IDEAS home Printed from https://ideas.repec.org/a/igg/jisscm/v16y2023i1p1-11.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISSCM.316182
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sankhadeep Chatterjee & Sarbartha Sarkar & Nilanjan Dey & Soumya Sen, 2018. "Non-Dominated Sorting Genetic Algorithm-II-Induced Neural-Supported Prediction of Water Quality with Stability Analysis," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 17(02), pages 1-20, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jisscm:v:16:y:2023:i:1:p:1-11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.