In order to assess the effects of e-learning integration on university performance and competitiveness, this study uses Oman as a model for the Gulf. Analyzing how e-learning impacts technology integration, diversity, community engagement, infrastructure, financial strength, institutional reputation, student outcomes, research and innovation, and academic quality can reveal whether universities are effectively incorporating digital tools to enhance teaching and learning. By offering a framework for comparable institutions in the Gulf area, this study provides insights into optimizing e-learning techniques to improve university performance and competitiveness. This study uses the Structural Equation Modeling (SEM) with a dataset comprising 424 participants and 55 indicators, analyzed using both measurement and structural models. The results of the hypothesis testing, which indicate that e-learning has a positive effect on factors like student outcomes (B = 0.080, t = 2.859, P = 0.004) and institutional reputation (B = 0.058, t = 2.770, P = 0.005), lend credence to these beliefs. Omani universities need culturally sensitive e-learning, stronger institutional support, and training to enhance diversity (B = 0.002, t = 0.456, P = 0.647) and technology integration (B = −0.009, t = 0.864, P = 0.387). These improvements increase the visibility of Gulf institutions abroad, attracting the best students from all around the world and fostering an inclusive learning atmosphere. Financially speaking, e-learning offers reasonably priced solutions such as digital libraries and virtual laboratories, which are especially beneficial in a region where education plays a major role in socioeconomic development.

The food insecurity and inadequate management of family farm production is a problem that per-sists today in all corners of the world. Therefore, the purpose of this study was to analyze the socioeconomic and agricultural production management factors associated with food insecurity in rural households in the Machángara river basin in the province Azuay, Ecuador. The information was collected through a survey applied to households that were part of a stratified random sample. Based on this information, the Latin American and Caribbean Household Food Security Measurement Scale (ELCSA) was constructed to estimate food insecurity as a function of socioeconomic factors and agricultural production management, through the application of a Binomial Logit model and an Ordinal Logit model, in the STATA^® 16 program. The results show that head house a married head of household, living in an informal house, having a latrine, producing medicinal or ornamental plants, and the relationship between expenses and income are significant variables that increase the probability of being food insecure. In this way, this research provides timely information to help public policy makers employ effective strategies to benefit rural household that are food vulnerable.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

This study aims to: (1) analyze the need for digital marketing capabilities in Thai MSME; (2) develop an online digital marketing course; and (3) enhance Thai MSME’s digital marketing capabilities, particularly in Thailand’s manufacturing sectors. The survey was conducted using questionnaires distributed to a sample group of 400 digital marketing staff, executives, or business owners, complemented by in-depth interviews with marketing experts, business managers, and owners, totaling 10 participants. The research findings reveal a significant demand for digital marketing skills among MSME entrepreneurs in the manufacturing sector. The top three skills identified as most crucial for enhancement are: (1) communication and marketing information presentation skills; (2) brand building and public relations; and (3) video marketing execution. The study further revealed that the design of the digital marketing course, along with the developed online learning platform, attracted and successfully enrolled 104 MSMEs who participated in the online program. The pre- and post-training assessment results demonstrated a statistically significant difference in test scores, with a mean post-training score of 16.10 ( Mean = 16.10, S.D. = 1.396), representing a notable increase from the pre-training mean score of 6.47 ( Mean = 6.47, S.D. = 3.634) at the 0.05 significance level. Furthermore, the results of the follow-up evaluation on the application of acquired knowledge revealed that the overall level of knowledge and skills application is at its highest, with an average score of 4.64. This indicates that the developed course and online learning platform effectively enhance learners’ knowledge.

The Malaysian dilemma presents a complex challenge in the wake of the COVID-19 pandemic, requiring a comprehensive statistical analysis for the formulation of a sustainable economic framework. This study delves into the multifaceted aspects of reconstructing Malaysia’s economy post-COVID-19, employing a data-driven approach to navigate the intricacies of the nation’s economic landscape. The research focuses on key statistical indicators, including GDP growth, unemployment rates, and inflation, to assess the immediate and long-term impacts of the pandemic. Additionally, it examines the effectiveness of government interventions and stimulus packages in mitigating economic downturns and fostering recovery. A comparative analysis with pre-pandemic data provides valuable insights into the extent of economic resilience and identifies sectors that require targeted support for sustained growth. Furthermore, the study explores the role of technology and digital transformation in building a resilient economy, considering the accelerated shift towards remote work and digital transactions during the pandemic. The analysis incorporates data on technological adoption rates, digital infrastructure development, and innovation ecosystems to gauge their contributions to economic sustainability. Addressing the Malaysian Dilemma also involves an examination of social and environmental dimensions. The study investigates the impact of economic policies on income distribution, social equity, and environmental sustainability, aiming to achieve sustainable economic growth. The study contributes a nuanced analysis to guide policymakers and stakeholders in constructing a sustainable post-COVID-19 economy in Malaysia.

This study explores the influence of digital technologies, including media, on pre-service teachers’ interactions and engagement patterns. It underscores the significance of promoting digital competence to empower pre-service teachers to navigate the digital world responsibly, make informed decisions, and enhance their digital experiences. The objective is to identify key themes and categories in research studies related to pre-service teachers’ digital competence and skill preparations. Conducting a systematic literature review, we searched databases such as SCOPUS, ScienceDirect, and Taylor & Francis, including forty-three articles in the dataset. Applying qualitative content analysis, we identified four major themes: digital literacy, digital competencies, digital skills, and digital thinking. Within each theme, categories and their frequencies were examined. Preliminary findings reveal a growing prevalence of digital competence and literacy articles between 2019 and 2024. The paper concludes by offering recommendations for further research and implementations, with specific criteria used for article selection detailed in the paper. A digital literacy policy for teacher education preparedness is included.