The Role of Universities' Electronic Management in Achieving Organizational Excellence: Example of Al Hussein Bin Talal University
Bibliographic record
Abstract
The study aimed at identifying the level of applying electronic management and the organizational excellence atAl-Hussein Bin Talal University (AHU). It also attempted to predict the organizational excellence level through thedegree of applying electronic management. The study sample consisted of (249) administration members (academicmanagers, managers) at AHU. The study tool considered two sections: the first concerned measuring the level ofapplying electronic management with regard to three dimensions (administrative, materialistic, and technical); thesecond concerned identifying the organizational excellence level with regard to three dimensions (leadershipexcellence of university management, human staff excellence, and services excellence). A descriptive methodologywas used to accomplish the study objectives.The study results showed that the level of applying electronic management at AHU is average with regard toadministrative and technical dimensions, while it is poor regarding the materialistic dimension. It also revealed that thelevel of organizational excellence is average on the leadership excellence dimension but poor on human staffexcellence and services excellence dimensions. The study includes detailed analysis of the classified variables (workposition, experience years, work place). Linear regression analysis also showed that the level of organizationalexcellence achievement's degree can be predicted through identifying the degree level of applying electronicmanagement at AHU.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".