THE FUTURE OF DIGITAL BRANDING THROUGH GENERATIVE MEDIA
DOI:
https://doi.org/10.29121/shodhkosh.v6.i5s.2025.6895Keywords:
Generative Media, Digital Branding, AI Storytelling, Diffusion Models, Consumer Engagement, Virtual InfluencersAbstract [English]
A major change in digital branding in the future is the accelerated generation of generative media which includes image creation and video making, and audio creation and narrative systems founded on text. As expanding brands operate within algorithmic, hyper-personal and interacting space, generative technologies present unmatched potential in the creation of dynamic, responsive and emotionally charged brand experiences. In this paper, the concept of generative media as the means of altering the brand identity formation, consumer perception, and storytelling is discussed in terms of its ability to enable scalable creativity, allow content to be varied in real-time and enable brand to be transformed over time. The changes in the field of emotional branding, the participatory culture in the digital world, and psychology of personalized experiences with the media are reflected in the theoretical bases. We also talk about the technological background of the work GANs, diffusion models, and transformer-based architectures which can be applied to provide adaptive pipelines to create context-sensitive brand assets. More and more, with the developing case studies, we evaluate the contribution of generative systems towards offering greater engagement, flexibility of the narrative, and the assistance of constructing virtual influencers and responsive brand avatars. The analysis of the quantitative and qualitative impact may indicate a balance shift between brand consistency and creative variability on what AI-generated content may be able to reinforce the trust, increase consumer immersion, and accelerate the designing processes when addressed accordingly.
References
Amer, S. (2023). AI Imagery and the Overton Window. arXiv Preprint. DOI: https://doi.org/10.2139/ssrn.4776793
Borji, A. (2023). Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL·E 2. arXiv Preprint.
Brisco, R., Hay, L., and Dhami, S. (2023). Exploring the Role of Text-To-Image AI in Concept Generation. Proceedings of the Design Society, 3, 1835–1844. https://doi.org/10.1017/pds.2023.184 DOI: https://doi.org/10.1017/pds.2023.184
Ferreira, Â., and Casteleiro-Pitrez, J. (2023). Inteligência Artificial no Design de Comunicação em Portugal: Estudo de Caso Sobre as Perspetivas de 10 Designers Profissionais de Pequenas e Médias Empresas. ROTURA – Revista de Comunicação, Cultura e Artes, 3, 114–133.
Feuerriegel, S., Hartmann, J., Janiesch, C., and Zschech, P. (2024). Generative AI. Business and Information Systems Engineering, 66(1), 111–126. https://doi.org/10.1007/s12599-023-00834-7 DOI: https://doi.org/10.1007/s12599-023-00834-7
Islam, T., Miron, A., Liu, X., and Li, Y. (2022). Svton: Simplified Virtual Try-On. In Proceedings of the 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (369–374). IEEE. https://doi.org/10.1109/ICMLA55696.2022.00059 DOI: https://doi.org/10.1109/ICMLA55696.2022.00059
Islam, T., Miron, A., Liu, X., and Li, Y. (2023). FashionFlow: Leveraging Diffusion Models for Dynamic Fashion Video Synthesis from Static Imagery. arXiv Preprint. DOI: https://doi.org/10.3390/fi16080287
Islam, T., Miron, A., Liu, X., and Li, Y. (2024). StyleVTON: A Multi-Pose Virtual Try-On with Identity and Clothing Detail Preservation. Neurocomputing, 594, Article 127887. https://doi.org/10.1016/j.neucom.2024.127887 DOI: https://doi.org/10.1016/j.neucom.2024.127887
Kshetri, N., Dwivedi, Y. K., Davenport, T. H., and Panteli, N. (2023). Generative Artificial Intelligence in Marketing: Applications, Opportunities, Challenges, and Research Agenda. International Journal of Information Management, 71, Article 102716. https://doi.org/10.1016/j.ijinfomgt.2023.102716 DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102716
Martínez, G., Watson, L., Reviriego, P., Hernández, J., Juarez, M., and Sarkar, R. (2023). Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet. arXiv Preprint. DOI: https://doi.org/10.1007/978-3-031-57963-9_5
Oppenlaender, J. (2022). The Creativity of Text-to-Image Generation. In Proceedings of the 25th International Academic Mindtrek Conference (Academic Mindtrek ’22) (192–202). Association for Computing Machinery. https://doi.org/10.1145/3569219.3569352 DOI: https://doi.org/10.1145/3569219.3569352
Oppenlaender, J. (2023). The Cultivated Practices of Text-to-Image Generation. arXiv Preprint. DOI: https://doi.org/10.1007/978-3-031-66528-8_14
Paananen, V., Oppenlaender, J., and Visuri, A. (2023). Using Text-to-Image Generation for Architectural Design Ideation. arXiv Preprint. DOI: https://doi.org/10.1177/14780771231222783
Samuelson, P. (2023). Generative AI Meets Copyright. Science, 381(6654), 158–161. https://doi.org/10.1126/science.adi0656 DOI: https://doi.org/10.1126/science.adi0656
Schetinger, V., Di Bartolomeo, S., El-Assady, M., McNutt, A., Miller, M., Passos, J., and Adams, J. (2023). Doom or Deliciousness: Challenges and Opportunities for Visualization in the Age of Generative Models. Computer Graphics Forum, 42(3), 423–435. https://doi.org/10.1111/cgf.14841 DOI: https://doi.org/10.1111/cgf.14841
Soni, V. (2023). Adopting Generative AI in Digital Marketing Campaigns: An Empirical Study of Drivers and Barriers. Sage Science Review of Applied Machine Learning, 6, 1–15.
Yang, X. (2023). The Effects of AI Service Quality and AI Function–Customer Ability Fit on Customer’s Overall Co-Creation Experience. Industrial Management and Data Systems, 123(7), 1717–1735. https://doi.org/10.1108/IMDS-08-2022-0500 DOI: https://doi.org/10.1108/IMDS-08-2022-0500
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Dr. S. Balaji, Shilpa Bhargav, Nidhi Tewatia, Ms. Banashree Dash, Dr. Smita Meena, Siddharth Sriram, Archana Haribhau Bhapkar
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
