Dynamic Association Analysis of Corporate Social Responsibility and Human Capital Management Utilizing the Grey Relational Algorithm

DOI

10.2991/978-2-38476-309-2_16How to use a DOI?

Keywords

Human resource management; grey relation; enterprise

Abstract

As large enterprises continue to embrace diversified and specialized management philosophies, their social roles within society are becoming increasingly varied. With the integration of computer technology into enterprise management, challenges in human resource management pertinent to collective social responsibility frequently arise. This paper introduces a novel approach by using a discrete data modeling method to explore the coupling relationship between collective social responsibility and employee relations management, utilizing the grey correlation technique. Unlike the commonly utilized hierarchical data analysis and clustering techniques in contemporary human resource management studies, this novel method examines the dynamic data of corporate social responsibility and human capital, creating a coupled model linked to shared social responsibility.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xinyue Wang
AU  - Kunxiang Luo
PY  - 2024
DA  - 2024/12/09
TI  - Dynamic Association Analysis of Corporate Social Responsibility and Human Capital Management Utilizing the Grey Relational Algorithm
BT  - Proceedings of the 2024 9th International Conference on Modern Management, Education and Social Sciences (MMET 2024)
PB  - Atlantis Press
SP  - 131
EP  - 137
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-309-2_16
DO  - 10.2991/978-2-38476-309-2_16
ID  - Wang2024
ER  -