Research on Human Resource Management Performance Evaluation Based on Entropy Weight TOPSIS Method
- DOI
- 10.2991/978-94-6463-102-9_139How to use a DOI?
- Keywords
- Entropy weight method; TOPSIS method; Human resources; Performance evaluation; Closeness
- Abstract
In order to improve the accuracy and comprehensiveness of performance evaluation results, this paper proposes a human resource management performance evaluation method based on entropy weight TOPSIS. Establish a data analysis platform for human resource management to provide data basis for performance evaluation; Under the condition of meeting the principles of simplicity, scientificity, systematicness and consistency, an evaluation system containing multiple evaluation factors shall be established; Entropy weight method and TOPSIS method are used to calculate the closeness of each evaluation object to the positive ideal solution in the evaluation system, and this value is used to measure the performance level of human resource management. Through experiments, it is verified that this method has higher evaluation accuracy and comprehensiveness, and can provide reference for enterprise human resource management.
- Copyright
- © 2023 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.
Cite this article
TY - CONF AU - Li Li PY - 2022 DA - 2022/12/29 TI - Research on Human Resource Management Performance Evaluation Based on Entropy Weight TOPSIS Method BT - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022) PB - Atlantis Press SP - 1347 EP - 1354 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-102-9_139 DO - 10.2991/978-94-6463-102-9_139 ID - Li2022 ER -