Algorithmic Management, Perceived Precarity, and Collective Identity Formation Among Indonesian Gig Economy Workers

Abstract

Background: Platform-mediated algorithmic control can reshape employment relationships, perceived precarity, and the mechanisms through which gig workers form collective occupational identities, yet evidence from developing digital economies remains limited.

Objective: To examine the relationships between algorithmic management intensity, perceived precarity, digital literacy, and social identity transformation among Indonesian gig economy workers.

Methods: A cross-sectional survey included 324 workers recruited from ride-hailing (n=128), food-delivery (n=112), and freelance digital-service (n=84) platforms in Jakarta and Surabaya. Four validated instruments measured algorithmic management, precarity, social identity transformation, and digital literacy. Hierarchical multiple regression was performed.

Results: The combined model explained 41.8% of variance in social identity transformation (R²=0.418; adjusted R²=0.395; F(15,308)=14.78; p<0.001). Automated deactivation threat (β=-0.385), performance-rating pressure (β=-0.318), algorithmic surveillance (β=-0.267), and income instability (β=-0.209) were the strongest negative predictors, whereas digital literacy was protective (β=0.198; all p<0.001).

Conclusion: Algorithmic management and perceived precarity were associated with weaker collective identity, while digital literacy showed a protective association, with implications for worker organization, platform governance, and labour policy.