Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/116429
DC FieldValueLanguage
dc.contributorDepartment of Management and Marketing-
dc.contributorSchool of Accounting and Finance-
dc.creatorNgai, EWT-
dc.creatorLee, MCM-
dc.creatorKei, BCW-
dc.date.accessioned2025-12-29T04:02:48Z-
dc.date.available2025-12-29T04:02:48Z-
dc.identifier.issn0018-9391-
dc.identifier.urihttp://hdl.handle.net/10397/116429-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.rightsThe following publication E. W. T. Ngai, M. C. M. Lee and B. C. W. Kei, 'A Generative Artificial Intelligence (GenAI) System for Fashion Design: A Case Study,' in IEEE Transactions on Engineering Management, vol. 72, pp. 1320-1333, 2025 is available at https://doi.org/10.1109/TEM.2025.3554248.en_US
dc.subjectCreative industriesen_US
dc.subjectDesign principleen_US
dc.subjectDesign science researchen_US
dc.subjectDesign theoryen_US
dc.subjectFashion industryen_US
dc.titleA generative artificial intelligence (GenAI) system for fashion design : a case studyen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1320-
dc.identifier.epage1333-
dc.identifier.volume72-
dc.identifier.doi10.1109/TEM.2025.3554248-
dcterms.abstractThis study employs a design science research approach to propose a foundational information system design theory tailored for generative artificial intelligence (GenAI) applications in the fashion design process. It delineates meta-requirements and design principles that address both the transformative potential of GenAI, and the unique challenges faced by the fashion sector. To validate the practicality of the proposed design theory, a prototype system was developed and evaluated with feedback from 30 experienced fashion practitioners, confirming its feasibility and effectiveness. Insights from case studies conducted with two Hong Kong-based fashion companies further highlight the benefits and challenges of integrating GenAI into fashion design. While GenAI demonstrates promise in enhancing communication, accelerating design processes, and improving customer engagement and satisfaction, key challenges remain, including the need for high-quality datasets, significant computational resources, and ethical considerations related to AI-generated designs. The design principles derived from this study provide a structured guideline for system designers, offering a practical framework for developing GenAI systems that cater to the specific needs of the fashion industry. By contributing both theoretical and practical insights, this study advances understanding of how GenAI can drive innovation in fashion design and lays a foundation for future research in this domain.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on engineering management, 2025, v. 72, p. 1320-1333-
dcterms.isPartOfIEEE transactions on engineering management-
dcterms.issued2025-
dc.identifier.scopus2-s2.0-105003036845-
dc.identifier.eissn1558-0040-
dc.description.validate202512 bcjz-
dc.description.oaAccepted Manuscripten_US
dc.identifier.SubFormIDG000527/2025-12en_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextThe work of E. W. T. Ngai was supported in part by RIAIoT, PolyU, under Grant CD4T, and in part by RCTFF, PolyU, under Grant BBEY and Grant BBFM. The work of B. C. W. Kei was supported by PolyU under Grant R-ZDDM and Grant ZDEX. Review of this article was arranged by Department Editor A. Kumar. The authors would like to thank the constructive comments provided by the Editors and three anonymous reviewers on previous versions of this article.en_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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