Mapping the Digital Frontier: Bibliometric and Machine Learning Insights Into Public Administration Transformation

Abstract

Digital transformation significantly influences public administration by integrating advanced technologies such as artificial intelligence, blockchain, and big data analytics across various governmental functions. In this study, the use of LDA alongside advanced bibliometric techniques such as citation analysis and co-citation networks to explore the evolution and current status of digital transformation in public administration provides a structured examination of large data sets obtained from the Web of Science, providing a thematic and intellectual insight into the field. Analyzing 628 articles, our research highlights how traditionally engineering-associated technologies are increasingly applied within the social sciences, transforming public management processes and policies. Our findings reveal that digital transformation in public administration is predominantly driven by external pressures rather than internal organizational initiatives. The importance of this study lies in its ability to map and visualize the transformative impact of digital technologies on governance structures, policy-making processes, and public engagement. Revealing that the databased, more transparent and participatory nature of public administration has increased, the study not only maps the thematic evolution of public administration but also discusses the consequences of these transformations for policy makers and public administrators. Integrating digital tools has enhanced service delivery and operational efficiency but also presents challenges, such as the need for comprehensive cybersecurity strategies and continuous adaptation to technological advancements.