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Enregistrement W3142700129

Consumers’ Willingness-to-pay for Health-enhancing Attributes in Food Products: A Meta-analysis

2016· article· en· W3142700129 sur OpenAlexaboutno aff
Irina Dolgopolova, Ramona Teuber

Notice bibliographique

Revue2016 Annual Meeting, July 31-August 2, Boston, Massachusetts · 2016
Typearticle
Langueen
DomaineMedicine
ThématiqueConsumer Attitudes and Food Labeling
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWillingness to payProduct (mathematics)MarketingConsumption (sociology)BusinessPromotion (chess)PopulationPerceptionFood productsFunctional foodFood choiceEnvironmental healthPublic economicsEconomicsMedicinePsychologyFood science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Assessing potential demand for functional or healthy foods is crucial from several perspectives. First, foods with functional attributes in many cases require more expensive production process than traditional foods, for example, when the functional attribute is provided by enhancing or enriching the products with additional substances. It is necessary, then, to estimate potential demand for functional foods prior to the delivering product to the consumers. Second, the promotion of healthier food options is related to the fact that an unhealthy diet is among the four main behavioral risk factors of non-communicable diseases (NCD) and are mostly spread in low- and middle-income countries. It has been demonstrated that prices can be a barrier for healthy food consumption, especially among low-income groups of the population. From this perspective, it needs to be clearly stated if consumers indeed are ready to pay price premiums for foods aimed at improving their health. Third, market introduction of functional foods and foods with health benefits has not always been successful. Despite the importance of a healthy diet in the prevention of some diseases and sustaining well-being in general, economists and marketing researchers observe some uncertainty in consumers’ perception and acceptance of foods with health benefits. This paper investigates the body of research that has been performed so far on consumers’ valuations of healthy attributes in food products by means of a meta-analysis. It explores if variation in willingness to pay (WTP) for healthy attributes in foods that have been reported in scientific papers on the topic can be attributed to common factors related to the choice of the methodology, the place and time of data collection, the choice of the carrier product and the health benefit specified. Thus, our study contributes to the existing literature on health-enhancing foods by (i) reviewing the existing empirical evidence on consumer valuations of different healthy attributes, (ii) identifying the major underlying drivers of differences in WTP estimates via meta-analysis and (iii) deriving directions of research to be taken into account for the future developments in the field. Literature search resulted in 28 studies which provided 175 WTP estimates. Stata meta-regression command specifically designed for meta-analyses was employed. This command allows analyzing study-level data and estimates the between-study variance and the coefficients by weighted least squares when the outcome variable is continuous. The results of the meta-regression imply that the elicitation method, the carrier product, the specific health benefit, and the place of the study significantly influence variations in WTP estimates across studies. First, hypothetical methods of willingness to pay elicitation produce higher valuations compared to non-hypothetical methods like experimental auction and real purchase data. Second, with respect to the base product the results indicate that in case of dairy products (milk, yogurt, cream cheese, cheese, butter and ice cream) and fruits and vegetables the WTP estimates for a specific health attribute are significantly lower than for all other product categories included. Third, according to our results the specific health attribute “Cholesterol lowering” leads to significantly higher WTP estimates than any other health/nutrition claim. The valuations of this attribute varied from 0% to 200% with the highest values referring to the spread for lowering cholesterol. Finally, the place where the data was collected influences WTP estimates. Our results indicate that there are no significant differences between studies conducted in Europe, the United States and other regions. However, studies conducted with Canadian consumers report statistically significant lower WTP values. In general, it can be noticed that despite an established connection between diet and the development of non-communicable diseases, economics and marketing research so far fails to provide systematic view on the consumer valuations of different healthy attributes in food and, consequently, on the perspective demand for these products. Studies reviewed reported very different valuations of healthy attributes in foods. Studies also differ greatly in basically all parameters of the research: data collection, methodology, and analysis of the results. Although it seems rather difficult to draw general conclusions about consumers’ willingness to pay for healthy attributes in foods, this research summarizes the efforts performed so far and may be employed to determine the directions for future analysis.

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 machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,034
score de la tête « metaresearch » (Gemma)0,061
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,179

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0340,061
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0100,064
Bibliométrie0,0040,005
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0020,002
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0060,000

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 Opus0,048
Tête enseignante GPT0,313
Écart entre enseignants0,265 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeMéta-analyse
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
Publié2016
Routes d'admission1
Résumé présentoui

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