Mapping the Panorama of Enterprise Digital Transformation: An LLM-Driven Variable Relational Network Perspective

Abstract Purpose While the imperative of enterprise digital transformation (EDT) has been widely acknowledged, a systematic understanding of its intricate network of antecedents and consequences remains fragmented. This study proposes a novel knowledge representation framework that leverages large language models (LLMs) to construct a variable relational network (VRN), offering a panoramic, micro-level perspective on EDT. Design/methodology/approach We extract five types of variable relationships from a vast corpus of academic publications on EDT to generate the VRN. Subsequently, we apply network topology analysis to uncover the temporal and regional characteristics of the VRN. Its hierarchical structure is then analyzed through K -shell decomposition. Findings Our results show that, over the past two decades, the scale of the VRN has experienced rapid growth, driven collectively by multi-layered external factors such as the rapid advancement of digital technologies, and its internal connections have become increasingly tighter. Regional comparisons of the VRN reveal that different economies, shaped by institutional theories, exhibit distinct transformation paradigms while striving toward common goals. K -shell analysis uncovers a clear hierarchical structure, distinguishing peripheral, intermediate, and core variables, with these layers corresponding to varying degrees of strategic significance and transformation maturity. Research limitations The study’s limitations primarily concern the accuracy of the VRN, which depends on the LLM’s extraction performance and its potential for hallucinations, which may introduce noise into the network topology. Practical implications The VRN and its network topology structure serve as a diagnostic tool for strategic decision-making, enterprises and policymakers can also use these insights to design targeted support programs. Originality/value This study contributes a data-driven, LLM-assisted framework for mapping the evolving and multidimensional landscape of enterprise digital transformation, thereby validating and extending the theoretical boundaries of EDT.