The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Transforming Digital Marketing with Generative AI
Open accessAbstract
The current marketing landscape faces challenges in content creation and innovation, relying heavily on manually created content and traditional channels like social media and search engines. While effective, these methods often lack the creativity and uniqueness needed to stand out in a competitive market. To address this, we introduce MARK-GEN, a conceptual framework that utilises generative artificial intelligence (AI) models to transform marketing content creation. MARK-GEN provides a comprehensive, structured approach for businesses to employ generative AI in producing marketing materials, representing a new method in digital marketing strategies. We present two case studies within the fashion industry, demonstrating how MARK-GEN can generate compelling marketing content using generative AI technologies. This proposition paper builds on our previous technical developments in virtual try-on models, including image-based, multi-pose, and image-to-video techniques, and is intended for a broad audience, particularly those in business management.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“where research shows individuals partly blame AI rather than attributing all responsibility to their conversation partners [36].”
From Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production (2025) · cites this paper as [36]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712
An overview of gradient descent optimization algorithms
Ruder, S. (2016) · arXiv · doi:10.48550/arxiv.1609.04747 · Cited by 4,962
Han (2023) · doi:10.1109/tpami.2022.3152247 · Cited by 4,682
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., et al. (2021) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.08.002 · Cited by 4,611 · cited 1×
“Unfortunately, some businesses find it challenging to implement new marketing strategies due to their complexity and the need for skilled resources [15] and the lack of a clear framework [16].”
BackgroundHybrid Recommender Systems: Survey and Experiments
Burke, R. (2002) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1021240730564 · Cited by 4,119
Research works printed but not linked: 10
- Bala, 2018, A critical review of digital marketing, Int. J. Manag. IT Eng., 8, 321
- Soni, 2023, Adopting Generative AI in Digital Marketing Campaigns: An Empirical Study of Drivers and Barriers, Sage Sci. Rev. Appl. Mach. Learn., 6, 1
- Boughton, 2005, Search engine marketing, Perspect. Bus., 2, 29
- Kushwaha, 2020, Search engine marketing a new way of marketing in digital age, Palarch’s J. Archaeol. Egypt/Egyptol., 17, 2053
- Rawson, 2013, The truth about customer experience, Harv. Bus. Rev., 91, 90
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Adv. Neural Inf. Process. Syst., 27, Available online: https://proceedings.neurips.cc/paper_files/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf.
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30, Available online: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
- Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., and Van Gool, L. (2017). Pose guided person image generation. Adv. Neural Inf. Process. Syst., 30, Available online: https://www.researchgate.net/publication/317205156_Pose_Guided_Person_Image_Generation.
- Iwata, T., Watanabe, S., and Sawada, H. (2011, January 16–22). Fashion coordinates recommender system using photographs from fashion magazines. Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, Barcelona, Spain.
- Chilimbi, T., Suzue, Y., Apacible, J., and Kalyanaraman, K. (2014, January 6–8). Project adam: Building an efficient and scalable deep learning training system. Proceedings of the 11th USENIX symposium on operating systems design and implementation (OSDI 14), Broomfield, CO, USA.
Cited by
Sorted by citations
Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production
Fu, Y., Newman, M., Going, L., Feng, Q., & Lee, J. H. (2025) · Proceedings of the 2025 ACM Designing Interactive Systems Conference · doi:10.1145/3715336.3735829 · Cited by 30
Exploring the Collaborative Co-Creation Process with AI: A Case Study in Novice Music Production
Fu, Y., Newman, M., Going, L., Feng, Q., & Lee, J. H. (2025) · arXiv · Cited by 30
Metadata
- Authors
- Islam, T., Miron, A., Nandy, M., Choudrie, J., Liu, X., & Li, Y.
- Affiliations
- Brunel University of London (GB)
- University of Hertfordshire (GB)
- Corresponding author
- Li, Yongmin
- Venue
- Computers
- Publisher
- MDPI AG
- Published
- 2024-07-08
- Volume
- 13
- Issue
- 7
- Pages
- 168
- ISSN
- 2073-431X (ISSN-L)
- OpenAlex
- W4400418528
- Cited by
- 68
- References
- 84
- Citations by year
- 2026: 27 · 2025: 34 · 2024: 7
- FWCI
- 48.78
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers (publishedVersion, cc-by)
- University of Hertfordshire Research Archive (University of Hertfordshire) (acceptedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
- University of Hertfordshire Research Archive (University of Hertfordshire) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EPSRC) (EP/T518116/1)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
- Keywords
- generative AI, digital marketing, marketing content creation, fashion industry, virtual try-on, image-to-video generation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
