Understanding Gig Workers Resistance to Algorithmic Control

Companies are increasingly using digital technologies and algorithms to control employees. Control measures that were originally performed by human managers are in part completely automated by technologies. Such systems require large amounts of behavioral data to enable constant surveillance and performance evaluation. Now, the use of algorithms for management and control triggers various forms of resistance behavior. Thus, the goal of the project is to gain a nuanced understanding of the mechanisms behind gig workers` resistance to algorithmic control. The research project focuses on the so-called gig economy, i.e. that part of the labor market in which temporary jobs are assigned flexibly on an ad-hoc basis. Well-known examples are so-called ride hailing services, such as Uber and Lyft, which control the behavior of their drivers through the mandatory use of an app. For example, not only is the route specified by the app, but the algorithm also tries to manipulate the drivers into accepting certain rides by means of incentives and sanctions. But also in the field of crowd working, algorithmic management is increasingly used. A nuanced understanding of the mechanisms behind gig workers` resistance to algorithmic control is urgently needed for research in this area, such as the socio-emotional and economic impact on workers, as well as ethical considerations for the use of algorithmic management. In addition, the results of this project also have important practical implications, whether for explaining the causes of negative consequences of algorithmic control, such as high turnover rates, or for designing algorithmic management to help improve worker job satisfaction.