管理胜任力模型在员工晋升中的应用——AHP与VIKOR方法的结合

IF 0.6 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE International Journal of Biometrics Pub Date : 2020-06-08 DOI:10.5539/ijbm.v15n7p77
Hsien-Yu, Shun, Kuo-Yan Wang
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引用次数: 3

摘要

管理胜任力模型是列出管理者职位所需胜任力的主要结构。本文结合管理胜任力模型和AHP-VIKOR方法,建立了一个员工晋升应用程序。研究采用定性和定量相结合的方法。通过文献综述和内部访谈来解释管理胜任力模型,以确定适合案例公司的具体胜任力项目。专家小组的研究用于评估案例研究中候选人的管理能力。定量工具为层次分析法和VIKOR模型。运用层次分析法计算管理胜任力项目的准则权重。VIKOR多准则决策模型对备选方案进行排序,选出最合适的晋升人选。将VIKOR模型的结果作为推荐最佳候选人的决策依据。本研究期望在客观评价结果的基础上,运用量化的方法来支持企业高层对内部人才的开发。
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The Application of a Managerial Competency Model in Employee Promotion—Combination of AHP and VIKOR Methodology
Managerial competency model is a main structure that lists competencies needed for manager’s position. This paper established a employee promotion application combined with managerial competency model and AHP-VIKOR methodology. Research was carried using qualitative and quantitative methods. The managerial competency model is explained through a literature review and internal interview is to confirm the specific competency items that suitable for case company. Research from expert panels is used to evaluate the managerial competency of the candidates in the case study. The quantitative tool was AHP and VIKOR model. AHP was applied to calculate the criteria weights of managerial competency items. VIKOR multi-criteria decision making model ranked the alternatives and the most suitable promotion candidate was selected. The results from the VIKOR model were used as the decision-making basis for the promotion of the best candidate. This research expected to use the quantitative method to support the executive management level to develop internal talent base on a objective evaluate results.
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来源期刊
International Journal of Biometrics
International Journal of Biometrics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
1.50
自引率
0.00%
发文量
46
期刊介绍: Biometrics and human biometric characteristics form the basis of research in biological measuring techniques for the purpose of people identification and recognition. IJBM addresses the fundamental areas in computer science that deal with biological measurements. It covers both the theoretical and practical aspects of human identification and verification.
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