Latinx and White Adolescents’ Preferences for Latinx-Targeted Celebrity and Noncelebrity Food Advertisements: Experimental Survey Study
Notice bibliographique
Résumé
BACKGROUND: Exposure to food advertisements is a major driver of childhood obesity, and food companies disproportionately target Latinx youth with their least healthy products. This study assessed the effects of food and beverage advertisements featuring Latinx celebrities versus Latinx noncelebrities on Latinx and White adolescents. OBJECTIVE: This web-based within-subjects study aims to assess the effects of food and beverage advertisements featuring Latinx celebrities versus Latinx noncelebrities on Latinx and White adolescents' preferences for the advertisements and featured products. METHODS: Participants (N=903) were selected from a volunteer sample of adolescents, aged 13-17 years, who self-identified as Latinx or White, had daily internet access, and could read and write in English. They participated in a web-based Qualtrics study where each participant viewed 8 advertisements for novel foods and beverages, including 4 advertisements that featured Latinx celebrities and the same 4 advertisements that featured Latinx noncelebrities (matched on all other attributes), in addition to 2 neutral advertisements (featuring bland, nontargeted products and did not feature people). Primary outcomes were participants' ratings of 4 advertisements for food and beverage brands featuring a Latinx celebrity and the same 4 advertisements featuring a Latinx noncelebrity. Multilevel linear regression models compared the effects of celebrities and differences between Latinx and White participants on attitudes (advertisement likeability; positive affect; and brand perceptions) and behavioral intentions (consumption; social media engagement-"liking;" following; commenting; tagging a friend). RESULTS: Latinx (n=436; 48.3%) and White (n=467; 51.7%) participants rated advertisements featuring Latinx celebrities more positively than advertisements featuring noncelebrities on attitude measures except negative affect (Ps≤.002), whereas only negative affect differed between Latinx and White participants. Two of the 5 behavioral intention measures differed by celebrity advertisement status (P=.02; P<.001). Additionally, the interaction between celebrity and participant ethnicity was significant for 4 behavioral intentions; Latinx, but not White, participants reported higher willingness to consume the product (P<.001), follow brands (P<.001), and tag friends (P<.001). While White and Latinx adolescents both reported higher likelihoods of "liking" advertisements on social media endorsed by Latinx celebrities versus noncelebrities, the effect was significantly larger among Latinx adolescents (P<.01). CONCLUSIONS: This study demonstrates the power of Latinx celebrities in appealing to both Latinx and White adolescents but may be particularly persuasive in shaping behavioral intentions among Latinx adolescents. These findings suggest an urgent need to reduce celebrity endorsements in ethnically targeted advertisements that promote unhealthy food products to communities disproportionately affected by obesity and diabetes. The food industry limits food advertising to children ages 12 years and younger, but industry self-regulatory efforts and policies should expand to include adolescents and address disproportionate marketing of unhealthy food to Latinx youth and celebrity endorsements of unhealthy products.
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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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».