Watched by the algorithm: algorithmic monitoring, worker resistance and well-being in the gig economy

Purpose This study examines how algorithmic monitoring shapes gig workers’ psychological well-being through passive and active algorithmic resistance. It further investigates whether algorithmic fairness alters the relationship between algorithmic monitoring and resistance behaviors. Design/methodology/approach The study uses a two-study quantitative design. Study 1 applies a three-wave survey of 224 gig workers in Pakistan, with four-week intervals between each wave. Algorithmic monitoring and algorithmic fairness were measured at Time 1, passive and active algorithmic resistance at Time 2 and psychological well-being at Time 3. Study 2 uses a 2 × 2 experiment with 115 participants, manipulating algorithmic monitoring and algorithmic fairness and examining resistance responses and psychological well-being. Findings Study 1 shows that algorithmic monitoring is positively related to passive and active algorithmic resistance, and that both forms of resistance are negatively related to psychological well-being. Passive and active resistance also mediate the relationship between algorithmic monitoring and well-being. Algorithmic fairness weakens the relationship between monitoring and active resistance. Study 2 shows significant interactions between algorithmic monitoring and algorithmic fairness for passive resistance, active resistance and psychological well-being, although the observed resistance patterns differ from the hypothesized form. Originality/value This study advances algorithmic human resource management (HRM) research by showing that gig workers do not merely experience algorithmic monitoring passively but respond through distinct forms of resistance that have implications for psychological well-being. By integrating labor-process theory, organizational justice theory and a stress-and-coping perspective, the study explains how algorithmic monitoring becomes psychologically consequential and how fairness shapes workers’ responses to platform control.