HEIs service quality as a predictor of students’ satisfaction affecting academic performance: The extension of the Embryonic HESQUAL model
Bibliographic record
Abstract
To question the dominanceof the dimensions of service quality using the embryonic HESQUAL model onstudents' satisfaction. Besides, the study will identify the mediating effect ofa department’s commitment and a moderating effect of self-efficacy on therelationship between students’ satisfaction and academic performance. The study was a one-time dimensionaland used a simple random sampling method which is suitable for quantitativestatistical analysis and homogeneous populations in nature. Analysis resultingfrom SmartPLS by using 431 responses from undergraduate and graduate students of public universities inBangladesh. The study found that students arefocused on core educational qualities and physical environmental facilities tobe satisfied. Thoroughly,students' satisfaction has a direct impact on academic performance butsurprisingly no moderating precedence of self-efficacy on the connectionbetween student satisfaction and academic performance was seen. Noticeably, theextension of the HESQUAL model of department commitment finds significant influenceson both satisfaction and academic performance. However, the modified HESQUEL will quantify thequality dimensions in higher education, and its application in this study candepict a greater understanding of the quality dimensions required to beprioritized by university authorities and policymakers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".