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.

Smart contracts offer automation for various decentralized applications but suffer from vulnerabilities that cause financial losses. Detecting vulnerabilities is critical to safeguarding decentralized applications before deployment. Automatic detection is more efficient than manual auditing of large codebases. Machine learning (ML) has emerged as a suitable technique for vulnerability detection. However, a systematic literature review (SLR) of ML models is lacking, making it difficult to identify research gaps. No published systematic review exists for ML approaches to smart contract vulnerability detection. This research focuses on ML-driven detection mechanisms from various databases. 46 studies were selected and reviewed based on keywords. The contributions address three research questions: vulnerability identification, machine learning model approaches, and data sources. In addition to highlighting gaps that require further investigation, the drawbacks of machine learning are discussed. This study lays the groundwork for improving ML solutions by mapping technical challenges and future directions.