Implementing AI-driven Decision Support Systems to Enhance Small Business Operations

Thesis Abstract

Small businesses form the backbone of the global economy, yet they often operate with limited access to sophisticated decision-making tools that can enhance efficiency, competitiveness, and growth. The rapid advancement of artificial intelligence (AI) and information and communication technologies (ICT) presents an opportunity to develop decision support systems (DSS) tailored for small business contexts, enabling data-driven decision making and strategic planning. However, the adoption and implementation of AI-driven DSS in small enterprises face challenges related to technological readiness, cost, skills, and organizational change. This study aims to investigate the impact of implementing AI-driven decision support systems on small business operations, with the overarching goal of providing actionable insights into the adoption process, operational improvements, and strategic benefits. The specific objectives of the research include (1) to identify the key factors influencing the adoption of AI-driven DSS in small businesses; (2) to assess the effects of these systems on operational efficiency and decision-making effectiveness; (3) to evaluate the barriers and facilitators to successful implementation; and (4) to develop a conceptual framework applicable to small business contexts for the integration of AI-driven DSS. The study adopts a mixed-methods research design, combining quantitative and qualitative approaches to ensure comprehensive analysis. The quantitative phase involves a survey of 200 small business owners and managers across manufacturing, retail, and service sectors, selected through stratified random sampling to ensure representativeness. The data collection instrument comprises a structured questionnaire validated through pilot testing and expert review, measuring variables such as technological capacity, perceived benefits, organizational readiness, and implementation challenges. For the qualitative component, in-depth interviews are conducted with a purposive sample of 20 key stakeholders involved in adopting AI-driven DSS, including IT consultants, decision-makers, and employees, to explore contextual and perceptual factors influencing adoption. The quantitative data are analyzed using multiple regression analysis to establish relationships between variables, while thematic analysis is employed for qualitative data to identify recurring themes and insights. Structural Equation Modeling (SEM) is used to test the conceptual framework and assess the direct and indirect effects of factors on system adoption and operational outcomes. Expected findings include evidence that technological readiness, perceived ease of use, and organizational support significantly influence AI-driven DSS adoption. The results are anticipated to demonstrate that successful implementation correlates with improvements in operational efficiency, accuracy of decision-making, and strategic agility. Barriers such as limited technical skills, high implementation costs, and resistance to change are likely to be prominent, alongside facilitators like management commitment, external support, and user training. This research contributes to the existing body of knowledge by developing a tailored conceptual framework for AI-DSS adoption in small enterprises, integrating technology acceptance theories such as the Technology Acceptance Model (TAM) and the Diffusion of Innovations theory. It offers practical insights for small business owners, technology providers, and policymakers seeking to promote digital transformation. The study concludes that effective implementation of AI-driven decision support systems can significantly enhance small business operations, but requires strategic planning, capacity building, and stakeholder engagement. Recommendations include developing affordable, user-friendly AI tools tailored for small businesses, enhancing digital literacy through targeted training, and fostering supportive policy environments. The research suggests further exploration into longitudinal studies to assess long-term impacts and case studies focusing on specific industry contexts, thereby extending the applicability and depth of understanding concerning AI-driven DSS in the small business sector.