The effect of knowledge management on firm performance. mediating role of production technology, supply chain integration, and green supply chain
Bibliographic record
Abstract
The company's technology implementation is inseparable from employees' ability to operate and use it optimally. Therefore, companies must maintain a developed and adequate process knowledge management according to the latest needs to improve performance while considering environmental impacts. This study investigated the role of knowledge management in adopting production technology, supply chain integration, and green supply chain toward firm performance. The study surveyed the manufacturing industry in East Java that has implemented green supply chain management, with respondents having at least two years of experience working. Respondents have filled in as many as 115 questionnaires considered valid from the 145 questionnaires received. Data processing used the partial least squares software 4.0 version. The result indicated that knowledge management significantly influenced production technology, supply chain integration, and green supply chain adoption. However, knowledge management does not influence firm performance directly. Production technology enables supply chain integration, green supply chain adoption, and firm performance. Furthermore, supply chain integration affects green supply chain adoption and firm performance. Moreover, the result indicated that supply chain adoption directly influences the firm’s performance. The practical implication of the results enlightens the managers and top management on the importance of updated technology and knowledge for employees, enabling the adoption of supply chain integration and green supply chain in the pursuit of enhanced firm performance. These results enrich the current research in supply chain management theory.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".