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Record W6986630335

Quality perception in a meat department of a retail store

2014· dissertation· en· W6986630335 on OpenAlexaboutno aff

Bibliographic record

VenueK-State Research Exchange (Kansas State University) · 2014
Typedissertation
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)PerceptionIndex (typography)Statistical analysisMetric (unit)Competition (biology)
DOInot available

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.092
GPT teacher head0.361
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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