Visualizing supply chain concentration: A systematic scientometric review
Bibliographic record
Abstract
With the increasing complexity of supply chain management, supply chain concentration (SCC) has become a prominent research topic in academia and practice. To clarify the developmental context and research trends within this field, this study utilizes the Web of Science core collection as the data source, selecting 362 English-language publications from 1975 to 2025. CiteSpace 6.2 was employed to conduct a visual bibliometric analysis, systematically examining the social structure, conceptual structure, and intellectual structure of SCC research through co-authorship, co-word, and co-citation analyses. The results indicate rapid growth in SCC research since 2020, with China and the United States being the major contributing countries, and collaborations exhibiting regional characteristics. High-frequency keywords prominently include "customer concentration," "supplier concentration," and "performance," with research themes progressively extending toward frontier topics such as "digital transformation," "green innovation," and "corporate social responsibility." Co-citation analysis identified representative works by authors such as Panos Patatoukas, Dan Dhaliwal, and Murillo Campello, highlighting a shift in research focus from traditional performance perspectives to governance mechanisms and sustainable strategies within a digital context. This study summarizes core literature clusters, evolutionary paths of clusters, and significant citation bursts, revealing interdisciplinary integration and paradigm shifts in SCC research. The paper provides a systematic review of future directions in SCC studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.225 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.210 | 0.195 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".