This study determines the efficiency and productivity of Mexico’s urban and rural municipalities in generating economic welfare between 1990 and 2020. It establishes the incidence of context and space on efficiency, using Data Envelopment Analysis, the Malmquist-Luenberger Metafrontier Productivity Index, and Nonparametric Regression. The results indicate that 4 of the 2456 municipalities analyzed were efficient, that productivity increased, and that context and space influenced efficiency. This highlights the need for policies that optimize resource utilization, enhance investment in education, stimulate local business development, encourage inter-municipal cooperation, reduce rural-urban disparities, and promote sustainability.

This study investigates the career expectations of individuals in Thailand’s emerging economy, emphasizing the critical factors that shape these expectations within the context of a rapidly evolving labour market in the digital era. A quantitative approach was employed, collecting data from 1230 Thai respondents through convenience sampling, utilizing a structured survey as the primary research instrument. Data analysis involved the use of percentages, means and logistic regression to provide a comprehensive understanding of the findings. The results indicate that factors such as gender, age, monthly income, professional identity, values, culture and technology usage (including devices like laptops, social media platforms, home internet access and usage hours) significantly influence career expectations. Understanding these influential factors is crucial for developing targeted strategies to enhance career satisfaction, preparedness and overall competitiveness in an increasingly globalized and digital economy. By addressing the unique needs and aspirations of the Thai workforce, particularly in this digital age, stakeholders can cultivate a more responsive and adaptive professional environment, ultimately contributing to national economic growth in the digital era.

The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

The article presents the experience of formation and development of economic competences of non-economic specialty students. The modern world is quite complex, diverse, and multidimensional, in order to adapt to it, work effectively, it is necessary to have information about market relations, relations in the sphere of production, consumption, exchange, distribution, and also to be able to connect these areas, navigate the laws operating in these areas. It should be noted that the formation and development of a specialist’s economic competence occurs throughout his or her entire professional life. In our study, the process of forming economic competence is considered as its formation at the stage of mastering economic disciplines, relevant special courses and methodical support. Training in higher education should lead to the acquired knowledge being transferred into the activity of combining elements into an interconnected structure, into the skillful distribution of resources, into the activity that brings profit and has the form of capital investment, in other words, the individual, acquiring knowledge for himself, should be able to transform it into a socially significant value. This requires the search for and implementation of new approaches in the content and organization of the educational process at all levels of education. Research devoted to the role of education in the preparation of future non-economists for economic competence focuses on the preparation of an individual for the economic literacy of an entrepreneur. One of the main tasks of the education system should be preparation for successful socialization in the context of involvement in entrepreneurial relations. It is students and young specialists who have advantages in entrepreneurship in the current conditions: they have the opportunity to obtain specialized knowledge and skills in the field of economics; they can start their own business, relying on economic knowledge. Therefore, the role of higher education is increasing, since it helps to meet the needs of society and implement its socially significant goals. This poses new challenges for universities to transfer the necessary economic knowledge, skills and abilities to students, and to develop their economic competence. The development of basic economic competences in a student is a guarantee of his competitiveness in the labor market and the basis for making reasonable economic decisions in the daily life of every person.

This research presents a novel approach utilizing a self-enhanced chimp optimization algorithm (COA) for feature selection in crowdfunding success prediction models, which offers significant improvements over existing methods. By focusing on reducing feature redundancy and improving prediction accuracy, this study introduces an innovative technique that enhances the efficiency of machine learning models used in crowdfunding. The results from this study could have a meaningful impact on how crowdfunding campaigns are designed and evaluated, offering new strategies for creators and investors to increase the likelihood of campaign success in a rapidly evolving digital funding landscape.