Anatomy of Digital Leadership Studies: An Analysis with Topic Modeling Approaches

This study aims to identify trends in digital leadership and analyze frequently used terms and their underlying topic structures in a significant data context. To achieve this, the study used topic modeling approaches (TMA) applied to the academic literature on digital leadership. A total of 847 articles published between 2014 and 2024 from Web of Science, Scopus, DergiPark, and Council of Higher Education Thesis Center databases were collected and analyzed using TMA. In particular, latent Dirichlet allocation (LDA) and BERTopic, which are among the TMA approaches, were used to provide a comprehensive review of the field as well as a comparative analysis of the methodologies. Generative artificial intelligence is used to make the results obtained with the topic modeling approaches more meaningful. In particular, OpenAI's GPT-3.5-turbo model was used to automatically summarize the topics identified with LDA and BERTopic and generate appropriate thematic headings. The results reveal that the concept of digital leadership in the literature is primarily focused on overcoming organizational challenges through innovation and transformation. Key themes identified include the adoption of innovative strategies for digital transformation, the development of new business models, and the role of digital leadership in quality management, technological analysis, and performance improvement through effective technology, knowledge, and social management. Overall, these findings provide valuable insights for future research by suggesting potential variables and research questions, clarifying academic trends in the field, and providing a new method for mapping this emerging area of study.