Strategic Management Knowledge Map via BERTopic (1980–2025): Evolution, Integration, and Application
Problem: Amid digital disruption and the cross-fertilization of RBV, DCV, and KBV, strategic management knowledge has grown fragmented with blurred boundaries. Conventional mapping (citation/co-word, LDA) lacks semantic and temporal resolution, obscuring overlaps, divergences, and turning points and hindering links to actionable indicators (e.g., the Balanced Scorecard). Hence, an integrated, semantically faithful, time-stamped map is needed to bridge research and operational metrics. Gap: Prior maps rely on citation/co-word signals, miss textual meaning, and treat RBV/DCV/KBV in isolation—lacking a theory-aligned, time-stamped, manager-oriented synthesis. Objectives: This study aims to (1) reveal how RBV, DCV, and KBV evolve and interrelate over time; (2) produce an integrated, semantically grounded map; and (3) translate selected themes into actionable managerial indicators. Method: We analyzed 25,907 WoS articles (1980–2025) with BERTopic (Sentence-BERT + UMAP + HDBSCAN + c-TF-IDF). We used an RBV/DCV/KBV lexicon to guide retrieval/interpretation (not to constrain modeling). We discovered 230 topics, retained 33 via coherence (C_V), and benchmarked them against LDA. Key findings: A concise set of 33 high-quality themes with a higher C_V than LDA on this corpus was established. A Fish-Scale view (overlapping subfields across economics, management, sociology) that clarifies RBV–DCV–KBV intersections was achieved. Era-sliced prevalence shows how themes emerge and recombine over 1980–2025. Selected themes mapped to Balanced Scorecard (BSC) indicators linking capabilities → processes → customer outcomes → financial results. Contribution: A clear, time-aware synthesis of RBV–DCV–KBV and a scalable, reproducible pipeline for structuring fragmented theory landscapes are presented in this study—bridging scholarly integration with managerial application via BSC mapping.
Authors
- Kuei‐Kuei Lai (ORCID: https://orcid.org/0000-0001-6049-1161)
- Yarsun Hsu (ORCID: https://orcid.org/0009-0006-5434-6498)
- Chih-Wen Hsiao (ORCID: https://orcid.org/0009-0008-5791-4280)
Institutions
- Chaoyang University of Technology (TW)
Publication Details
- Journal
- Applied System Innovation
- Published
- 2025-09-29
- DOI
- https://doi.org/10.3390/asi8050142
- Citations
- 2
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 4.57