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Record W2428060010 · doi:10.1108/ijwbr-09-2015-0041

À votre santé – conceptualizing the AO typology for luxury wine and spirits

2016· article· en· W2428060010 on OpenAlexaff
Jeannette Paschen, Ulrich Paschen, Jan Kietzmann

Bibliographic record

VenueInternational Journal of Wine Business Research · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMarketingMarket segmentationTypologyOriginalityContext (archaeology)WineBusinessAdvertisingMarketing strategySociologySocial science

Abstract

fetched live from OpenAlex

Purpose The status of icewine as a luxury item is largely undisputed in popular perception. Despite this, icewine has received very little attention in the management literature. This paper aims to close this gap by developing a theoretical framework to segment the luxury wine and spirits market with a focus on icewine. Design/methodology/approach This paper is conceptual in nature. The authors adapt Berthon et al. ’s (2009) aesthetics and ontology (AO) framework for luxury brands to provide a theoretical lens for segmenting the luxury wine and spirits market into four distinct segments. Findings The main contribution of this paper is a theoretical framework for segmenting the market for luxury wines and spirits into four distinct segments: cabinet collectors, cellar collectors, connoisseurs and carousers. The authors then apply their framework to the icewine category and outline considerations for the marketing mix of icewine producers. Practical implications The AO framework for luxury wines and spirits is beneficial for icewine producers to help differentiate their current and future market segments. In addition, this paper outlines practical implications for icewine maker’s marketing mix that could enhance their competitive position today and in the future. Originality/value This is the first paper examining icewine in the context of luxury marketing.

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)0.001
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: none
Teacher disagreement score0.634
Threshold uncertainty score0.355

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.378
Teacher spread0.296 · 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

Citations9
Published2016
Admission routes1
Has abstractyes

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