Mapping Strategic Management Research in the Public Sector: An Integrated Bibliometric and Machine Learning Approach - Mohammad Nuh; M. Rizki Pratama (Universitas Kadiri, 2026)
Strategic management is a key lens for understanding how public organizations respond to increasing performance demands, yet existing studies remain fragmented and lack systematic synthesis. This study maps the evolution of strategic management research in the public sector, identifies core and emerging themes, and examines shifts in research trajectories over time. Using the PRISMA protocol, 5,749 records were screened and reduced to 132 Scopus-indexed publications. Bibliometric analysis using Biblioshiny® identified publication patterns and influential contributors, while topic modeling using Latent Dirichlet Allocation (LDA) in Python uncovered latent thematic structures and changes in topic probabilities over time. The findings reveal ten topics grouped into four major domains: organizational learning, leadership, reform and change, and urban innovation. The literature shows a shift from a dominant focus on strategic planning and performance tools toward change resistance, leadership engagement, and adaptive learning capabilities. Leadership remains a central driver, while increasing attention to urban innovation reflects the growing importance of collaborative governance and digital transformation. Overall, this study provides a methodological contribution by integrating bibliometric analysis for the macro-mapping of research actors and Latent Dirichlet Allocation (LDA) topic modeling to extract latent thematic structures at a granular level.