The effect of top management commitment on improving operational performance through green purchasing and green production
Bibliographic record
Abstract
This research has investigated the impact of top management commitment to enhance operational performance through green purchasing and green production Practices in the manufacturing industry. The study has surveyed 122 from 578 manufacturing companies domiciled in East Java, Indonesia, using a questionnaire designed with a seven-point Likert scale. Data analysis used the partial least square. The result revealed that top management commitment affects green purchasing, green production practices, and operational performance. Furthermore, operational performance is directly affected by green purchasing and green production. The green purchasing affects green production. In addition, top management commitment indirectly improves operational performance through green purchasing and green production. This result provides essential insight for the manager in the manufacturing industry that top management commitment and practicing green purchasing, and green production enhances operational performance. Furthermore, this research extends the acceptance of previous research related to top management commitment, green purchasing, and green production in improving operational performance. The novelty of this study is the revelation of the mediating role of green purchasing and green production in the influence of top management commitment on operational performance. Hence, this study contributes to enriching the current research in supply chain management.
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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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".