Unveiling the Impact of Big Data Analytics on Supply Chain Resilience: Theorisation and Empirical Evidence Using a Multi‐Method Approach

ABSTRACT Organisations should harness big data analytics to transform their supply chains and archive resilience. Despite the critical role of big data analytics in shaping supply chain resilience, existing literature on their relationship remains starkly fragmented and inconclusive. To address this critical paucity and reconcile the prevailing inconsistencies, we employed a multi‐method research design, combining both qualitative and quantitative approaches to confirm our theoretical model. We first utilise deductive qualitative analysis across multiple cases to validate the underlying mechanisms and boundary conditions through which big data analytics capability influences supply chain resilience. Subsequently, our quantitative analysis unveils that the nexus between big data analytics capability and supply chain resilience operates through two mediated pathways (traditional and digital SC capabilities) under institutional environments (i.e., government intervention and guanxi). Specifically, government intervention amplifies both mediating effects, whereas guanxi weakens the mediation effect of digital supply chain capability. Furthermore, our results reveal contingency‐dependent mediation pathways linking big data analytics capability and supply chain resilience. We find that weak institutional forces position digital supply chain capability as the primary pathway, whereas strong institutional forces shift the emphasis toward traditional supply chain capability as the central mechanism linking BDA capability to supply chain resilience. This study contributes to the emerging literature on Information Systems (IS) by theoretically exploring and empirically validating the mechanisms and boundary conditions through which big data analytics capability affects supply chain resilience.