Consumers and AI-generated content in digital communication

The present work analysed users' attitudes towards the use of AI in media and marketing communications, focusing on the evaluation of the quality of AI-generated content. Based on the analyses, it was found that the gender of the respondents does not have a significant impact on the evaluation of AI content. The frequency of contact with AI technologies proved to be a particularly significant factor influencing the perception of quality, with respondents who came into contact with AI technologies more frequently evaluating the generated content more positively. The facts defined are translated into the proposition that experience with AI is key to consumer adoption of AI, however, education and transparency in the implementation of AI systems is extremely important. Despite the stated benefits of AI being defined as automation and personalization, there are also significant concerns related to privacy, trust, and ethical aspects of the use of these technologies. The low explanatory power of the tested model was clarified. A number of other factors influence the evaluation of AI content, which should be the subject of further research. The investigation confirmed that artificial intelligence has the potential to be a significant asset for marketing and media communications. The success of implementation depends mainly on the ability to respond to legitimate user concerns and needs in a responsible and transparent manner. In conclusion, AI technologies have the potential to significantly change media and marketing communications. The results are useful in marketing strategies for marketing managers, media, policy makers and other stakeholders who plan to use AI to create content and communicate with their target audiences.

1.        Ahmed, Z., Shanto, S. S., Rime, Most. H. K., Morol, Md. K., Fahad, N., Hossen, Md. J., & Abdullah-Al-Jubair, Md. (2024). The generative ai landscape in education: Mapping the terrain of opportunities, challenges, and student perceptions. IEEE Access, 12, 147023-147050. https://doi.org/10.1109/ACCESS.2024.3461874 2.        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 3.        Celik, I. (2023). Exploring the determinants of artificial intelligence (Ai) literacy: Digital divide, computational thinking, cognitive absorption. Telematics and Informatics, 83, 102026. https://doi.org/10.1016/j.tele.2023.102026 4.        Cui, Y. (Gina), Van Esch, P., & Phelan, S. (2024). How to build a competitive advantage for your brand using generative AI. Business Horizons, 67(5), 583-594. https://doi.org/10.1016/j.bushor.2024.05.003 5.        Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19(1), 17. https://doi.org/10.1007/s40979-023-00140-5 6.        Elkhatat, A. M., Elsaid, K., & Almeer, S. (2023). Evaluating the efficacy of AI content detection tools in differentiating between human and AI-generated text. International Journal for Educational Integrity, 19(1), 17. https://doi.org/10.1007/s40979-023-00140-5 7.        Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552-568. https://doi.org/10.1016/j.jbusres.2022.02.088 8.        Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.1126/science.adh2586 9.        Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.1126/science.adh2586 10.     Robles, P., & Mallinson, D.. J. (2025). Artificial intelligence technology, public trust, and effective governance. Review of Policy Research, 42(1), 11-28. https://doi.org/10.1111/ropr.12555