Patterns of decision-making and driving factors among Indian wine drinkers: When picking out a bottle of wine
Bibliographic record
Abstract
In today's highly competitive global consumer market, it is crucial to understand the factors that influence customers' decisions to select a particular product (in this case, wine) from among thousands of brands. Many hours have been spent investigating what factors influence people to buy specific products. However, there is scant research on the preferences of wine buyers when shopping online. Understanding why consumers pick one brand of wine over another when faced with literally thousands of options is crucial in today's hyper-competitive global consumer market. Researchers have paid close attention to how customers behave when deciding which products to purchase. However, there is a lack of research into the habits of people who buy wine online. The evolution of the Indian wine industry, common grape-growing regions and products, and other motivating factors are all discussed. A questionnaire for the study was also developed based on previously established models and ideas. Information was collected and analysed from numerous resources. The 619 responses provided a detailed portrait of Indian wine consumers and numerous strategies for reaching them.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".