The Digital Doorway: Technology Access and Digital Literacy as Determinants of Youth Participation in the Gig Economy in Embakasi Sub- County, Nairobi County, Kenya

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Published Jan 13, 2026
https://doi.org/10.71064/spu.amjr.10.2.2025.494
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Vol. 10 No. 2 (2025): African Multidisciplinary Journal of Research

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Denis Ngetha Mwangi
St. Paul's University

Abstract

This study investigated how technology access and digital literacy determine youth participation in the gig economy in Embakasi Sub-County, Nairobi City County, Kenya. Employing a cross-sectional mixed-methods research design, the study utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) as its theoretical framework. Quantitative data were collected from 315 randomly selected respondents, supplemented by qualitative insights from nine key informants. A combination of cluster sampling and simple random sampling was used to select survey participants. Four constituencies within Embakasi Sub-County (Embakasi East, Embakasi West, Embakasi Central, and Embakasi South) served as primary clusters, further grouped into nineteen wards. Key findings revealed that while 92.1% of gig workers reported reliable internet access, primarily via mobile data (48.6%), a significant portion (52.1%) perceived their current technology as limiting. Technical difficulties frequently impacted gig opportunities (64.4%), and 79.7% believed better technology would increase earnings, leading 71.1% to invest in gig-specific technology. Bivariate analysis indicated significant associations between gig participation and education level, marital status, age, training, and specific skills (managerial, creative, technical). Interestingly, perceived freedom and flexibility were not statistically significant motivators for sustained participation. The study recommends subsidized internet, accessible device financing, and comprehensive digital literacy programs integrated into education. Furthermore, it suggests tailored skills training focusing on high-demand areas and life skills, improved digital infrastructure, and awareness campaigns on technology investment to enhance earnings. Collaborative efforts for accessible technical support are also advised to reduce missed opportunities.

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How to Cite
Ngetha Mwangi , D. (2026). The Digital Doorway: Technology Access and Digital Literacy as Determinants of Youth Participation in the Gig Economy in Embakasi Sub- County, Nairobi County, Kenya. African Multidisciplinary Journal of Research, 10(2), 135–189. https://doi.org/10.71064/spu.amjr.10.2.2025.494

References

  1. Anderson, M. (2018). Teens, social media & technology 2018. Pew Research Center. https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/
  2. Chinn, M. D., & Fairlie, R. W. (2018). ICT use in the developing world: An analysis of differences in computer and internet penetration. Review of International Economics, 18(1), 153–167. https://doi.org/10.1111/j.1467-9396.2009.00861.x
  3. Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE.
  4. Graham, M., Hjorth, I., & Lehdonvirta, V. (2017). Digital labour and development: Impacts of global digital labour platforms and the gig economy on worker livelihoods. Transfer: European Review of Labour and Research, 23(2), 135-162.
  5. Harris, L. R. (2020). Cross-sectional research methods in education and the social sciences. Routledge. https://doi.org/10.4324/9780429263788
  6. Housman, M. (2013). ICT and human capacity building: Why technology alone won’t help solve unemployment. Brookings Institution.
  7. Kenya National Bureau of Statistics (KNBS). (2022). 2022 economic survey. KNBS. https://www.knbs.or.ke/?wpdmpro=economic-survey-2022
  8. Kumar, R. (2020). Technological advancements and youth employment: Opportunities and challenges. International Journal of Research in Social Sciences, 10(4), 113–123.
  9. Lane, M. (2021). Digital transformation and the future of work: Opportunities and challenges for youth. OECD Publishing. https://doi.org/10.1787/b94b26f8-en
  10. Luan, W. S., & Teo, T. (2009). Investigating the technology acceptance among student teachers in Malaysia. Asian Social Science, 5(8), 123–128. https://doi.org/10.5539/ass.v5n8p123
  11. Marchewka, J. T., Liu, C., & Kostiwa, K. (2007). An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA, 7(2), 93–104.
  12. Mwakatumbula, H., & Moshi, G. (2020). Digital literacy and challenges facing Uber drivers in Tanzania. Journal of African Digital Economy, 5(1), 45–59.
  13. Rivard, S., Raymond, L., Verreault, D., & Bergeron, F. (2014). Development of information technology: A conceptual and empirical exploration. Information & Management, 51(5), 590–605. https://doi.org/10.1016/j.im.2014.03.002
  14. Saadat, S. (2020). The impact of technological advancement on labor market dynamics. Journal of Economic Perspectives and Policy, 12(3), 89–102.
  15. Stefano, V. D. (2019). Digital labour platforms and the future of work: Towards decent work in the online world. International Labour Office (ILO). https://www.ilo.org/global/publications/books/WCMS_645337/lang--en/index.htm
  16. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.