Unlocking the potential of digital technologies for sustainable supply chain management strategies
Bibliographic record
Abstract
The advent of digital technologies (DTs) has transformed traditional supply chains into agile, efficient, and sustainable networks, known as Supply Chain 4.0. Digital supply chain management (DSCM) is crucial in enhancing supply chain (SC) sustainability by integrating digital tools to improve efficiency, transparency, and environmental stewardship. This study aims to examine the impact of DSCM on SC sustainability, assess the role of a firm’s intellectual capital (IC) in adopting DSCM, and explore the mediating role of supply chain mapping between DSCM and SC sustainability. Focusing on Pakistan’s textile sector, we employed a mixed-method approach, starting with a quantitative analysis using Partial Least Squares Structural Equation Modelling (PLS-SEM), followed by a qualitative case study of two textile companies. Our findings reveal a significant positive relationship between IC and DSCM adoption, underscoring the importance of knowledge resources in leveraging DTs for SC improvement. However, the direct impact of DSCM on SC sustainability was not supported, indicating the need for intermediary processes or factors to enhance the sustainability outcomes of DSCM.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".