Ethnic Minorities in Advertising

Rößner, Anna; Eisend, Martin

doi:10.1080/00913367.2023.2255247

Cited by 5 publications

(1 citation statement)

References 32 publications

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“…We present a table to help conceptualize the knowledge gap that we address with this research (see Appendix A). Specifically, we show that a range of articles focus on the effects of AI-generated models (i.e., Franke et al, 2023;Lou et al, 2023;Mende et al, 2023;Sands, Campbell, et al, 2022;Thomas & Fowler, 2021), while others focus on diversity representation in advertising (Campbell et al, 2024;Eisend et al, 2023;Rößner & Eisend, 2023;Rößner et al, 2021;Tsai, 2011). However, to the best of our knowledge, very few studies have addressed in combination both AI-generated models and diversity representation.…”

Section: Introductionmentioning

confidence: 77%

Inauthentic inclusion: Exploring how intention to use AI‐generated diverse models can backfire

Sands,

Demsar,

Ferraro

et al. 2024

Psychology and Marketing

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Rapid advances in AI technology have important implications for, and effects on, brands and advertisers. Increasingly, brands are creating digital models to showcase clothing and accessories in a similar way to human models, with AI used to customize various body types, ages, sizes, and skin tones. However, little is known about how the underrepresented consumers respond to a brand's intention to use AI‐generated models to represent them. We explore this by conducting four studies. We find evidence that a brand's intention to use AI‐generated (vs. human) models negatively affects brand attitude (study 1). We further investigate this effect using two different underrepresented consumer groups: LGBTQIA+ consumers (study 2) and consumers with disabilities (study 3). We show the effect to be serially mediated by consumers' perception of greater threat to their self‐identity and a lower sense of belonging, subsequently having a negative effect on brand attitude. Finally, we show that the perception of a brand's motivation for representing diverse consumer groups can attenuate these negative effects (study 4). Specifically, when consumers believe a brand is intrinsically motivated to use AI‐generated diversity representations, they report a significantly lower social identity threat which in turn is associated with a significantly higher sense of belonging to the brand. Our research findings suggest that a brand's well‐meaning intentions to represent diversity can in fact have negative effects on the very consumers whom a brand is trying to attract. While catering to diversity is of critical importance, our results indicate that brand managers should exercise caution when using AI to appeal to diverse groups of potential consumers.

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Section: Introductionmentioning

confidence: 77%

Inauthentic inclusion: Exploring how intention to use AI‐generated diverse models can backfire

Sands,

Demsar,

Ferraro

et al. 2024

Psychology and Marketing

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White Americans’ preference for Black people in advertising has increased in the past 66 years: A meta-analysis

Lenk,

Hartmann,

Sattler

2024

Proc. Natl. Acad. Sci. U.S.A.

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This study investigates Black and White consumers’ preferences for Black versus White people in United States advertising contexts over 66 y, from 1956 until 2022, a time in which the United States has experienced significant ethno-racial diversification. Examining Black and White consumers’ reactions to visual advertising over more than half a century offers a unique and dynamic view of interracial preferences. Mass advertising reaches an audience of billions and can shape people’s attitudes and behavior, emphasizing the relevance of clarifying the influence of race in advertising, how it has evolved over time, and how it may contribute to mitigating discrimination based on racial perceptions. A meta-analysis of extant experiments into the relationship between the depicted endorser’s race (i.e., the model in a visual ad) and the reaction of Black and White viewers pertains to 332 effect sizes from 62 studies reported in 52 scientific papers, comprising 10,186 Black and White participants. Our results are anchored in a conceptual framework, including a comprehensive set of perceiver (viewer), target (endorser), social/societal context, and publication characteristics. Without accounting for temporal dynamics, the results indicate ingroup favoritism, such that White viewers prefer White models and Black viewers prefer Black models. But by controlling for the publication year, it is possible to observe a time-dependent trend: Historically, White consumers preferred endorsers of the same race, but this preference has significantly shifted toward Black endorsers in recent years. In contrast, the level of Black consumers’ reactions to endorsers of the same race remains largely unchanged over time.

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