Assessing the role of green supply chain management on operational performance: mediating role of information technology infrastructure, internal and external integration Indonesian manufacturing
Bibliographic record
Abstract
Manufacturing companies constantly strive to build sustainable performance to survive fierce business competition. Besides, the company should be committed to protecting the environment by paying attention to the role of supply chain members. Companies should collaborate with external partners, enabling them to fulfill customers' needs for environmentally friendly products. This study explores the effect of green supply chain management on operational performance with the mediating role of information technology infrastructure, internal integration, and external integration. This study surveyed manufacturing companies in Indonesia using structured questionnaires designed with a five-point Likert scale. The questionnaire is designed using Google Forms, and the links are distributed to respondents through email, WhatsApp, and other social media. As many as 245 responses were obtained and valid for analysis. The data processing used SmartPLS software version 4.0. The hypothesis test results found that green supply chain management influences information technology infrastructure, internal integration, external integration, and operational performance. Information technology infrastructure impacts improving internal integration, external integration, and operational performance. Internal integration has an impact on external integration and operational performance. External integration has an impact on improving operational performance. The research contributes to managerial practice by adopting the ISO 14001 standard in green supply chain management. for companies to make improvements. These findings also enrich the current research in supply chain management theories.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".