An investigation of the impact of organizational big data analytics capabilities on healthcare supply chain resiliency

Abstract

Evaluating organizational big data analytics capabilities (BDAC) is crucial for strengthening resilience in healthcare supply chains (HSCs). This study employs an integrated multi-criteria decision-making (MCDM) approach, combining the Decision-making Trial and Evaluation Laboratory (DANP) and Multi-Attributive Border Approximation Area Comparison (MABAC) methods in a fuzzy environment. The goal is to assess the interdependence of BDAC and its impact on resilience within the HSC. The research draws on organizational information processing (OIP) and knowledge-based view (KBV) theoretical lenses to identify relevant BDAC components. The study yields context-specific insights into the role of big data analytics in fortifying the HSC Using a case study in a public hospital. The findings contribute to the understanding of supply chain resilience, emphasizing the pivotal role of BDAC in organizational preparedness. This knowledge can guide healthcare sector managers in making informed decisions to enhance overall resilience, allowing organizations to navigate uncertainties and challenges proactively. Ultimately, leveraging insights from this study can foster a more adaptive and resilient HSC, benefiting both patients and stakeholders.