Effect of geographical origin on consumers' emotional response to alcoholic beverages: A study with wine and cider
Bibliographic record
Abstract
Abstract Consumers do not evaluate food products exclusively using sensory properties, but rather they judge them using both intrinsic (ex. sensory properties) and extrinsic (ex. brand, packaging, geographic origin) factors. Alcoholic beverages include a wide variety of information on their packaging, with one factor being geographic origin. The purpose of this study was to determine how geographic origins can influence a consumer's emotional response to alcoholic beverages (wine and cider). First, consumers were asked to identify wine and cider‐producing countries. Then in the first trial, consumers (n = 112) evaluated wine samples from different geographic origins (France, Italy, Canada, USA, Nova Scotia [NS]). In the second trial, consumers (n = 102) evaluated cider samples labeled with different geographic origins (France, USA, Canada, United Kingdom, NS). In both trials, the samples were evaluated using the check‐all‐that apply variant of the 25‐item version of the EsSense Profile® and a purchase intent scale. The geographic origin affected the consumers' purchase intent of both wines and ciders. Participants that had higher knowledge about wine or cider, were not influenced by the geographic origin. In both trials, purchase intent scores increased when positive emotions were selected. However, the consumer's level of knowledge did not affect their emotional response to the different geographical origins. Future research should investigate how cultural background affects consumers' emotional responses, and the influence of other extrinsic factors. Practical Applications Consumers have emotional responses to foods and these responses begin prior to purchase. Consumers rely on the packaging (extrinsic cues) to make purchasing decisions and alcoholic beverages contain a large number of extrinsic cues. Geographical origin is an extrinsic cue that is especially important for alcoholic beverages. This study aimed to evaluate consumers' emotional responses to alcoholic beverages from different geographical origins. The geographical origin affected the consumers' emotional response and purchase intent. When consumers experienced positive emotions their purchase intent increased. Future studies should evaluate the effect of other extrinsic cues.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".