Driving Innovation in Agri-Startups Through Sustainable Supply Chain Integration: Evidence from Vietnam
Bibliographic record
Abstract
Integrating into sustainable supply chains positively influences innovation efficiency in agristartups.This study examines the impact of such integration on the innovation performance of 476 agri-startups in Vietnam.Using the Ordinary Least Squares (OLS) model, we assess the effects of key factors including market access, collaboration, risk management, high-tech applications, sustainable practices, and strategy on innovation outcomes.Regression analysis is also employed to identify the determinants driving the adoption of high-tech solutions that support sustainable supply chains.The results indicate that participation enhances operational efficiency, fosters technological adoption, and strengthens innovation and sustainability capacity.By engaging in sustainable practices and leveraging high-tech tools, agri-startups can improve product and process innovation while mitigating risks.Based on these findings, practical recommendations are provided for agri-startups to optimize their innovation capabilities through sustainable supply chain integration, contributing to Vietnam's national innovation initiatives and broader sustainable development objectives.
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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 | 0.001 |
| 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) | 0.002 | 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".