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The purpose of this study is to develop a novel hybrid approach that incorporates the structural equation model (SEM) and fuzzy cognitive map (FCM) to investigate the impacts of the variation in project complexity on project success.

This study adopts SEM to identify and validate a correlation between project complexity variables and PS. Standardized causal coefficients estimated in SEM are used to construct an FCM model to illustrate the effect of complexity on PS with linkage direction and weights. Predictive and diagnostic analyses are performed to dynamically model the variation in project complexity on the evolution of PS.

Results indicate that (1) the hybrid SEM–FCM approach is capable of modeling the dynamic interactions between project complexity and PS; (2) information, goal and environmental complexities are negatively correlated with PS, and technological, task and organizational complexities are positively correlated with PS and (3) the recommendations of complexity management for construction projects are put forward under the guideline of success monitoring.

This research contributes to (1) the state of knowledge by proposing a hybrid methodology that can model the dynamic interactions between project complexity and PS and (2) the state of practice by providing a new perspective of PS evaluation to enhance the probability of success in complex construction projects.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

This paper proposes a novel hybrid simulation approach that incorporates the structural equation model (SEM) and system dynamics (SD) to investigate the impacts of leadership dynamics on project performance under different scenarios.

SEM is used to identify and validate a correlation between leadership variables and project performance statically. On this basis, the SD model is constructed to depict a system model connecting the leadership and project performance. Different scenarios are simulated to dynamically model the variation in leadership on the evolution of project performance.

Results indicate that (1) leadership can be divided into personal ability, relationship atmosphere and organizational strategy in complex construction projects; (2) personal ability, relationship atmosphere and organizational strategy positively correlate with project performance over time; and (3) L1 (stress management ability), L7 (team building) and L17 (institution support) are the leading factors influencing project performance and should be paid more attention under limited resources.

This research contributes to (1) the state of the knowledge by proposing a hybrid methodology that can systematically model the impacts of leadership dynamics on project performance over time and (2) the state of the practice by gaining a better understanding of the strategy of resource distribution for enhancing project performance in complex construction projects.

The purpose of this paper was to map the safety management research of construction industry by scientometric analysis, which can predict important highlights and future research directions of safety management research in the construction industry. As an important issue in the construction industry, safety management issues have been researched from different perspectives. Although previous studies make knowledge contributions to the safety management research of construction industry, there are still huge obstacles to distinguish the comprehensive knowledge map of safety management research in the construction industry.

This study applies three scientometric analysis methods, collaboration network analysis, co-occurrence network analysis and cocitation network analysis, to the safety management research of construction industry. 5,406 articles were retrieved from the core collection database of the Web of Science. CiteSpace was used for constructing a comprehensive analysis framework to analyze and visualize the safety management research of construction industry. According to integrating the analysis results, a knowledge map for the safety management research of construction industry can be constructed.

The analysis results revealed the academic communities, key research topics and knowledge body of safety management research in the construction industry. The evolution paths of safety management research in the construction industry were divided into three development stages: “construction safety management”, “multi-objective safety management” and “comprehensive safety management”. Five research directions were predicted on the future safety management research of construction industry, including (1) comprehensive assessment indicators system; (2) intelligent safety management; (3) cross-organization collaboration of safety management; (4) multilevel safety behavior perception and (5) comparative analysis of safety climate.

The findings can reveal the overall status of safety management research in the construction industry and represent a high-quality knowledge body of safety management research in the construction industry that accurately reflects the comprehensive knowledge map on the safety management research of construction industry. The findings also predict important highlights and future research directions of safety management research in the construction industry, which will help researchers in the safety management research of construction industry for future collaboration and work.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.

Purpose: This chapter looks specifically at the sources of economic policy uncertainty in Nigeria, and discusses their impact on the Nigerian economy while drawing implications for Africa. It identifies factors that transmit uncertainty in economic policy in Nigeria and draw implications for other African countries.

Methodology: This chapter uses a literature survey methodology to identify the sources of economic policy uncertainty in Nigeria.

Findings: The identified sources of economic policy uncertainty in Nigeria are: the frequent changes in central bank policy, unexpected changes in government policy, political interference, unexpected fall in global oil price, recession, and unethical practices.

Implications: The implication of the study is that rising economic policy uncertainty in Nigeria can have a significant effect on the Nigerian economy and for connected African countries.

Originality: Previous studies have not examined the sources of economic policy uncertainty in Nigeria.

Several parameters set economic growth, and one of them is innovation. Thus, it is vital to realize the proper motives for a firm to innovate. The key actors in innovation systems – such as firms, research institutions, academia, and standards developing communities – affect knowledge ­creation, contribute to its diffusion and use, and set the global innovation capacity. Additionally, the market-driven and the worldwide open paradigm may aid to ensure stable and reliable integration, interactive abilities, and active collaborations to drive international innovation and its ­concepts forward. The World Trade Organization (WTO) is an international organization that assembles principles to produce such standards as mentioned above, so as to ensure a prosperous future globally. Global cooperation, openness, interaction, and the generation of motives for innovation are some of the principles that WTO embraces, and all of these are to benefit humanity from free market and trade growth, leading to triumph through innovation. Overall, this chapter examines the significant role of WTO in trade policy uncertainty and how innovation is ensured and stimulated by WTO regarding the specific case of the Chinese market.

This paper aims to explore the pathways of carbon transfer in 200 US corporations along with the motivations that drive such transfers. The particular focus is on each firm’s embeddedness in the global value chain (GVC) and the influence of environmental law, operational costs and corporate social responsibility (CSR). The insights gleaned bridge a gap in the literature surrounding GVCs and corporate carbon transfer.

The methodology comprised a two-step research approach. First, the authors used a two-sided fixed regression to analyse the relationship between each firm’s embeddedness in the GVC and its carbon transfers. The sample consisted of 217 US firms. Next, the authors examined the influence of environmental law, operational costs and CSR on carbon transfers using a quantitative comparison analysis. These results were interpreted through the theoretical frameworks of the GVC and legitimacy theory.

The empirical results indicate positive relationships between carbon transfers and GVC embeddedness in terms of both a firm’s position and its degree. From the quantitative comparison, the authors find that the pressure of environmental law and operational costs motivate these transfers through the value chain. Furthermore, CSR does not help to mitigate transfers.

The findings offer insights for policymakers, industry and academia to understand that, with globalised production and greater value creation, transferring carbon to different parts of the GVC – largely to developing countries – will only become more common. The underdeveloped nature of environmental technology in these countries means that global emissions will likely rise instead of fall, further exacerbating global warming. Transferring carbon is not conducive to a sustainable global economy. Hence, firms should be closely regulated and given economic incentives to reduce emissions, not simply shunt them off to the developing world.

Carbon transfer is a major obstacle to effectively reducing carbon emissions. The responsibilities of carbon transfer via GVCs are difficult to define despite firms being a major consideration in such transfers. Understanding how and why corporations engage in carbon transfers can facilitate global cooperation among communities. This knowledge could pave the way to establishing a global carbon transfer monitoring network aimed at preventing corporate carbon transfer and, instead, encouraging emissions reduction.

This study extends the literature by investigating carbon transfers and the GVC at the firm level. The authors used two-step research approach including panel data and quantitative comparison analysis to address this important question. The authors are the primary study to explore the motivation and pathways by which firms transfer carbon through the GVC.

The aim of this study is to examine the effect of trade wars between countries on social factors. For this purpose, six different criteria are selected based on the comprehensive literature review. Additionally, fuzzy DEMATEL approach is considered to weight these criteria. The findings indicate that working life is the most significant criterion. However, free time and leisure and family are at the bottom of the list. Additionally, it is also defined that working life is the most influencing criterion. These results demonstrate that trade wars have the highest impact on the working life of the people. In other sayings, people’s fear of losing their jobs goes up in case of the trade wars. To solve this problem, companies should make official statements to their employees about the situation caused by the trade wars. By making clear explanations, uncertainties in this process can be minimized.

Recent empirical studies by Antonakakis, Chatziantoniou and Filis (2013), Brogaard and Detzel (2015) and Christou et al. (2017) present evidence, which supports the notion that a rise in economic policy uncertainty (EPU) will lead to a decline in stock prices. The purpose of this paper is to examine US categorical policy uncertainty on stock returns while controlling for implied volatility and downside risk. In addition to the domestic impacts of policy uncertainty, this paper also presents evidence that changes in US policy uncertainty promptly propagates to the global stock markets.

This study uses a GED-GARCH (1, 1) model to estimate changes of uncertainties in US monetary, fiscal and trade policies on stock returns for the sample period of January 1990–December 2018. Robustness test is conducted by using different set of data and modeling techniques.

This paper contributes to the literature in several aspects. First, testing of US aggregate data while controlling for downside risk and implied volatility, consistently, shows that responses of stock prices to US policy uncertainty changes, not only display a negative effect in the current period but also have at least a one-month time-lag. The evidence supports the uncertainty premium hypothesis. Second, extending the test to global data reveals that US policy uncertainty changes have a negative impact on markets in Europe, China and Japan. Third, testing the data in sectoral stock markets mainly displays statistically significant results with a negative sign. Fourth, the evidence consistently shows that changes in policy uncertainty present an inverse relation to the stock returns, regardless of whether uncertainty is moving upward or downward.

The current research is limited to the markets in the USA, eurozone, China and Japan. This study can be extended to additional countries, such as emerging markets.

This paper provides a model that uses categorical policy uncertainty approach to explain stock price changes. The parametric estimates provide insightful information in advising investors for making portfolio decision.

The estimated coefficients of changes in monetary policy uncertainty, fiscal policy uncertainty and trade policy uncertainty are informative in assisting policymakers to formulate effective financial policies.

This study extends the existing risk premium model in several directions. First, it separates the financial risk factors from the EPU innovations; second, instead of using EPU, this study investigates the effects from monetary policy, fiscal policy and trade policy uncertainties; third, in additional to an examination of the effects of US categorical policy uncertainties on its own markets, this study also investigates the spillover effects to global major markets; fourth, besides the aggregate stock markets, this study estimates the effects of US policy uncertainty innovations on the sectoral stock returns.