This study proposes a fuzzy analytic hierarchy process (FAHP) method to support strategic decision-makers in choosing a project management research agenda. The analytical hierarchy process (AHP) model is the basic tool used in this study. It is a mathematical tool for evaluating decisions with multiple alternatives by decomposing them into successive levels according to their degree of importance. The Sustainable Development Goals (SDG) oriented theme of project management was chosen from among four themes that emerged from a strategic monitoring study. The FAHP method is an effective decision-making tool for multiple aspects of project management. It eliminates subjectivity and produces decisions based on consistent judgment.

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.

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 contemporary landscape characterized by technological advancements and a progressive economic environment, the utilization of currency has undergone a paradigm shift. Despite the growing prevalence of digital currency, its adoption among the Vietnamese population faces several challenges, including limited financial literacy, concerns over security, and resistance to change from traditional cash-based transactions. This research aims to identify these challenges and propose solutions to encourage the widespread use of digital currency in Vietnam. This research adopts a quantitative approach, utilizing Likert scale questionnaires, with a dataset of 330 records. The interrelationships among variables are analyzed using partial least squares structural equation modeling (PLS-SEM). The analysis results substantiate the viability of the research model, confirming the hypotheses. The findings demonstrate a positive relationship and the significance impact of factors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived trust (PT), social influence (SI), openness to innovation (OI), and financial knowledge (FK) to intention to use digital currency (IUDC). Thereby aiming to inform policymakers, industry stakeholders, and the wider community, fostering a deeper understanding of consumer behavior and providing solutions to enhance the adoption of digital currency in the evolving landscape of digital finance.