Customer support for nudge strategies to promote fruit and vegetable intake in a university food service
Notice bibliographique
Résumé
BACKGROUND: Diverse nudges, also known as choice architectural techniques, have been found to increase fruit and vegetable (FV) selection in both lab and field studies. Such strategies are unlikely to be adopted in mass eating settings without clear evidence of customer support; confirmation in specific contexts is needed. Inspired by the Taxonomy of Choice Architecture, we assessed support for eight types of nudging to increase the choice of FV-rich foods in a university food service. We also explored whether and to what extent nudge support was associated with perceived effectiveness and intrusiveness. METHODS: An online survey was conducted with students who used on-campus cafeterias. Multiple recruitment methods were used. Participants were given 20 specific scenarios for increasing FV selection and asked about their personal support for each nudge, as well as perceived intrusiveness and effectiveness. General beliefs about healthy eating and nudging were also measured. Results were assessed by repeated measures ANOVA for the 8 nudge types. RESULTS: All nudge scenarios achieved overall favourable ratings, with significant differences among different types of nudging by the 298 respondents. Changing range of options (type B3) and changing option-related consequences (type B4) received the highest support, followed by changing option-related effort (type B2) and making information visible (type A2). Translating information (type A1), changing defaults (type B1) and providing reminders or facilitating commitment (type C) were less popular types of nudging. Providing social reference points (type A3) was least supported. Support for nudge types was positively associated with the belief that food services have a role in promoting healthy eating, perceived importance of FV intake, trustworthiness of the choice architect and female gender. Lastly, support for all types of nudges was positively predicted by perceived effectiveness of each nudge and negatively predicted by perceived intrusiveness above and beyond the contribution of general beliefs about healthy eating and nudging. CONCLUSIONS: Findings from the current study indicate significant differences in support for nudge techniques intended to increase FV selection among university cafeteria users. These findings offer practical implications for food service operators as well as public health researchers.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».