Taxation and customs strategies in Jordanian supply chain management: Shaping sustainable design and driving environmental responsibility
Bibliographic record
Abstract
This study explores the role of taxation and customs strategies in promoting sustainable design and environmental responsibility in supply chain management. The research examines their direct effects on sustainable and environmental responsibility in Jordan, as well as the moderating influence of socioeconomic factors. It also investigates the mediating effect of sustainable practices adoption. Quantitative analysis using Smart PLS4 software was conducted. The results highlight the positive influence of taxation strategies on sustainable and environmental responsibility. Effective taxation strategies contribute to a stronger commitment to sustainable practices and environmental concerns within supply chains. Similarly, customs strategies positively influence sustainable and environmental responsibility, demonstrating that efficient customs management aligns with sustainable practices. In addition, socioeconomic factors were identified as significant moderators. The relationship between taxation strategies and sustainable responsibility, as well as environmental responsibility, varied based on the socioeconomic context. This suggests that the effectiveness of these strategies depends on the specific circumstances in which organizations operate. Furthermore, sustainable practices adoption was found to moderate the relationship between taxation and environmental strategies, and sustainable responsibility. This indicates that the successful implementation of sustainable practices enhances the impact of taxation and environmental strategies in promoting sustainability within the supply chain. This paper emphasizes the importance of developing and implementing effective taxation and customs policies that align with sustainable practices. Organizations should consider the influence of socioeconomic factors and prioritize the sustainable practices adoption to maximize the impact of these strategies. The findings provide valuable insights for strategic decision-making and encourage businesses to integrate sustainability into their supply chain management, contributing to environmental conservation and long-term success.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".