Driving sustainable supply chains: Blockchain-enabled eco-efficiency for resilient customs ports
Bibliographic record
Abstract
This paper investigates the driving factors behind sustainable supply chains in Jordan, focusing on the implementation of blockchain technology, customs ports practices, and technological infrastructure. The primary data for the study was collected through questionnaires distributed to employees working in the Jordanian customs. A random sampling method was employed to select participants, and a total of 184 valid questionnaires were retrieved for analysis. The collected data was analyzed using the statistical software Smartpls PLS4. The results of quantitative research reveal that the implementation of blockchain technology and technological infrastructure positively affects the driving of sustainable supply chains in Jordan, also customs ports practices also have a positive impact on driving sustainable supply chains, emphasizing the significance of efficient and resilient customs operations for sustainability. Additionally, compliance with environmental regulations enhances the effectiveness of blockchain technology in achieving sustainability objectives. Moreover, underscoring the role of robust technological capabilities in supporting sustainable operations within customs ports. The study contributes to the understanding of the key drivers of sustainable supply chains in Jordan, providing valuable insights for policymakers, supply chain managers, and other stakeholders involved in promoting sustainability within the customs ports industry. The findings can guide decision-making and inform strategies aimed at enhancing eco-efficiency and resilience in supply chain operations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".