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基于数据仓库和计算智能的人力资本投资预警风险与控制模型。

An Early Warning Risk and Control Model for Manpower Capital Investment Using Data Warehousing and Computational Intelligence.

机构信息

School of Economics and Management, Harbin University of Science and Technology, Harbin, Heilongjiang 150080, China.

School of Electronics and Information Engineering, Heilongjiang University of Science and Technology, Harbin, Heilongjiang 150022, China.

出版信息

Comput Intell Neurosci. 2022 Mar 24;2022:7624135. doi: 10.1155/2022/7624135. eCollection 2022.

DOI:10.1155/2022/7624135
PMID:35371241
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8970945/
Abstract

Human capital plays an important role in the development of enterprises. Investing in human capital is the main focus of enterprises to improve personnel quality and enhance their core competitiveness. With the development of market economy, the function of human resource market allocation has been improved and the mobility of enterprise human resources has been enhanced leading to the increase in investment risk of enterprise human capital. Enterprise human capital investment risk has a negative impact on enterprises, reduces the income of enterprises' human capital investment, and affects their growth. Hence, enterprises need to avoid the risk of human capital investment or minimize the negative impact of risk. Using the data warehouse and computational intelligence, this paper constructs the early warning and control model for human capital investment risk and analyzes the existing approaches during the recruitment process and training, investment, and production, among enterprises. Finally, this paper proposes the corresponding control method according to the model inspiration.

摘要

人力资本在企业发展中起着重要作用。投资人力资本是企业提高人员素质、增强核心竞争力的主要关注点。随着市场经济的发展，人力资源市场配置功能得到改善，企业人力资源流动性增强，导致企业人力资本投资风险增加。企业人力资本投资风险对企业产生负面影响，降低企业人力资本投资收益，影响其成长。因此，企业需要避免人力资本投资风险或最小化风险的负面影响。本文利用数据仓库和计算智能构建人力资本投资风险预警与控制模型，并对企业招聘过程中和培训、投资、生产等阶段的现有方法进行分析。最后，根据模型启示提出相应的控制方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/a5a329416414/CIN2022-7624135.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/a54ec79dd36a/CIN2022-7624135.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/9b2f70916e44/CIN2022-7624135.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/c78d2c338c62/CIN2022-7624135.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/a5a329416414/CIN2022-7624135.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/a54ec79dd36a/CIN2022-7624135.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/7f020c3a7e62/CIN2022-7624135.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/91007731d4b6/CIN2022-7624135.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/d8930c821887/CIN2022-7624135.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/76f5f9f44397/CIN2022-7624135.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/32398d392c85/CIN2022-7624135.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/9b2f70916e44/CIN2022-7624135.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/c78d2c338c62/CIN2022-7624135.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ea6/8970945/a5a329416414/CIN2022-7624135.009.jpg

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