Algorithmic platform management and risk-taking behavior among Chinese food delivery riders

Food delivery riders in China face widespread risk-taking behavior. This behavior is tied to algorithmic platform management. It is become more pressing as the digital platform economy expands. Drawing on Cognitive Appraisal Theory and Persistent Cognition Theory, this study develops a dual-pathway model integrating online immediate and offline sustained cognition, using two-wave questionnaire data from 320 riders in Changsha. Results show algorithmic platform management promotes risk-taking behavior via perceived algorithmic control and emotional rumination, while inhibiting it through problem-solving contemplation. Self-control resource depletion moderates the mediating role of perceived algorithmic control, and learning agility moderates that of perceived algorithmic control and emotional rumination. These findings clarify the cognitive mechanism linking algorithmic platform management to riders' risk-taking behavior, offering actionable insights for platforms to optimize algorithms, protect rider safety, and advance harmonious digital labor relations.