Customs intelligence and risk management in sustainable supply chain for general customs department logistics
Bibliographic record
Abstract
In this descriptive analytical study conducted within the Jordanian Customs Department, the influence of Customs Intelligence and Risk Management on Sustainable Supply Chain practices and their subsequent effects on Customs Department Logistics were investigated. Data collected through a structured questionnaire distributed to department employees were analyzed using Smart PLS-4. The findings revealed that Customs Intelligence significantly shapes Sustainable Supply Chain practices, emphasizing the importance of data driven decision-making in achieving sustainability goals. Effective Risk Management strategies were found to positively contribute to Sustainable Supply Chain initiatives, highlighting the symbiotic relationship between risk mitigation and sustainability. Sustainable Supply Chain practices, in turn, were demonstrated to enhance the efficiency of Customs Department Logistics. Furthermore, the study unveiled that Sustainable Supply Chain acts as a mediator, enhancing the impact of both Risk Management and Customs Intelligence on Logistics outcomes. These findings collectively underscore the intricate dynamics of these factors in the context of the Jordanian Customs Department, providing valuable insights for optimizing logistics operations, ensuring compliance, and fostering sustainability. This paper contributes valuable insights and empirical evidence to the fields of customs-related logistics, risk management, sustainable supply chain management, and logistics operations within the Jordanian Customs Department. The paper provides recommendations that have the potential to inform and improve logistics practices, benefiting stakeholders in both the public and private sectors and advancing the understanding of these critical dynamics in the broader logistics and supply chain management discipline.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| 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".