The daily dynamics of algorithmic pressure and traffic safety in gig delivery: a multilevel mediation model of risky riding.

The rapid expansion of on-demand food delivery has brought algorithmic management, the use of artificial intelligence to oversee labor processes, into everyday working life. The same system that maximizes operational efficiency may also place substantial psychosocial burdens on riders. This study examines the daily dynamics linking algorithmic performance pressure, risky riding behaviors, and near-miss incidents among motorcycle delivery workers. Using a daily diary design, we collected longitudinal data from 50 experienced riders in South Korea over seven consecutive days, yielding 317 person-day observations. A multilevel mediation model decomposed the effects into within-person and between-person components while controlling for individual traits, including baseline risky riding tendencies and work experience. Daily algorithmic performance pressure raised the likelihood of near-miss incidents (total effect), but this relationship ran predominantly through an increase in risky riding behaviors (indirect effect). Once the mediator was included, the direct effect of pressure on near-misses was no longer significant, a pattern consistent with an indirect (mediator-dominant) pathway rather than full mediation in the strict sense. Even with riders' baseline risk tendencies held constant, then, algorithmic pressure compromises traffic safety mainly by inducing rule-violating behaviors as compensatory strategies. These results point to algorithmic architecture as a structural antecedent of occupational hazard. Because the safety risk stems from systemic pressure channeled through behavioral pathways rather than from individual negligence, policy should shift from individual-level enforcement toward systemic reform of platform algorithms, including realistic delivery windows and transparent performance metrics.