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Social Network Analysis of Sustainable Human Resource Management from the Employee Training’s Perspective

Authors: Lu Zhang; Xiaochao Guo; Zhimei Lei; Ming K. Lim;

Social Network Analysis of Sustainable Human Resource Management from the Employee Training’s Perspective

Abstract

Employee training is not only important for the continuous growth of human resources but also guarantees sustainable human resource management in enterprises. It is very important to understand corporate behaviour related to employee training not only from the perspective of a single enterprise but also from that of multiple enterprises. The purpose of this study is to explore multiple enterprises’ employee training behaviours by conducting a content analysis of corporate social responsibility (sustainability) reports and a social network analysis. This study also seeks to find a way to achieve sustainable employee training by analysing the similarities in the different types of corporate training behaviours. Our analysis shows that, in 2017, 108 types of training activities were implemented by 53 enterprises; the key employee trainings (e.g., security training and skills training) and enterprises (e.g., bank of communication) are identified. The training behaviours of some of the enterprises are similar to some extent, and eight groups of firms that are very similar are identified. The results of this study show that social network analysis performs well for studying corporate employee training behaviours. Some suggestions to minimize the investment costs of training and to improve the sustainability of human resource management from the employee training perspective are provided.

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Keywords

sustainable human resource management, social network analysis, Environmental effects of industries and plants, TJ807-830, sustainability reports, TD194-195, Renewable energy sources, Environmental sciences, employee training, GE1-350

  • BIP!
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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    34
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
34
Top 10%
Top 10%
Top 10%
gold