A Review of Literature on the Gaps Model on Service Quality: A 3-Decades Period: 1985–2013
Bibliographic record
Abstract
The study aims to contribute to the research on service quality, analyzing almost 30 years of research on the Gaps Model proposed by Parasuraman, Zeithaml and Berry in the 1980s. A literature review has been conducted from 1985 to 2013 with the purpose of underlining the model evolution and its criticisms. Major international academic databases have been consulted. On this basis the paper summarizes some theoretical-conceptual and methodological-operational critical aspects identified by scholars who analyzed and applied the model and the scale. Despite that, the Gaps Model and the SERVQUAL scale are still the most used instruments to study service quality in marketing literature. The analysis allows to identify interesting points for future research on the topic of service quality. The conceptual framework presented in the paper does not include any empirical research that could be eventually implemented to validate the findings.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".