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A Bibliometric and Co-Occurrence Analysis of Work-Life Balance: Related Literature Published Pre- and During COVID-19 Pandemic

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  • Soumi Majumder

    (Vidyasagar University, India)

  • Debasish Biswas

    (Vidyasagar University, India)

Abstract

Work-life balance helps to maintain an attractive organizational culture and remove work-life conflicts and show the path to employees of how to be more efficient in different work roles. This balanced practice is giving a care and feeling of protection to the employees. It motivates better performance that contributes to employee engagement indices. The main purpose of this study is to report work-life balance pre- and during the COVID-19 pandemic by bibliometric analysis. This study analyzed 4,030 “work-life balance” studies published between January 1, 2010 and December 31, 2019, from the pre-pandemic era, and 1,143 studies published during the pandemic (between January 1, 2020-March 24, 2021). The data were extracted from the Scopus database using keywords “work-life balance” and keywords in titles (items) analyzed using VOSviewer software. Co-occurrence connection between keywords in titles and density visualization based on the total link strength clearly shows that COVID-19 significantly impacted work-life balance and related research.

Suggested Citation

  • Soumi Majumder & Debasish Biswas, 2023. "A Bibliometric and Co-Occurrence Analysis of Work-Life Balance: Related Literature Published Pre- and During COVID-19 Pandemic," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 16(1), pages 1-11, January.
    • Handle: RePEc:igg:jisscm:v:16:y:2023:i:1:p:1-11
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    References listed on IDEAS

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    1. Vijay M. Khadse & Parikshit Narendra Mahalle & Gitanjali R. Shinde, 2020. "Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests: A Comparative Analysis and Recommendations," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 11(3), pages 80-105, July.
    2. Debi Acharjya & A. Anitha, 2017. "A Comparative Study of Statistical and Rough Computing Models in Predictive Data Analysis," International Journal of Ambient Computing and Intelligence (IJACI), IGI Global, vol. 8(2), pages 32-51, April.
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