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

◐ medium · 2026-08-20 · technology-acceptance-model, ai-education, higher-ed, governance, educational-policy-ai, administrator

Synthesis: This study empirically models the organisational, 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–Organisation–Environment (TOE) framework and Diffusion of Innovations (DOI) theory, the author surveyed 300 academic and administrative staff and used PLS-SEM. Findings show internal organisational conditions (culture, sustainability practices, waste management) strengthen social drivers, while technological attributes (compatibility, relative advantage, low complexity) shape perceived innovation attributes that catalyse 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 organisational drivers matter most: Organisational 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 300 academic and administrative staff (decision makers, faculty, IT specialists, instructional technologists, curriculum specialists) across multiple Saudi Arabian higher education institutions.
  • Integrated Technology–Organisation–Environment (TOE) framework and Diffusion of Innovations (DOI) theory; distinguishes the study from individual-level models like TAM and UTAUT, which it argues miss organisational-level factors.
  • 12 latent constructs measured on a five-point Likert scale: social trends, organisational 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.

Implications for AI in Education

  • Confirms AI adoption in higher education is not merely a technical upgrade but a pedagogical and organisational shift requiring alignment between institutional culture, technological readiness, and external policy.
  • For higher education leaders and policymakers, the results argue for capacity-building of educators, investment in compatible infrastructure, and clear regulatory frameworks (data privacy, ethical transparency, quality assurance) to accelerate responsible adoption.
  • Highlights a gap the study addresses: adoption in emerging regions (Saudi Arabia) differs from developed-country contexts due to differences in culture, policy, and resources, so frameworks must be context-sensitive.
  • Supports data-driven decision support and smart learning platforms as vehicles for personalised learning, learning analytics, and at-risk learner identification, provided institutional readiness and digital literacy are developed first.

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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). https://doi.org/10.53761/fskfah39 (CC BY-ND 4.0, open access).