Human-Centred Information Systems in the Gig Economy: How Personal Resources Buffer Algorithmic Stressors among Female Platform Workers

Abstract

Gender-agnostic algorithmic management on digital platforms creates severe psychological stressors for frontline service workers. Grounded in Human-Centred Information Systems (HCIS) and the Job Demands-Resources (JD-R) framework, this study examines how app-based demands (algorithmic tracking, rating pressure, physical safety hazards, and economic precarity) affect the psychological well-being and job performance of female gig service partners in Delhi-NCR, India. It also tests whether psychological resilience (an internal resource) and perceived social support (an external resource) buffer these effects. Data were collected from 385 female platform workers in the beauty, domestic, and care sectors on platforms such as Urban Company and Housejoy, using the CD-RISC-10, the Multidimensional Scale of Perceived Social Support, and Ryff's Psychological Well-Being Scale, and analysed with Structural Equation Modelling (SEM). Rigid app tracking, rating pressure, and fear of being blocked significantly reduced well-being and job performance. Moderation analysis revealed a two-way buffering effect: resilience weakened the negative effect of algorithmic control on well-being, while social support protected against rating pressure and loneliness. The findings identify key human-computer interaction (HCI) friction points and support three design recommendations: human-in-the-loop recourse for automated deactivation, transparent algorithmic rules, and stress-aware, micro-flexible interfaces.