Africa’s informality, institutional hybridity and resource constraints reshape supply‑chain theory and open up high‑value opportunities for AI economics research; IJOPM’s AfIn initiative aims to build local capacity and publish theory and empirical work leveraging these contexts.

Main Finding

The paper argues that Africa’s distinctive OSCM (operations and supply chain management) contexts—centred on a dominant informal economy, deep socio-cultural heterogeneity, traditional/survival-oriented values, weak formal but strong informal institutions, rapid population growth potential, abundant natural resources with low economic outcomes, and acute environmental/logistics constraints—offer underexploited opportunities to refine existing OSCM theories and generate new ones. The authors introduce IJOPM’s Africa Initiative (AfIn) to promote, support and disseminate OSCM research “on and from Africa,” and they map how these contextual factors underpin five OSCM research themes where African data can generate novel theoretical contributions.

Key Points

• Seven contextual factors salient for OSCM in Africa:
• Informal economy and organizations: very high prevalence (e.g., ~85.8% of workforce in informal sector), many micro and unregistered firms, low industrialization, large role of subsistence agriculture and low-productivity sectors.
• Socio-cultural diversity and complexity: extreme linguistic, ethnic and cultural heterogeneity across and within countries that complicates consumer demand, coordination and institutional interactions.
• Traditional and survival-oriented cultures: stronger emphasis on religion, family, authority and survival values vs. secular/self-expression values—this shapes organizing, governance and collective mechanisms (e.g., market “queens”, solidarity practices).
• Weak formal institutions coexisting with powerful informal institutions: low scores on rule of law, governance and logistics, but strong normative and cultural-cognitive actors (chiefs, religious leaders, trade associations) that influence markets and can act as governance intermediaries.
• Population growth potential: youthful populations and expanding consumer-market potential.
• Abundant resources but low outcomes: natural resource endowment that has not translated into commensurate productivity, trade or development outcomes.
• High environmental constraints: poor infrastructure and logistics performance, low ease-of-doing-business indicators, macro-political and economic instability in places.
• Five OSCM themes through which African contexts can advance theory and practice:
• Serving heterogeneous consumer markets.
• Managing (scarce or contested) resources.
• Managing factor-market rivalry (competition for labor, land, finance).
• Managing environmental hostility (infrastructure, logistics, shocks).
• Managing institutions (interplay between formal and informal governance).
• Much existing OSCM theory was developed outside Africa and often overlooks the operational realities shaped by informality, institutional hybridity and socio-cultural complexity. Africa-based empirical research can both refine existing concepts and produce genuinely new theory.
• IJOPM’s Africa Initiative (AfIn), launched January 2025, creates a dedicated outlet/section and support platform to guide, promote and facilitate African OSCM research; the paper outlines the initiative’s motivation, objectives and support/review structures.

Data & Methods

• Research type: conceptual and integrative research paper (research review and synthesis), not an original empirical study.
• Method: multi-disciplinary literature synthesis — authors draw on OSCM literature on Africa plus complementary literatures (international business, entrepreneurship, organization studies, policy reports and major datasets) to identify and unpack salient contextual factors and map them to OSCM research themes.
• Theoretical framing: contingency theory and context-sensitive approaches to examine how external and internal environments shape OSCM choices and outcomes.
• Outputs: conceptual mapping (seven contextual factors → five OSCM research themes) and a practical agenda for advancing Africa-centered OSCM research, together with description of IJOPM’s AfIn.

Implications for AI Economics

The paper’s contextual characterization of African OSCM environments has several concrete implications for AI economics research and applications:

Research design and empirical strategy - Data representativeness: AI/economic models trained on Western or aggregated global data will likely mis-specify demand, labor supply, productivity and adoption dynamics in African contexts—researchers should prioritize Africa-sourced datasets (mobile, administrative, firm surveys, satellite/remote sensing, marketplace/platform logs). - Heterogeneous agents: socio-cultural diversity and high informality argue for micro-founded, heterogeneous-agent models (agent-based models, structural models with rich heterogeneity) rather than homogeneous-equilibrium frameworks. - Causal evidence: field experiments, RCTs and quasi-experimental designs are valuable for estimating AI-driven interventions (pricing, matching, credit, logistics) across informal/formal strata and culturally diverse subpopulations.

Platform and market design - Platform economics must account for informality: marketplace algorithms, matching, reputation and incentive mechanisms need to handle unregistered sellers, opaque transaction histories, and nonstandard governance (e.g., trust mediated by chiefs, market queens). - Pricing and personalization: highly heterogeneous tastes and language fragmentation suggest localized, low-data personalization methods (few-shot learning, transfer learning with local fine-tuning) and robust pricing models for low-margin markets.

Labor, automation and distributional effects - Informal employment dominance: studies on automation/AI impacts should model spillovers to informal sector livelihoods and mechanisms of labor reallocation (not just formal-sector job displacement). - Human-AI hybrids: low levels of advanced technologies and skills call for hybrid human-AI systems (decision support, mobile-assisted workflows) and evaluation of complementarities between AI tools and local organizational practices.

Resource allocation and factor rivalry - AI for scarce-resource allocation: algorithms for allocation of land, water, transport capacity or energy must consider entrenched informal institutions and competing claims; mechanism design should incorporate legitimacy from informal actors. - Competition modelling: AI-enabled bidding, procurement or procurement-auction designs need to factor in institutional capture risks and uneven access to digital infrastructure.

Infrastructure, logistics and spatial economics - Logistics optimization: AI for routing, inventory and demand forecasting must incorporate poor physical infrastructure, fragmented networks, and high uncertainty—robust, constraint-aware optimization and reinforcement learning under partial observability are appropriate. - Spatial data use: leverage remote sensing, mobile phone metadata and supply-chain traceability to model resource endowments and logistical constraints.

Governance, fairness and regulation - Algorithmic fairness: fairness definitions must reflect local normative institutions and survival-oriented priorities (e.g., fairness toward households depending on subsistence incomes or market-queen organized groups). - Institutional design for AI: policymakers must coordinate formal regulation with informal institutions; AI governance research should study hybrid governance regimes and their effects on adoption and welfare.

Actionable research pathways for AI economists - Build and share Africa-grounded datasets (marketplaces, informal firm panels, mobile payments, high-resolution satellite). - Run field experiments and deployment pilots that evaluate AI tools in informal-market settings (e.g., demand forecasting for micro-retailers, credit-scoring for unregistered firms). - Develop models of AI adoption and labor impacts that explicitly include informal/formal sector interactions and institutional intermediaries. - Design platform mechanisms and matching algorithms that incorporate non-digital governance actors (chiefs, market leaders) as coordination nodes. - Use agent-based and spatial economic simulation to study resource rivalry and infrastructure investments under alternative policy/AI interventions.

Policy and practice - Capacity building: investments in data infrastructure, digital literacy and local model development to ensure AI tools are usable and appropriate. - Co-design with local institutions: AI interventions should be co-designed with informal leaders and community institutions to gain legitimacy and align incentives. - Targeted regulation: tailor AI governance to hybrid institutional contexts—balance formal rules with engagement strategies for informal actors.

Short conclusion Africa’s OSCM contexts (informality, institutional hybridity, cultural heterogeneity, infrastructure constraints, demographic dynamics) present both challenges and rich opportunities for AI economics. To produce valid, equitable and policy-relevant insights, AI economists must use Africa-specific data, adopt models that capture heterogeneity and institutional complexity, design hybrid human-AI solutions, and engage with local governance structures.