The Impact of Learning Organizations Dimensions on the Organisational Performance: An Exploring Study of Saudi Universities
Bibliographic record
Abstract
The education sector is crucial to any nation committed to building future human capital. The Higher Education sector in the Kingdom of Saudi Arabia (KSA) is at the centre of transforming the nation's future in a radical move to end oil-dependency. But this is only possible if universities make a decisive change and start working as learning organisations in all employee's levels. The present study investigates the direction of higher education in becoming learning organisations. We collected data from 840 staff members in 20 public Saudi universities. We designed a questionnaire exploring the seven dimensions of learning organisation found in the literature. Regression analyses were used to assess the impact of those dimensions on the organisational performance. Results showed that universities that adhered most to the learning organisation principles demonstrated a better organisational performance, particularly concerning research and knowledge performance. We recommend that universities should (1) use change agents to help transform effectively and meet rising demands and (2), promote continuous learning for all employees to increase productivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".