The gig satisfaction gap: a comparative machine learning analysis of job satisfaction determinants between gig and traditional employees
Yeye Li, Jinkai Cheng · 2026
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Description
Yeye Li and Jinkai Cheng compared job satisfaction between gig workers and staff employees in a 2026 study. Past work could not agree on whether gig workers, who take task-based jobs through apps, feel worse about their work. What drives any gap was also unclear.
The pair mined 19,610 online staff reviews posted from 2011 to 2025. A learning tool sorted the reviews into gig and staff piles with high accuracy. They then weighed which factors mark a happy worker in each pile. Local gig staff, such as drivers and couriers, were the gig group in view. The split view shows where platforms could lift daily work life.
Catalog record
| CREATOR | Yeye Li, Jinkai Cheng |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 57 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |