Journal of Advertising and Sales Management

Journal of Advertising and Sales Management

A Conceptual Review of Individuals' Reactions to Artificial Intelligence Identity Disclosure, Developing a Framework to Explain These Reactions in Advertising

Authors
Helia Alamdari
SeyedAlireza Mirbagheri
Department of Management, Faculty of Management and Economics, Sharif University of Technology, Tehran, Iran
10.22034/asm.2026.2087794.3521
Abstract
Objective: With the rapid growth of generative artificial intelligence (AI), AI based content creation has become a dominant trend in contemporary marketing. Although disclosing the use of AI has been recommended as a strategy to promote transparency and ethical accountability, empirical evidence reveals heterogeneous and often negative consumer reactions. To explain this inconsistency, this study adopts a conceptual review approach to identify the processes and factors influencing individuals’ attitudes toward AI generated advertisements.
Method: This research employs a conceptual review methodology through an extensive search in Google Scholar using keywords related to AI disclosure. During the screening process, empirical and conceptual studies examining attitudes, perceptions, and reactions to AI disclosure were included, while studies focused solely on technical aspects of AI without addressing user or consumer responses were excluded. The selected studies were classified into eight core domains. Subsequently, drawing on persuasion knowledge model, the findings were integrated to propose a conceptual framework explaining audience reactions to AI disclosure in advertising, comprising five mediating mechanisms and two moderating variables.
Findings: The proposed conceptual framework suggests that disclosing AI identity tends to have a direct negative effect on audience attitudes, yet this effect operates through several mediating processes. Perceived transparency exerts a positive influence, while inferences of manipulative intent, reduced perceived creativity, algorithm aversion, and decreased perceived legitimacy form the negative pathways. Furthermore, audience experience with AI moderates these effects: positive experience mitigates the negative impact, whereas negative experience amplifies it. Product type also moderates responses, disclosure tends to have weaker negative effects for utilitarian products and stronger ones for hedonic products. The framework’s theoretical contribution lies in consolidating fragmented literature, jointly explaining mediating and moderating mechanisms within a testable model, and extending persuasion knowledge model to the context of intelligent technologies.
Conclusion: This research proposes a set of evidence based practical recommendations for optimizing AI use in advertising content creation. The results indicate that the impact of AI disclosure is neither uniform nor fixed but contingent upon audience perception and contextual factors. Accordingly, when disclosure is mandatory on certain platforms or situations, employing human AI co creation models can be effective. Conversely, in non mandatory contexts, overtly highlighting AI involvement is not always advisable and may be avoided. Disclosure strategies should also align with product type: for utilitarian products, revealing AI use generally has limited negative influence and can be done cautiously; for hedonic products, where emotional, creative, and human elements dominate, AI disclosure may reduce perceived authenticity of the message. In such cases, either avoiding direct disclosure or emphasizing human participation (e.g., implying human collaboration) helps maintain emotional connection and message acceptance. Finally, the study emphasizes the need for clear organizational policies and frameworks for responsible AI utilization. Viewing AI as a tool serving human creativity rather than replacing it allows leveraging technological benefits while preserving authentic human expression, an approach that, in the long run, enhances the effectiveness of marketing communications.
Keywords
disclosure
artificial intelligence
advertising
attitudes
Subjects
زارعی,عظیم , فیض,داود , مدرسی,میثم و یوسفی,محمد . (1404). کاوش نقش هوش مصنوعی مولد در بازاریابی: یک مطالعه کیفی با رویکرد تحلیل تماتیک. مدیریت تبلیغات و فروش, 6(3), 224-244. doi: 10.22034/asm.2025.2078592.3474
سنجری نادر,بهزاد , بلوچی,حسین و ملکی ورکی,مهرداد . (1403). بررسی تأثیر عوامل روانشناختی در تمایل به استفاده از نرم‌افزارهای چهره‌آرایی مبتنی بر هوش مصنوعی در بازاریابی لوازم آرایشی. مدیریت تبلیغات و فروش, 5(4), 59-79. doi: 10.22034/asm.2025.2049478.3331
نصیری,علی , امنی,حمید و خسرویان چم پیری,یاسمن . (1404). مدل‌سازی تأثیر شخصی‌سازی تبلیغات مبتنی بر هوش مصنوعی بر وفاداری مشتری در برندهای دیجیتال. مدیریت تبلیغات و فروش, 6(3), 139-152. doi: 10.22034/asm.2025.2076565.3462
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
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.
Arango, L., Singaraju, S. P., & Niininen, O. (2023). Consumer responses to AI-generated charitable giving ads. Journal of Advertising, 52(4), 486–503.
Baek, T. H., Kim, J., & Kim, J. H. (2024). Effect of disclosing AI-generated content on prosocial advertising evaluation. International Journal of Advertising, 1–22. https://doi.org/10.1080/02650487.2024.2401319
Bakpayev, M., Baek, T. H., Van Esch, P., & Yoon, S. (2022). Programmatic creative: AI can think but it cannot feel. Australasian Marketing Journal, 30(1), 90–95. https://doi.org/10.1016/j.ausmj.2020.04.002
Bashardoust, A., Feuerriegel, S., & Shrestha, Y. R. (2024). Comparing the willingness to share for human-generated vs. AI-generated fake news (arXiv:2402.07395). arXiv. http://arxiv.org/abs/2402.07395
Bellaiche, L., Shahi, R., Turpin, M. H., Ragnhildstveit, A., Sprockett, S., Barr, N., Christensen, A., & Seli, P. (2023). Humans versus AI: Whether and why we prefer human-created compared to AI-created artwork. Cognitive Research: Principles and Implications, 8(1), 42.
Bigman, Y. E., & Gray, K. (2018). People are averse to machines making moral decisions. Cognition, 181, 21–34.
Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257.
Bruna Horvath. (2024). Coca-Cola causes controversy with AI-made ad. https://www.nbcnews.com/tech/innovation/coca-cola-causes-controversy-ai-made-ad-rcna180665
Campbell, C., Plangger, K., Sands, S., & Kietzmann, J. (2022). Preparing for an era of deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. Journal of Advertising, 51(1), 22–38.
Campbell, M. C. (1995). When attention-getting advertising tactics elicit consumer inferences of manipulative intent: The importance of balancing benefits and investments. Journal of Consumer Psychology, 4(3), 225–254.
Cao, L. (2021). Artificial intelligence in retail: Applications and value creation logics. International Journal of Retail & Distribution Management, 49(7), 958–976.
Carl, W. J. (2008). The role of disclosure in organized word‐of‐mouth marketing programs. Journal of Marketing Communications, 14(3), 225–241. https://doi.org/10.1080/13527260701833839
Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-Dependent Algorithm Aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788
Chiarella, S. G., Torromino, G., Gagliardi, D. M., Rossi, D., Babiloni, F., & Cartocci, G. (2022). Investigating the negative bias towards artificial intelligence: Effects of prior assignment of AI-authorship on the aesthetic appreciation of abstract paintings. Computers in Human Behavior, 137, 107406.
Cicek, M., Gursoy, D., & Lu, L. (2025). Adverse impacts of revealing the presence of “Artificial Intelligence (AI)” technology in product and service descriptions on purchase intentions: The mediating role of emotional trust and the moderating role of perceived risk. Journal of Hospitality Marketing & Management, 34(1), 1–23.
Damian, R. I., & Simonton, D. K. (2015). Four Psychological Perspectives on Creativity. In R. A. Scott & S. M. Kosslyn (Eds.), Emerging Trends in the Social and Behavioral Sciences (1st ed., pp. 1–15). Wiley. https://doi.org/10.1002/9781118900772.etrds0134
Deephouse, D. L., & Carter, S. M. (2005). An Examination of Differences Between Organizational Legitimacy and Organizational Reputation*. Journal of Management Studies, 42(2), 329–360. https://doi.org/10.1111/j.1467-6486.2005.00499.x
Diakopoulos, N., & Koliska, M. (2017). Algorithmic Transparency in the News Media. Digital Journalism, 5(7), 809–828. https://doi.org/10.1080/21670811.2016.1208053
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.
Du, D., Zhang, Y., & Ge, J. (2023). Effect of AI Generated Content Advertising on Consumer Engagement. In F. Nah & K. Siau (Eds.), HCI in Business, Government and Organizations (pp. 121–129). Springer Nature Switzerland.
Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961–974.
Fransen, M. L., Verlegh, P. W. J., Kirmani, A., & Smit, E. G. (2015). A typology of consumer strategies for resisting advertising, and a review of mechanisms for countering them. International Journal of Advertising, 34(1), 6–16. https://doi.org/10.1080/02650487.2014.995284
Friestad, M., & Wright, P. (1994). The Persuasion Knowledge Model: How People Cope with Persuasion Attempts. Journal of Consumer Research, 21(1), 1–31. https://doi.org/10.1086/209380
Grassini, S., & Koivisto, M. (2024). Understanding how personality traits, experiences, and attitudes shape negative bias toward AI-generated artworks. Scientific Reports, 14(1), 4113.
Gray, K., & Wegner, D. M. (2012). Feeling robots and human zombies: Mind perception and the uncanny valley. Cognition, 125(1), 125–130.
Hofeditz, L., Mirbabaie, M., Holstein, J., & Stieglitz, S. (2021). Do You Trust an AI-journalist? A Credibility Analysis of News Content with AI-Authorship. ECIS. https://www.researchgate.net/profile/Lennart-Hofeditz/publication/351348564_Do_you_Trust_an_AI-Journalist_A_Credibility_Analysis_of_News_Content_with_AI-Authorship/links/61d898ebda5d105e5526dd10/Do-you-Trust-an-AI-Journalist-A-Credibility-Analysis-of-News-Content-with-AI-Authorship.pdf
Hulland, J. (2020). Conceptual review papers: revisiting existing research to develop and refine theory. 10(1), 27–35. https://doi.org/10.1007/S13162-020-00168-7
Ishowo-Oloko, F., Bonnefon, J.-F., Soroye, Z., Crandall, J., Rahwan, I., & Rahwan, T. (2019). Behavioural evidence for a transparency–efficiency tradeoff in human–machine cooperation. Nature Machine Intelligence, 1(11), 517–521.
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4). https://svn.bmj.com/content/2/4/230.
Johar, J. S., & Sirgy, M. J. (1991). Value-expressive versus utilitarian advertising appeals: When and why to use which appeal. Journal of Advertising, 20(3), 23–33.
Kaplan, A. D., Kessler, T. T., Brill, J. C., & Hancock, P. A. (2023). Trust in Artificial Intelligence: Meta-Analytic Findings. Human Factors: The Journal of the Human Factors and Ergonomics Society, 65(2), 337–359. https://doi.org/10.1177/00187208211013988
Keppeler, F. (2024). No thanks, dear AI! Understanding the effects of disclosure and deployment of artificial intelligence in public sector recruitment. Journal of Public Administration Research and Theory, 34(1), 39–52.
Kietzmann, J., Mills, A. J., & Plangger, K. (2021). Deepfakes: Perspectives on the future “reality” of advertising and branding. International Journal of Advertising, 40(3), 473–485.
Kim, M., Lee, J. K., & Lee, K. Y. (2019). Interplay of content type and product type in the consumer response to native advertising on social media. Asian Journal of Communication, 29(6), 464–482. https://doi.org/10.1080/01292986.2019.1679852
Kirkby, A., Baumgarth, C., & Henseler, J. (2023). To disclose or not disclose, is no longer the question – effect of AI-disclosed brand voice on brand authenticity and attitude. Journal of Product & Brand Management, 32(7), 1108–1122. https://doi.org/10.1108/JPBM-02-2022-3864
Kirmani, A., & Zhu, R. (2007). Vigilant against manipulation: The effect of regulatory focus on the use of persuasion knowledge. Journal of Marketing Research, 44(4), 688–701.
Köbis, N., & Mossink, L. D. (2021). Artificial intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry. Computers in Human Behavior, 114, 106553.
Koning, B., & Voorveld, H. A. (2025). Disclaimer! This Content Is AI-Generated: How AI-Disclosures Influence Trust in Advertisements and Organizations. Journal of Interactive Advertising, 25(3), 240–253.
Kreps, S., McCain, R. M., & Brundage, M. (2022). All the news that’s fit to fabricate: AI-generated text as a tool of media misinformation. Journal of Experimental Political Science, 9(1), 104–117.
Lee, D., & Ham, C.-D. (2023). AI versus Human: Rethinking the Role of Agent Knowledge in Consumers’ Coping Mechanism Related to Influencer Marketing. Journal of Interactive Advertising, 23(3), 241–258. https://doi.org/10.1080/15252019.2023.2217830
Lehnert, K., Till, B. D., & Ospina, J. M. (2014). Advertising Creativity: The Role of Divergence Versus Meaningfulness. Journal of Advertising, 43(3), 274–285. https://doi.org/10.1080/00913367.2013.851630
Lim, S., & Schmälzle, R. (2024). The effect of source disclosure on evaluation of AI-generated messages. Computers in Human Behavior: Artificial Humans, 2(1), 100058.
Liu, Y., Wang, S., & Yu, G. (2023). The nudging effect of AIGC labeling on users’ perceptions of automated news: Evidence from EEG. Frontiers in Psychology, 14, 1277829.
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019a). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650.
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019b). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650.
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. Humans: The Impact of Artificial Intelligence Chatbot Disclosure on Customer Purchases. Marketing Science, mksc.2019.1192. https://doi.org/10.1287/mksc.2019.1192
MacInnis, D. J. (2011). A Framework for Conceptual Contributions in Marketing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1845968
Ma, X., & Huo, Y. (2024). Drawing a satisfying picture: An exploratory study of human-AI interaction in AI Painting through breakdown–repair communication strategies. Information Processing & Management, 61(4), 103755.
Mahmud, H., Islam, A. 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.
McKinsey. (2023). https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2023-generative-ais-breakout-year
Mende, M., Scott, M. L., Van Doorn, J., Grewal, D., & Shanks, I. (2019). Service Robots Rising: How Humanoid Robots Influence Service Experiences and Elicit Compensatory Consumer Responses. Journal of Marketing Research, 56(4), 535–556. https://doi.org/10.1177/0022243718822827
Meyer, C & Schwager, A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 116.
Minton, E. A., Kaplan, B., & Cabano, F. G. (2022). The influence of religiosity on consumers’ evaluations of brands using artificial intelligence. Psychology & Marketing, 39(11), 2055–2071. https://doi.org/10.1002/mar.21727
Morosan, C., & Dursun-Cengizci, A. (2024). Letting AI make decisions for me: An empirical examination of hotel guests’ acceptance of technology agency. International Journal of Contemporary Hospitality Management, 36(3), 946–974.
Mozafari, N., Weiger, W. H., & Hammerschmidt, M. (2022). Trust me, I’m a bot–repercussions of chatbot disclosure in different service frontline settings. Journal of Service Management, 33(2), 221–245.
Ochmann, J., Michels, L., Zilker, S., Tiefenbeck, V., & Laumer, S. (2020). The influence of algorithm aversion and anthropomorphic agent design on the acceptance of AI-based job recommendations. https://aisel.aisnet.org/icis2020/is_workplace_fow/is_workplace_fow/4/
Palmeira, M., & Spassova, G. (2015). Consumer reactions to professionals who use decision aids. European Journal of Marketing, 49(3–4), 302–326.
Panda, T. K., Panda, T. K., & Mishra, K. (2013). Does Emotional Appeal Work in Advertising? The Rationality Behind Using Emotional Appeal to Create Favorable Brand Attitude. IUP Journal of Brand Management, 10(2).
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. In International Journal of Research in Marketing (Vol. 40, Number 2, pp. 269–275). Elsevier. https://www.sciencedirect.com/science/article/pii/S0167811623000162
Plaisance, P. L. (2007). Transparency: An Assessment of the Kantian Roots of a Key Element in Media Ethics Practice. Journal of Mass Media Ethics, 22(2–3), 187–207. https://doi.org/10.1080/08900520701315855
Qin, X., & Jiang, Z. (2019). The Impact of AI on the Advertising Process: The Chinese Experience. Journal of Advertising, 48(4), 338–346. https://doi.org/10.1080/00913367.2019.1652122
Raziq, M. M., Ahmed, Q. M., Ahmad, M., Yusaf, S., Sajjad, A., & Waheed, S. (2018). Advertising skepticism, need for cognition and consumers’ attitudes. Marketing Intelligence & Planning, 36(6), 678–693.
Rob Watts & Katherine Haan. (2023). Over 75% Of Consumers Are Concerned About Misinformation From Artificial Intelligence – Forbes Advisor. https://www.forbes.com/advisor/business/artificial-intelligence-consumer-sentiment/
Runco, M. A., & Jaeger, G. J. (2012). The Standard Definition of Creativity. Creativity Research Journal, 24(1), 92–96. https://doi.org/10.1080/10400419.2012.650092
Schmitt, B. (2020). Speciesism: An obstacle to AI and robot adoption. Marketing Letters, 31(1), 3–6.
Shaouf, A., Lü, K., & Li, X. (2016). The effect of web advertising visual design on online purchase intention: An examination across gender. Computers in Human Behavior, 60, 622–634.
Smit, E. G., & van Reijmersdal, E. A. (2024). 10. The Importance of Consumer Empowerment in Dealing with Digital Persuasion. Communication Research into the Digital Society Fundam, 177.
Sohn, K., Sung, C. E., Koo, G., & Kwon, O. (2020). Artificial intelligence in the fashion industry: Consumer responses to generative adversarial network (GAN) technology. International Journal of Retail & Distribution Management, 49(1), 61–80.
Studio, A. C. (2024, March 14). How Advertisers Can Unlock The Promise Of AI With Transparency. AdExchanger. https://www.adexchanger.com/content-studio/how-advertisers-can-unlock-the-promise-of-ai-with-transparency/
Suchman, M. C. (1995). Managing Legitimacy: Strategic and Institutional Approaches. The Academy of Management Review, 20(3), 571. https://doi.org/10.2307/258788
Thomas, V. L., Fowler, K., & Grimm, P. (2013). Conceptualization and exploration of attitude toward advertising disclosures and its impact on perceptions of manipulative intent. Journal of Consumer Affairs, 47(3), 564–587.
Thompson, D. V., & Malaviya, P. (2013). Consumer-Generated Ads: Does Awareness of Advertising Co-Creation Help or Hurt Persuasion? Journal of Marketing, 77(3), 33–47. https://doi.org/10.1509/jm.11.0403
To, R. N., Wu, Y.-C., Kianian, P., & Zhang, Z. (2025). When AI Doesn’t Sell Prada: Why Using AI-Generated Advertisements Backfires for Luxury Brands. Journal of Advertising Research, 65(2), 202–236. https://doi.org/10.1080/00218499.2025.2454120
U.S.: AI ads trust levels 2023. (n.d.). Statista. Retrieved September 27, 2025, from https://www.statista.com/statistics/1456992/consumer-trust-levels-ai-disclosure-ads-usa/
Waddell, T. F. (2018). A Robot Wrote This?: How perceived machine authorship affects news credibility. Digital Journalism, 6(2), 236–255. https://doi.org/10.1080/21670811.2017.1384319
Weismueller, J., Harrigan, P., Wang, S., & Soutar, G. N. (2020). Influencer Endorsements: How Advertising Disclosure and Source Credibility Affect Consumer Purchase Intention on Social Media. Australasian Marketing Journal, 28(4), 160–170. https://doi.org/10.1016/j.ausmj.2020.03.002
Wortel, C., Vanwesenbeeck, I., & Tomas, F. (2024). Made with Artificial Intelligence: The Effect of Artificial Intelligence Disclosures in Instagram Advertisements on Consumer Attitudes. Emerging Media, 2(3), 547–570. https://doi.org/10.1177/27523543241292096
Wu, L., Dodoo, N. A., & Wen, T. J. (2025). Disclosing AI’s Involvement in Advertising to Consumers: A Task-Dependent Perspective. Journal of Advertising, 54(1), 20–38. https://doi.org/10.1080/00913367.2024.2309929
Wu, L., & Jing Wen, T. (2021). Understanding AI Advertising From the Consumer Perspective: What Factors Determine Consumer Appreciation of AI-Created Advertisements? Journal of Advertising Research, 61(2), 133–146. https://doi.org/10.2501/JAR-2021-004
Wu, L., & Wen, T. J. (2021). Understanding AI advertising from the consumer perspective: What factors determine consumer appreciation of AI-created advertisements? Journal of Advertising Research, 61(2), 133–146.
Wu, Y., Mou, Y., Li, Z., & Xu, K. (2020). Investigating American and Chinese subjects’ explicit and implicit perceptions of AI-generated artistic work. Computers in Human Behavior, 104, 106186.
Xu, Y. W., Cai, R. R., & Gursoy, D. (2024). When disclosing the artificial intelligence (AI) technology integration into service delivery backfires: Roles of fear of AI, identity threat and existential threat. International Journal of Hospitality Management, 122, 103829.
Yagci, M. I., Biswas, A., & Dutta, S. (2009). Effects of comparative advertising format on consumer responses: The moderating effects of brand image and attribute relevance. Journal of Business Research, 62(8), 768–774. https://doi.org/10.1016/j.jbusres.2008.03.005
Yang, Y., & Shi, X. (2022). Disclose the bot: Chatbot disclosure and anxiety in customer–chatbot interactions. https://aisel.aisnet.org/pacis2022/237/
Yokoi, R., Eguchi, Y., Fujita, T., & Nakayachi, K. (2021). Artificial intelligence is trusted less than a doctor in medical treatment decisions: Influence of perceived care and value similarity. International Journal of Human–Computer Interaction, 37(10), 981–990.
Zhang, Y., & Gosline, R. (2023). Human favoritism, not AI aversion: People’s perceptions (and bias) toward generative AI, human experts, and human–GAI collaboration in persuasive content generation. Judgment and Decision Making, 18, e41.