Applied Mathematics and Nonlinear Sciences
Journal license

Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

May 3, 2024


Pages


DOI

Article

Gray correlation analysis of strategic human resource management and corporate performance

Check for updates


Authors

Yuxuan Liu Affiliation:
School of Management Engineering and Business, Hebei University of Engineering, Handan, Hebei, 056000, China.
and Huiling Zhang Affiliation:
School of Management Engineering and Business, Hebei University of Engineering, Handan, Hebei, 056000, China.


Abstract

In today’s competitive business landscape, effective human resource management (HRM) is pivotal for the growth and performance of enterprises. This study uses gray correlation and multiple regression analyses to investigate the connection between HRM practices and corporate performance. We developed an evaluative framework to assess the impact of strategic talent management on company outcomes. Survey data were analyzed to unveil the dynamics between HRM variables and corporate performance. Our findings highlight a significant relationship between employee recruitment, incentive programs, promotion pathways, and an innovative culture with corporate success, with p-values less than 0.01. Notably, stock options (0.354), bonus schemes (0.325), and promotion opportunities (0.331) show the strongest correlations. The gray correlation analysis further indicates that the HRM-performance linkage in three surveyed companies exceeds 0.7%, underscoring HRM’s vital role in enhancing enterprise performance. This research presents gray correlation analysis as an effective tool for businesses to evaluate the impact of their HRM strategies on performance.


Keywords

Gray correlation analysis, Multiple linear regression, Index system, Human resource management, Enterprise performance, 91B32


Citation

Liu, Y. & Zhang, H. (2024). Gray correlation analysis of strategic human resource management and corporate performance. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0980

Published by: Engineering Journals

Engineering Journals Logo