Digital Technologies for Firms’ Competitive Advantage and Improved Supply Chain Performance
Bibliographic record
Abstract
Supply chain operation is more competitive in a dynamic business environment. Developing supply chain capability is, hence, important for gaining a competitive advantage and overall improved supply chain performance. The purpose of this study is to explore the potential of digital technologies to enhance supply chain performance and for firms to gain competitive advantage through improved supply chain capabilities. This study, through a survey questionnaire, gathered a total of 150 sample data from supply chain executives and managers in the ready-made garments (RMG) industry in Bangladesh. Findings of the study demonstrate that the digital supply chain is a significant contributor to improving the supply chain capabilities of RMG firms, and it subsequently leads to competitive advantage with a direct positive effect on firms’ supply chain performance. The findings also indicate that digital technology has a direct effect on supply chain capability and supply chain performance in RMG firms. Based on these empirical findings, the study draws conclusion that digital technology integration in the supply chain would have a positive contribution to supply chain agility and flexibility, which would enable firms to effectively engage supply chain partners in dealing with unexpected situations in business operations. This study contributes to the current literature on digital supply chain capabilities, and it also provides insights for supply chain managers, policymakers, and practitioners in the fields of supply chains, logistics, and business performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".