Service Quality and Student Satisfaction Using ServQual Model: A Study of a Private Medical College in Saudi Arabia
Bibliographic record
Abstract
The study assessed the service quality and satisfaction among Pharmacy students at one of the private medical colleges in Saudi Arabia using ServQual model. Specifically, it sought to determine the respondents’ profile in terms of gender, year level, and grade point average; service quality using SERVQUAL Model; over-all students’ satisfaction of students on the provided college services; and which profile and service quality dimensions best predict the over-all satisfaction of the students. Using a descriptive statistics and Multiple Regression Analysis for data analysis, this paper had 189 respondents. Based on the results, majority of the respondents were female from levels 2, 3, and 4 with above average GPA. Responsiveness, empathy and tangibility dimensions of service quality had a negative gap, which means that the expected services did not meet the perceived services on the cited service quality dimensions. Meanwhile, when overall satisfaction was measured, students expressed satisfaction to the college services. Year level was a predictor for all measures of student satisfaction; while GPA was identified as a negative predictor to student satisfaction in terms of faculty. Students’ satisfaction in terms of faculty was best predicted by responsiveness, assurance, tangibility. Meanwhile, assurance was a predictor of students’ satisfaction in terms of curriculum. Responsibility, responsiveness and assurance, on the other hand were predictors of students’ satisfaction in terms of students services and facilities. Finally, the overall student satisfaction was predicted by responsiveness, assurance and tangibility.
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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 | 0.001 | 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) | 0.002 | 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".