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

Yeye Li, Jinkai Cheng

Open source

DOI: 10.3389/fpsyg.2026.1871662
Published: 2026-09-03
Container: Frontiers in Psychology
Publisher: Frontiers Media SA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3389/fpsyg.2026.1871662,
  title = {The gig satisfaction gap: a comparative machine learning analysis of job satisfaction determinants between gig and traditional employees},
  author = {Yeye Li and Jinkai Cheng},
  year = {2026},
  journal = {Frontiers in Psychology},
  doi = {10.3389/fpsyg.2026.1871662},
  url = {https://doi.org/10.3389/fpsyg.2026.1871662}
}

RIS

TY  - JOUR
TI  - The gig satisfaction gap: a comparative machine learning analysis of job satisfaction determinants between gig and traditional employees
AU  - Yeye Li
AU  - Jinkai Cheng
PY  - 2026
JO  - Frontiers in Psychology
DO  - 10.3389/fpsyg.2026.1871662
UR  - https://doi.org/10.3389/fpsyg.2026.1871662
ER  -

APA

Li, Y., & Cheng, J. (2026). The gig satisfaction gap: a comparative machine learning analysis of job satisfaction determinants between gig and traditional employees. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2026.1871662

Source records

crossref · retrieved 2026-09-25T10:12:33.579Z