Perception and Acceptance of AI-Generated Advertising Images among Generation Z

Main Article Content

Athip Techapongsathon
Saroj Waikongkha

Abstract

This research investigated the perception and acceptance of AI-generated advertising imagery. The goals are to 1) analyze the perception of advertising images generated by artificial intelligence, 2) examine opinions on the acceptance of AI-generated product images, and 3) investigate whether people in Generation Z from different demographic groups perceive AI-generated advertising images differently. Cochran’s formula considered the sample size, and the confidence level was set at 95%. A quantitative study using questionnaires was conducted on 400 participants in Generation Z. Descriptive statistics like frequency distribution, percentage, and mean and inferential statistics like one-way ANOVA were employed to assess the research.


The research results were as follows: 1) The result revealed that most Generation Z participants (aged 19-27) can moderately distinguish between AI-generated and human-created images. 2) They generally hold a positive attitude towards AI-generated images, perceiving that AI helps to reduce work time, makes images appear more modern, and effectively captures attention. These findings suggest that artificial intelligence technology will continue to play an increasingly significant role in advertising and media creation. 3) Hypothesis testing further indicates that individuals who have received engineering, technology, and science education display more positive attitudes than those in other disciplines. Conversely, Generation Z respondents studying humanities and social sciences are less accepting of AI-generated product images than their peers in other fields. Therefore, technological literacy significantly influences the acceptance of AI technology in media production processes.

Article Details

How to Cite
Techapongsathon, A., & Waikongkha, S. (2025). Perception and Acceptance of AI-Generated Advertising Images among Generation Z. Arts of Management Journal, 9(1), 196–218. retrieved from https://so02.tci-thaijo.org/index.php/jam/article/view/274728
Section
Research Articles

References

Allen, T. J., & Scott Morton, M. S. (1994). Information technology and the corporation of the 1990s: research studies. Oxford University Press.

Arsabarn. C., & Wetprasit. W. (2023). The impact of technological acceptance factors and marketing communication affecting mobile banking usage behavior of the elderly in Phetchaburi province. Journal of Management Science Udon Thani Rajabhat University, 5(5), 49-63.

Campbell, C., Plangger, K., Sands, S., & Kietzmann, J. (2021). Preparing for an era of deepfakes and AI-generated ads: A framework for understanding responses to manipulated advertising. Journal of Advertising, 1–17. https://doi.org/10.1080/00913367.2021.1909515

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

Chaisamrong, C. (2018). Acceptance of language learning technology through online applications of consumers in Bangkok Metropolitan Region (No. 147497). Thammasat University.

Chau, P. Y. K. (1996). An empirical assessment of a modified technology acceptance model. Journal of Management Information Systems: JMIS, 13(2), 185–204. https://doi.org/10.1080/07421222.1996.11518128

Cochran, W.G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons.

Cuesta-Valiño, P., Kazakov, S., Gutiérrez-Rodríguez, P., & Rua, O. L. (2023). The effects of the aesthetics and composition of hotels’ digital photo images on online booking decisions. Humanities and Social Sciences Communications, 10(1), 1–11. https://doi.org/10.1057/s41599-023-01529-w

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319. https://doi.org/10.2307/249008

Edwards, D. T., Shroff, B., Lindauer, S. J., Fowler, C. E., & Tufekci, E. (2008). Media advertising effects on consumer perception of orthodontic treatment quality. The Angle Orthodontist, 78(5), 771–777. https://doi.org/10.2319/083106-357.1

El-aasy, H. A. (2023). Employing artificial intelligence (AI) technology in advertising design on social media. Journal of Design Sciences and Applied Arts, 4(2), 247–263. https://doi.org/10.21608/jdsaa.2023.194906.1260

Elliott, A. (2019). The culture of AI: Everyday life and the digital revolution. Routledge.

Feldman, R. S. (2014). Understanding psychology (12th ed.). McGraw-Hill Higher Education.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51–90. https://doi.org/10.2307/30036519

Gülaçti, İ. E., & Kahraman, M. E. (2021). The impact of artificial intelligence on photography and painting in the post-truth era and the issues of creativity and authorship. Medeniyet Sanat Dergisi, 7(2), 243–270. https://doi.org/10.46641/medeniyetsanat.994950

Harsanto, W., & Jakti, P. (2023). Post-photography: the disruption effect of artificial intelligence on photography for product advertising. Information Sciences Letters an International Journal, 9(12), 2141–2151. http://dx.doi.org/10.18576/isl/120920

Holden, R. J., & Karsh, B. T. (2010). The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159-172. https://doi.org/10.1016/j.jbi.2009.07.002

Kagan, M., & Lissitsa, S. (2023). Generations X, Y, Z: Attitudes toward social workers in the age of media technologies. Technology in Society, 75, 102353. https://doi.org/10.1016/j.techsoc.2023.102353

Koktong, T., & Promrat, T. (2023). The creation of photographic art based on the perception of woman’s beauty myth for advertising purposes. Kurdish Studies, 11(2), 1820–1835. https://doi.org/10.58262/ks.v11i02.127

Kosar, S. A., Muruthi, B. A., Shivers, C., Zarate, J., & Byron, J. (2023). Millennials and generation Z: men’s perspectives on hashtag feminism. The Journal of Men’s Studies, 31(3), 478–499. https://doi.org/10.1177/10608265231175832

Lee, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095–1104. https://doi.org/10.1016/j.im.2003.10.007

Lyngdoh, T., El-Manstrly, D., & Jeesha, K. (2023). Social isolation and social anxiety as drivers of generation Z’s willingness to share personal information on social media. Psychology & Marketing, 40(1), 5–26. https://doi.org/10.1002/mar.21744

McLeod, D. M., Wise, D., & Perryman, M. (2017). Thinking about the media: A review of theory and research on media perceptions, media effects perceptions, and their consequences. Review of Communication Research, 5, 35-83. https://doi.org/10.12840/issn.2255-4165.2017.05.01.013

Makridakis, S. (2017). The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. https://doi.org/10.1016/j.futures.2017.03.006

Mazzone, M., & Elgammal, A. (2019). Art, creativity, and the potential of artificial intelligence. Arts, 8(1), 26. https://doi.org/10.3390/arts8010026

Mesch, G. S., & Liu, X. J. (2023). Differential media exposure and perceptions of fear and behavior change in China and Israel during the COVID-19 pandemic. New Media & Society. https://doi.org/10.1177/14614448231164638

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134. https://doi.org/10.1080/10864415.2003.11044275

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

Rahmat, W., Tiawati, R. L., Kemal, E., Tatalia, R. G., Azri, H., & Wulandari, Y. (2023). How do the young people picture out their use, activeness, and connectivity on social media? a discourse analysis approach. Journal of Communication Inquiry. https://doi.org/10.1177/01968599231174848

Robertson, J., Fossaceca, J., & Bennett, K. (2022). A cloud-based computing framework for artificial intelligence innovation in support of multidomain operations. IEEE Transactions on Engineering Management, 69(6), 3913–3922. https://doi.org/10.1109/tem.2021.3088382

Rožukalne, A. (2020). Perception of media and information literacy among representatives of mid-age and older generations: the case of Latvia. ESSACHESS-Journal for Communication Studies, 13(26).

Sadaf, A. (2011). Public perception of media role. International Journal of Humanities and Social Science, 1(5), 228–236.

Saulīte, L., Ščeulovs, D., & Pollák, F. (2022). The influence of non-product-related attributes on media brands’ consumption. Journal of Open Innovation Technology Market and Complexity, 8(3), 105. https://doi.org/10.3390/joitmc8030105

Shi, B., & Wang, H. (2023). An AI-enabled approach for improving advertising identification and promotion in social networks. Technological Forecasting and Social Change, 188(122269). https://doi.org/10.1016/j.techfore.2022.122269

Stahl, C. C., & Literat, I. (2023). #GenZ on TikTok: the collective online self-portrait of the social media generation. Journal of Youth Studies, 26(7), 925-946. https://doi.org/10.1080/13676261.2022.2053671

Sun, Y., & Xing, J. (2022). The impact of social media information sharing on the green purchase intention among Generation Z. Sustainability, 14(11), 6879. https://doi.org/10.3390/su14116879

Tan, E. M. (2022). An overview: visual communication in photography as healing therapy. Silpa Bhirasri. Journal of Fine Arts, 8(1–2), 302–316.

Techapongsathon, A., & Waitayasin, P. (2022). The study of creation process in combination between digital media and installation art for representing the identity of generation C. The Journal of Social Communication Innovation, 10(2), 140-156. https://so06.tci-thaijo.org /index.php/jcosci/article/view/255892

Harsanto, P., & Jakti, J. W. (2023). Post-photography: the disruption effect of artificial intelligence on photography for product advertising. Information Sciences Letters an International Journal, 9(12), 2141-2151. http://dx.doi.org/10.18576/isl/120920

Yu, Y. (2022). The role and influence of artificial intelligence on advertising industry. Advances in Social Science, Education and Humanities Research. DOI: 10.2991/assehr.k.220105.037

Zhou, Y. (2022). Research and practice of ai intelligence and depth integration of photography. In The International Conference on Cyber Security Intelligence and Analytics (pp. 931–935). Springer International Publishing. https://doi.org/10.1007/978-3-030-97874-7_135