The role of top management commitment to improve operational performance through it adoption, supply chain integration, and green supply chain management
Bibliographic record
Abstract
Manufacturing companies constantly strive to produce products that promote better competitiveness. In addition, the current business environment requires manufacturing companies to adopt environmentally friendly concepts, which have become a global customer concern. Therefore, companies must inevitably meet environmental protection requirements through ecologically friendly processes and products. Meanwhile, environmentally friendly adoption requires a capital-intensive investment, which doubts the management regarding the investment return. Hence, top management commitment is highly needed to maintain eco-friendly products and contribute to the company's performance. This study examines the role of top management commitment to operational performance through adopting information technology, supply chain integration, and green supply chain management. This study surveyed manufacturing companies that have implemented ISO 14000, as many as 73 companies with criteria of having more than 100 employees. Data is collected using questionnaires directly and online with Google Forms. The results of data processing analysis found that top management commitment influences information technology adoption with a priority scale to maintain competitiveness and strategies that increase competitiveness. Top management commitment through information technology can improve supply chain integration and green supply chain management. In addition, supply chain integration improves green supply chain management and operational performance. Implement environmental-friendly measures by involving external partners to impact operational performance. The results of this study contribute to enriching supply chain management theory by significantly adopting green supply chain management to improve sustainable development and performance. It also makes a practical contribution by providing insight for practitioners to generate added value for customers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".