Consumer engagement with AI-generated advertising: Visual appeal, transparency, and anthropomorphism

Putu Laksmita Dewi Rahmayanti; Cokorda Gede Govinda Partha; Fikrul Hakim; Estanislau de Sousa Saldanha; Leonie Nesselhauf

doi:10.24198/jmk.v11i1.72719

Consumer engagement with AI-generated advertising: Visual appeal, transparency, and anthropomorphism

Putu Laksmita Dewi Rahmayanti, Cokorda Gede Govinda Partha, Fikrul Hakim, Estanislau de Sousa Saldanha, Leonie Nesselhauf

Abstract

Background: The growing use of generative artificial intelligence (AI) in advertising has revolutionized marketing communications and highlighted challenges regarding consumer responses to AI-generated advertising. While previous research has explored particular technology or advertising attributes, few studies have merged AI transparency, AI anthropomorphism, and perceived visual appeal to explain consumer engagement via advertising attitude and brand trust. Purpose: The objective of this study is to examine the effects of AI transparency, AI anthropomorphism, and perceived visual appeal on attitude toward AI advertising, brand trust, and consumer engagement, as well as the effects of attitude and trust on consumer engagement. Methods: An explanatory sequential mixed-methods design was adopted. The quantitative data were collected from 220 Indonesian social media users who had been exposed to AI-generated advertisements on platforms including Instagram, TikTok, YouTube, X, WhatsApp, and Facebook Results: Quantitatively, visual appeal significantly influenced all three outcomes, transparency influenced Brand Trust and Consumer Engagement, and anthropomorphism had no significant effects. Attitude and Brand Trust also enhanced engagement. Qualitatively, visual appeal attracted attention, transparency reinforced trust, and anthropomorphism elicited mixed responses depending on naturalness and relatability. Conclusion: Visual appeal and trust-related brand trust play more important roles in consumer engagement with AI-generated advertising than anthropomorphic features alone, highlighting the importance of attractive visuals and credible brand communication assessments are more closely related to consumer engagement than anthropomorphic features alone. Implications: This study adds to the AI advertising literature by merging technological, visual, attitudinal and relational elements and indicates the benefit of integrating PLS-SEM and QNA. The practical implications for marketers are to concentrate on the visual attractiveness and transparency of the advertising on the use of content produced by AI and to ensure organic and believable human-like aspects.

Keywords

AI-generated advertising; consumer engagement; AI transparency; brand trust; visual appeal

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References

Ahn, R. J., Cho, S. Y., & Zhao, X. (2026). Virtual perfection: visual cues, disclosure, and adolescent responses to virtual influencers. Journal of Interactive Advertising, 1–18. https://doi.org/10.1080/15252019.2026.2681492

Akarsu, H., & Sever, N. S. (2023). Engagement publicitario: conceptualización, desarrollo de escala y validación. Fonseca, Journal of Communication, 26, 283–301. https://doi.org/10.14201/fjc.31217

Arghashi, V., & Arsun Yuksel, C. (2023). Customer brand engagement behaviors: The role of cognitive values, intrinsic and extrinsic motivations and self-brand connection. Journal of Marketing Theory and Practice, 31(2), 146–172. https://doi.org/10.1080/10696679.2021.2018936

Ayertey, S., Panditharathna, R., Mohamad, M., & Laryeafio, M. N. (2026). Generative AI as a digital influencer on visual content marketing. In Strategies, Dynamics, and Societal Impacts of Digital Influencer Marketing (pp. 159–174). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-5916-8.ch006

Babu, M. A., Mir, J. A., Roy, R., Roy, A., & Rasel, M. (2026). AI-driven advertising and consumer engagement: Explaining cognitive, emotional, and behavioral responses through the stimulus–organism–response framework. Strategic Business Research, 2(1), 100247. https://doi.org/10.1016/j.sbr.2026.100247

Blut, M., Wang, C., Wünderlich, N. V., & Brock, C. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632–658. https://doi.org/10.1007/s11747-020-00762-y

Braun, V., Clarke, V., & Hayfield, N. (2022). ‘A starting point for your journey, not a map’: Nikki Hayfield in conversation with Virginia Braun and Victoria Clarke about thematic analysis. Qualitative Research in Psychology, 19(2), 424–445. https://doi.org/10.1080/14780887.2019.1670765

Chae, M.-J., & Kim, M. (2026). Too human to trust? How AI human-likeness and context orientation shape consumer preferences in premium high-tech markets. Journal of Retailing and Consumer Services, 88, 104513. https://doi.org/10.1016/j.jretconser.2025.104513

Chaisatitkul, A., Luangngamkhum, K., Noulpum, K., & Kerdvibulvech, C. (2024). The power of AI in marketing: Enhancing efficiency and improving customer perception through AI-generated storyboards. International Journal of Information Technology, 16(1), 137–144. https://doi.org/10.1007/s41870-023-01661-5

Chen, Y., Wang, H., Rao Hill, S., & Li, B. (2024). Consumer attitudes toward AI-generated ads: Appeal types, self-efficacy and AI’s social role. Journal of Business Research, 185, 114867. https://doi.org/10.1016/j.jbusres.2024.114867

Cheng, X., Zhang, X., Cohen, J., & Mou, J. (2022). Human vs. AI: Understanding the impact of anthropomorphism on consumer response to chatbots from the perspective of trust and relationship norms. Information Processing & Management, 59(3), 102940. https://doi.org/10.1016/j.ipm.2022.102940

Destrity, N. A., & Wardasari, N. (2025). Message fatigue on social media and Covid-19 health behavior intention. Jurnal Manajemen Komunikasi, 9(2), 283–302. https://doi.org/10.24198/jmk.v9i2.52231

Draucker, C. B., Rawl, S. M., Vode, E., & Carter-Harris, L. (2020). Integration through connecting in explanatory sequential mixed method studies. Western Journal of Nursing Research, 42(12), 1137–1147. https://doi.org/10.1177/0193945920914647

Du, D., Zhang, Y., & Ge, J. (2023). Effect of AI generated content advertising on consumer engagement. In Lecture Notes in Computer Science (pp. 121–129). Springer, Cham. https://doi.org/10.1007/978-3-031-36049-7_9

Fang, H., Li, B., Zhou, Z., Li, M., Lai, H., Zheng, Y., Hu, B., & Chen, W. (2026). Exploring the aesthetic cognition and artistic acceptance of AIGC-generated urban sculptures: A structural equation modeling and visual content analysis approach. PLOS One, 21(3), e0344501. https://doi.org/10.1371/journal.pone.0344501

Filieri, R., Rasty, F., Bazi, S., & Zheng, L. (2026). Different engagement dimensions, different outcomes: Consumer engagement with tech influencers and behavioural outcomes. Journal of Marketing Management, 1–30. https://doi.org/10.1080/0267257X.2026.2712510

Gan, C. L., Lee, Y. Y., Liew, T. W., Tan, S.-M., Ahmad, F., & Prasetio, A. (2025). Cognitive and affective factors in AI virtual influencer marketing: A stimulus–organism–response and pleasure–arousal–dominance model approach. Digital Business, 5(2), 100150. https://doi.org/10.1016/j.digbus.2025.100150

Greilich, A., Bremser, K., & Wüst, K. (2025). Consumer response to anthropomorphism of text-based AI chatbots: A systematic literature review and future research directions. International Journal of Consumer Studies, 49(5), 1–22. https://doi.org/10.1111/ijcs.70108

Hair, J., & Alamer, A. (2022). Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example. Research Methods in Applied Linguistics, 1(3), 100027. https://doi.org/10.1016/j.rmal.2022.100027

Hanaysha, J. R., & Alhyasat, K. M. K. (2025). Examining the effect of social media advertising features on customer perceived value and brand love in the retail industry. Telematics and Informatics Reports, 18, 100208. https://doi.org/10.1016/j.teler.2025.100208

Hollebeek, L. D., Sarstedt, M., Menidjel, C., Sprott, D. E., & Urbonavicius, S. (2023). Hallmarks and potential pitfalls of customer‐ and consumer engagement scales: A systematic review. Psychology & Marketing, 40(6), 1074–1088. https://doi.org/10.1002/mar.21797

Ipsos. (2025). Ipsos AI monitor 2025. Www.Ipsos.Com. https://www.ipsos.com/en-dk/ipsos-ai-monitor-2025

Jayaprakash, S. (2025). Transforming Consumer Perceptions: Shifting Emphasis from Ads to Informed Decision-Making Through Content Marketing. In Lecture Notes in Mechanical Engineering (pp. 419–431). Springer. https://doi.org/10.1007/978-981-96-5511-3_33

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

Kumar, S., & Thankachan, J. (2025). Advances in consumer research aesthetic of consumption: A review of sensory branding and visual semiotics in consumer experience. Advances in Consumer Research, 2(5), 1068–1083. https://www.acr-journal.com/article/aesthetic-of-consumption-a-review-of-sensory-branding-and-visual-semiotics-in-consumer-experience-1787/

Le, K. B. Q., Khan, H., Li, F., & Kunz, W. H. (2026). More than saying “it’s AI”: How role disclosure transparency in AI-generated ads influences persuasion. Psychology & Marketing, 43(10), 2532–2551. https://doi.org/10.1002/mar.70175

Li, M., & Suh, A. (2022). Anthropomorphism in AI-enabled technology: A literature review. Electronic Markets, 32(4), 2245–2275. https://doi.org/10.1007/s12525-022-00591-7

Ma, L., Ou, W., & Sian Lee, C. (2022). Investigating consumers’ cognitive, emotional, and behavioral engagement in social media brand pages: A natural language processing approach. Electronic Commerce Research and Applications, 54, 101179. https://doi.org/10.1016/j.elerap.2022.101179

Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. The MIT Press.

Miskolczi, M. (2026). The illusion of reality: How AI-generated images (AIGIs) are fooling social media users. Computers in Human Behavior, 176, 108876. https://doi.org/10.1016/j.chb.2025.108876

Nikhashemi, S. R., & Altinay, L. (2026). Decoding consumer–AI Interaction through perceived cognitive capability and anthropomorphism: Evidence from unstructured big data. Psychology & Marketing, 43(8), 1951–1978. https://doi.org/10.1002/mar.70151

Nurfaizal, Y., Hafshah, L. N., Nafisa, F. K., & Barros, A. (2026). Digital customer experience and social media marketing on purchase intention. Jurnal Manajemen Komunikasi, 10(2), 148–166. https://doi.org/10.24198/jmk.v10i2.67084

Park, K., & Yoon, H. Y. (2025). AI algorithm transparency, pipelines for trust not prisms: Mitigating general negative attitudes and enhancing trust toward AI. Humanities and Social Sciences Communications, 12(1), 1160. https://doi.org/10.1057/s41599-025-05116-z

Qadri, U. A., & Moustafa, A. M. A. (2026). They made it just for me! How AI transparency and influencer well-being shape consumer responses to AI-driven content. Psychology & Marketing, 43(3), 591–608. https://doi.org/10.1002/mar.70075

Ratnasari, E., & Sumartias, S. (2025). Ethical considerations of using generative Artificial Intelligence in advertising. PRofesi Humas, 10(1), 72–96. https://doi.org/10.24198/prh.v10i1.62726

Riswanto, A. L., Ha, S., Lee, S., & Kwon, M. (2024). Online reviews meet visual attention: a study on consumer patterns in advertising, analyzing customer satisfaction, visual engagement, and purchase intention. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3102–3122. https://doi.org/10.3390/jtaer19040150

Silvennoinen, J., Kujala, T., & Haapakoski, M. (2026). Differentiating visual usability from aesthetics in visual experience. International Journal of Human–Computer Interaction, 1–21. https://doi.org/10.1080/10447318.2026.2639826

Spence, M. (1974). Competitive and optimal responses to signals: An analysis of efficiency and distribution. Journal of Economic Theory, 7(3), 296–332. https://doi.org/10.1016/0022-0531(74)90098-2

Sufyan Ramish, M., Qamar Zia, M., Saraih, U. N., Suanda, J., & Ansari, J. (2023). Does visual appeal moderates the impact of attitude towards advertising on brand attitude, attachment and loyalty? ReMark - Revista Brasileira de Marketing, 22(3), 1250–1275. https://doi.org/10.5585/remark.v22i3.22160

Sugunapriya, T., & Ramya, S. S. (2026). Decoding the power of visual cues in marketing: An empirical study on the impact of colours, imagery, and aesthetic design on consumer trust, engagement, and purchase intentions. In Sustainable development agenda 2030. https://doi.org/10.1007/978-3-032-11612-3_73

Tiwari, A., & Kumari, N. (2024). The impact of visual appeal, perceived price, and trust on consumer buying intention toward cosmetic products: An analysis using structural equation modelling. Proceedings of the 5th International Conference on the Role of Innovation, Entrepreneurship and Management for Sustainable Development (ICRIEMSD-2024), 456–467. https://doi.org/10.2991/978-94-6463-612-3_30

Tsiotsou, R. H., Hatzithomas, L., & Wetzels, M. (2024). Display advertising: The role of context and advertising appeals from a resistance perspective. Journal of Research in Interactive Marketing, 18(2), 198–219. https://doi.org/10.1108/JRIM-09-2022-0302

van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

Wang, Y., Sauka, K., & Situmeang, F. B. I. (2025). Anthropomorphism and transparency interplay on consumer behaviour in generative AI-driven marketing communication. Journal of Consumer Marketing, 42(4), 512–536. https://doi.org/10.1108/JCM-04-2024-6806

We Are Social. (2025). Digital 2026: Top digital and social media trends in Indonesia. Wearesocial.Com. https://wearesocial.com/id/blog/2025/11/digital-2026-top-digital-and-social-media-trends-in-indonesia/

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

Yang, J., Dong, C., Chu, S.-C., & Rheu, M. (2026). Transforming advertising in the age of generative AI: Exploring advertising professionals’ perceptions of human-AI value co-creation. International Journal of Advertising, 45(1), 139–170. https://doi.org/10.1080/02650487.2025.2604910

DOI: https://doi.org/10.24198/jmk.v11i1.72719