The experience of New Zealand in the evolving wine markets of Japan and Singapore
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the developing wine markets of Japan and Singapore for New Zealand (NZ) wine. It is principally an opinion piece with some reference to the academic literature, to the trade literature and quite a bit of the authors' own experiences as marketing academics conducting research in East Asia on the growth of wine drinking in this region of the globe. Design/methodology/approach – This paper is atypical in that it is more of a descriptive commentary, or “Viewpoint”, that draws on the literature interspersed with the autoethnographic reflections regarding the experiences in looking at NZ wine in Japan and Singapore as well as drawing on data from face-to-face interviews and focus groups with a variety of participants with knowledge of the global wine industry. Informal meetings were held with individuals representing NZ wineries, Japanese and Singapore wine distributors, restaurant food and beverage managers, wine journalists, wine shop proprietors and sommeliers data. Personal reflections and opinions are interspersed with the trade and academic literature in relation to the exploration of the NZ experience in the wine markets of Japan and Singapore. Findings – The major finding is that there are marked differences between Japanese and Singaporean consumers and that the adoption of wine drinking or the incorporation of wine into one's non-traditionally wine-drinking society involves individuals who play cultural intermediary roles as communicators and distributors of “cultural products” and as translators of cultural products into meaningful local, consumption experiences. Based on personal observations, there appears to be a functional aspect to this facet of globalisation in that cultural intermediaries facilitate the adoption of wine consumption in emerging Asian markets simply through promoting it as a social accompaniment much like local alcoholic beverages, but also that wine has the capacity to enhance local cuisine. Practical implications – The insights gained through personal reflection and an examination of perspectives from participants with knowledge of the wine industry in Japan and Singapore should help NZ wine producers with specific knowledge to navigate through the complexity of emerging wine markets in the Asian context. Originality/value – The contribution is in looking at “sophisticated globalization” in the context of NZ wine producers looking to market a cultural product such as wine to specific Asian societies not traditionally known for wine drinking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".