Determinants and Consequences of Strategic Silence in the Gig Economy:Evidence from Indonesian Platform Workers
Keywords:
gig economy platform work strategic silence algorithmic governance economic dependence collective networks legal awareness labor process theoryAbstract
Deactivation without due process, opaque rating systems, and automated task allocation make speaking up risky for many platform workers in Indonesia yet most research treats silence as an attitude rather than a governance-shaped behavior. Despite the promise of flexibility, gig workers face structural vulnerabilities: unstable income, opaque algorithmic management, and limited social protection. This study examines the political economy of silence in Indonesia’s gig economy, viewing silence as a strategic survival response to asymmetric platform power. Using data from 1,050 gig workers across six cities, and PLS-SEM, the model specifies five antecedents (fear of deactivation, economic dependency, reputation concerns, algorithmic control, lack of institutional support), two moderators (collective networks, job alternatives), one mediator (strategic silence), and two outcomes (worsened working conditions, mental-health strain). The measurement model meets reliability and convergence thresholds (α ≥ .80; CR ≥ .85; AVE ≥ .50). Structural results indicate that algorithmic control (β = .316) and fear of deactivation (β = .279) are the most salient drivers of strategic silence, with additional effects from economic dependency (β = .244), lack of institutional support (β = .221), and reputation concerns (β = .174). Silence strongly predicts worsened working conditions (β = .697; R2 = .451) and mental-health strain (β = .706; R2 = .492). Collective networks and job alternatives attenuate fear-, dependency-, and support-deficit pathways to silence, and mediation tests confirm that silence transmits governance pressures to adverse outcomes. Findings reframe silence as governance-shaped behavior and support interventions that lower the private cost of voice through transparent metrics, auditable ratings, and due-process deactivation review with human oversight.
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