Outsourcing in supply chain: A bibliometric analysis
Bibliographic record
Abstract
The purpose of this paper is to conduct a bibliometric analysis to analyze trends and patterns of outsourcing and supply chain research results using statistical and co-word analysis. The search results in the Scopus database yielded 787 relevant documents with the keywords outsourcing and supply chain from 1997 to early 2022. The results of the bibliometric analysis resulted in the number of publications, citations, subject areas, country, keywords, and topic clusters. The trend of outsourcing and supply chain research tends to increase every year although it had a significant decline in 2011. The documents with the most citations are related to supplier selection using analytical network processes, optimization of outsourcing partners, and ICT in the supply chain. The four fields that dominate the research area are business management, engineering, decision science, and computer science. The United States of America is the most productive country with the most citations. There are four cluster topics formed: the importance of supply chain management in outsourcing in industry and ICT in company activities, the impact of supply chain on business performance and outsourcing decisions, logistics outsourcing decisions to third parties and their effectiveness, lastly the benefit costs of outsourcing and the implications for product management. This paper provides an additional updated overview of the outsourcing and supply chain research map.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.160 | 0.250 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".