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Record W2068453904 · doi:10.1108/17511060710817230

Customer engagement and the operational efficiency of wine retail stores

2007· article· en· W2068453904 on OpenAlexaff
J.E. Barth

Bibliographic record

VenueInternational Journal of Wine Business Research · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsWine tastingOriginalityMarketingBusinessAdvertisingWineData envelopment analysisSample (material)Value (mathematics)Style (visual arts)SignageComputer scienceMathematicsQualitative researchGeographySociologyStatistics

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to show that new‐style retail wine stores with features such as tasting rooms, lecture theatres and demonstration kitchens used to educate and engage customers have better retail efficiency than old‐style stores. Design/methodology/approach Sales dollars, labour hours and litres of inventory depletion from a paired sample of old‐style and new‐style facilities located in five different communities are submitted to a data envelopment analysis to determine the retail efficiency of the stores. Findings All the new‐build stores had higher retail efficiency than the older stores, and input reductions in older stores were unlikely to bring their performance up to the level of the new store concepts. Originality/value One of the shortcomings of this research is that the old and new stores in the paired samples are different in size and location within each municipality. While it is clear that the new store features (tasting rooms, seminars, cooking demonstrations, etc.) increase retail efficiency, it remains to know the contribution of each of feature to the improvement in retail performance.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.716

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.070
GPT teacher head0.347
Teacher spread0.277 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations27
Published2007
Admission routes1
Has abstractyes

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