Development of a DEMATEL-ANP Model for Analyzing Determinants of Artificial Intelligence Adoption Intention among SMEs

Abstract

Artificial Intelligence (AI) adoption provides significant opportunities for Small and Medium Enterprises (SMEs) to improve operational efficiency, decision-making, and competitiveness. However, SMEs still face various challenges in adopting AI technologies, including limited financial resources, digital capability, technological readiness, and organizational preparedness. This study aims to develop a DEMATEL-ANP model to identify causal relationships and prioritize determinants influencing AI adoption intention among SMEs. A quantitative approach using Multi-Criteria Decision-Making (MCDM) was applied by integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) methods. The proposed model incorporates technological, organizational, and environmental factors based on the Technology Acceptance Model (TAM) and Technology-Organization-Environment (TOE) framework. Data were collected through expert judgment from five experts with knowledge and experience in artificial intelligence, digital transformation, SME development, and technology adoption. The DEMATEL results indicate that all evaluated factors belong to the cause group, with Digital Capability (DC) identified as the most influential factor. Furthermore, ANP results reveal that Cost (0.15) and Digital Capability (0.15) are the highest priority factors, followed by Competitive Pressure (0.14) and Perceived Usefulness (0.13). The consistency evaluation confirms that all ANP comparison matrices meet the consistency requirement with a Consistency Ratio below 0.10. This study contributes by providing a comprehensive decision-making framework for understanding AI adoption determinants among SMEs and identifying key factors that should be prioritized to support successful AI implementation