A study of human resource management competency model based on data science
- DOI
- 10.2991/978-94-6463-102-9_133How to use a DOI?
- Keywords
- human resource management; competency; data Science; competency model
- Abstract
In this paper, word frequency statistics of HRM competency factors in related literature were conducted, and high-frequency competency factors were selected as HRM generic competency factors, and other data science competency factors were added by analyzing new requirements of HRM personnel through data science, which were finally sorted and summarized as HRM competency factors of mathematical science. On the basis of the initial proposed competency factors, the statistical data were subjected to reliability analysis, descriptive statistical analysis and exploratory factor analysis, and the dimensions of the data science-based human resource management personnel competency model were finally determined. To a certain extent, it enriches and improves the research on data science employee competency requirements and HRM personnel competency transformation, and also provides a theoretical basis for enterprises to promote HRM data science and enhance HRM talent training mechanism.
- 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 - Kang Wang PY - 2022 DA - 2022/12/29 TI - A study of human resource management competency model based on data science BT - Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022) PB - Atlantis Press SP - 1292 EP - 1296 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-102-9_133 DO - 10.2991/978-94-6463-102-9_133 ID - Wang2022 ER -