A review of twenty years of SERVQUAL research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper reviews 20 years (1988‐2008) of research on the SERVQUAL scale for measuring service quality. Design/methodology/approach A range of studies that have applied the SERVQUAL scale in this 20‐year period are examined in a non‐exhaustive review of the literature. These studies are selected from well‐known databases – such as “ABI/Inform”, “ScienceDirect”, and “EBSCOhost”. Findings The paper identifies and summarizes numerous theoretical and empirical criticisms of the SERVQUAL scale. Despite these criticisms, the paper concludes that SERVQUAL remains a useful instrument for service‐quality research. Originality/value The paper provides a useful source of information on SERVQUAL and its applications. In particular, the paper summarizes a selection of 30 applications of SERVQUAL.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it