Digital transformation and supply chain competitiveness: Evidence of dynamic capabilities from an emerging economy
Bibliographic record
Abstract
The manufacturing firms are trying to attain digitalization of operations and supply chains in the fast changing technological era. Digital transformation was more agile and efficient. However, the issue of how and why digital transformation is going to create a sustained competitive advantage is a question. The survey was carried out in the form of a quantitative survey among the managers of 326 Jordanian manufacturing firms, which were involved in digital projects. SEM and AMOS were used to test the hypotheses. The results illustrate that the digital transformation has positive and significant effects on the competitive advantage of the supply chains. The mediation analysis showed that the dynamic capabilities are important to the competitive advantage of the digital transformation. The results suggest that digital transformation is a strategic enabler that enhances the dynamic capabilities of a company, which is later translated into the high-level of supply chain performance. Digitalization is an enabler of building of capability, according to the Dynamic Capabilities Theory. These increased capabilities, in turn, enable firms to attain and maintain competitive advantages in the form of faster delivery, increased flexibility, and reduced costs. The research builds on theoretical knowledge by introducing digital transformation to the capability-based perspective on competitiveness, in which the value of digital investments is achieved by the capabilities of organizations and managers to a large extent.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".