Unlocking flow in the gig economy: A configurational study based on an integrated JD-R and regulatory fit perspective.

Abstract

Research by platform management and academic scholars on how to improve the flow experience for platform workers has yet to yield clear conclusions. By integrating the Job Demands-Resources (JD-R) model with Regulatory Fit Theory, this study develops a configurational framework to examine how the interplay between job demands, job resources, and workers' regulatory focus shapes the flow experience. This relationship is analyzed using fuzzy-set Qualitative Comparative Analysis (fsQCA). Its findings reveal that: (1) no single antecedent variable independently constitutes a necessary condition for high flow experiences, underscoring the phenomenon's causal complexity; (2) two types of configurations are identified across five pathways associated with the absence of flow experience in gig workers: "predominant promotion motivation under high demands-resources type" and "Dual motivation under high demands-resources type"; (3) two types of configuration are identified across five pathways that undermine flow experience in gig workers: "error-based deprivation of resources-motivation type" and "underload-based dual deprivation of resources-motivation type". This research not only deepens our understanding of the complexity and multidimensionality of the causes of flow experience in gig workers but also provides practical guidance for platform managers to enhance flow experience effectively.