Digitalization and green supply chain integration to build supply chain resilience toward better firm competitive advantage
Bibliographic record
Abstract
Organizations rely on information technology to integrate internally and externally to create process efficiency in increasing competitiveness. Information technology can support digitalization in companies to maintain green supply chain management. Manufacturing companies are required to be able to pay attention to the environment by maintaining the balance of nature. The object of this research is manufacturing companies located in East Java. Data were collected from respondents through questionnaires which were distributed using Google form. The results of the questionnaire distribution were obtained from a total of 108 companies analyzed using the partial least squares method. The analysis shows that digitalization affects supply chain integration, green supply chain, and resilience. Digitalization in the supply chain can form a strong integration, energy efficiency, and effectiveness to survive. Supply chain integration affects the green supply chain and supply chain resilience. Integration in the supply chain system, able to overcome environmental problems and optimize resources. A green supply chain affects supply chain resilience. Supply chain integration, green supply chain, and supply chain resilience affect a firm competitive advantage. Practical research contributions for management to allocate budgets with the needs of application development and supply chain systems within the company. Supply chain digitization is a solid foundation for companies to have a competitive advantage against competitors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".