Looking at both sides of algorithmic control and employee well-being: a job demands−resources model

Objectives: Drawing on the job demands-resources (JD-R) theory, this study aims to analyze the impact of algorithmic control on the well-being of delivery drivers by focusing on the mediating role of job demands (work overload and time pressure) and job resources (feedback quality and role clarity). Methods: This study obtained three-wave data from 435 delivery drivers and examined the hypotheses using structural equation modelling. Results: The results indicated that algorithmic control reduced delivery drivers' well-being by increasing the job demands (time pressure and work overload). In addition, algorithmic control enhanced delivery drivers' well-being by increasing their level of job resources (feedback quality and role clarity). Conclusion: Integrating job demands-resources (JD-R) theory, this study provides a more balanced view of how algorithmic control influences well-being by demonstrating the opposing mediating roles of job demands (work overload and time pressure) and job resources (feedback quality and role clarity).