Tracing the evolution of digitalisation research in business and management fields: Bibliometric analysis, topic modelling and deep learning trend forecasting

Abstract

Research on digitalisation trends and digital topics has become one of the most prolific streams of research within the fields of business and management during the course of the past few years. The purpose of this study is to provide a general picture of the intellectual structure and the conceptual space of this research realm. To this purpose, 6067 publications related to digital topics, indexed in the business and management categories of Web of Science (WoS), and dated from 1990 to 2020 are explored based on the approaches of bibliometric analysis, topic modelling and trend forecasting. The results of the bibliometric analysis comprise insights into the publication and citation structure, the most productive authors, the most productive universities, the most productive countries, the most productive journals, the most cited studies and the most prevalent themes and sub-themes on digitalisation in business and management. In addition, the outcomes of the topic modelling give new knowledge on the latent topical structure along with the rising, falling and fluctuating trends of this literature. In addition, the results of the trend forecasting enable readers to have a glimpse of how the underlying trends of the literature will probably change within the next years until 2025. These results provide guidance and orientation for both academics and practitioners who are initiating or currently developing their efforts in this discipline.

A Bibliometric Study of Machine Learning in Biofilm

“…The objective of this study is to conduct a comprehensive bibliometric analysis of the use of ML in biofilm research to gain insights into the current state of the field [11]. Bibliometrics is a quantitative analysis of scientific publications that provides valuable insights into the structure, dynamics, and trends of a particular research area [12,13]. A bibliometric analysis of ML in biofilm research can identify the most influential publications, authors, and institutions in the field, as well as the most common research topics and trends [14].…”