Do Distinct Servqual Dimensions Emerge From Mystery Shopping Data? A Test of Convergent Validity
Bibliographic record
Abstract
Abstract: Service quality is commonly thought to encompass five generic dimensions: responsiveness, assurance, tangibles, empathy, and reliability. These dimensions form the basis for service measurement tools such as SERVQUAL. Research in this area using tools such as SERVQUAL has predominantly focused on customer perceptions of quality. However, another approach used by many organizations is to send trained raters into the service environment, posing as customers, to evaluate service levels. This approach is often called “mystery shopping” and is very commonly used in both private- and public-sector organizations. This study examines whether the accepted service quality dimensions derived from customer perceptions studies are reflected in service quality evaluations using mystery shopping. It finds that the dimensions that emerge from mystery shopping data resemble SERVQUAL dimensions. Furthermore, a replication found that those dimensions are reasonably stable over time. The findings suggest that data from mystery shopping surveys can exhibit convergent validity.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".