Labour market segmentation and the experiences of web-based platform workers in Nigeria
Abstract This study examines how labour market segmentation shapes the rise and lived experiences of web-based platform workers in a developing country, focusing on how digital platforms both reproduce peripheral employment and create new forms of economic opportunity and meaningful work amid local constraints. Adopting a qualitative research design, we conducted semi-structured interviews with 23 Nigerian web-based platform workers and utilised thematic analysis to capture their subjective experiences. We found that web-based platforms have significant implications for work and employment in Nigeria. This is discussed within the study’s three overarching themes, including ‘precarity and economic security’, ‘perception of work meaningfulness’, and ‘country-specific issues’, which point to an upward trajectory in the number of web-based platform workers in the near future. By developing the notion of algorithmic segmentation as a context-sensitive extension of labour market segmentation theory, we show how platform-mediated mechanisms, worker dependence, infrastructure, and reputational conditions shape unequal access to work, income stability, and bargaining power. Our findings highlight the socio-cultural dimensions of meaningful digital labour and inform policy aligned with SDG 8 (decent work), calling for formal legal recognition of platform workers, minimum income guarantees, social protections, and strategic infrastructure investment.
Authors
- Olatunji David Adekoya (ORCID: https://orcid.org/0000-0003-4785-4129)
- Chima Mordi (ORCID: https://orcid.org/0000-0003-1921-1660)
- Hakeem Ajonbadi
- Juliet Jayasuria
Institutions
- University of Doha for Science and Technology (QA)
- Sheffield Hallam University (GB)
Publication Details
- Journal
- The Economic and Labour Relations Review
- Published
- 2026-10-05
- DOI
- https://doi.org/10.1017/elr.2026.10083
- Primary Topic
- Digital Economy and Work Transformation
- Type
- article
- Field-Weighted Citation Impact
- 0.00