10 years of service quality measurement: reviewing the use of the SERVQUAL instrument
Bibliographic record
Abstract
En 1988, Parasuraman, Zeithaml y Berry elaboraron un instrumento para medir la calidad del servicio. Desde esa fecha, este instrumento ha sido utilizado en numerosos estudios sobre distintas industrias y en diferentes países, tanto por académicos como por profesionales. Sin embargo, a pesar de su amplia difusión, pocos estudios tratan los aspectos de dimensionalidad y validez de esta escala de medición. El presente artículo describe las prácticas observadas con relación a estos aspectos a través del análisis de los estudios que han usado SERVQUAL durante los últimos diez años. A partir de una muestra de 60 trabajos empíricos que usan la escala SERVQUAL, se analiza los principales aspectos de validez tratados por cada autor, empleando una plantilla de análisis adaptada del estudio de Stokes y Miller (1975). Con base en los datos disponibles, el estudio sugiere que la escala desarrollada por Parasuraman, Zeithaml y Berry (1988) no presenta una estructura dimensional estable de cinco factores. Finalmente, el artículo evalúa la influencia de las caraterísticas del diseño de la investigación sobre la confiabilidad de SERVQUAL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".