Marisol Velázquez-Salazar

This study examines the relationship between financial education and the development of SFSCs in vulnerable communities in Ecuador, Peru, and Colombia—a connection rarely addressed in the literature. Using a qualitative methodology, the research involved in-depth interviews with cocoa producers in Esmeraldas (Ecuador), banana by-product producers in Condorcanqui (Peru), and blackberry producers in Cauca (Colombia). The findings reveal that financial education—tailored to the cultural and productive context of each community—strengthened chain internal governance, understood as producers’ capacity to organize, manage, and make collective decisions around shared values. Key outcomes include improved resource management, expense control, productive investment, responsible access to and use of credit, and enhanced planning and commercial negotiation skills. These capabilities enabled producers to reduce intermediaries, access better markets, innovate products and processes, and improve both economic and social conditions. The three experiences overcame typical governance challenges in SFSCs—such as structural uncertainty, small scale, and lack of coordination—through business planning, local associations, and partnerships with universities, companies, and NGOs. Nevertheless, limitations remain, including regulatory barriers, insufficient government support, and shortages of key infrastructure. The study highlights financial education as a potentially relevant, yet underexamined, dimension of internal governance in SFSCs, contributing to ongoing debates on resilience, hybrid supply chain participation, and rural development in Latin America.

Research on how corporate social responsibility (CSR) practices linked to sustainable operations (SO) affect corporate financial performance (FP) is still limited. This study presents a novel methodological proposal to measure the individual impact of such practices on the profitability of companies listed on the Mexican Stock Exchange. The method employed consists of a Random Forest (RF) model complemented by Explainable Machine Learning (XML) techniques, namely Individual Conditional Expectation (ICE), Partial Dependence Plots (PDPs) and SHapley Additive exPlanations (SHAP), to calculate the individualized marginal effect in the return on assets (RoA), return on equity (RoE) and return on investment capital (ROIC) for each company, explained by the environmental, social, and governance scores provided by Bloomberg (Bloomberg Finance, L.P., New York, NY, USA), such as the market capitalization, debt-to-equity ratio, sales growth, and years since listing. The novelty of this model lies in the application of RF and XML, which offers a comprehensive and interpretable perspective on the CSR–FP relationship and the use of lagged explanatory variables to avoid endogeneity problems, overcoming the limitations of traditional analyses. The results indicate that environmental scores exhibit the most consistent contribution to FP, whereas social and governance effects are highly metric-dependent. The SHAP analysis reveals substantial heterogeneity in the drivers of firm FP, highlighting the relevance of XML methods.

Small-scale food production is declining worldwide, primarily due to the corporatization of food systems. The lack of economic inclusion, the difficulty in preserving culture, and low social integration are some of the primary barriers this economic activity faces. Based on grounded theory and through in-depth interviews with small-scale farmers and other community stakeholders, these components are analyzed in case studies of immigrant Latin American-owned farms and markets in Ohio, USA. This study analyzes SFSC in Ohio Farms as alternatives to preserve food culture as heritage and combat social inequities. The primary findings show that family traditions and cropping methods are closely related and both are important factors for farmers to preserve their cultural heritage. The common typology of SFSC in the analyzed cases is the farmers’ market type. By prioritizing cultural preservation, farmers sacrifice the use of technology, crop fashionable or market-demand food, and therefore forego potential economic benefits. This prioritization creates restrictions to economic inclusion that escalate when coupled with the lack of institutional support.

Objetivo. El objetivo de este estudio fue, por un lado, mapear las principales temáticas de la literatura sobre inteligencia artificial en administración y, por otro lado, explorar las relaciones entre dichos temas. Diseño/Metodología/Enfoque. Se realizó un análisis de co-palabras sobre 15 835 artículos indexados en Scopus (2015–2024), empleando como unidad de análisis las palabras clave del autor en el área de administración. La red semántica se construyó con los 50 términos más frecuentes, aplicando normalización por asociación y el algoritmo Walktrap para la detección de clústeres. Resultados/Discusión. Los resultados revelan que la literatura se organiza en torno a tres grupos temáticos. El primero se centra en interfaces conversacionales, el segundo en transformación digital y el tercero adopta un enfoque computacional. La estructura temática identificada refleja un campo en proceso de consolidación, con predominio de enfoques técnicos y una especialización funcional limitada. Conclusiones. La investigación actual se centra más en el desarrollo metodológico que en su aplicación estratégica en contextos organizativos específicos. Estos hallazgos ponen de manifiesto la necesidad de enfoques más integrales que articulen tecnología, gestión y gobernanza, así como de una agenda futura centrada en su adopción desde perspectivas sociotécnicas.

The impact that Non-Communicable Diseases (NCDs) have on the health status of the population has generated the need for an in-depth analysis of health habits and NCDs. In addition to its significant impact on population health, this phenomenon also translates into substantial economic consequences for countries. This study delves into the analysis of the relationship between health habits and NCDs among the economically active population of Mexico. Through a hybrid approach that integrates the use of machine learning (ML) models and a structural equation model (SEM), we seek to quantify the direct and indirect causal effects between health habits and NCDs. For this study, information from the 2022 National Health and Nutrition Survey carried out in Mexico for the working-age population is used. According to the results obtained in the first stage of analysis using ML, the most relevant variables (health habits) that impact the probability of individuals presenting with NCDs were identified (random forest precision of 78.66% and Lasso with 71.27%). The second stage of analysis through SEM using the most relevant variables, which were selected through ML, allowed us to measure the direct and indirect causal effect of health habits on NCDs. The SEM model was statistically significant (Chi-square: 449.186; p-value = 0.0000) and revealed that negative health habits, such as a poor diet, physical inactivity, smoking and alcohol consumption, significantly increase the risk of NCDs in the working-age population in Mexico (0.23), while vigorous physical activity and salary has a negative impact (−0.17 and −0.23, respectively) on the presence of NCDs. This study highlights the ability of machine learning and SEM approaches to model the impact of health habits on NCDs for the economically active population in Mexico.

The aim of this paper is to present the results of an impact evaluation of a community center in health, capacity building, and digital access, which form an approximation of quality of life, in the population of Santa Fe town in Mexico City, from 2022 to 2024. The methodology is quantitative, using an impact index and the differences in differences (DD) technique. The data were obtained from primary sources with surveys undertaken via questionnaires. The center is operated by a private university and funded by private firms. The results show a positive impact of 0.287127 out of 1 on the weighted impact index, which allows us to consider this program successful in improving the quality of life of the target population. Through impact evaluation, the effectiveness of interventions and opportunities for improvement are identified, fostering collaboration among local actors, including community members, state-run public programs, and community centers. This collaborative effort improves the quality of life, creating a sustainable community wherein each actor addresses specific needs. Impact evaluation plays a crucial role in measuring sustainability because it is a continuous improvement process that, when combined with other actions, enhances the community’s overall well-being.