Political risk is one of the most prominent dangers for International Construction Companies (ICCs) to deal with in overseas countries. Despite considerable studies on political risk management in international business, few have been focused on quantitative political risk assessment methods in international construction projects. Thus, this study aims to establish a quantitative model for assessing political risk in such projects. Through the comprehensive literature review and the pilot interviews with 10 industry experts, 36 political risk source variables were first identified. Then, a questionnaire survey was conducted with 155 respondents, and a confirmatory factor analysis (CFA) was performed to group these variables into six exogenous driver components and six endogenous vulnerability components. Subsequently, a quantitative assessment model was developed, based on the vulnerability theory and the Cannikin Law, and validated by 294 cases obtained from 45 Chinese Construction Companies (CCCs). This study incorporated the vulnerability variables and the Cannikin Law in political risk assessment and provides ICCs with a more effective approach to assess political risk using the available data from international research institutes and project systems. The application of the model can also facilitate in political risk warning and management in the international construction business. As the model proposed in this study can be applied to ICCs in different countries, the findings and outcomes of this study can contribute to the global body of knowledge of political risk management.

International construction enterprises (ICEs) have been plagued with political risks in the global market. To ensure the success of political risk management, it is necessary to comprehend the political risks in international construction projects. This study aims at exploring the political risk paths in international construction projects. The preliminary political risk factors and paths were proposed from the literature review. A survey questionnaire was developed to collect political risk cases faced by Chinese construction enterprises (CCEs) performing international construction projects, and 264 valid cases were received and analyzed for this study. Adopting the confirmatory factor analysis (CFA) method, 6 macrofactors (“sociopolitical stability” (A), “legal and regulatory” (B), “social safety” (C), “economy performance” (D), “attitude towards foreigners” (E), and “international environment” (F)) and 2 microfactors (“low exposure” (G) and “capability of enterprises” (H)) were identified. Moreover, the results of path analysis illustrated that 7 factors (A, B, C, D, E, G, and H) had the significant direct negative effect on “risk consequences” and 3 factors (A, D, and F) had the indirect negative effect on “risk consequences.” The findings from this study help practitioners gain an in‐depth understanding of political risks in international construction projects and provide a useful reference for ICEs to manage political risks when venturing outside their home countries.

The surging demand for logistics systems brought about by the vigorous development of e-commerce makes urban traffic more and more congested. The need for a sustainable transition in terms of urban transportation infrastructure also encourages the further innovation of logistics systems. The urban underground logistics system (ULS) emerges as a promising alternative for realizing efficient large-scale freight distribution in megacities. However, there are relatively few studies that have explored the factors that determine the uptake of ULSs in practice. This paper thus aims to identify the critical success factors of ULSs throughout project life cycle stages. Firstly, a desktop study and a study using the Delphi method were conducted to extract the critical success factors (CSFs) of ULS projects. Secondly, a questionnaire survey was conducted to collect data on the perceived significance of the selected success factors from ULS professionals. Thirdly, the intergroup comparison of the significance of CSFs and exploratory factory analysis were used to ascertain the critical factors and latent determinants influencing the development of ULS projects. In total, 36 CSFs in the four life cycle stages of ULS projects were finalized. The identified factors represent the seven latent determinants in developing a ULS project, namely, overall feasibility and acceptance of the ULS, prototype system, and business model, competence and resources for ULS construction and operation, station layout and intermodal transportation, government policies and incentives, long-term planning of the underground space and logistics network, and market investigation and forecasting. The research findings of the paper help guide practitioners and policy makers on decisions made during ULS planning and construction and provide a reference performance evaluation framework for ULS projects.

Under the influence of pervasive digital revolution, the accessibility and analysis of ‘big data’ can provide useful insights and help various industries evolve. Despite the popularity of big data, the construction industry is lagging behind other industries in adopting big data technologies. This paper fills the knowledge gap by examining the status quo of big data adoption in companies with different sizes and roles, as well as that in projects with different types, and ascertaining the drivers for and challenges in adopting big data. This paper employed a structured questionnaire survey and statistical analyses to investigate the significance of factors influencing the drivers, challenges, and enhancement strategies of big data adoption, and validated the results with post-study interviews with construction professionals. The results show that big data adoption in the construction industry is affected by the size of companies and the work experience of their employees. Technology advancement, competitiveness, and government plan and policy initiatives are identified as the top three drivers of big data adoption in the construction sector. Moreover, a lack of appropriate supporting systems, difficulties in data collection, and the shortage of knowledge and experience are found to be the major challenges in big data adoption. Finally, the identified top three strategies for overcoming these challenges and promoting big data adoption are ‘clear organization structure’, ‘government incentives’, and ‘the training of information technology (IT) personnel’. The paper suggests the necessity of creating differentiated strategies for big data adoption for companies with different scales and roles, and helps provide useful insights for policy-makers in promoting big data applications.