This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

This study uses dynamic capability theory and a resource-based view to examine whether intellectual capital (human, relational, and structural capital) mediates entrepreneurial leadership and innovation success. Drawing on data from 422 senior-level employees working in Peruvian I.T. companies, the proposed relationships were analyzed using SmartPLS 4. Entrepreneurial leadership was found to foster employees’ innovative performance through the mediating role of human capital, relational capital, and structural capital. Practically, businesses often rely on innovation for survival and growth, so they should consider entrepreneurial leadership to create intellectual capital (human capital, relational capital and structural capital) for innovation performance. Businesses should provide entrepreneurial training that emphasizes role modeling intellectual capital and encourages employees to recognize and pursue entrepreneurial opportunities. With significantly limited research, the study contributes by investigating the interrelationship of entrepreneurial leadership, intellectual capital, and innovation performance. The study contributes to the Resource Based View and Dynamic Capability Theory by demonstrating how entrepreneurial leadership contributes to innovation performance through human capital, relational capital, and structural capital.

This paper assesses South Africa’s massive infrastructure drive to revive growth and increase employment. After years of stagnant growth, this is now facing a deep economic crisis, exacerbated by the COVID-19 pandemic. This drive also comes after years of weak infrastructure investment, widening the infrastructure deficit. The plan outlines a R1 trillion investment drive, primarily from the private sector through the Infrastructure Fund over the next 10 years (Government of South Africa, 2020). This paper argues that while infrastructure development in South Africa is much-needed, the emphasis on de-risking for private sector buy-in overshadows the key role the state must play in leading on structurally transforming the economy.

The world has complex mega-cities and interdependent infrastructures. This complication in infrastructure relations makes it sensitive to disasters and failures. Cascading failure causes blackouts for the whole system of infrastructures during disasters and the lack of performance of the emergency management stakeholders is clear during a disaster due to the complexity of the system. This research aimed to develop a new concurrent engineering model following the total recovery effort. The objectives of this research were to identify the clustered intervention utilized in the field of resilience and developing a cross-functional intervention network to enhance the resilience of societies during a disaster. Content analysis was employed to classify and categorize the intervention in the main divisions and sub-divisions and the grouping of stakeholders. The transposing system was employed to develop an integrated model. The result of this research showed that the operations division achieved the highest weight of information interchange during the response to improve the resilience of the system. The committee of logistics and the committee of rescue and relief needed the widest bandwidth of information flow in the concurrent engineering (CE) model. The contributed CE model helped the stakeholders provide a resilient response system. The final model and the relative share value of exchanging information for each workgroup can speed up recovery actions. This research found that concurrent engineering (CE) is a viable concept to be implemented as a strategy for emergency management. The result of this research can help policymakers achieve a collaborative teamwork environment and to improve resilience factors during emergency circumstances for critical infrastructures.

Given the heavy workload faced by teachers, automatic speaking scoring systems provide essential support. This study aims to consolidate technological configurations of automatic scoring systems for spontaneous L2 English, drawing from literature published between 2014 and 2024. The focus will be on the architecture of the automatic speech recognition model and the scoring model, as well as on features used to evaluate phonological competence, linguistic proficiency, and task completion. By synthesizing these elements, the study seeks to identify potential research areas, as well as provide a foundation for future research and practical applications in software engineering.