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dc.contributor.authorChuang, Yen-Chingen_US
dc.contributor.authorHu, Shu-Kungen_US
dc.contributor.authorLiou, James J. H.en_US
dc.contributor.authorTzeng, Gwo-Hshiungen_US
dc.date.accessioned2020-10-05T01:59:42Z-
dc.date.available2020-10-05T01:59:42Z-
dc.date.issued2020-01-01en_US
dc.identifier.issn2029-4913en_US
dc.identifier.urihttp://dx.doi.org/10.3846/tede.2020.12366en_US
dc.identifier.urihttp://hdl.handle.net/11536/154840-
dc.description.abstractPersonnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts' judgements as input for the derivation of solutions. However, the subjectivity of the experts' experience can have a negative influence on this type of decision-making process. With the arrival of today's data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future.en_US
dc.language.isoen_USen_US
dc.subjecthuman resource developmenten_US
dc.subjectpersonnel selection and improvementen_US
dc.subjectdata-driven decision-making environmenten_US
dc.subjectdata-driven multiple attribute decision-making (Data-driven MADM)en_US
dc.subjectrough set theory (RST)en_US
dc.subjectDEMATEL-based analytical network process (DANP)en_US
dc.subjectpreference ranking organization method for enrichment evaluation with aspiration level (PROMETHEE-AS)en_US
dc.titleA DATA-DRIVEN MADM MODEL FOR PERSONNEL SELECTION AND IMPROVEMENTen_US
dc.typeArticleen_US
dc.identifier.doi10.3846/tede.2020.12366en_US
dc.identifier.journalTECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMYen_US
dc.citation.volume26en_US
dc.citation.issue4en_US
dc.citation.spage751en_US
dc.citation.epage784en_US
dc.contributor.department科技管理研究所zh_TW
dc.contributor.departmentInstitute of Management of Technologyen_US
dc.identifier.wosnumberWOS:000546134600004en_US
dc.citation.woscount0en_US
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