人力资源管理领域的数据挖掘应用展望——以基于灰色关联模型的离职管理实证分析为例
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摘要
数据挖掘作为知识经济时代的发展新动力,其应用已涉及金融业、零售业、保险业、医疗等诸多领域并取得显著成效,但数据挖掘在人力资源管理领域的应用目前仍处于初始阶段,其推行力度、普及程度、技术成熟度等均存在很大局限性。本文在此背景下以离职管理为例,基于数据和风险层面角度,借助数据挖掘技术的灰色关联分析法对离职因素进行量化评估,通过实证分析结果指导管理者做出决策,并且进一步强调数据对企业和人力资源管理者的必要性。
data mining as a new drive the development of knowledge economy era, the application has been involved in many fields such as finance, retail, insurance, medical treatment and achieved remarkable results, but the application of data mining in the field of human resource management is still at the initial stage, its commitment, popularity, technical maturity, etc. There are big limitations. In this context, taking dimission management as an example, this paper USES the grey relational analysis method of data mining technology to conduct quantitative evaluation of dimission factors from the perspective of data and risk, guides managers to make decisions through empirical analysis results, and further emphasizes the necessity of data for enterprises and human resource managers.
data mining as a new drive the development of knowledge economy era, the application has been involved in many fields such as finance, retail, insurance, medical treatment and achieved remarkable results, but the application of data mining in the field of human resource management is still at the initial stage, its commitment, popularity, technical maturity, etc. There are big limitations. In this context, taking dimission management as an example, this paper USES the grey relational analysis method of data mining technology to conduct quantitative evaluation of dimission factors from the perspective of data and risk, guides managers to make decisions through empirical analysis results, and further emphasizes the necessity of data for enterprises and human resource managers.
引文
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[2]朱近贤.数据挖掘技术在人力资源管理中的应用研究[D].上海交通大学,2008,5.
[3]陈章良.基于数据挖掘的预测决策模型应用与研究[J].中国管理信息化,2009:57-59.
[4]瞿晓理.数据挖掘技术在员工流失问题上的应用[J].技术与创新管理,2014,35(5):452-456.
[5]杨帆.基于数据挖掘技术的人力资源信息化管理[J].信息通信,2017:152-153.
[6]李亚.基于灰色关联度模型的中小企业信用风险评估[J].经济师,2014,(06):79-80.
[7]张勉,张德.企业雇员离职意向的影响因素:对一些新变量的量化研究[J].人力资源管理,2007,(04).
[8]令狐克睿.基于Price-Mueller(2000)模型的新生代农民工离职原因及对策分析[J].贵州民族大学学报,2013:9-14.
[9]张雪军,齐辉.高科技企业员工离职风险量化评估机制研究——以LC公司为例[J].中国人力资源开发,2013.
[10]周霞,司马经子.以离职倾向为研究对象的人力资源风险管理[J].华南理工大学学报,2015,17(4).
[11]张言彩,韩玉启.国外员工离职研究文献描述性分析及综述[J].技术经济,2007,26(8):73-77.
[12]汪明.数据挖掘综述[J].河北软件职业技术学院学报.2012,14(1).
[13]高艳.人力资源管理理论研究综述[J].西北大学学报.2005,35(2).
[14]Danping Zhang,Jin Deng.The Data Mining of the Human Resources Data Warehouse in University Based on Association Rule[J].Journal of Computers,2011,V01.6(1):139-146.
[15]Siyu Zang,Maolin Ye.Human Resource Management in the Era of Big Data[J].Journal of Human Resource and Sustainability Studies,2015,V01.03(01):41-45.[l6]Nelson R.Data processing as a tool in human resource management[J].World hospital S,1983,V01.19(4):28-30.
[17]Paul Zikopoulos,Chris Eaton.Understanding Big Data:Analytics for Enterprise Class Hadoop and Streaming Data[M].Mc Graw-Hill Osborne Media,2011.
[18]Manyika J,Chui M,Brown B,et al.Big data:the next frontier for innovation,competition,and productivity.Mc Kinsey Global Institute,2011.
[19]Junqiang Liu,Yunhe Pan,Ke Wang,and Jiawei Hart.Mining frequent item sets by opportunistic projection[A].Proceedings of the 8th ACM SIGKDD International Co Bference on Knowledge Discovery and Data Mining[C].USA:ACM Press,2002.
[20]Stephen G,Jean-Louis N,Walker,Paul L.Enterprise Risk Management:A Process for Enhanced Management and Improved Performance[J].Management Accounting Quarterly,2012,13(3):28-38.
[21]Keen.P.GW.A Decade Perspective,Information&Management,decision support system.1986,1:362~363.