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

Abstract

This cross-sectional study examines the relationships between algorithmic management intensity, perceived precarity, digital literacy, and social identity transformation among Indonesian gig economy workers (n=324). Drawing on social identity theory and precarious work frameworks, we investigate how platform-mediated algorithmic control systems affect collective identity formation processes in one of Southeast Asia’s largest digital labor markets. Participants were 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 intensity (16 items, α=0.89), perceived precarity (12 items, α=0.86), social identity transformation (20 items, α=0.91), and digital literacy (8 items, α=0.84). Hierarchical multiple regression analysis revealed that 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, Cohen’s f²=0.718). The strongest predictors were automated deactivation threat (β=-0.385, p<0.001), performance rating pressure (β=-0.318, p<0.001), algorithmic surveillance (β=-0.267, p<0.001), and income instability (β=-0.209, p<0.001). Digital literacy emerged as a significant protective factor (β=0.198, p<0.001), suggesting that workers with greater digital competencies maintain stronger collective identities despite algorithmic pressures. These findings extend social identity theory to platform labor contexts and demonstrate that algorithmic management systems fundamentally disrupt traditional mechanisms of collective identity formation, with implications for worker organizing, platform governance, and labor policy in developing digital economies.