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This study aimed at exploring the differential effects of different corporate governance (CG) indicators on risk management practices in Islamic financial institutions (IFIs) and conventional financial institutions (CFIs) of Pakistan. It also investigated the moderating role of institutional quality (IQ) in shaping the effects of CG practices on financial institutions of Pakistan.

A sample of 57 financial institutions including commercial banks, insurance companies and Modarba companies over the period 2006–2017 is used to carry out the empirical analysis. The authors applied the robust two-step system-generalized method of moments estimator, which is also called the dynamic panel data estimator. They also built the PCA-based composite index of CG and IQ by using different indicators to investigate the moderating role of IQ. They used three proxies for risk taking, five for CG and one for Shari’ah governance. To test the validity of the instruments, they applied the Arellano and Bond’s (1991) AR (1) and AR (2) tests and the J-statistic of Hansen (1982).

The results provided strong evidence that several individual characteristics of CG and the composite index are significantly related to the operational risk, the liquidity risk and the Z-score (a proxy for solvency risk). The results also revealed that IQ significantly and substantially contributes in reducing the level of risks. Finally, the estimation results indicated that the effects of CG on risk management are significantly different at IFIs and CFIs. This differential impact is mainly attributed to the fundamental differences in business models, operational strategies and contractual obligations of both types of institutions.

The findings of this study are important for enhancing our understanding of how CG relates to risk taking in Islamic and conventional financial services industries and how good quality institutions are important for formulating the governance effects on the risk-taking behavior of financial institutions. The findings suggest that a suitable size of board should be chosen to manage the risk effectively. As the findings show that the risk-taking behavior of IFIs differs from that of CFIs, the regulators and international standard setting bodies should tailor the regulatory frameworks accordingly.

This paper is different from the existing studies in four aspects. First, to the best of the authors’ knowledge, this is the first empirical investigation in Pakistan, which does the comparison of IFIs and CFIs while examining the impacts of CG on risk management. Second, the paper constructs the composite index of CG by considering several different indicators of governance and examines the combined effect of governance indicators on risk management process. Third, this paper adds to the growing literature on the role of IQ by investigating whether it acts as a moderator between CG structures and risk management and if yes, then whether this moderating role is different for IFIs and CFIs. Finally, the paper builds upon the existing research work on the CG effects for different types of financial institutions by proposing a single regression based analytical framework for comparing the effects across two different types of institutions, harvesting the benefits of higher degrees of freedom and avoiding/minimizing the measurement error.

This study aimed to propose a performance-oriented approach of automatically generative design and optimization of hospital building layouts in consideration of public health emergency, which intended to conduct reasonable layout design of hospital building to meet different performance requirements for both high efficiency during normal periods and low risk in the pandemic.

The research design follows a sequential mixed methodology. First, key points and parameters of hospital building layout design (HBLD) are analyzed. Then, to meet the requirements of high efficiency and low risk, adjacent preference score and infection risk coefficient are constructed as constraints. On this basis, automatic generative design is conducted to generate building layout schemes. Finally, multi-objective deviation analysis is carried out to obtain the optimal scheme of hospital building layouts.

Automatic generative design of building layouts that integrates adjacent preferences and infection risks enables hospitals to achieve rapid transitions between normal (high efficiency) and pandemic (low risk) periods, which can effectively respond to public health emergencies. The proposed approach has been verified in an actual project, which can help systematically explore the solution for better decision-making.

The form of building layouts is limited to rectangles, and future work can explore conducting irregular layouts into optimization for the framework of generative design.

The contribution of this paper is the developed approach that can quickly and effectively generate more hospital layout alternatives satisfying high operational efficiency and low infection risk by formulating space design rules, which is of great significance in response to public health emergency.

This study aims to investigate the effect of the diversity of the board of directors (BOD) and the shariah supervisory board (SSB) on credit risk, insolvency, operations, reputation, rate of deposit return risk (RDRR) and equity-based financing risk (EBFR) of Islamic banks (IB).

The study uses 68 IBs from 19 countries covering 2009 to 2019. BOD and SSB diversity attributes data were hand-collected from the annual reports. Financial data were collected from the bankscope database. The robustness test and two-step system generalized method of moment estimation technique were used to address potential endogeneity issues.

This study provides evidence that diversity in the experience and cross-membership of board members decreases the risk. Gender diversity increases the risk, but the BOD’s education level diversity has no relationship with risk. More interestingly, influences in the experience and cross-membership of the SSB’s members positively influence risk. However, members’ education levels and gender diversity have not been proven to affect risk.

The paper recommends that Islamic banking authorities play a stronger role and make a greater effort in driving corporate governance reform. Also, determining individual characteristics of the board is a requirement to become a member of a BOD or an SSB.

This paper expands the commitment literature through the diversity of the BOD’s and the SSB’s members in terms of their education levels, experience, cross-membership and gender. This study expands the list of potential risks for IBs, by including the RDRR and EBFR.

This study aims to examine the impact of intellectual capital (IC) on default risk in Indian companies listed on the National Stock Exchange.

This study applies panel data regression analysis to derive a relationship between IC and default risk for the sample period 2013–2022. The value-added intellectual coefficient (VAIC) of Pulic (2000) has been applied to measure IC performance, and default risk is estimated using the revised Z-score model of Altman (2000).

The results revealed a positive association between Z-score and VAIC. It implies that a higher value of VAIC improves financial stability and leads to a lower likelihood of default. The findings further suggest that new default forecasting models can be experimented with IC indicators for better default prediction.

The findings can have implications for investors and banks. This paper provides evidence of IC performance in improving the financial solvency of firms. Investors and financial institutions should invest their resources in a healthy firm that effectively manages and invests in their IC. It will eventually award investors and creditors high returns through efficient value-creation processes.

This study provides evidence of IC performance in improving the financial solvency of Indian high-defaulting firms, which lacks sufficient evidence in this domain of research. Numerous studies exist examining the relationship between firm performance and IC value, but this area is inadequately focused and underresearched. This study, therefore, fills the research gap from an Indian perspective.

Expanding on the real-world financial market framework and considering the current market turmoil, with cryptocurrencies (where contracts for difference (CFDs) are extremely common) (Hasso et al., 2019) displaying unprecedented volatility, the authors aim to test in an online laboratory setting whether displaying a risk warning message is truly effective in reducing the level of risk taken and whether the placement of this method makes a difference.

To explore the impact of risk disclosure framing on risk-taking behavior, the authors conducted an online pair-wise lottery choice experiment. In addition to manipulating risk awareness through the presence or absence of risk warning messages of varying intensity, the authors also considered dynamic inconsistency, cognitive ability and questionnaire-based financial risk tolerance (FRT) scores. The authors aimed to identify potential relationships between these variables and experimentally elicited risk aversion. The authors' study offers valuable insights into the complex nature of risky decision-making and sheds light on the importance of considering dynamic inconsistency in addition to risk awareness and aversion.

The authors' results provide statistical evidence for the efficacy of informative and very salient messages in mitigating risky decision, hinting at several policy implications. The authors also provide some statistical evidence in support of the relationship between cognitive abilities and risk preferences. The authors detect that individual with low cognitive abilities scores display great risk aversion.

This study investigates the impact of risk warning messages on investment decisions in an online laboratory setting – a unique approach. However, the authors go beyond this and also examine the potential influence of dynamic inconsistency on decision-making, adding further value to the literature on this topic. To ensure a comprehensive understanding of the participants, the authors collect data on cognitive ability and FRT using questionnaires. This study provides a simple and cost-effective framework that can be easily replicated in future research – a valuable contribution to the field.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

This study aims to assess the association of Healthy Eating Index (HEI) with levels of fasting blood sugar (FBS) and lipid profile in normoglycemic and elevated FBS patients.

This case-control study was conducted on 144 participants, namely, 72 normoglycemic subjects (FBS < 100 mg/dl) and 72 high-glycemic patients (FBS ≥ 100 mg/dl) aged 20–60 years of age, who were selected from the nutrition and diet clinics in Tehran city. The dietary intake was collected by using a validated food frequency questionnaire to determine the HEI score.

The mean±SD age and body mass index of participants were 47.1 ± 12.7 years and 29.6 ± 6.0 kg/m², respectively. The median (interquartile range) of HEI scores in the normoglycemic group and the high-glycemia group were 19.34 (15.24–24.31) and 16.53 (13.35–24.07), respectively. In the overall population, the findings of the multi-variable linear regression model indicated a positive association between the HEI score and high-density lipoprotein-cholesterol (HDL-C) (ß = 0.34; 95%CI: 0.05–0.64, P = 0.01). However, there is no significant association between HEI and HDL-C in normoglycemic (ß = 0.19; 95%CI: −0.31, 0.69, P = 0.45) and hyperglycemic subjects (ß = 0.28; 95%CI: −0.10–0.66, P = 0.15). Furthermore, the association of HEI with levels of FBS, triglycerides (TGs) and low-density lipoprotein-cholesterol (LDL-C) was not significant in any of the analyzed groups, including the total population, normoglycemic individuals and hyperglycemic subjects.

This study was the first study to assess the role of HEI and its components with levels of FBS and lipid profile in normoglycemic and hyperglycemic individuals in Iran. The findings suggested that higher adherence to HEI may be associated with an increase in the HDL-C level. However, HEI could not predict FBS, TGs and LDL-C levels in the adult population.

This study aims to identify the regions with a high risk of natural disaster, estimate the proportion of households potentially participating in natural disaster insurance and analyze the relationship between disaster risk index and proportion of household potentially participating in natural disaster insurance.

Descriptive and quadrant analysis was applied on the 2019 Indonesia Disaster Risk Index (IRBI) scores and the 2019 National Socio-Economic Survey data.

The results showed there are only two categories of disaster risks in Indonesia based on IRBI categorization: “Medium” and “High.” Some districts in Aceh Province such as Simeuleu, Pidie Jaya and Banda Aceh City were observed to have a high proportion of households potentially participating in the natural disaster insurance while some in Jawa Tengah provinces have fairly low level even though they were categorized as high disaster-prone areas. Moreover, the quadrant analysis showed that 43 districts have high IRBI scores but low insurance participation rates with most discovered to be in Jawa Barat and Sumatera Selatan provinces.

Indonesia does not have a financial mitigation program up to the present time because almost all disaster resolutions are formulated based on emergency funds from the state budget even though it is important to use insurance schemes in all stages of disaster management. To the best of the authors’ knowledge, this study is the first attempt to identify households potentially participating in natural disaster insurance through the National Socio-Economic Survey in Indonesia.

The novel coronavirus (COVID-19) has caused financial stress and limited their lending agility, resulting in more non-performing loans (NPLs) and lower performance during the II wave of the coronavirus crisis. Therefore, it is essential to identify the risky factors influencing the financial performance of Indian banks spanning 2018–2022.

Our sample consists of a balanced panel dataset of 75 scheduled commercial banks from three different ownership groups, including public, private and foreign banks, that were actively engaged in their operations during 2018–2022. Factor identification is performed via a fixed-effects model (FEM) that solves the issue of heterogeneity across different with banks over time. Additionally, to ensure the robustness of our findings, we also identify the risky drivers of the financial performance of Indian banks using an alternative measure, the pooled ordinary least squares (OLS) model.

Empirical evidence indicates that default risk, solvency risk and COVAR reduce financial performance in India. However, high liquidity, Z-score and the COVID-19 crisis enhance the financial performance of Indian banks. Unsystematic risk and systemic risk factors play an important role in determining the prognosis of COVID-19. The study supports the “bad-management,” “moral hazard” and “tail risk spillover of a single bank to the system” hypotheses. Public sector banks (PSBs) have considerable potential to achieve financial performance while controlling unsystematic risk and exogenous shocks relative to their peer group. Finally, robustness check estimates confirm the coefficients of the main model.

This study contributes to the knowledge in the banking literature by identifying risk factors that may affect financial performance during a crisis nexus and providing information about preventive measures. These insights are valuable to bankers, academics, managers and regulators for policy formulation. The findings of this paper provide important insights by considering all the risk factors that may be responsible for reducing the probability of financial performance in the banking system of an emerging market economy.

The empirical analysis has been done with a fresh perspective to consider unsystematic risk, systemic risk and exogenous risk (COVID-19) with the financial performance of Indian banks. Furthermore, none of the existing banking literature explicitly explores the drivers of the I and II waves of COVID-19 while considering COVID-19 as a dependent variable. Therefore, the aim of the present study is to make efforts in this direction.

In the event of a disaster, educational institutions like schools serve as lifeline buildings. Hence, it is crucial to safeguard these buildings for the communities that may depend on the school as a disaster shelter and aid center. Thus, this paper aims to conduct a multihazard risk assessment survey at 50 schools (with 246 building blocks) in Dehradun.

The past few decades have witnessed the impact of multihazard frequency in Uttarakhand, India, due to the geographical features of the Himalayas and its neo-tectonic mountain-building process. Dehradun is the capital of Uttarakhand state and comes under seismic zone IV, which is highly prone to earthquakes.

The hazard assessment is divided into two types of surveys: first, building-level surveys that include rapid visual screening, nonstructural risk assessment and fire safety audit, and second, campus-level surveys that include vulnerability analysis for earthquake, flood, industrial hazard, landslide and wind.

This paper will list several gaps and unrecognized practices in the region that increase the schools’ multihazard risk. The study’s outcome will help prioritize the planning of disaster awareness, retrofitting execution, future construction practices and decision-making to minimize the risk and prepare the school for the upcoming disasters.

Physical data were collected by the author to determine the multihazard risk analysis in 50 schools in the Dehradun District of Uttarakhand, India. The building- and campus-level surveys have been used to generate a database for the retrofit and renovation process for each individual school to use their budget fruitfully and in a planned way. The survey conducted is more effort and a more detailed risk evaluation which necessitates effectively mitigating and ensuring the potential safety of the region’s schools.