Cheese perception in the North American market
Bibliographic record
Abstract
Purpose – The purpose of this paper is to detect market segments where consumers have a different knowledge of domestic and imported Parmesan cheese in USA and Canada. The results may be helpful in understanding to what extend North America consumers appreciate Parmesan cheese and brands, Parmesan consumption and price while recognizing market segments according to consumer awareness, involvement and covariate effects. Design/methodology/approach – A class of mixture models, known as combination uniform binomial (CUB), is applied to survey data collected in USA and Canada. A questionnaire, filled out by 540 restaurant customers, collects opinions about consumption, purchase features and price. The CUB model estimates the two latent variables, known as feeling and uncertainty, explaining the respondent’s behavior as awareness and involvement variability while the CUB clustering procedure detects market segments. Findings – CUB results show that the Parmesan is a well-known cheese but also that a small share of consumers look for the place of origin. The model detects market segments where consumers express better awareness on taste, price and origin while the knowledge of imported Parmesan brands is lacking. Most of consumers, not paying attention to the origin, would hardly switch to the imported Parmesan because of higher price or because they are already satisfied of the domestic cheese. Research limitations/implications – The results suffer some restrictions in the sample representativeness. A further analysis, where the survey is done at retail and advances in CUB models, may improve the market segmentation procedure allowing a better generalization of results. Practical implications – The survey results highlights the appreciation and consumption of Parmesan cheese, especially for its taste, as well as a low perception of Italian brands. Consequently, trade companies should focussed their communication strategy on activities encouraging North American consumers to taste Italian Parmesan brands (e.g. tasting sessions, price promotions) instead of costly and less effective advertising campaigns. Social implications – Parmesan brand misunderstandings are often associated with market information asymmetry. The paper results show a market segmentation where purchases are mainly driven by Parmesan taste regardless of domestic or imported brands. Likely, the consumption of domestic Parmesan is well consolidated and it is not a consequence of brand information asymmetry. Originality/value – The CUB model is an innovative and flexible no parametric approach for evaluating consumer behavior and for segmenting the market while dealing with complex problems of food knowledge.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".