The validity of the SERVQUAL and SERVPERF scales
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 The purpose is to investigate, the difference between SERVQUAL and SERVPERF's predictive validity of service quality. Design/methodology/approach Data from 17 studies containing 42 effect sizes of the relationships between SERVQUAL or SERVPERF with overall service quality (OSQ) are meta‐analyzed. Findings Overall, SERVQUAL and SERVPERF are equally valid predictors of OSQ. Adapting the SERVQUAL scale to the measurement context improves its predictive validity; conversely, the predictive validity of SERVPERF is not improved by context adjustments. In addition, measures of services quality gain predictive validity when used in: less individualistic cultures, non‐English speaking countries, and industries with an intermediate level of customization (hotels, rental cars, or banks). Research limitations/implications No study, that were using non‐adapted scales were conducted outside of the USA making it impossible to disentangle the impact of scale adaptation vs contextual differences on the moderating effect of language and culture. More comparative studies on the usage of adapted vs non‐adapted scales outside the USA are needed before settling this issue meta‐analytically. Practical implications SERVQUAL scales require to be adapted to the study context more so than SERVPERF. Owing to their equivalent predictive validity the choice between SERVQUAL or SERVPERF should be dictated by diagnostic purpose (SERVQUAL) vs a shorter instrument (SERVPERF). Originality/value Because of the high statistical power of meta‐analysis, these findings could be considered as a major step toward ending the debate whether SERVPERF is superior to SERVQUAL as an indicator of OSQ.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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