The influence of eco-design, green information systems, green manufacturing, and green purchasing on manufacturing performance
Bibliographic record
Abstract
Companies today strive to integrate manufacturing processes with the environment to strike a balance. This study aims to examine the impact of green implementation for companies on the performance of manufacturing companies. Data was collected using Google Forms distributed online to the manufacturing companies domiciled in East Java. The criterion for the companies is that they have been committed to implementing a green approach to the production process, procurement of environmentally friendly raw materials, and green products. The partial least square technique analyzed data from as many as 115 respondents, with the position as senior staff level and higher, who have worked for at least two years and are permanent employees. The results showed that the implementation of eco-design has an impact on green purchasing and green manufacturing. Eco-design and green information systems implemented by the company can improve manufacturing performance by producing adequate overall product quality, and the number of products produced varies according to market demand. Therefore, the green information system affects green manufacturing and purchasing in manufacturing companies. Therefore, green manufacturing and green purchasing can impact manufacturing performance. The research results contribute to practitioners, especially top management, in committing to implementing Green which affects the performance of manufacturing companies. The theoretical contribution of the research is to enrich green supply chain management and sustainable performance for manufacturing companies.
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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 | 0.004 | 0.014 |
| 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) | 0.002 | 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".