Diagnosing Core Topics in Digital Transformation Studies via Topic Model Approach

The field of digital transformation continues to develop and expand rapidly due to technological advances. This study uses text mining techniques to analyze 5280 articles published between 2014 and 2024 using the LDA model and the Gibbs sampling method, the study identifies the most prominent topics on digital transformation research. Traditional methods, which rely on predefined categories and subjective judgment, are inadequate for identifying underlying themes in large datasets. The study identifies the most prominent topics in digital transformation research and tracks trends by tracking changes in topic rankings across different periods. It also explores sub- specialization areas across 1065 digital transformation journals and assesses how shifts in these areas impact the broader topic landscape. The findings provide valuable insights for practitioners, researchers, journal editors, and policymakers involved in digital transformation.