Abstract
Background
Algorithmic management (AM) is increasingly taking over leadership tasks that were previously handled by human managers, such as task allocation and performance monitoring, and transferring them to data-driven systems. AM is currently particularly used in platform work and logistics and affects psychosocial working conditions: it can reduce social resources such as social support and cohesion at work, which may have a negative impact on well-being. The aim is to systematically search study findings on AM and social support (interventions) and provide an overview of potential organizational measures.
Methods
A systematic search was conducted in accordance with the Population–Concept–Context (PCC) framework. Key inclusion criteria comprised employees, aspects of social support, and the use of AM at work. Searches were performed in Web of Science, PsycINFO, and Google Scholar up to March 2026, complemented by expert consultation. Two reviewers conducted title, abstract screening with support from a third reviewer. The extracted data will be analyzed both descriptively and thematically.
Results
In total, 2303 articles were identified. Of these, via expert consultations eight articles were provided. After the removal of duplicates (n = 124), 69 studies were assessed in full text screening. Preliminary results suggest that AM reduces social support and increases isolation, partly offset by informal peer networks. Strategies fostering interaction e.g., (informal), social spaces and participatory AM features (e.g., shift voting), along with transparent and supportive leadership, may buffer negative effects. Further results will be presented at the conference.
Conclusions
Current evidence suggests that algorithmic management may negatively affect employees’ perceived social support at work. At the same time, research on how to mitigate these effects remains limited, highlighting the need for effective organizational strategies.
Key messages
• Algorithmic management can reduce social support and cohesion at work, increasing isolation and potentially harming employee well-being.
• Evidence on solutions is limited; interaction, participatory systems, and supportive leadership may help buffer negative effects.