SA-HEIKSP: Development and Empirical Validation of a Cloud- Based Adoption Framework for Cross-Institutional Knowledge Sharing in Saudi Higher Education Institutions

Abstract

This study investigates the adoption of a cloud-based knowledge sharing platform (SA-HEIKSP) among higher education institutions (HEIs) in Saudi Arabia within the context of digital transformation and Vision 2030. Knowledge is widely recognised as a critical organisational asset, particularly in universities, which serve as key institutions for knowledge creation, dissemination, and exchange. However, knowledge sharing across Saudi universities remains limited due to technological, organisational, environmental, and individual barriers.

To address this gap, the study develops and empirically validates a conceptual framework integrating the Technology–Organisation–Environment (TOE) framework, the Diffusion of Innovation (DOI) theory, and individual behavioural factors. A mixed- methods approach was employed, combining quantitative survey data (n = 448) with qualitative insights. The data were analysed using SPSS and AMOS (version 29) through Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM).

The CFA results confirmed strong measurement validity and reliability. After refinement, all factor loadings exceeded 0.70, except one item (CO3), which was removed due to a low loading (0.11). Convergent validity was established with AVE values ranging from 0.57 to 0.75, while Composite Reliability (0.75–0.86) and Cronbach’s alpha (> 0.70) confirmed strong internal consistency. Discriminant validity was achieved using the Fornell–Larcker criterion, with MSV values lower than AVE for all constructs. Model fit indices demonstrated an acceptable and improved model (CMIN/DF = 2.22, CFI = 0.935, TLI = 0.923, RMSEA = 0.052, SRMR = 0.017), confirming a strong fit between the proposed model and empirical data.

Structural equation modelling revealed that complexity and compatibility did not directly influence SA-HEIKSP adoption; instead, both factors influenced adoption indirectly through intention toward knowledge sharing. This modification improved theoretical consistency with DOI and enhanced model fit.

This study makes significant theoretical, methodological, and practical contributions. Theoretically, it extends existing TOE and DOI models by integrating technological, organisational, environmental, and individual factors into a unified framework for cloud-based knowledge sharing in HEIs. Methodologically, it demonstrates rigorous empirical validation using CFA and SEM to test a newly developed adoption model. Practically, the study enhances understanding of cloud-based knowledge-sharing adoption across Saudi universities, an underexplored context despite its importance. It provides actionable insights for the Ministry of Education and university leaders to support cloud-based knowledge-sharing initiatives, ultimately improving collaboration, facilitating knowledge exchange, and enhancing institutional performance in alignment with Saudi Vision 2030.