Unlocking project SDGs in furniture manufacturing: The mediating role of green innovation resilience
Bibliographic record
Abstract
This study aims to examine the effect of Green Supply Chain Management (GSCM) and Lean Manufacturing (LM) on the achievement of Sustainable Development Goals (SDGs) by including Green Innovation Resilience (GIR) as a mediating variable and Environmental Regulation (ER) as a moderating variable. The problem raised is the lack of research linking GSCM and LM directly or indirectly to SDGs, especially in the furniture industry sector in developing countries. This study uses a quantitative approach with a purposive sampling technique of 230 medium and large-scale furniture companies in East Java. Data was collected through questionnaires distributed online to managers. Data analysis was conducted with SmartPLS 3.2. The uniqueness of this research lies in developing and testing the new concept of GIR as a mediating variable that strengthens the relationship between GSCM and LM on the achievement of SDGs in the furniture industry of developing countries, as well as considering the moderating role of environmental regulation-an integrative approach that has not been comprehensively explored in previous research. The results showed that GSCM and LM have a significant effect on SDGs both directly and through the mediation of GIR. In addition, ER was shown to strengthen the relationship between GSCM and LM on SDGs. These findings indicate the importance of integrating green practices and process efficiency with sustainable innovation and regulatory support in improving industry sustainability performance.
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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 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".