The consumer preferences of Parmesan cheeses in foreign countries: a non parametric analysis using CUB models
Notice bibliographique
Résumé
In the last decade the 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 US consumers. This 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 CUS models. The survey, started in 2011, was done in some restaurants chains in US and Canada. A brief questionnaire was fill out by 540 consumers 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. A descriptive analysis of result show that the Parmesan (as general cheese) is known and appreciated by the majority of respondents, but most of them do not know the difference between Parmigiano Reggiano and Grana Padano. To deeply investigated a CUB model was employed. In particular, the CUB model allows us to better explore the relationship between the consumers and items (answers) and to summarize the distribution of preferences (ranking) expressed by the respondents (as 7 point Likert scale). Using the qualitative judgments of preferences and the overall distributions of rating, the CUB model makes comparisons in a inference context and it associates the consumers features to their evaluations. The CUB model is able to explain, in a direct way, the option that a subject expresses by interpreting it as a mixture of feeling and uncertainty. Feeling and uncertainty and latent components are, therefore, essentially continuous and never observable, and allow a connection with the individual characteristics (covariates) of consumers. The CUB has firstly investigated the following questions: "Level of knowledge of Parmesan", "Degree of appreciation of Parmesan", "Frequency of purchase", "Knowledge of the differences between Parmesan Padano and Parmesan Reggiano" and "Willingness to pay for Parmesan.” The analysis starts by analyzing all consumers and it generates alternative models according to differences in consumers ratings; next the analysis chooses the best models outcome and introduces covariates (consumer’s features) to recognize factors affecting feeling and uncertainty. Firsts results seem to shows a variability in ratings expressed by consumers, except for the level of knowledge where the distribution of scores is similar to the distribution of probability assessed by the model. Secondly, the introduction of covariates (such as country, age, etc.) has identified the effect of factors affecting consumer ratings. Among results, the CUB model shows a different consumer behavior in consuming Parmesan while the comparison between US and Canada revealed significant differences in consumer habits and expectations.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,004 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».