Perceived Communicative Sameness and Brand Authenticity in Generic and Brand-Specific Brand Messages

Generative artificial intelligence (GenAI) enables brands to produce polished and scalable marketing communication, but fluency and efficiency do not necessarily create a distinctive or credible brand voice. This study examines perceived communicative sameness, the perception that a brand message could be used by many competing brands with little change, as a cue through which consumers judge brand authenticity. A conceptual framework linking perceived sameness, brand voice distinctiveness, inferred brand commitment, perceived brand authenticity, and engagement intention is developed from research on brand authenticity, brand voice, signaling, and consumer inference. Six hypotheses derived from the framework were tested in a between-subjects online experiment in which 200 adult participants were assigned to read either a generic or a brand-specific message from a fictional brand. Compared with the generic message, the brand-specific message was rated lower on perceived sameness and higher on brand voice distinctiveness, inferred brand commitment, perceived brand authenticity, and engagement intention, with large effects on every outcome. In a regression model, brand voice distinctiveness, inferred brand commitment, and message condition predicted perceived authenticity, whereas perceived sameness did not make a significant unique contribution. Perceived authenticity in turn predicted engagement intention. Because the manipulation concerned message specificity and not artificial intelligence (AI) authorship or disclosure, the findings inform the role of brand-specific meaning in authenticity judgments but do not establish effects of AI use. Implications for AI-assisted brand communication and directions for future research are discussed.