Exploring the effect of blockchain technology on supply chain resilience and transparency: Evidence from the healthcare industry
Bibliographic record
Abstract
Blockchain technology is revolutionizing all sectors of industries including the healthcare one. Blockchain technology applications have been realized in healthcare as they have the potential to revolutionize healthcare systems. Integrating blockchain into the design of supply chain design leads to decentralizing the management of the supply chain, and, in turn, improves workflow efficiency and reduces the various security threats. Therefore, this study aimed at examining the impact of integrating blockchain technology, into supply chain practices, on supply chain transparency and resilience. By adopting the organizational information processing theory, this study developed a research model that explored the impact of blockchain technology features including data quality, smart contracts, and traceability on supply chain resilience and transparency. Furthermore, this study examined the effect of supply chain transparency on supply chain resilience. The data was collected, from healthcare industry personnel, in Jordan, using an electronic survey. In total, 215 participants responded to the questionnaire. RStudio- 2022.07.1 was used to conduct the data analysis. Results revealed that data quality significantly and positively affects supply chain transparency and resilience. In addition to that, results indicated that while smart contracts are positively related to supply chain transparency, they do not affect supply chain resilience. Also, traceability was found to be positively related supply chain transparency and resilience. Finally, it was found that blockchain-driven supply chain transparency positively impacts blockchain-driven supply chain resilience. The results imply that integrating blockchain technology into the supply chain can enhance both supply chain transparency and resilience, and, in turn, provide the supply chain the capability of recovering to its original state should it encounter disruptive events. The outcomes of this study can help supply chain managers and other stakeholders to develop strategies and tactics to best utilize blockchain technology to enhance blockchain-driven supply chain transparency and resilience.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".