To analyze the effect of an increase in the quantity or quality of public investment on growth, this paper extends the World Bank’s Long-Term Growth Model (LTGM), by separating the total capital stock into public and private portions, with the former adjusted for its quality. The paper presents the LTGM public capital extension and accompanying freely downloadable Excel-based tool. It also constructs a new infrastructure efficiency index, by combining quality indicators for power, roads, and water as a cardinal measure of the quality of public capital in each country. In the model, public investment generates a larger boost to growth if existing stocks of public capital are low, or if public capital is particularly important in the production function. Through the lens of the model and utilizing newly-collated cross-country data, the paper presents three stylized facts and some related policy implications. First, the measured public capital stock is roughly constant as a share of gross domestic product (GDP) across income groups, which implies that the returns to new public investment, and its effect on growth, are roughly constant across development levels. Second, developing countries are relatively short of private capital, which means that private investment provides the largest boost to growth in low-income countries. Third, low-income countries have the lowest quality of public capital and the lowest efficient public capital stock as a share of GDP. Although this does not affect the returns to public investment, it means that improving the efficiency of public investment has a sizable effect on growth in low-income countries. Quantitatively, a permanent 1 ppt GDP increase in public investment boosts growth by around 0.1–0.2 ppts over the following few years (depending on the parameters), with the effect declining over time.

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.

The existing studies on the association between the built environment and health mainly concentrates on urban areas, while rural communities in China have a huge demand for a healthy built environment, and research in this area remains insufficient. There is a lack of research on the health impact of the built environment in rural communities in China, where there is a significant demand for advancements in the healthy built environment. Exploring the Influence of built environment satisfaction on self-rated health outcomes in New-type village communities has positive significance for advancing research on healthy village community. This paper selects four new-type village communities as typical cases, which are located in the far suburbs of Shanghai, China. A questionnaire survey was conducted on individual villagers, and 223 valid questionnaire samples were obtained. A PLS-SEM model was developed using survey data to examine how built environment satisfaction influences dwellers’ self-rated health while taking into account the mediating function of the perceived social environment. Moreover, multi-group analysis was performed based on age. The results show that built environment satisfaction indirectly influences residents self-rated health through its impact on perceived social environment. The research also discovered that the relationship between built environment satisfaction, social environment satisfaction and self-rated health is not influenced by age as a moderating factor. The research offers new insights for the planning and design of new-type village community from a health perspective.

This study examined the factors influencing online purchases among consumers in Bangladesh, employing a modified version of the Technology Acceptance Model (TAM). Data from 353 individuals in Bangladesh revealed that perceived ease of use, social influence, security, convenience, trust, emotional experience, and functional experience significantly positively affect the intention to purchase online. Additionally, results show that the intention to purchase online significantly positively affects actual online purchases. Findings further highlighted that intention to make online purchases mediated the influence of perceived ease of use, social influence, security, convenience, trust, emotional experience, and functional experience over online purchases. The study provides significant practical recommendations to help businesses and consumers support online purchasing with diverse advantages.

Thailand and the EU started negotiating a free trade agreement (FTA) in 2005, but negotiations were subsequently suspended in 2014 after the country’s military coup. The significance of these negotiations are important because of the mutual benefit of achieving higher levels of trade and investment between the world’s largest single market and the second largest ASEAN economy. The Specific Factors (SF) model of production and trade is applied to identify potential winner and loser industries and factors of production in Thailand. The model identifies short-run loses for some labor inputs, return to capital, and output in agriculture and services. In the manufacturing and energy sectors, higher output will benefit some labor inputs and capital owners. Understanding the short-run impact of an FTA could allow policymakers in Thailand to reinforce the institutional infrastructure such as implementing trade adjustment assistance programs (TAA), to help re-train workers who may become unemployed due to free trade.