ScholarWorks@중앙대학교

초록

This study empirically analyzes how perceived job replacement anxiety among dawn delivery workers in digital platform labor environments is moderated by employment type and algorithmic control. Analyzing survey data from 942 dawn delivery workers, we measured perceived job insecurity across three dimensions: automation risk, platform substitution risk, and skill deficiency replacement anxiety. Results indicate that independent contractors and gig workers experience higher levels of employment insecurity compared to regular employees; however, paradoxically, as algorithmic control intensity increases, employment insecurity among these precarious workers significantly diminishes. This paradoxical phenomenon can be interpreted as workers developing “practical skill” through continuous exposure to algorithmic control, whereby they become acclimated to platform-based work environments and internalize algorithmic rules and operational mechanisms concerning task selection, work pace, and route optimization. While this competence enables predictable work performance and income security despite precarious labor conditions, it simultaneously functions as an ideological mechanism that obscures structural inequalities and manufactures tacit consent to exploitation. This study provides theoretical and policy implications by empirically analyzing the complex interactions between algorithmic control and subjective employment insecurity among dawn delivery platform workers, highlighting both the structural contradictions of platform labor and the necessity for institutional interventions to protect workers.

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