Service quality and student/customer satisfaction in the private tertiary education sector in Singapore
Bibliographic record
Abstract
Purpose This paper focuses on students’ perceptions of the quality of non-academic services received in higher education. While the important role played by expectations and perceptions in students’ evaluations of such services has been discussed in much of the service quality literature, there is insufficient work in the private tertiary educational sector (PTES). Thus, the purpose of this paper is to examine the relationships between service quality, student satisfaction, and behavioural intentions in the PTES, using Singapore as a case study. Design/methodology/approach This study adopted quantitative research to address the research questions. Primary data were collected from 324 valid responses from a survey conducted in two private tertiary educational institutes (PTEIs) in Singapore. Findings The results suggested that perceived service quality is positively correlated to satisfaction; perceived service quality and satisfaction are positively correlated to favourable behavioural intentions; and the relationships among perceived service quality and loyalty and paying more for a service are mediated by satisfaction. Originality/value This study is significant as the results provide better insights for Singaporean administrators in PTEIs, which is an under-researched area. Generally, the results will have far-reaching implications for all stakeholders in the delivery and consumption of education services in PTEIs, within and beyond Singapore.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".