Exploring research trends and patterns in leadership research: a machine learning, co-word, and network analysis

Abstract Leadership is recognized as playing a crucial role in the organization’s performance and success. As a result, the scientific literature on leadership has become quite extensive, making it difficult to identify and understand the current state of research. Most literature studies focus on a specific aspect of the field or a limited time frame, providing a fragmented view of the overall landscape. Therefore, this research aims to provide new insights into the current state of research through two studies. Using advanced Natural Language Processing (NLP) techniques, the first study focuses on identifying emerging research trends in the field through a Latent Dirichlet Allocation (LDA) model, providing insights into future areas of interest and investigation. The second study centers on analyzing consolidated research patterns through co-word and network analysis, shedding light on the connections and interrelationships between leadership research topics. By applying these techniques to a comprehensive dataset of 56,547 research papers gathered from Web of Science and Scopus, this study provides a detailed understanding of the current state of leadership research and identifies potential areas for future exploration. Five research trends were identified: (1) Leadership and Digital Transformation Research (LDTR); (2) Leadership and Organizational Performance Research (LOPR); (3) Educational Leadership Research (ELR); (4) Leadership Practices and Development Research (LPDR); and (5) Gender and Diversity Leadership Research (GDLR). Combining these five research trends with the consolidated research patterns identified, we propose several research directions identified for advancing leadership studies.