Visual Analysis of Human Resource Management Research Under the Background of Big Data
- DOI
- 10.2991/978-94-6463-030-5_83How to use a DOI?
- Keywords
- Big Data; Human Resource Management; Citespace; Knowledge Graph
- Abstract
With the rapid development of digital economy, the application of new generation information technology such as big data has gradually transformed enterprise human resource management, forming human resource management under the background of big data. In order to explore its research status and development trend, this paper takes 143 relevant documents collected in the web of Science Database in recent ten years as the research object, makes quantitative analysis of relevant documents in this field by using CiteSpace software, and constructs relevant knowledge map. The results show that documents produced by USA are the largest, followed by England, and India, Spain, Italy, Germany and China have some influence; the documents are mainly published in journals in the field of management; the documents content involve performance, management, impact, artistic intelligence, etc.; in recent years, there are more and more related documents, and they involve other fields, which may show the trend of cross domain development.
- 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 - Bindi Wu PY - 2022 DA - 2022/12/20 TI - Visual Analysis of Human Resource Management Research Under the Background of Big Data BT - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) PB - Atlantis Press SP - 843 EP - 850 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-030-5_83 DO - 10.2991/978-94-6463-030-5_83 ID - Wu2022 ER -