Identification of Future Skills Using Data-Driven Methods: A Systematic Literature Review and Directions for Future Research

dc.contributor.author Brasse, Julia
dc.date.accessioned 2023-12-26T18:46:14Z
dc.date.available 2023-12-26T18:46:14Z
dc.date.issued 2024-01-03
dc.identifier.isbn 978-0-9981331-7-1
dc.identifier.other da550635-3c4c-44ac-9b5d-9b6bdf1d5512
dc.identifier.uri https://hdl.handle.net/10125/107015
dc.language.iso eng
dc.relation.ispartof Proceedings of the 57th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Digitalization of Work
dc.subject data-driven methods
dc.subject future skills
dc.subject job advertisements
dc.subject literature review
dc.title Identification of Future Skills Using Data-Driven Methods: A Systematic Literature Review and Directions for Future Research
dc.type Conference Paper
dc.type.dcmi Text
dcterms.abstract The labor market is rapidly changing due to swift technological progress, economic disruptions, and societal demands, leading to frequent job transitions and skill disruptions. Understanding the skills required to tackle future challenges, commonly referred to as future skills, is of paramount importance. The availability of accessible data and rapid technological advancements have given rise to an emerging research field focused on data-driven methods for identifying future skills. However, there is a lack of cumulative knowledge. By conducting a systematic review of 118 papers published in diverse interdisciplinary outlets, we examine and describe the applied analysis methods and research objectives, aiming to enhance understanding within this field of research. Furthermore, we identify four distinct directions for future research.
dcterms.extent 10 pages
prism.startingpage 5248
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