BEYOND ENTERTAINMENT: HOW TRUST CONVERTS AI-GENERATED CONTENT VALUE INTO GENERATION Z ENGAGEMENT
คำสำคัญ:
AI-Generated Content, Trust, Content Value, Generation Z, Social Media Engagementบทคัดย่อ
Although generative artificial intelligence enables brands and content creators to produce social media content rapidly and at scale, favorable evaluations of AI-generated content do not necessarily translate into user engagement because users may remain uncertain about its accuracy, authenticity, and credibility. Understanding how such evaluations are converted into engagement is therefore important for brands seeking to use AI-generated content effectively. Drawing on the advertising value perspective and research on trust in artificial intelligence, this study examines the relationships among perceived creativity, perceived entertainment, perceived informativeness, trust, and Generation Z engagement with AI-generated content. Using purposive eligibility screening with convenience-based online recruitment, survey data were collected from 385 Generation Z social media users in Thailand and analyzed using partial least squares structural equation modeling (PLS-SEM).
The results showed that perceived informativeness had the strongest positive relationship with trust and was also directly related to engagement. Perceived creativity was related to engagement only indirectly through trust. Perceived entertainment was not significantly related to trust, and its indirect relationship with engagement through trust was not significant. Trust significantly predicted engagement and mediated the relationships between creativity and informativeness and engagement. The model explained 69.5% of the variance in trust and 74.1% of the variance in engagement. These findings demonstrate that creativity, entertainment, and informativeness follow different pathways to engagement. For AI-generated content, useful and relevant information supported by trust is more important for Generation Z engagement than entertainment appeal alone.
เอกสารอ้างอิง
Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?. Smart Learning Environments, 10, Article 60. https://doi.org/10.1186/s40561-023-00269-3
Cochran, W. G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons.
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
Dimock, M. (2019, January 17). Defining generations: Where Millennials end and Generation Z begins. Pew Research Center. https://www.pewresearch.org/short-reads/2019/01/17/where-millennials-end-and-generation-z-begins/
Ducoffe, R. H. (1996). Advertising value and advertising on the web. Journal of Advertising Research, 36(5), 21-35.
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J. S., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., Medaglia, R., … Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160. https://doi.org/10.3758/BRM.41.4.1149
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
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. https://doi.org/10.1108/EBR-11-2018-0203
Hamouda, M., & Aissaoui, A. (2026). Cracking the code: How can AI-generated advertising be effective? Empirical evidence on the antecedents and consequences of advertising value. European Business Review, 38(2), 224-243. https://doi.org/10.1108/EBR-06-2025-0214
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Jackson, K. M., Kiss, H., & Bergman, M. E. (2025). The power of personalization: Generation Z’s emotional response to AI food marketing under the EU AI Act. British Food Journal. Advance online publication. https://doi.org/10.1108/BFJ-05-2025-0727
Jung, T., Koghut, M., Lee, E., & Kwon, O. (2025). Artificial creativity in luxury advertising: How trust and perceived humanness drive consumer response to AI-generated content. Journal of Retailing and Consumer Services, 87, 104403. https://doi.org/10.1016/j.jretconser.2025.104403
Kholkina, V., Chesnokova, E., & Zelenskaya, E. (2025). Virtual or human? The impact of the influencer type on Gen Z consumer outcomes. Journal of Product & Brand Management, 34(1), 104–118. https://doi.org/10.1108/JPBM-12-2023-4885
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, 186, 114984. https://doi.org/10.1016/j.jbusres.2024.114984
Logan, K., Bright, L. F., & Gangadharbatla, H. (2012). Facebook versus television: Advertising value perceptions among females. Journal of Research in Interactive Marketing, 6(3), 164-179. https://doi.org/10.1108/17505931211274651
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359. https://doi.org/10.1287/isre.13.3.334.81
Muntinga, D. G., Moorman, M., & Smit, E. G. (2011). Introducing COBRAs: Exploring motivations for brand-related social media use. International Journal of Advertising, 30(1), 13-46. https://doi.org/10.2501/IJA-30-1-013-046
Ringle, C. M., Wende, S., & Becker, J. M. (2024). SmartPLS 4 [Computer software]. SmartPLS GmbH. https://www.smartpls.com
Siau, K., & Wang, W. (2018). Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal, 31(2), 47–53.
Smith, R. E., MacKenzie, S. B., Yang, X., Buchholz, L. M., & Darley, W. K. (2007). Modeling the determinants and effects of creativity in advertising. Marketing Science, 26(6), 819-833. https://doi.org/10.1287/mksc.1070.0272
Till, B. D., & Baack, D. W. (2005). Recall and persuasion: Does creative advertising matter?. Journal of Advertising, 34(3), 47–57. https://doi.org/10.1080/00913367.2005.10639201
Whiting, A., & Williams, D. (2013). Why people use social media: A uses and gratifications approach. Qualitative Market Research: An International Journal, 16(4), 362-369. https://doi.org/10.1108/QMR-06-2013-0041
Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197-206. https://doi.org/10.1086/651257
Zhou, T., & Lu, H. (2025). The effect of trust on user adoption of AI-generated content. The Electronic Library, 43(1), 61–76. https://doi.org/10.1108/EL-08-2024-0244
ดาวน์โหลด
เผยแพร่แล้ว
รูปแบบการอ้างอิง
ฉบับ
ประเภทบทความ
สัญญาอนุญาต
ลิขสิทธิ์ (c) 2026 วารสารวิชาการสังคมศาสตร์เครือข่ายวิจัยประชาชื่น
อนุญาตภายใต้เงื่อนไข Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
บทความที่ได้รับการตีพิมพ์เป็นลิขสิทธิ์ของวารสารวิชาการสังคมศาสตร์เครือข่ายวิจัยประชาชื่น
