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dc.contributor.authorIslam, Tasin
dc.contributor.authorMiron, Alina
dc.contributor.authorNandy, Monomita
dc.contributor.authorChoudrie, Jyoti
dc.contributor.authorLiu, Xiaohui
dc.contributor.authorLi, Yongmin
dc.date.accessioned2024-07-08T09:45:02Z
dc.date.available2024-07-08T09:45:02Z
dc.date.issued2024-07-08
dc.identifier.citationIslam , T , Miron , A , Nandy , M , Choudrie , J , Liu , X & Li , Y 2024 , ' Transforming Digital Marketing with Generative AI ' , Computers , vol. 13 , no. 7 , 168 , pp. 1-24 . https://doi.org/10.3390/computers13070168
dc.identifier.issn2073-431X
dc.identifier.otherJisc: 2171980
dc.identifier.urihttp://hdl.handle.net/2299/28015
dc.description© 2024 The Author(s). Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/
dc.description.abstractThe 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, multipose, and image-to-video techniques, and is intended for a broad audience, particularly those in business managementen
dc.format.extent24
dc.format.extent14302458
dc.language.isoeng
dc.relation.ispartofComputers
dc.subjectdeep learning
dc.subjectdigital marketing
dc.subjecte-commerce
dc.subjectgenerative AI
dc.subjectHuman-Computer Interaction
dc.subjectComputer Networks and Communications
dc.titleTransforming Digital Marketing with Generative AIen
dc.contributor.institutionCentre for Future Societies Research
dc.contributor.institutionHertfordshire Business School
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85199639540&partnerID=8YFLogxK
rioxxterms.versionofrecord10.3390/computers13070168
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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