Quality perception in a meat department of a retail store
Bibliographic record
Abstract
HEB is a privately-held grocery retailer founded in 1905 in Kerrville, TX. Since then, HEB has grown to 399 stores in 155 communities. Although the majority of its operations have been in southern Texas, nearly 10 percent of HEB’s stores (39) are in Mexico. This may be considered an impressive feat since its entry into Mexico occurred in 1997 to take advantage of the growth opportunities in Mexico and the North American Free Trade Agreement involving Canada, the U.S. and Mexico. The research was conducted using primary data collected through a survey. Secondary data from the Shapiro Index were also employed to explain the observations from the survey. Econometric and statistical models were used in the analyses. Customer quality perception is an important metric for the retail industry. This research evaluates the effect of purchase history, frequency of shopping, price perception, quality and service changes through time on the quality perception of a meat department in a supermarket. The impact of additional labor was analyzed to determine the effect on those variables. The quality perception of the customers of other meat retailers in the same trading areas was also evaluated. The results of the study were then compared to the actual metric used to measure quality perception (Shapiro Index). The study found that the company has a significant higher quality perception than other supermarkets, that labor had a positive effect on quality and service change, customers noticed the change, and with time, it will increase their quality perception. The results show a different perception from customers than the Shapiro Index, customers do not notice a decrement on quality in the meat departments. Based on these results, a further research on the actual methodology used was performed, training and new purchasing specifications were applied to improve the intrinsic characteristics of the products and a new marketing campaign was launched based on quality and freshness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".