The Measurement of Service Quality in the Tour Operating Sector: A Methodological Comparison
Bibliographic record
Abstract
Service quality in the tourism industry receives increasing attention in the literature, yet confusion still exists as to which measure offers the greatest validity. The two main research instruments are Importance-Performance Analysis (IPA) and SERVQUAL. However, both measures have been questioned and research has introduced measures that multiply SERVQUAL by Importance, as well as a measure of just Performance (SERVPERF). This article assesses these four main methods of measuring customer service quality. The data were obtained in cooperation with a major U.K. tour operator. Of the respondents, 220 completed a questionnaire before departure on what elements were important to them and what their expectations were for these elements. Toward the end of their holiday, respondents were issued a second questionnaire measuring performance on the same elements. The research found that although there was variety in the rankings of the 13 different elements, there was no statistical difference between the four methodologies. The final section of this article considers the implications of this finding for tourism managers and future research in the area of service quality.
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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 | 0.061 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".