This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.

Primary reason for interpretation the paper was the creation of a starting position for setting up e-learning in the structures of the executive forces of the Slovak Republic, which absent in the current dynamic environment. Problems with education arose mainly in connection with the global problem of Europe, such as the influence of illegal migrants, and it was necessary to retrain a large number of police officers in a short time. We reflect on the combined model of LMS Moodle and proctored training through MS TEAMS and their active use in practice. We focused on the efficiency in the number of participants in individual trainings and costs per participant according to the field of training. We compared the processed data with the costs of the pilot introduction of analytical organizational unit providing e-learning and interpreted the positive results in the application of e-learning compared to conventional (face-to-face) educational activities. As a basic (reference) comparative indicator, the costs of educational activities of selected organizational unit of state institution represented by own educational organizations and the number of trained employees for the periods in question were chosen. To measure effectiveness, we set financial—cost KPIs. Our findings clearly demonstrated that it is possible to significantly optimize costs when changing the current form of ICT education to e-learning. The implementation of another educational activities form of education, e-learning, within public institutions, according to the results of the analysis, can simplify and at the same time make education processes more efficient in the context of individual subjects of the Ministry of the Interior of the Slovak Republic.

Entrepreneurial Orientation (EO) emphasizes the identification and exploitation of business opportunities, while entrepreneurial action learning (EAL) underscores the acquisition of knowledge through practical experience and continuous improvement. Breakthroughs in both aspects contribute to maintaining flexibility, adapting to changes, and enabling success in competitive markets. The key to the development of small and medium-sized enterprises (SMEs) lies in a clear Entrepreneurial Orientation, a focus on Entrepreneurial Action Learning, and the cultivation of innovation spirit through continuous practice and experience accumulation, thereby enhancing entrepreneurial performance (EP). This study aims to explore the impact of Entrepreneurial Orientation on the Entrepreneurial Performance of SMEs, clarify the mediating role of Entrepreneurial Action Learning between Entrepreneurial Orientation and Entrepreneurial Performance, and investigate the variability of Entrepreneurial Performance among different industries. By means of data collection from 598 SMEs, data analysis was conducted using Structural Equation Modeling (SEM) and Analysis of Variance (ANOVA). The analysis results indicate that entrepreneurial orientation has a positive impact on entrepreneurial action learning and entrepreneurial performance, and entrepreneurial action learning has a positive impact on entrepreneurial performance. The study also found that entrepreneurial action learning partially mediates the relationship between entrepreneurial orientation and entrepreneurial performance. There are certain differences in entrepreneurial performance among different industries. This study enriches the relevant literature in the field of entrepreneurship. Additionally, research on entrepreneurial orientation, entrepreneurial action learning, and entrepreneurial performance in specific regional contexts is very limited, making this study valuable for subsequent research in related areas.

 This research aims to empirically examine the role of learning organization practices in enhancing sustainable organizational performance, utilizing knowledge management and innovation capability as mediating variables. The study was conducted in public IT companies across China, which is a vital sector for driving innovation and economic growth. A mixed-methods approach was employed, with quantitative methods accounting for 70% and qualitative methods for 30% of the research. Purposive sampling was utilized to distribute questionnaires to 546 employees from 10 public IT companies. Statistical analysis was conducted using Structural Equation Modeling (SEM). The findings indicate that learning organization practices significantly influence knowledge management practices (β = 0.785, p < 0.001) and innovation capability (β = 0.405, p < 0.001). Furthermore, knowledge management practices positively contribute to sustainable organizational performance (β = 0.541, p < 0.001), while innovation capability also has a positive effect (β = 0.143, p < 0.001). Moreover, knowledge management practices partially mediate the relationship between learning organization practices and sustainable performance, with a total effect of 0.788 (p < 0.001). The mediating role of innovation capability is also significant, with a total effect of 0.422 (p = 0.045). The study further includes qualitative in-depth interviews with 20 managers from 10 IT companies across five regions in China: East, South, West, North, and Central. Senior managers were selected through a stratified sampling method to ensure comprehensive representation by including both the largest and smallest companies in each region. These findings underscore the critical role of learning organizations in promoting sustainability through effective knowledge management and innovation capabilities within the IT sector.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.