Supply chain collaboration for the staple food product competitiveness
Bibliographic record
Abstract
Food supply chain collaboration (FSCC) is a prevalent business strategy in developed countries. Despite the widespread adoption, there is still limited literature on FSCC in developing countries, including Indonesia. Therefore, the study aimed to analyze the factors of information sharing, relationship quality, and corporate shared value on supply chain collaboration and their effect on improving the competitiveness of rice main food products in Indonesia. The conceptual framework was developed from the Relational View theory as the basis for supply chain collaboration, which could prove beneficial in increasing the competitiveness of food products. The data were collected from three rice supply chain actors, namely farmers, rice milling units, and retailers, which were analyzed using partial least squares Structural Equation Modeling. The results showed that information sharing was the variable with the greatest effect on supply chain collaboration, followed by corporate shared value and relationship quality. Information sharing carried out by supply chain actors was due to dependence, trust, and commitment to relationships. Collaboration and information sharing among actors showed the potential to positively improve the competitiveness of rice main food products. These results provided valuable insights for food supply chain actors, emphasizing the importance of collaboration by considering economic, social, and environmental aspects.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".