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Dependence and non-intervention: Explaining drivers’ experience of taxi platforms’ algorithmic management in Oslo, Norway

Sigurd MN Oppegaard

Economic and Industrial Democracy: an international journal2026https://doi.org/10.1177/0143831x261437378article
AJG 3ABDC A
Weight
0.50

Abstract

While attracting workers by promising autonomy, gig platforms exercise significant control through algorithmic management. This article explores the algorithmic management of taxi platforms in Oslo, Norway and how it is experienced by the drivers. Drawing on extensive fieldwork in the industry, it shows that although the algorithmic management is opaque, unpredictable, and non-negotiable, the drivers nonetheless highlight their flexibility and do not necessarily experience it as control. The analysis highlights two factors explaining these experiences. First, drivers in Oslo have access to multiple platforms and non-platform market segments and therefore remain economically independent of each individual platform. Second, the platforms’ algorithmic management is a non-interventionist form of control, operating primarily through market mechanisms and without intervening directly in the drivers’ labor process. This underscores the importance of market structures in conditioning algorithmic management and emphasizes how platforms are able to provide autonomy at work while maintaining authority over the market.

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https://doi.org/https://doi.org/10.1177/0143831x261437378

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@article{sigurd2026,
  title        = {{Dependence and non-intervention: Explaining drivers’ experience of taxi platforms’ algorithmic management in Oslo, Norway}},
  author       = {Sigurd MN Oppegaard},
  journal      = {Economic and Industrial Democracy: an international journal},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/0143831x261437378},
}

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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.