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

Miguel Reyna-Castillo (), Alejandro Santiago (), Ana Xóchitl Barrios-del-Ángel (), Francisco Manuel García-Reyes, Fausto Balderas and José Ignacio Anchondo-Pérez
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Miguel Reyna-Castillo: Postdoctoral Internships, Secretaría de Ciencia, Humanidades, Tecnología e Innovación (Secihti), Av. Insurgentes Sur, Ciudad de México 03940, Mexico
Alejandro Santiago: Faculty of Engineering Tampico, Autonomous University of Tamaulipas, Centro Universitario Sur, Tampico 89336, Tamaulipas, Mexico
Ana Xóchitl Barrios-del-Ángel: Tampico Faculty of Commerce and Administration, Autonomous University of Tamaulipas, Centro Universitario Sur, Tampico 89109, Tamaulipas, Mexico
Francisco Manuel García-Reyes: Tecnológico Nacional de México—Instituto Tecnológico de Ciudad Madero, Av. Primero de Mayo 1610, Los Mangos, Cd Madero 89460, Tamaulipas, Mexico
Fausto Balderas: Tecnológico Nacional de México—Instituto Tecnológico de Ciudad Madero, Av. Primero de Mayo 1610, Los Mangos, Cd Madero 89460, Tamaulipas, Mexico
José Ignacio Anchondo-Pérez: Faculty of Architecture, Design and Urbanism, Autonomous University of Tamaulipas, Centro Universitario Tampico-Madero, Tampico 89000, Tamaulipas, Mexico

Logistics, 2025, vol. 9, issue 2, 1-32

Abstract: 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.

Keywords: social sustainability; pre-post-pandemic COVID-19; supply chain resilience; Mexico; PLS-SEM; evolutionary fuzzy knowledge transfer (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2025
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