Assessing the role of digital logistics agility in enhancing intelligent quality management: The mediating influence of green sourcing strategies, an empirical investigation
Bibliographic record
Abstract
This research paper seeks to research the place of logistics agility and intelligent quality management. The conundrum is represented by rapid evolution in the industrial supply chain operations due to digital transformation especially in the developing countries. Thus, this research aims at exploring how digital logistics agility impacts intelligent quality management with the moderated impact of green sourcing strategy in the Jordanian industrial sector. This was a quantitative research methodology; the study was done by the use of a structured survey questionnaire which was sent to those working in supply chain and operations of a sample size of Jordanian industrial firms. We employed the partial least squares structural equation modeling (PLS-SEM) to test the hypothesis of the direct and indirect correlations between digital logistics agility and green sourcing strategies and intelligent quality management. This analysis established that the idea of digital logistics agility has a considerable impact on green sourcing policies and smart quality management activities. The indirect impact of the logistics on the quality of the outcomes through the mediation of a green-sourcing is also substantial, meaning that, in as much as an agile logistics system can enhance the quality outcomes, its ability to do so will be greater in case it also involves the introduction of environmentally friendly sourcing practices. The current research demonstrates that the digital logistics agility can be used to improve the intelligent quality control in the industrial environment when combined with the green sourcing strategies. The findings explain the ways that supply chain managers, sustainability officers, as well as policymakers can educate the design of resilient, evidence-based, and sustainable industrial systems in Jordan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".