Regression Analysis of the Influence of Human Resource Management on Enterprise Performance
Available Online January 2020.
- https://doi.org/10.2991/msie-19.2020.1How to use a DOI?
- human resource management, employee satisfaction, enterprise performance, regression analysis
- Firstly, this paper constructs a model with employee satisfaction as the intermediate variable, forming the basic hypothesis of three variables: human resource management, employee satisfaction and enterprise performance; secondly, it designs the reasonable items of the questionnaire combined with the variables, and the questionnaire is issued and the survey data is recovered; finally, it integrates the data and conducts regression analysis to study the relationship between human resource management and enterprise performance, and puts forward the suggestions for human resource management. In recent years, with the continuous acceleration of China's economic development, enterprises conform to the background of the development of the times, showing unprecedented vitality, and become an important part of China's economic construction. Talents are the core strategic resources of an enterprise and the key guarantee to improve the performance of an enterprise. This paper analyzes the influence mechanism of human resource management on enterprise performance, constructs and demonstrates the relationship mechanism between the two, which is of great significance to improve enterprise management efficiency, enhance enterprise performance, and promote the healthy and orderly development of enterprises.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jing Chen PY - 2020/01 DA - 2020/01 TI - Regression Analysis of the Influence of Human Resource Management on Enterprise Performance BT - 2019 International Conference on Management Science and Industrial Economy (MSIE 2019) PB - Atlantis Press SP - 1 EP - 7 SN - 2352-5428 UR - https://doi.org/10.2991/msie-19.2020.1 DO - https://doi.org/10.2991/msie-19.2020.1 ID - Chen2020/01 ER -