The Impact of Algorithmic Management on Shared Mobility Platforms on the Labor Sustainability of Ride-Hailing Drivers: A Multilevel Analysis of Psychological Alienation and Perceptions of Decent Work

Against the rapid growth of the platform economy, the labor sustainability of ride-hailing drivers has become an important issue in digital economy governance and social sustainability. Existing studies have mainly focused on the operational efficiency, income mechanisms, and labor control of shared mobility platforms, while paying insufficient attention to how algorithmic management affects drivers’ long-term work sustainability through psychological mechanisms. Drawing on labor process theory, this study develops a multilevel analytical framework to examine how platform algorithmic management influences the labor sustainability of ride-hailing drivers. Algorithmic management is conceptualized as a multidimensional structure consisting of algorithmic control, algorithmic opacity, and income uncertainty. Psychological alienation and perceived decent work are introduced as key mechanisms, while the cross-level role of the local institutional context is also examined. Based on survey data from ride-hailing drivers in 39 cities across China, with 20 drivers from each city and 780 valid responses in total, this study applies multilevel modeling for empirical analysis. The findings show that platform algorithmic management shapes drivers’ psychological experiences through a multidimensional control structure and further affects labor sustainability through perceived decent work. The local institutional environment mainly plays a direct protective role rather than amplifying the psychological pathway.