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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.

The numerous spoil grounds brought about by mega transportation infrastructure projects which can be influenced by the ecological environment. To achieve better management of spoil grounds, this paper aims to assess their comprehensive risk levels and categorize them into different categories based on ecological environmental risks.

Based on analysis of the environmental characteristics of spoil grounds, this paper first comprehensively identified the ecological environmental risk factors and developed a risk assessment index system to quantitatively describe the comprehensive risk levels. Second, this paper proposed a comprehensive model to determine the risk assessment and categorization of spoil ground group in mega projects integrating improved projection pursuit clustering (PPC) method and K-means clustering algorithm. Finally, a case study of a spoil ground group (includes 50 spoil grounds) in a mega infrastructure project in western China is presented to demonstrate and validate the proposed method.

The results show that our proposed comprehensive model can efficiently assess and categorize the spoil grounds in the group based on their comprehensive ecological environmental risk. In addition, during the process of risk assessment and categorization of spoil grounds, it is necessary to distinguish between sensitive factors and nonsensitive factors. The differences between different categories of spoil grounds can be recognized based on nonsensitive factors, and high-risk spoil grounds which need to be focused more on can be identified according to sensitive factors.

This paper develops a comprehensive model of risk assessment and categorization of a group of spoil grounds based on their ecological environmental risks, which can provide a reference for the management of spoil grounds in mega projects.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

Businesses are increasingly vulnerable and exposed to physical climate change risks, which can cascade through local, national and international supply chains. Currently, few methodologies can capture how physical risks impact businesses via the supply chains, yet outside the business literature, methodologies such as sustainability assessments can assess cascading impacts.

Adopting a scoping review framework by Arksey and O'Malley (2005) and the PRISMA extension for scoping reviews (PRISMA-ScR), this paper reviews 27 articles that assess climate risk in supply chains.

The literature on supply chain risks of climate change using quantitative techniques is limited. Our review confirms that no research adopts sustainability assessment methods to assess climate risk at a business-level.

Alongside the need to quantify physical risks to businesses is the growing awareness that climate change impacts traverse global supply chains. We review the state of the literature on methodological approaches and identify the opportunities for researchers to use sustainability assessment methods to assess climate risk in the supply chains of an individual business.

In the current economic scenario characterized by turbulence, innovation is a requisite for company's growth. The innovation activities are implemented through the realization of innovative project. This paper aims to prospect the promising opportunities coming from the application of Machine Learning (ML) algorithms to project risk management for organizational innovation, where a large amount of data supports the decision-making process within the companies and the organizations.

Moving from a structured literature review (SLR), a final sample of 42 papers has been analyzed through a descriptive, content and bibliographic analysis. Moreover, metrics for measuring the impact of the citation index approach and the CPY (Citations per year) have been defined. The descriptive and cluster analysis has been realized with VOSviewer, a tool for constructing and visualizing bibliometric networks and clusters.

Prospective future developments and forthcoming challenges of ML applications for managing risks in projects have been identified in the following research context: software development projects; construction industry projects; climate and environmental issues and Health and Safety projects. Insights about the impact of ML for improving organizational innovation through the project risks management are defined.

The study have some limitations regarding the choice of keywords and as well the database chosen for selecting the final sample. Another limitation regards the number of the analyzed papers.

The analysis demonstrated how much the use of ML techniques for project risk management is still new and has many unexplored areas, given the increasing trend in annual scientific publications. This evidence represents an opportunities for supporting the organizational innovation in companies engaged into complex projects whose risk management become strategic.

A comprehensive understanding of the determining factors and implications of the frameworks for appreciating the relationships between climate risks and cultural heritage remains deficient. To address the gap, the review analysed literature on the management of climate risk in cultural heritage. The review examines the strengths and weaknesses of climate risk management (CRM) frameworks and attendant implications for the conservation of cultural heritage.

The study adopted a two-phased systematic review procedure. In the first phase, the authors reviewed related publications published between 2017 and 2021 in Scopus and Google Scholar. Key reports published by organisations such as the United Nations Educational, Scientific and Cultural Organisation (UNESCO) and International Council on Monuments and Sites (ICOMOS) were identified and included in Phase Two to further understand approaches to CRM in cultural heritage.

Results established the changes in trend and interactions between factors influencing the adoption of CRM frameworks, including methods and tools for CRM. There is also increasing interest in adopting quantitative and qualitative methods using highly technical equipment and software to assess climate risks to cultural heritage assets. However, climate risk information is largely collected at the national and regional levels rather than at the cultural heritage asset.

The review establishes increasing implementation of CRM frameworks across national boundaries at place level using high-level technical skills and knowledge, which are rare amongst local organisations and professionals involved in cultural heritage management.

The review established the need for multi-sectoral, bottom-up and place-based approaches to improve the identification of climate risks and decision-making processes for climate change adaptation.

Power plant projects are very complex and encounter serious cost overruns worldwide. Their cost overrun risks are not independent but interrelated in many cases, having structural relationships among each other. The purpose of this study is, therefore, to establish the complex structural relationships of risks involved.

In total, 76 published articles from the previous literature are reviewed using the content analysis method. Three risk networks in different phases of power plant projects are depicted based on literature review and case studies. The possible methods of solving these risk networks are also discussed.

The study finds critical cost overrun risks and develops risk networks for the procurement, civil and mechanical works of power plant projects. It identifies potential models to assess cost overrun risks based on the developed risk networks. The literature review also revealed some research gaps in the cost overrun risk management of power plants and similar infrastructure projects.

This study will assist project risk managers to understand the potential risks and their relationships to prevent and mitigate cost overruns for future power plant projects. It will also facilitate decision-makers developing a risk management framework and controlling projects’ cost overruns.

The study presents conceptual risk networks in different phases of power plant projects for comprehending the root causes of cost overruns. A comparative discussion of the relevant models available in the literature is presented, where their potential applications, limitations and further improvement areas are discussed to solve the developed risk networks for modeling cost overrun risks.

The purpose of this study is to extend previous literature and examine risk management efficiency among Takaful (TI) and conventional insurance (CI) firms in the Kingdom of Saudi Arabia (KSA) and the United Arab Emirates (UAE). This study also aims to determine whether Takaful firms are more efficient in managing risks, compared to CI firms.

This study examines risk management efficiency among Takaful and CI firms in the KSA and the UAE for a sample of 20 insurance firms comprising 10 TI firms and 10 CI firms for the period 2018–2020. The authors use Data Envelopment Analysis to estimate efficiency scores among insurance companies to compare risk management efficiency between CI and TI companies and apply two-way analysis of variance to statistically analyze the data.

The results of this study show that TI firms have a higher efficiency score than CI firms, but not significantly and that insurance firms in KSA have higher efficiency scores than insurance firms in UAE. The results also reveal that TI firms did not significantly outperform CI firms in managing risks; however, there is a significant difference in efficiency scores among insurance firms in KSA and UAE.

The authors also contribute to the literature by providing important insights into how the operational business environment of the country can influence the risk management efficiency of CI and TI companies.

This study promotes understanding the insurance industry, its efficiency and risk management, thus offering key implications for decision-makers, regulators and managers associated with the insurance industry in UAE, KSA and other emerging insurance markets. Regulators could provide enabling policies that foster and promote the business environment, as there is a need to improve risk management efficiency in the insurance industry. Also, the results of this study show that the operating status of the UAE insurance industry in terms of efficiency and risk management is lower than that of KSA. Hence, it would be useful for UAE managers and regulators in taking steps to improve the overall insurance industry market.

The results of this study make significant contributions by providing new insights to the existing literature on the risk management efficiency in the insurance industry, as it adopts a different methodological approach that examines risk management efficiency among TI and CI companies.

This study aims to evaluate performance and map the science of research on International Organization for Standardization (ISO) 31000 standard through published articles. Specifically, this study determines the current state of the art, identifies research gaps and guides future studies related to ISO 31000.

This work investigates and examines the research papers acquired from the Scopus and Web of Science databases. Inclusion and exclusion criteria were applied to obtain relevant papers. Bibliometric analysis using Biblioshiny was conducted to answer the research objectives.

The results show growing interest in ISO 31000 research but limited interconnectivity among articles. Influential journals have emerged, highlighting key research trends in risk management's (RM) practical application and its significance in organizational decision-making. Key research areas include risk assessment (RA) methods, enterprise RM and system integration, endorsing ISO 31000 as a valuable tool. Future research should prioritize longitudinal studies to track ISO 31000's impact, study effective risk communication strategies, explore sector-specific RM practices and assess ISO 31000's application in emerging technologies.

This research reveals key themes and diverse methods that aid practitioners in customizing industry risk strategies, adapting to emerging trends, engaging global collaboration and improving risk communication. Nevertheless, the study might overlook non-English contributions, urging broader language inclusion for ISO 31000's profundity.

This paper's originality lies in its comprehensive bibliometric analysis of ISO 31000 research, providing valuable insights into the standard's growing significance and global impact. The study identifies key research themes and influential authors, guiding future research and improving RM practices.

The growing business complexity has caused many risks (e.g. operational, financial, reputational, cybersecurity, regulatory and compliance) that threaten companies' sustainability and have received attention from regulators, investors, and businesses. The authors present a model for assessing and reporting corporate risk by examining the indicators underlying corporate risk reporting.

A thorough review of the literature and semi-structured interviews with experts were conducted and the fuzzy Delphi technique was used to obtain consensus and screening of risks. The relationships between these risk indicators were recognized, weighted and prioritized by employing a hybrid Decision Making Trial and Evaluation Laboratory Model (DEMATEL) method integrated with Analytic Network Process (ANP) (DEMATEL-ANP [DANP]) approach. Finally, using the Iranian setting of corporate risk reporting, a model was developed to calculate the risk-reporting scores.

The results indicate that risk disclosure quality is more important than risk disclosures' textual properties and quantity. According to the experts, reporting the key risks that the company faces, management's approach to dealing with these risks and quantifying their impact are more important than the other indicators. The results also show that risk reporting in Iran lacks quantitative and specific information, and most risk disclosures are sticky.

The data have been prepared and analyzed according to the unique Iranian reporting environment, which should be considered when interpreting the results.

The results of this research can be used by the regulators of the Stock Exchange Organizations (SEO) to evaluate corporate risk reports and rank companies. Results are also relevant to investors and policymakers to identify companies with poor risk disclosure and to take necessary measures to improve their reporting practices.

This paper contributes to the social and governance literature by presenting the importance of risk reporting in corporate disclosures.

The unique Iranian setting of corporate risk reporting furthers the understanding of risk reporting and thus provides education, policy, practice and research implications for other emerging economies like Iran. Many prior studies focus mainly on the quality of risk disclosure, and other aspects of corporate risk disclosure presented in the study have remained largely overlooked. The corporate risk reporting attributes identified in the study are relevant to the rise of non-financial risks, the textual and qualitative nature of risk reporting and textual risk disclosures.