AIGC in digital media as a factor in the development of social infrastructure

Michal Kubovics ,Peter Murár

doi:10.24294/jipd11634

Home - JIPD - Volume 9 - Issue 4

Submit to JIPD

Journal Browser

Volume | Year

Issue

• Current lssue

View All

News and Announcements

View All

AIGC in digital media as a factor in the development of social infrastructure

Michal Kubovics

Peter Murár

Journal of Infrastructure Policy and Development 2025, 9(4), 11634; https://doi.org/10.24294/jipd11634

Submitted：25 Mar 2025

Accepted：06 Aug 2025

Published：28 Nov 2025

Download PDF(344.62KB)

XML

Abstract

The aim was to explore the relationships between selected demographic and digital citizenship factors and public trust and citizens' willingness to accept content generated by advanced technological innovations (AIGC) in social infrastructure. AIGC is defined as a proposition of social infrastructure that includes digital public services, education, and public administration, where its implementation has direct political and regulatory implications. The sample consisted of 1,308 respondents. Spearman's correlation coefficient was used to examine the relationships between the ordinal variables. To assess the differences between groups of respondents, a one-way analysis of variance was used, during which multiple linear regression analysis was used to confirm the predictive power of awareness and experience of AI-generated content in relation to the propensity to accept such content. The study confirmed a statistically significant but weak negative relationship between the age of respondents and their willingness to accept AIGC, with younger age groups showing slightly higher acceptance rates. Respondents' attitudes towards the use of personal data through AI and their overall awareness of technological trends had a more significant impact on acceptance. The findings show that respondents who are open to data collection through AI technologies show significantly higher levels of acceptance of automatically generated content. Similarly, respondents who rate the current quality of AIGC positively also have higher expectations regarding the future transformation of marketing strategies and media practices. The decisive factors in the social infrastructure for AIGC acceptance are not so much the age of respondents, but rather their awareness, technological literacy and level of trust in the technology itself. We recommend introducing regulatory frameworks to ensure the transparency of AIGC in public infrastructure and supporting educational programmes focused on digital literacy and accessibility of AI-based services, which would increase citizens' trust in AIGC in digital public services. The results have direct implications for policy-making, digital citizenship and the setting of rules for fair access to AIGC within the social infrastructure.

Keywords

Contemporary Construction

References

1. Bharadiya, J. P., Thomas, R. K., & Ahmed, F. (2023). Rise of Artificial Intelligence in Business and Industry. Journal of Engineering Research and Reports, 25(3), 85–103. https://doi.org/10.9734/jerr/2023/v25i3893
2. Bhatnagr, P., & Rajesh, A. (2024). Artificial intelligence features and expectation confirmation theory in digital banking apps: Gen Y and Z perspective. Management Decision. https://doi.org/10.1108/md-07-2023-1145
3. Borden, S.-L., Codina, L., & Ufarte-Ruiz, M.-J. (2024). Introduction. Our relationships with GenAI and the media: Testing the limits of transparency, trust and moral agency. Communication & Society, 217–221. https://doi.org/10.15581/003.37.4.217-221
4. Bown, O. (2024). From genies performing magic to sages imparting wisdom: a value-centred survey of music AI user interfaces, creative affordances and artist objectives. Journal of New Music Research, 53(1–2), 5–18. https://doi.org/10.1080/09298215.2024.2442360
5. Brown, O., Davison, R. M., Decker, S., et al. (2024). Theory‐Driven Perspectives on Generative Artificial Intelligence in Business and Management. British Journal of Management, 35(1), 3–23. Portico. https://doi.org/10.1111/1467-8551.12788
6. Davenport, T., Guha, A., Grewal, D., et al. (2019). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0
7. Elhajjar, S. (2024). Unveiling the marketer’s lens: exploring experiences and perspectives on AI integration in marketing strategies. Asia Pacific Journal of Marketing and Logistics, 37(2), 498–517. https://doi.org/10.1108/apjml-04-2024-0485
8. Gonçalves, A. R., Pinto, D. C., Rita, P., et al. (2023). Artificial Intelligence and Its Ethical Implications for Marketing. Emerging Science Journal, 7(2), 313–327. https://doi.org/10.28991/esj-2023-07-02-01
9. Graham, C., & Stough, R. (2025). Consumer perceptions of AI chatbots on Twitter (X) and Reddit: an analysis of social media sentiment and interactive marketing strategies. Journal of Research in Interactive Marketing, 19(7), 1096–1124. https://doi.org/10.1108/jrim-05-2024-0237
10. Grewal, D., Satornino, C. B., Davenport, T., et al. (2024). How generative AI Is shaping the future of marketing. Journal of the Academy of Marketing Science, 53(3), 702–722. https://doi.org/10.1007/s11747-024-01064-3
11. Guo, D., Chen, H., Wu, R., et al. (2023). AIGC challenges and opportunities related to public safety: A case study of ChatGPT. Journal of Safety Science and Resilience, 4(4), 329–339. https://doi.org/10.1016/j.jnlssr.2023.08.001
12. Huang, M.-H., & Rust, R. T. (2020). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50. https://doi.org/10.1007/s11747-020-00749-9
13. Ioku, T., Song, J., & Watamura, E. (2024). Trade-offs in AI assistant choice: Do consumers prioritize transparency and sustainability over AI assistant performance? Big Data & Society, 11(4). https://doi.org/10.1177/20539517241290217
14. Khan, A. W., & Mishra, A. (2023). AI credibility and consumer-AI experiences: a conceptual framework. Journal of Service Theory and Practice, 34(1), 66–97. https://doi.org/10.1108/jstp-03-2023-0108
15. Kumar, V., Rajan, B., Venkatesan, R., et al. (2019). Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317
16. Li, H., Ren, T., & Zhang, Z. (2024). Assistive Tools or Insecurity: The Impact of Technological Readiness on Willingness to Use AI. International Journal of Human–Computer Interaction, 41(18), 11657–11667. https://doi.org/10.1080/10447318.2024.2443802
17. Miyazaki, K., Murayama, T., Uchiba, T., et al. (2024). Public perception of generative AI on Twitter: an empirical study based on occupation and usage. EPJ Data Science, 13(1). https://doi.org/10.1140/epjds/s13688-023-00445-y
18. Park, H. E. (Grace). (2024). The double‐edged sword of generative artificial intelligence in digitalization: An affordances and constraints perspective. Psychology & Marketing, 41(11), 2924–2941. Portico. https://doi.org/10.1002/mar.22094
19. Pramanik, P., & Jana, R. K. (2025). A consumer acceptance model in the artificial intelligence era. Management Decision, 63(9), 3136–3163. https://doi.org/10.1108/md-03-2024-0574
20. Qadri, U. A., Moustafa, A. M. A., & Abd Ghani, M. (2025). They misused me! Digital literacy’s dual role in AI marketing manipulation and unethical young consumer behavior. Young Consumers. https://doi.org/10.1108/yc-08-2024-2207
21. Robles, P., & Mallinson, D. J. (2023). Catching up withAI: Pushing toward a cohesive governance framework. Politics & Policy, 51(3), 355–372. Portico. https://doi.org/10.1111/polp.12529
22. Toff, B., & Simon, F. M. (2024). “Or They Could Just Not Use It?”: The Dilemma of AI Disclosure for Audience Trust in News. The International Journal of Press/Politics, 30(4), 881–903. https://doi.org/10.1177/19401612241308697
23. Tully, S. M., Longoni, C., & Appel, G. (2025). Lower Artificial Intelligence Literacy Predicts Greater AI Receptivity. Journal of Marketing, 89(5), 1–20. https://doi.org/10.1177/00222429251314491
24. Wang, T., Zhang, Y., Qi, S., et al. (2024). Security and Privacy on Generative Data in AIGC: A Survey. ACM Computing Surveys, 57(4), 1–34. https://doi.org/10.1145/3703626
25. Xu, Y., Liu, X., Cao, X., et al. (2021). Artificial intelligence: A powerful paradigm for scientific research. The Innovation, 2(4), 100179.

Previous
article in this issue

Next
article in this issue