Algorithmic Management, Perceived Algorithmic Injustice, and Gig Worker Outcomes in Pakistan: A Time-Lagged, Moderated-Mediation Analysis
DOI:
https://doi.org/10.69565/jems.v4i4.492Keywords:
algorithmic management, gig economy, organizational justice theory, PLSpredict, workforce coordinationAbstract
While algorithmic management (AM) has emerged as the dominant form of workforce coordination on digital labor platforms, there is limited empirical evidence from South Asian gig economies, where norms of informal labor dominate and regulations are weak. This study builds and tests a moderated-mediation model that champions the concept of organizational justice theory and the concept of conservation of resources (COR) theory, with the moderating effect of psychological capital (PsyCap) and the mediating effect of perceived algorithmic injustice between AM and two worker outcomes: emotional exhaustion and resistance behavior. A three-wave, time-lagged research design was used, recruiting 438 ride-hailing and delivery gig workers across five cities of Punjab, Pakistan: Lahore, Faisalabad, Multan, Rawalpindi and Gujranwala. The results for PLS SEM (SmartPLS 4) revealed that AM was significantly positively associated with perceived algorithmic injustice, which subsequently could predict emotional exhaustion and resistance, supported by PLSpredict (out-of-sample) and importance-performance map analysis. Bootstrapped indirect effects confirmed the mediating effect of perceived injustice. Results from the moderated-mediation analysis using the PROCESS macro (Model 7) indicated that the indirect effects of AM on both outcomes were significantly lower for workers with high PsyCap. The results contribute to the algorithmic management literature because they empirically address the input-process-output logic that was proposed in the recent systematic reviews and show the protective relationship between psychological resources and work in an under-investigated, high power-distance context. Theoretical and practical implications for platform design, labor policy and worker training to Pakistan and similar emerging gig economies are discussed.
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