Supply Chain Risk Management in Young and Mature SMEs
Bibliographic record
Abstract
In this article, we examine how external factors such as demand, security regulation, cyber risks, and relative performance influence supply chain risk management (SCRM) in young and mature small and medium-sized enterprises (SMEs) in Turkey. For this, we utilised fuzzy set qualitative comparative analysis (fsQCA) using data from 137 Turkish SMEs. Our results suggest a single significant path for explaining SCRM in young SMEs, while we found three significant paths for explaining SCRM in mature SMEs. Furthermore, the results indicate that demand risk is the only external factor for young SMEs to realise SCRM success. For mature SMEs, demand risk and/or relative performance are essential to explain SCRM performance. Based on our findings, we theoretically contribute by unravelling the pathways through which external factors influence SCRM performance. Moreover, practitioners could align their strategies towards these pathways when constructing a strategy for achieving SCRM performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".