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

## TL;DR

This study shows how algorithmic control affects delivery drivers' well-being through both increased stress and improved job resources.

## Contribution

It introduces a balanced view of algorithmic control using the JD-R model to show opposing effects on well-being.

## Key findings

- Algorithmic control increases job demands like time pressure and work overload, reducing well-being.
- Algorithmic control also increases job resources like feedback quality and role clarity, improving well-being.
- The study demonstrates opposing mediating roles of job demands and resources in the JD-R model.

## Abstract

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).

This study obtained three-wave data from 435 delivery drivers and examined the hypotheses using structural equation modelling.

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).

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).