Implementing AI-Driven Decision Support Systems in Small Business Management

Thesis Abstract

The rapid digitization of business processes and advancements in artificial intelligence (AI) have significantly transformed decision-making paradigms in small business management, yet the adoption and implementation of AI-driven decision support systems (DSS) remain limited among small enterprises due to resource constraints, technical expertise deficits, and organizational resistance. This study aims to explore the integration of AI-driven DSS into small business management practices, with the specific objectives of assessing the factors influencing adoption, evaluating the impact on managerial decision-making efficiency, and identifying challenges associated with implementation. Employing a mixed-methods research design, the study combines quantitative surveys and qualitative interviews to capture a comprehensive understanding of the phenomenon. The population comprises small business managers and owners operating within the manufacturing and retail sectors across a metropolitan region with a population of approximately 2,000 small enterprises. A stratified random sampling technique is employed to select a sample of 300 respondents for the quantitative component, and 30 participants for qualitative interviews, ensuring representativeness across business size, industry type, and years of operation. Data collection instruments include a structured questionnaire validated through pilot testing and confirmed for reliability with Cronbach’s alpha coefficients exceeding 0.80, alongside semi-structured interview guides. Quantitative data are analyzed using descriptive statistics, correlation analysis, and multiple regression models to assess the relationships between perceived technological readiness, organizational support, managerial attitude, and the extent of AI DSS adoption. Qualitative data are analyzed thematically, guided by the Technology Acceptance Model (TAM) and the Diffusion of Innovations Theory, to elucidate contextual factors influencing implementation processes and resistance behaviors. Model specification involves regression analysis with variables such as perceived ease of use, perceived usefulness, and organizational culture as predictors of successful adoption. Expected findings of the study indicate a positive correlation between technological readiness, perceived benefits, and adoption levels of AI DSS, with organizational support and managerial openness serving as critical mediators. Moreover, the research anticipates identifying key barriers including financial constraints, limited digital literacy, and organizational inertia, which hinder optimal integration. These insights are expected to contribute a nuanced understanding of the distinctive challenges faced by small businesses, providing empirical evidence on the influence of organizational and individual factors in AI adoption. The research further aims to develop a conceptual framework consolidating the factors impacting AI-driven DSS implementation in small businesses, aligning with overarching theories such as TAM and DOI to inform theoretical development. The study’s contribution advances knowledge on digital transformation within small enterprise contexts, identifying practical strategies for enhancing AI adoption through targeted training, resource allocation, and organizational change management. It also proposes a model for tailored implementation frameworks adaptable to varying small business environments. The main conclusion underscores that successful integration of AI-driven decision support systems hinges on improving digital literacy, fostering managerial buy-in, and providing affordable, user-friendly AI solutions tailored to small enterprise needs. Based on these findings, recommendations include policy initiatives promoting digital skill development, incentives for technology adoption, and further research into long-term impacts on business performance. The study’s limitations acknowledge the regional specificity, recommending longitudinal studies and cross-context comparisons to deepen understanding and generalizability. Overall, this research offers valuable insights into harnessing AI technologies to enhance decision-making efficacy in small business management, fostering competitive advantage and sustainable growth in the digital economy.