Flexibility, Satisfaction, and the Performance of Ride-Hailing Platform Job

This study examines the influence of work motivation, flexible working hours, and job satisfaction on the performance of ride-hailing platform jobs in emerging economies, thereby addressing the issue of global algorithmic flexibility paradox in digital platform economy. A causal-explanatory quantitative research design was employed, analysing primary data from 213 ride-hailing drivers in an emerging metropolitan market. Data were evaluated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Our findings indicate that flexible working hours directly improve performance but fail to generate job satisfaction. This confirms the flexibility paradox, wherein algorithmic management neutralizes the psychological benefits of schedule autonomy, reducing flexibility to a mere operational utility rather than an emotional resource. This study advances management theory by repositioning Herzberg’s Two-Factor Theory and the Job Demands-Resources model within algorithmically mediated labour environments, demonstrating that work motivation requires full mediation through job satisfaction to influence performance. Digital platform administrators globally must transition from opaque, punitive algorithmic management to transparent, human-centric algorithmic design. This study highlights the vulnerability of workers who bear the operational risks of digital platform economies, advocating for regulatory interventions to protect digital platform workers. It empirically validates the flexibility paradox, offering a framework for understanding digital platform worker behaviour in emerging economy contexts.