employees in fast-food chain outlets were analyzed using partial least squares structural equation modeling. Algorithmic support was positively associated with work engagement and negatively associated with exhaustion, whereas algorithmic control showed the opposite pattern. Indirect-effect estimates were consistent with support being associated with greater promotion-focused crafting through engagement and lower prevention-focused crafting through exhaustion. Control was associated with greater prevention-focused crafting through exhaustion and lower promotion-focused crafting through engagement. The support–engagement relationship was stronger at higher perceived managerial decision-support orientation, while the control–exhaustion relationship was stronger at higher perceived enforcement orientation. These findings highlight the value of examining algorithmic functions across both motivational and strain-related pathways and considering how managers enact system outputs. The observational design supports associations consistent with the proposed mechanisms rather than causal conclusions.