Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104026
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dc.contributorDepartment of Industrial and Systems Engineeringen_US
dc.creatorNazeer, Sen_US
dc.creatorSumbal, MSen_US
dc.creatorLiu, Gen_US
dc.creatorMunir, Hen_US
dc.creatorTsui, Een_US
dc.date.accessioned2024-01-17T02:44:51Z-
dc.date.available2024-01-17T02:44:51Z-
dc.identifier.issn2514-9342en_US
dc.identifier.urihttp://hdl.handle.net/10397/104026-
dc.language.isoenen_US
dc.publisherEmerald Publishing Limiteden_US
dc.rights© Emerald Publishing Limited. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher.en_US
dc.rightsThe following publication Nazeer S, Sumbal MS, Liu G, Munir H, Tsui E (2025), "The next big thing: role of ChatGPT in personal knowledge management challenges and opportunities for knowledge workers across diverse disciplines". Global Knowledge, Memory and Communication, Vol. 74 No. 7-8 pp. 2255–2269 is published by Emerald and is available at https://doi.org/10.1108/GKMC-07-2023-0246.en_US
dc.subjectPersonal knowledge managementen_US
dc.subjectGenerative AIen_US
dc.subjectChatGPTen_US
dc.subjectKnowledge workeren_US
dc.subjectKnowledge management theoriesen_US
dc.subjectKnowledge augmentationen_US
dc.titleThe next big thing : role of ChatGPT in personal knowledge management challenges and opportunities for knowledge workers across diverse disciplinesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage2255en_US
dc.identifier.epage2269en_US
dc.identifier.volume74en_US
dc.identifier.issue7-8en_US
dc.identifier.doi10.1108/GKMC-07-2023-0246en_US
dcterms.abstractPurpose: The purpose of this paper is to embark on evaluating the role of Chat Generative-Trained Transformer (ChatGPT) in personal knowledge management (PKM) practices of individual knowledge workers across varied disciplines.en_US
dcterms.abstractDesign/methodology/approach: The methodology involves four steps, i.e. literature search, screening and selection of relevant data, data analysis and data synthesis related to KM, PKM and generative artificial intelligence (AI) with a focus on ChatGPT. The findings are then synthesized to develop a viewpoint on the challenges and opportunities brought by ChatGPT for individual knowledge workers in enhancing their PKM capability.en_US
dcterms.abstractFindings: This work highlights the prevailing challenges and opportunities experienced by knowledge workers while leveraging PKM through implying ChatGPT. It also encapsulates how some management theories back the cruciality of generative AI (specifically ChatGPT) for PKM.en_US
dcterms.abstractResearch limitations/implications: This study identifies the challenges and opportunities. from existing studies and does not imply empirical data/result. The authors believe that findings can be adjusted to diverse domains regarding knowledge workers’ PKM endeavors. This paper draws some conclusions and calls for further empirical research.en_US
dcterms.abstractOriginality/value: ChatGPT’s capability to accelerate organizational performance compelled scholars to focus in this domain. The linkage of ChatGPT to Knowledge Management is an under-explored area specifically the role of ChatGPT on PKM hasn't been given attention in the existing work. This is one of the earliest studies to explore this context.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationGlobal knowledge, memory and communication, 30 Oct. 2025, v. 74, no. 7-8, p. 2255-2269en_US
dcterms.isPartOfGlobal knowledge, memory and communicationen_US
dcterms.issued2025-10-30-
dc.identifier.scopus2-s2.0-85180253701-
dc.identifier.eissn2514-9350en_US
dc.description.validate202401 bcchen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera2580-
dc.identifier.SubFormID47908-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextDepartment of ISE, The Hong Kong Polytechnic Universityen_US
dc.description.pubStatusPublisheden_US
dc.description.oaCategoryGreen (AAM)en_US
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