Mystery Shopper Benchmarking of Durable-Goods Chains and Stores
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With increased attention being paid to retail performance, a considerable amount of academic work has been devoted to the assessment of retail services. This work has moved beyond an initial reliance on classical test theory methods to the more managerially relevant perspective provided by generalizability theory and begun to compare the quality of data provided by customers with that collected by mystery shoppers. However, initial work on mystery shopping is limited to the evaluation of individual retail outlets, not retail chains, and to convenience-goods retailers, where personal selling is of only minor importance. This article examines the psychometric quality of mystery shopping data for retail chains and durable-goods retailers. At issue are whether more visits are required to evaluate retail chains than individual stores and whether the more extended period of sales interaction characteristic of durable retailing increases or reduces the number of shopper visits needed to make reliable decisions when evaluating retailers.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it