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The Bibliometric Keywords Network Analysis of Human Resource Management Research Trends: The Case of Human Resource Management Journals in South Korea

Author

Listed:
  • Chungil Chae

    (College of Business and Public Management (CBPM), Kean University (Wenzhou), Wenzhou 325060, China)

  • Jeong-Ha Yim

    (Department of Lifelong Education, Administration, and Policy, University of Georgia, Athens, GA 30602, USA)

  • Jaeeun Lee

    (Department of Adult Learning and Counselling, Sangji University, Wonju 26339, Korea)

  • Sung Jun Jo

    (Department of Global Business, Gachon University, Seongnam 13120, Korea)

  • Jeong Rok Oh

    (Graduate School of Public Administration, Korea University, Sejong 30019, Korea)

Abstract

Although previous studies on human resource management (HRM) research trends in the global context provided very useful information about the structures of HRM research trends, they have critical limitations. Despite the growing contribution and significance of HRM practices in Korea, a dominant group of scholars has rarely focused on what is going on in the research community in the country. To overcome the limitations and fill the gaps found in studies on the global HRM trends, the purpose of this study is to conduct the keyword network analysis investigating the semantic network structure composed of Korean HRM studies. A total of 1158 research papers published by three top peer-reviewed HRM journals in Korea that were published from 2007 to 2018 were analyzed. The result shows that the whole network structure of Korean HRM has a complex semantic structure that is socially constructed. Additionally, this study identified the top 10 prominent keywords and its ego-centric networks, and nine thematic clusters. By adopting keywords network analysis in bibliometric methods, this study provides an accurate structural interpretation of Korean HRM research practice to facilitate the sustainable development of the studies on the global HRM trends.

Suggested Citation

  • Chungil Chae & Jeong-Ha Yim & Jaeeun Lee & Sung Jun Jo & Jeong Rok Oh, 2020. "The Bibliometric Keywords Network Analysis of Human Resource Management Research Trends: The Case of Human Resource Management Journals in South Korea," Sustainability, MDPI, vol. 12(14), pages 1-37, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:14:p:5700-:d:384931
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    References listed on IDEAS

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    3. Waltman, Ludo & van Eck, Nees Jan, 2013. "A systematic empirical comparison of different approaches for normalizing citation impact indicators," Journal of Informetrics, Elsevier, vol. 7(4), pages 833-849.
    4. Dotsika, Fefie & Watkins, Andrew, 2017. "Identifying potentially disruptive trends by means of keyword network analysis," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 114-127.
    5. Nees Jan van Eck & Ludo Waltman, 2009. "How to normalize cooccurrence data? An analysis of some well‐known similarity measures," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(8), pages 1635-1651, August.
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    Cited by:

    1. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.

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