Developing an AI-powered Platform to Support Micro-Entrepreneurs' Business Growth

Thesis Abstract

The rapid proliferation of micro-enterprises in emerging economies underscores the pressing need for innovative digital solutions that enhance their growth potential amid resource constraints and limited access to formal financial and business development services. Despite increasing adoption of ICTs, micro-entrepreneurs often lack tailored support tools that provide real-time insights, decision-making guidance, and market connectivity. This study aims to develop and evaluate an AI-powered platform designed to support micro-entrepreneurs’ business growth by facilitating access to timely information, personalized recommendations, and networking opportunities. The specific objectives are to identify critical challenges faced by micro-entrepreneurs, to design an AI-driven platform tailored to their needs, and to empirically assess the platform’s impact on business performance and entrepreneurial confidence. Employing a mixed-methods research design, the study combines qualitative exploratory interviews and focus groups with quantitative evaluation. The qualitative phase involved 30 micro-entrepreneurs selected through purposive sampling from small business clusters in an urban region, providing insights into operational challenges, ICT literacy levels, and desired platform features. Based on these findings, a prototype AI-driven platform was developed incorporating machine learning algorithms for personalized business advice, natural language processing for customer interaction, and data analytics for market insights. The quantitative phase engaged a sample of 200 micro-entrepreneurs, randomly assigned into experimental and control groups, over a six-month period. Data collection instruments included structured surveys measuring business performance (sales volume, customer base), entrepreneurial confidence, and platform usability, complemented by platform analytics logs. Data analysis entailed descriptive statistics, inferential testing via paired t-tests, and multiple regression analyses using SPSS. Thematic analysis was employed for qualitative data using NVivo to interpret interview transcripts. The platform’s efficacy was evaluated through hypotheses testing that postulated significant improvements in business growth metrics and entrepreneurial confidence among users exposed to the AI-driven intervention compared to controls. It is anticipated that results will demonstrate a positive correlation between platform usage and increased sales, expanded customer reach, and enhanced entrepreneurial self-efficacy. This research is expected to contribute theoretical insights by applying the Technology Acceptance Model (TAM) and Diffusion of Innovations theory to an AI-enabled micro-entrepreneurial context, highlighting factors influencing adoption and sustained use of intelligent platforms. Empirically, it fills the literature gap concerning scalable, AI-based support systems tailored for micro-entrepreneurs in resource-limited settings. The study provides evidence-based models for integrating AI solutions into existing entrepreneurial ecosystems, emphasizing usability, contextual relevance, and affordability. Anticipated findings will indicate that tailored AI-driven interventions significantly improve micro-entrepreneurs’ business outcomes and confidence levels, thereby validating the potential of digital intelligence to democratize entrepreneurial support. The main conclusion underscores the transformative potential of AI-powered platforms in bridging informational and market gaps faced by micro-entrepreneurs, advocating for policy frameworks that promote digital literacy, infrastructure development, and adoption incentives. Recommendations include scaling the platform through public-private partnerships, integrating local context-specific data, and fostering entrepreneurial digital literacy programs. Future research should explore longitudinal impacts, adaptive algorithms for diverse sectors, and integration with broader financial and governmental support systems. Overall, this study provides a scalable blueprint for leveraging artificial intelligence to catalyze micro-entrepreneurial development, with implications for technology deployment, entrepreneurial ecosystems, and inclusive economic growth.