Exploring adoption of E-learning in the Gulf Cooperation Council (GCC) education sector

This study explores e-learning adoption among higher education students in Private Higher Educational Institutions (PHEIs) in the Gulf Cooperation Council (GCC) region. Using the Technology Acceptance Model (TAM), the research examines how student perceptions influence the use of e-learning systems. The primary goals include identifying adoption factors, enhancing e-learning quality, and optimizing educational resource allocation. A cross-sectional survey was conducted with 539 respondents via an online questionnaire. Structural Equation Modeling (SEM) was performed using SmartPLS 4. Key findings indicate that perceived usefulness and perceived ease of use significantly influence system usage, mediated by system availability. The study underscores the rapid growth of e-learning in GCC higher education, emphasizing the need to account for country- and age-specific differences. This research offers actionable insights for administrators at PHEIs and lays a foundation for future studies. Researchers are encouraged to examine e-learning adoption in diverse educational settings, incorporate moderating variables, and use larger sample sizes to improve the generalizability of findings.

Authors

Institutions

Publication Details

Journal
Discover Sustainability
Published
2025-03-26
DOI
https://doi.org/10.1007/s43621-025-00950-2
Citations
1
Primary Topic
Online and Blended Learning
Type
article
Field-Weighted Citation Impact
8.93
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring adoption of E-learning in the Gulf Cooperation Council (GCC) education sector

M. Akbar, Habil Slade Ogalo, T. Ramayah, Jayendira P. Sankar
1 citations
Discover Sustainability
Online and Blended Learning
8.93
article

Exploring adoption of E-learning in the Gulf Cooperation Council (GCC) education sector

M. Akbar, Habil Slade Ogalo, T. Ramayah, Jayendira P. Sankar
article en
1 citations

Abstract

This study explores e-learning adoption among higher education students in Private Higher Educational Institutions (PHEIs) in the Gulf Cooperation Council (GCC) region. Using the Technology Acceptance Model (TAM), the research examines how student perceptions influence the use of e-learning systems. The primary goals include identifying adoption factors, enhancing e-learning quality, and optimizing educational resource allocation. A cross-sectional survey was conducted with 539 respondents via an online questionnaire. Structural Equation Modeling (SEM) was performed using SmartPLS 4. Key findings indicate that perceived usefulness and perceived ease of use significantly influence system usage, mediated by system availability. The study underscores the rapid growth of e-learning in GCC higher education, emphasizing the need to account for country- and age-specific differences. This research offers actionable insights for administrators at PHEIs and lays a foundation for future studies. Researchers are encouraged to examine e-learning adoption in diverse educational settings, incorporate moderating variables, and use larger sample sizes to improve the generalizability of findings.

Discover SustainabilityVol. 6(1)
Chandigarh University (IN), University of Jordan (JO), Universiti Sains Malaysia (MY), Asia Pacific University of Technology & Innovation (MY), Daffodil International University (BD), University of Indonesia (ID), Arab Open University (BH), Sohar University (OM), Sunway University (MY), Punjab Agricultural University (IN)
Partnerships for the goals
Openalex Percentile: Top 5%
Online and Blended Learning
8.93
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.