The consumer preferences of Parmesan cheeses in foreign countries: a non parametric analysis using CUB models
Bibliographic record
Abstract
In the last decade the Italian exports of Parmesan cheese has been reinforced in the north American countries (US and Canada) because of a strong increase in consumption. However, factors boosting this consumption are not always so obvious. For instance, in US both Reggiano and Grana Padano are generally classified as “Parmesan Reggiano and Parmesan Padano” where the term Parmesan is well-known while the differentiation between Reggiano and Padano is often not appreciated or not so clear for North American consumers even because the imported Parmesan is about 6-7% of the total consumption. \nThis paper analyzes data of a survey carried out on US and Canada about the Parmesan’s consumption through a class of mixture models with covariates know as CUB models. The survey was done in some restaurants chains in US and Canada. A questionnaire was filled out by 540 customers to get information about the of knowledge and appreciation of Parmesan, purchase features as well as factors influencing the purchase and willingness to pay. Information about consumer’s profile were also collected. \nCUB models, applied to ordinal scale data, allow us to estimate the latent variables known as feeling and uncertainty. The feeling indicates the conviction of the respondent and the attraction/repulsion he feels towards the evaluation, while the uncertainty is a random component related to factors such as lack of knowledge or interest, high times for valuation, laziness / apathy. \nThe CUB model detected a high feeling on the level of knowledge, the appreciation and frequency of purchase of the Parmesan while the CUB model with covariates showed a discriminatory effect of the age and country of residence on feeling as well. By contrast, the results show a high uncertainty on the knowledge of differences between Parmesan Reggiano and Parmesan Padano. The simulations with covariates revealed no discriminatory effect of demographic, geographic and behavioral variables while confirming that the Parmesan cheese is a well-known but also that Reggiano-Padano cheeses are poorly understood. This phenomenon may be interpreted as positive for the reputation of Parmesan but it could hide a lack of information for consumers which may encourage the local production at the expense of Made in Italy one.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".