Luxury wine brands as gifts: ontological and aesthetic perspectives
Bibliographic record
Abstract
Purpose The purpose of this paper is to address the issues of luxury gift giving and the giving of luxury wines as gifts from a conceptual perspective. Design/methodology/approach The article considers the OA (aesthetic and ontology) model as proposed by Berthon et al. that permits the integration of various conceptualisations of different authors in the area of luxury branding. The model offers a typology of luxury brands that draws on Heidegger's theory of arts and Whitehead's process philosophy. This means that one can differentiate luxury brands along two dimensions: aesthetics and ontology. Findings The paper contends that the four modes as set out in the AO model of Berthon et al. can be used as a typology of luxury wines, from both gift giving, and gift receiving, perspectives. Practical implications Luxury wine marketers can make use of the proposed typology to target wine gift givers effectively, by understanding where on the proposed matrix both the giver and the receiver are positioned. The four modes that emerge can be seen as different target markets, with different motivations and different behaviors with regard to luxury wines as gifts. Originality/value By applying the OA model to luxury wines and specifically to the giving and receiving of luxury wines, this paper offers wine marketers the insight to formulate different marketing mix strategies based on the different target markets that emerge from the proposed model.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".