This study highlights the importance of social capital within third sector organizations, as in other sectors of the economy, and confirms the influence of social capital on human capital. In this case, it contributes to the analysis of the structure and quality of relationships among members of a social organization, which enables motivation and commitment to collective action. Based on exploratory and confirmatory factor analysis, from a 45-item survey applied to 190 workers in social organizations; the constructs were reconfigured for the construction of the model of organizational social capital, was carried out using the structural equation methodology. It is argued that the cognitive and structural dimensions of social capital affect its relational dimension in terms of identification, trust and cooperation, which in turn influences worker motivation and other key aspects of human capital. The relational dimension, measured by workers’ identification, trust, and cooperation, has significant effects on their motivation and work engagement, which leads to important practical considerations for human resource policies in these organizations. The article contributes to the existing literature on human capital management by exploring the perception of workers in nonprofit organizations that are part of Ecuador’s third sector.

The freight transport chain brings together several types of players, particularly upstream and downstream players, where it is connected to both nodal and linear logistics infrastructures. The territorial anchoring of the latter depends on a good level of collaboration between the various players. In addition to the flow of goods from various localities in the area, the Autonomous Port of Lomé generates major flows to and through the port city of Lomé, which raises questions about the sustainability of these various flows, which share the road with passenger transport flows. The aim of this study is to analyse the challenges associated with the sustainability of goods flows. The methodology is based on direct observations of incoming and outgoing flows in the Greater Lomé Autonomous District (DAGL) and semi-directive interviews with the main players in urban transport and logistics. The results show that the three main challenges to the sustainability of goods transport are congestion (28%), road deterioration (22%) and lack of parking space (18%).

The Malaysian government has been actively strengthening the information and communication industry’s ecosystem through talent retention to realize Malaysia 5.0 and transform the country into a developed human-centered society that balances economic advancement with the resolution of talent problems. This is done to recognize the significance of emerging in building a vibrant and dynamic economy for the country. Few of these studies, however, had developed comprehensive policy recommendations for keeping information specialists in Malaysia’s information businesses. To address this gap, a comprehensive literature review was conducted to understand the factors driving information professionals to leave the sector. The findings aim to inform talent retention strategies that will strengthen the industry’s sustainability and attract skilled leaders, ensuring the information sector’s readiness for a successful digital transition.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

The business environment in the modern era is witnessing numerous Intellectual Changes, Technological developments, and increasingly Complex Situations, which has led to a need for effective Leadership in the Business Sectors. This leadership plays a role in transforming companies into giant corporations that serve as a true foundation for enhancing and improving Job Competencies (JC)., The study aimed to analyze the impact of the Soft Skills approach in Human Resources (analytical and critical thinking, decision-making and problem-solving, planning and organization, teamwork) on developing Job Competencies (productivity, technical, managerial) in Petroleum Sector Companies in Egypt. The researchers employed the descriptive-analytical method to study the phenomenon, conducting the study on stratified random samples consisting of 379 managers and a sample of 382 employees from Petroleum Sector Companies. The study utilized the SPSS and AMOS Software Programs. The study found statistically significant differences at the (0.01) level between the average scores of managers and employees regarding soft skills in human resources and job competencies, with managers scoring higher. Additionally, the study revealed a statistically significant direct causal effect at the (0.01) level of Human Resources Soft Skills on Job Competencies in Petroleum Sector Companies., Finally, a proposal was developed for enhancing Job Competencies in Petroleum Companies in Egypt based on the application of human resources Soft Skills, alongside future research directions and practical implications.

In the third national communication submitted by Ecuador, the total greenhouse gases (GHG) emission was calculated at 80,627 GgCO_2-eq, considering the country’s commitment to the Framework on Climate Change. In 2018, Ecuador ratified its nationally determined contribution (NDC) to reduce its GHG emissions by 11.87% from the business-as-usual (BAU) scenario by 2025. The macroeconomic impacts of NDC implementation in the energy sector are discussed. A Computable Equilibrium Model applied to Ecuador (CGE_EC) is used by developing scenarios to analyze partial and entry implementation, as well as an alternative scenario. Shocks in exogenous variables are linked to NDC energy initiatives. So, the NDC’s feasibility depends on guaranteeing the consumption of hydropower supply, either through local exports or domestic demand. In the last case, the government’s Energy Efficiency Program (PEC) and electricity transport have important roles, but the high levels of investment required and poor social conditions would impair its implementation. NDC implementation implies a GDP increase and price index decrease due to electricity cost reductions in the productive sector. These conditions depend on demand-supply guarantees, and the opposite case entails negative impacts on the economy. The alternative scenario considers less dependence on the external market, achieving higher GDP, but with only partial fulfillment of the NDC goals.