Nonlinear impact of supply chain finance on the performance of seafood firms: A case study from Vietnam
Bibliographic record
Abstract
Supply chain finance has become an interesting research topic which attracts lots of attention from scholars recently, particularly after the global financial crisis. However, only few studies have examined the causal relationship between supply chain finance and firm performance. More specially, there is a big research gap when almost none of existing research has analysed the nonlinear impact of supply chain finance on firm performance. With this aim, this paper succeeds in giving first empirical evidence on the U-shaped nonlinear relationship between supply chain finance and the performance of seafood firms in Vietnam. Specifically, a bad performance of supply chain finance (the increase in cash conversion cycle -CCC) causes a lower firm performance (FP). Nevertheless, if any decrease in firm performance reaches its minimum (CCC*), the restructuring of the firm will gradually improve it. In addition, firm performance is significantly influenced by controlled variables of firm-specific, firm size (SIZE) and capital structure (CAP), and macroeconomic, economic growth (EG), factors. The findings are valuable for the management as well as scholars in bringing a more comprehensive perspective on the causal relationship between supply chain finance and firm performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".