Analysis of innovative strategies to alleviate the influence of COVID-19 on sustainable supply chain performance: PLS-SEM and deep learning artificial neural network analysis

Abstract The worldwide spread of the COVID-19 epidemic caused significant upheavals, especially impacting supply chain (SC) systems. Addressing these issues requires the development of creative and novel approaches. Therefore, this study investigates how digital innovation (DI), information processing capability (IPC), and supply chain integration (SCI) alleviate the influence of the COVID-19 epidemic on sustainable supply chain performance (SSCP). The study also examines the mediating roles of DI, IPC, and SCI and the moderating role of institutional support (IS). Data were gathered from Pakistani food supply chain enterprises and employed a two-phase method integrating partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) techniques. The result reveals that while COVID-19 disruption negatively impacted SSCP, it positively influenced DI, IPC, and SCI. Moreover, DI, IPC, and SCI intervene in the link between COVID-19 disruption and SSCP. Institutional support strengthens the favorable effects of DI, IPC, and SCI on SSCP. Drawing on the resource-based view, dynamic capability theory, and organizational information processing theory, this study explains how firms can improve resilience and sustainability during disruptions. This model offers potential strategies for managers and policymakers to enhance supply chain resilience and maintain performance in the face of crises.