Institutt for markedsføring

Journal of Retailing and Consumer Services 94(105058) Doi: https://doi.org/10.1016/j.jretconser.2026.105058

Generative artificial intelligence (GenAI) is moving from back-end support into the creative core of advertising, raising questions about how consumers respond when brands use GenAI for visible, consumer-facing content. This study investigates the consumer backlash to GenAI advertising by analyzing 7822 YouTube and Reddit comments on Coca-Cola's AI-generated remake of its iconic Holidays Are Coming campaign. Using large-scale data collection and a LLM-assisted, researcher-validated thematic analysis, we identify a three-dimensional pattern of aversion against GenAI ads, which we organize as the CPS (Content–Psychological–Socio-ethical) framework. The framework consists of content-related judgments focused on visual realism, execution quality, and contextual fit; psychological reactions centered on authenticity, creativity, and human connection; and socio-ethical attributions involving corporate motives, labor displacement, and broader ethical concerns. The findings demonstrate that backlash against GenAI ads emerges not only from perceived inferiority to human work, but also from what the use of GenAI signals to consumers about a brand's values and intentions. Built on real-world consumer discourse from a GenAI-enabled ad campaign, the CPS framework empirically grounds how backlash develops along the three dimensions. For practitioners, the framework offers a diagnostic tool to anticipate potential backlash from using GenAI in ads.

Gyldendal Akademisk Doi: https://www.gyldendal.no/hoyere-utdanning-og-profesjon/okonomi-og-administrasjon/organisasjon-og-ledelse/forbrukerpsykologi-9788205611498

Journal of Public Policy & Marketing 45(3 Special Issue: Reclaiming Marketplace Inclusion as a Marketing Imperative) s. 227-230 Doi: https://doi.org/10.1177/07439156261431324

Creativity and Innovation Management Doi: https://doi.org/10.1111/caim.70069

Frontiers in Artificial Intelligence Doi: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2026.1900160/abstract

Accurate brain tumor classification from Magnetic Resonance Imaging (MRI) remains challenging due to dataset heterogeneity, class imbalance, and limited inte...