The integration of the third generation balanced scorecard with a student loyalty model to enhance financial performance in higher education
Bibliographic record
Abstract
We have developed a set of appropriate performance evaluation measurements for the private higher education (PHE) sector, based mainly on the integration between the third generation balanced scorecard (third GBSC) and a students' loyalty model (SLM). We describe the process of the development of customers' (students') loyalty, taking into consideration the improvement of the quality of the education process by increasing students' satisfaction, loyalty and financial performance respectively, by improving key performance indicators (KPIs) of the third GBSC. Furthermore, we pursue a case study methodology of the application of the third GBSC integrated with a SLM at Egypt's Canadian International College (CIC). We also investigate students' satisfaction of the CIC's Faculty of Engineering. We find that the application of the suggested model has a significant association with the improvement of the CIC's KPIs related to education quality (EQ). Furthermore, the application of this integration model increases the anticipation of profitability.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".