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Record W25906239 · doi:10.35808/ersj/390

Brewing the Recipe for Beer Brand Equity

2013· article· en· W25906239 on OpenAlex
Cristina, Normand Bourgault, Domingo

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEUROPEAN RESEARCH STUDIES JOURNAL · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsRecipeBrewingBusinessBrand equityAdvertisingMarketingBrand namesCommerceFood scienceChemistry

Abstract

fetched live from OpenAlex

This research study aims to analyze the sources and consequences of beverages’ Brand Equity, and more specifically, the beer Brand Equity in a Sothern European mature market. For this purpose, based on the customer-based Aaker’s Brand Equity model, we developed an empirical study, using structural equation modeling (SEM) in order to assess how beer Brand Equity stems from in the brewery industry and to analyze its consequences in consumer behavior. Our findings suggest that the beer brand image is the most important dimension for beer Brand Equity. Moreover, a significant positive influence was found for all the dimensions analyzed, namely brand awareness, perceived quality and loyalty; while we found empirical support for the influence of beer Brand Equity on purchase intention and the consumer willingness to pay a premium price. This research brings relevant implications for brewery marketing managers, who should strengthen their beer brand image, and further consider beer Brand Equity as a key variable in consumer behaviour.

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 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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.583
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.349
GPT teacher head0.431
Teacher spread0.082 · 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