AI Disclosure in Applied Marketing Practice: Effects of an AI Label on Advertising Credibility, Brand Authenticity, and Purchase Intention for a Sensory Experience Product
DOI:
https://doi.org/10.51137/wrp.ijaaib.1063Keywords:
AI Disclosure, Brand Authenticity, Generative Artificial Intelligence, Advertising CredibilityAbstract
Regulatory initiatives such as the EU AI Act oblige advertisers to disclose AI-generated content, raising a transparency dilemma for applied marketing practice: an ethically required disclosure may erode consumer trust. This study examines whether an explicit AI label ("Created by AI") on an advertising image for a fictional organic natural cosmetics brand reduces advertising credibility, brand authenticity, and purchase intention, and whether individual AI literacy moderates these effects. A randomized single-factor between-subjects online experiment was conducted and analyzed in two scenarios: an intention-to-treat sample (N = 179) representing realistic exposure conditions, and a per-protocol sample (N = 125) isolating conscious label perception. No significant label effects emerged in either scenario, and brand authenticity did not mediate an effect on purchase intention. Given the limited sample size, moderate negative effects cannot be excluded, particularly for brand authenticity. More than half of the exposed participants overlooked the label entirely, indicating pronounced banner blindness in applied advertising contexts.
References
Altay, S., & Gilardi, F. (2024). People are skeptical of headlines labeled as AI-generated, even if true or human-made, because they assume full AI automation. PNAS Nexus, 3(10), pgae403. https://doi.org/10.1093/pnasnexus/pgae403
Arango, L., Singaraju, S. P., & Niininen, O. (2023). Consumer responses to AI-generated charitable giving ads. Journal of Advertising, 52(4), 486–503. https://doi.org/10.1080/00913367.2023.2183285
Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2021). Multivariate Analysemethoden: Eine anwendungsorientierte Einführung (16th ed.). Springer Gabler. https://doi.org/10.1007/978-3-658-32425-4
Baek, T. H., Kim, J., & Kim, J. H. (2026). Effect of disclosing AI-generated content on prosocial advertising evaluation. International Journal of Advertising, 45(1), 171–192. https://doi.org/10.1080/02650487.2024.2401319
Beckert, J. (2024). Friestad & Wright (1994): The Persuasion Knowledge Model. In O. Hoffjann & S. Sandhu (Eds.), Key Works in Strategic Communication Research (pp. 365–376). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-45292-6_32
Biagini, G. (2024). Assessing the assessments: Toward a multidimensional approach to AI literacy. Media Education, 15(1), 91–101. https://doi.org/10.36253/me-15831
Bortz, J., & Schuster, C. (2010). Statistik für Human- und Sozialwissenschaftler (7th ed.). Springer.
Campbell, C., Plangger, K., Sands, S., & Kietzmann, J. (2022). How deepfakes and artificial intelligence could reshape the advertising industry: The coming reality of AI fakes and their potential impact on consumer behavior. Journal of Advertising Research, 62(3), 241–251. https://doi.org/10.2501/JAR-2022-017
Carolus, A., Koch, M. J., Straka, S., Latoschik, M. E., & Wienrich, C. (2023). MAILS - Meta AI literacy scale: Development and testing of an AI literacy questionnaire based on well-founded competency models and psychological change- and meta-competencies. Computers in Human Behavior: Artificial Humans, 1(2), 100014. https://doi.org/10.1016/j.chbah.2023.100014
De Cicco, R., Francioni, B., Curina, I., & Cioppi, M. (2025). AI, human, or a blend? How the educational content creator influences consumer engagement and brand-related outcomes. Journal of Services Marketing, 39(10), 54–72. https://doi.org/10.1108/JSM-10-2024-0539
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
European Parliament. (2023, June 8). EU AI Act: First regulation on artificial intelligence. https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.
Friestad, M., & Wright, P. (1994). The Persuasion Knowledge Model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1–31.
Gkourla, A. (2024). Disclosure in advertising: How do consumers perceive AI-generated images? [Master's thesis, Tilburg University].
Hartmann, J., Exner, Y., & Domdey, S. (2025). The power of generative marketing: Can generative AI create superhuman visual marketing content? International Journal of Research in Marketing, 42(1), 13–31. https://doi.org/10.1016/j.ijresmar.2024.09.002
Hou, W., Xu, J., & Ren, J. (2026). Exploring the advertising effectiveness of generative artificial intelligence: An empirical study using the hierarchy-of-effects model. International Journal of Advertising, 45(1), 6–26. https://doi.org/10.1080/02650487.2025.2592466
Kaya, A., & İşçioğlu, T. E. (2026). Is transparency valued or doubted? The impact of AI disclosure on consumer responses to sustainability messages and the mediating role of green scepticism. Pazarlama ve Pazarlama Araştırmaları Dergisi, 19(1). https://doi.org/10.15659/ppad.19.1.1811603
Kirk, C. P., & Givi, J. (2025). The AI-authorship effect: Understanding authenticity, moral disgust, and consumer responses to AI-generated marketing communications. Journal of Business Research. Advance online publication.
Koning, B., & Voorveld, H. A. M. (2025). Disclaimer! This content is AI-generated: How AI disclosures influence trust in advertisements and organizations. Journal of Interactive Advertising, 25(3), 240–253. https://doi.org/10.1080/15252019.2025.2554149
Kučinskas, G. (2025). Revealing AI involvement in ad creation: Effects on authenticity, brand perceptions and consumer intentions. Journal of Information Systems Engineering and Management, 10(16s), 727–740. https://doi.org/10.52783/jisem.v10i16s.2659
Lim, S., & Schmälzle, R. (2024). The effect of source disclosure on evaluation of AI-generated messages: A two-part study. Computers in Human Behavior: Artificial Humans, 2(1), 100058.
Lin, M.-H., & Wu, P.-H. (2026). Creativity in advertising: The roles of implicit theory and AI. International Journal of Advertising, 45(1), 81–113. https://doi.org/10.1080/02650487.2025.2570559
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
Longoni, C., & Cian, L. (2022). Artificial intelligence in utilitarian vs. hedonic contexts: The "Word-of-Machine" effect. Journal of Marketing, 86(1), 91–108. https://doi.org/10.1177/0022242920957347
Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. (2022). What influences algorithmic decision-making? A systematic literature review on algorithm aversion. Technological Forecasting and Social Change, 175, 121390. https://doi.org/10.1016/j.techfore.2021.121390
Mazzù, M. F., Andria, A., Baccelloni, A., & De Angelis, M. (2025). The impact of human vs. AI recommendations on consumer reactions to product exposure. Electronic Commerce Research. https://doi.org/10.1007/s10660-025-10048-6
Meinert, J. (2020). All you need is a (heuristic) cue?: An empirical investigation of the use of social media cues and features and underlying mechanisms for credibility judgments of news and political communication [DuEPublico: Duisburg-Essen Publications online, University of Duisburg-Essen, Germany]. https://doi.org/10.17185/DUEPUBLICO/72856
Morhart, F., Malär, L., Guèvremont, A., Girardin, F., & Grohmann, B. (2015). Brand authenticity: An integrative framework and measurement scale. Journal of Consumer Psychology, 25(2), 200–218. https://doi.org/10.1016/j.jcps.2014.11.006
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Schilke, O., & Reimann, M. (2025). AI disclosure and trust.
Van Berlo, Z. M. C., Campbell, C., & Voorveld, H. A. M. (2024). The MADE framework: Best practices for creating effective experimental stimuli using generative AI. Journal of Advertising, 53(5), 732–753. https://doi.org/10.1080/00913367.2024.2397777
Zhang, L., & Hur, C. (2025). The impact of generative AI images on consumer attitudes in advertising. Administrative Sciences, 15(10), 395. https://doi.org/10.3390/admsci15100395
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Copyright (c) 2026 Felix Bettenworth (Author)
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