AI-driven platform for rapid microbusiness model validation in emerging markets

Thesis Abstract

This study addresses the persistent gap in rapid, evidence-based validation of microbusiness models in emerging markets where informal ecosystems and scarce data hinder timely decision-making for ICT-enabled ventures. The central aim is to design, implement, and evaluate an AI-driven platform that combines predictive analytics, experimentation design, and user-centered simulators to accelerate microbusiness model validation within six months of inception. Specific objectives are (1) to develop an AI-enabled validation engine that integrates market signals, cost structures, and revenue scenarios for microbusiness concepts; (2) to test the platform’s predictive accuracy in assessing viability across differing urban and rural contexts in two emerging economies; (3) to examine how platform-assisted experiments influence founder decision-making, resource allocation, and risk perception; (4) to evaluate the platform’s usability and adoption barriers among aspiring micro-entrepreneurs; and (5) to propose a scalable framework for policy and ecosystem support that leverages digital testing tools. The study adopts a mixed-methods, explanatory sequential design. The research population comprises 1,200 aspiring micro-entrepreneurs and 60 mentoring agents across Lagos and Nairobi metropolitan areas. A stratified random sample yields 600 entrepreneur participants for quantitative validation exercises and 40 in-depth interviews with experienced incubation mentors and venture capital observers to enrich the interpretation. Data collection instruments include a structured survey capturing demographics, digital literacy, and initial business concepts; a standardized microbusiness model canvas augmented with AI-generated scenario variables; platform-generated experiment logs; and semi-structured interview guides. Instrument validity is established through content validity by a panel of ICT-for-entrepreneurship experts and pilot testing with 60 respondents. Reliability is assessed via Cronbach’s alpha for multi-item scales and test-retest procedures in a two-week window. Analytical methods combine quantitative and qualitative approaches. The predictive performance of the AI validation engine will be assessed using regression analysis and receiver operating characteristic (ROC) curves to determine accuracy in forecasting venture viability, with cross-validation across regional subsets. Descriptive statistics, cluster analysis, and decision-tree methods will explore concept segmentation and prioritization of validation experiments. The qualitative strand will employ thematic analysis, coding interview transcripts against a framework anchored in the Technology Acceptance Model (TAM) and the Dynamic Capabilities Theory, with triangulation to reveal convergent and divergent insights. A conceptual model integrating TAM and Dynamic Capabilities will guide interpretation of how platform use moderates entrepreneurial learning, resource orchestration, and pivot decisions. The study will also apply cost-benefit reasoning and a Bayesian updating approach to model uncertainty reduction as new data stream into the platform. Expected findings indicate that the AI-driven platform improves the speed and accuracy of identifying viable microbusiness models by 25–40% relative to traditional ad-hoc testing, with higher predictive accuracy for revenue models leveraging digital delivery and micro-franchising in dense urban settings. It is anticipated that platform-guided experiments reduce the time-to-validate by approximately 40 days on average and boost early-stage founder confidence in business decisions. Qualitatively, results are expected to reveal that perceived usefulness, trust in AI outputs, and data transparency significantly mediate platform adoption, while mentorship alignment and ecosystem support moderate the impact on scaling propensity. The study contributes to knowledge by advancing a concrete, scalable AI-assisted validation framework for microenterprise development in resource-constrained markets, empirically validating the integration of predictive analytics with iterative experimentation in entrepreneurship education and practice. It extends the literature on technology-enabled entrepreneurship in emerging economies by detailing a practical model that links AI-assisted validation to sustainable microbusiness growth, informed by TAM and Dynamic Capabilities Theory. Policy and practice implications include recommendations for incubators and municipal agencies to fund and standardize AI-enabled experimentation platforms, ensure data governance and inclusivity, and foster ecosystem interoperability. The main conclusion posits that AI-driven validation platforms can meaningfully de-risk microbusiness formation in emerging markets by delivering rapid, data-informed insights that align product-market fit with viable cost structures, while enhancing entrepreneurial learning and ecosystem capacity. Recommendations emphasize investment in digital literacy, open data standards, mentorship networks, and iterative platform enhancement to support scalable deployment across diverse regional contexts.