Driving Digital Adoption: A Conceptual Framework for Artificial Intelligence (AI) Integration in Small and Medium Enterprises (SMEs)

Abstract

Artificial Intelligence (AI) technology has transitioned from an advanced corporate application into an accessible operational utility for contemporary business management. For Small and Medium Enterprises (SMEs), integrating AI capabilities offers substantial potential to optimize internal workflows, enhance decision-making precision, and strengthen market positioning. However, actual AI adoption among smaller enterprises in developing economies remains fragmented due to resource limitations and technical risks. Moving beyond a conventional application of the Technology-Organization-Environment (TOE) framework, this paper proposes an extended theoretical framework that integrates AI-specific trustworthiness and organizational digital maturity as contingency mechanisms. The conceptual model evaluates primary drivers across technological (relative advantage, complexity), organizational (top management support, financial readiness), and environmental contexts (competitive pressure, regulatory support), while incorporating Perceived AI Trustworthiness as a mediating mechanism and Organizational Digital Maturity as a moderating condition. The paper formulates eight testable propositions and details a quantitative methodology based on Partial Least Squares Structural Equation Modeling (PLS-SEM) for a target sample of N=300 SMEs. This framework advances theoretical understandings of technology adoption in emerging economies and offers actionable directives for policymakers and business managers seeking to accelerate digital maturity.