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Text mining for human resources competencies: Taiwan example

Text mining for human resources competencies: Taiwan example The purpose of this study is to explore the capabilities required by entry-level human resources (HR) professionals based on job advertisements by using text mining (TM) technique.Design/methodology/approachThis study used TM techniques to explore the capabilities required by entry-level HR professionals based on job advertisements on HR agency 104’s website in Taiwan. Python was used to crawl the advertisements on the website, and 841 posts were collected. Next, the author used TM to explore and understand hidden trends and patterns in numerous data sets.FindingsThe results of this study revealed four critical success factors (specific skills, educational level, experience and specific capabilities), five clusters and ten classifications.Practical implicationsThe results can aid HR curriculum developers and educators in customizing and improving HR education curricula, such that HR students can develop capabilities required to secure employment in the current HR job market.Originality/valueOur results may facilitate the understanding of the current trends in the HR job market and provide useful suggestions to HR curriculum developers for improving training and professional course design, such that students’ competitiveness is enhanced and professional capabilities improved. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Journal of Training and Development Emerald Publishing

Text mining for human resources competencies: Taiwan example

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2046-9012
DOI
10.1108/ejtd-07-2018-0060
Publisher site
See Article on Publisher Site

Abstract

The purpose of this study is to explore the capabilities required by entry-level human resources (HR) professionals based on job advertisements by using text mining (TM) technique.Design/methodology/approachThis study used TM techniques to explore the capabilities required by entry-level HR professionals based on job advertisements on HR agency 104’s website in Taiwan. Python was used to crawl the advertisements on the website, and 841 posts were collected. Next, the author used TM to explore and understand hidden trends and patterns in numerous data sets.FindingsThe results of this study revealed four critical success factors (specific skills, educational level, experience and specific capabilities), five clusters and ten classifications.Practical implicationsThe results can aid HR curriculum developers and educators in customizing and improving HR education curricula, such that HR students can develop capabilities required to secure employment in the current HR job market.Originality/valueOur results may facilitate the understanding of the current trends in the HR job market and provide useful suggestions to HR curriculum developers for improving training and professional course design, such that students’ competitiveness is enhanced and professional capabilities improved.

Journal

European Journal of Training and DevelopmentEmerald Publishing

Published: Sep 16, 2021

Keywords: Cluster analysis; Classification; Human resource; Text mining; Job advertisement; Professional competence

References