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Enregistrement W4414883357 · doi:10.1007/s44187-025-00563-8

Evaluating the influence of traffic light labels on consumer sugar sweetened beverage choices using a discrete choice experiment in Sri Lanka

2025· article· en· W4414883357 sur OpenAlexfundno aff
Priyanka Jayawardena, Nisha Arunatilake, Usha Perera

Notice bibliographique

RevueDiscover Food · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueConsumer Attitudes and Food Labeling
Établissements canadiensnon disponible
Organismes subventionnairesInternational Development Research CentreAmerican University of Beirut
Mots-clésConsumption (sociology)Product (mathematics)Food choiceFood labellingFood productsValue (mathematics)Nutrition facts labelConsumer behaviourDiscrete choice

Résumé

récupéré en direct d'OpenAlex

Fostering healthy eating has gained momentum as the link between poor diets and the rise in noncommunicable diseases (NCDs) has become more widely recognised. The current evidence suggests that food labels may help consumers make informed decisions and avoid unhealthy eating practices. However, available research mostly evaluates the impact of food labels on consumption habits or the effectiveness of various food label types. Consumers consider several factors when purchasing, including food labels, the kind of food, price, and nutritional value. Furthermore, a sizable unofficial food market may lessen exposure to packaged foods with food labels, thereby decreasing the value of food labels. Consequently, while proposing food labelling regulations, it is essential to consider the characteristics of the retail sector as well as customer behaviour. Addressing the gaps in previous literature, we examine the effectiveness of food labels on consumer choices based on various product attributes such as beverage type and price, as well as the food labels. Specifically, the study focuses on the efficacy of traffic light labels (TLLs) on sugar-sweetened beverages (SSBs) in Sri Lanka, where the regulation of TLLs on SSBs was introduced in 2016. We also assess the efficacy of food labels in informing consumers from different backgrounds. In this study, we evaluated the impact of TLLs on the consumption behaviour of SSBs in the presence of other product features using a Discrete Choice Experiment (DCE) model. We collected data through a choice experiment that assesses consumer choice of beverages in the presence of three product categories: beverage type, TLL, and price. The survey involved around 2,500 consumers across 14 districts, representing both urban and rural areas and all provinces of Sri Lanka. The data is analysed using a mixed logit model. We used the Household Income and Expenditure Survey (HIES) and primary consumer survey data to assess the exposure to products containing TLLs across different socioeconomic groups, applying the quintile distribution. Consumers base their beverage choices on a combination of product attributes, notably price, beverage type, and TLLs, assessing these both independently and jointly. The TLL regulation on SSBs significantly influences the selection of low-sugar SSBs. However, this influence considerably varies by socioeconomic group. Low-income groups are not very concerned about the TLL when making beverage choices. Product attributes, such as price and beverage type, also significantly impact beverage choices. While price sensitivity is evident across all consumer groups, it is notably higher among low-income consumers compared to their middle- and high-income counterparts. Furthermore, limited coverage of the SSB regulations disproportionately negatively affects economically disadvantaged groups, as around 75% of sweetened beverages fall outside the regulatory framework. Healthy consumer choices can be effectively influenced by the TLL system on SSBs. However, the acceptance of nutritional labelling is significantly impacted because its effectiveness differs among customers’ socioeconomic backgrounds. For various reasons, consumers from lower socioeconomic backgrounds are less influenced by TLL codes when it comes to their beverage preferences. The TLL system has little effect on those from low socioeconomic backgrounds because it does not cover the types of beverages that they typically drink. Furthermore, even if they are, their comprehension of TLL is limited. Finally, consumers from lower socioeconomic backgrounds are less affected by TLLs when making purchasing decisions, regardless of their awareness of the TLL system. This is partly because such consumers are more price-sensitive. This emphasises consumer awareness of TLL codes and making healthy beverage options affordable.

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)0,007
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,016
Score d'incertitude au seuil0,038

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0070,007
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,002
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,047
Tête enseignante GPT0,378
Écart entre enseignants0,331 · 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'étudeObservationnel
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
Publié2025
Routes d'admission1
Résumé présentoui

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