How does supply chain management affect financial performance? Evidence from coffee sector
Bibliographic record
Abstract
The study investigates the impact of supply chain management on the corporate financial performance of the coffee industry in Vietnam.Particularly, supply chain management is measured in three dimensions, namely relationship with suppliers, relationship with intermediaries and distributors, and relationship with customers.Data are collected by conducting a survey among 248 coffee company representatives of supply chain participants in Vietnam.The multiple regression analysis is adopted in the model estimation.The findings reveal that financial performance (FP) was positively influenced by relationship with intermediaries and distributors (RID), relationship with customers (RC), and relationship with suppliers (RS).In specific, the relationship with intermediaries and distributors (RID) is the most significant driver of financial performance (FP).The study greatly succeeds in providing an unprecedented finding which is the considerable effect of the participants representing supply chain management on financial performance.The findings are essential to the management of supply chain members in the coffee sector.Accordingly, to boost the financial performance, the companies should pay more attention on improving supply chain management efficiency.Supply chain management can be achieved not only by improving processes internally but also by working with suppliers, customers and most notably partners like intermediaries and distributors..
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".