Over the course of many years, the Mekong Delta region has experienced relatively low and inconsistent levels of business attraction and low quality of the enterprise environment compared to other regions in Vietnam. To delve into whether this discrepancy reflects a negative perception of the business environment in the area, this study employs a dataset comprising the aggregate Provincial Competitiveness Index (PCI) and nine of its component scores, alongside other significant control variables, to analyze business attraction trends spanning from 2010 to 2020. It based on the modeling analysis for the panel data that includes Pool-OLS, FEM and REM models. The findings indicate that PCI serves as an important indicator influencing the quality of the business environment and plays a role in determining the location preferences of businesses. It is observed that public investment has exerted an impact on enticing new businesses to the region throughout this period. These research outcomes carry several policy implications, suggesting that public policy interventions can positively shape the business environment, consequently bolstering the appeal of business investments in the region.

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.

This study analyzes the highly disruptive transportation business in Indonesia. The purpose of observation is to completely synthesize disruptive transportation that causes bad externalities in society. Data sources come from primary data of interviews and secondary data of related literature. The research method uses critical qualitative with a combination of in-depth interviews with several stakeholders. Key findings suggest that trust, consistency, capital ownership and proximity of new entrants to incumbents are important in disruptive innovation processes, empirical implications that transportation in Indonesia has undergone a definite economic shift. The results showed that although the government has publicly expressed its full support for any individual who will develop a business in the digital economy model, it is not effective enough to be consistent in the transportation business. Policy recommendations include adaptive training incentive programs for incumbent groups and accelerated funding assistance for new entrant groups, in addition to strengthening active collaboration between the government and the private sector is urgently needed.

Improving the practical skills of Science, Technology, Engineering and Mathematics (STEM) students at a historically black college and university (HBCU) was done by implementing a transformative teaching model. The model was implemented on undergraduate students of different educational levels in the Electrical Engineering (EE) Department at HBCU. The model was also extended to carefully chosen high and middle schools. These middle and high school students serve as a pipeline to the university, with a particular emphasis on fostering growth within the EE Department. The model aligns well with the core mission of the EE Department, aiming to enhance the theoretical knowledge and practical skills of students, ensuring that they are qualified to work in industry or to pursue graduate studies. The implemented model prepares students for outstanding STEM careers. It also increases enrolment, student retention, and the number of underrepresented minority graduates in a technology-based workforce.

An extensive assessment index system was developed to evaluate the integration of industry and education in higher vocational education. The system was designed using panel data collected from 31 provinces in China between 2016 and 2022. The study utilized the entropy approach and coupled coordination degree model to examine the temporal and spatial changes in the level of growth of the integration of industry and education in higher vocational education, as well as the factors that impact it. In order to examine how the integration of industry and education in higher vocational education develops over time and space, as well as the factors that affect it, we utilized spatial phasic analysis, Tobit regression model, and Dagum’s Gini coefficient. The study’s findings suggest that between 2016 and 2022, the integration of industry and education in higher vocational education showed a consistent improvement in overall development. Nevertheless, there are still significant regional differences, with certain areas showing limited levels of integration, while the bulk of regions are either in a state of low integration with high clustering or low integration with low clustering. Most locations showed either a “low-high” or “low-low” level of agglomeration, indicating a significant degree of spatial concentration, with a clear trend of higher concentration in the east and lower concentration in the west. The progress of industrial structure and the degree of regional economic development have a substantial impact on the amount of integration of industry and education in higher vocational education. There is a notable increase in the amount of integration between industry and education in higher vocational education, which has a favorable effect. Conversely, the local employment rate has a substantial negative effect on this integration. Moreover, the direct influence of industrial structure optimization is restricted. The Gini coefficient of the development level of integration of industry and education in higher vocational education exhibits a slight rising trend. Simultaneously, there is a varying increase in the Gini coefficient inside the group and a decrease in the Gini coefficient between the groups. The disparities in the level of integration between Industry and Education in the provincial area primarily stem from inter-group variations across the locations. To promote the integration of industry and education in higher vocational education, it is recommended to strengthen policy support and resource allocation, address regional disparities, improve professional configuration, and increase investment in scientific and technological innovation and talent development.