Digital-Local Food Networks: Design, Implementation, and Evaluation in Urban Slums

Thesis Abstract

Urban slums face persistent food insecurity and fragmented informal markets, with residents’ access to fresh, affordable produce constrained by geographic, economic, and digital divides. This study addresses the problem of how digital-local food networks can be designed, implemented, and evaluated to enhance food access, resilience, and livelihoods in urban slums, while ensuring inclusivity for informal vendors and marginalized consumers. The aim is to develop a scalable, participatory digital platform that integrates local food suppliers, micro-entrepreneurs, and residents into a transparent, affordable, and reliable supply chain. Specific objectives are (1) to map existing food exchange practices, informal networks, and mobile device usage among slum communities; (2) to co-design and pilot a digital-local food network prototype that facilitates producer–consumer linkages, price transparency, and order fulfillment; (3) to implement the platform in two slum contexts for a six-month period and assess adoption, usability, and impact on food access; (4) to evaluate economic, social, and health dimensions of the network using mixed methods; and (5) to produce a set of policy and practice recommendations for scale-up and governance. The methodology employs an explanatory sequential mixed-methods design. The population comprises residents, informal food vendors, and small-scale farmers supplying urban slums in Lagos and Nairobi. A purposive sample of 200 households will be surveyed to measure food security, expenditure on fresh produce, and digital literacy, complemented by 40 in-depth interviews with vendors, platform facilitators, and community leaders to capture contextual and operational factors. For the pilot, 60 vendors and 400 households will participate in the digital network over six months. Data collection instruments include a structured household survey, semi-structured interview guides, focus group discussion protocols, platform usage analytics, and market price records. Validity and reliability will be ensured through pilot testing, triangulation, and inter-rater coding checks (Cohen’s kappa) for qualitative data. Data analysis will integrate descriptive statistics, difference-in-differences (DID) analysis to assess changes in food expenditure and variability, regression models to identify determinants of platform adoption and food access outcomes, and thematic analysis for qualitative data. A conceptual model grounded in the Resource-Based View and Technology Acceptance Model will guide interpretation, with diffusion of innovations theory informing adoption dynamics. The thematic analysis will be conducted using NVivo, and regression analyses will be performed in R, with robustness checks including propensity score matching to address selection bias. Key expected findings include (i) enhanced access to affordable, fresh produce via digital aggregations that reduce time and transaction costs for households; (ii) increased visibility and bargaining power for small vendors and farmers, leading to improved margins and reduced spoilage; (iii) evidence of user-friendly design features that accommodate low digital literacy, multilingual interfaces, and inclusive payment options; (iv) identification of barriers such as device access, network reliability, trust, and regulatory constraints; (v) differential impacts by gender, household income, and proximity to markets, with policy-relevant insights on equitable benefits. The study anticipates that the digital-local network will produce measurable improvements in per capita fresh produce expenditure efficiency, dietary diversity scores, and vendor income stability, while maintaining acceptable levels of platform-enabled food safety and quality controls. Contributions to knowledge include a practical framework for co-designing digital-local food networks in resource-constrained urban settings, an evidence base on the social and economic implications of digitizing informal food systems, and an evaluative model combining DID, regression, and thematic analysis to gauge impact across multiple dimensions. The study concludes that carefully designed digital platforms, underpinned by community governance and interoperability with existing informal economies, can materially enhance food security and resilience in urban slums. Recommendations emphasize participatory governance structures, capacity-building for vendors and residents, scalable payment and logistics solutions, and policy interventions to support digital inclusion, data privacy, and platform regulation to ensure equitable benefit distribution.