Use Balanced Scorecard (BSC) Perspectives in the Service Sector: A Case Study on the Jordanian Private Universities
Bibliographic record
Abstract
This study aimed to identify on how to use Balanced Scorecard (BSC) perspectives in the service sector in general and in the Jordanian private universities in particular. The study was designed questionnaire to achieve the study objective, the questionnaire was distributed to(48), has recovered from (42) identify the rate of recovery was (87.5%), and after analyzing the data and test hypothesis using analysis methods through the program (SPSS) we found many of the results was the most important: There is using of the Balanced Scorecard (BSC) perspectives in the Jordanian private universities partially, The researcher recommends that The universities hve to use all the perspectives of Balanced Scorecard (BSC) in the work as well as follow-up students after graduation in order to give the Promotional League. and The university has to work feedback about the services provided to the students.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".