Abstract As brands seek to increase racial representation in their advertising, many are turning to AI‐generated images of humans (i.e., synthetic models) as a scalable solution. This research demonstrates that consumer reactions to synthetic models are asymmetric, depending on the racial identity being depicted. Across five studies, we demonstrate that disclosing the AI‐generated nature of models lowers firm evaluations when the featured models are from racially underrepresented groups (e.g., Black, Hispanic, and Asian models), but not when they are White. This effect is driven by reduced perceptions of the authenticity of the firm's inclusion efforts, which in turn elevate inferences of manipulative intent. Importantly, these negative effects can be mitigated when consumers are informed that the firm is owned by members of the depicted racial community, suggesting that credible signals of authentic community ties can neutralize the evaluative penalty of AI disclosure. Our findings extend research on racialized depictions in advertising by identifying synthetic depiction as a novel threat to the evaluative benefits of minority representation, and by theorizing why this threat operates asymmetrically by race. More broadly, our results highlight that technological innovations in advertising cannot be divorced from the social and historical contexts that shape their meaning.
Abstract This research reveals a psycholinguistic effect whereby more precise words in product communications increase consumer perceptions of product uniqueness. Drawing on Categorization Theory, the authors propose that more precise words in product descriptions are situated deeper within their semantic categories and denote narrower subsets of referents, thereby eliciting perceptions of product uniqueness. [Correction added on 15 October 2026, after first online publication: The reference in the preceding sentence has been removed.] Four experiments provide robust evidence supporting the “precise‐unique effect” and its underlying psycholinguistic mechanism driven by categorical depth. The findings further suggest that consumers' need for product uniqueness moderates the downstream effects of language precision on perceived attractiveness and behavioral intention via perceived uniqueness. This research contributes to the growing literature on language effects in consumer psychology and provides practical implications for making strategic lexical choices that enhance product positioning and differentiation.
Abstract We shine a spotlight on marketing misinformation—claims about products and brands that contradict or distort verifiable facts. Misinformation may be created unintentionally or intentionally by companies, competitors, customers, or third parties seeking to harm companies or consumers. We focus on the role of AI in creating, obfuscating, and disseminating misinformation in the form of text, audio, images, and videos generated using large language models. This type of information can pose a substantial risk for brand safety. Consumers may be aware that AI can generate misinformation but may lack the ability to guard against it. We discuss how different stakeholders in the information ecosystem can help in the prevention and correction of misinformation and propose a future research agenda in this critical and fast‐growing area.
Abstract This paper examines exploitation in the marketplace from a consumer behavior perspective. We define marketplace exploitation as occurring when one party leverages structural and/or situational resource asymmetries to extract disproportionate value in ways that undermine fairness or autonomy, even when exchanges appear voluntary and mutually beneficial. We distinguish marketplace exploitation from related concepts including fairness, deception, and discrimination, arguing that it centers on the morally problematic use of contextual advantage. Adopting a dual lens, we develop conceptual frameworks for both firm‐to‐consumer exploitation (e.g., price gouging, dark patterns) and consumer‐to‐firm exploitation (e.g., free trial abuse, exploiting firm mistakes). These frameworks delineate how structural and situational asymmetries give rise to objective exploitative acts, when such acts are perceived as exploitative, and how they shape emotional, cognitive, and behavioral responses. We synthesize a research agenda and outline methodological approaches for studying the antecedents, perceptions, and consequences of exploitation. This work contributes to consumer psychology by conceptualizing marketplace exploitation as both a subjective psychological experience and a strategic marketplace behavior, with implications for theory, policy, and ethical firm practices.
This paper introduces generative grounded theory (GGT), an AI-enhanced method for inductive theory building that adapts grounded theory-and qualitative research procedures more broadly-to contemporary digital and algorithmic research conditions. GGT integrates generative artificial intelligence (GenAI) into grounded-theory analysis while preserving human interpretive authority and theoretical responsibility. The method specifies a step-by-step process that supports systematic comparison and structured engagement with qualitative data. We demonstrate the method by outlining two new consumer-psychology studies and reanalyzing two established grounded-theory studies. We also address concerns surrounding GenAI use and outline future developments in the use of GenAI for qualitative analyses. GGT enables greater scale and efficiency in working with qualitative data, which are increasingly heterogeneous and multimodal, while supporting rigorous and precise theory construction and enhancing transparency and traceability. GGT can also be extended to other qualitative approaches that rely on similar analytic procedures.
