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The present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.

The cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.

The findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.

This research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.

This research aims to examine the moderating role of the existence of risk management committee between risk-taking behavior and companies’ performance.

Research sample includes 383 manufacturing company-year that listed on the Indonesian Stock Exchange period of 2017–2020. The risk-taking behavior includes the use of leverage, capital intensity, research and development intensity, and earnings uncertainty. The hypothesis test uses company fixed-effect regression.

The result shows that risk management committee moderates the effect of risk-taking behavior on companies’ performance. This research also finds the similar result when risk management committee and risk-taking behavior are examined on the future performance. In the further analysis, the result also finds that the expertise of risk management committee moderates the effect of risk-taking behavior on companies’ performance.

This research contributes to fill the previous gap of risk-taking behavior and companies’ performance by considering the existence of risk management committee to promote oversight role on risk-taking behavior. This research also contributes to give new evidence in Indonesia about the role of risk management committee to improve the benefits or to reduce the costs of risk-taking behavior.

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

This study seeks to build a financial uncertainty index for Saudi Arabia. This index serves as a leading indicator of Saudi economic activity and helps to describe economic fluctuations and forecast economic trends.

This study adopts an extension of the Jurado et al. (2015) procedure by combining financial uncertainty factors with their net spillover effects on GDP and inflation to construct an aggregate financial uncertainty index. The authors consider 13 monthly financial variables for Saudi Arabia from January 2010 to June 2021.

The empirical results show that the constructed financial uncertainty estimates are good leading indicators of economic activity. The robustness analysis suggests that the authors’ proposed financial uncertainty estimators outperform the alternative estimates used by other existing approaches to estimate the financial conditions index.

To the best of the authors’ knowledge, this is the first attempt at constructing a financial uncertainty index for Saudi Arabia. This study extends the empirical literature, from which the authors propose a novel conceptual framework for building a financial uncertainty index by combining the approach of Jurado et al. (2015) and the time-varying connectedness network approach proposed by Antonakakis et al. (2020)

The purpose of this study is to explore how the unprecedented rise in the economic policy uncertainty influence Indian banking sector stability. The unprecedented rise in the economic policy uncertainty during the recent pandemic has garnered the attention of policymakers to investigate its consequences on different sectors of the economy.

In this quest, the present study uses system generalized method of moments and other econometric tools to examine the influence of economic policy uncertainty on the Indian banking sector, covering the time frame from 2000 to 2022. In addition, the current study also investigates the mediating role of regulation and supervision in the nexus of economic policy uncertainty and the Indian banking sector stability.

The empirical outcome reveals that economic policy uncertainty negatively influences banking stability. However, when economic policy uncertainty interacts with stringent banking regulations, private monitoring and supervisions, it assists in diversifying the negative impact of economic policy uncertainty on the Indian banking sector stability.

To the best of the author’s knowledge, the study is an original work and provides robust estimates that will assist policymakers in understanding the influence of policy uncertainty on the banking stability. Moreover, the study also helps in understanding the role of supervision and regulation in mitigating the negative consequences of policy uncertainty on the banking stability.

This paper attempts to document the effect of economic uncertainty on financing constraints faced by private firms.

Ordered logistic regression is used to analyze the data of private firms from 101 developing countries. The data was provided by the World Bank's Enterprise Surveys and was gathered during the period between 2006 and 2019.

The findings show that firms headquartered in countries with high economic uncertainty face more financing constraints than firms headquartered in countries with low economic uncertainty. The authors argue that the increase in economic uncertainty allows capital providers to adjust their lending decisions by reducing the provision of capital to firms. The paper also shows that firms headquartered in countries with strong institutional infrastructure and well-functioning firms are less likely to be affected by economic uncertainty while accessing finance.

The findings would help managers, investors, regulators, and policymakers better understand the implication of economic policy uncertainty on the real economy. This study also sheds the light on the importance of minimizing volatility, ambiguity, and randomness in governmental decisions and policies. Regardless of the pertinence of these policies, arbitrariness surrounding their development and communication can limit their effectiveness and produce unwanted effects.

This paper is closely related to prior literature that documents the behavior of credit providers and investors (the supply side) during the periods of economic uncertainty. The authors differ from this strand of literature by taking the perspective of firms – the demand side.

Exploiting novel measures of climate change exposure and corporate culture generated by a powerful textual analysis of earnings conference calls, this study aims to explore the effect of firm-specific climate change exposure on corporate innovation through the lens of corporate culture.

The authors apply the standard regression analysis as well as a variety of sophisticated techniques, namely, propensity score matching, entropy balancing and an instrumental-variable analysis with multiple alternative instruments.

The authors find that more exposure to climate change risk results in more innovation, as indicated by a significantly stronger culture of innovation. The findings are consistent with the notion that firms more exposed to climate change risk are pressed to be more innovative to adapt to the numerous changes caused by climate change. Finally, the authors also find that the effect of firm-level exposure on innovation is considerably less pronounced during uncertain times.

The authors are among the first studies to take advantage of a novel measure of firm-specific exposure to climate change and investigate how climate change exposure influences an innovative culture. Since climate change is a timely issue, the findings offer important implication to several stakeholders, such as shareholders, executives and investors in general.

This study aims to investigate the effect of political turmoil on the firm financial performance, particularly in presence of politically affiliated board of directors.

The study applied panel regression analyses on a data set of Pakistan’s listed companies ranged over 14 years, spanning from 2007 to 2021. Political turmoil was first gauged through three determinants, i.e. political protest, government election and constitutional reform, and thereafter, economic uncertainty index was used as a proxy for political turmoil. For the purpose of political connection, the study used political affiliation of the board of directors.

The study finds that political turmoil has deleterious effect on the return on assets and Tobin’s Q. The study further unveils that politically affiliated firms are relatively insulated from the volatility posed by the political uncertainty and exhibit significantly better financial outcomes.

Findings of the study suggest that appropriate composition of the board is imperative in offsetting the risk posed by the political turmoil. Hence, the results are useful for investors, policymakers and regulators to ensure financial soundness of firms in the wake of political turmoil.

To the best of the authors’ knowledge, this is the first study that investigates the moderating impact of political connection on the performance of companies in presence of political turmoil.

The primary aim of this study is to evaluate the impacts of institutional quality (IQ) and economic policy uncertainty (EPU) on bank risk-taking behavior, especially after the global financial crisis of 2007–2008.

After considering the outlier effect, missing figure and inconsistent data, the study’s final sample contains 24,364 firm-year observations of 4,367 banks. A total of 27 countries were considered as those data are available on the “EPU index” introduced by Baker et al. (2016) for 2011–2020. To estimate the core results, the dynamic panel generalized method of moments (GMM) has been used to examine the effects of IQ and EPU on bank risk-taking behavior. Later, this study also validates the core results by using two-stage least squares (2SLS).

The authors found a positive relationship between EPU and banks' risk-taking behavior of banks', but imperatively, a significant and negative relationship exists between IQ and bank risk-taking behavior. This study also has a remarkable and distinct findings from Uddin et al. (2020) one of the vital indicators of IQ quality measurement “voice and accountability” (VACC) impacted negatively on bank risk-taking behavior. It indicates that when VACC is well established, banks tend to take the low risk under the prevailing EPU conditions and vice-versa. Moreover, the lagged dependent variable significantly impacted the bank's risk-taking negatively.

To the best of the authors' knowledge, very few studies endeavored to investigate the dominance or impact level of IQ and EPU on the area, i.e. bank risk-taking behavior which inspired us to contribute to the banking literature to address this issue in a broader aspect – the connection between EPU and bank risk-taking behavior, also a relationship between IQ and bank risk-taking behavior and finally linking them with bank risk-taking behavior.

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.

To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.

This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.

The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.

A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.

The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.