Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/29338
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Islam, T | - |
dc.contributor.author | Miron, A | - |
dc.contributor.author | Nandy, M | - |
dc.contributor.author | Choudrie, J | - |
dc.contributor.author | Liu, X | - |
dc.contributor.author | Li, Y | - |
dc.date.accessioned | 2024-07-12T15:50:10Z | - |
dc.date.available | 2024-07-12T15:50:10Z | - |
dc.date.issued | 2024-07-08 | - |
dc.identifier | ORCiD: Tasin Islam https://orcid.org/0000-0001-7568-9322 | - |
dc.identifier | ORCiD: Alina Miron https://orcid.org/0000-0002-0068-4495 | - |
dc.identifier | ORCiD: Monomita Nandy https://orcid.org/0000-0001-8191-2412 | - |
dc.identifier | ORCiD: Jyoti Choudrie https://orcid.org/0000-0001-9349-7690 | - |
dc.identifier | ORCiD: Xiaohui Liu https://orcid.org/0000-0003-1589-1267 | - |
dc.identifier | ORCiD: Yongmin Li https://orcid.org/0000-0003-1668-2440 | - |
dc.identifier.citation | Islam, T. et al. (2024) 'Transforming Digital Marketing with Generative AI', Computers, 13 (7), 168, pp. 1 - 24. doi: 10.3390/computers13070168. | en_US |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29338 | - |
dc.description | Data Availability Statement: This paper did not generate any new data. | - |
dc.description | The repositories for the projects are as follows: https://github.com/1702609/SVTON; https://github.com/1702609/FashionFlow (accessed on 6 July 2024). | - |
dc.description.abstract | 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. | en_US |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) grant number EP/T518116/1. | en_US |
dc.format.extent | 1- 24 | - |
dc.format.medium | Electronic | - |
dc.language | English | - |
dc.publisher | MDPI | en_US |
dc.rights | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | generative AI | en_US |
dc.subject | deep learning | en_US |
dc.subject | e-commerce | en_US |
dc.subject | digital marketing | en_US |
dc.title | Transforming Digital Marketing with Generative AI | en_US |
dc.type | Article | en_US |
dc.date.dateAccepted | 2024-07-05 | - |
dc.identifier.doi | https://doi.org/10.3390/computers13070168 | - |
dc.relation.isPartOf | Computers | - |
pubs.issue | 7 | - |
pubs.publication-status | Published online | - |
pubs.volume | 13 | - |
dc.identifier.eissn | 2073-431X | - |
dc.rights.license | https://creativecommons.org/licenses/by/4.0/legalcode.en | - |
Appears in Collections: | Dept of Computer Science Research Papers Brunel Business School Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | 13.97 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License