Determinants of Artificial Intelligence Adoption Intention in Small and Medium-sized Enterprises (SME): Empirical Evidence from Sri Lanka

Abstract

Artificial Intelligence (AI) is rapidly transforming business operations across diverse sectors globally, yet its integration within Small and Medium-sized Enterprises (SMEs) in developing economies remains considerably underexplored. In the context of Sri Lanka, SMEs constitute approximately 75% of all enterprises and contribute nearly 52% to the national Gross Domestic Product (GDP); however, the country ranks 92nd out of 174 economies with a score of 0.44 on the IMF AI Preparedness Index, underscoring a critical gap between economic significance and technological readiness. This disparity motivates a pressing need to examine the determinants that shape AI adoption intention within the Sri Lankan SME sector. Accordingly, this study investigates five predetermined factors Competitive Pressure (CP), External Support (ES), Top Management Commitment (TMC), Employee Adaptability (EA), and Organization Readiness (OR) categorised as internal and external determinants of AI adoption intention for main business functions. Grounded in the Technology-Organization-Environment (TOE) framework, the study employs a quantitative deductive approach, collecting data from 385 SME owners and managers across manufacturing, services, agriculture, construction, and logistics sectors via an online questionnaire, with analysis conducted using SPSS. Multiple linear regression results reveal that Organization readiness (β = 0.287, p < 0.001), Top Management Commitment (β = 0.206, p = 0.002), and Employee Adaptability (β = 0.311, p < 0.001) exhibit statistically significant positive relationships with AI adoption intention, collectively explaining 78% of its variance (R2 = 0.78). Conversely, Competitive Pressure and External Support demonstrated no statistically significant influence. These findings suggest that Sri Lankan SMEs should strategically prioritise internal capability building — particularly workforce adaptability, managerial commitment, and organisational preparedness to accelerate meaningful AI integration. The study contributes empirical evidence to the technology adoption literature within developing economic contexts and offers actionable insights for SME practitioners and policymakers.