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An Analysis of Consumer Response to Plant-based Meat Alternative Labelling Policy

2022· dissertation· en· W6989174028 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueAgriculture Sustainability and Environmental Impact
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLabellingAgency (philosophy)AppealMeat packing industryConsumer demandConsumer choiceFood productsConsumer behaviour
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Plant-based meat alternatives, defined as products made with plant-based protein that imitate the taste, texture, and appearance of real meat, have been subject to rapid market growth in recent years. These products tend to appeal to consumers who are actively reducing their meat consumption, typically due to concerns about animal welfare, environmental sustainability, or health issues. The simulant nature of these products introduces the need for regulation of labels to facilitate informed consumer decision-making when selecting meat and plant-based alternatives at the grocery store. In Canada, guidelines exist which regulate the use of meat-related terms (e.g., burger, ground, etc.) on the labels of plant-based meat alternatives, nutritional content, and other aspects of these products. While meat-related terms are permitted in Canada, provided certain disclaimers are also present, some jurisdictions abroad have banned such labels entirely. In Canada, some meat industry groups have called for the removal of such terms, and in 2020 the Canadian Food Inspection Agency (CFIA) conducted a consultation on its guidelines for plant-based meat alternative labelling. Despite a dynamic policy environment, research that investigates the consumer demand effects of plant-based meat alternative labelling policy remains elusive. \n\nA survey of 1203 Canadian consumers was conducted to assess the consumer demand effects of different regulatory approaches to the use of meat-related terms on plant-based meat alternative labels. The survey included a discrete choice experiment, where respondents were assigned to one of three labelling treatments – unregulated labels, current Canadian regulations, and a meat-related terms ban. Choice sets featured ground beef and plant-based alternatives with varying attributes and prices. The choice experiment facilitated the investigation of two secondary research objectives: consumer response to regulated protein label claims, and an assessment of preference heterogeneity for plant-based meat alternatives under different labelling policy scenarios. The data was analyzed using multinomial logit, random parameters logit, and latent class logit models, eliciting marginal utility and willingness-to-pay estimates for the attributes and policy effects. \n\nResults show that the labelling policy environment does impact consumer preferences for ground beef and plant-based alternatives. Ground beef is preferred by most consumers in the Canadian market under all three labelling treatments. Further, consumers prefer meat alternatives in an unregulated market relative to the current Canadian regulations and the meat-related terms ban treatments. On average, consumers exhibit similar reductions in willingness-to-pay under the two regulated treatments. However, these effects diverge when preference heterogeneity is accounted for. Five classes of consumers were identified in the latent class logit model, with varying preferences, characteristics, and responses to labelling policy. Preferences for protein claims are generally strong and positive, and there is a significant degree of heterogeneity in preferences for products, attributes, and labelling policy frameworks. The analysis reveals numerous insights into both market and policy issues of plant-based meat alternative labelling. It is in the firm’s best interest to utilize meat-related terms on product labels. However, the disparity in preferences among policy treatments indicates that the provision of information in the form of label disclaimers alongside meat-related terms likely provides valuable information to consumers who may be confused or inattentive otherwise.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,278
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0120,000

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.

Tête enseignante Opus0,005
Tête enseignante GPT0,194
Écart entre enseignants0,190 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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