The agri-food supply chain (AFSC) needs resilience beyond organizational and dyadic levels due to its complex adaptive nature and increasing number of disruptions. Based on the investigation of expert perceptions, this study aims to explore the influence structure of social-ecological resilience capabilities to aid in understanding the complexity of resilience and informing decision making. Moreover, the prioritization of big data analytics (BDA) practices can support agri-food entities in adopting best practices for resilience improvement by using a fuzzy hybrid multiple-criteria decision analysis approach. To contribute to the two aims, first, responses from 26 distinguished supply chain resilience scientists were analyzed using a fuzzy DANP approach to uncover the influence relationships and priority weights of 19 organization, supply chain, and industry level social-ecological resilience capabilities. Second, 14 BDA practices categorized into three groups of sensing, seizing, and transforming―based on the dynamic capabilities perspective―were prioritized as judged by a total of 19 managers in three large food retailers using a fuzzy TOPSIS model, considering their assessed contribution to strengthening resilience capabilities. These capabilities have also been triangulated with secondary data to contextualize and corroborate case descriptions. The findings suggest the high prominence and net influence of adaptability and agility, alongside the centrality of collaboration, supply flexibility, and risk-aware culture within the elicited influence structure in AFSCs. Production and supply chain managers and policymakers in AFSCs can use the results to assess organizational, supply chain, and industry resilience, guiding strategic planning based on identified capability interdependencies and priority weights. In addition, retail managers can use the evaluation method to reach a consensus in their organization to better understand and implement the critical BDA practices that are prioritized for resilience enhancement in their specific context. • Adopting the theoretical angles of social-ecological resilience and dynamic capabilities • Analyzing data from 26 supply chain resilience scientists and 19 managers in three large food retailers • Uncovering the influence structures between social-ecological resilience capabilities • Developing an empirical ranking model of Big Data Analytics practices to support resilience at different levels in agri-food supply chains • Guiding future theory testing in supply chain resilience and Big Data Analytics
Asgari et al. (2026) studied this question.