The effect of environmentally oriented leadership and public sector management quality on supply chain performance: The moderating role of public sector environmental policy
Bibliographic record
Abstract
This study examines the complex relationship between environmentally oriented leadership, public sector management quality, environmental policies, and supply chain performance within the public sector context in Indonesia. The research combines quantitative analysis and qualitative insights from key stakeholders to explore these dynamics comprehensively. The findings underscore the multifaceted nature of supply chain performance optimization, where environmentally oriented leadership demonstrates a nuanced connection to performance outcomes. Public sector management quality significantly enhances efficiency and overall supply chain performance. Notably, well-structured environmental policies positively impact supply chain performance and contribute to organizational sustainability. However, a weak policy framework can dilute the positive influence of leadership. The moderating effect of environmental policies on the relationship between management quality and supply chain performance might be less pronounced. The implications of this study offer both theoretical and managerial insights. The findings emphasize the need for holistic supply chain management strategies that integrate leadership styles, management practices, and policy frameworks to achieve sustainable performance outcomes. The study acknowledges limitations related to the specific context and variables studied. Future research can explore broader variables, diverse sectors, and regions by enhancing the understanding of these complex interactions. The novelty of this study lies in its comprehensive examination of the intricate relationship between leadership, management quality, policies, and supply chain performance within the public sector context, contributing to the growing body of knowledge in this vital area.
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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 | 0.003 | 0.008 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".