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Looking at both sides of algorithmic control and employee well-being: a job demands-resources model
Lu Zhang1, Xuehang Ling2, Chen Yang2
1Department of Business Administration, Guizhou University of Finance and Economics, Guiyang, China.
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).
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