Academic Journal
The daily dynamics of algorithmic pressure and traffic safety in gig delivery: a multilevel mediation model of risky riding.
| Τίτλος: | The daily dynamics of algorithmic pressure and traffic safety in gig delivery: a multilevel mediation model of risky riding. |
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| Συγγραφείς: | Lee J; Department of Police Science, Konkuk University, Chungju 27478, Republic of Korea., Yu T; Department of Human Care, Namseoul University, Cheonan 31020, Republic of Korea., Song N; Department of Transportation Engineering, Myongi University, Yongin 17058, Republic of Korea., Park HC; Department of Transportation Engineering, University of Seoul, Seoul 02504, Republic of Korea. Electronic address: hcpark@uos.ac.kr. |
| Πηγή: | Accident; analysis and prevention [Accid Anal Prev] 2026 Dec; Vol. 238, pp. 108701. Date of Electronic Publication: 2026 Sep 05. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Pergamon Press Country of Publication: England NLM ID: 1254476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2057 (Electronic) Linking ISSN: 00014575 NLM ISO Abbreviation: Accid Anal Prev Subsets: MEDLINE |
| Imprint Name(s): | Publication: Oxford : Pergamon Press Original Publication: [New York, Pergamon Press] |
| Ιατρικοί όροι (MeSH): | Accidents, Traffic*/prevention & control , Accidents, Traffic*/statistics & numerical data , Accidents, Traffic*/psychology , Risk-Taking* , Algorithms* , Motorcycles* , Safety*, Humans ; Republic of Korea ; Adult ; Male ; Female ; Longitudinal Studies ; Multilevel Analysis |
| Περίληψη: | 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. (Copyright © 2026 Elsevier Ltd. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Algorithmic management; Food delivery riders; Multilevel mediation; Near-miss incidents; Performance pressure; Risky riding behavior |
| Entry Date(s): | Date Created: 20260905 Date Completed: 20261009 Latest Revision: 20261009 |
| Update Code: | 20261010 |
| DOI: | 10.1016/j.aap.2026.108701 |
| PMID: | 42700555 |
| Βάση Δεδομένων: | MEDLINE |
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