Exploring Organisational Drivers and Innovation Attributes of Artificial Intelligence Adoption in Higher Education

Abstract

This study examines the organisational and technological determinants influencing the adoption of artificial intelligence (AI) technologies specifically data-driven decision support systems (DSS) and smart learning platforms in Saudi Arabian higher education institutions. Guided by the integrated Technology–Organisation–Environment (TOE) framework and Diffusion of Innovations (DOI) theory, the study employs a quantitative approach using a structured survey administered to 300 academic and administrative staff across multiple institutions. The survey captures key constructs, including social trends, organisational culture, sustainability practices, waste management, technology compatibility, relative advantage, complexity, social drivers, innovation attributes, intention to use data-driven DSS, and government regulatory support. Structural equation modelling (SEM) was applied to test the hypothesised relationships and evaluate the interplay among organisational, technological, and environmental factors. The results reveal that internal organisational conditions, particularly organisational culture, sustainability policies, and waste-management practices- significantly strengthen internal social drivers that promote technological change. Technological characteristics, including Compatibility with existing systems and perceived relative advantage, strongly enhance the perceived innovation attributes of AI technologies. These innovation attributes, in turn, serve as a major catalyst for both the intention to use and actual adoption of AI-enabled systems. Adoption within the higher education context was associated with improvements in teaching practices, enhanced learner engagement, and curriculum innovation. Moreover, government regulations and policy incentives emerged as crucial external enablers that facilitate institutional readiness and adoption. This study contributes to the growing body of knowledge on AI integration in higher education by providing an empirically validated model that highlights the interaction between organisational drivers, innovation characteristics, and environmental enablers. The findings offer practical insights for policymakers, higher education leaders, and system designers seeking to accelerate responsible and effective AI adoption in academic environments.