Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/9990
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dc.contributor.authorProf. LAU Kung Wong, Nicken_US
dc.date.accessioned2024-05-21T09:23:38Z-
dc.date.available2024-05-21T09:23:38Z-
dc.date.issued2022-
dc.identifier.citationPRESENCE: Virtual and Augmented Reality, 2022, vol. 31, pp. 229-244.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11861/9990-
dc.description.abstractThis paper aims to explore the possibilities of implementing the tacit knowledge transfer process and practices in the metaverse and remote workplaces. Tacit knowledge transfer is crucial for organizational knowledge management and maintaining an organization's sustainable development. The research team believes that the use of the metaverse in remote workplaces is expected to revolutionize established organizational learning, knowledge management, and tacit knowledge transfer models. This research attempts to understand employees’ experiences from three perspectives: (1) the acceptance by both senior and junior staff of using the metaverse for training; (2) the experiences of tacit knowledge transfer over the metaverse; and (3) the role of immersion and interactivity during the knowledge transfer process. The significance of this research is the theoretical investigation of the tacit knowledge transfer model in the metaverse. This research is explorative in nature; therefore, this research is not going to generalize or prove the effectiveness of using the metaverse in the tacit knowledge transfer process, but rather explore the factors and deepen our understanding of these fundamental attributes. The findings of this research suggest a modification of the classical model SECI Matrix by adding a new component of “integration” in the transfer process.en_US
dc.language.isoenen_US
dc.relation.ispartofPRESENCE: Virtual and Augmented Realityen_US
dc.titleRethinking the knowledge transfer process through the use of metaverse: A qualitative study of organizational learning approach for remote workplaceen_US
dc.typePeer Reviewed Journal Articleen_US
dc.identifier.doi10.1162/pres_a_00395-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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