Navigating the interplay between innovation orientation, dynamic capabilities, and digital supply chain optimization: empirical insights from SMEs
Bibliographic record
Abstract
This study empirically explores the influence of innovation orientation on the digital supply chain, mediated by dynamic capabilities within the context of Small and Medium Enterprises (SMEs). This study, rooted in a comprehensive evaluation of dynamic capabilities and innovation orientation, introduces a framework that could serve as a valuable resource for subsequent research. It contributes to the global discourse on optimizing digital supply chain practices among SMEs. Quantitative method was employed, gathering data from 212 professionals in SMEs in Dubai, UAE, via an online questionnaire. The collected data were analyzed using SmartPLS 4, focusing on reliability, validity, discriminant validity, and hypothesis testing. The findings show a significant influence of innovation orientation on the digital supply chain. Dynamic capabilities also exhibit an indirect yet substantial impact on the relationship between innovation orientation and the digital supply chain. The identified dynamic capabilities are instrumental in refining decisions associated with the circular economy and leveraging technology to enhance supply chain mechanisms. These capabilities foster positive correlations between the digital supply chain and innovation. The predominant advantage of digital supply chains for consumers lies in their agility and speed, facilitating swift response to customer demand and bolstering business efficiency. The model presented is a template for future research, which could expand its scope to include other industries like manufacturing or services, augmenting the robustness of the findings.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".