Examining Service Quality in Ghanaian Higher Education: A Comparative Analysis of Private and Public Universities
Bibliographic record
Abstract
Little is known about how students perceive the quality of service provided by Ghanaian tertiary institutions and how this perception influences their enrolment choices. Using the Higher Education Quality (HiEdQUAL) model for service quality measurement, this study examined service quality across five key dimensions; teaching and course content, administrative services, academic facilities, campus infrastructure, and support services in Ghanaian private and public universities. A structured questionnaire based on HiEdQUAL model consisting of 27 items with five dimensions, measured on a five-point Likert-Scale was used to gather data. Out of the initial 2,266 sampled respondents surveyed from private and public universities, a total of 1,758 correctly completed questionnaires were returned. This gave a high response rate of 76.43%. The paired t-test results that examined the equality of means between students’ perceptions of service quality at private and public universities across five dimensions, helped to determine the presence of statistically significant differences in some areas. The results suggested that public universities may be making better use of their resources or benefitting from their large numbers of enrollment and state financial support. Given that the quality of service provided by both private and public universities in Ghana falls short of students’ expectations, the paper provides administrators with practical insights to improve service quality in Ghanaian universities and suggests the need for continuous quality improvement in institutions of higher learning in Ghana. Keywords: HiEdQUAL, Service Quality, Higher Education, Public Universities, Private Universities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".