Generative AI-Driven Marketing: Effects on Consumer Trust and Perceived Brand Authenticity
DOI:
https://doi.org/10.70577/asce.v5i2.868Keywords:
generative artificial intelligence, consumer trust, brand authenticity, AI disclosure, digital marketing.Abstract
This study examines how the adoption of generative artificial intelligence (GAI) tools in corporate marketing strategies affects two critical dimensions of the brand-consumer relationship: trust and perceived authenticity. Starting from the observation that 67% of Fortune 500 marketing departments already integrate some form of GAI into their content creation workflows (McKinsey & Company, 2024), the paper addresses a gap that remains underexplored in the literature: the absence of models that simultaneously articulate the cognitive and affective mechanisms through which exposure to AI-generated content reconfigures—or erodes—consumer trust and perceived brand authenticity.
The methodology combines a 2×2 quasi-experimental design (content type: AI-generated vs. human; disclosure presence: yes vs. no) with a cross-validation survey conducted on a sample of 487 adult consumers recruited via Prolific Academic, preceded by a statistical power analysis using G*Power 3.1 (f² = 0.15, α = 0.05, power = 0.80). Measurement scales were drawn from validated instruments in prior literature: Moulard et al.'s (2021) brand authenticity scale, Cheng et al.'s (2022) AI system trust scale, and Longoni et al.'s (2022) AI disclosure responsiveness scale. Structural analysis was conducted using PLS-SEM with SmartPLS 4.0.
Results reveal that exposure to content explicitly labeled as AI-generated reduces consumer trust by 0.34 standard deviations compared to human-authored content (β = −0.34, p < 0.001), but this effect is significantly attenuated when transparent and contextualized disclosure is provided (interaction β = 0.21, p = 0.008). Perceived authenticity displays a more complex pattern: GAI does not inherently erode it, but does so when consumers detect incongruence between the algorithmic tone of the message and the brand's historical values. Technological literacy acts as a significant moderator of both relationships.
Practical implications suggest that brands can preserve consumer trust without sacrificing GAI efficiency, provided they implement proactive disclosure strategies and maintain identity coherence in their automated messages. Theoretically, the study contributes to technology acceptance models by introducing perceived authenticity as a previously overlooked mediating variable in AI-driven digital marketing contexts.
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