Leveraging AI Marketplaces for Micro-Entrepreneurship in Emerging Economies

Thesis Abstract

The rapid proliferation of AI marketplaces presents a transformative opportunity for micro-entrepreneurs in emerging economies, yet systematic understanding of how such platforms influence entrepreneurial outcomes, skill development, and income resilience remains limited. This study addresses the problem of how AI-enabled marketplaces can reduce entry barriers, lower transaction costs, and augment value creation for micro-entrepreneurs operating in low-resource contexts, while also identifying systemic risks and inequities that may arise from platform-driven asymmetries. The aim is to elucidate the mechanisms through which AI marketplaces affect micro-entrepreneurial performance and to develop a framework for leveraging these platforms to enhance sustainable livelihoods. Specific objectives are to (i) map the adoption patterns and usage intensity of AI marketplaces among micro-entrepreneurs in three emerging economies; (ii) examine the impact of platform features (algorithmic matching, pricing transparency, and AI-powered analytics) on income, sales growth, and market reach; (iii) assess the moderating roles of digital literacy, access to finance, and social networks on platform-driven outcomes; (iv) identify barriers to entry, trust issues, and ethical considerations in AI-mediated transactions; and (v) propose a practical model for policy and platform design to maximize inclusive benefits. A mixed-methods research design is employed. The quantitative strand uses a cross-sectional survey of 600 micro-entrepreneurs across Kenya, Bangladesh, and Vietnam, stratified by sector (handicrafts, food products, and services) and by urban-rural location. Data collection instruments include a structured questionnaire measuring AI marketplace usage (frequency, features employed), entrepreneurial performance indicators (monthly revenue, profit margin, customer base growth), digital literacy, access to finance, and perceived risks. Descriptive statistics, multiple regression, and hierarchical linear modeling (HLM) are used to test hypothesized relationships between AI marketplace engagement and performance outcomes, with robustness checks via propensity score matching to control for self-selection bias. The qualitative strand collects 40 in-depth interviews with select respondents from the survey cohort and 12 focus group discussions with platform operators and local ecosystem actors to triangulate findings. Thematic analysis guided by Braun and Clarke’s approach identifies mechanisms, constraints, and ethical concerns, while a cross-case synthesis highlights contextual variations. The theoretical framework integrates the Technology Acceptance Model (TAM) extended for AI-enabled marketplaces, the Resource-Based View (RBV) of digital capabilities, and the Platform Ecosystems theory to explain how platform governance, data governance, and network effects shape micro-entrepreneur outcomes. The study contributes to knowledge by (a) providing empirical evidence on how AI marketplaces influence micro-entrepreneurial performance in diverse emerging economies; (b) identifying critical platform features and individual/dyadic capabilities that drive success; (c) revealing how digital literacy, financial access, and social capital interact with platform design to affect outcomes; and (d) offering a pragmatic framework for policymakers and platform designers to foster inclusive value creation. Expected findings include positive associations between AI marketplace engagement and revenue growth, enhanced market reach through algorithmic matching, and higher profit margins when users leverage analytics dashboards and pricing tools. Digital literacy and access to microfinance are anticipated to strengthen these effects, while concerns about data privacy, algorithmic bias, and dependency on platform incentives may pose risks to equitable benefits. The study also anticipates contextual variation in adoption due to infrastructure, regulatory environments, and cultural norms. The?? contributions are theoretical, empirical, and practical. Theoretically, the research extends TAM, RBV, and Platform Ecosystems to the context of micro-entrepreneurship in low-resource settings with AI-enabled marketplaces, offering a nuanced model of capability development and platform interaction. Empirically, it provides cross-country comparative evidence and a rich qualitative understanding of mechanisms and barriers. Practically, it informs policy interventions, financial inclusion strategies, and platform design choices aimed at maximizing inclusive growth, such as targeted digital literacy programs, affordable financing, data ownership rights, and transparent algorithmic governance. The study concludes that AI marketplaces hold substantial potential to uplift micro-entrepreneurs in emerging economies when accompanied by supportive digital literacy, affordable finance, trustworthy data practices, and inclusive platform governance; it recommends stakeholder collaboration among governments, financial institutions, and platform operators to implement standardized benchmarks for fairness, security, and user empowerment.