Latinx and White Adolescents’ Preferences for Latinx-Targeted Celebrity and Noncelebrity Food Advertisements: Experimental Survey Study
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".