Although algorithmic management’s impact on gig workers is extensively studied, its psychological spillover into private life—particularly the emergence of intimate partner surveillance (IPS) as a control-compensation mechanism—remains a critical blind spot. This investigation theorizes algorithmic management as a catalyst for IPS behaviors, testing perceived loss of control as a mediating mechanism and psychological capital (PsyCap) and algorithmic transparency as moderators.
Using a two-wave design, survey data were collected from 220 gig workers in Pakistan’s on-demand transport and delivery sectors. Relationships were tested via PROCESS Macro after controlling for key covariates.
Algorithmic management was significantly associated with both directly and indirectly via perceived loss of control. PsyCap and transparency each buffered algorithmic management’s effect on control loss but did not significantly attenuate its indirect effect on IPS.
This study’s causal claims are tempered by its focus on a platform-governed workforce—without comparison to traditional service workers unaffected by algorithmic control. While the male sample mirrors Pakistan’s gig economy reality, self-reported IPS measures and cultural specificity may limit transferability to contexts with different gender-power dynamics.
Platform-level interventions (PsyCap initiatives, transparency enhancements) may partially mitigate control erosion but appear insufficient alone to prevent relational spillover. Complementary family-level strategies (e.g. open communication and mutual privacy agreements) may be needed.
By integrating algorithmic management and work-family scholarship through a multi-theoretical lens, this study offers a groundbreaking analysis of how digital labor platforms extend their control beyond the workplace, disrupting personal relationships in ways previously overlooked.
