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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.001 |
| 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".