The increase in the use of information technology (IT) poses a challenge derived from the risks and threats of computer security in all areas of society. In this sense, cybersecurity emerges as an important pillar of support for protecting infrastructures essential for countries’ sustainable economic and social development. This paper explores the possible links between cybersecurity and sustainable development within the high-impact scientific literature. The study uses a systematic literature review methodology based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, ensuring a rigorous and structured approach to selecting and analyzing relevant literature. The scientific database Web of Science is used to ensure the integrity and quality of the data collected, following criteria widely validated in this type of methodology. The study reveals a significant interrelationship between cybersecurity and sustainable development in the business sphere. It highlights cybersecurity’s contribution to economic sustainability by protecting critical infrastructure and minimizing financial risks. Concerning environmental sustainability, cybersecurity facilitates the implementation of cleaner and more efficient technology. Social sustainability ensures personal data protection and fosters a culture of responsibility and trust within organizations.

The number of research papers interested in studying the social dimension of supply chain sustainability and resilience is increasing in the literature. However, the social dimension is complex, with several uncertainty variables that cannot be expressed with a traditional Boolean logic of totally true or false. To cope with uncertainty, Fuzzy Logic allows the development of models to obtain crisp values from the concept of fuzzy linguistic variables. Using the Structural Equation Model by Partial Least Squares (SEM-PLS) and Evolutionary Fuzzy Knowledge, this research aims to analyze the predictive power of social sustainability characteristics on supply chain resilience performance in the context of the COVID-19 pandemic with representative cases from Mexico and Chile. We validate our approach using the Chile database for training our model and the Mexico database for testing. The fuzzy knowledge database has a predictive power of more than 80%, using social sustainability features as inputs regarding supply chain resilience in the context of the COVID-19 pandemic disruption. To our knowledge, no works in the literature use fuzzy evolutionary knowledge to study social sustainability in correlation with resilience. Moreover, our proposed approach is the only one that does not require a priori expert knowledge or a systematic mathematical setup.

Esta investigación tiene como objetivo explicar, en contexto de la pandemia por COVID-19, el efecto que las características del teletrabajo que los profesores tienen sobre el tecnoestrés en las organizacioneseducativas mexicanas pertenecientes al sector público de preescolar a licenciatura. La metodología fue empírica estadística, los datos se obtuvieron de la aplicación de 835 encuestas a profesores y se analizaronmediante la técnica de ecuaciones estructurales utilizando variables de segundo orden. Los resultados indican que los recursos, la autonomía y el balance vida-trabajo, son características del teletrabajo realizado por el profesorado que explican el incremento del tecnoestrés en términos de fatiga, ansiedad e ineficacia cuando se desarrollan las clases a distancia, además se presentan hallazgos diferenciados a partir del sexo del profesorado. Los resultados implican información para el aprendizaje y toma de decisiones organizacionales por parte de las autoridades educativas.

The global crisis caused by the COVID-19 pandemic has taught us the importance of reflecting on the essential resources and capabilities that enable companies to react to disruptions. In this regard, studies have shown that social sustainability is a crucial resource for the operational performance of supply chains in emerging contexts. Although the literature has responded to the call for research on the social dimension of sustainability in emerging economies, most research has focused on emerging Asia, leaving a void in Latin America. Two socially focused frameworks are used to address the ontological challenge of defining sustainable human well-being around the firm. Amartya Sen’s capabilities approach and the theoretical extension of the Social-Resource-Based View (SRBV) are appropriate to address social sustainability under two essential aspects: (1) the firm as a generator of social performance and (2) social sustainability as a generator of firm performance. This paper aims to analyze the predictive capacity of Social Sustainability Orientation on social performance and supply chain operational performance in the context of emerging Latin America, with representative cases from Mexico, Colombia, and Chile. The methodology was empirical–statistical and based on a structured questionnaire applied to 217 purchasing managers of large multisector companies (Mx n = 64, Co n = 100, and Cl n = 53). Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show the intrasample and extrasample predictive ability of Social Sustainability Orientation on social and Supply Chain Performance. It is concluded that socially sustainable culture and investment orientation is a valuable resource that provides the capability for Latin supply chain welfare and operational performance. A call is made to procurement and public policy managers to disseminate and care for the social aspects of sustainability as a resource that enhances business competitiveness and social justice in the Latin American region.

Health care is an essential factor in the social sustainability of the university; therefore, it is a challenge and a responsibility to monitor a safe return to school that ensures the support of the physical and emotional well-being of students. In this sense, the Maslach Burnout Inventory-Student Survey (MBI-SS) is a validated resource with robust techniques in several regions of the world to diagnose school burnout. However, few efforts appear in the literature to validate it from a predictive approach in the Latin region. This study aims to validate, from a predictive approach, measures of school burnout in Latino university students from Mexico and Colombia. A total of 235 surveys were administered (Mx. n = 127, Co. n = 108), and a Partial Least Squares (PLS) measurement model was validated using the statistical program SmartPLS 3.3.7. As a result, 22 valid items were obtained in four reliable subconstructs: burnout, family cynicism, inefficacy, and somatization. The value of this research is its contribution to filling two gaps related to the MBI-SS scale (1) to contribute to the validation of the MBI-SS in a Latin context and (2) the use of the nonparametric statistical technique PLS focused on prediction.

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.