A cross-cultural comparison of social media usage in the wine business
Bibliographic record
Abstract
A vast number of studies are being published about social media in the wine business, but less is known about their usage comparing different wine producing countries. Therefore, a cross-cultural study was conducted, in which we investigated the use of social media tools by wineries in European and in overseas countries like Germany, Austria, Italy, France, Hungary, UK as well as USA, Australia, New Zealand, Canada and South Africa.\nThe questionnaire was developed and tested first in Germany. In order to use the question catalogue for this cross-cultural study, it was translated into the official language of the certain country. Using different online survey software, the link of the questionnaire was sent via email and was distributed on social media pages. The response rate varies between 25 up to 427 wineries which participated in the study.\nThis study demonstrates that social media has been accepted and is already widely used as a communication tool in the wine business. A high level of acceptance of social media tools could be observed in each participating countries. Facebook is the most important among the available social media tools; however video- and photo-sharing systems are developing rapidly. There is a significant difference between European and overseas countries. Wineries of the latter utilize more frequently social media than those from Europe.\nSocial media has been developing very rapidly. Facebook is still the number one, even in the wine industry, but new and innovative platforms occur day by day. Winery owners should be aware of the effectiveness of this modern communication tool, however at the same time they have to define a communication strategy, in which social media is integrated too.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".