The mystery shopper: a tool to measure public service delivery?
Bibliographic record
Abstract
Originally the exclusive preserve of the private sector, the mystery shopper technique is increasingly being used in the public sector. In the wake of the reforms to modernise the state, accountability and performance-monitoring exercises are on the rise. They focus, in particular, on service quality and user-customer satisfaction. The article makes a twofold contribution to this topic: methodological and substantive. First of all, the article undertakes a scoping review of the literature on the mystery shopper. This review makes it possible to present the mystery shopper technique and its use in the public sector. For this bibliometric study, a sample of 34 papers was analysed. Second, the article offers a summary of the research into the mystery shopper technique, its potential and its limitations. Points for practitioners This article describes the use of the mystery shopper technique in the public sector. The areas for which mystery shopper surveys are commissioned are relatively limited, most being undertaken in the health sector. However, the scoping review emphasises the potential importance of mystery shopping for the purposes of the evaluation. As such, investigating other areas can be very interesting and promising for the public authorities. We also observe from this literature review that the challenges identified during mystery shopper studies can be overcome.
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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 | 0.021 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.030 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".