Exploring the impact of blockchain technology, green supply chain, green practice, supply chain flexibility on green supply chain performance
Bibliographic record
Abstract
Changes are occurring in customers demanding that companies produce environmentally friendly products. Apart from that, there is pressure from the government so that companies carrying out business activities can follow the rules and regulations set. The company has developed a system that uses technology to support production process activities properly and adequately. Blockchain technology makes green supply chain implementation faster and more flexible to continue improving supply chain performance. Data is collected on companies implementing blockchain technology and environmentally friendly programs. Data was collected at 512 manufacturing companies in Indonesia using Google Forms, which was distributed via email and social media. Data analysis used PLS to answer all research hypotheses. The research results showed that blockchain technology significantly influenced green supply chain management of 0.816, green practice of 0.370, supply chain flexibility of 0.115, and green supply chain performance of 0.150. Green supply chain management is a commitment for companies that have become top management commitments to adopt by reducing consumption of materials that impact the environment, which affects green supply practices of 0.473, supply chain flexibility of 0.244, and green supply chain performance of 0.247. Green supply practice, as a form of company best practice application, can influence supply chain flexibility by 0.428 and green supply chain performance by 0.214. Supply chain flexibility that has been running in manufacturing companies has had a significant impact on green supply chain performances of 0.331. This research provides a practical contribution to top management's commitment to running a green supply chain to increase company performance. Contribution for regulators and rule makers to continue to carry out continuous monitoring of business actors. Theoretical contributions to enrich theories about the green environment, green supply chain management, and blockchain technology.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".