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September 16, 2024
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December 24, 2024
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February 27, 2025
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Abstract

This study uses a scientometric approach to examine the relationship between learning agility and work engagement in modern management. Using the Scopus database, it identified trends, significant authors, and influential institutions from 1994 to 2023. The data sources in this study were taken from the Scopus database with the keywords “Learning Agility” AND “Work Engagement” AND “Modern Management” from 1994-2023, with a total of 720 documents. Then, it was visualized and analyzed using VOSviewer, RStudio, CiteSpace visualization, and bibliometric mapping software. The results showed that learning agility, the ability to quickly adapt to new experiences, work commitment, focus on completing tasks, and achieving goals are closely related. Machine learning, artificial neural networks, and predictive analytics can improve learning agility and work engagement. Transformational leadership, mental workload, social support, digital competence, and new technology adaptability also improve learning ability and work engagement. Theoretical implications of the study include understanding the dynamics of learning agility and work engagement dynamics. In contrast, practical implications include strategies to increase employee productivity through skill development and targeted interventions. The limitation of this research is the data selection process, which only provides general limitations. Therefore, this research suggests that in the future, data should be explicitly limited by selecting the data to be analyzed one by one by adopting a mixed-method approach.

Keywords

Learning agility Work engagement Scientometric analysis Modern management Bibliometric visualization

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Copyright (c) 2025 Farira Nareswari, Rini Juni Astuti

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Nareswari, F., & Astuti, R. J. (2025). A scientometric review of the relationship between learning agility and work engagement in modern management context. Asian Management and Business Review, 5(1), 196–215. https://doi.org/10.20885/AMBR.vol5.iss1.art13
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