Females' attitude and preference for beer: a conjoint analysis study
Bibliographic record
Abstract
Summary In order to sustain the fast‐expanding beer industry, companies need to attract new female consumers. The main objective of this study was to identify the extrinsic and intrinsic attributes that drive female consumers' purchase of beer. A literature review and focus group ( n = 6) were conducted, and six attributes were identified as purchase drivers of beer. These attributes included flavour, appearance (colour), packaging, brand, production methods and beer style. These attributes were used to design a choice‐based conjoint analysis survey. The survey was administered to 277 females (aged 35.09 ± 15.2) residing in Nova Scotia (Canada). The results indicated that sweetness has a positive effect on liking. Bitterness has a strong negative effect on liking of beer. Black colour, stouts and macro‐brewed beers also had negative effects on liking of beer products. The consumer clusters showed that generally all of the consumer groups liked sweet beers with low bitterness.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".