Assessing the Impact of AI-Driven Decision Support Systems on Small Business Performance

Thesis Abstract

Small businesses increasingly adopt artificial intelligence-driven decision support systems (AI-DSS) to enhance operational efficiency, improve strategic decision-making, and competitive positioning amidst dynamic market conditions. However, empirical evidence on the actual impact of these systems on small business performance remains limited, particularly regarding how AI-DSS influences key performance indicators such as profitability, customer satisfaction, and operational resilience. This study aims to critically assess the impact of AI-DSS implementation on small business performance, with specific objectives to evaluate the extent of AI adoption, determine the relationship between AI-DSS utilization and business performance metrics, and identify moderating factors such as business size, industry, and managerial skills. Employing a descriptive correlational research design, the study utilizes a mixed-methods approach combining quantitative and qualitative data collection techniques to provide a comprehensive analysis. The population consists of small businesses within the manufacturing, retail, and service sectors operating in a metropolitan region, totaling approximately 2,500 firms. Using stratified random sampling, a sample size of 380 small business owners and managers was selected, ensuring adequate representation across sectors. Data were collected through structured questionnaires measuring the level of AI-DSS adoption, perceived usefulness, and business performance indicators, supplemented by semi-structured interviews to explore contextual and implementation challenges. The quantitative data were analyzed using multiple regression analysis to examine the relationship between AI-DSS adoption and performance outcomes, while structural equation modeling (SEM) was employed to test the proposed conceptual framework grounded in the Technology-Organisation-Environment (TOE) theory and the Diffusion of Innovations theory. Reliability and validity of the instruments were ensured through Cronbach’s alpha (above 0.8) and confirmatory factor analysis. Qualitative data were subjected to thematic analysis to extract nuanced insights related to implementation experiences, organizational barriers, and managerial perceptions. It is anticipated that findings will reveal a statistically significant positive correlation between AI-DSS utilization and key performance metrics such as revenue growth, customer satisfaction, and operational efficiency. Moreover, the study is expected to identify critical moderating factors, like managerial technological competence and organizational readiness, which influence the extent of AI impact. The results will contribute to the existing literature by providing empirical evidence on AI adoption’s tangible outcomes within the context of small enterprises, an area currently underexplored in technology adoption research. This research significantly advances understanding of how AI-driven decision support systems influence small business performance, offering theoretical insights into the mechanisms of technology adoption and practical implications for policymakers, small business associations, and technology providers. The study recommends strategies for effective AI integration tailored for small enterprises, emphasizing capacity building, managerial training, and customization of AI tools to fit small business operational contexts. In conclusion, the study underscores the potential of AI-DSS as a transformative lever for small business growth and sustainability. It advocates for supportive policies and targeted interventions to facilitate widespread, effective AI adoption among small enterprises. Future research avenues include longitudinal studies to track performance over time and cross-national comparisons to explore contextual variations in AI impact.