Bibliometric Knowledge Mapping of Teaching Staff Satisfaction of High-Level Talents Construction System on Big Data

DOI

10.2991/978-2-38476-378-8_36How to use a DOI?

Keywords

High-level talent; Satisfaction and loyalty; Bibliometric knowledge mapping; Dynamic capability theory

Abstract

The primary premise of school development is to have a sufficient number of teachers, reasonable structure, and excellent quality of teachers. Because of these “prophets”, school educational activities can only be organized, carried out, improved, and developed. In the new era, to promote the further development of our school, it is necessary to introduce more powerful measures to build a good team of teachers, to ensure that the university achieves the goal of building a vocational university at the undergraduate level. Therefore, how to improve the satisfaction and loyalty of high-level talents has become a concern of the school.

In this paper, this study utilizes bibliometric knowledge mapping to guide dynamic capability theory. High-level talents as the research object, mainly in literature research, questionnaire, and data analysis, improve the satisfaction of high-level talents, to a certain extent, make up for the shortage of academic research, and enrich the university faculty of literature research materials. Data and information were collected by consulting relevant materials, literature, and a questionnaire survey, and the satisfaction survey was conducted from four aspects of school image, teacher experience, teacher welfare, and teacher salary, and the satisfaction of high-level talents was comprehensively analyzed and evaluated by SPSS 23.0 and Amos software. According to the analysis results, it will inspire relevant schools and provide theoretical support to improve the overall quality and innovation ability of teachers.

Copyright

© 2025 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.

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TY  - CONF
AU  - Jinrong Huang
AU  - Min Ye
PY  - 2025
DA  - 2025/03/31
TI  - Bibliometric Knowledge Mapping of Teaching Staff Satisfaction of High-Level Talents Construction System on Big Data
BT  - Proceedings of the 2024 4th International Conference on Public Art and Human Development (ICPAHD 2024)
PB  - Atlantis Press
SP  - 309
EP  - 321
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-378-8_36
DO  - 10.2991/978-2-38476-378-8_36
ID  - Huang2025
ER  -