We investigate the obesity transition at the country- and regional-levels, by age, gender, and socioeconomic status (SES) and its relationship to three health behavior attributes, including physical activity (PA), sedentary activities (ST), and consumption of ultra-processed foods (CUPF) within the urban population of Colombia, from 20,010 to 2050. The study is informed by cross-sectional data from ENSIN survey. We used these data to develop a system dynamics model that simulates the dynamics of obesity by body mass index (BMI) categories, gender, and SES. This model also uses a conservative co-flow structure for three health-related behaviors (PA, ST, and CUPF). At the national level, our results indicate that the burden of obesity is shifting towards populations with lower SES as the gross domestic product (GDP) increases, particularly women aged 20-59 years with lower SES. Among this group of women, the highest burden of obesity is among those who do not meet the PA, ST and CUPF recommendations. At the regional level, our findings suggest that the regions are at different stages in the obesity transition. The burden of obesity is shifting towards women with lower SES as GDP increases at the national level and across several regions. This obesity transition is paralleled by a high prevalence of women from low SES groups who do not meet the minimum recommendations for PA, CUPF, and ST. Our findings can be used by decision-makers to inform age- and SES- specific policies seeking to tackle the obesity.

Purpose: The aim of this study is to increase the understanding of collaborative relationships and assess according to the project size, the influence of the contributory factors in shaping collaboration network structure in projects developed in global supply chains (GSC).Design/methodology/approach: The paper used a case study methodology applied to eight global projects developed by an Austrian company leader in global market intra-logistics solutions and warehouse automation. The cases were studied by two approaches in network analysis. First, visual and descriptive analysis to describe structural aspects of the network. Second, stochastic network analysis to evaluate the influence of contributory factors in the structure of the collaboration network.Findings: The results evidence that independently of the project size and project manager influence, project team roles (PTR) who have a reciprocal communication among other PTR tend to have a higher collaboration intensity (CI). Additionally, the results highlight the influence of the project manager in shaping the collaboration network in standard projects (STP) and small projects (SMP). According to the project size, the results show that the PTR that form complete triangles or cluster or who communicate frequently among each other tend to have a high CI, being more evident these tendencies in large-scale projects than STP and SMP.Originality/value: This research provides a framework to identify the key actors and contributory factors in shaping collaborative relationships in GSC. The findings could be used to support the decision-making process and formulation strategies for effective collaborative relationship management in GSC.

Behavioral theory assumes that leaders can be identified by their daily behaviors. Social network analysis helps to understand behavioral patterns within their social networks. This work considers leaders as the managerial personnel of the organization and differentiates managements from non-managerial staff by their behavior with five different types of interactions with PageRank and their attributes in modern organizations. PageRank and word embedding using word2vec with phrases from features are adopted to extract new features for the identification of managerial staff. Both traditional machine learning methods and graph neural networks are utilized with real-world data from an Austrian IT company called Knapp System Integration. Our experimental results show that the proposed new features extracted using PageRank with different types of interactions and word2vec with phrases significantly improve the identification accuracy. We also propose to use graph neural networks as an effective learning algorithm to identify managers from organizations. Our approach can identify managerial staff with an accuracy of around 80%, which demonstrates that managers could be identified through social network analysis. By analyzing the behaviors of members, the proposed method is effective as a performance appraisal tool for organizations. The study facilitates sustainable management by helping organizations to retain managerial talents or to invite potential talents to join the management team.

Over the past few decades, the study of leadership theory has expanded across various disciplines, delving into the intricacies of human behavior and defining the roles of individuals within organizations. Its primary objective is to identify leaders who play significant roles in the communication flow. In addition, behavioral theory posits that leaders can be distinguished based on their daily conduct, while social network analysis provides valuable insights into behavioral patterns. Our study investigates five and six types of social networks frequently observed in different organizations. This study is conducted using datasets we collected from an IT company and public datasets collected from a manufacturing company for the thorough evaluation of prediction performance. We leverage PageRank and effective word embedding techniques to obtain novel features. State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). More specifically, our approach can achieve state-of-the-art performance with an accuracy close to $90\%$ for leaders identification with data from projects of different types. This investigation contributes to the establishment of sustainable leadership practices by aiding organizations in retaining their leadership talent.

El presente artículo propone estrategias para el desarrollo de competencias blandas (Trabajo en Equipo, Comunicación Efectiva y Pensamiento Crítico) en el marco de la experiencia COIL entre la Universidad de Ibagué (Colombia) y la Universidad de Piura (Perú). Las propuestas de estrategias se basan en la ejecución y perfeccionamiento de seis (06) experiencias COIL, desde el 2020 hasta la fecha. El artículo describe la estructura de organización de la experiencia COIL, las actividades desarrolladas y las herramientas tecnológicas que las soportan. Finalmente, se describen las conclusiones referidas a la aplicación de la metodología COIL como herramienta para potenciar el desarrollo de habilidades blandas de los estudiantes de Instituciones Educativas Superior (IES).

Este artículo tiene como objetivo evaluar el nivel de apropiación e implementación de competencias blandas en los estudiantes de la Universidad de Ibagué (Colombia) y la Universidad de Piura (Perú), en el marco de la implementación de la Experiencia COIL. Se propuso un plan de formación basado en un esquema de tres fases con el fin de evaluar la aplicación del plan de formación trazado. La primera fase evaluó los resultados intermedios para medir el progreso de los estudiantes en el desarrollo de competencias blandas, identificar áreas de mejora en el plan de formación y realizar ajustes según fuese necesario. La segunda fase evaluó los resultados al concluir la experiencia formativa y, por último, la tercera fase realizó un seguimiento posterior a la graduación para evaluar el aporte a las competencias blandas adquiridas por los estudiantes durante la experiencia en su práctica profesional. Los resultados de este estudio proporcionan información valiosa para el diseño y la implementación de programas de formación en competencias blandas en la educación superior.