Ethnic Diversity in Advertising: A Meta-Analysis

Abstract

Although research on ethnic diversity in advertising is extensive, its findings remain fragmented and often inconclusive. This limits practical guidance for marketers. Beyond marketing- and consumer-specific factors, differences between ethnic minority groups within a country and cross-national socioeconomic and cultural factors can play a role in shaping responses to ethnic diversity in advertising of consumers belonging to either ethnic majority or minority groups. This study develops and empirically tests a theoretical framework to examine the impact of ethnic diversity in advertising along with the factors that moderate this impact. The authors conduct a meta-analysis based on 1,276 effect sizes derived from 106 papers published between 1969 and 2024, encompassing 155 unique datasets from 21 countries. The findings reveal that ethnic diversity effects are small, but are moderated by country-level variables such as migration rates and rule of law, in addition to product, advertising, and consumer characteristics, and that some of these moderator effects work differently for ethnic minority compared with ethnic majority consumers. Furthermore, within the U.S. context, the sociopolitical power position of ethnic minorities enhances advertising effectiveness among majority consumers. These insights offer implications for both researchers and practitioners.

Keywords

advertising marketing ethnic diversity inclusion meta-analysis

Many countries have experienced significant demographic shifts, resulting in increased ethnic diversity (Bai, Ramos, and Fiske 2020). Ethnic diversity refers to both the fragmentation and composition of multiple ethnic groups within a society (Dinesen, Schaeffer, and Sonderskov 2020). This growing diversity has become a critical consideration for marketers worldwide, prompting a greater incorporation of ethnically diverse representations in advertising in efforts to appeal to increasingly ethnically diverse consumer audiences (Unstereotype Alliance 2024). Recent research indicates that ethnic diversity in advertising—operationalized by the percentage of ethnic minority endorsers—has increased globally, reflecting the rising proportion of ethnic minorities in many societies (Rößner and Eisend 2023). Correspondingly, U.S. advertising and marketing spending in media targeting Hispanic, Black, and Asian American audiences rose by 5.7% to USD 34.64 billion in 2023 and is projected to increase by 8.3% to USD 45.83 billion in 2024 (PQ Media 2024).¹ Although multiethnic advertising and marketing accounts for approximately 5% of total ad and marketing spending (PQ Media 2024), most advertisers still perceive progress in diversity and inclusion as limited (Association of National Advertisers Alliance for Inclusive and Multicultural Marketing 2024). This is notable given that diversity in advertising has been associated with significant business benefits, including an optimistic 16.3% increase in long-term sales and a 15% rise in customer loyalty (Unstereotype Alliance 2024).

Beyond its managerial relevance, ethnic diversity in advertising continues to attract substantial academic attention (e.g., Davis, Smith, and Sevilla 2025; Khan et al. 2025). Numerous studies have examined the impact of ethnic diversity by comparing advertisements featuring either ethnic majority or minority endorsers, and by assessing responses from consumers. However, findings have been mixed and have shown positive, negative, and neutral effects of ethnic diversity in advertising (see Web Appendix A). These inconsistencies continue when distinguishing between ethnic minority and majority consumers. While ethnic minority consumers tend to respond neutrally or positively to ads featuring coethnic endorsers (e.g., Aaker, Brumbaugh, and Grier 2000; Shao et al. 2023), a pattern attributed to mechanisms such as targeting, perceived similarity, and identification, responses from ethnic majority consumers range from positive (e.g., Gan and Chen 2024) to neutral (e.g., Antioco, Smeesters, and Boedec 2012) to negative (e.g., Rajabi et al. 2017).

Previous efforts to explain the heterogeneity in ethnic diversity effects have focused on marketing-related factors (e.g., product type) and consumer differences (e.g., age, gender, ethnicity). For example, Desphandé and Stayman (1994) show that consumers’ reactions to endorsers of varying ethnicities depended on the ethnic composition of U.S. cities. Both minority and majority consumers showed a preference for endorsers of their own ethnicity—a tendency that is amplified when the respective group represents a numerical minority in a city. However, such findings are often context-specific and may not generalize across national or cultural boundaries. While data on ethnic diversity in advertising have been collected from various countries over several decades, cross-national differences remain underexplored. Furthermore, the composition and status and power positions of ethnic minority groups vary across countries (Omi and Winant 2014), which suggests that endorsers belonging to different minority groups might lead to varying effects on consumers within the same country. A more comprehensive understanding of the effects of ethnic diversity in advertising—along with how these effects are moderated by cross-national differences, ethnic diversity composition in a country, and marketing-related factors—is thus essential. Importantly, these moderator effects can differ between ethnic minority and majority consumers. Together, these insights can offer novel theoretical contributions and practical guidance for both domestic and international campaigns.

Against this backdrop, we develop a theoretical framework to reconcile the inconsistencies in existing research. We examine the effects of ethnic diversity in advertising and aim to explain the heterogeneity in findings. Specifically, we consider (1) between-country differences, (2) within-country diversity characteristics, and (3) marketing-related factors (e.g., product category, advertising medium). We test how these factors moderate the effects of ethnic diversity on all consumers, as well as minority and majority consumers separately. We explain differences in responses by minority and majority consumers by social categorization.

To explain between-country variations, we draw on two theoretical perspectives—exposure and contact and institutional theory—both of which posit that broader sociocultural contexts shape minority and majority consumer responses to ethnic diversity in advertising. Research in fields such as sociology has long highlighted the importance of socioeconomic and cultural factors in shaping the relationships between ethnic majority and minority groups (e.g., Dima and Dima 2016; Dinesen and Sonderskov 2015). However, advertising research has rarely considered comparisons between countries. When it has, such studies have typically included only a few countries and yielded limited generalizability (e.g., Gao, Xu, and Kim 2013; Lwin and Wee 2000). To examine within-country variation in ethnic diversity composition, we focus on U.S.-based data and analyze the effects of using different ethnic groups as endorsers, including consideration of their status and power positions.

To test our theoretical framework, we conduct a meta-analysis of 1,276 effect sizes related to ethnic diversity in advertising, drawn from 106 papers published between 1969 and 2024. These studies comprise 155 unique datasets from 21 countries, offering a unique opportunity to examine the influence of time-varying country-level moderators such as migration rates. To explore within-country dynamics, we pay particular attention to the United States, which accounts for over 60% of the data in our sample.

Our findings offer empirical, theoretical, and managerial contributions. First, we synthesize prior results and show that the overall effects of ethnic diversity in advertising are modest, while they are significantly more positive for ethnic minority consumers than for majority consumers. This supports the strategic use of ethnic diversity in advertising to engage minority audiences. Moreover, since such advertisements do not negatively affect majority consumers, a key concern among advertisers (Schlegelmilch, Khan, and Hair 2016) is mitigated. Second, we identify important country-level moderators, demonstrating that migration rates and institutional strength (e.g., rule of law) influence consumer responses to ethnic diversity in advertising. Third, we find that the impact of minority endorsers is contingent on their power and social status within a given national context. These results clarify how ethnic diversity functions within and between countries and underscore the importance of adapting advertising strategies to both local and international contexts. Finally, we identify several marketing-related factors that moderate advertising effects, thereby providing actionable insights for practitioners seeking to optimize ethnically inclusive campaigns. Overall, these findings can help marketers navigate the trade-off between inclusive versus targeted ethnic advertising strategies, addressing the longstanding challenge of “targeting without alienating” (Johnson and Grier 2011). Ethnic diversity in advertising can be effectively used not only in targeted media but also in mainstream media without risking backlash from majority consumers.

Conceptual Background

Definitions and Research Framework

Ethnicity is defined as a social construct representing “clusters of people who have common culture traits that they distinguish from those of other people” (Smedley and Smedley 2005, p. 17). These traits refer to a common language, place of origin, historical background, traditions, values, beliefs, and a sense of collective identity. In line with Dinesen, Schaeffer, and Sonderskov (2020), ethnic diversity refers to the presence of multiple distinct ethnic groups within a society, capturing the degree of ethnic fragmentation and composition. In advertising, ethnic diversity is typically implemented through the inclusion of endorsers who belong to ethnic minority groups. Due to limited consumer attention and cognitive processing during advertising exposure, identification of endorsers’ ethnicity is often guided by salient visual cues, such as facial features, skin tone, eye color, and hair texture. Less commonly, advertisers employ cultural cues such as clothing, names, or references to heritage to signal ethnicity (e.g., Rößner, Gvili, and Eisend 2021).

Consumer perceptions of and responses to ethnic minority representations in advertising are shaped by various attributes associated with those minority groups. These include their relative population size, social status and power, categorical distinction from the majority, group-specific characteristics, and the nature of their treatment by the majority population (Seyranian, Atuel, and Crano 2008). Ethnic group distinctions can be either manifest (e.g., observable traits or group characteristics) or latent (e.g., perceived social power or status), each of which can influence advertising responses in different ways.

Figure 1 outlines the research framework guiding this meta-analysis. The central relationship under investigation is the effect of ethnic diversity in advertising on persuasion (i.e., positively valenced responses toward advertising, including attitudes and behavioral responses). The framework incorporates four categories of moderator variables to account for heterogeneity in advertising effects: (1) minority versus majority consumers, (2) country-level variables, (3) ethnic diversity variables, and (4) other (including marketing) variables. For the first category, we distinguish between ethnic minority and majority consumers, drawing conceptually on social categorization theory. Moderators in the second to fourth categories are theorized and analyzed both as main effects and in interaction with consumer groups (i.e., minority vs. majority). To explain country-level differences in response to ethnic diversity in advertising, we draw on two complementary theoretical perspectives: exposure and contact, and institutional theory. In addition, other factors—including time, product and brand characteristics, consumer demographics, media, and methodological differences—are considered to account for further variability across studies and effect sizes. The composition of and relationship between different ethnic diversity groups, endorsers, and consumers are country-specific and difficult to assess by a meta-analysis, as most studies capture only few selected minority groups in a specific country. To better understand within-country effects of ethnic diversity, we investigate variations in advertising effectiveness based on the specific identity of the ethnic endorser and the ethnic group's relative social status and power. We use data from the United States, which comprise a large proportion of our meta-analytic dataset, and assess multiple ethnic minority group endorsers and consumers.

Figure 1.

Research Framework.

Explaining Responses of Minority and Majority Consumers to Ethnic Diversity: Social Categorization

People tend to think categorically about others (Macrae and Bodenhausen 2000). Social categorization is a fundamental psychological process through which individuals classify others into distinct groups, allowing them to anticipate behavior and situate themselves within the social world (Tajfel 1978). These categorizations often draw on stereotypes—simplified, generalized beliefs about social groups formed through prior experience and selective exposure to environmental cues (Fiske and Neuberg 1990). While stereotypes can be either positive or negative, those applied to outgroup members tend to be more negative in valence, whereas ingroup stereotypes are typically more favorable (Allport 1954).

Social categorization theory provides a valuable lens for understanding consumer responses to ethnic diversity in advertising. For ethnic majority consumers, exposure to minority-group endorsers can activate implicit biases, resulting in automatic negative reactions—even when individuals consciously support principles of diversity and inclusion. These reactions are often not rooted in overt prejudice but rather in ingroup favoritism—the unconscious tendency to show trust, empathy, and positive regard toward those perceived as similar or belonging to the same social group (Hewstone, Rubin, and Willis 2002). Consequently, majority-group consumers may withhold positive evaluations of diverse advertisements not necessarily because they dislike the minority outgroup, but because they reserve positive affect for the ingroup.

Country-specific influences on ethnic diversity effects can differ between minority and majority consumers, as both the salience of social categories and the stereotypes that are associated with them vary (Rhodes and Baron 2019). For example, majority-group consumers in ethnically more homogeneous societies such as China, South Korea, and Japan tend to respond more negatively to multiethnic advertising (Baek, Lee, and Oh 2022). In contrast, consumers in multicultural societies like Canada often associate such advertising with inclusiveness and social openness (Strebinger et al. 2018). Ethnic minority consumers are more likely to respond positively to advertisements featuring minority-group endorsers, whom they categorize as members of their ingroup (Tajfel 1978). This identification promotes feelings of representation, recognition, and self-relevance, which in turn enhance positive advertising responses (Campbell et al. 2025).

Explaining Between-Country Effects of Ethnic Diversity on Minority and Majority Consumers with Country-Level Variables

Drawing from sociological and related interdisciplinary research, we propose two overarching theoretical perspectives to account for country-level differences in consumer responses to ethnic diversity: (1) exposure and contact and (2) institutional frameworks.²

The first theoretical perspective refers to exposure and contact. Individuals’ exposure to ethnically diverse environments significantly shapes their acceptance of ethnic minorities (Dinesen and Sonderskov 2015), thereby influencing attitudes toward ethnic diversity in advertising. Exposure and contact vary across countries due to differences in ethnic compositions in a country, which in turn has differential effects depending on ethnic group membership. Two opposing predictions emerge for the impact of such exposure. First, increased exposure to ethnic outgroups may lead to negative reactions among ethnic majority individuals. This is attributed to perceived competition over limited resources such as employment. This heightens intergroup threat—the perception that outgroups pose a challenge to the welfare or goal attainment of the ingroup (Riek, Mania, and Gaertner 2006). In contrast, for minority consumers, exposure to minority-group endorsers can reduce perceived threat, as they are less likely to see themselves in competition with majority members. As a result, increased exposure to ethnic diversity is expected to decrease (increase) ethnic diversity effects on persuasion for majority (minority) consumers; that is, exposure leads to less (more) favorable responses toward ethnic diversity among majority (minority) consumers.

Conversely, intergroup contact theory (Allport 1954) posits that contact with members of different ethnic groups reduces prejudice and promotes favorable attitudes. Meta-analytical evidence supports the benefits of intergroup contact (Pettigrew and Tropp 2006), attributing them to factors such as interethnic cohesion, cross-group friendships, and increased familiarity. Accordingly, environments that facilitate exposure of majority consumers to minority-group endorsers can enhance positive responses, while minority consumers benefit from identification with the endorsers (Tropp and Pettigrew 2005). From this perspective, increasing ethnic diversity should yield positive persuasion effects for both groups.

To empirically assess these mechanisms, we use two proxy variables: net migration rate³ and ethnic fractionalization (Drazanova 2020), and test the interaction with minority versus majority consumers. Net migration rate reflects the likelihood of exposure to outgroups due to migration flows, while ethnic fractionalization captures the diversity of ethnic groups within a population. The former is more closely linked to perceptions of intergroup threat, as it signals an increase in the number of outgroup members. The latter captures ethnic variety without necessarily indicating increases in any minority group's size.

The second theoretical framework refers to institutions, which are “the humanly devised constraints that structure political, economic and social interaction. They consist of both informal constraints (sanctions, taboos, customs, traditions, and codes of conduct), and formal rules (constitutions, laws, property rights)” (North 1991, p. 97). Institutions establish societal expectations and shape individual behaviors. The institutional perspective suggests that country-level rules and norms influence the societal acceptance of ethnic diversity but vary between minority and majority group members, who are differently affected by the level of acceptance of ethnic diversity. Burgess and Steenkamp (2006) identify three interrelated institutional subsystems: regulative, cultural, and socioeconomic.

The regulative system refers to the formal rules and legal frameworks that govern behavior. Stable democratic institutions are linked to greater tolerance (Peffley and Rohrschneider 2003). Strong regulative systems promote equality and freedom, reducing perceived intergroup differences and fostering inclusivity (Lehmann and Seitz 2016). Weak regulative institutions, by contrast, may reinforce perceived disparities, prompting minority consumers, but not majority consumers, to respond more favorably to diverse advertising due to increased solidarity with other underrepresented groups. We measure the regulative environment using rule of law indicators and use a democracy index to conduct a robustness check.

The cultural system encompasses societal values and norms. Two of Hofstede’s (2001) cultural dimensions are particularly relevant: power distance and achievement values (also known as masculinity). Power distance—the extent to which unequal power distribution is accepted—discourages diversity, as it reinforces hierarchical group distinctions (Van der Vegt, Van de Vliert, and Huang 2005). While societal norms of high achievement-oriented cultures include “ego orientation” and “money and things are important,” societal norms of low achievement-oriented cultures cover “relationship orientation” and “quality of life and people are important” (Hofstede 2001, p. 299). High achievement-oriented cultures emphasize individual success, assertiveness, and toughness over interpersonal relations, empathy, and altruism, often at the expense of inclusivity, as has been shown for gender equality and stereotyping in advertising in different countries (Eisend 2010). High achievement values are associated with lower tolerance for diversity in advertising (Eisend and Hermann 2019) and negative attitudes toward immigration (Leong and Ward 2006). In cultures high in power distance and achievement values, ethnic majority consumers may respond less favorably to minority-group endorsers. Conversely, minority consumers may experience heightened group identity and solidarity, leading to more positive responses toward ethnic diversity (Bernstein 1997).

The socioeconomic system relates to the influence of economic structures and entrepreneurial activity. Income inequality, often measured by the Gini index, signals how evenly resources are distributed. High inequality tends to foster perceptions of a competitive, fragmented society, reducing trust and increasing social intolerance (Dima and Dima 2016; Uslaner 2002), which is in line with the preceding arguments of intergroup threat and perceived competition over limited resources. Conversely, entrepreneurship and innovation—measured by population-weighted patent registrations—are associated with openness, talent attraction, and creativity, all of which foster inclusivity and positive attitudes toward diversity (Florida 2003; Freitag and Rapp 2015; Qian 2013). Therefore, we expect responses to ethnic diversity in advertising to deteriorate with rising inequality and to improve with innovation for both minority and majority consumers. To control for broader economic conditions and as a robustness check, we also control for GDP growth rate and GDP per capita, both of which have been linked to greater societal tolerance and diversity acceptance (Eagle, Macy, and Claxton 2010).

Explaining Effects of Ethnic Diversity Variables on Minority and Majority Consumers Within a Country

Ethnic diversity is a multifaceted construct shaped by cultural complexities and specific historical trajectories. Ethnic groups can be distinguished not only by overt characteristics such as group membership but also by more nuanced, latent factors—particularly those related to power and status. These factors influence individuals’ access to social, economic, and political resources, as well as their broader standing within the societal hierarchy (Omi and Winant 2014).

The composition and relative positions of ethnic minority groups vary across countries. To explore within-country differences in consumer responses to ethnic diversity, we focus on the United States—the only country in our dataset with sufficient data points to distinguish between major ethnic minority subgroups. Specifically, we examine three groups: Black, Hispanic, and Asian Americans. These groups differ not only in demographic representation—19.1% Hispanic/Latino, 12.6% Black, and 6.1% Asian American as of 2022 (USA Facts 2024)—but in their historical experiences and socioeconomic positioning (Humes and Hogan 2009).

The social positioning of Black Americans has been profoundly shaped by the transatlantic slave trade and its enduring legacy of institutionalized racism, including slavery and legalized segregation. Although significant legal and political advances were achieved during the Civil Rights Movement of the 1960s, structural inequalities persist, particularly in wealth accumulation, education, and the criminal justice system (Humes and Hogan 2009). The Hispanic/Latino population has deep roots in the United States, linked to historical territorial shifts following the Mexican-American War in 1848, and continued migration from Latin American countries, particularly Mexico, Cuba, and Puerto Rico. Hispanics often encounter systemic barriers such as lower educational attainment, labor market discrimination, and multifaceted discrimination based on language, phenotype, and generational status (Cano et al. 2021). By contrast, Asian Americans—though subject to exclusionary policies in earlier periods—experienced a shift following the Immigration and Nationality Act of 1965. It eliminated national origin quotas and enabled the immigration of highly educated individuals (Humes and Hogan 2009). Today, Asian Americans enjoy higher average levels of education and income, comparable to or exceeding those of White Americans, and in contrast to other minority groups.

To empirically assess ethnic diversity effects, we run two models: one that investigates group categorization influences (i.e., whether the ethnic minority endorser is Black, Hispanic, or Asian American) and one that investigates differences in power and social standing, for which we use education and income of the endorser minority group as proxies. These structural indicators help capture the hierarchical positioning of ethnic groups within the United States. Research on social dominance orientation (Jost and Thompson 2000) provides insight into how such hierarchies influence persuasion: Majority group members tend to score higher on social dominance orientation and are more likely to endorse social hierarchies, whereas minority group members are more egalitarian. Powerful minority endorsers may be perceived by their own group as atypical exemplars that do not fit existing schemas, which can lead to negative responses (Campbell et al. 2025), as identification and ingroup favoritism is reduced (Tajfel 1978). Research shows that individuals who succeed in counterstereotypical, high-status domains can experience social backlash, as their success challenges existing group hierarchies and norms (Phelan and Rudman 2010). Backlash arises when such atypical exemplars are perceived as violating prescriptive status expectations, leading to resistance and reduced identification within their own ingroup (Rudman et al. 2012). Consequently, majority consumers may respond more positively to minority endorsers with high status, while minority consumers may not favor such high-status minority endorsers due to perceived distance or lack of shared identity.

Effects of Other Variables

Table 1 provides an overview of the moderator variables illustrated in Figure 1, along with their operational definitions, theoretical explanation, and expected effects. The group of other variables includes several moderators pertaining to time, consumers, endorsers, products, media channels, and methodological approaches.⁴ Temporal variation in effects of ethnic diversity in advertising acknowledge that immigration trends, economic shifts, and political developments can cause fluctuations over time (Meuleman, Davidov, and Billiet 2009). Recent meta-analytic work (Lenk, Hartmann, and Sattler 2024) finds a temporal trend showing increased acceptance of minority endorsers by White majority consumers over time. However, given the complex and dynamic nature of these factors, the direction of temporal influences remains difficult to predict. Apart from the moderators related to methodological differences, we do not offer specific hypotheses for the other variables, neither for their main effects nor for the potential interaction with minority/majority consumers. An exploratory analysis of their influence can still yield valuable managerial insight and inform more effective marketing decisions, as the marketing-specific factors are endogenous to marketers and thus subject to strategic manipulation.

Table 1.

Explaining Effects of Ethnic Diversity in Advertising: Moderators and Their Expected Effects.

Moderator	Description	Expected Responses Toward Ethnic Diversity	Explanation
Minority/majority consumer	Whether respondents belong to an ethnic minority or majority group.	Persuasion effects of ethnic diversity are weaker for majority consumers than for minority consumers.	Social categorization/ingroup favoritism: Majority consumers show negative responses to outgroup members, whereas minority consumers respond favorably due to ingroup identification (Tajfel 1978).
Country-Level Variables
Net migration	Net immigration minus emigration per capita.	Intergroup threat: Net migration and ethnic fractionalization decrease (increase) the persuasion effects of ethnic diversity for majority (minority) consumers. Intergroup contact: Net migration and ethnic fractionalization increase the persuasion effects of ethnic diversity for both majority and minority consumers.	Intergroup threat increases perceptions of competition (Dinesen and Sonderskov 2015), while intergroup contact promotes positive attitudes through increased interaction and identification (Allport 1954).
Ethnic fractionalization	Probability that two randomly selected individuals belong to different ethnic groups.
Rule of law	Extent to which citizens (and local legal experts) perceive that their countries/governments adhere to the rule of law in practice (constraints on government powers, absence of corruption, open government, fundamental rights, order and security, regulatory enforcement, civil justice, criminal justice).	Rule of law decreases the persuasion effect of ethnic diversity for minority consumers.	Rule of law fosters tolerance, equality, and social trust, reducing group-based distinctions (Peffley and Rohrschneider 2003).
Power distance	Cultural acceptance of hierarchical power distribution.	Power distance and achievement values decrease (increase) the persuasion effect of ethnic diversity for majority (minority) consumers.	Power distance reinforces perceived group differences and marginalizes minorities (Van der Vegt, Van de Vliert, and Huang 2005), who then develop stronger collective identities. Achievement orientation is associated with lower support for diversity and preference for dominant groups (Eisend and Hermann 2019; Leong and Ward 2006).
Achievement values	Cultural emphasis on achievement over harmony.
Gini coefficient (Gini)	Extent to which the distribution of income among individuals or households deviates from an equal distribution.	Inequality (Gini) decreases the persuasion effect of ethnic diversity and innovation (patents) increases the effect of ethnic diversity for both majority and minority consumers.	Inequality undermines social cohesion and increases intolerance (Dima and Dima 2016; Uslaner 2002), while innovation fosters openness, creativity, and tolerance (Solt 2011).
Patents	Worldwide patent applications (per capita) filed through Patent Cooperation Treaty procedures or with a patent office.
Ethnic Diversity Variables (U.S. Data Only)
Black, Hispanic, Asian	Ethnic identity of the endorser.	Endorsers from high-power minority groups increase (decrease) the persuasion effects of ethnic diversity for majority (minority) consumers.	Persuasion of majority consumers is stronger when outgroup members hold higher status; minority consumers may not identify with high-power minority endorsers.
Education	Share of minority group with college education.
Income	Per capita income of endorser's minority group.
Other Variables
Year	Year of data collection.	No directional hypothesis.	Attitudes toward diversity fluctuate with immigration rates, politics, and economic changes (Meuleman, Davidov, and Billiet 2009).
Participant type	Whether respondents are students.	No prior expectation.
Participant age	Average respondent age.	No prior expectation.
Participant gender	Percentage of male respondents.	No prior expectation.
Celebrity status	Whether the endorser is a celebrity.	No prior expectation.
Endorser gender	Whether the endorser is female.	No prior expectation.
Brand type	Whether the advertised brand is real.	No prior expectation.
Product type	Whether the ad is for a product (vs. service).	No prior expectation.
Product involvement	Whether the advertisement refers to a low involvement product/service.	No prior expectation.
Product benefit	Whether the advertisement refers to a utilitarian product/service.	No prior expectation.
Media	Whether the ad appeared in online (vs. offline) media.	No prior expectation.
Sample size	Number of participants in the study.	Samples size decreases the effect size.	Smaller effect sizes are linked to larger samples if publication bias exists (Levine, Asada, and Carpenter 2009).
Journal quality	Whether the paper is published in a top-tier journal.	Top journals (vs. other outlets) increase the effect size.	Selection and publication biases can inflate effect sizes in high-impact journals (Eisend and Tarrahi 2014).

Method

Paper Retrieval

We identified both published and unpublished studies that quantitatively assessed the effects of ethnic diversity in advertising—specifically, the impact of ethnic minority endorsers relative to ethnic majority endorsers—on consumer responses. To identify relevant papers, we performed a keyword search in electronic databases (Web of Science, EBSCO, ProQuest, Google Scholar, and SSRN).⁵ We scanned two seminal articles (Aaker, Brumbaugh, and Grier 2000; Desphande and Stayman 1994) and reviews of the extant literature (Kareklas and Polonsky 2011; Sierra, Hyman, and Heiser 2010, 2012) for references, as well as articles citing them. Next, we reviewed the reference lists of obtained papers. The search encompassed all studies published up to December 2024. Our approach follows procedures used in recent meta-analyses (e.g., Roschk and Hosseinpour 2020; Rosengren et al. 2020).

We included studies that provided quantitative comparisons of ethnic minority versus majority endorsers in advertising and measured their impact on at least one consumer response variable. Studies were excluded if they fell outside this scope or did not provide sufficient data for the computation of effect sizes, and where such data could not be obtained from the original authors. Eligible studies were required to be written in English. All studies are based on experimental designs, thereby ensuring causal inference and directional clarity in the effect sizes.

To avoid duplication, we defined a “paper” as any unique document presenting original analyses (e.g., journal articles, working papers, conference proceedings, or unpublished theses). A single paper could contain multiple datasets, also labeled as “studies.” For our analysis, we used these datasets as the primary unit of analysis. Each dataset could contribute one or more effect sizes, depending on how many consumer response variables it addressed.

Coding

To ensure generalizability, we focused on 13 consumer response variables that were sufficiently represented across studies (i.e., each had at least ten reported effect sizes). These response variables, along with their definitions, are presented in Table 2. Two independent coders (i.e., one author and a trained research assistant who was unaware of the research hypotheses) assigned each effect size to the appropriate response category. Intercoder reliability was high (Cohen's κ = .958; agreement rate = 96.5%), and any discrepancies were resolved through discussion. Moderator variables are presented in Table 3. Coders received instructions in line with the definitions and description in Table 1 and Table 3. Where coding ambiguity existed, two independent coders conducted the assessments. Intercoder reliability scores are noted in the table. Disagreements were discussed until consensus was reached. For variables involving low-inference judgments, coding was conducted by a single coder and subsequently validated by one of the study authors. With the exception of power distance and achievement values, all country-level variables and ethnic group characteristics were time-varying. We matched values to each dataset based on the country and year of data collection. For example, if a study was conducted in the United States in 2020, we assigned the U.S. value for net migration in that year. Similarly, if an Asian endorser was used in a 2020 study, we assigned the 2020 per capita income for Asians in the United States. In cases where the year of data collection was not reported, we estimated it by subtracting three years from the publication year—an approach based on the typical time lag between data collection and publication.

Table 2.

Consumer Response Variables.

Consumer Response	Definition and Operationalization	Examples
Attitude toward the ad	Evaluation of the advertisement/the advertised message.	Attitude toward the ad/the advertised message, ad/advertised message evaluation/judgment/rating/credibility, preference of ad, ad strategy evaluation, ad creativity.
Attitude toward the brand	Evaluation of the sponsored brand.	Attitude toward the brand, brand attitude(s), evaluation/friendliness/liking/trust/impressions/creativity/image, personal preference, response to the brand, perception of luxuriousness.
Attitude toward the company	Evaluation of the sponsoring company.	Attitude toward the company, organizational attractiveness, company rating, compatibility with organization, image of organization, attraction to organization, company's altruism.
Attitude toward the product	Evaluation of the advertised product.	Attitude toward the product, evaluation of product attribute performance, product evaluation, (perceived) product quality, product-attribute beliefs, taste perception, perceptions of product variety.
Attitude toward the spokesperson	Evaluation of the advertising endorser(s).	Attitude toward the (ad) spokesperson, attitude toward model, attitude toward actor, (perceived) model credibility/likeability/attractiveness/expertise, spokesperson trustworthiness, source evaluations, model minority attributes.
Behavioral response	Consumers’ likelihood of engaging in a brand-favoring behavior.	Purchase likelihood, buying/behavioral/purchase/consumption intention, intention to buy/purchase, likelihood of/willingness to purchase/pay more/spend, purchase behavior, intention to donate money, choice.
Cognitive responses, negative	Number of consumers’ negative cognitive reactions.	Negative cognitive responses, number of negative thoughts, number of counterarguments.
Cognitive responses, positive	Number of consumers’ positive cognitive responses.	Number of positive cognitive responses.
Intolerance	Activation of racist/intolerant thoughts/reactions.	Feelings of ostracism, implicit prejudice, race implicit association test, institutional and blatant racism beliefs, racial privilege beliefs differed.
Memory	Consumers’ ability to recall or recognize the advertising message elements and product-related or brand-related information.	Recall of product information, correct recall of message claims, product/brand recall, cue recognition, aided recall, comprehension of message claims, open-ended recall.
Model identification	Consumers’ perception of being similar to the endorser, or consumers’ identification with the advertising endorser.	Perceived/felt similarity, model similarity, identification with ad spokesperson, symbolic role identification with actors, perceived similarity to characters, identification with ad models/spokesperson, symbolic role identification with actors.
Relevance	Consumers’ perception of being targeted by the advertisement.	Felt targetedness, belief that the ad was intended for them, self-brand connection, self-referencing.
Self-image evaluation	Consumers’ evaluation of their appearance.	Appearance self-esteem, satisfaction with attractiveness, perception of body image, body dissatisfaction.

Table 3.

Moderator Variables.

Moderator Variable^a	Source	Descriptives^b
Minority/majority consumer^c	When ethnic composition in the sample was clearly stated, for instance, 39 participants identified as Black and 84 as White (Aaker, Brumbaugh, and Grier 2000), and effect sizes were reported for each group, we assigned the effect sizes accordingly. For the minority dummy, we assigned a value of 1 to the responses from the 39 Black participants and 0 to the majority dummy; the opposite coding was applied to the White participants. If the ethnic composition was not unambiguously reported in the paper or if the results were reported for a sample that included both minority and majority respondents, we assigned a value of 0 to the dummy variables.	0 = ethnic majority/mixed (672 effect sizes); 1 = ethnic minority (347 effect sizes)
Minority/majority consumer^c		0 = ethnic minority/mixed (578 effect sizes); 1 = ethnic majority (441 effect sizes)
Country-Level Variables
Net migration	World Bank DataBank (https://databank.worldbank.org)	Mean = .006, SD = .012
Ethnic fractionalization^d	Historical Index of Ethnic Fractionalization (https://cadmus.eui.eu/handle/1814/64606)	Mean = .457, SD = .124
Rule of law	World Justice Project Rule of Law Index (https://worldjusticeproject.org/rule-of-law-index/)	Mean = .699, SD = .075
Power distance	https://www.theculturefactor.com/	Mean = 46.2, SD = 14.5
Achievement values	https://www.theculturefactor.com/	Mean = 59.9, SD = 5.8
Gini coefficient (Gini)	World Bank DataBank (https://databank.worldbank.org)	Mean = 39.3, SD = 3.1
Patents	World Bank DataBank (https://databank.worldbank.org)	Mean = .0006, SD = .0003
Ethnic Diversity Variables (U.S. Data Only)
Black^e	Ethnic identity of the endorser as indicated in paper.	0 = other (323 effect sizes)^e; 1 = Black (438 effect sizes)
Hispanic^e	Ethnic identity of the endorser as indicated in paper.	0 = other (718 effect sizes)^e; 1 = Hispanic (43 effect sizes)
Asian^e	Ethnic identity of the endorser as indicated in paper.	0 = other (575 effect sizes)^e; 1 = Asian (186 effect sizes)
Education^e	World Bank DataBank (https://databank.worldbank.org)	Mean = 22.6, SD = 15.0^e
Income^e	World Bank DataBank (https://databank.worldbank.org)	Mean = 19097.1, SD = 10,120.5^e
Other Variables
Year	Continuous variable for the year of data collection. If not provided in the paper, publication year minus three as the average difference between year of data collection and publication year.	Mean = 2,005.6, SD = 13.8
Participant type	Student/nonstudent status of participants as indicated in paper.	0 = only student sample (469 effect sizes); 1 = nonstudent/mixed sample (550 effect sizes)
Participant age^d	Average respondent age as indicated in paper.	Mean = 27.9, SD = 5.5
Participant gender^d	Percentage of male respondents as indicated in paper.	Mean = 35.6, SD = 23.3
Celebrity status	Endorser celebrity status as indicated in paper.	0 = other (954 effect sizes); 1 = celebrity (65 effect sizes)
Endorser gender^c	Endorser gender as indicated in paper and/or evaluated by two coders.	0 = other (555 effect sizes); 1 = female model (464 effect sizes)
Brand type^c	Ad for real or fictitious brand as indicated in paper and/or evaluated by two coders.	0 = real (294 effect sizes); 1 = fictitious (725 effect sizes)
Product type^c	Ad for product or other as indicated in paper and/or evaluated by two coders.	0 = product (817 effect sizes); 1 = other (202 effect sizes)
Product involvement^c	Ad for low involvement product or other as indicated in paper and/or evaluated by two coders.	0 = low involvement (441 effect sizes); 1 = other (578 effect sizes)
Product benefit^c	Ad for utilitarian or other product as indicated in paper and/or evaluated by two coders.	0 = utilitarian (338 effect sizes); 1 = other (681 effect sizes)
Media	Ad appears in online or offline media as indicated in paper.	0 = offline (968 effect sizes); 1 = online (51 effect sizes)
Sample size	Number of study participants as indicated in paper.	Mean = 131.5, SD = 138.8
Journal quality	University of Texas at Dallas (UT Dallas) list (https://jsom.utdallas.edu/the-utd-top-100-business-school-research-rankings/).	0 = other journals (856 effect sizes); 1 = journals on UT Dallas list (163 effect sizes)

For definitions of the variables, refer to Table 1.

Descriptives refer to the effect sizes and datasets that were used for the main meta-regression model.

The intercoder reliabilities (agreement rate [kappa]) were as follows: minority and majority (100%), endorser gender (100%), brand type (95.1% [.89]), product type (99% [.99]), product involvement (98% [.96]), and product benefit (91.1% [.88]).

For ethnic fractionalization, participant age, and participant gender, missing value imputations were applied, following the procedure in former meta-analyses (e.g., Babic Rosario et al. 2016; Peng et al. 2023). Ethnic fractionalization has 40 missing values (effect sizes); participant age, 549; and participant gender, 129. We apply alternative imputation techniques as robustness checks.

The descriptive data refer to the reduced set of data collected in the United States.

We used the correlation coefficient as an effect size metric. A positive value indicates that positively (negatively) valenced consumer responses such as attitude toward the brand (negative cognitions) were more (less) favorable toward ethnic minority endorsers than toward ethnic majority endorsers. A negative value indicates that positively (negatively) valenced consumer responses were less (more) favorable toward ethnic minority endorsers than toward ethnic majority endorsers. Alternative statistical measures (e.g., Student's t-tests) were converted into correlation coefficients using established procedures (e.g., Borenstein et al. 2021; Morris and DeShon 2002). We contacted the authors when insufficient statistical information was provided. All effect sizes were corrected for measurement unreliability. If a study did not report reliability statistics or used single-item measures, we applied the average reliability coefficient for the respective construct, aggregated across all studies.

The final database included 106 papers with 155 distinct datasets (studies), providing 1,276 effect sizes. The sample included peer-reviewed journal articles, unpublished theses, and conference proceedings, thereby mitigating the risk of publication bias (see Web Appendix B for an overview). Data were collected across diverse geographic contexts, including countries from all continents except South America—specifically Australia, Belgium, Canada, China, France, Germany, Greece, India, Indonesia, Iran, Israel, Malaysia, Qatar, Singapore, South Africa, South Korea, Spain, Taiwan, Turkey, the United Kingdom, and the United States.

Integration of Effect Sizes

We integrated the effect sizes using a mixed-effects multilevel model to account for the hierarchical structure of the data and for multiple effect sizes derived within a single dataset. We estimated the following model:

{ES}_{ij} = γ_{00} + μ_{0 j} + e_{ij},

(1)

where i = 1, 2, 3, …, I effect sizes, and j = 1, 2, 3, …, J datasets. This formula estimates the average effect size γ₀₀, the deviation in the average effect size in a dataset from γ₀₀ (μ_0j), and the deviation in each effect size in the jth dataset from γ₀₀ (e_ij). The latter two terms have variances that follow a normal distribution and are uncorrelated. We estimated a multilevel model and performed separate analyses for all consumer response variables. To address publication bias, we computed fail-safe Ns for all statistically significant (p < .05) integrated correlations. We conducted a regression test for funnel plot asymmetry; a significant result would suggest the presence of bias. Additionally, we included sample size as a predictor in our meta-regression models to detect small-study effects indicative of publication bias. We tested for heterogeneity using the Q statistic, which approximates a chi-square distribution with K − 1 degrees of freedom. Outliers were identified using Z-scores; effect sizes exceeding ±3 were flagged. Outliers were detected in two response variables (brand attitudes and self-image evaluation). Excluding these outliers reduced heterogeneity without substantively altering the results.

Meta-Regression

To examine potential moderating effects, we conducted multilevel meta-regression analyses. We aggregated all attitude variables (attitude toward the ad, attitude toward the brand, attitude toward the company, attitude toward the product, attitude toward the spokesperson), and behavioral response into a “persuasion” variable, following prior research (e.g., Eisend and Tarrahi 2022). In line with prior meta-analyses (e.g., Weingarten and Goodman 2021), we controlled for differences between dependent variables and included dummy variables for each attitude variable, using “behavioral response” as the reference category. The estimated model is expressed as follows.

\begin{aligned} r_{ij} & = γ_{000} + γ_{001} \times Net migratio n_{j} + γ_{002} \\ \times Ethnic {fractionalization}_{j} + γ_{003} \times Rule of {law}_{j} \\ + γ_{004} \times Power {distance}_{j} + γ_{005} \\ \times Achievement {values}_{j} + γ_{006} \times {Gini}_{j} + γ_{007} \times {Patents}_{j} \\ + γ_{008} \times {Year}_{j} + γ_{009} \times {Participant type}_{j} \\ + γ_{010} \times {Participant age}_{j} + γ_{011} \times {Media}_{j} + γ_{012} \\ \times Sample {size}_{j} + γ_{013} \times Journal {quality}_{j} + γ_{10} \\ \times Minority / majority {consumer}_{ij} + γ_{20} \\ \times Participant {gender}_{ij} + γ_{30} \times Celebrity {status}_{ij} \\ + γ_{40} \times Endorser {gender}_{ij} + γ_{50} \times Brand {type}_{ij} \\ + γ_{60} \times {Product type}_{ij} + γ_{70} \times Product {involvement}_{ij} \\ + γ_{80} \times Product {benefit}_{ij} + γ_{90} \times {Attitude ad}_{ij} + γ_{100} \\ \times {Attitude brand}_{ij} + γ_{110} \times {Attitude product}_{ij} + γ_{120} \\ \times {Attitude spokesperson}_{ij} + γ_{130} \times {Attitude company}_{ij} \\ + γ_{140} \times Minority / majority consumer \times {MODERATOR}_{ij} \\ + u_{0 j} + e_{ij}, \end{aligned}

(2)

where r_ij denotes the ith correlation reported in the jth dataset. Some of the moderators vary between datasets (e.g., country moderators), but some vary within datasets (e.g., the same dataset can provide effect sizes for both minority and majority consumer samples). Sampling variances were used as weights. As indicated by the interaction term in Equation 2, we included interaction terms between the minority variable and all other moderators (excluding sample size and journal quality) to examine differential effects by consumer groups. To mitigate multicollinearity, we introduced interaction terms individually, following established meta-analytic procedures (Sethuraman, Tellis, and Briesch 2011). We then repeated the analysis for attitude responses (i.e., we dropped effect sizes referring to “behavioral responses” and used “attitude toward the ad” as reference category) to verify consistency.⁶

Model robustness was assessed through various diagnostic checks. First, the distribution of effect sizes conformed to the assumption of normality (see Web Appendix C). Second, we examined multicollinearity, which is a common issue in cross-national research and meta-analyses (Steensma et al. 2000). The highest variance inflation factor (VIF) was observed for power distance (6.29 in the persuasion model, 6.23 in the attitude model). Removing this variable reduced the maximum VIF to 3.14 and 3.11, respectively, though results remained stable. Thus, we retained power distance in the model, as specified previously. Correlations among moderators are reported in Web Appendix D.

To assess within-country heterogeneity, we conducted a subsample analysis using only U.S.-based studies. Building on Equation 2, we excluded all country-level moderators and added three ethnic minority group indicators (Black, Hispanic, Asian) to capture endorser ethnic diversity. Then, we reran the same model without the dummy variables but added either education or income. Additionally, interaction terms between each diversity variable and consumer ethnicity (minority) were added individually. The model specification for these analyses is as follows, where DIVERSITY refers to either a specific ethnic group (i.e., Black, Hispanic, Asian) or socioeconomic characteristic (i.e., education, income):

\begin{aligned} r_{ij} & = γ_{000} + γ_{001} \times {Year}_{j} + γ_{002} \times Participant {type}_{j} \\ + γ_{003} \times Participant {age}_{j} + γ_{004} \times {Media}_{j} + γ_{005} \\ \times Sample {size}_{j} + γ_{006} \times Journal {quality}_{j} + γ_{10} \\ \times Minority / majority {consumer}_{ij} + γ_{20} \times {DIVERSITY}_{ij} \\ + γ_{30} \times Participant {gender}_{ij} + γ_{40} \times Celebrity {status}_{ij} \\ + γ_{50} \times Endorser {gender}_{ij} + γ_{60} \times Brand {type}_{ij} + γ_{70} \\ \times Product {type}_{ij} + γ_{80} \times Product {involvement}_{ij} + γ_{90} \\ \times Product {benefit}_{ij} + γ_{100} \times Attitude a d_{ij} + γ_{110} \\ \times {Attitude brand}_{ij} + γ_{120} \times {Attitude product}_{ij} + γ_{130} \\ \times {Attitude spokesperson}_{ij} + γ_{140} \times {Attitude company}_{ij} \\ + γ_{150} \times Minority / majority consumer \times {DIVERSITY}_{ij} \\ + u_{0 j} + e_{ij} . \end{aligned}

(3)

Results

Effect Size Integration

Table 4 reports the results of the effect size integration. When we aggregated the data across both ethnic minority and majority consumers, five consumer response variables yielded statistically significant average effect sizes. Specifically, exposure to ethnic diversity significantly increased (1) attitude toward the brand, (2) attitude toward the company, (3) attitude toward the product, (4) memory, and (5) model identification. However, when the results were disaggregated by consumer group, distinct response patterns emerged. Among ethnic minority consumers, exposure to ethnic diversity led to significantly more favorable outcomes for attitude toward the ad, attitude toward the brand, attitude toward the company, behavioral responses, positive cognitive responses, model identification, and perceived relevance, and it reduced negative cognitive responses. In contrast, ethnic majority consumers demonstrated significantly increased negative cognitive responses and decreased positive cognitive responses in response to ethnic diversity. Differences between the two consumer groups can be inferred from the nonoverlapping confidence intervals. In addition, we directly tested for differences between ethnic minority and majority consumers (see Web Appendix E). These analyses confirm that minority consumers, compared with their majority counterparts, exhibit more positive attitudes toward the advertisement, company, and spokesperson; stronger behavioral responses; lower levels of negative cognitive responses; higher levels of positive cognitive responses; reduced intolerance; greater model identification; and stronger ad relevance in response to ethnic diversity.

Table 4.

Effect Size Integration.

Consumer Response Variable	Sample/Subsample	No. of Datasets	No. of Effect sizes	Sample Size	Average Effect Size (r)	p-Value	95% CI Lower Limit	95% CI Upper Limit	Q-Value^a	p-Value	Fail-Safe N^b	Regression Test^c	p-Value
Attitude toward the ad	Full	87	360	34,676	.035	.202	−.019	.088	113,796.782	<.001	—	—
	Minority consumer	38	159	9,265	.150	.002	.054	.245	15,378.526	<.001	90,558	−1.488	.137
	Majority consumer	62	173	22,140	−.047	.197	−.118	.024	23,732.076	<.001	—	—
Attitude toward the brand	Full	58	150	33,007	.086	.002	.031	.140	3,632.293	<.001	18,296	−2.627	.009
	Without outliers	58	149	32,766	.090	<.001	.039	.141	2,227.162	<.001	23,962	−3.419	<.001
	Minority consumer	8	16	2,312	.164	.007	.046	.283	210.666	<.001	575	−.866	.386
	Majority consumer	39	74	10,810	.050	.180	−.023	.123	2,896.597	<.001	—	—
Attitude toward the company	Full	7	27	2,812	.187	.018	.033	.342	1,658.201	<.001	12,084	−3.648	<.001
	Minority consumer	4	16	1,028	.333	.001	.130	.537	641.134	<.001	11,226	−3.671	<.001
	Majority consumer	3	7	830	−.055	.312	−.162	.052	7.775	.255	—	—
Attitude toward the product	Full	28	102	13,789	.096	.002	.035	.156	1,680.053	<.001	6,191	−2.190	.029
	Minority consumer	6	24	1,624	.019	.781	−.113	.151	99.210	<.001	—	—
	Majority consumer	14	38	6,318	.026	.548	−.060	.113	251.498	<.001	—	—
Attitude toward spokesperson	Full	31	124	11,341	.056	.366	−.065	.177	40,695.713	<.001	—	—
	Minority consumer	15	39	2,898	.102	.233	−.065	.269	12,193.390	<.001	—	—
	Majority consumer	23	55	5,550	−.054	.484	−.205	.097	5,399.386	<.001	—	—
Behavioral response	Full	74	256	35,060	.049	.061	−.003	.100	27,494.055	<.001	—	—
	Minority consumer	20	93	6,230	.148	.011	.034	.262	8,099.717	<.001	33,375	−2.064	.039
	Majority consumer	47	99	12,862	−.013	.679	−.073	.048	8,229.879	<.001	—	—
Cognitive responses, negative	Full	3	23	1,073	−.022	.897	−.348	.305	251,932.038	<.001	—	—
	Minority consumer	3	13	611	−.650	<.001	−.860	−.440	316.060	<.001	104,015	3.907	<.001
	Majority consumer	2	10	462	.816	<.001	.644	.988	123.375	<.001	1,861,875	−10.562	<.001
Cognitive responses, positive	Full	3	21	1,106	−.201	.185	−.498	.096	4,501.309	<.001	—	—
	Minority consumer	2	8	416	.593	<.001	.370	.816	111.296	<.001	2,565	−4.152	<.001
	Majority consumer	2	12	578	−.717	<.001	−.864	−.570	73.722	<.001	15,296	7.843	<.001
Intolerance	Full	5	14	1,127	−.062	.613	−.303	.179	1,803.702	<.001	—	—
	Minority consumer	3	5	410	.246	.145	−.085	.576	111.102	<.001	—	—
	Majority consumer	2	3	430	−.503	.117	−1.133	.126	166.517	<.001	—	—
Memory	Full	6	17	1,978	.152	.034	.012	.292	1,459.099	<.001	4,385	−1.391	.164
	Minority consumer	5	12	1,181	.180	.055	−.003	.364	915.004	<.001	—	—
	Majority consumer	4	5	797	.093	.210	−.053	.239	13.844	.008	—	—
Model identification	Full	22	118	9,995	.168	.004	.054	.282	8,731.462	<.001	98,768	−2.762	.006
	Minority consumer	15	60	4,784	.368	<.001	.220	.516	2,452.360	<.001	109,083	−3.291	.001
	Majority consumer	15	54	4,333	−.073	.363	−.230	.084	2,482.047	<.001	—	—
Relevance	Full	17	53	3,349	.126	.121	−.033	.286	4,590.983	<.001	—	—
	Minority consumer	11	26	1,437	.302	.002	.113	.490	964.489	<.001	60,955	−4.244	<.001
	Majority consumer	12	27	1,912	−.040	.737	−.272	.192	2,879.010	<.001	—	—
Self-image evaluation	Full	4	11	519	−.054	.216	−.140	.032	5.121	.883	—	—
	Without outliers	4	10	495	−.071	.115	−.159	.017	2.418	.983	—	—
	Minority consumer	3	6	255	−.046	.463	−.168	.076	3.935	.559	—	—
	Majority consumer	3	5	264	−.063	.310	−.183	.058	1.149	.886	—	—

The multilevel Q-statistic refers to heterogeneity between datasets.

The fail-safe N is the number of additional statistically nonsignificant correlations needed to render the results nonsignificant.

The regression test assessed funnel plot asymmetry, and a statistically significant result indicated asymmetry.

Tests for homogeneity revealed significant heterogeneity for all consumer response variables except self-image evaluation. The removal of outliers had minimal impact and resulted in only slight changes to the effect sizes for attitude toward the brand and self-image evaluation.

To assess publication bias, we calculated fail-safe N values for all statistically significant average effect sizes. The results suggest that the findings are robust. However, several funnel plot asymmetry tests yielded statistically significant results, with most showing a negative association. This indicates that effect sizes tend to be larger in studies with smaller sample sizes and suggests the presence of small-study effects, which may stem from publication bias. Nevertheless, small-study effects may also arise from differences in study design between small and large studies or from random variation. Moreover, the power of regression-based tests for funnel plot asymmetry is limited, especially when the number of effect sizes is small—as is the case for certain outcome variables in Table 4 (Sterne, Gavaghan, and Egger 2000). Finally, the sample size moderator in the meta-regression model was not statistically significant, providing no further support for the presence of small-study effects.

Meta-Regression

Tables 5 and 6 presents the results of the persuasion meta-regression models (Equation 2) that include statistically significant interaction effects (p < .05). Comprehensive results for all meta-regression models—covering both persuasion and attitudes, and including all tested interaction effects—are available in Web Appendix F. Each model explained a significant proportion of heterogeneity in the effect sizes, as confirmed by model test statistics. The residual heterogeneity remained statistically significant, and variance component estimates indicated that within-dataset variance exceeded between-dataset variance.

Table 5.

Influence of Moderator Variables on the Effects of Ethnic Diversity in Advertising on Persuasion: Meta-Regression Results—Part 1.

	Main Effects Model				Interaction with Net Migration			Interaction with Rule of Law			Interaction with Year
	b	SE	p	Predicted r^a	b	SE	p	b	SE	p	b	SE	p
Intercept	−12.229	6.175	.048		−10.902	6.125	.075	−17.988	6.731	.008	−45.699	10.340	.000
Minority/majority consumer	.180	.031	.000	−.006, .175	−.001	.094	.994	2.127	.953	.026	24.081	6.026	.000
Net migration	−6.201	14.433	.667	.072, .058, .044	−53.309	27.162	.050	−.506	14.579	.972	−3.545	14.080	.801
Ethnic fractionalization	.115	.247	.641	.045, .058, . 071	.163	.244	.505	.064	.246	.796	.088	.241	.716
Rule of law	−.946	.484	.051	.131, .058, −.015	−.761	.484	.116	1.692	1.376	.219	−.801	.471	.089
Power distance	−.005	.003	.141	.116, .058, .001	−.004	.003	.153	−.005	.003	.088	−.005	.003	.129
Achievement values	.004	.005	.430	.037, .058, .079	.004	.005	.376	.004	.005	.440	.003	.005	.539
Gini	−.010	.008	.214	.087, .058, .029	−.009	.008	.259	−.014	.008	.089	−.007	.008	.353
Patents	31.458	67.856	.643	.048, .058, .068	44.431	67.253	.509	.648	68.989	.993	15.684	66.323	.813
Year	.007	.003	.039	−.009, .058, .125	.006	.003	.062	.009	.003	.009	.023	.005	.000
Participant type	.032	.053	.543	.041, .073	.032	.052	.543	.040	.053	.449	.023	.052	.654
Participant age	−.004	.004	.359	.081, .058, .035	−.004	.004	.404	−.005	.004	.227	−.004	.004	.368
Participant gender	−.000	.001	.786	.064, .058, .052	−.000	.001	.778	−.000	.001	.613	−.000	.001	.688
Celebrity status	−.160	.072	.026	.070, −.090	−.160	.071	.024	−.164	.071	.021	−.165	.070	.018
Endorser gender	.019	.043	.660	.049, .068	.019	.042	.654	.014	.042	.737	.004	.042	.918
Brand type	.052	.050	.295	.021, .073	.057	.049	.244	.056	.050	.261	.050	.049	.302
Product type	−.067	.050	.177	.073, .006	−.083	.049	.095	−.067	.049	.174	−.082	.048	.091
Product involvement	−.032	.037	.388	.076, .044	−.028	.037	.443	−.029	.037	.438	−.025	.037	.489
Product benefit	.002	.036	.959	.057, .059	.006	.036	.866	−.006	.036	.877	.007	.036	.855
Media	−.008	.076	.921	.058, .051	.003	.075	.973	−.008	.075	.914	.005	.074	.943
Sample size	−.000	.000	.918	.060, .058, .056	−.000	.000	.997	−.000	.000	.872	−.000	.000	.953
Journal quality	.041	.069	.553	.052, .092	.035	.068	.605	.024	.069	.729	.012	.068	.863
Attitude toward the ad	.044	.032	.162	.044, .088	.044	.031	.164	.045	.031	.157	.041	.031	.185
Attitude toward the brand	.022	.036	.548	.055, .077	.019	.036	.601	.022	.036	.537	.017	.036	.636
Attitude toward the product	.034	.044	.435	.054, .089	.032	.044	.462	.034	.044	.435	.027	.044	.536
Attitude toward the spokesperson	.184	.043	.000	.034, .218	.183	.042	.000	.185	.042	.000	.181	.042	.000
Attitude toward the company	.158	.088	.072	.054, .212	.169	.087	.053	.154	.087	.078	.172	.086	.047
Interaction term					36.377	17.848	.042	−2.689	1.315	.041	−.012	.003	.000
No. of effect sizes (Level 1)	891^b				891^b			891^b			891^b
No. of datasets (Level 2)	131				131			131			131
Explained variance	92.826, d.f. = 26, p < .001				97.977, d.f. = 27, p < .001			97.724, d.f. = 27, p < .001			111.092, d.f. = 27, p < .001
Residual heterogeneity	51,402.02, d.f. = 864, p < .001				49,872.8, d.f. = 863, p < .001			51,335.26, d.f. = 863, p < .001			51,274.36, d.f. = 863, p < .001
σ² (within-dataset variance)	.074				.074			.074			.073
τ² (between-dataset variance)	.023				.022			.022			.021

We predicted the correlations by setting each dummy variable to 0 and 1. For continuous moderators, we predicted correlations at one standard deviation below the mean, at the mean, and at one standard deviation above the mean. All other variables in the model were held at their sample mean values.

The number of 891 effect sizes is smaller than the 1,019 persuasion-related effect sizes in Table 4 due to missing values in some country moderators.

Table 6.

Influence of Moderator Variables on the Effects of Ethnic Diversity in Advertising on Persuasion: Meta-Regression Results—Part 2.

	Interaction with Participant Age			Interaction with Participant Gender			Interaction with Endorser Gender			Interaction with Real Brand
	b	SE	p	b	SE	p	b	SE	p	b	SE	p
Intercept	−12.666	5.953	.033	−12.557	6.219	.043	−10.917	6.297	.083	−11.322	6.110	.064
Minority/majority consumer	.777	.152	.000	.047	.063	.449	.241	.038	.000	.434	.094	.000
Net migration	−6.649	13.926	.633	−4.922	14.538	.735	−4.919	14.679	.738	−6.357	14.263	.656
Ethnic fractionalization	.171	.238	.474	.066	.249	.790	.099	.251	.694	.140	.244	.567
Rule of law	−.935	.464	.044	−.935	.488	.055	−.988	.493	.045	−.877	.478	.066
Power distance	−.004	.003	.184	−.005	.003	.119	−.005	.003	.115	−.004	.003	.153
Achievement values	.005	.005	.329	.003	.005	.560	.003	.005	.537	.004	.005	.421
Gini	−.008	.008	.272	−.010	.008	.205	−.010	.008	.205	−.010	.008	.214
Patents	49.408	65.734	.452	17.710	68.486	.796	18.840	69.050	.785	27.986	67.098	.677
Year	.006	.003	.040	.007	.003	.032	.006	.003	.067	.006	.003	.062
Participant type	.033	.051	.519	.035	.053	.515	.045	.054	.408	.044	.052	.400
Participant age	.025	.008	.003	−.004	.004	.334	−.004	.004	.311	−.004	.004	.323
Participant gender	−.000	.001	.676	−.005	.002	.022	−.000	.001	.865	−.000	.001	.687
Celebrity status	−.163	.069	.019	−.145	.072	.045	−.151	.073	.038	−.167	.071	.019
Endorser gender	.013	.041	.759	.024	.043	.570	.237	.094	.012	.027	.042	.525
Brand type	.057	.048	.236	.062	.050	.221	.051	.051	.319	.273	.091	.003
Product type	−.087	.048	.072	−.070	.050	.164	−.076	.051	.134	−.071	.049	.149
Product involvement	−.018	.036	.619	−.033	.037	.370	−.032	.038	.392	−.028	.037	.453
Product benefit	.008	.036	.821	.003	.037	.936	−.003	.037	.928	.007	.036	.846
Media	−.006	.073	.938	−.001	.076	.992	−.024	.077	.753	.003	.075	.968
Sample size	−.000	.000	.715	−.000	.000	.823	−.000	.000	.784	−.000	.000	.981
Journal quality	.023	.067	.724	.041	.069	.556	.034	.070	.626	.026	.068	.701
Attitude toward the ad	.040	.031	.195	.041	.031	.197	.042	.031	.182	.044	.031	.156
Attitude toward the brand	.016	.036	.667	.021	.036	.567	.019	.036	.597	.019	.036	.595
Attitude toward the product	.030	.044	.488	.028	.044	.523	.027	.044	.532	.026	.044	.558
Attitude toward the spokesperson	.185	.042	.000	.183	.042	.000	.184	.042	.000	.183	.042	.000
Attitude toward the company	.167	.086	.053	.144	.088	.101	.164	.088	.063	.168	.087	.054
Interaction term	−.023	.006	.000	.004	.002	.014	−.155	.060	.009	−.168	.059	.004
No. of effect sizes (Level 1)	891^a			891^a			891^a			891^a
No. of datasets (Level 2)	131			131			131			131
Explained variance	112.005, d.f. = 27, p < .001			99.133, d.f. = 27, p < .001			99.636, d.f. = 27, p < .001			102.316, d.f. = 27, p < .001
Residual heterogeneity	50,886.05, d.f. = 863, p < .001			51,399.51, d.f. = 863, p < .001			51,065.94, d.f. = 863, p < .001			51,112.510, d.f. = 863, p < .001
σ² (within-dataset variance)	.073			.073			.073			.073
τ² (between-dataset variance)	.020			.023			.024			.024

The number of 891 effect sizes is smaller than the 1,019 persuasion-related effect sizes in Table 4 due to missing values in some country moderators.

Across the various models, several main effects emerged consistently. Rule of law, year of data collection, minority/majority consumer, celebrity endorser status, and attitudes toward the spokesperson all significantly predicted effect size variations. In line with findings from the effect size integration, these effects were generally stronger among ethnic minority consumers. Specifically, the rule of law was associated with attenuated advertising effects, while effect sizes increased over time. Advertising featuring noncelebrity endorsers yielded stronger effects, as did advertisements evaluated on the basis of attitudes toward the spokesperson and, to a lesser extent, attitudes toward the company.

Consistent interaction effects were observed across both the persuasion and attitude models, particularly between several moderator variables and minority/majority consumer: net migration, rule of law, year, participant age, participant gender, endorser gender, and brand type. As illustrated in Figure 2, the effects of ethnic diversity in advertising increase with net migration for minority consumers but decrease for majority consumers, consistent with predictions from intergroup threat theory. Rule of law moderates these effects by reducing the divergence in response between majority and minority consumers. The increasing impact of ethnic diversity over time is evident only among majority consumers. Additional interaction patterns reveal that participant age negatively moderates effects among minority consumers but not among majority consumers. Further, a higher proportion of male consumers strengthens the effects for minority groups but weakens them for majority groups. The gender of the ethnic minority endorser also matters: Female endorsers tend to be more effective among majority consumers, whereas male endorsers have a stronger influence on minority consumers. Finally, the differential effect of ethnic diversity is stronger for real brands than for fictitious brands or products.

Figure 2.

Interaction Plots: Meta-Regression Results.

Robustness checks, reported in Web Appendix G, support the stability of these findings. First, we reestimated the models after removing power distance, which had the highest VIF. Second, we recoded the minority/majority variable to distinguish between majority-only versus minority or mixed consumer samples. Third, we substituted rule of law with democracy (due to high collinearity between the two), and we introduced GDP growth and GDP per capita as alternative economic indicators, removing multicollinear variables as necessary.⁷ We tested alternative imputation methods—median of nearby values and linear interpolation—for variables where mean imputation was applied (participant age, participant gender, and ethnic fractionalization). Then, we examined an interaction between net migration and ethnic fractionalization. Finally, we estimated a noncontextualized model excluding all country-level variables. Across all robustness tests, the results remained largely consistent with the main findings presented in Tables 5 and 6, affirming the validity of the meta-regression results.

Over 60% of the datasets in our sample were collected in the United States, a proportion that aligns with the country distribution reported in previous meta-analyses (e.g., Geyskens, Steenkamp, and Kumar 1998). Table 7 presents the results of the U.S.-focused regression model (Equation 3), with corresponding results for the attitude models reported in Web Appendix H.

Table 7.

Influence of Moderator Variables on the Effects of Ethnic Diversity in Advertising on Persuasion: U.S. Data.

	Interaction with Black			Interaction with Hispanic			Interaction with Asian			Interaction with Education			Interaction with Income
	b	SE	p	b	SE	p	b	SE	p	b	SE	p	b	SE	p
Intercept	−5.218	4.035	.196	−5.212	4.056	.199	−5.415	4.075	.184	.096	4.967	.985	−1.474	7.867	.851
Minority/majority consumer	.209	.049	.000	.224	.034	.000	.209	.039	.000	.362	.063	.000	.468	.075	.000
Black	.036	.090	.687	.072	.047	.124	.074	.047	.115
Hispanic	−.064	.075	.397	−.109	.194	.576	−.062	.075	.410
Asian	−.040	.068	.554	−.043	.068	.528	−.123	.117	.293
Education										.012	.004	.005
Income													.000	.000	.024
Year	.002	.002	.242	.002	.002	.247	.003	.002	.227	.000	.003	.917	.000	.004	.924
Participant type	.020	.074	.790	.015	.074	.838	.012	.075	.874	.044	.080	.585	.048	.083	.565
Participant age	−.003	.006	.571	−.003	.006	.568	−.003	.006	.594	−.007	.007	.308	−.005	.007	.524
Participant gender	.001	.001	.277	.001	.001	.283	.001	.001	.293	.003	.001	.040	.001	.001	.326
Celebrity status	−.277	.137	.044	−.275	.138	.047	−.276	.139	.046	−.363	.140	.010	−.242	.147	.101
Endorser gender	.084	.051	.097	.086	.051	.092	.081	.051	.115	.060	.055	.283	.066	.057	.246
Brand type	.125	.069	.072	.126	.070	.070	.125	.070	.073	.033	.073	.656	.108	.076	.156
Product type	−.041	.062	.506	−.041	.062	.515	−.043	.062	.493	.000	.069	.998	−.059	.069	.389
Product involvement	−.033	.046	.481	−.033	.046	.470	−.035	.046	.447	−.055	.050	.268	−.043	.052	.405
Product benefit	−.015	.042	.712	−.015	.042	.714	−.013	.042	.758	−.020	.044	.651	−.015	.047	.756
Media	−.026	.097	.786	−.023	.097	.816	−.021	.097	.833	−.020	.099	.837	.016	.105	.878
Sample size	.000	.000	.899	.000	.000	.893	.000	.000	.969	.000	.000	.858	.000	.000	.984
Journal quality	.094	.079	.234	.096	.079	.224	.097	.080	.223	.093	.089	.298	.073	.090	.416
Attitude toward the ad	.053	.037	.150	.053	.037	.148	.053	.037	.146	.046	.039	.238	.064	.040	.113
Attitude toward the brand	.028	.044	.520	.029	.044	.519	.028	.044	.528	.029	.048	.551	.033	.052	.531
Attitude toward the product	.019	.053	.727	.019	.053	.722	.019	.053	.726	.041	.063	.516	.041	.064	.518
Attitude toward the spokesperson	.216	.051	.000	.216	.051	.000	.215	.051	.000	.307	.057	.000	.227	.054	.000
Attitude toward the company	.116	.089	.190	.113	.089	.204	.110	.089	.214	.130	.089	.144	.119	.093	.200
Interaction term	.027	.061	.654	.033	.135	.808	.060	.071	.400	−.008	.003	.002	−.000	.000	.001
No. of effect sizes (Level 1)	761			761			761			573			650
No. of datasets (Level 2)	98			98			98			79			84
Explained variance	92.281, d.f. = 23, p < .001			92.222, d.f. = 23, p < .001			93.038, d.f. = 23, p < .001			88.257, d.f. = 21, p < .001			93.612, d.f. = 21, p < .001
Residual heterogeneity	44,231.68, d.f. = 737, p < .001			50,269.23, d.f. = 737, p < .001			44,524.09, d.f. = 737, p < .001			20,098.18, d.f. = 551, p < .001			41,216.8, d.f. = 628, p < .001
σ² (within-dataset variance)	.079			.079			.079			.069			.084
τ² (between-dataset variance)	.038			.038			.039			.037			.044

The results show that persuasive effects do not systematically vary by specific ethnic group categorizations within the United States, for both minority and majority consumers. However, significant interaction effects emerged between education and minority/majority consumer, as well as between income and minority/majority consumer. As illustrated in Figure 3, higher education and income levels of ethnic minority endorsers lead to stronger persuasive effects among majority consumers, consistent with theoretical expectations regarding perceived power and status. The same characteristics diminish persuasive effects among minority consumers. Other significant main effects—specifically for celebrity status, minority/majority consumer, and attitudes toward the spokesperson—were generally consistent with the broader findings reported in Tables 5 and 6, further supporting the robustness of our model.

Figure 3.

Interaction Plots: U.S. Data.

Discussion

This meta-analysis examined the effects of ethnic diversity in advertising, revealing small effects. The effects are influenced by multiple moderating variables related to country context, ethnic diversity characteristics, and factors linked to time, product, media, consumer, context, and method and often vary across consumer groups. The findings offer valuable implications for both academic research and managerial practice, which are summarized in Table 8.

Table 8.

Summary of Key Findings, Research Implications, and Managerial Implications.

Research Questions	Key Findings	Research Implications	Managerial Implications
Is ethnic diversity in advertising effective?	Persuasion effects of ethnic diversity yield small overall effects (r = .035–.187).	Clarifies previously inconclusive findings by showing that aggregate effects mask meaningful subgroup variation. Highlights the need to disaggregate effects by ethnic group and to examine underexplored downstream outcomes such as trust and engagement.	Ethnic diversity in advertising is especially effective for targeting ethnic minority consumers, while not negatively affecting majority consumer responses. Targeting efficiency therefore increases in markets with larger minority populations.
Does the effect of ethnic diversity differ between minority and majority consumers?	Persuasion effects are significantly stronger and more favorable among ethnic minority consumers (r = .019–.333) compared with majority consumers (r = −.055–.050).
How does the effect of ethnic diversity vary across countries?	A higher net migration rate amplifies differences in consumer responses: Majority consumers react less favorably (r_pred = −.045^a), while minority consumers react more favorably (r_pred = .183). A stronger rule of law reduces both the differential and overall effects (r_pred reduced from .131 to −.015).	Intergroup threat theory explains that perceived threat increases negative responses among majority consumers, while enhancing positive reactions among minorities. Institutional strength promotes perceived equality, thereby dampening differences. This underscores the necessity of considering contextual and country factors in theory and future research.	Advertising strategies should be tailored to national contexts and adapted to minority and majority consumers. Countries with high migration and weaker institutions may benefit most from diversity-focused messaging. Web Appendix I offers country-specific predictions for ad effectiveness.
How does the effect of ethnic diversity vary by endorser characteristics across ethnic groups?	With increasing education levels and income, minority-group endorsers become more persuasive to majority consumers (r_{pred education} increased from −.056 to .089; r_{pred income} increased from −.067 to .036) but less effective for minority consumers (r_{pred education} decreased from .249 to .164; r_{pred income} decreased from .286 to .127).	Status-based evaluations, rather than group identity alone, explain differential effects. Positive responses from majority consumers may stem from deference to high-status figures; negative responses among minority consumers may reflect weakened identification.	High-status minority endorsers are effective for engaging majority consumers but may alienate minority consumers. Endorser selection should account for shifting power dynamics across ethnic groups.
How do effects of ethnic diversity vary by other marketing- and consumer-related factors?	Effects of ethnic diversity have increased over time (r_pred increased from −.009 to .125). Celebrity endorsers are less effective (r_pred = −.090). Younger and male minority consumers respond more favorably (r_{pred age} = .260, r_{pred gender} = .232). Male endorsers are more persuasive for minority consumers (r_pred = .201); female endorsers are more persuasive for majority consumers (r_pred = .197). Real brands elicit less favorable responses from majority consumers (r_pred = −.073).	Ingroup identification and prototype congruence explain reduced effectiveness of celebrity endorsers and greater resonance with younger, male minority audiences. Status signaling and status affirmation account for the enhanced impact of male minority endorsers.	Avoid celebrity endorsers in ethnic diversity ads. Target campaigns toward younger and male minority consumers. Use male minority endorsers to appeal to minority consumers, and female endorsers to engage majority consumers.

Predicted correlations from Table 5, Figure 2, and Figure 3. For low and high values of continuous moderators, we report predicted correlations at one standard deviation above and below the mean.

Research Implications

Although the average effects of ethnic diversity in advertising are relatively modest, thereby questioning the optimistic estimates of business benefits due to diversity in advertising as indicated by recent industry reports (Unstereotype Alliance 2024), they are more pronounced and positive for ethnic minority consumers compared with majority consumers. This finding clarifies mixed results found in prior research and highlights the necessity of disaggregating effects by consumer groups. The persuasive effects explored here may extend to other important outcomes—such as trust, engagement, or brand attachment—that warrant further empirical investigation, as they remain understudied in the context of ethnic diversity in advertising.

Prior literature emphasizes the need to consider societal as well as commercial outcomes of diversity in advertising, given that consumers often respond favorably to socially responsible brands (Eisend 2019). While this meta-analysis primarily focused on commercial outcomes—reflecting the emphasis in the primary studies—it also included two noncommercial outcomes: self-evaluation and tolerance. Although we did not find significant effects for these, this does not undermine the potential societal relevance of ethnic diversity in advertising. Future studies should explore broader societal outcomes, such as intergroup attitudes, ethnic identity affirmation, perceived social norms, and civic engagement.

Importantly, this meta-analysis incorporates multiple country-level moderators simultaneously, avoiding the limitations of earlier work that adopted simplistic cultural frameworks (e.g., Gao, Xu, and Kim 2013; Lwin and Wee 2000). Our results support theories referring to both contact/exposure and institutions. Perceived intergroup threat explains divergent consumer responses: As threat increases, majority group consumers respond less favorably, while minority group consumers respond more favorably to ethnic diversity in advertising. Strong institutions, characterized by effective rule of law, reduce both the differential impact across consumer groups and the overall effect of ethnic diversity, likely due to enhanced equality and lower perceived intergroup differences. The findings highlight the need to consider sociopolitical and economic environments in both interpreting prior studies and designing future research.

Within-country moderator analyses revealed that differences in consumer responses to specific ethnic minority groups are best explained by power and status differences (i.e., education and income difference of the endorser's ethnic group) rather than mere group categorizations (i.e., whether the endorser is Black, Hispanic, or Asian American). Higher status and power of minority endorsers enhances effects for majority consumers but reduces them for minority consumers—likely because powerful minority endorsers are perceived by their own group as atypical exemplars that do not fit existing schemas and status expectations, leading to backlash and reducing identification and ingroup favoritism (Rudman et al. 2012). This is consistent with findings from recent meta-analytic work (Lenk, Hartmann, and Sattler 2024), which show stronger ingroup preference among U.S. consumers for same-race endorsers, and a temporal trend showing increased acceptance of minority endorsers by White consumers over time. This parallels our finding that effects of ethnic diversity (related to multiple ethnic minority groups) on majority consumers have increased in recent years.

Moreover, social categorization may explain why ethnic minority celebrities tend to have weaker persuasive effects: They are often perceived as atypical and not representative of their group, thereby weakening identification and prototype-based effects. A lack of prototype congruence is particularly likely in cultural contexts where ethnic minorities have historically lower status or face structural exclusion, such as in France or Germany: Minority celebrities may be perceived as unrepresentative exceptions rather than group prototypes, activating contrast effects or stereotype threat rather than identification. However, prototype congruence and identification vary depending on the social status of ethnic minorities. For instance, in countries with highly selective immigration policies such as Canada or Australia, high-status migration is more common, which could increase the identification of ethnic minority consumers with same-ethnicity celebrities. Our results further show that younger and male minority consumers respond more favorably to diversity in advertising, possibly due to a stronger need for identity affirmation. Additionally, male minority endorsers are more effective with minority consumers, while female endorsers work better with majority consumers. This may be attributed to stereotypical gender roles. Male endorsers may convey strength and competence (appealing to status needs), while female endorsers may be perceived as warm and nurturing, qualities broadly favored by general audiences (Fiske, Cuddy, and Glick 2007). Finally, we found that majority consumers respond less favorably to ethnic diversity when the advertised brand is real rather than fictitious, potentially due to stronger expectations or brand schemas based on traditional advertising portrayals for real brands and products. Real brands can activate preexisting brand knowledge and stereotypical expectations (e.g., German or U.S. brands that are associated with tradition and national identity), which can amplify the diagnosticity of diversity cues and make them more likely to be scrutinized for authenticity or consistency. In contrast, fictitious brands that lack prior schemas allow for more flexible interpretation of diversity cues, potentially reducing dissonance or resistance. In sum, these moderator effects provide opportunities for more thorough investigation into the proposed underlying mechanisms.

Managerial Implications

From a practical standpoint, the findings challenge the efficacy of using ethnic diversity in advertising aimed primarily at majority consumers. Despite concerns among advertisers about alienating majority audiences (Schlegelmilch, Khan, and Hair 2016), our results suggest that featuring ethnic minority endorsers does not undermine advertising effectiveness—particularly when the target audience includes minority consumers. While the average effects are modest, they are notably more substantial among minority audiences, making ethnic diversity advertising especially viable in markets with significant minority populations. Given increasing ethnic diversity in many countries—for example, Hispanic/Latino individuals comprise 19.1% of the U.S. population (USA Facts 2024)—targeted ethnic diversity advertising is both relevant and necessary. It seems particularly effective for products specifically relevant to minority consumers (e.g., food, films) but may also be adapted to mainstream products.

Because advertising strategies are typically implemented on a national level, advertisers require country-specific insights, especially when contextual factors (e.g., migration rates, institutional strength) exert countervailing influences. To this end, we conducted an additional country-level prediction analysis (Web Appendix I). The results suggest that ethnic diversity advertising is least effective in countries with both strong institutions and high migration rates (e.g., Sweden, Singapore, Netherlands), but more effective in countries with stricter migration controls (e.g., Austria) or weaker regulatory frameworks (e.g., Mexico). Although national categorizations are imperfect, patterns in immigration histories and public sentiment regarding immigration regulation can help explain the results. Advertisers should carefully interpret them, as local demographic and cultural factors may moderate advertising effectiveness. For instance, majority consumers in countries with low migration rates might respond more favorably to ethnic diversity, but the group of ethnic minority consumers itself might be too small to be targeted efficiently.

Endorser selection is also critical. High-status ethnic minority endorsers resonate more with majority audiences but less with their own group. These dynamics may change over time and vary by country, depending on the composition of ethnic groups and their relative power. For example, the U.S. has a high ethnic fractionalization index (Drazanova 2020), indicating frequent contact with diverse ethnic groups and making status differences more salient. Similar high fractionalization exists in countries such as Belgium, Canada, Indonesia, Spain, or the United Arab Emirates. The findings may not apply in more homogeneous societies with low migration rates and low ethnic fractionalization (e.g., Japan), where such distinctions are less visible or relevant. Thus, the findings should be generalized with caution across different countries, taking into account their unique demographic and migration characteristics.

Additional moderators suggest avoiding celebrity endorsers in ethnic diversity advertising. Focusing on younger and male minority audiences can increase the effectiveness of ethnic diversity advertising. Male minority endorsers are most effective for targeting minority consumers, while female endorsers appeal more to majority consumers. To mitigate backlash among majority audiences, brands may benefit from using both minority and majority endorsers in initial campaigns to foster familiarity before transitioning to minority-only representation.

Limitations and Future Research

This study has several limitations. Although the meta-analysis includes data from countries across multiple continents, South America is not represented, and the dataset skews toward Western, industrialized nations. This is a common limitation in the field and highlights the need for broader geographic replication, particularly for a topic as context-sensitive as ethnic diversity. Future studies should collect data from underrepresented regions and test more granular country-level moderators.

Moreover, current implementations of ethnic diversity in advertising often rely on visible markers such as skin color. This oversimplifies the construct and risks obscuring more nuanced identity cues. Future research should explore alternative indicators of ethnicity, such as names, symbols, or short narrative descriptions that signal ethnic affiliation.

Our analysis also does not account for within-country regional variation. Ethnic diversity tends to be higher in urban areas than in rural regions, likely influencing consumer responses. Unfortunately, most primary studies do not report regional data, limiting the ability to make subnational recommendations. Future research should address ethnic diversity advertising effectiveness below the national level to inform more targeted marketing strategies.

Supplemental Material

sj-pdf-1-jmx-10.1177_00222429251408344 - Supplemental material for Ethnic Diversity in Advertising: A Meta-Analysis

Supplemental material, sj-pdf-1-jmx-10.1177_00222429251408344 for Ethnic Diversity in Advertising: A Meta-Analysis by Martin Eisend, Anna Rößner and Erik Hermann in Journal of Marketing

Footnotes

Coeditor

Jan-Benedict E.M. Steenkamp

Associate Editor

Marnik Dekimpe

Ethical Considerations

There are no human participants in this study and informed consent is not required.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received financial support in the form of a research grant from the German Research Foundation (Deutsche Forschungsgemeinschaft), Germany (grant number EI 508/21-1).

ORCID iDs

Martin Eisend

Erik Hermann

Data Availability

The data that support the findings of this article are publicly available in the JM Dataverse.

Notes

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