Algorithmic management and working conditions in Europe. Evidence from the AIM-WORK Survey

The diffusion of algorithmic management (AM) is transforming the organisation of work, raising important questions about its implications for job quality and workers’ well-being. However, empirical evidence on these effects remains limited, largely due to the lack of large-scale, comparable, and representative data on the adoption of different AM practices across countries. This paper addresses this gap by exploiting the novel AIM-WORK survey, the first dataset providing detailed information on multiple algorithmic management practices and working conditions among the working population across the 27 European Union Member States. Using this unique source of evidence, we provide the first cross-country quantitative assessment of the relationship between specific AM practices and a broad set of working condition indicators. Our analysis shows that algorithmic management is not a homogeneous phenomenon: different practices have distinct implications for workers. Overall, AM is associated with lower levels of autonomy, reduced ability to take breaks, and higher work-related stress, although the magnitude and direction of these relationships vary substantially depending on the specific technology implemented. Practices involving direct algorithmic direction of work, particularly those regulating task execution and work pace, display the strongest associations with reduced worker discretion and increased work intensification. We also find that exposure to multiple AM practices tends to reinforce negative outcomes, suggesting cumulative effects of algorithmic control. Finally, the analysis reveals substantial cross-country heterogeneity, highlighting the role of institutional and organisational contexts in shaping the consequences of algorithmic management.