Text Mining in Management Research: A Bibliometric Analysis

The goal of this paper is to provide a bibliometric analysis of scientific publications that employ text mining in management. To accomplish this, the authors collected 1282 documents from the Web of Science and performed performance analysis and science mapping with the help of the Bibliometrix package in Rstudio. The performance analysis used a range of bibliometric indicators such as productivity, citations, h-index, and m-quotient, in order to identify research trends and the most influential journals, authors, countries, and literature in the study. Science mapping used author keywords co-occurrence, co-authorship, and co-citation analysis to reflect the conceptual, social, and intellectual structure of the research. Specifically, we have seen an exponential increase in the use of text mining in management in recent years. The United States is the dominant country for research, having the earliest studies and the highest number of literature and citations. Furthermore, the research themes showed that topic modeling is at the forefront of current text mining research about management. This study will help scholars and management practitioners interested in the intersection of text mining and management to quickly understand the latest advances in research.