Platform gig work conditions and workers’ perceptions of decent work: a configurational and necessity perspective

Abstract This study adopts a configurational perspective to investigate how diverse conditions jointly shape platform gig workers’ perception of decent work. Focusing on six key variables, namely perceived algorithmic control, surface acting, deep acting, Smart Technology, AI, Robotics, and Algorithms (STARA) awareness, gender, and working hours, the analysis integrates fuzzy set Qualitative Comparative Analysis (fsQCA) with Necessary Condition Analysis (NCA). Drawing on 316 longitudinally collected questionnaires from gig workers, the analysis demonstrates that perception of decent work emerges through multiple distinct pathways, including a “deep acting–female gig worker” type, a “low STARA awareness–low working hours” type, and a “deep acting–driven” type. Particularly noteworthy is the finding that functions as an important contextual condition associated with positive perceptions, while female gig workers tend to report higher evaluations of decent work. By revealing these complex and asymmetric mechanisms, the study advances understanding of how individual, technological, and structural factors intersect in shaping workers’ experiences. Beyond its empirical insights, the research contributes to the growing use of NCA in conjunction with fsQCA, offering a more nuanced lens, offering a more nuanced lens for analyzing decent work in the platform economy.