Employees subject to algorithmic management are more likely to experience job strain, according to a new report from the European Foundation for the Improvement of Living and Working Conditions (Eurofound), which identified that performance monitoring was most strongly associated with poorer health and wellbeing outcomes.
The report, Algorithmic Management in European Workplaces, analysed how algorithmic management affects working conditions and employee wellbeing. Researchers focused on three functions: performance monitoring, automated task allocation, and automated scheduling.
“Performance monitoring is most strongly associated with poorer health and well-being outcomes and exacerbates the adverse effects of job strain in already strained jobs,” the report said.
Eurofound said that 19% of EU employees said a computer program monitored their performance at work, 18% said a computer program allocated their tasks, and 10% said a computer program determined their working hours.
Economic sector is a significant predictor of the use of algorithmic management, the report found, with such practices commonplace in finance, real estate, information and communications, and transport and storage industries. Denmark had the highest algorithmic management intensity, followed by Belgium and Ireland, the report said. Employees subjected to algorithmic management reported higher job demands, and subsequently job strain, but its relationship with access to job resources was more mixed.
Performance monitoring emerged as the form most strongly associated with poorer health and wellbeing outcomes, the report said. By contrast, automated task allocation was the strongest predictor of job transformation, including the creation of new tasks, the removal of others, and greater interaction with colleagues. Automated scheduling and task allocation produced more mixed results than performance monitoring.
However, “this pattern should not be interpreted as evidence that automated task allocation enhances the availability of these job resources. Rather, it may reflect the organisational characteristics of workplaces in which automated task allocation is more commonly used,” the report said.
The relationship between algorithmic management and wellbeing is complex, researchers emphasised. Ultimately, the findings suggest that the implications of algorithmic management for job quality depend on the specific functions through which it is implemented and their interaction with existing working conditions, particularly the balance between job demands and job resources.
The report encourages employers to assess if the current use of those functions is proportionate and fair.
“As [algorithmic managements’] adoption expands, attention should be paid to the implications for job quality by ensuring that its implementation is accompanied by adequate job resources, employee support and safeguards for healthy working conditions,” the report said.
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