Big Data-driven management system has attracted significant attention worldwide as it provides several capabilities to improve strategic, tactical, and operational decisions to eventually create a notable impact on the COVID-19 pandemic. Monitoring, surveillance, detection, and prevention of the global pandemic cases are provided with simultaneous access and management with big data tools. Bringing the pandemic to a normalization level and taking it under control in the health supply chain could be achieved through big data-driven management which is lifesaving applications. However, the adoption of the management system in the fight against the COVID-19 pandemic includes many obstacles all over the world. Mainly, these are data-related characteristics, technological incompetence, socio-economic structure, and governmental policies. In this scope, the main motivation of this study is determining and evaluating critical criteria by using a Multi-Criteria Decision-Making (MCDM) approach. The Spherical Fuzzy AHP methodology proposed as one of the novel MCDM methods enables to obtain managerial implications by comparing to the significance of criteria. The findings can assist to understand the actual nature of the barriers and potential benefits of big data-driven in COVID-19 pandemic management system and make policy regarding curb the pandemic. Therefore, this study is a contribution to academicians, researchers, and practitioners with a different perspective on COVID-19 pandemic management. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Big Data-Driven in COVID-19 Pandemic Management System: Evaluation of Barriers with Spherical Fuzzy AHP Approach