Assessing growth potential of careers with occupational mobility network and ensemble framework

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dc.contributor.authorLiu, Jiamin
dc.contributor.authorWang, Tao
dc.contributor.authorYao, Feng
dc.contributor.authorPedrycz, Witold
dc.contributor.authorSong, Yanjie
dc.contributor.authorHe, Renjie
dc.date.accessioned2024-05-19T14:42:30Z
dc.date.available2024-05-19T14:42:30Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe growth potential of a career reflects its future prospects and is an important consideration for individuals and organizations when career planning. There is still a lack of quantitative assessment tools for growth potential of careers. In this study, considering the key role of human capital in human resource management, as well as the excellent performance of complex network and machine learning in big data analysis and prediction, a career growth potential assessment model with human capital ensemble is proposed through human capital-based occupational mobility network and ensemble learning. First, an occupational mobility network is constructed based on online professional dataset to associate occupations with each other. Then, five dimensions of human capital measurements are designed to quantify human capital in terms of education, experience, social capital, occupational size, and concentration. These are then combined with the occupational mobility network to create a new network that depicts human capital flows among occupations. Finally, an ensemble framework for assessing career growth potential is constructed to integrate multidimensional human capital information in the network and obtain quantitative scores of growth potential. This study is the original attempt to adopt a data-driven idea and an intelligent approach to understand career growth potential. The experimental results show that it also makes a useful exploration for modeling human capital flows and intelligent assessment of career prospects.en_US
dc.identifier.doi10.1016/j.engappai.2023.107306
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85174360248en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.engappai.2023.107306
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5249
dc.identifier.volume127en_US
dc.identifier.wosWOS:001097930800001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectHuman Capitalen_US
dc.subjectOccupational Mobility Networken_US
dc.subjectGrowth Potential Of Careeren_US
dc.subjectMachine Learningen_US
dc.titleAssessing growth potential of careers with occupational mobility network and ensemble frameworken_US
dc.typeArticleen_US

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