E-learning's influence on organizational excellence in UAE universities: Exploring the moderat-ing role of demographic variables
Bibliographic record
Abstract
This study employs a quantitative approach to investigate the relationship between E-learning and Organizational Excellence in UAE universities. The study focuses on 250 senior and middle administrators from public universities selected through a convenience sampling method. The advanced statistical tools SPSS and AMOS were used to analyze the research data through Structural Equation Modeling (SEM). The findings reveal the impact of e-learning dimensions–Technical Knowledge and Management Willingness—on Organizational Excellence. Additionally, the study delves into moderation effects, revealing that Gender, Age, Educational Level, and Experience play pivotal roles in influencing perceptions of e-learning among senior and middle administrators at UAE public universities. This research contributes to the understanding of how e-learning impacts Organizational Excellence within the UAE university landscape while also highlighting the crucial moderating roles of demographic variables. These findings offer valuable insights for educational institutions aiming to optimize e-learning strategies and enhance organizational performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".