Algorithmic management and wellbeng at work

This thesis explores the relationship between Algorithmic Management (AM) and employee well-being in standard work environments. AM, which refers to the use of algorithms to assume managerial functions traditionally handled by humans, is an emerging phenomenon that promises efficiency and objectivity. However, its impact on well-being is still under heated debate, particularly regarding when and why AM leads to positive or negative well-being outcomes for employees. This research seeks to address this debate by examining AM from different perspectives. Specifically, a multi-paradigmatic approach is employed to study when AM enhances or erodes engagement, autonomy, and overall well-being, as well as why these outcomes occur. Drawing on both positivist and interpretivist approaches, this thesis integrates quantitative and qualitative methods to investigate the mechanisms, boundary conditions, as well as sensemaking processes that shape the AM-well-being relationship. First, in line with a positivist perspective, we use quantitative surveys to explore how AM relates to employee engagement, mediated by social and economic exchanges. The findings of the first study suggest AM shifts work interactions from social to economic, often correlating with lower engagement. A close leader’s moderating role is highlighted, showing that strong interpersonal relationships can buffer these negative effects. The second study addresses job autonomy, revealing that AM’s association with reduced autonomy is influenced by factors like systemic justice and individual proactivity, with high justice and proactivity mitigating the loss the job autonomy. The final study adopts an interpretivist approach, using qualitative methods to explore how employees make sense of and respond to AM in environments marked by uncertainty and complexity. It shows that employees actively reinterpret or resist AM’s influence on their well- being and emphasizes employees’ political potential to reshape workplace dynamics in an AM context. The thesis concludes that AM poses challenges to employee well-being in standard work settings, despite efficiency gains. To balance these aspects, organizations should implement AM systems that prioritize genuine interpersonal relationships, justice, and autonomy. This research provides a nuanced understanding of AM’s dual nature and practical insights for its ethical use at work.