Knowledge effects on the exploratory acquisition of wine
Bibliographic record
Abstract
Purpose – This paper aims to determine whether exploratory wine purchasing behaviour is affected by consumers’ objective and subjective wine knowledge. Design/methodology/approach – A questionnaire was developed using recognised scales for exploratory consumer tendencies, objective and subjective wine knowledge. The survey was administered using the MTurk platform. A factor analysis was first used to test the psychometric properties of the measures of the three constructs. Once the robustness of the measures was ascertained, cross-tabulations and testing via ANOVA’s of the demographics of age, gender, weekly wine consumption and education on the constructs was undertaken. In addition the causal relationship of subjective and objective wine knowledge on exploratory purchase behaviour was investigated via the use of multiple regression analysis. Findings – The results show that consumers with more real (objective) knowledge of wines are more likely to participate in exploratory wine purchasing. Objective wine knowledge is greatest amongst older consumers and those who consume more wine. Research limitations/implications – While attempts were made to limit biases due to the research approach, the results may lack generalisability because a US sample only, was used. Recommendations for future research extending the sample population as well as for changes to the question formats are suggested. Practical implications – The findings of this study have implications for wine marketers in that marketing strategies and activities (labelling, distribution, media, etc.) may need to be adapted depending on the exploratory purchasing behaviour and wine knowledge of their target customers. Originality/value – Exploratory wine acquisition behaviour is important to wine marketers. This behaviour encourages trial but, at the same time, impacts brand loyalty. This paper identifies the characteristics of consumers in terms of wine knowledge, consumption and demographics most likely to exhibit this behaviour and provides support for the need for marketers to identify these consumers and adapt their marketing activities targeting 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".