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ŞEFFAFLIK DEĞER Mİ GÖRÜR, ŞÜPHE Mİ UYANDIRIR? YAPAY ZEKA AÇIKLAMASININ TÜKETİCİLERİN SÜRDÜRÜLEBİLİRLİK MESAJLARINA VERDİĞİ TEPKİLER ÜZERİNDEKİ ETKİSİ VE YEŞİL ŞÜPHECİLİĞİN ARACILIK ROLÜ

Year 2026, Volume: 19 Issue: 1, 144 - 168, 27.01.2026

Abstract

Markaların pazarlama iletişimlerinde yapay zekâ (YZ) kullanımını açıklayıp açıklamaması gerektiği konusu, hem akademik hem de uygulama alanında halen önemli bir tartışma konusudur. Bu çalışma, çevresel sürdürülebilir temalı mesajlarda YZ açıklamasının tüketici tutumları ve satın alma niyetleri üzerindeki etkisini çok boyutlu bir çerçevede araştırmakta ve yeşil şüpheciliğin aracılık rolünü incelemektedir. Bu amaçla, denekler arası, tek faktörlü tasarıma sahip iki deneysel çalışma yürütülmüştür. Birinci çalışmada, 81 katılımcı YZ açıklaması olan veya olmayan reklamlara rastgele atanmıştır. İkinci çalışmada ise 61 katılımcı YZ açıklaması veya insan açıklaması olan mesajlara yine rastgele maruz bırakılmıştır. Hipotezler SPSS 20.0 ve PROCESS Macro kullanılarak bağımsız örneklem t-testi, lojistik regresyon ve basit aracılık (model 4) analizleri ile test edilmiştir. Sonuçlar, YZ açıklamasının marka tutumları ve satın alma niyetleri üzerinde olumsuz bir etkiye sahip olduğunu, ancak reklama yönelik tutumlar üzerinde anlamlı bir etkiye sahip olmadığını göstermektedir. Ayrıca, her iki çalışmada da yeşil şüpheciliğin tüm ilişkilere anlamlı bir şekilde aracılık ettiği bulunmuştur. Genel olarak, bulgular ikna bilgisi modeli, algoritma kaçınma çerçevesi ve sinyalleme teorisi açısından pazarlama literatürüne teorik katkılar sağlamakta ve yapay zeka destekli sürdürülebilirlik iletişim stratejileri tasarlamak için pratik öneriler sunmaktadır.

References

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IS TRANSPARENCY VALUED OR DOUBTED? THE IMPACT OF AI DISCLOSURE ON CONSUMER RESPONSES TO SUSTAINABILITY MESSAGES AND THE MEDIATING ROLE OF GREEN SCEPTICISM

Year 2026, Volume: 19 Issue: 1, 144 - 168, 27.01.2026

Abstract

Whether brands should disclose their use of artificial intelligence (AI) in their communication remains a significant debate in both academia and practice. This study investigates the impact of AI disclosure in environmentally sustainable messages on consumer attitudes and purchase intentions, within a multidimensional framework, and examines the mediating role of green scepticism. To this end, two experimental studies with between-subjects, single-factor design were conducted. In Study I, 81 participants were randomly assigned to advertisements with or without AI disclosure. In Study II, 61 participants were again randomly exposed to messages with either AI disclosure or human disclosure. Using SPSS 20.0 and PROCESS Macro, the hypotheses were tested with independent samples t-test, logistic regression and simple mediation (model 4) analyses. The results indicate that AI disclosure has a negative impact on brand attitudes and purchase intentions, while having no significant influence on attitudes toward the advertisement. Moreover, green scepticism was found to mediate all relationships significantly in both studies. Overall, the findings provide theoretical contributions to marketing literature with respect to the persuasion knowledge model, algorithm aversion framework and signalling theory and offer practical insights for designing AI-generated sustainability communication strategies.

References

  • Albayrak, T., Aksoy, Ş., & Caber, M. (2013). The effect of environmental concern and scepticism on green purchase behaviour. Marketing Intelligence & Planning, 31(1), 27–39. https://doi.org/10.1108/02634501311292902
  • 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
  • Baek, T. H., Kim, J., & Kim, J. H. (2024). Effect of disclosing AI-generated content on prosocial advertising evaluation. International Journal of Advertising, Advance online publication, 1–22. https://doi.org/10.1080/02650487.2024.2401319
  • Boerman, S. C., & Van Reijmersdal, E. A. (2016). Informing consumers about “hidden” advertising: A literature review of the effects of disclosing sponsored content. In E. De Pelsmacker (Ed.), Advertising in new formats and media (pp. 115–146). Emerald. https://doi.org/10.1108/978-1-78560-313-620151005
  • Boerman, S. C., Willemsen, L. M., & Van der Aa, E. P. (2017). “This post is sponsored”: Effects of sponsorship disclosure on persuasion knowledge and electronic word of mouth in the context of Facebook. Journal of Interactive Marketing, 38(1), 82–92. https://doi.org/10.1016/j.intmar.2016.12.002
  • Brüns, J. D., & Meißner, M. (2024). Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity. Journal of Retailing and Consumer Services, 79, 104010. https://doi.org/10.1016/j.jretconser.2024.103790
  • Bui, H. T. (2025). Examining the effect of AI advertising involvement disclosure on advertising value and purchase intentions. Journal of Research in Interactive Marketing, 1-20. https://doi.org/10.1108/JRIM-02-2025-0066
  • Cambier, F., & Poncin, I. (2020). Inferring brand integrity from marketing communications: The effects of brand transparency signals in a consumer empowerment context. Journal of Business Research, 109, 260–270. https://doi.org/10.1016/j.jbusres.2019.11.060
  • Campbell, C., Plangger, K., Sands, S., Kietzmann, J., & Bates, K. (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
  • Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. https://doi.org/10.1007/s11747-019-00696-0
  • De Jans, S., Cauberghe, V., & Hudders, L. (2018). How an advertising disclosure alerts young adolescents to sponsored vlogs: The moderating role of a peer-based advertising literacy intervention through an informational vlog. Journal of Advertising, 47(4), 309–325. https://doi.org/10.1080/00913367.2018.1539363
  • 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
  • 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
  • Ganesan, P., Sridhar M. & Priyadharsani S. (2016). Advertisement attitude, brand attitude and purchase intention - reciprocal and mediation effect study. International Journal of Business Excellence, 9(4), 488-510. https://doi.org/10.1504/IJBEX.2016.076781
  • Goh, S. K., & Balaji, M. S. (2016). Linking green skepticism to green purchase behavior. Journal of Cleaner Production, 131, 629–638. https://doi.org/10.1016/j.jclepro.2016.04.122
  • Guzman, A. L., & Lewis, S. C. (2020). Artificial intelligence and communication: A human–machine communication research agenda. New media & society, 22(1), 70-86. https://doi.org/10.1177/1461444819858691
  • Ham, C. D., Ryu, S., Lee, J., Chaung, U. C., Buteau, E., & Sar, S. (2022). Intrusive or relevant? Exploring how consumers avoid native Facebook ads through decomposed persuasion knowledge. Journal of Current Issues & Research in Advertising, 43(1), 68–89. https://doi.org/10.1080/10641734.2021.1944934
  • Hayes, A. F. (2022). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (3rd ed.). Guilford Press.
  • Huang, M. H., & Rust, R. T. (2022). A framework for collaborative artificial intelligence in marketing. Journal of Retailing, 98(2), 209–223. https://doi.org/10.1016/j.jretai.2021.03.001
  • Hughes, D. (2025). Best examples of AI in marketing. Digital Marketing Institute Blog. Retrieved from https://digitalmarketinginstitute.com/blog/some-inspiring-uses-of-ai-in-digital-marketing
  • Isaac, M. S., & Grayson, K. (2016). Beyond skepticism: Can accessing persuasion knowledge bolster credibility? Journal of Consumer Research, 43(6), 895–912. https://doi.org/10.1093/jcr/ucw063
  • Kim, J., Giroux, M., & Lee, J. C. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychology & Marketing, 38(7), 1140–1155. https://doi.org/10.1002/mar.21498
  • 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
  • Lim, S., & Schmälzle, R. (2024). The effect of source disclosure on evaluation of AI-generated messages. Computers in Human Behavior: Artificial Humans, 2(1), 100061. https://doi.org/10.1016/j.chbah.2024.100058
  • Longoni, C., Bonezzi, A., & Morewedge, C. K. (2020). Resistance to medical artificial intelligence is an attribute in a compensatory decision process: Response to Pezzo and Beckstead (2020). Judgment and Decision Making, 15(3), 446–448. https://doi.org/10.1017/S1930297500007233
  • 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
  • Manoj, D., & Lee, G. (2025, January). Navigating consumer sentiment: Exploring opinions on AI-generated content across domains. In International Textile and Apparel Association Annual Conference Proceedings (Vol. 81, No. 1). Iowa State University Digital Press.
  • MacKenzie, S. B., Lutz, R. J., & Belch, G. E. (1986). The role of attitude toward the ad as a mediator of advertising effectiveness: A test of competing explanations. Journal of Marketing Research, 23(2), 130–143. https://doi.org/10.1177/002224378602300205
  • Magni, F., Park, J., & Chao, M. M. (2023). Humans as creativity gatekeepers: Are we biased against AI creativity? Journal of Business and Psychology, Advance online publication. https://doi.org/10.1007/s10869-023-09910-x
  • 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
  • Messer, U. (2024). Co-creating art with generative artificial intelligence: Implications for artworks and artists. Computers in Human Behavior: Artificial Humans, 2(1), 100056. https://doi.org/10.1016/j.chbah.2024.100056
  • Mileva, G. (2025). Top 10 AI influencers making waves on Instagram. Influencer Marketing Hub. Retrieved from https://influencermarketinghub.com/ai-influencers-instagram/
  • Mohr, L. A., Eroǧlu, D., & Ellen, P. S. (1998). The development and testing of a measure of skepticism toward environmental claims in marketers’ communications. Journal of Consumer Affairs, 32(1), 30–55. https://doi.org/10.1111/j.1745-6606.1998.tb00399.x
  • Montecchi, M., Plangger, K., West, D., & de Ruyter, K. (2024). Perceived brand transparency: A conceptualization and measurement scale. Psychology & Marketing, 41(10), 2274–2297. https://doi.org/10.1002/mar.22048
  • Nelson, M. R., Wood, M. L. M., & Paek, H. J. (2009). Increased persuasion knowledge of video news releases: Audience beliefs about news and support for source disclosure. Journal of Mass Media Ethics, 24(4), 220–237. https://doi.org/10.1080/08900520903332626
  • Nunnally, J. C. (1978). Psychometric methods. McGraw-Hill, New York.
  • Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. https://doi.org/10.3758/BRM.40.3.879
  • Qiu, X., Wang, Y., Zeng, Y., & Cong, R. (2025). Artificial intelligence disclosure in cause-related marketing: A persuasion knowledge perspective. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 193. https://doi.org/10.3390/jtaer20030193
  • Sands, S., Demsar, V., Ferraro, C., Campbell, C., & Cohen, J. (2024). Inauthentic inclusion: Exploring how intention to use AI-generated diverse models can backfire. Psychology & Marketing, 41(6), 1396–1413. https://doi.org/10.1002/mar.21987
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There are 56 citations in total.

Details

Primary Language English
Subjects Marketing Communications, Social Marketing, Consumer Behaviour
Journal Section Research Article
Authors

Ahmet Kaya 0000-0001-5707-5386

Tutku Eker İşcioğlu 0000-0002-4794-6368

Submission Date October 27, 2025
Acceptance Date January 21, 2026
Publication Date January 27, 2026
Published in Issue Year 2026 Volume: 19 Issue: 1

Cite

APA Kaya, A., & Eker İşcioğlu, T. (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), 144-168.