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Quantifying the gig economy’s evolution: A bibliometric exploration of historical patterns and future projections

Aldijana Bunjak & Olivier Zehnder

Electronic Markets2026https://doi.org/10.1007/s12525-026-00878-zarticle
AJG 2ABDC A
Weight
0.50

What the paper says

This paper provides an overview of gig economy literature up to June 2025, identifying key themes and future research directions. Using multiple bibliometric techniques (co-citation analysis, co-word analysis, bibliographic coupling, and the “invisible colleges” framework), this paper examines major trends in the field. Co-citation analysis highlights topics such as defining the gig economy, innovation, worker well-being, spatial crowdsourcing, and volunteered geographical information. Co-word analysis emphasizes data generation, conceptualization, and enabling mechanisms as influential themes. Bibliographic coupling identifies emerging areas, including gig economy applications in innovation, health research, data collection, and ecological prevention. The analysis covers data up to 2021, with findings compared against literature from 2022 to 2025 to assess predictive value and integrate new developments. Results reveal an expanding conceptual and thematic landscape, demonstrating the role of bibliometric analysis in anticipating research directions and informing future inquiry.

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Cite this paper

https://doi.org/https://doi.org/10.1007/s12525-026-00878-z

Or copy a formatted citation

@article{aldijana2026,
  title        = {{Quantifying the gig economy’s evolution: A bibliometric exploration of historical patterns and future projections}},
  author       = {Aldijana Bunjak & Olivier Zehnder},
  journal      = {Electronic Markets},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s12525-026-00878-z},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.