Original Paper
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.
doi:10.2196/53188
Keywords
Introduction
Developing obesity during adolescence increases future risk of diabetes, cardiovascular disease, and diet-related cancers during adulthood [-]. Youth of color experience higher rates of obesity relative to White youth [-]. More than 37% of Latinx adolescents are overweight or obese relative to 26.5% of White adolescents [,], indicating an urgent need to reduce rates of obesity among Latinx youth.
Food marketing is a major driver of adolescent and childhood obesity [-]. Food companies spend more than US $1.8 billion on youth-directed marketing [,], which primarily promotes products that are energy-dense and nutrient-poor [-]. Such exposure is concerning given the large body of evidence showing that viewing food advertising increases children’s preferences for promoted foods and brands, caloric intake, purchases, and requests for advertised products [,,-].
Adolescents and youth of color represent key consumer groups to food marketers not only because of their spending power [] and trendsetting abilities [,] but also because of their unique responsiveness to advertising []. Compared to children and adults, adolescents are more impulsive and have lower inhibitory control []. They use brands to help elevate their feelings of self-worth [], fit in with their desired peer group [], and distinguish themselves from their caregivers []. Food and beverage brands aim to appeal to Latinx youth through targeted or multiethnic marketing, which refers to identifying a community that shares some common needs or characteristics that an organization—in this case, marketers—uses to appeal to that group []. While multiethnic advertising aims to reach consumers of diverse ethnicities, monoethnic advertising reaches members of a single ethnic group []. A study by Shao et al [] found that socially distinctive consumers (eg, Latinx minorities) are more likely to prefer same-race monoethnic advertisements because this exposure promotes a sense of shared identity and belonging []. Janssen et al [] explored advertising on Instagram and found that celebrity influencers with a high number of followers have more “likes” and more followers with positive attitudes toward the products they endorse; this results in greater purchase intention []. While targeted marketing is not inherently problematic, monoethnic advertising coupled with celebrity or influencer sponsorships can potentially target Latinx adolescents and Generation Z teens with health-harming products that may exacerbate health disparities.
Adolescents are exposed to food marketing on social media [-], but few studies have examined how adolescents respond to such marketing. One cross-sectional study showed that among a nationally representative sample of adolescents, more than 70% of them self-reported that they “liked,” shared, or followed food and beverage brand accounts []. The same study also found that Black and less acculturated Latinx adolescents were more likely to self-report that they engaged with brand accounts than White adolescents []. Recent experimental studies have revealed that adolescents rate social media advertisements highly when those advertisements have high numbers of “likes” []. One randomized trial examining the impact of influencer marketing—the promotion of products by popular web-based celebrities—showed that influencer marketing of unhealthy foods on social media significantly increased children’s consumption of unhealthy snacks [,].
Exposure to unhealthy food advertisements that feature celebrity influencers is concerning as it can capture adolescents’ attention through its appealing features. Viewing these advertisements can increase positive affect and feelings of connection to the celebrity or influencer because they are often seen as trustworthy and are already well-liked by the adolescent [-]. These positive feelings toward the celebrity or influencer then elicit positive feelings (greater positive affect, liking, and desire for the product) toward the unhealthy food and beverage they are promoting []. Previous studies have assessed participants’ responses to advertisements using 3 attitudinal ratings, including how much they like the advertisement, brand, and product, which allows researchers to compare such ratings across experimental conditions []. Such attitudinal ratings are measured through self-report questions that ask how much the participant likes the advertisement, how much they like the brand, and how much they like the product featured in the advertisement. Consequently, the likelihood of the adolescent engaging with the advertisement posts (eg, through “likes,” comments, and shares) increases, along with the likelihood that they will subsequently purchase or consume unhealthy food [,-]. Purchase intentions are measured via a self-report question and defined as a participant’s willingness to purchase the product in the near future. Previous studies have used purchase intention questions as an outcome when objective measurements of purchasing behavior are not possible [-]. Previous research has measured and experimentally compared this engagement by participants with brands and advertisements on social media. Further, Social Norms Theory proposes that adolescents in particular, may be especially vulnerable to many of the racially targeted marketing practices that companies use on social media [,] because adolescents are highly sensitive to their peers [,], popularity cues, and conformity pressures [,].
Research has shown that these feelings of connection may be even stronger when the race of the person featured in the advertisement matches the race of the adolescent [,]. A number of theoretical frameworks in social psychology may inform an understanding of this effect. According to the social identity theory, individuals derive a significant portion of their self-concept from their group memberships, leading to the formation of social identities []. Emphasizing the importance of social categorization, social comparison, and group identification in shaping attitudes and behaviors, social identity theory suggests that Latinx adolescents’ ethnic identity may play a crucial role in shaping their self-concept. Racially targeted marketing that features Latinx celebrities can tap into this social identity, thereby fostering a sense of belonging and positively influencing attitudes toward the advertised product. Relatedly, Distinctiveness Theory may help explain the heightened appeal and influence of food advertisements featuring Latinx celebrities. Distinctiveness Theory explains that individuals define themselves based on rare traits in their environment [,]. Ethnicity in particular is an attribute related to distinctiveness and has been shown to hold greater significance in self-definition than other social categories []. Because Latinx adolescents are part of a distinctive minority group, distinctiveness theory suggests that they may be acutely attuned to and influenced by ethnically targeted marketing.
Despite the pervasiveness of targeted marketing, no experimental studies have examined the effects of Latinx-targeted food marketing on Latinx and White adolescents. This study assessed the effects of food and beverage advertisements featuring a Latinx celebrity on the same advertisements featuring a Latinx noncelebrity (ie, an individual who is not famous), including differential effects on Latinx and White adolescents. Examining the influence of celebrity-endorsed food advertisements on the preferences of Latinx and White youth will enhance the field’s theoretical understanding of how ethnically targeted marketing uniquely affects Latinx youth, which can help inform practical implications on how public policies can address unhealthy food marketing that targets youth of color. By harnessing a rigorous 2×2 mixed factorial design, this study is the first to objectively compare celebrities’ and noncelebrities’ food endorsements on Latinx and White youth. Our research questions, therefore, are as follows:
- To what extent does exposure to Latinx celebrities’ food advertisements increase adolescents’ preferences for the advertisement and the featured product, compared to Latinx-noncelebrities’ food advertisements?
- To what extent does exposure to Latinx people in food advertisements increase preferences for the advertisements among Latinx adolescents compared to White adolescents?
We predicted that (1) both Latinx and White adolescents would report higher preferences, including more positive attitudes (advertisement likeability; positive affect; and brand perceptions) and behavioral intentions (product consumption and social media engagement) when advertisements featured Latinx celebrities versus Latinx noncelebrities; (2) Latinx adolescents would report higher preferences for all advertisements compared to White adolescents; and (3) Latinx adolescents would show a stronger positive response to Latinx celebrity advertisements than White adolescents.
Methods
Study Design
This web-based study used a rigorous 2×2 mixed-factorial experimental design with 2 independent variables: the ethnicity of the participant (Latinx or White) and whether the person in the advertisement was a celebrity or noncelebrity. Latinx and White adolescent participants (aged 13-17 years) each 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. Novel food advertisements feature brands that participants are likely to be unfamiliar with, either created by researchers or brands from other countries. Using novel brands minimizes the likelihood that participants’ ratings will be driven by previous brand preferences and experiences. For example, using a well-known brand such as Coca Cola would drown out our ability to examine the effects of Latinx celebrities’ endorsements due to preexisting attitudes and preferences by consumers. After viewing each advertisement, participants provided attitude measures (for someone like me, advertisement likeability, positive and negative affect, and brand perceptions) and behavioral intentions (consumption, “liking,” following, commenting, and tagging a friend), before viewing the next advertisement. These outcome measures have been used in other food marketing research studies as primary and secondary outcomes [,,].
Participant Recruitment
In January 2019, we recruited participants through Dynata, a survey firm that maintains panels of thousands of adolescents and adults in the United States who are interested in completing surveys in exchange for incentives such as cash, prize drawings, or donations to charity. Dynata’s recruitment involves a rigorous process that uses multiple steps to recruit and screen potential participants. First, randomly selected participants from members of Dynata’s panels are combined with a pool of potential participants who are joining Dynata for the first time after responding to web-based recruitment materials. Those recruitment materials include banner advertisements on social media platforms and other commonly visited websites where participants might notice and respond to the flyers. This combined group of potential participants then receives an invitation to “take a survey,” but are not provided with additional details about surveys that might introduce selection bias (eg, “take a survey about food” might attract people with an interest in food). Second, to be eligible for a study, potential participants complete quality control questions and are randomly matched with surveys for which they likely qualify. Finally, among the randomly selected participants who expressed an interest in and have been randomly matched with a survey, a subset is randomized to complete a specific survey and then receives a link to the survey.
Pretest (N=50) and study participants (N=903) were adolescents, aged 13-17 years, who self-identified as Latinx, or White, had daily internet access, and could read and write in English. Of the 1509 eligible participants who responded to the survey, 992 participants (65.7%) completed all questions. An additional 89 participants did not answer 8 of 10 attention-check questions correctly and were excluded from the analyses. These attention-check questions asked, “What does this advertisement say?” with 3 multiple-choice answers. The final sample included 903 adolescents (436 (48.3%) Latinx adolescents and 467 (51.7%) White adolescents).
Ethical Considerations
The Institutional Review Board for the New York University School of Medicine approved the study (protocol #115-0087), in accordance with all applicable regulations. Parents voluntarily provided consent and adolescent participants provided assent after reading general information about the study, explaining the nature and possible consequences of the study, and the length of the survey. Study data were deidentified to protect participants’ privacy and confidentiality. Participants were compensated by our recruitment firm, Dynata, with incentives such as cash, prize drawings, or donations to charity.
Materials
To develop the 8 food and beverage advertisements, we first created pairs of advertisements for 8 novel foods and beverages using Photoshop (Adobe). Each pair included 1 advertisement that featured a Latinx celebrity and a matched advertisement with a Latinx noncelebrity. Because brand familiarity for well-known products (eg, Coca Cola) heavily influences advertisement ratings and the difficulty of changing preexisting attitudes about known brands [], we selected brands that were only available in countries outside the United States. To increase the likelihood that the advertisements were perceived as Latinx-targeted and that participants would recognize the celebrities, the advertisements included a slogan with at least 1 Spanish word or the name of the celebrity in the slogan or as a signature.
The 16 advertisements (8 pairs) for pretesting included 4 male and 4 female celebrity or noncelebrity pairs and 4 foods and 4 beverages. We pretested these advertisements among 20 Latinx and White adolescents to match the advertisement pairs on the attractiveness of the person in the advertisement, perceived target audience, and celebrity recognition.
Pretest participants first rated the attractiveness of the person in the advertisement, indicated the perceived race or ethnicity of the person, and identified whether the person in the advertisement was a celebrity. They also indicated how much they thought the advertisement was intended for people like them and for people in their age group.
We excluded 1 pair of advertisements because only 10% of participants (n=2) perceived the celebrity to be Latinx and another pair because the majority did not recognize the celebrity. We excluded 2 additional pairs because they scored the lowest on both celebrity recognition and attractiveness measures. The final set of 4 advertisement pairs (
Figure 1) included 3 advertisement pairs that featured Latina women alongside a sugar-sweetened beverage (n=2 pairs) or unhealthy food product (n=1 pair) and one that featured a Latinx male alongside a sugar-sweetened beverage. Similar to previous research, we blurred the faces and names of the celebrities in the advertisements in Figure 1. The participants saw unblurred images [,].
We also used Photoshop to create 2 additional advertisements to serve as neutral nontargeted stimuli. These 2 advertisements featured bland, unsavory products (ie, water and saltine crackers) and did not show people (
Figure 2).
They were used to compare advertisement ratings by Latinx and White adolescents to enable us to control for overall differences in advertisement ratings in our final models.
Survey Procedures
The web-based survey was designed using Silver Lake and CPP Investment’s Qualtrics software, and the functionality of the survey was tested by the investigators before launching the study. Participants were required to complete the survey on a computer (instead of a mobile device) to ensure the visibility of advertisements. Participating on a mobile device might have made it more difficult to view the features of the advertisements in the survey due to the smaller screen size compared to desktops. Median survey completion time was 23 minutes. The study setting was anywhere a participant could access a computer to complete the experimental survey.
After providing assent and answering eligibility questions, participants were told the following cover story to conceal the true purpose of the study: “We are working with MTV to select commercials that their viewers might like and are interested in your attitudes and opinions on products featured in these advertisements. Participation in this study will involve viewing several different food and beverage advertisements and answering questions about them.” All participants viewed and rated each of the 10 advertisements in a specific order as follows: they first viewed the neutral advertisement for water; then 2 celebrity advertisements and 2 noncelebrity advertisements for different brands presented in random order; then viewed the neutral advertisement for crackers; and finally, the remaining celebrity and noncelebrity advertisements presented in random order. This order ensured that participants did not see advertisements for 2 brands in the same group of 4 advertisements.
Primary Outcome Measures
The primary outcome measures and dependent variables are listed in 
Table 1.
| Outcomes | Survey questions | ||
| Advertisement rating questions: Cognitive responses to advertisements and advertisement efficacy | |||
| Likeabilitya | “How much did you like this ad?” (0=“strongly disliked it” to 100=“liked it a lot”) [,] | ||
| Positive affect | “Did the ad for [Coke] make you feel: cheerful; good; pleased; stimulated; soothed?” (0=“not at all” to 100=“very much so”. Scores were averaged into a composite positive affect score []. | ||
| Negative affect | “Did the ad for [Coke] make you feel: insulted; irritated; repulsed?” (0=“not at all” to 100=“very much so”. Scores were averaged into a composite negative affect score []. | ||
| Taste preference | “How much do you think you would like the taste of this product” (0=“Not at all” to 100=“A lot”) []. | ||
| Advertisement efficacyb | “I like the brand in this ad,” “I react favorably to the brand in this ad,” “The brand in this ad is appealing,” “The brand in this ad is good,” and “The brand in this ad is pleasant,” (0=“disagree” to 100=“agree”) []. Brand liking statements were averaged for a combined brand perception rating. | ||
| Behavioral intention questions | |||
| Likelihood to consume the advertised brand | “How likely would you be to consume this product if it was offered to you?” (0=“very unlikely” to 100=“very likely”) [] | ||
| Likelihood of following or liking the brand on social mediaa | “What is the likelihood you would Follow or Like this brand on Facebook, Instagram, Twitter, Snapchat, or Tumblr?” (0=“very unlikely” to 100=“very likely”) [,] | ||
| Willingness to engage with the brand or advertisement on social mediaa | How likely would you be to engage with this brand on social media?: “I would comment on the brand’s post” (0=“very unlikely” to 100=“very likely”) ; “I would ”Like“ this brand’s post” (0=“very unlikely” to 100=“very likely”) ; “I would tag a friend” (0=“very unlikely” to 100=“very likely”) [,] | ||
| Likelihood they were the intended audience for the post | “I feel the advertisement was intended for people like me” (0=“disagree” to 100=“agree”); “I do not believe the company was targeting consumers like me” (0=“disagree” to 100=“agree”); “The advertiser made this advertisement for people like me.” (0=“disagree” to 100=“agree”). The inverse of question 2 was calculated and the scores of the 3 questions were averaged into a composite score []. | ||
aThis item has not been validated but has been used before in previous research [,].
bThis item is based on a published commonly used marketing scale study but was adapted [].
Participants provided their attitudes about advertisements and brands immediately after viewing each advertisement, before viewing the next advertisement, using a sliding scale (0-100) for all questions (Table 1). We used 0-100 response scales to minimize bias and lack of reliability versus Likert scales [], and, among some participant populations (eg, under-resourced communities), Likert scales contradict other response formats []. Other studies have shown that 0-100 scales offer stronger psychometric properties than Likert scales [].
The question, “How much did you like this ad?” (0=“strongly disliked it” to 100=“liked it a lot”) assessed advertisement likeability. A commonly used and validated marketing survey was used to evaluate affective advertisement responses []. Participants responded to the following 8 prompts: “Did the ad for [Coke] make you feel…cheerful; good; pleased; stimulated (positive affect); soothed; insulted; irritated; repulsed (negative affect)?” Affective advertisement responses included positive (cheerful, good, pleased, stimulated, and soothed) and negative (insulted, irritated, and repulsed) attributes, rated as 0=“not at all” to 100=“very much so.” Positive and negative attributes were averaged into separate composite positive and negative affect scores. Taste preference was measured with 1 question: ”How much do you think you would like the taste of this product” (0=“Not at all” to 100=“A lot”) []. Advertisement efficacy was measured with an adapted version of a commonly used marketing scale that asked participants the following 5 questions: “I like the brand in this ad,” “I react favorably to the brand in this ad,” “The brand in this ad is appealing,” “The brand in this ad is good,” and “The brand in this ad is pleasant,” and rated from 0=“disagree” to 100=“agree” []. Brand liking statements were averaged for a combined brand perception rating [].
Behavioral intention measures included the likelihood to consume the advertised brand [] and willingness to engage with the brand or advertisement on social media, including the following questions: “What is the likelihood you would Follow or Like this brand on Facebook, Instagram, Twitter, Snapchat, or Tumblr?”; How likely would you be to engage with this brand on social media? (“I would comment on the brand’s post” [0=“very unlikely” to 100=“very likely”]; “I would “Like” this brand’s post” [0=“very unlikely” to 100=“very likely”]; “I would tag a friend” [0=“very unlikely” to 100=“very likely”]). The willingness to engage measures have not been validated but have been used before in previous research [,]. All responses were assessed on a sliding scale from 0=“very unlikely” to 100=“very likely” (
Figure 3).
Participants rated the likelihood they were the intended audience for the post across all 8 advertisements that featured people, using a scale developed by Aaker et al []. The scale asked three questions: (1) “I feel the advertisement was intended for people like me,” (2) “I do not believe the company was targeting consumers like me,” and 3) “The advertiser made this advertisement for people like me.” We assessed these responses using a sliding scale from 0=“disagree” to 100=“agree.” We reverse-coded question 2 and then averaged the scores.
Other Measures
Participants provided demographic characteristics, including age, race, ethnicity, grade level, parental education, and height and weight. They then indicated how often they checked social media if they had access to a smartphone, and which social media platforms they used. Participants who identified as Latinx also completed the Multigroup Ethnic Identity Measure-Revised, a 6-item measure that assesses exploration of and commitment to ethnic identity on a 5-point Likert scale: 1=strongly disagree to 5=strongly agree. The overall composite Multigroup Ethnic Identity Measure-Revised score is calculated by averaging the scores for each of the 6 items. The scale has been validated among adolescents to measure ethnic identity (among Latinx adolescents, α=.930) [-].
Statistical Analysis
We used 2-tailed t tests (continuous variables) and chi-square tests (categorical variables) to compare the demographic characteristics of Latinx and White participants. We also compared Latinx and White participants’ ratings of the 2 neutral advertisements on all outcomes to determine whether the 2 groups systematically rated advertisements differently on any of the measures.
We used multilevel linear regression to account for multiple ratings by each participant. Dependent variables included all attitude and behavioral measures. Independent variables included binary terms for participant ethnicity and presence of a celebrity in the advertisement, an interaction term between participant ethnicity and celebrity, random effect of participant, and categorical brand indicator. There were no differences in Latinx and White adolescents’ ratings of the neutral advertisements that did not feature people, so we did not control for that variable in the model. Across all the outcomes except targetedness, Latinx participants did not rate the neutral advertisements any differently than the White participants. They differed on targetedness—Latinx participants said they thought the neutral advertisements were more targeted to them. Due to differences in age by participant ethnicity (P=.04), we included a categorical indicator of age as a covariate in all models. All analyses were conducted in R (R Foundation for Statistical Computing) in February 2020.
Results
Overview
More than half of participants (n=540, 59.8%) were aged 13-15 years and 40.2% (n=363) were aged 16-17 years (Table 2). Approximately one-half identified as Latinx (n=436, 48.3%). There were no significant differences in parental education (P=.21) or participant sex (P=.13) between Latinx and White participants. The majority of participants (n=870, 96.3%) reported having one or more social media accounts, and 41.1% reported using a smartphone “almost constantly” to access social media.
| Total sample (N=903) | Latinx adolescents (n=436) | White adolescents (n=467) | P values for chi-squared testsa | ||||||||
| Sociodemographic characteristics | |||||||||||
| Ageb (years), n (%) | .04 | ||||||||||
| 13-15 | 540 (59.8) | 276 (63.3) | 264 (56.5) | ||||||||
| 16-17 | 363 (40.2) | 160 (36.7) | 203 (43.5) | ||||||||
| Highest parent educational levelc, n (%) | .21 | ||||||||||
| High school, GEDd, or lower | 191 (21.2) | 81 (18.6) | 110 (23.6) | ||||||||
| Vocational school or some college | 327 (36.2) | 167 (38.3) | 160 (34.3) | ||||||||
| Bachelor degree | 239 (26.5) | 111 (25.5) | 128 (27.4) | ||||||||
| Master degree or advanced graduate work | 138 (15.3) | 71 (16.3) | 67 (14.4) | ||||||||
| Not sure or unknown | 8 (0.9) | 6(1.4) | 2 (0.4) | ||||||||
| Sex, n (%) | .13 | ||||||||||
| Male | 148 (16.4) | 79 (18.1) | 69 (14.8) | ||||||||
| Female | 753 (83.4) | 355 (81.4) | 398 (85.2) | ||||||||
| Nonbinary or other | 2 (0.2) | 2 (0.5) | 0 (0.0) | ||||||||
| MEIM-Re (α=.930), n (%) | N/Af | 60.23 (24.3) | N/A | ||||||||
| Less than or equal to median (60.8), n (%) | N/A | 219 (50.2) | N/A | ||||||||
| Greater than median (60.8)), n (%) | N/A | 217 (49.8) | N/A | ||||||||
| Behavioral characteristics | |||||||||||
| How often do you use your smartphone to access social media accounts, such as Facebook, Instagram, and Snapchat, each day?c, n (%) | .07 | ||||||||||
| Almost constantly | 371 (41.1) | 193 (44.3) | 178 (38.1) | ||||||||
| Several times a day | 354 (39.2) | 169 (38.8) | 185 (39.6) | ||||||||
| Once a day or less frequently | 178 (19.7) | 74 (16.97) | 104 (22.3) | ||||||||
| Social media access on smartphonesc, n (%) | .12 | ||||||||||
| I have my own smartphone | 833 (92.3) | 409 (93.8) | 424 (90.8) | ||||||||
| I do not have my own smartphone | 70 (7.8) | 27 (6.2) | 43 (9.2) | ||||||||
| Which of the following social media accounts do you have?, n (%) | |||||||||||
| Facebookb | 644 (71.3) | 296 (67.9) | 348 (74.5) | ||||||||
| Instagramb | 650 (71.98) | 334 (76.6) | 316 (67.7) | ||||||||
| Snapchatb | 601 (66.6) | 325 (74.5) | 276 (59.1) | ||||||||
| 308 (34.1) | 147 (33.7) | 161 (34.5) | |||||||||
| Tumblr | 66 (7.3) | 37 (8.5) | 29 (6.2) | ||||||||
| Other | 14 (1.6) | 6 (1.4) | 8 (1.7) | ||||||||
| How many active social media accounts do you have?b, n (%) | .10 | ||||||||||
| 0 | 33 (3.1) | 11 (2.5) | 22 (4.7) | ||||||||
| 1 | 177 (19.8) | 75 (17.2) | 102 (21.8) | ||||||||
| 2 | 228 (24.8) | 110 (25.2) | 118 (25.3) | ||||||||
| 3 | 254 (28.0) | 136 (31.2) | 118 (25.3) | ||||||||
| 4 | 171 (18.9) | 80 (18.4) | 91 (19.5) | ||||||||
| 5 | 36 (3.99) | 22 (5.1) | 14 (3) | ||||||||
| 6 | 4 (0.4) | 2 (0.4) | 2 (0.4) | ||||||||
| Average number of social media accounts, mean (SD)b | 2.53 (1.2) | 2.63 (1.2) | 2.44 (1.3) | .02 | |||||||
| Correctly identified celebrity status, (N=7224, 8 advertisements per person)b, n (%) | 5006 (69.3) | 2572 (73.4) | 2434 (65.2) | <.001 | |||||||
aP values for chi-square tests comparing the demographic data between Latinx and White adolescents.
bP<.05 comparing distributions between Latinx and White adolescents.
c.05<P<.25 comparing distributions between Latinx and White adolescents.
dGED: General Educational Development.
eMEIM-R: Multigroup Ethnic Identity Measure-Revised.
fN/A: not applicable.
Attitudes About the Advertisement and the Brand
On average, participants reported liking the 8 advertisements that featured people (mean 53.58, SD 28.70; Table 3).
Positive affective responses were rated neutrally (mean 49.44, SD 28.85), while participants reported overall low negative affective responses to advertisements (mean 18.77, SD 25.79). Participants rated their perception of the brand on average as 51.63 (SD 29.25) across all 8 advertisements that featured people.
For all attitude measures except negative affect, all participants rated advertisements significantly more positively for advertisements with celebrities than with noncelebrities; there were no main effects of participant ethnicity nor interactions between celebrity and participant ethnicity. However, Latinx participants reported significantly more negative affect toward all advertisements (P=.01).
For brand perception, we found a main effect of celebrity but did not find an interaction, indicating that Latinx and White adolescents rated the brands with celebrities significantly higher than the brands without celebrities.
| Outcome | Latinx participants (n=436) | White participants (n=467) | Main effect of celebrity in the advertisement | Main effect of participant ethnicity | Interaction (participant ethnicity and celebrity in advertisement) | ||||||||||
| Celebrity, mean (SD) | Noncelebrity, mean (SD) | Celebrity, mean (SD) | Noncelebrity, mean (SD) | β (P value) | β (P value) | β (P value) | |||||||||
| Intended audience (targeting) | 58.71 (0.49) | 56.51 (0.47) | 55.51 (0.51) | 55.48 (0.50) | .03 (.95) | 1.12 (.33) | 2.17 (<.001) | ||||||||
| Attitudes | |||||||||||||||
| How much did you like or dislike the ad? | 55.53 (0.69) | 52.72 (0.67) | 53.85 (0.68) | 52.30 (0.66) | 2.15 (<.001) | 1.22 (.44) | —c | ||||||||
| Positive affective response | 51.52 (0.68) | 48.93 (0.67) | 49.45 (0.69) | 47.96 (0.67) | 2.02 (<.001) | 1.66 (.32) | — | ||||||||
| Negative affective response | 20.69 (0.66) | 20.69 (0.64) | 17.05 (0.57) | 16.58 (0.55) | 0.24 (.46) | 3.77 (.01) | — | ||||||||
| How much do you think you would like the taste of this product? | 57.15 (0.71) | 55.65 (0.71) | 55.49 (0.72) | 54.73 (0.71) | 1.12 (.01) | 1.42 (.38) | — | ||||||||
| Brand perception | 53.55 (0.69) | 50.94 (0.68) | 51.54 (0.70) | 50.59 (0.69) | 0.96 (.07) | 0.52 (.76) | 1.66 (.03) | ||||||||
| Behavioral intentions | |||||||||||||||
| How likely would you be to consume this product if it was offered to you? | 58.88 (0.73) | 56.12 (0.73) | 56.67 (0.75) | 56.09 (0.73) | 0.59 (.36) | 0.13 (.94) | 2.17 (.02) | ||||||||
| What is the likelihood you would “Follow” or “Like” this brand on Facebook, Instagram, Twitter, Snapchat, or Tumblr? | 43.23 (0.82) | 40.38 (0.78) | 37.44 (0.78) | 36.53 (0.76) | 0.92 (.08) | 4.01 (.049) | 1.94 (.01) | ||||||||
| How likely would you be to engage with this brand on social media? | |||||||||||||||
| I would comment on the brand’s post | 40.13 (0.81) | 37.85 (0.79) | 35.74 (0.78) | 34.88 (0.76) | 1.54 (.01) | 3.79 (.06) | — | ||||||||
| I would “Like” this brand’s post | 43.91 (0.82) | 40.65 (0.79) | 37.84 (0.80) | 36.56 (0.77) | 1.28 (.02) | 4.27 (.04) | 1.98 (.01) | ||||||||
| I would tag a friend | 39.28 (0.83) | 36.63 (0.80) | 33.71 (0.79) | 32.90 (0.76) | 0.81 (.12) | 3.92 (.06) | 1.85 (.01) | ||||||||
aAll models included age as a covariate.
bAll response items were on a scale ranging from 0 to 100.
cNot available.
Behavioral Intentions
Participants reported that they would likely consume the product (mean 56.92, SD 31.22), and there was no main effect of celebrity or participant ethnicity. However, we found a significant interaction between participant ethnicity and celebrity (Table 3). Only Latinx participants indicated significantly higher consumption intention when the advertisement featured a celebrity compared to advertisements featuring a noncelebrity (P<.001).
On measures of social media engagement, participants rated their likelihood of following the brand on average 39.31 (SD 33.51) across all 8 advertisements. They indicated a similar likelihood to comment on the social media post (mean 37.09; SD 33.34), “like” the post (mean 39.65, SD 33.81), and “tag” a friend in the post (mean 35.55, SD 33.88).
The main effect of celebrity was significant for two of the four social media engagement measures: (1) participants were more likely to comment on or “Like” posts that featured celebrities in their advertisements compared to posts that featured noncelebrities (Table 3); (2) Latinx participants were more likely to “Follow” or “Like” brands or posts than White participants. There was a main effect of celebrity on participant’s likelihood of commenting on the post such that participants reported a higher likelihood of commenting on posts that featured celebrities (mean 38.1, SE 1.04 compared to those without (mean 36.5, SE 1.04; β=1.54; t6320=4.25; P<.001). We also found the main effects of celebrity (β=1.28; t6319=2.38; P=.02) and participant ethnicity (β=4.27; t968=2.09; P=.04) for willingness to “like” the brand. The main effects were qualified by an interaction (β=1.98; t6319=2.57; P=.01). While White participants reported higher willingness to “like” advertisements with celebrities compared to noncelebrities (mean 36.7, SE 1.42 vs mean 38.0, SE 1.42; z=2.38; P=.02); the effect of celebrity was significantly greater among Latinx participants (mean 41.0, SE 1.49 vs mean 44.3, SE 1.49; z=5.87; P<.001).
For willingness to “tag” a brand, we found no main effects of celebrity (t6319=1.55; P=.12) or participant ethnicity (t963=1.91; P=.06). However, we did find a significant interaction (t6319=2.46; P=.01). While White participants were equally likely to “tag” an advertisement with a celebrity (mean 33.9, SE 1.43) compared with an advertisement without a celebrity (mean 33.1, SE 1.43; z=1.55; P=.12), Latinx participants were more likely to “tag” an advertisement with a celebrity (mean 39.7, SE 1.50) compared to an advertisement without a celebrity (mean 37.0, SE 1.50; z=4.91; P<.001).
Finally, for willingness to follow a brand on social media, we found no main effect of celebrity (t6319=1.76; P=.08), but did find evidence of a main effect of participant ethnicity (β=4.01; t964=1.98; P=.05), as well as a significant interaction (β=1.94; t6319=2.59; P=.01). White adolescents reported the same willingness to follow a brand on social media regardless of whether the advertisement featured a celebrity (mean 37.6, SE 1.42) or not (mean 36.7, SE 1.42; P=.08). However, Latinx participants reported higher likelihood of following the brand on social media when the advertisement featured a celebrity (mean 43.6, SE 1.48 vs mean 40.7, SE 1.48; z=5.30; P<.001).
Discussion
Principal Findings
Our hypothesis for research question 1, is that both Latinx and White adolescents would report higher preferences, including more positive attitudes (advertisement likeability; positive affect; and brand perceptions) and behavioral intentions (product consumption; social media engagement), when advertisements featured Latinx celebrities versus Latinx noncelebrities was mostly supported. Specifically, both Latinx and White participants in our sample reported significantly higher positive attitudes and brand perceptions for brands endorsed by Latinx celebrities compared to Latinx noncelebrities. However, we found significant main effects of celebrity advertisements for just 2 behavioral intentions: likelihood to comment and like brand posts. The findings that celebrities increased the appeal of the advertisements and products reinforces previous research that showed celebrity-endorsed products are more appealing to adolescents [] and preadolescents [] compared to products endorsed by noncelebrities.
Our hypothesis for research question 2, that Latinx participants would report more positive attitudes and behavioral intentions for both Latinx celebrity and Latinx noncelebrity advertisements compared to White participants was supported for just 2 outcomes: likelihood to follow brands on social media and like a brand post. We found no significant main effects of participant ethnicity on positive attitudes, brand perceptions, or intent to consume. However, we did find the hypothesized interaction between celebrity and participant ethnicity for most behavioral intentions. Relative to White adolescents, Latinx adolescents reported a higher willingness to “like” and follow brands that featured celebrities compared to brands that featured noncelebrities. In addition, Latinx adolescents reported higher consumption intentions and likelihood to tag a post when advertisements featured celebrities compared to noncelebrities.
These results reinforce other studies on the effects of celebrity endorsements and targeted food marketing. Our findings that both Latinx and White adolescents reported a higher likelihood to comment, “like,” or tag a friend in the post on social media when the advertisements featured celebrities versus noncelebrities is similar to previous research showing that celebrity endorsements increase willingness to engage with brands []. Like other studies demonstrating that celebrity endorsements made adolescents more likely to choose the endorsed product [], we identified a main effect of celebrity on adolescents’ willingness to consume the product—but only for Latinx adolescents.
Latinx celebrities may disproportionately influence Latinx adolescents’ behavioral intentions, including intent to consume and follow brands on social media, compared to White adolescents and noncelebrity advertisements. One potential explanation for the disproportionate influence of Latinx celebrities on Latinx youth is that Latinx communities are underrepresented in popular media, and positive portrayals in the media may heighten Latinx adolescents’ responses to targeted advertisements. Future studies should examine the extent to which exposure to Latinx-targeted advertisements increases Latinx adolescents’ actual food purchases and caloric consumption.
This study makes several contributions to the literature on the effects of food advertising and exposure to racially targeted food advertising. First, this is the first experimental study to examine the intersection of food advertising and celebrity endorsements among Latinx youth. The results indicate a direct causal effect of celebrities on willingness to consume the product and follow or “like” the brand on social media for both Latinx and White youth, even though—after controlling for other possible explanations—Latinx youth were more likely to believe that the advertisements with Latinx celebrities targeted them. These findings demonstrate the power of celebrity endorsements on adolescents’ preferences. Second, this study demonstrates that the intersection between celebrity endorsements and Latinx ethnicity is complex and warrants further research to disentangle these effects. Celebrities influenced adolescents’ attitudes about the advertisements, but it appears that Latinx celebrities may have a disproportionate influence on Latinx adolescents’ behavioral intentions.
Limitations
This study has several limitations. First, although we only included brands from outside the United States to avoid brand familiarity potentially influencing outcomes, our pretest did not assess brand familiarity among participants. Some adolescents may have been familiar with the international brands in our advertisements, thereby influencing their ratings. An additional limitation is that we only included Latinx celebrities and Latinx noncelebrities. The lack of White people in advertisements limits our ability to discern if our outcomes were shaped by ethnic congruity or some other factor. Another limitation is that in using Dynata for participant recruitment, our sample was not nationally representative. Participants in Dynata’s sample are more likely to use the internet than the general US population, but since our research studies social media, this sample may better reflect our population of interest. Another limitation of opt-in panels is the risk of response bias (eg, providing socially desirable answers). However, we believe this risk is greatly reduced because the survey responses are anonymous, and research involving Dynata shows that adolescents express a variety of preferences (ie, they do not give socially desirable answers) [-]. Another limitation is that within-subjects study designs introduce possible “order effects” bias. Because our participants saw each advertisement (eg, the popcorn advertisement featuring a celebrity) and later saw its counterpart (eg, popcorn advertisement featuring a noncelebrity), order effects bias would suggest that the ratings of the second advertisement in a given pair could be affected by their reaction to the first advertisement in that pair. We aimed to reduce such bias, however, by (1) separating the presentation order of advertisements so that participants never saw two similar advertisements in a row; and (2) randomizing the order (eg, for each advertisement pair, some participants saw the celebrity advertisement first, whereas others saw the noncelebrity advertisement first). Although we would have preferred to avoid any possibility of order effects by using randomization via a between-subjects design, within-subjects is valuable when large sample sizes are not feasible—when designing the study, we knew we would not be able to recruit a large enough sample of Latinx adolescents to warrant a between-subjects design with multiple study conditions. Within-subjects’ designs are commonly used in experimental research studies, including studies focused on food-related behaviors [-]. Finally, we did not assess participants’ familiarity with the people featured in the advertisements, nor did we ask how much they liked the person in the advertisement. Both of those ratings should have been controlled in our analyses because they could have affected participants’ ratings. Our study has major strengths, though, including the tightly controlled manipulation of images and the pretesting of those matched celebrities and noncelebrities on perceived attractiveness and target audience. Research on the effects of monoethnic and celebrity advertising effectiveness on the Generation Z cohort should also be explored.
Conclusions
Our data advance the theoretical understanding of targeting marketing by showing that viewing ethnicity-congruent food advertisements is particularly powerful for Latinx youth, which is concerning given Latinx communities experience high rates of diet-related diseases. These findings can help support policies that address unhealthy food marketing that targets communities of color, similar to how tobacco research on targeted marketing helped shed light on tobacco companies’ targeted marketing practices [-]. Furthermore, in response to concerns about targeted marketing, Facebook now prohibits any advertisement from targeting users based on race []. New York State’s proposed Predatory Marketing Prevention Act would aim to prevent advertisements from misleading consumers. These findings can be communicated to policymakers to consider including racial/ethnic-targeted marketing in such bills []. 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. Celebrities should consider choosing endorsements that support the health of their young fans, and powerful celebrities may be able to encourage companies to allow them to endorse healthier products. The food industry limits food advertising to children aged 12 years and younger, but our findings indicate that industry self-regulatory efforts should expand to include adolescents and address disproportionate advertising of unhealthy food to Latinx youth.
Acknowledgments
The authors would like to thank the following New York University Food Environment and Policy Research Coalition research assistants and staff who have no conflicts of interest to report: Alysa Miller, Yrvane Pageot, Margaret Eby, Tenay Greene, Joshua Arshonsky, Chelsea Mangold, Shirley Valerio, Robert Suss, Sana Husain, Natasha Pandit, Carolyn Fan, Anne Dumadag, Andrea Rodriguez Barrio, Ruchi Desai, Ingrid Wells, Jessica Osterman, and Jemar Bather. Generative artificial intelligence was not used in any portion of the manuscript writing. Data collection and analyses were supported by the National Institutes of Health (NIH) Early Independence Award (DP5OD021373-01; principal investigator: MAB) from the NIH Office of the Director. The NIH had no role in the design and conduct of the study.
Data Availability
The data generated during and/or analyzed during this study are available from the corresponding author on reasonable request.
Conflicts of Interest
None declared.
References
- Al-Hamad D, Raman V. Metabolic syndrome in children and adolescents. Transl Pediatr. 2017;6(4):397-407. [FREE Full text] [CrossRef] [Medline]
- Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med. 2003;157(8):821-827. [CrossRef] [Medline]
- Kansra AR, Lakkunarajah S, Jay MS. Childhood and adolescent obesity: a review. Front Pediatr. 2021;8:581461. [FREE Full text] [CrossRef] [Medline]
- Lister NB, Baur LA, Felix JF, Hill AJ, Marcus C, Reinehr T, et al. Child and adolescent obesity. Nat Rev Dis Primers. 2023;9(1):24. [CrossRef] [Medline]
- Todd AS, Street SJ, Ziviani J, Byrne NM, Hills AP. Overweight and obese adolescent girls: the importance of promoting sensible eating and activity behaviors from the start of the adolescent period. Int J Environ Res Public Health. 2015;12(2):2306-2329. [FREE Full text] [CrossRef] [Medline]
- Alberga AS, Sigal RJ, Goldfield G, Prud'homme D, Kenny GP. Overweight and obese teenagers: Why is adolescence a critical period? Pediatr Obes. 2012;7(4):261-273. [CrossRef] [Medline]
- Major JM, Cross AJ, Watters JL, Hollenbeck AR, Graubard BI, Sinha R. Patterns of meat intake and risk of prostate cancer among African-Americans in a large prospective study. Cancer Causes Control. 2011;22(12):1691-1698. [CrossRef] [Medline]
- Cuevas AG, Krobath DM, Rhodes-Bratton B, Xu S, Omolade JJ, Perry AR, et al. Association of racial discrimination with adiposity in children and adolescents. JAMA Netw Open. 2023;6(7):e2322839. [FREE Full text] [CrossRef] [Medline]
- Johnson VR, Acholonu NO, Dolan AC, Krishnan A, Wang EHC, Stanford FC. Racial disparities in obesity treatment among children and adolescents. Curr Obes Rep. 2021;10(3):342-350. [FREE Full text] [CrossRef] [Medline]
- Mahmood N, Sanchez-Vaznaugh EV, Matsuzaki M, Sánchez BN. Racial/ethnic disparities in childhood obesity: the role of school segregation. Obesity. 2022;30(5):1116-1125. [FREE Full text] [CrossRef] [Medline]
- Alemán JO, Almandoz JP, Frias JP, Galindo RJ. Obesity among Latinx people in the United States: a review. Obesity. 2023;31(2):329-337. [FREE Full text] [CrossRef] [Medline]
- Boyland E, Muc M, Kelly B, Halford JCG, Vohra J, Rosenberg G, et al. Indirect associations between commercial television exposure and child body mass index. J Nutr Educ Behav. 2021;53(1):20-27. [CrossRef] [Medline]
- Cairns G, Angus K, Hastings G, Caraher M. Systematic reviews of the evidence on the nature, extent and effects of food marketing to children. A retrospective summary. Appetite. 2013;62:209-215. [FREE Full text] [CrossRef] [Medline]
- Lobstein T, Jackson-Leach R, Moodie ML, Hall KD, Gortmaker SL, Swinburn BA, et al. Child and adolescent obesity: part of a bigger picture. Lancet. 2015;385(9986):2510-2520. [FREE Full text] [CrossRef] [Medline]
- Harris JL, Taillie LS. More than a nuisance: implications of food marketing for public health efforts to curb childhood obesity. Annu Rev Public Health. 2024;45(1):213-233. [FREE Full text] [CrossRef] [Medline]
- Bragg M, Lutfeali S, Greene T, Osterman J, Dalton M. How food marketing on Instagram shapes adolescents' food preferences: online randomized trial. J Med Internet Res. 2021;23(10):e28689. [FREE Full text] [CrossRef] [Medline]
- Bankole E, Harris N, Rutherford S, Wiseman N. A systematic review of the adolescent-directed marketing strategies of transnational fast food companies in low- and middle-income countries. Obes Sci Pract. 2023;9(6):670-680. [FREE Full text] [CrossRef] [Medline]
- Food marketing to kids. Center for Science in the Public Interest. URL: https://www.cspinet.org/advocacy/nutrition/food-marketing-kids [accessed 2024-03-15]
- Vassallo AJ, Kelly B, Zhang L, Wang Z, Young S, Freeman B. Junk food marketing on Instagram: content analysis. JMIR Public Health Surveill. 2018;4(2):e54. [FREE Full text] [CrossRef] [Medline]
- Barker AB, Parkin M, Sinha S, Wilson E, Murray RL. A content analysis of 'junk food' content in children's TV programmes: a comparison of UK broadcast TV and video-on-demand services. J Public Health. 2022;44(4):e506-e513. [FREE Full text] [CrossRef] [Medline]
- Edwards CG, Pollack CC, Pritschet SJ, Haushalter K, Long JW, Masterson TD. Prevalence and comparisons of alcohol, candy, energy drink, snack, soda, and restaurant brand and product marketing on Twitch, Facebook gaming and YouTube gaming. Public Health Nutr. 2022;25(1):1-12. [FREE Full text] [CrossRef] [Medline]
- Review of food marketing to children and adolescents—follow-up report. Federal Trade Commission. 2012. URL: https://www.ftc.gov/reports/review-food-marketing-children-adolescents-follow-report [accessed 2024-03-15]
- Backholer K, Gupta A, Zorbas C, Bennett R, Huse O, Chung A, et al. Differential exposure to, and potential impact of, unhealthy advertising to children by socio-economic and ethnic groups: a systematic review of the evidence. Obes Rev. 2021;22(3):e13144. [CrossRef] [Medline]
- Boyland E, Maden M, Coates AE, Masterson TD, Alblas MC, Bruce AS, et al. Food and non-alcoholic beverage marketing in children and adults: a systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies. Obes Rev. 2024;25(1):e13643. [CrossRef] [Medline]
- Boyland E, McGale L, Maden M, Hounsome J, Boland A, Angus K, et al. Association of food and nonalcoholic beverage marketing with children and adolescents' eating behaviors and health: a systematic review and meta-analysis. JAMA Pediatr. 2022;176(7):e221037. [FREE Full text] [CrossRef] [Medline]
- Harris JL, Yokum S, Fleming-Milici F. Hooked on junk: emerging evidence on how food marketing affects adolescents’ diets and long-term health. Curr Addict Rep. 2021;8(1):19-27. [CrossRef]
- Heller L. Move over Millennials, Generation Z is in charge. Forbes. 2015. URL: https://www.forbes.com/sites/lauraheller/2015/08/14/move-over-millennials-generation-z-is-in-charge/ [accessed 2024-03-15]
- Zmuda N. How Coke is targeting Black consumers. Ad Age. 2009. URL: https://adage.com/article/the-big-tent/marketing-coke-targeting-african-american-consumers/137716 [accessed 2009-07-01]
- Brooks R, Christidis R, Carah N, Kelly B, Martino F, Backholer K. Turning users into 'unofficial brand ambassadors': marketing of unhealthy food and non-alcoholic beverages on TikTok. BMJ Glob Health. 2022;7(6):e009112. [FREE Full text] [CrossRef] [Medline]
- Pechmann C, Levine L, Loughlin S, Leslie F. Impulsive and self-conscious: adolescents' vulnerability to advertising and promotion. J Public Policy Mark. 2005;24(2):202-221. [CrossRef]
- Gorrese A, Ruggieri R. Peer attachment: a meta-analytic review of gender and age differences and associations with parent attachment. J Youth Adolesc. 2012;41(5):650-672. [CrossRef] [Medline]
- Armsden GC, Greenberg MT. The inventory of parent and peer attachment: individual differences and their relationship to psychological well-being in adolescence. J Youth Adolesc. 1987;16(5):427-454. [CrossRef] [Medline]
- Kumanyika S, Grier S. Targeting interventions for ethnic minority and low-income populations. Future Child. 2006;16(1):187-207. [CrossRef] [Medline]
- Shao W, Zhang Y, Cheng A, Quach S, Thaichon P. Ethnicity in advertising and millennials: the role of social identity and social distinctiveness. Int J Advert. 2023;42(8):1377-1418. [CrossRef]
- Janssen L, Schouten AP, Croes EAJ. Influencer advertising on Instagram: product-influencer fit and number of followers affect advertising outcomes and influencer evaluations via credibility and identification. Int J Advert. 2022;41(1):101-127. [CrossRef]
- Lutfeali S, Ward T, Greene T, Arshonsky J, Seixas A, Dalton M, et al. Understanding the extent of adolescents' willingness to engage with food and beverage companies' Instagram accounts: experimental survey study. JMIR Public Health Surveill. 2020;6(4):e20336. [FREE Full text] [CrossRef] [Medline]
- Ares G, Antúnez L, de León C, Alcaire F, Vidal L, Natero V, et al. 'Even if you don't pay attention to it, you know it's there': a qualitative exploration of adolescents' experiences with digital food marketing. Appetite. 2022;176:106128. [CrossRef] [Medline]
- Arya V, Sambyal R, Sharma A, Dwivedi YK. J Consum Behav. 2023;23(2):556-585. [CrossRef]
- Fleming-Milici F, Harris JL. Adolescents' engagement with unhealthy food and beverage brands on social media. Appetite. 2020;146:104501. [CrossRef] [Medline]
- Klassen KM, Borleis ES, Brennan L, Reid M, McCaffrey TA, Lim MS. What people "Like": analysis of social media strategies used by food industry brands, lifestyle brands, and health promotion organizations on Facebook and Instagram. J Med Internet Res. 2018;20(6):e10227. [FREE Full text] [CrossRef] [Medline]
- Coates AE, Hardman CA, Halford JCG, Christiansen P, Boyland EJ. Social media influencer marketing and children's food intake: a randomized trial. Pediatrics. 2019;143(4):e20182554. [CrossRef] [Medline]
- Forbes-Bell S, Bardey AC, Fagan P. Testing the effect of consumer-model racial congruency on consumer behavior. Int J Market Res. 2019;62(5):599-614. [CrossRef]
- Sherman LE, Payton AA, Hernandez LM, Greenfield PM, Dapretto M. The power of the like in adolescence: effects of peer influence on neural and behavioral responses to social media. Psychol Sci. 2016;27(7):1027-1035. [FREE Full text] [CrossRef] [Medline]
- Scissors L, Burke M, Wengrovitz S. What's in a Like?: Attitudes and behaviors around receiving Likes on Facebook. 2016. Presented at: CSCW '16: Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing; February 27-March 2, 2016:1501-1510; San Francisco, CA. [CrossRef]
- Keller KL. Conceptualizing, measuring, and managing customer-based brand equity. J Mark. 1993;57(1):1-22. [CrossRef]
- Harris JL, Sacco SJ, Fleming-Milici F. TV exposure, attitudes about targeted food ads and brands, and unhealthy consumption by adolescents: modeling a hierarchical relationship. Appetite. 2022;169:105804. [CrossRef] [Medline]
- Fazio RH, Powell MC, Williams CJ. The role of attitude accessibility in the attitude-to-behavior process. J Consum Res. 1989;16(3):280-288. [CrossRef]
- Muntinga DG, Moorman M, Smit EG. Introducing COBRAs. Int J Advertising. 2015;30(1):13-46. [CrossRef]
- Bargh JA, Chen M, Burrows L. Automaticity of social behavior: direct effects of trait construct and stereotype-activation on action. J Pers Soc Psychol. 1996;71(2):230-244. [CrossRef] [Medline]
- Bongers P, Jansen A, Havermans R, Roefs A, Nederkoorn C. Happy eating: the underestimated role of overeating in a positive mood. Appetite. 2013;67:74-80. [CrossRef] [Medline]
- Evers C, Adriaanse M, de Ridder DTD, de Witt Huberts JC. Good mood food. Positive emotion as a neglected trigger for food intake. Appetite. 2013;68:1-7. [CrossRef] [Medline]
- Bragg MA, Miller AN, Kalkstein DA, Elbel B, Roberto CA. Evaluating the influence of racially targeted food and beverage advertisements on Black and White adolescents' perceptions and preferences. Appetite. 2019;140:41-49. [FREE Full text] [CrossRef] [Medline]
- Roberto CA, Bragg MA, Schwartz MB, Seamans MJ, Musicus A, Novak N, et al. Facts up front versus traffic light food labels: a randomized controlled trial. Am J Prev Med. 2012;43(2):134-141. [CrossRef] [Medline]
- Gorski Findling MT, Werth PM, Musicus AA, Bragg MA, Graham DJ, Elbel B, et al. Comparing five front-of-pack nutrition labels' influence on consumers' perceptions and purchase intentions. Prev Med. 2018;106:114-121. [FREE Full text] [CrossRef] [Medline]
- Moran AJ, Roberto CA. Health warning labels correct parents' misperceptions about sugary drink options. Am J Prev Med. 2018;55(2):e19-e27. [FREE Full text] [CrossRef] [Medline]
- Musicus AA, Moran AJ, Lawman HG, Roberto CA. Online randomized controlled trials of restaurant sodium warning labels. Am J Prev Med. 2019;57(6):e181-e193. [CrossRef] [Medline]
- VanEpps EM, Roberto CA. The influence of sugar-sweetened beverage warnings: a randomized trial of adolescents' choices and beliefs. Am J Prev Med. 2016;51(5):664-672. [FREE Full text] [CrossRef] [Medline]
- Schultz PW, Nolan JM, Cialdini RB, Goldstein NJ, Griskevicius V. The constructive, destructive, and reconstructive power of social norms. Psychol Sci. 2007;18(5):429-434. [CrossRef] [Medline]
- Cialdini RB, Kallgren CA, Reno RR. A focus theory of normative conduct: a theoretical refinement and reevaluation of the role of norms in human behavior. In: Zanna MP, editor. Advances in Experimental Social Psychology. Amsterdam, Netherlands. Elsevier; 1991:201-234.
- Casey BJ, Getz S, Galvan A. The adolescent brain. Dev Rev. 2008;28(1):62-77. [FREE Full text] [CrossRef] [Medline]
- Lakon CM, Hipp JR, Wang C, Butts CT, Jose R. Simulating dynamic network models and adolescent smoking: the impact of varying peer influence and peer selection. Am J Public Health. 2015;105(12):2438-2448. [CrossRef] [Medline]
- Cho H, Li W, Shen L, Cannon J. Mechanisms of social media effects on attitudes toward E-cigarette use: motivations, mediators, and moderators in a national survey of adolescents. J Med Internet Res. 2019;21(6):e14303. [FREE Full text] [CrossRef] [Medline]
- Anderson L, McCabe DB. A coconstructed world: adolescent self-socialization on the internet. J Public Policy Mark. 2012;31(2):240-253. [CrossRef]
- Downs JS, Bruine de Bruin W, Fischhoff B, Murray PJ. Behavioral decision research intervention reduces risky sexual behavior. Curr HIV Res. 2015;13(5):439-446. [FREE Full text] [CrossRef] [Medline]
- Tajfel H, Turner J. Social identity theory and self-categorization theory: a historical review. In: Hornsey MJ, editor. Social and Personality Psychology Compass. Oxford, United Kingdom. Blackwell Publishing; 2008.
- Grier SA, Brumbaugh AM. Consumer distinctiveness and advertising persuasion. In: Diversity in Advertising. London, United Kingdom. Psychology Press; 2004:217-237.
- Harris JL, Haraghey KS, Lodolce M, Semenza NL. Teaching children about good health? Halo effects in child-directed advertisements for unhealthy food. Pediatr Obes. 2018;13(4):256-264. [CrossRef] [Medline]
- Schouten AP, Janssen L, Verspaget M. Celebrity vs. influencer endorsements in advertising: the role of identification, credibility, and product-endorser fit. Int J Advertising. 2019;39(2):258-281. [CrossRef]
- Olney TJ, Holbrook MB, Batra R. Consumer responses to advertising: the effects of ad content, emotions, and attitude toward the ad on viewing time. J Consum Res. 1991;17(4):440-453. [CrossRef]
- Delgado-Ballester E, Alemán JLM. Brand trust in the context of consumer loyalty. Eur J Mark. 2001;35((11/12)):1238-1258. [CrossRef]
- Spears N, Singh SN. Measuring attitude toward the brand and purchase intentions. J Curr Issues Res Advertising. 2004;26(2):53-66. [CrossRef]
- Aaker JL, Brumbaugh AM, Grier SA. Nontarget markets and viewer distinctiveness: the impact of target marketing on advertising attitudes. J Consum Psychol. 2008;9(3):127-140. [CrossRef]
- Dolnicar S. 5/7-point “Likert scales” aren't always the best option. Ann Tourism Res. 2021;91(4):103297. [CrossRef]
- Ogden J, Lo J. How meaningful are data from Likert scales? An evaluation of how ratings are made and the role of the response shift in the socially disadvantaged. J Health Psychol. 2012;17(3):350-361. [CrossRef] [Medline]
- Kan A. Effect of scale response format on psychometric properties in teaching self-efficacy. Eurasian J Educ Res. 2009;8(34):215-228. [FREE Full text]
- Norris AE, Ford K, Bova CA. Psychometrics of a brief acculturation scale for Hispanics in a probability sample of urban Hispanic adolescents and young adults. Hisp J Behav Sci. 1996;18(1):29-38. [CrossRef]
- Phinney JS, Ong AD. Conceptualization and measurement of ethnic identity: current status and future directions. J Couns Psychol. 2007;54(3):271-281. [CrossRef]
- Burrow-Sanchez JJ. Measuring ethnic identity in Latino adolescents with substance use disorders. Subst Use Misuse. 2014;49(8):982-986. [CrossRef] [Medline]
- Dixon H, Scully M, Niven P, Kelly B, Chapman K, Donovan R, et al. Effects of nutrient content claims, sports celebrity endorsements and premium offers on pre-adolescent children's food preferences: experimental research. Pediatr Obes. 2014;9(2):e47-e57. [CrossRef] [Medline]
- Duthie E, Veríssimo D, Keane A, Knight AT. The effectiveness of celebrities in conservation marketing. PLoS One. 2017;12(7):e0180027. [FREE Full text] [CrossRef] [Medline]
- Goffe L, Wrieden W, Penn L, Hillier-Brown F, Lake AA, Araujo-Soares V, et al. Reducing the salt added to takeaway food: within-subjects comparison of salt delivered by five and 17 holed salt shakers in controlled conditions. PLoS One. 2016;11(9):e0163093. [FREE Full text] [CrossRef] [Medline]
- Liu Y, Roefs A, Nederkoorn C. Food palatability directs our eyes across contexts. Front Psychol. 2021;12:664893. [FREE Full text] [CrossRef] [Medline]
- Passia N, Chaar MS, Krummel A, Nagy A, Freitag-Wolf S, Ali S, et al. Influence of the number of implants in the edentulous mandible on chewing efficacy and oral health-related quality of life—a within-subject design study. Clin Oral Implants Res. 2022;33(10):1030-1037. [CrossRef] [Medline]
- Harris JL. Targeted food marketing to Black and Hispanic consumers: the tobacco playbook. Am J Public Health. 2020;110(3):271-272. [CrossRef] [Medline]
- Cruz TB, Rose SW, Lienemann BA, Byron MJ, Meissner HI, Baezconde-Garbanati L, et al. Pro-tobacco marketing and anti-tobacco campaigns aimed at vulnerable populations: a review of the literature. Tob Induc Dis. 2019;17:68. [FREE Full text] [CrossRef] [Medline]
- Moore DJ, Williams JD, Qualls WJ. Target marketing of tobacco and alcohol-related products to ethnic minority groups in the United States. Ethn Dis. 1996;6(1-2):83-98. [Medline]
- Kelly B, Backholer K, Boyland E, Kent MP, Bragg MA, Karupaiah T, et al. Contemporary approaches for monitoring food marketing to children to progress policy actions. Curr Nutr Rep. 2023;12(1):14-25. [FREE Full text] [CrossRef] [Medline]
- Bond S. Facebook scraps ad targeting based on politics, race and other 'sensitive' topics. National Public Radio. 2021. URL: https://www.npr.org/2021/11/09/1054021911/facebook-scraps-ad-targeting-politics-race-sensitive-topics [accessed 2024-03-15]
- Senate Bill S7487C: 2021-2022 legislative session. The New York State Senate. URL: https://www.nysenate.gov/legislation/bills/2021/S7487 [accessed 2024-03-15]
Edited by A Mavragani; submitted 29.09.23; peer-reviewed by S Liu, S Bidmon, D Dinh; comments to author 23.02.24; revised version received 28.03.24; accepted 28.10.24; published 31.01.25.
Copyright©Marie A Bragg, Samina Lutfeali, Daniela Godoy Gabler, Diego A Quintana Licona, Jennifer L Harris. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 31.01.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.