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In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

This study aims to explore the relative efficiency of the working capital management (WCM) for Emirati firms before and during the coronavirus crisis. Next, this study explores the potential impact of WCM on the likelihood of financial distress.

A data envelopment analysis (DEA) was applied to assess the relative efficiency of the WCM. This study uses the emerging market Z-score model to predict the likelihood of financial distress. The logistic regression was applied to investigate the impact of the efficiency of WCM on firms’ financial distress.

The results of this study model showed a negative and significant influence of the efficiency of WCM on firms’ financial distress likelihood.

The findings have important implications for many stakeholders, including decision makers, WC managers, financiers, investors, financial consultants, researchers and others, in increasing their awareness of firms’ WCM performance before and during the crisis. Further, the results could have implications for trading strategies as investors seek attractive economic gains from their investment in firms that care about WCM.

The implications of WCM performance on social interests would cause firms’ decision makers to operate efficiently and achieve the best practices to minimise the probability of firms' financial distress.

This study advances a novel contribution to the literature by introducing a novel model to assess WCM based on DEA technology.