Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Dec 12.
Published in final edited form as: Appetite. 2019 May 2;140:41–49. doi: 10.1016/j.appet.2019.05.001

Evaluating the influence of racially targeted food and beverage advertisements on Black and White adolescents’ perceptions and preferences

Marie A Bragg a,b,*, Alysa N Miller a, David A Kalkstein c, Brian Elbel a,d, Christina A Roberto e
PMCID: PMC9743992  NIHMSID: NIHMS1788993  PMID: 31055011

Abstract

Introduction:

The present study measures how racially-targeted food and beverage ads affect adolescents’ attitudes toward ads and brands, purchase intentions for advertised products, and willingness to engage with brands on social media.

Methods:

Black and White adolescents were recruited through Survey Sampling International in 2016. Participants completed an online survey in which they were randomized to view either four food and beverage ads (e.g., soda, candy commercials) featuring Black actors or four food and beverage ads featuring White actors.

Results:

For the two components of the attitudinal outcome, Black participants were more likely to report a positive affective response toward racially-similar ads compared to Whites. However, White participants were more likely to like ads that were racially-dissimilar compared to Black participants. Data were analyzed in 2016–2017, and we used an alpha level of 0.05 to denote statistical significance.

Conclusions:

Both Black and White adolescents reported more positive affective responses to ads that featured Blacks compared to ads that featured Whites. Because there were no differences on two outcomes, future research should examine the influence of racially-targeted marketing in real-world contexts (e.g., social media) and longitudinal exposure to targeted advertising on dietary behavior.

Keywords: Racially-targeted marketing, Adolescent, Brand engagement, Advertisement, Social media

1. Introduction

Over 20% of adolescents ages 12–19 years in the United States are obese or overweight (Ogden, Carroll, Fryar, & Flegal, 2015). Further, 19.5% of Black youth ages 2–19 years meet criteria for obesity compared to 14.7% percent of White youth (Ogden et al., 2015). Developing obesity at a young age places youth at a greater risk of negative health outcomes in adulthood (Tirodkar & Jain, 2003).

Food and beverage advertising has been identified as a major contributor to childhood obesity (Halford et al., 2004, 2008; Harris, Bargh, & Brownell, 2009a; McGinnis, Gootman, & Kraak, 2006). Nielsen data estimating television advertising exposure rates in the U.S. show that adolescents view over 4000 food and beverage television ads annually (Frazier & Harris, 2017), the vast majority of these commercials promoting unhealthy products (Boyland & Halford, 2013; Harris et al., 2013, 2014). Numerous studies have demonstrated exposure to food marketing associated with increased food intake (even for foods not advertised), purchase requests, and stronger brand preferences among children (Boyland & Halford, 2013; Cairns, Angus, Hastings, & Caraher, 2013; McGinnis et al., 2006).

Targeted marketing (i.e. using common characteristics and behaviors that appeal to a certain consumer group, and positioning products in mediums most likely to reach them) is one tool used by food companies to reach specific demographics (e.g. airing commercials during shows popular among adolescents) (Grier & Kumanyika, 2010). This may be especially powerful among adolescents, who seek products and brands that provide them with a sense of identity (Grier & Kumanyika, 2010), aim to distinguish themselves from parents (Kersting, 2004), and may respond more favorably to adolescent-targeted ads featuring someone with desired qualities (e.g. fame, beauty). (Marwick, 2010).

Nielsen reports that it is common industry practice for companies to include Black celebrities as spokespersons in marketing because Black consumers are more likely to purchase products with Black spokespersons compared to White spokespersons (Harris et al., 2009b; Nielsen Company And. Incr, 2015). Targeted ads convey inclusivity themes (e.g. ethnic symbols, linguistic styles), reinforcing a sense of identity with young minority consumers (Adeigbe, Baldwin, Gallion, Grier, & Ramirez, 2015; Grier & Kumanyika, 2008; Scott, 2012). Thus, it is alarming that food and beverage companies advertise more unhealthy food products on television channels popular among Black youth, while healthier products appear on channels popular among White youth (Harris, Shehan, Gross, & Others, 2015). Compared to White youth, Black youth are exposed to 70% more fast food and beverage TV commercials than commercials promoting healthier foods, and these advertisements primarily feature unhealthy products (Grier & Kumanyika, 2008). Black youth are especially appealing to marketers because of their high media use, spending patterns, and companies perceive them as trendsetters with more cultural influence than other demographic groups (Grier & Kumanyika, 2008).

Numerous studies have demonstrated that youth are heavily targeted by food companies (Grier & Kumanyika, 2010; Harris et al., 2013, 2014, 2015; Montgomery & Chester, 2009; Powell et al., 2007, 2014; Story & French, 2004; Weber, Story, & Harnack, 2006), but only a few have examined the influence of such ads on adolescents (Cairns et al., 2009, 2013; Dixon et al., 2014; Grier, 2009; Kelly et al., 2016; Scully et al., 2012; Woodward et al., 1997). Although numerous studies demonstrate the pervasiveness of racially-targeted marketing (Adeigbe et al., 2015; Grier, 2009; Grier & Kumanyika, 2008; Harris et al., 2015), none have assessed the effects of racially-targeted ads on Black and White adolescents. Because adolescents are in a critical stage of identity development that facilitates the adoption of strong brand loyalty and product preferences (Grier, 2009; Grier & Kumanyika, 2010; Kersting, 2004; Marwick, 2010), and they account for millions in spending power (McGinnis et al., 2006), more research is needed on the influence of food and beverage marketing on adolescents because targeted marketing may play an influential role in various health behaviors (e.g. caloric intake). This study aims to examine the effect of Black- and White-targeted commercials on Black and White adolescents’ attitudes toward ads, product preferences, and willingness to engage with food and beverage brands on social media.

2. Methods

2.1. Participants

In 2016, we recruited 1533 participants ages 12–17 years to complete an online survey. Survey Sampling International (SSI) recruited participants for the study. Survey Sampling International is a firm that maintains panels of adults and adolescents in the United States who are recruited via online communities and panels, social media sites, and online banner advertising. Their recruitment materials invite participants to answer survey questions, using incentives such as cash, sweepstakes, and charity donations. SSI’s participant panels have been included in public health studies that span a range of topics (e.g., sugary beverage warning labels (Moran & Roberto, 2018; Roberto, Wong, Musicus, & Hammond, 2016; VanEpps & Roberto, 2016), food advertising (Fleming-Milici, Harris, & Liu, 2018; Goren, Harris, Schwartz, & Brownell, 2010), food and tobacco policy (Fong et al., 2006; Harris, Thompson, Schwartz, & Brownell, 2011; Loureiro & Umberger, 2007; Petrescu, Hollands, Couturier, Ng, & Marteau, 2016), and media messaging (Gollust, Lantz, & Ubel, 2009; Puhl et al., 2013)). The firm uses a three-stage randomization process to match participants with surveys for which they likely meet eligibility criteria. First, randomly selected participants who are already members of SSI’s panels are combined with a pool of potential participants who are joining SSI for the first time after responding to SSI’s recruitment materials. This combined group of potential participants receives an invitation to “take a survey,” but the recruitment materials do not provide the participants with additional details about the surveys that might introduce systematic selection bias (e.g., “take survey on food commercials” might lead people with an interest in food to click the survey link). Second, to become eligible for a specific study that is available at that time point, the potential participants complete quality control questions and are randomly matched with surveys they for which they likely meet eligibility criteria that is available at that time. Finally, among the randomly selected participants who have expressed an interest in and have been randomly matched with a survey, a subset are randomized to complete a specific survey. That subset makes up the final participant sample. For the present study, SSI recruited participants who identified as Black/African American or White (Table 1). A total of 1533 participants started the survey, 1503 completed it, and 30 were excluded because they: (1) they self-identified as a race/ethnicity other than Black or White; (2) were outside the eligible age range (12–17 years); or (3) incorrectly answered the data integrity question that appeared after survey television commercials (i.e. “What was the name of the product being shown?“).

Table 1.

Sociodemographic characteristics of sample.

Demographic Characteristics Full Sample N (%) Black Adolescents n (%) White Adolescents N (%)
N 1502 769 (51.2) 733 (48.8)
Female 44.6 413 (61.5)b 258 (38.5)b
Male 55.3 356 (42.8)b 475 (57.2)b
Average age, y 14.73 14.9 14.5
Race
 White 733 (48.8) 0 (0) 733 (48.8)
 Black 769 (51.2) 769 (51.2) 0 (0)
Average parent 1 education
 Less than high school 126 (8.4) 69 (4.6) 57 (3.8)
 High school/GED 279 (18.6) 156 (10.4) 123 (8.2)
 Vocational/Trade school 59 (3.9) 39 (2.6)b 20 (1.3)b
 Some college 317 (21.1) 215 (14.3)b 102 (6.8)b
 Bachelor’s degree 395 (26.3) 180 (12.0)b 215 (14.3)b
 Master’s degree 225 (15.0) 72 (4.8)b 153 (10.2)b
 Advanced graduate work or PhD 85 (5.7) 25 (1.7)b 60 (4.0)b
 Not sure 14 (0.9) 11 (0.7) 3 (0.2)
 Not applicable 2 (0.1) 2 (0.1) 0 (0)
Average parent 2 education
 Less than high school 49 (3.3) 25 (1.7) 24 (1.6)
 High school/GED 284 (18.9) 173 (11.5)b 111 (7.4)b
 Vocational/Trade school 60 (4.0) 41 (2.7)b 19 (1.3)b
 Some college 343 (22.8) 220 (14.6)b 123 (8.2)b
 Bachelor’s degree 401 (26.7) 157 (10.5)b 244 (16.2)b
 Master’s degree 175 (11.6) 45 (3.0)b 130 (8.7)b
 Advanced graduate work or PhD 78 (5.2) 27 (1.8)b 51 (3.4)b
 Not sure 48 (3.2) 35 (2.3)b 13 (0.9)b
 Not applicable 64 (4.3) 46 (3.0)b 18 (1.2)b
  How often are you exposed to advertisements of this product?a 2.62 2.6 2.7 (0.2)
Open in a new tab
a

Response choices were: 0 = Never; 1 = A few times a year; 2 = Once a month; 3 = Once a week; 4 = Every day. There was no significant difference between self-reported exposure rates between groups, and the average rating for the whole sample was 2.62.

b

If p < .05 when comparing Black and White adolescents.

2.2. Selecting and pre-testing food and beverage commercials

Using a 2 × 2 design, participants were randomized to view four food and beverage commercials featuring Black actors (Black-targeted condition) or four food and beverage commercials for the same brands featuring White actors (White-targeted condition). This generated four groups: 1) Black participants who saw Black-targeted ads; 2) Black participants who saw White-targeted ads; 3) White participants who saw Black-targeted ads; and 4) White participants who saw White-targeted ads. Television commercials were purchased through Kantar Media, a company that maintains AdScope’s database of all television, website, radio station, newspaper, and magazine advertisements since 1996 (AdScope Intelligence, 2017). We obtained recent food and beverage advertisements that aired in 2010 or later using search terms associated with brands that spend more than other food and beverage companies on advertising (e.g. “Coca-Cola”) (Harris et al., 2012, 2013, 2014, 2015) as well as race/ethnicity keywords. In a set of 945 potential AdScope commercials, we used the keywords “Black” and “African American” to identify a list of 46 potential food and beverage commercials including Black actors. We then searched for 46 ads featuring White actors that were most similar to the ads with Black actors. We excluded ads that did not feature adolescents or have a potential match. Two commercials were considered matches when they each promoted the same brand, featured only White or only Black adolescents, and had an ad duration within 15 s of its counterpart. For example, a Gatorade ad featuring Black individuals would be considered a match for a Gatorade ad featuring White individuals.

The final set of options included six Black- and six White-targeted commercials, which were pretested through SSI with 50 Black and 50 White adolescents ages 12–17 years to ensure ads were rated as adolescent-targeted. All adolescents in the pretest viewed the commercials and reported the perceived target audience by answering three questions evaluated on 7-point scales with responses ranging from 1 (disagree completely) to 7 (agree completely): “I feel the advertisement was intended for people like me,” “I do not believe I was in the target market the company created the advertisement for,” and “The advertiser made that advertisement for people like me.” (Aaker, Brumbaugh, & Grier, 2000) The four pairs of commercials with the highest pre-test ratings were selected for inclusion in the final survey. To ensure the ads we were studying were perceived by adolescents as targeted towards them, we excluded two pairs of commercials with the lowest ratings for the pre-test questions related to the perceived target audience. Our final sample of ads included four Black- and four White-targeted ads. The selected advertisements represented four well-known brands: Gatorade, Skittles, Pebbles Cereal, and Trolli Gummi Candy. Three Black-targeted ads were 15 s in duration, and one Black-targeted ad was 30 s in duration. Three White-targeted ads were 30 s in duration and one White-targeted ad was 15 s in duration (See Appendix 1 for descriptions of the ad content).

2.3. Survey procedure

Eligible participants provided informed consent and completed the survey, which involved viewing four commercials and answering questions about each one (see Supplementary Table 1 for survey questions). The average duration of each commercial was 22.5 s, and the median survey completion time was 22 min. The Institutional Review Board at NYU Langone Medical Center approved this study.

2.4. Primary outcomes

2.4.1. Liking of the ad

Supplementary Table 1 displays all survey items and response choices. After viewing a commercial, participants responded to the question, “Did you like the ad?,” adapted from Spears and Singh’s (Spears & Singh, 2004) attitude toward the brand measure. The response options included eight choices that enabled the participant to indicate that they liked or disliked the ad. We dichotomized this variable into “liked the ad” if participants indicated a “positive” response (e.g. The ad is good) or “did not like the ad” if they indicated a “negative” response (e.g. I disliked the ad).

2.4.2. Affective response to the ad

We adapted the Attitude Toward the Ad Inventory (Madden, Allen, & Twible, 1988) and participants responded to the question, “How did the ad for this product make you feel?” Participants selected one affective reaction among a list. Responses were dichotomized into “positive affective reaction” to the ad if participants reported feeling cheerful, excited, pleased, stimulated, and soothed in response to the ad, while repulsed, insulted, and irritated were considered “negative affective reaction.”

2.4.3. Taste perception

We measured perceived tastiness using responses to the question, “How good/bad do you think this product would taste?” on a 7-point Likert scale (1 = Very Bad; 7 = Very Good).

2.4.4. Response to the brand

Participants responded to four questions assessing their reaction to the brand being advertised (see Supplementary Table 1 for survey items). A composite measure for each ad was created by averaging participants’ responses to these items, and the brand trust question was linearly transformed from a 5-point scale to a 7-point scale. This expanded the range of the 5-point scale to 7 points with even spacing between each point (Howell, 2012), ultimately allowing us to combine this item into a composite measure with the other three items.

Participants also responded to the question, “How much do you like the brand?,” which we adapted from Spears and Singh’s (Spears & Singh, 2004) attitude toward the brand measure. Responses were coded as liking the brand (e.g. I like the brand) or not liking the brand (e.g. The brand is bad).

2.5. Secondary outcomes

2.5.1. Brand engagement

Participants were asked whether they would “follow” or “like” product accounts on Facebook, Twitter, and Instagram if they did not follow the brand or product account already. Participants’ responses were combined across all three platforms into a single dichotomous variable indicating whether or not they would follow the product on any social media platform. We excluded responses indicating the participant did not use that platform or already followed the product because we were interested in whether the ad would influence brand engagement on social media.

2.5.2. Purchasing and consumption intentions

Participants indicated how likely they were to purchase the product shown in the ad using a 7-point Likert scale. Participants also reported how often they would consume the product if possible (measured on a 5-point scale).

2.5.3. Statistical analyses

To explore the influence of ad targeting on Black and White adolescents, we used R50 and lme451 to conduct a multilevel regression on each of the outcome measures described above. We analyzed binary outcomes using logistic regressions, and continuous outcomes using linear regressions. In each model we included participant race, whether participants viewed ads that were congruent or incongruent with their own race, and the interaction of these two variables as between subjects fixed effects. Because this was a randomized experiment, we did not control for any other variables in our primary analysis. We did, however, repeat our main analysis adjusting for age, gender, and parental education (measured as the maximum education achieved by either parent) because two of these variables were not balanced across the groups, suggesting the need to adjust for demographic covariates. To account for individual differences, we included a random intercept of participant in each model. When applicable, we broke down significant interactions to explore the simple effects of ad targeting within Black and White adolescents, respectively, and estimated the effect of ad targeting within Black and White adolescents separately.

3. Results

3.1. Liking of the ad

On average, participants liked 84% of the ads (SD = 26%). Using a multilevel logistic regression, we found a main effect of participant race such that on average, Black participants were more likely to like the ad (adjusted probability = .96) than were White participants (adjusted probability = .90), b = 0.50, se = 0.07, z = 6.71, p < .001 (Table 2). We did not find evidence for a main effect of ad targeting (p = .51), however, the interaction between participant race and ad targeting was significant, b = 0.17, se = 0.08, z = 2.16, p = .03. Breaking down this interaction to look at the simple effect of targeting within Black and White adolescents respectively, we found that White participants were more likely to like Black-targeted ads than White-targeted ads, b = −0.21, se = 0.10, z = 2.08, p = .04. For Black participants, we did not find evidence for an effect of targeting on the probability of liking the ad, b = 0.11, se = 0.11, z = 1.01, p = .31. For White participants, the adjusted probability of liking a White-targeted ad was 88% while the adjusted probability of liking a Black-targeted ad was 91% (Fig. 1). For Black participants, the adjusted probability of liking a Black-targeted ad was 96% and the adjusted probability of liking a White-targeted ad was 95%. Further analyses revealed that this interaction effect held for the Gatorade and Skittles ads, but was not significant for the Trolli and Cocoa/Fruity Pebbles ads.

Table 2.

Outcomes by advertising condition.

Percentages (SEs) Black Participants White Participants
Targeted Non-Targeted Targeted Non-Targeted
 Liking of Ad 96.0% (.01) 95.0% (.01) 88.0% (.02) 91.0% (.01)*
 Positive Affective Reaction 97.0% (.01) 95.0% (.01)* 95.0% (.01) 97.0% (.01)*
 Liking Brand 96.0% (.01) 95.0% (.01) 95.0% (.01) 96.0% (.01)
 Brand Engagement 77.0% (.04) 75.0% (.04) 80.0% (.03) 84.0% (.03)
Means (SEs)
 Taste 6.06 (.04) 6.08 (.05) 6.04 (.05) 6.02 (.05)
 Response to Brand 5.88 (.05) 5.85 (.05) 5.86 (.05) 5.93 (.05)
 Likelihood of Purchasing 5.70 (.06) 5.74 (.06) 5.77 (.06) 5.68 (.06)
 Desired Consumption Frequency 2.69 (.05) 2.71 (.05) 2.83 (.05) 2.82 (.05)
Open in a new tab
*

Indicates significant differences between targeting conditions for Black and White participants, respectively.

*

p < .05.

Fig. 1.

Fig. 1.

Open in a new tab

Estimated adjusted probability of liking ad based on participants’ race and whether the ad they viewed was targeted toward their own racial group or was targeted toward another racial group*p < .05.

3.2. Affective response to the ad

On average, participants reported a positive affective reaction to 90% of the ads (SD = 20%). Although we found neither a main effect of participant race (p = .47) nor of ad targeting (p = .57), there was a significant interaction, b = 0.27, se = 0.07, z = 3.75, p < .001. Breaking down this interaction into its simple effects, we found that White participants were more likely to report a positive affective reaction to ads that were Black-targeted than White-targeted, b = −0.32, se = 0.10, z = 3.09, p = .002; the adjusted probability of reporting a positive affective reaction to a White-targeted ad was 95% while the adjusted probability of reporting a positive affective reaction to an Black-targeted ad was 97% (Fig. 2). Conversely, we found that Black participants were more likely to report positive affect in response to Black-targeted ads than White-targeted ads, b = 0.22, se = 0.10, z = 2.20, p = .03. The adjusted probability of Black participants reporting a positive affective reaction to a Black-targeted ad was 97% and the adjusted probability of reporting a positive affective reaction to a White-targeted ad was 95%. Follow up analyses revealed that this effect was driven by the Trolli and Skittles ads, while the interaction between participant race and ad targeting was not significant for Gatorade and Cocoa/Fruity Pebbles ads.

Fig. 2.

Fig. 2.

Open in a new tab

Estimated adjusted probability of a positive affective reaction to an ad based on participants’ race and whether the ad they viewed was targeted toward their own racial group or was targeted toward another racial group*p < .05.

3.3. Taste perception

Participants’ average taste rating of products was M = 6.05 (SD = 1.94). We failed to find evidence for an effect of participant race (p = .42), ad targeting (p = .97), or their interaction (p = .72) on participants’ taste ratings for the product featured in the ads.

3.4. Response to the brand

Overall, participants reported an average of score of M = 5.88 (out of 7) (SD = 1.13) on the composite measure of participants’ response to the brand. We failed to detect a significant effect of participant race (p = .57), ad targeting (p = .68), or their interaction on participants’ response to the brand (p = .24). We found that on average, participants liked 91% of the brands (SD = 18%). We found neither a main effect of participant race (p = .52) nor of ad targeting (p = .60), and the interaction was not statistically significant, b = 0.12, se = 0.07, z = 1.83, p = .07.

3.5. Brand engagement

Participants said they would “like” or “follow” 64% of the ads. We failed to find evidence that participant race (p = .10), ad targeting (p = .69), or their interaction had a significant effect on participants’ likelihood of social media engagement (p = .32).

3.6. Purchase intentions

Participants reported an average of M = 5.72 (SD = 1.60) on the item measuring likelihood of purchasing the featured product. We failed to detect a significant effect of participant race (p = .86), ad targeting (p = .67), or their interaction (p = .29).

3.7. Consumption intentions

The average rating on the item asking participants how often they would eat/drink the featured product if possible was M = 2.76, SD = 1.26 (0 = Never; 4 = Daily). We found a significant main effect of participant race such that black participants said they would be less likely to consume the featured product b = −0.06, se = 0.03, t (1497) = 2.60, p < .001. We failed to detect a significant effect of either ad targeting (p = .88) or the interaction between ad targeting and participant race (p = .80). When we included age, gender, and parental education in the model as covariates, we found that the main effect of participant race was no longer significant (p = .45).

4. Discussion

This study aimed to examine the influence of Black- and White-targeted food and beverage commercials on Black and White adolescents’ attitudes toward ads, perceived tastiness of advertised products, attitudes toward brands, willingness to engage with brands on social media, and purchase and consumption intentions. Overall, Black and White adolescents reported positive responses to both Black- and White-targeted ads indicating the broad appeal of both types of food and beverage ads. Our hypothesis that participants would report favorable attitudes toward commercials featuring actors whose race matched their own was partially supported. When asked how the ad made them feel, Black participants were more likely to report a positive emotional response to ads featuring Black actors. Unexpectedly, White participants were also more likely to report a positive emotional response to ads featuring Black actors. For the more overt attitudinal question “Did you like the ad?,” White adolescents also preferred Black-targeted ads compared to White-targeted ads, but there was no difference between Black adolescents’ ratings of Black- and White-targeted ads.

White adolescents’ preference for Black-targeted ads may not be surprising given that companies often view Black adolescents as trendsetters (Zmuda, 1377). For example, the Assistant Vice President of African American Marketing at Coca-Cola remarked on the trendsetting role of young Black consumers: “Teens really are the future of America, and African-American teens, in particular, have proven to be trendsetters in the U.S. Their ability to shape culture is really critical.” (Zmuda, 1377) Further, the Senior Vice President of U.S. Strategic Community Alliances and Consumer Engagement, Nielsen reinforced the trendsetting nature of Black consumers, stating, “Our research shows that Black consumer choices have a ‘cool factor’ that has created a halo effect, influencing not just consumers of color but the mainstream as well.” (Black Dollars Matter, 2018) Although we did not hypothesize that White adolescents would have some stronger preferences for Black-targeted ads relative to White-targeted ads, these industry insights suggest that Black-targeted media may be highly appealing to other racial/ethnic groups.

Our observed effect sizes were small, which is consistent with industry reports and academic literature that suggests enormous amounts of repeated exposure are needed for consumers to maintain brand awareness (Holman & Hecker, 1983; Online or Traditional Adv, 2016; Reuters, 2013), which is a prerequisite for brand loyalty and leads to increased purchases (How does advertising work, 2018; Macdonald & Sharp, 2000; Reuters, 2013). As highlighted by industry reports, brief ad exposure can produce very small effects, but when aggregated over time, can lead to more substantial influence on purchasing (How does advertising work, 2018). Because industry literature indicates that repeated exposure is needed to affect consumers’ perception and behavior, it is possible that repeated exposure to racially targeted advertisements would increase product preferences among racially targeted group. Although racially targeted marketing is not inherently problematic, it raises issues of social justice when the advertisements promote unhealthy products. Social cognitive theories predict that repeated exposure to food advertising can also lead directly to beliefs and behaviors without active, deliberate processing of the information presented (Bargh & Ferguson, 2000; PsycNET, 2018; Strack & Deutsch, 2004; Wilson & Bar-Anan, 2008).

This study makes a contribution to the literature on racially-targeted marketing because much of the existing research in this area has focused on tobacco and alcohol and much of the food marketing work has only documented the prevalence of racially targeted marketing. Research shows racially targeted marketing is prevalent and promotion of unhealthy products appears more frequently in Black communities and media outlets compared to White communities and media outlets (Tirodkar & Jain, 2003; Yancey et al., 2009; Cruz, Wright, & Crawford, 2010; Seidenberg, Caughey, Rees, & Connolly, 2010; Henriksen, Schleicher, Dauphinee, & Fortmann, 2012; Ohri-Vachaspati et al., 2015; Duerksen et al., 2005; Home -onn Rudd Center, 1115; Bang & Reece, 2003; Henderson & Kelly, 2005; Morland, Wing, & Roux, 2002; Harrison, 2006; WebsiteSteinburg, 2012; Hackbarth et al., 2001). Survey research on tobacco ads showed that adolescents who responded favorably to tobacco advertisements were more likely to like tobacco brands which, in turn, predicted increased likelihood of smoking (Image Advertisements’ Inf, 2018; Díaz, Villalbí, Nebot, Aubà, & Sanz, 1998; Armstrong, de Klerk, Shean, Dunn, & Dolin, 1990). Consistent with other research that found that racial/ethnic minorities are more likely to respond to racially-similar ads (Appiah, 2001; Brumbaugh, 2009; Deshpandé & Stayman, 1994; Green, 1999), we found that Black participants had a more positive emotional response to racially-similar ads. This is the first study to our knowledge to experimentally examine the influence of racially-targeted ads on Black and White adolescents, and is novel because its focus on youth.

5. Conclusions

Although there was some evidence that Black-targeted marketing increased positive responses toward ads among Blacks, the effects only occurred with adolescents’ ratings of how much they liked the ad and how it made them feel. There were no observed differences on adolescents’ tastiness perceptions, brand liking, likelihood of following brands on social media, or purchase and consumption intentions. This may be because brand loyalty tends to be relatively stable over time (Cowie, Swift, Borland, Chaloupka, & Fong, 2014; Torres, Augusto, & Godinho, 2017), making it unlikely that this study’s single exposure to racially-targeted marketing ad would have immediate effects on brand loyalty. Although it is possible that racially-targeted marketing does not influence the other product and brand outcomes measured in this study, more research involving repeated exposure to such ads is needed to understand whether a relationship exists.

5.1. Limitations

This study has several limitations. First, it is impossible to match commercials that are identical in every aspect except race/ethnicity because each commercial has a slightly different theme (e.g. “kitten condominium” versus “candy dog” in Trolli Candy commercials). We included four commercials that varied in their music, themes, and actors to prevent narrow stimulus sampling and determine if any effects replicated across multiple commercials. The race/ethnicity of the individual in the ad was the only factor consistently manipulated. Our pre-test, however, did not include questions to assess the likeability of other ad features (e.g., music, attractiveness of actors). We therefore cannot rule out that other ad features may explain our results, which emerged for two, but not all four ads. It is also important to recognize that racially-targeted marketing often encompasses other features aside from just the race of actors, including featuring certain themes, music etc. This means that any differences driven by other ad features like music or themes, might also reflect racial-targeting. Finally, it is possible adolescents’ responses were affected by varying duration of the commercials (i.e., some were 15 s and others were 30 s in duration). In addition, because Black and White participants responded very favorably to ads, it may have been difficult to detect meaningful differences in adolescents’ responsiveness to the racially-targeted component of the ads. Our use of a convenience sample also means our results cannot be generalized to all Black and White adolescents. Finally, because this is one of the first studies examining the effects of targeted food marketing among Black and White adolescents, we used an alpha level of 0.05 to denote statistical significance and did not correct for multiple tests, suggesting the need for replication of this research before drawing firm conclusions.

This study has a number of strengths, including the large sample size and inclusion of adolescents, who are understudied in food and beverage marketing. This study is also the first to examine the influence of racially-targeted food and beverage ads on Black and White adolescents’ attitudes toward ads, brands, purchase intentions, and willingness to engage with brands on social media. Although this study provides some evidence that Black-targeted ads influence adolescents’ preferences for those ads, more research is needed to examine how these ads affect actual purchases and food consumption.

Supplementary Material

Supplementary Table 1

Acknowledgements

Support for this research was provided by the Robert Wood Johnson Foundation. The views expressed here do not necessarily reflect the views of the Foundation. This project was also supported by the Rudd Foundation and the NIH Early Independence Award (DP5OD021373-01) from the NIH Office of the Director, P60MD000538. Dr. Roberto is supported by the National Institute On Aging of NIH under Award Number P30AG034546. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of NIH. No financial disclosures were reported by the authors of this paper. This study was approved by the New York University IRB #i15-00879.

Dr. Marie Bragg originated the study idea and design, helped with data acquisition and analyses, led the writing of the manuscript, had full access to all of the data in the study, and takes responsibility for the integrity of the data and accuracy of the analysis. Ms. Alysa Miller helped with the data acquisition and analysis, and helped draft the manuscript. Dr. David Kalkstein helped lead the data analysis and with interpretation of study results. Dr. Brian Elbel helped interpret study results and provided critical feedback on the drafts of the manuscript. Dr. Christina Roberto helped with leading the data analysis, interpreting results, and provided critical manuscript feedback.

We would like to thank the following Research Assistants and staff from the NYU SeedProgram for their contributions in collecting data: Yrvane Pageot, Anna Davies, Ruchi Deasi, Tenay Greene, Joshua Arshonsky, Margaret Eby, Ann Palladino, DiAnna Brice, Anne Dumadag, Ryan Hand, Ingrid Wells, Susan Stork, Natasha Pandit, Alex Harvey, and Jessica Osterman.

Funding source

This study was supported by the NIH Early Independence Award (DP5OD021373-01) from the NIH Office of the Director. Funded by the National Institutes of Health.

Appendix 1

Descriptions of Ad Content

The Gatorade ads featured an athlete engaging in physical activity and drinking Gatorade. Both Pebbles cereal ads featured a celebrity athlete engaging in physical activity and eating cereal. The Trolli candy ads featured adolescents engaging in a silly activity involving the candy; in the Black-targeted ad, an adolescent was eating the candy while kittens sat on top of his head. In the White-targeted Trolli ad, the adolescent was playing with a toy dog covered in the candy. In the Skittles ad in the Black-targeted ad condition, a Black individual is working out in a gym filled with Skittles. In the Skittles ad in the White-targeted ad condition, two White individuals are talking next to bleachers on a football field. Although the duration and theme of the ads are not identical, these ads were the most similar among the 92 ads that were identified through the search process as either featuring White or Black individuals in the ads.

Appendix 2.

Pre-Test Results Exploring Adolescents’ Perceptions about Ad Stimuli*

Demographic Characteristics
Race/ethnicity Female (n) Male (n) Average Age (years)
 Black 34 16 15.4
 White 28 22 15.3
Average Pre-test Advertisement Ratings Across the Sample (1–7 Point Likert Scale)
“I do not believe I was in the target market the company created the advertisement for.” “I feel the advertisement was intended for people like me.” “The advertiser made that advertisement for people like me.”
Skittles Ad Featuring Black Individuals
 Black participants’ ratings 2.6 5.9 6.0
 White participants’ ratings 2.5 5.9 5.7
Skittles Ad Featuring White Individuals
 Black participants’ ratings 2.5 6.0 6.7
 White participants’ ratings 2.6 6.0 6.0
Gatorade Ad Featuring Black Individuals
 Black participants’ ratings 2.5 6.1 6.2
 White participants’ ratings 2.7 5.9 6.1
Gatorade Ad Featuring White Individuals
 Black participants’ ratings 2.6 6.0 5.9
 White participants’ ratings 2.5 5.7 6.0
Trolli Ad Featuring Black Individuals
 Black participants’ ratings 2.6 5.8 6.2
 White participants’ ratings 2.4 6.0 6.1
Trolli Ad Featuring White Individuals
 Black participants’ ratings 2.5 5.9 5.9
 White participants’ ratings 2.4 5.9 6.0
Fruity/Cocoa Pebbles Ad Featuring Black Individuals
 Black participants’ ratings 2.7 5.8 6.2
 White participants’ ratings 2.8 5.9 5.8
Fruity/Cocoa Pebbles Ad Featuring White Individuals
 Black participants’ ratings 2.4 6.0 6.1
 White participants’ ratings 2.4 6.1 6.0
McDonald’s Ad Featuring Black Individuals
 Black participants’ ratings 2.4 5.5 6.0
 White participants’ ratings 2.5 5.6 5.7
McDonald’s Ad Featuring White Individuals
 Black participants’ ratings 2.2 3.1 3.9
 White participants’ ratings 2.1 3.6 3.5
Coca-Cola Ad Featuring Black Individuals
 Black participants’ ratings 2.4 6.0 6.0
 White participants’ ratings 2.6 6.1 6.2
Coca-Cola Ad Featuring White Individuals
 Black participants’ ratings 4.7 2.5 2.8
 White participants’ ratings 5.7 2.3 2.7
Open in a new tab
*

Chi-squared tests and t-tests did not indicate any differences in the Black and White participants’ ratings of the 8 pairs of commercials. The two lowest scoring commercials (i.e., McDonald’s and Coca-Cola) were removed and the remaining 6 pairs were used as the stimuli for this study.

Footnotes

Financial disclosure

The authors have no financial relationships relevant to this article to disclose.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.appet.2019.05.001.

Conflicts of interest

The authors have no conflicts of interest relevant to this article to disclose.

References

  1. Aaker JL, Brumbaugh AM, & Grier SA (2000). Nontarget markets and viewer distinctiveness: The impact of target marketing on advertising attitudes. Journal of Consumer Psychology, 9, 127–140. [Google Scholar]
  2. Adeigbe RT, Baldwin S, Gallion K, Grier S, & Ramirez AG (2015). Food and beverage marketing to latinos: A systematic literature review. Health Education & Behavior, 42(5), 569–582. [DOI] [PubMed] [Google Scholar]
  3. AdScope Intelligence Kantar media. http://products.adscope.com/TNSMI/CreativeServices/ClientUI/SecurityTS/Default.aspx?ReturnUrl=http%3a%2f%2fproducts.adscope.com%2fTNSMI%2fCreativeServices%2fClientUI%2fHomePage.aspx#/, Accessed date: 23 June 2017.
  4. Appiah O. (2001). Ethnic identification on adolescents’ evaluations of advertisements. Journal of Advertising Research, 41(5), 7–22. [Google Scholar]
  5. Armstrong BK, de Klerk NH, Shean RE, Dunn DA, & Dolin PJ (1990). Influence of education and advertising on the uptake of smoking by children. Medical Journal of Australia, 152(3), 117–124. [DOI] [PubMed] [Google Scholar]
  6. Bang H-K, & Reece BB (2003). Minorities in children’s television commercials: New, improved, and stereotyped. Journal of Consumer Affairs, 37(1), 42–67. [Google Scholar]
  7. Bargh JA, & Ferguson MJ (2000). Beyond behaviorism: On the automaticity of higher mental processes. Psychological Bulletin, 126(6), 925–945. [DOI] [PubMed] [Google Scholar]
  8. Black Dollars Matter The sales impact of black consumers. Nielsen. http://www.nielsen.com/us/en/insights/news/2018/black-dollars-matter-the-sales-impact-of-black-consumers.print.html, Accessed date: 16 August 2018.
  9. Boyland EJ, & Halford JC (2013). Television advertising and branding. Effects on eating behaviour and food preferences in children. Appetite, 62, 236–241. [DOI] [PubMed] [Google Scholar]
  10. Brumbaugh AM (2009). Why do I identify with thee? Let me count three ways: How ad context influences race-based character identification. Psychology and Marketing, 26(11), 970–986. [Google Scholar]
  11. Cairns G, Angus K, Hastings G, & Caraher M. (2013). Systematic reviews of the evidence on the nature, extent and effects of food marketing to children: A retrospective summary. Appetite, 62, 209–215. [DOI] [PubMed] [Google Scholar]
  12. Cairns G, Angus K, Hastings G, & Others (2009). The extent, nature and effects of food promotion to children: A Review of the evidence to december 2008. Geneva: World Health Organization. [Google Scholar]
  13. Cowie GA, Swift E, Borland R, Chaloupka FJ, & Fong GT (2014). Cigarette brand loyalty in Australia: Findings from the ITC four country survey. Tobacco Control, 23(Suppl 1), i73–i79. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cruz TB, Wright LT, & Crawford G. (2010). The menthol marketing mix: Targeted promotions for focus communities in the United States. Nicotine & Tobacco Research, 12(Suppl 2), S147–S153. [DOI] [PubMed] [Google Scholar]
  15. Deshpandé R, & Stayman DM (1994). A tale of two cities: Distinctiveness theory and advertising effectiveness. Journal of Marketing Research, 31(1), 57–64. [Google Scholar]
  16. Díaz E, Villalbí JR, Nebot M, Aubà J, & Sanz F. (1998). Smoking initiation in students: Cross-sectional and longitudinal study of predictive factors. Medical Clinics of North America, 110(9), 334–339. [PubMed] [Google Scholar]
  17. Dixon H, Scully M, Niven P, Kelly B, Chapman K, Donovan R, et al. (2014). Effects of nutrient content claims, sports celebrity endorsements and premium offers on preadolescent children’s food preferences: Experimental research. Pediatr Obes, 9(2) 10.1111/j.2047-6310.2013.00169.x/full. [DOI] [PubMed] [Google Scholar]
  18. Duerksen SC, Mikail A, Tom L, Patton A, Lopez J, Amador X, et al. (2005). Health disparities and advertising content of women’s magazines: A cross-sectional study. BMC Public Health, 5, 85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fleming-Milici F, Harris JL, & Liu S. (2018). Race, ethnicity, and other factors predicting U.S. Parents’ support for policies to reduce food and beverage marketing to children and adolescents. Health Equity, 2(1), 288–295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Fong GT, Hyland A, Borland R, Hammond D, Hastings G, McNeill A, et al. (2006). Reductions in tobacco smoke pollution and increases in support for smoke-free public places following the implementation of comprehensive smoke-free workplace legislation in the republic of Ireland: Findings from the ITC Ireland/UK survey. Tobacco Control, 15(Suppl 3), iii51–iii58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Frazier WC III, & Harris JL (2017). Trends in food advertising to young people: 2016 update. UConn Rudd Center for Food Policy and Obesity. [Google Scholar]
  22. Gollust SE, Lantz PM, & Ubel PA (2009). The polarizing effect of news media messages about the social determinants of health. American Journal of Public Health, 99(12), 2160–2167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Goren A, Harris JL, Schwartz MB, & Brownell KD (2010). Predicting support for restricting food marketing to youth. Health Affairs, 29(3), 419–424. [DOI] [PubMed] [Google Scholar]
  24. Green CL (1999). Ethnic evaluations of advertising: Interaction effects of strength of ethnic identification, media placement, and degree of racial composition. Journal of Advertising, 28(1), 49–64. [Google Scholar]
  25. Grier S. (2009). African American and hispanic youth vulnerability to target marketing: Implications for understanding the effects of digital marketing. Second NPLAN/BMSG meeting on digital media and marketing to children. Berkeley, CA: Berkeley Media Studies Group. [Google Scholar]
  26. Grier SA, & Kumanyika SK (2008). The context for choice: Health implications of targeted food and beverage marketing to african Americans. American Journal of Public Health, 98(9), 1616–1629. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Grier SA, & Kumanyika S. (2010). Targeted marketing and public health. Annual Review of Public Health, 31, 349–369. [DOI] [PubMed] [Google Scholar]
  28. Hackbarth DP, Schnopp-Wyatt D, Katz D, Williams J, Silvestri B, & Pfleger M. (2001). Collaborative research and action to control the geographic placement of outdoor advertising of alcohol and tobacco products in Chicago. Public Health Reports, 116(6), 558–567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Halford JC, Boyland EJ, Hughes GM, Stacey L, McKean S, & Dovey TM (2008). Beyond-brand effect of television food advertisements on food choice in children: The effects of weight status. Public Health Nutrition, 11(9), 897–904. [DOI] [PubMed] [Google Scholar]
  30. Halford JCG, Gillespie J, Brown V, Pontin EE, & Dovey TM (2004). Effect of television advertisements for foods on food consumption in children. Appetite, 42(2), 221–225. [DOI] [PubMed] [Google Scholar]
  31. Harris JL, Bargh JA, & Brownell KD (2009a). Priming effects of television food advertising on eating behavior. Health Psychology, 28(4), 404–413. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Harris JL, Brownell KD, & Bargh JA (2009b). The food marketing defense model: Integrating psychological research to protect youth and inform public policy. Soc Issues Policy Rev, 3(1), 211–271. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Harrison K. (2006). Fast and sweet: Nutritional attributes of television food advertisements with and without black characters. Howard Journal of Communications, 17(4), 249–264. [Google Scholar]
  34. Harris JL, Schwartz MB, Brownell KD, Sarda V, Dembek C, Munsell C, et al. (2012). Cereal FACTS 2012: Limited progress in the nutrition quality and marketing of children’s cereals. Vol. 12Rudd Center for Food Policy & Obesity. www.cerealfacts.org/media/Cereal_FACTS_Report_2012_7 http://www.cerealfacts.org/media/cereal_facts_report_2012_7.12.pdf. [Google Scholar]
  35. Harris JL, Schwartz MB, LoDolce M, Munsell C, Fleming-Milici F, Elsey J, et al. (2014). Sugary drink FACTS 2014: Some progress but much room for improvement in marketing to youth. New Haven, CT: Rudd Center for Food Policy and Obesity. http://www.sugarydrinkfacts.org/resources/sugarydrinkfacts_report.pdf. [Google Scholar]
  36. Harris JL, Schwartz MB, Munsell CR, Dembek C, Liu S, LoDolce M, et al. (2013). Fast food FACTS 2013: Measuring progress in nutrition and marketing to children and teens. Yale Rudd Center for Food Policy and Obesity; https://www.rwjf.org/content/dam/farm/reports/reports/2013/rwjf408549. [Google Scholar]
  37. Harris JL, Shehan C, Gross R, & Others (2015). Food advertising targeted to Hispanic and Black youth: Contributing to health disparities. Rudd Report uconnruddcenter org/targetedmarketing. http://www.uconnruddcenter.org/files/Pdfs/272-7%20%20Rudd_Targeted%20Marketing%20Report_Release_081115%5B1%5D.pdf.
  38. Harris JL, Thompson JM, Schwartz MB, & Brownell KD (2011). Nutritionrelated claims on children’s cereals: What do they mean to parents and do they influence willingness to buy? Public Health Nutrition, 14(12), 2207–2212. [DOI] [PubMed] [Google Scholar]
  39. Henderson VR, & Kelly B. (2005). Food advertising in the age of obesity: Content analysis of food advertising on general market and african american television. Journal of Nutrition Education and Behavior, 37(4), 191–196. [DOI] [PubMed] [Google Scholar]
  40. Henriksen L, Schleicher NC, Dauphinee AL, & Fortmann SP (2012). Targeted advertising, promotion, and price for menthol cigarettes in California high school neighborhoods. Nicotine & Tobacco Research, 14(1), 116–121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Holman RH, & Hecker SS (1983). Advertising impact: Creative elements affecting brand saliency. Journal of Current Issues and Research in Advertising, (6), 157–172.
  42. Home - UConn Rudd center for food policy and obesity. http://www.uconnruddcenter.org/files/Pdfs/272-7%20%20Rudd_Targeted%20Marketing%20Report_Release_081115%5B1%5D.pdf, Accessed date: 16 August 2018.
  43. How does advertising work? - advertising association. Advertising association. https://www.adassoc.org.uk/advertisings-big-questions/how-does-advertising-work/, Accessed date: 16 August 2018.
  44. Image advertisements’ influence on adolescents’ perceptions of the desirability of beer and cigarettes on JSTOR. American Marketing Association. [Google Scholar]
  45. Howell DC (2012). Statistical methods for psychology. Cengage Learning. [Google Scholar]
  46. Kelly B, Freeman B, King L, Chapman K, Baur LA, & Gill T. (2016). The normative power of food promotions: Australian children’s attachments to unhealthy food brands. Public Health Nutrition, 19(16), 2940–2948. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Kersting K. (2004). Driving teen egos—and buying—through “branding,.”. Monitor on Psychology, 35(6), 60. [Google Scholar]
  48. Loureiro ML, & Umberger WJ (2007). A choice experiment model for beef: What US consumer responses tell us about relative preferences for food safety, country-of-origin labeling and traceability. Food Policy, 32(4), 496–514. [Google Scholar]
  49. Macdonald EK, & Sharp BM (2000). Brand awareness effects on consumer decision making for a common, repeat purchase product:: A replication. Journal of Business Research, 48(1), 5–15. [Google Scholar]
  50. Madden TJ, Allen CT, & Twible JL (1988). Attitude toward the ad: An assessment of diverse measurement indices under different processing“ sets. Journal of Marketing Research, 242–252.
  51. Marwick R. Target market: Children as consumers. Aboutkidshealth. (2006). http://www.aboutkidshealth.ca/en/news/newsandfeatures/pages/target-market-children-as-consumers.aspx, Accessed date: 30 October 2010 Accessed June 22, 2017.
  52. McGinnis JM, Gootman JA, & Kraak VI (2006). Committee on food marketing and the diets of children and youth; Institute of medicine. Food marketing to children and youth: Threat or opportunity.
  53. Montgomery KC, & Chester J. (2009). Interactive food and beverage marketing: Targeting adolescents in the digital age. Journal of Adolescent Health, 45(3 Suppl), S18–S29. [DOI] [PubMed] [Google Scholar]
  54. Moran AJ, & Roberto CA (2018). Health warning labels correct parents’ misperceptions about sugary drink options. American Journal of Preventive Medicine, 55(2), e19–e27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Morland K, Wing S, & Roux AD (2002). The contextual effect of the local food environment on residents’ diets: The atherosclerosis risk in communities study. American Journal of Public Health, 92(11), 1761–1768. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Nielsen company And. Increasingly affluent, educated, and diverse. African-American consumers: The untold story 2015 report. Nielsen Company and Essence Communications. [Google Scholar]
  57. Ogden CL, Carroll MD, Fryar CD, & Flegal KM (2015). Prevalence of obesity among adults and youth: United States, 2011–2014. NCHS Data Brief, (219), 1–8. [PubMed]
  58. Ohri-Vachaspati P, Isgor Z, Rimkus L, Powell LM, Barker DC, & Chaloupka FJ (2015). Child-directed marketing inside and on the exterior of fast food restaurants. American Journal of Preventive Medicine, 48(1), 22–30. [DOI] [PubMed] [Google Scholar]
  59. Online or traditional advertising, what’s better for brands? Bain. (2016). https://www.bain.com/insights/online-or-traditional-advertising-whats-better-for-brands/, Accessed date: 16 November 2016 Accessed August 16, 2018.
  60. Petrescu DC, Hollands GJ, Couturier D-L, Ng Y-L, & Marteau TM (2016). Public acceptability in the UK and USA of nudging to reduce obesity: The example of reducing sugar-sweetened beverages consumption. PLoS One, 11(6), e0155995. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Powell LM, Szczypka G, & Chaloupka FJ (2007). Adolescent exposure to food advertising on television. American Journal of Preventive Medicine, 33(4 Suppl), S251–S256. [DOI] [PubMed] [Google Scholar]
  62. Powell LM, Wada R, & Kumanyika SK (2014). Racial/ethnic and income disparities in child and adolescent exposure to food and beverage television ads across the U.S. media markets. Health & Place, 29, 124–131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. PsycNET http://doi.apa.org/psycinfo/2007-00387-002, Accessed date: 16 August 2018.
  64. Puhl R, Peterson JL, & Luedicke J. (2013). Motivating or stigmatizing? Public perceptions of weight-related language used by health providers. International Journal of Obesity, 37(4), 612–619. [DOI] [PubMed] [Google Scholar]
  65. Reuters T. Building top of mind awareness. Penheel. https://penheel.com/wp-content/uploads/2013/06/Thomson_Building_Top_of_Mind_Awareness_Whitepaper.pdf, Accessed date: 16 August 2018.
  66. Roberto CA, Wong D, Musicus A, & Hammond D. (2016). The influence of sugar-sweetened beverage health warning labels on parents’ choices. Pediatrics, 137(2), e20153185. [DOI] [PubMed] [Google Scholar]
  67. Scott J. (October 2012). McDonald’s baobab tree. Africa is a country. http://africasacountry.com/2012/10/mcdonalds-baobab-tree/.
  68. Scully M, Wakefield M, Niven P, Chapman K, Crawford D, Pratt IS, et al. (2012). Association between food marketing exposure and adolescents’ food choices and eating behaviors. Appetite, 58(1), 1–5. [DOI] [PubMed] [Google Scholar]
  69. Seidenberg AB, Caughey RW, Rees VW, & Connolly GN (2010). Storefront cigarette advertising differs by community demographic profile. American Journal of Health Promotion, 24(6), e26–e31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Spears N, & Singh SN (2004). Measuring attitude toward the brand and purchase intentions. Journal of Current Issues and Research in Advertising, 26(2), 53–66. [Google Scholar]
  71. Story M, & French S. (2004). Food advertising and marketing directed at children and adolescents in the US. International Journal of Behavioral Nutrition and Physical Activity, 1(1), 3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Strack F, & Deutsch R. (2004). Reflective and impulsive determinants of social behavior. Personality and Social Psychology Review, 8(3), 220–247. [DOI] [PubMed] [Google Scholar]
  73. Tirodkar MA, & Jain A. (2003). Food messages on African American television shows. American Journal of Public Health, 93(3), 439–441. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Torres P, Augusto M, & Godinho P. (2017). Predicting high consumer-brand identification and high repurchase: Necessary and sufficient conditions. Journal of Business Research, 79, 52–65. [Google Scholar]
  75. VanEpps EM, & Roberto CA (2016). The influence of sugar-sweetened beverage warnings: A randomized trial of adolescents’ choices and beliefs. American Journal of Preventive Medicine, 51(5), 664–672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Weber K, Story M, & Harnack L. (2006). Internet food marketing strategies aimed at children and adolescents: A content analysis of food and beverage brand web sites. Journal of the American Dietetic Association, 106(9), 1463–1466. [DOI] [PubMed] [Google Scholar]
  77. Website, & Steinburg B. (2012). More marketers join Magic Johnson’s Aspire channel, get big concessions. Retrieved from http://adage.com/article/media/magic-johnson-s-aspire-channel-hands-big-ad-concessions/235537/, Accessed date: 16 August 2018.
  78. Wilson TD, & Bar-Anan Y. (2008). Psychology. The unseen mind. Science, 321(5892), 1046–1047. [DOI] [PubMed] [Google Scholar]
  79. Woodward DR, Cumming FJ, Ball PJ, Williams HM, Hornsby H, & Boon JA (1997). Does television affect teenagers’ food choices? Journal of Human Nutrition and Dietetics, 10(4), 229–235. [Google Scholar]
  80. Yancey AK, Cole BL, Brown R, Williams JD, Hillier A, Kline RS, et al. (2009). A cross-sectional prevalence study of ethnically targeted and general audience outdoor obesity-related advertising. The Milbank Quarterly, 87(1), 155–184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Zmuda N. How coke is targeting black consumers. Advertising age. (). http://adage.com/article/the-big-tent/marketing-coke-targeting-african-american-consumers/137716/, Accessed date: 1 July 2009 Accessed June 24, 2017.

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1
NIHMS1788993-supplement-Supplementary_Table_1.docx (21.8KB, docx)

RESOURCES