Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal Mediasosian : Jurnal Ilmu Sosial dan Administrasi Negara
Mohammad Nuh
Department of Public Administration, Brawijaya University
Social Sciences

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Mapping Strategic Management Research in the Public Sector: An Integrated Bibliometric and Machine Learning Approach Mohammad Nuh; M. Rizki Pratama
Jurnal Mediasosian : Jurnal Ilmu Sosial dan Administrasi Negara Vol 10 No 1 (2026): April 2026
Publisher : Universitas Kadiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30737/mediasosian.v10i1.7375

Abstract

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.
Co-Authors M. Rizki Pratama