The effects of internal driver, external pressure and green entrepreneurial orientation (GEO) on green supply chain management (GSCM) performance through GSCM practice in wood processing companies in Lumajang district
Bibliographic record
Abstract
This study examines the correlation between internal drivers, external pressures, Green Entrepreneurial Orientation (GEO). In the context of wood processing companies in Lumajang, this study examines how green supply chain management (GSCM) practices and performance interact. The study relies on theoretical underpinnings grounded in both institutional theory and the Natural Resource-Based View (NRBV) theory to thoroughly explore and comprehend these complex interconnections. Data was collected from a sample of 98 wood processing companies registered as Primary Timber Forest Product Industries (IPHHK) in the Lumajang District Forestry Office up to 2020, using a saturated sampling technique over three months from January to March 2022. This study's data analysis was carried out using structural equation modeling (SEM), which uses the partial least squares (PLS) methodology. The results of the analysis indicate that internal drivers do not exert a significant influence on Green Supply Chain Management (GSCM) performance. In contrast, external pressure and Green Entrepreneurial Orientation (GEO) have a notable and statistically significant impact on GSCM performance. Furthermore, GSCM practices play a crucial mediating role, fully mediating the correlation between internal drivers and GSCM performance and partially mediating the correlation between external pressure, GEO, and GSCM performance. This research holds practical implications for managers, supply chain specialists, and Lumajang wood processing industry policymakers. It clarifies the significance of particular drivers in putting GSCM practices into practice and reaching improved performance levels. Future research should consider expanding the sample size, extending the scope of the survey, exploring additional research avenues, and implementing longitudinal designs to investigate green supply chain integration and firm behavior over time.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".