VUCA bibliometric and content analysis
DOI:
https://doi.org/10.3989/redc.2023.2.1968Keywords:
VUCA, Bibliometrics, content analysis, LDA, K-MeansAbstract
VUCA is an acronym for volatility, uncertainty, complexity, and ambiguity, used to describe an environment that defies confident predictions. An example of this environment is the Covid-19 pandemic, which has created uncertainty worldwide because it is an unknown and highly contagious disease that neither society nor institutions were prepared to face. This article aims to describe the scientific production of VUCA to understand its main research focus. This research analyzes 105 documents from the Web of Science (WoS) database using Bibliometrics and Content Analysis. The bibliometric analysis reported several production indexes: annual, personal, national, institutional, and journal productivity. The content analysis analyzed 95 article abstracts in nineteen clusters selected by comparing two clustering methods, Latent Dirichlet Allocation and K-Means, using the coherence and silhouette indices, respectively. VUCA is an emerging topic with increased scientific production in the last four years. However, there are no major producers to date. The most frequent topics are management, leadership, and change, where several works emphasize the role of the leader in dealing with change. The literature has focused on understanding the skills needed to cope with a VUCA environment and how to teach them. In addition, the use of two methods based on machine learning techniques to estimate the number of clusters of scientific papers is highlighted as an alternative to splitting articles into topics when the dataset is small.
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