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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.

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.

This study presents the impact of Economic Policy Uncertainty (EPU)-induced Trade Supply Chain Vulnerability (TSCV) on the Small and Medium-Sized Enterprises (SMEs) in India by leveraging the World Bank Enterprise Survey data for 2014 and 2022. Applying econometric techniques, it examines firm size’ influence on productivity and trade participation, providing insights for enhancing SME resilience and trade participation amid uncertainty.

The econometric techniques focus on export participation, along with variables such as total exports, firm size, productivity, and capital intensity. It addresses crucial factors such as the direct import of intermediate goods and foreign ownership. Utilizing the Cobb-Douglas production function, the study estimates Total Factor Productivity, mitigating endogeneity and multicollinearity through a two-stage process. Besides, the study uses a case study of North Indian SMEs engaged in manufacturing activities and their adoption of mitigation strategies to combat unprecedented EPU.

Results reveal that EPU-induced TSCV reduces exports, impacting employment and firm size. Increased productivity, driven by technological adoption, correlates with improved export performance. The study highlights the negative impact of TSCV on trade participation, particularly for smaller Indian firms. Moreover, SMEs implement cost-based, supplier-based, and inventory-based strategies more than technology-based and risk-based strategies.

Policy recommendations include promoting increased imports and inward foreign direct investment to enhance small firms’ trade integration during economic uncertainty. Tailored support for smaller firms, considering their limited capacity, is crucial. Encouraging small firms to engage in international trade and adopting diverse SC mitigation strategies associated with policy uncertainty are vital considerations.

This study explores the impact of EPU-induced TSCV on Indian SMEs’ trade dynamics, offering nuanced insights for policymakers to enhance SME resilience amid uncertainty. The econometric analysis unveils patterns in export behavior, productivity, and factors influencing trade participation during economic uncertainty.

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.

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.

The purpose of this paper to examine how global economic policy uncertainty (GEPU) affects export flows of emerging market economies.

This study examines the effect of GEPU on 28 emerging markets' export performance. GEPU variable used in the authors’ empirical analysis is measured by partial least square (PLS) factor loading model with the help of 24 countries' economic policy uncertainty index. A panel vector autoregression (VAR) model is employed for the estimations and monthly data over the 2006:01–2019:12 period are used.

The empirical findings show that while the real external income is the main factor that affects export flows, the real exchange rate is the least effective variable with regard to the variance decomposition, which is not expected by the related economic theory. Panel VAR estimations results confirm the previous studies and find that GEPU affects export flows negatively and significantly.

To the best of the authors’ knowledge, this is the sole study in terms of focusing on the impacts of GEPU on the export volume of emerging markets. The contribution of this paper is twofold. Firstly, a large set of countries with monthly frequented data that assist to capture uncertainties better is used. Secondly, the global economic policy index is obtained by employing the PLS method, which provides more robust results that are calculated with respect to the dependent variable.

Trade uncertainty does influence the firm’s new investment, profitability and supply chain finance. Consequently, it results in decreased consumption and low consumer confidence and eventually disrupts global economic activity. This paper aims to propose a model to uncover the effects of trade policy uncertainty (TPU) on the real economic activity and economy’s health measured in terms of the purchasing manager’s index (PMI).

This study uses the PMI, trade policy uncertainty index, economic policy uncertainty index and short-term interest rate. The relation between economic activity and uncertainty was studied using nested regression and vector autoregressive model.

The empirical results show that PMI of China and Japan were more responsive to the TPU of the USA and remained more fluctuating during the year 2018–2019. Importantly, this paper notices that the US’s PMI reached a low historically subject to its own trade policy and tension with China. Overall, TPU has shown more pronounced effects on PMI across China, Japan and the USA, followed by important economic and political events and major trade tariff uncertainty deals.

The empirical outcome holds some practical implications trade uncertainty affects not only the economic health of the economy but also market participants, global investors and international political environment, recent trade barriers, tariff wars and ambiguity raise question about free and fair global trade and competitiveness of the member country of the world trade organization.

The work is a novel that attempts to explain economic activity and supply chain through PMI. Unlike conventional economic indicators, e.g. gross domestic product, producer price index, consumer price index, employment, etc. PMI measures manufacturing industries’ overall status concerning the number of orders, inventory levels, productions, supplier deliveries and employment.

The study performs an empirical test to assess the impact of the Pakistan-China Free trade agreement (FTA) on Pakistan, China, and the World's exports under homogenous and differentiated products. This study employs the modeling with Poisson specification with Poisson Pseudo-Maximum Likelihood method for the estimations. The results of empirical test show that the effect of FTA on the FTA and Non-FTA countries is greater in the differentiated product as compared to the homogenous product. Therefore, one of the most important policy implications provided by this study is that export enterprises need to concentrate on differentiated products as compare to the homogenous products after the implementation of the Pakistan-China FTA. Moreover, the previous literature concluded that Pakistan-China FTA was more beneficial for China as compared to Pakistan. However, according to this study, if Pakistani enterprises focus more on differentiated products as compared to homogenous products, then it will be equally beneficial for both Chinese and Pakistani enterprises. This study will contribute to the literature by considering the Bertrand competition between asymmetric countries and find out the effect of the FTA on these three countries. It considers China, Pakistan, and the Rest of the World as first, second, and third countries.