The impact of algorithmic control on app-workers’ counterproductive work behavior: from an objectification perspective

Purpose Drawing on the stressor-strain-outcome model and an objectification perspective, this study examines how algorithmic control is associated with app-workers’ counterproductive work behavior through organizational objectification and self-objectification. It further investigates perceived algorithmic transparency as a boundary condition. Design/methodology/approach Data were collected from 498 DiDi Chuxing drivers in China through a three-wave survey. The measurement model was assessed using SmartPLS 4, while ordinary least squares regression and conditional process analysis in SPSS 27.0 were used to test the direct, indirect, serial mediation, and moderation effects. Findings Algorithmic control was positively associated with counterproductive work behavior through organizational objectification and self-objectification, both separately and sequentially. Perceived algorithmic transparency weakened the positive relationships between algorithmic control and both forms of objectification. Practical implications Algorithmic governance may backfire when workers perceive that they are being treated as instruments or begin to evaluate themselves in instrumental terms. Platforms should reduce instrumental treatment in evaluation and enforcement, provide explanatory feedback, and use targeted transparency to support workers’ understanding of algorithmic decisions. Originality/value This study identifies a sequential objectification mechanism linking algorithmic control to counterproductive work behavior and demonstrates how perceived algorithmic transparency conditions this process.