Effect of Social Sustainability on Supply Chain Resilience Before, During, and After the COVID-19 Pandemic in Mexico: A Partial Least Squares Structural Equation Modeling and Evolutionary Fuzzy Knowledge Transfer Approach

Recent disruptions have led to a growing interest in studying the social dimension of sustainability and its relationship to resilience within supply chains. Social sustainability is characterized as complex, often offering anomalous data and confounding variables that are impossible to categorically define as true or false axioms. This work starts from an epistemological premise, in which non-parametric statistical methodologies and mathematical analytics are complementary perspectives to comprehensively understand the same social phenomenon. Second-generation predictive statistics, such as the PLS-SEM algorithm, have demonstrated robustness in treating multivariate social information, making it feasible to prepare data for knowledge transfer with mathematical techniques specialized for fuzzy data. This research aimed to analyze evolutionary fuzzy knowledge transfer pre-, during-, and post-pandemic COVID-19, and its effect on the relationship between social sustainability and supply chain resilience in representative cases from Mexico. Based on empirical data collected from supply chain managers in 2019 (n = 153), 2021 (n = 159), and 2023 (n = 119), the methodological technique involved three phases: (1) PLS-SEM modeling, (2) fuzzy-evolutionary predictive evaluation based on knowledge transfer between latent data, and (3) comparative analysis of the predictive effects of social attributes (labor rights, health and safety, inclusion, and social responsibility) on supply chain resilience. The results found a moderate significant variance in the pre-in-post-COVID-19 effect of social dimensions on supply chain resilience. Social and management implications are presented.