The gig satisfaction gap: a comparative machine learning analysis of job satisfaction determinants between gig and traditional employees

Vollständiger Abstract

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Introduction Prior research has yielded inconsistent findings regarding whether gig workers experience lower job satisfaction than traditional employees, while the specific factors underlying these differences remain insufficiently understood. This study systematically compares job satisfaction between location-based gig workers and traditional employees and identifies the factors that characterize their respective work experiences. Methods Using 19,610 online employee reviews collected between 2011 and 2025, we developed a machine-learning text classifier with 92.22% accuracy to distinguish reviews written by gig workers from those written by traditional employees. We then combined independent-samples t -tests, sentiment analysis, and topic modeling to compare satisfaction levels, emotional patterns, and the factors associated with job satisfaction across the two groups. Results The results show that gig workers and traditional employees report comparable levels of work–life balance satisfaction, but gig workers exhibit significantly lower overall job satisfaction, with the largest disparity observed in compensation satisfaction. Textual analyses further reveal distinct patterns in the two groups’ work experiences. Gig workers express predominantly negative sentiment toward driver-related costs, job allocation, and wages, whereas traditional employees place greater emphasis on organizational culture, career growth, and employee benefits. Topic modeling identifies 25 factors associated with job satisfaction and highlights several gig-specific concerns, particularly driving costs and the allocation of work opportunities. Discussion These findings help reconcile inconsistent evidence on the gig–traditional worker satisfaction gap by showing that the disparity is dimension-specific rather than uniform across all aspects of work. By identifying work arrangements and economic conditions that are particularly salient to location-based gig workers, this study extends understanding of job satisfaction in platform-mediated work and provides actionable implications for platform managers and policymakers seeking to improve gig workers’ work experiences.