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

Synthesis: This study empirically models the organizational, technological, and environmental determinants of AI adoption in higher education, focusing on data-driven decision support systems (DSS) and smart learning platforms in Saudi Arabian universities. Guided by the Technology–Organization–Environment (TOE) framework and Diffusion of Innovations (DOI) theory, the author surveyed 300 academic and administrative staff and used PLS-SEM. Findings show internal organizational conditions (culture, Sustainability practices, waste management) strengthen social drivers, while technological attributes (compatibility, relative advantage, low complexity) shape perceived innovation attributes that catalyze intention to use and actual adoption. Government regulations and policy incentives emerged as crucial external enablers of institutional readiness, with adoption associated with improved teaching, learner engagement, and curriculum innovation.

Key Findings

• Internal organizational drivers matter most: Organizational culture, sustainability policies, and waste-management practices significantly strengthen the internal social drivers that promote technological change.
• Technological characteristics drive innovation perception: Compatibility with existing systems and perceived relative advantage strongly enhance the perceived innovation attributes of AI technologies; perceived complexity suppresses them.
• Innovation attributes are the pivotal catalyst: Perceived innovation attributes directly drive both intention to use AI-based DSS and actual AI adoption — the mediating bridge between technology perception and uptake.
• Social drivers shape both adoption and policy: Social drivers positively influence intention to use AI-based DSS and also influence government regulatory frameworks, which in turn feed back into adoption.
• Regulatory environment is an external enabler: Government regulations and policy incentives facilitate institutional readiness and adoption, reducing legal and ethical uncertainty for institutions.
• Educational benefits follow adoption: In the higher education context, adoption was associated with improved teaching practices, enhanced learner engagement, and curriculum innovation.

Study Design & Method

• Quantitative survey administered to 348 academic and administrative staff (from 370 returned) (decision makers, faculty, IT specialists, instructional technologists, curriculum specialists) across multiple Saudi Arabian higher education institutions.
• Integrated Technology–Organization–Environment (TOE) framework and Diffusion of Innovations (DOI) theory; distinguishes the study from individual-level models like TAM and UTAUT, which it argues miss organizational-level factors.
• 12 latent constructs measured on a five-point Likert scale: social trends, organizational culture, sustainability, waste management, compatibility, relative advantage, complexity, social drivers, innovation attributes of AI, intention to use AI-based DSS, government regulatory support, and AI adoption.
• 14 hypotheses tested via Partial Least Squares SEM (PLS-SEM) in SmartPLS 4.0, with measurement-model evaluation (factor loadings, AVE, Fornell–Larcker discriminant validity) and structural-model evaluation using 5,000-sample bootstrapping.
• Reliability strong: Cronbach's alpha ranged 0.74–0.90 across constructs; all constructs above the 0.70 threshold.

What this means for practice

• Administrators. Treat AI adoption as organizational change, not a technical upgrade: align institutional culture, infrastructure readiness, and external policy before scaling, since the study's model ties internal social drivers to both intention and actual adoption.
• Administrators. Invest in compatibility and demonstrable relative advantage and reduce perceived complexity, because perceived innovation attributes are the pivotal catalyst driving intention to use AI-based decision support systems and adoption itself.
• Administrators. Build educator capacity and clear regulatory frameworks covering data privacy, transparency, and quality assurance, which reduce the legal and ethical uncertainty that slows institutional readiness.
• Administrators. Recognize that organizational culture, sustainability, and waste-management practices strengthen the internal social drivers of change — culture work is adoption work, not a soft add-on to procurement.
• Administrators. Develop context-sensitive frameworks for emerging regions rather than importing developed-country models, because the study argues adoption in Saudi Arabia differs in culture, policy, and resources.

Limitations

• Data come from a cross-sectional self-report survey: 348 usable responses from an initial 370, collected between 5 January and 20 March 2024 at Saudi Arabian higher-education institutions, so the model tests perceptions and intentions rather than observed adoption outcomes.
• Respondents were reached by email invitation and by sharing the link through professional WhatsApp and LinkedIn groups, a non-probability convenience approach that leaves room for self-selection bias.
• The single-country sample limits generalization to other national and institutional contexts, a point the study itself makes about differences between emerging and developed-country settings.
• Educational benefits such as improved teaching, learner engagement, and curriculum innovation are reported as associations within the same survey, not as measured effects of adoption.

Citation

Al-Rahmi, W. (2026). Exploring Organisational Drivers and Innovation Attributes of Artificial Intelligence Adoption in Higher Education. Journal of University Teaching and Learning Practice, 23(6).