Ahac, M. (2025)

Master’s thesis, University of Gothenburg

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Ahac (2025)explores how consumers interpret AI-generated marketing, focusing on theinterplay of trust, authenticity, and emotional tone. While generative AIenables brands to produce content at scale with personalization and efficiency,the thesis emphasizes a research gap: little is known about how consumersperceive such automated communication when it attempts to simulate humanemotion.

Drawing on AlgorithmAversion Theory (Dietvorst et al., 2015), Parasocial Interaction Theory(Horton & Wohl, 1956), and Surveillance Capitalism (Zuboff, 2019),the study situates consumer skepticism within broader debates about automation,authenticity, and data ethics. Literature reviewed indicates that trust in AImarketing is conditional: disclosure alone does not guarantee credibility unlessit is contextual and specific. Consumers often interpret emotionally framed AIcontent as manipulative or “flat,” undermining brand sincerity. Authenticity,meanwhile, is linked not to linguistic fluency but to perceived humaninvolvement and alignment with brand values. Studies by Alm & Gustafsson(2024) and Naz & Kashif (2025) show that users accept AI content morereadily when human editing or oversight is visible.

Emotionalresponses emerge as ambivalent: personalization can increase relevance, butover-automation risks detachment and discomfort, especially in high-contextcultures where subtle tone mismatches carry weight. Generational and culturaldifferences also shape acceptance, with younger, digitally literate consumersmore open to AI messaging.

Overall, thethesis synthesizes fragmented research, highlighting ethical and psychologicaltensions in AI-mediated branding. It argues that successful deployment of AImarketing requires transparency, balanced human-AI collaboration, andculturally sensitive design to maintain credibility and consumer trust.