Luxury wine brand visibility in social media: an exploratory study
Bibliographic record
Abstract
Purpose The purpose of this paper is to address the visibility of luxury wine brands, in particular the Bordeaux first growth brands in social media. Design/methodology/approach The paper uses data from howsociable.com to portray similar luxury wine brands in multi‐dimensional space. To identify the associations between the brands and the social media visibility indicators, the paper uses correspondence analysis. Findings The findings of the paper show that some of the brands considered did not, at the time the data were gathered, have a clearly defined social media strategy. Practical implications The indication is that there are opportunities for luxury wine brand managers to use social media as a tool in their marketing strategies; also some threats may exist to these brands should they take a laissez faire approach to social media, particularly when social media are becoming as influential, if not more so than conventional media. Originality/value Brands can take directions in social media today that would have been unlikely if not impossible five years ago. While brand managers may not fully be able to control the destinies of these brands, this paper suggests that the approaches followed in this particular research will present brand managers with a tool that will assist them in directing conversations that occur around their brands.
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 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".