Analysis of SERVQUAL Application to Service Quality Measurement and Its Impact on Loyalty in Ghanaian Private Universities
Bibliographic record
Abstract
The purpose of this research is to use an adapted SERVQUAL method to measure service quality in Ghanaian Private Universities. The study use graphical technique for data presentation.The methodological approach follows the traditional SERVQUAL method of service quality perception and expectation as well as the difference scores determination. This approach is intended to improve SERVQUAL method analysis to achieve quality decision making based on graphical view of different relationships among the concepts used in the method. The study is on 321 students’ perception and expectations of five different Private Universities in Ghana.The study finds that students’ perception on Private Universities’ performance predict their loyalty better than the expectations. Managerial action can be better taken on service quality variables when the difference scores are used as percentage on perception. However, different service quality measurement methods such as SERVPERF and HEdPERF should be used and compared the results with this modified SERVQUAL method in Ghanaian Private Universities.This research finding has the strength to equip marketing professionals and researchers to increase SERVQUAL method adoption among different academic institutions.The value in this study is found in highlighting the importance of difference scores and the graphical demonstration of relationships among service quality perception and expectation as well as loyalty constructs in Ghanaian Private Universities.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".