Algorithmic Control, Financial Precarity, and Subjective Well-being Among Indonesian Gig Workers: Evidence from PLS-SEM Using the Job Demands-Resources Framework

Abstract

The rapid expansion of platform-mediated gig work in the Global South has intensified debate over whether digital labor constitutes economic liberation or structured precarity. This study examined the structural associations among algorithmic management, financial precarity, perceived autonomy, subjective well-being, and job satisfaction among Indonesian gig workers, anchored in the job demands-resources (JD-R) framework. This investigation represents the first large-scale PLS-SEM analysis to delineate the financial precarity pathway within Indonesia's on-demand economy, addressing a critical gap in the Global South platform labor literature. A cross-sectional quantitative design was employed, with data collected from 684 motorcycle taxi and courier drivers across Jakarta, Surabaya, and Medan between March and May 2024 using stratified random sampling. Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5,000-subsample bootstrapping was applied via SmartPLS 4.0. Common method bias was assessed using Harman's single factor test (variance explained = 28.4%, below the 50% threshold). Measurement model assessment confirmed strong reliability (rho_A range: 0.847–0.928; AVE range: 0.688–0.810). Structural analysis revealed that algorithmic management was significantly positively associated with financial precarity (β = 0.642, p < 0.001) and negatively associated with subjective well-being (β = −0.210, p < 0.001). Financial precarity mediated this relationship (indirect β = −0.328, 95% CI [−0.392, −0.265], p < 0.001), constituting partial mediation. Perceived autonomy was positively associated with job satisfaction (β = 0.315, p < 0.001). In conclusion, algorithmic management in Indonesia's on-demand economy operates primarily through financial fragility to suppress worker well-being. These findings support urgent regulatory reform mandating algorithmic transparency and minimum income protection floors for platform workers.