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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.

This study aims to investigate the intersection of financial technology (fintech) and credit risk exploring the impact of fintech on credit risk within the banking and financial sector.

Using a bibliometric analysis approach, this study comprehensively reviews existing literature to understand the evolving landscape of fintech and credit risk. Data were extracted from the Scopus database using a comprehensive query encompassing various fintech-related keywords and their synonyms.

This study pinpoints six research streams on fintech and credit risk, spanning credit risk management, risk-sharing, credit scoring, regulatory challenges, small business lending impact and consumer credit market influence. It also examines recent advancements like artificial intelligence, blockchain and big data analytics in managing risk obligations.

While this study offers a comprehensive assessment, limitations include the ever-evolving nature of technology and potential biases in the retrieval process. Researchers should consider these factors when building on this study's findings.

The findings have practical implications for financial institutions, policymakers and researchers, offering insights into the opportunities and challenges presented by fintech in credit risk management. This study highlights potential areas for the application of advanced technologies in risk assessment and mitigation.

This study underscores the transformative impact of fintech on financial services, emphasizing the potential for more inclusive access and improved risk management. It encourages further exploration of fintech's societal implications, including its role in small business lending and consumer credit markets.

This study contributes to the existing body of knowledge by conducting a thorough bibliometric review, surpassing previous analyses in scope. It encompasses an extensive set of keywords to ensure the comprehensive retrieval of relevant papers, providing a foundation for future research in the dynamic field of fintech and credit risk.

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.

This article aims to assess risks related to the supply chain 5.0 digitalization. It aims to analyze interdependencies and causal relationships between critical digital supply chain 5.0 risks, emphasizing the need for proactive management to address emerging challenges.

Through an extensive literature review and expert judgment, risks related to supply chain 5.0 digitalization are identified. An integrated approach for risk assessment is employed, where the Analytic Hierarchy Process (AHP) is utilized to prioritize these risks. Subsequently, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to investigate cause-and-effect relationships among the identified top 10 risks. This comprehensive analysis forms the basis for informed strategic management decision-making.

The analysis identifies significant influences of “Dependence on technology,” “Complexity”, “Potential system failures”, and “Cyber security” while “Environmental impact” and “Socio-economic disparities” emerge as prominent risks in supply chain 5.0 digitalization. These findings offer actionable insights for management decision-making, guiding the formulation of strategies to address and mitigate critical risks.

The proposed integrated approach (AHP-DEMATEL) provides valuable insights for managers to effectively mitigate digital supply chain 5.0 risks and strategically respond to disruptions. By prioritizing risks, organizations can allocate resources efficiently and address the most critical challenges first, minimizing long-term damage to resilience. Embracing this approach enables practitioners to enhance overall supply chain resilience, guiding key management decisions for the development of sustainable and adaptive strategies.

This paper marks the first comprehensive attempt to assess supply chain 5.0 digitalization risks using decision-making methods like AHP and DEMATEL. The integrated approach contributes novel insights to the field of supply chain risk management, specifically aiding management decision-making in the face of digitalization challenges.

Since the 1980s, New public management has fostered the introduction of managerial approaches similar to those of the private sector in public administrations. Recently, the advantages of performing risk management in the public sector have been recognized; however, to the best of our knowledge, research on risk management in public administrations is underdeveloped, and there is a need to understand how risk management is performed. This paper addresses these issues and investigates whether and how risk management is performed in Italian public administration.

This study focused on a sample of 503 Italian municipalities and used a mixed research method. Through a qualitative content analysis of documents published on municipalities’ websites, data and information were collected and elaborated using quantitative indicators.

The main results are that a high percentage of large Italian municipalities perform risk management and comply with theoretical provisions on risk management, sometimes displaying isomorphic behavior in risk management practices.

This study provides a new perspective on risk management in Italian municipalities, contributes to filling a gap in the literature and suggests a theoretical perspective on municipalities’ approaches when introducing new managerial practices.

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.

Managers of megaprojects face social risk management challenges throughout the various design, construction, and operation stages, owing to the various conflicts of interest among stakeholders, public skepticism, and opposition. However, most existing studies have not focused on the dynamic analysis of integrating social risks in these stages. This study developed a dynamic analysis approach to explore the dynamics of critical social risk factors and related stakeholders of megaprojects and built the managerial maps for various stakeholders.

Based on the social analysis network (SNA), a dynamic network analysis approach for understanding the dynamics of social risk and related stakeholders has been developed by literature and case analysis. The approach comprises the following steps: (1) generating social risk–stakeholder networks in different stages; (2) analysis of the critical stakeholders and social risk factors; (3) dynamic analysis of social risk factors; and (4) developing social risk management maps for various stakeholders. To verify the feasibility and effectiveness of the approach, 40 megaprojects from China were analyzed.

According to the results, the local government is a critical stakeholder during all stages, inadequate information promotion (IIP) and imperfect communication and coordination mechanism (ICCM) are key social risk sources throughout the megaproject life cycle. Furthermore, the management maps for government organizations, project implementation groups, and external stakeholders were constructed.

This research has three contributions. First, a dynamic analysis approach of stakeholder-associated social risks in megaprojects is developed, which enriches the social risk management theory of megaprojects and provides inspiration for future research focus. Second, the social risk–stakeholder networks and critical social risks in different stages are confirmed to provide a more valid and accurate picture of social risk management in megaprojects. Third, the social risk managerial maps for different stakeholders built in this research will be beneficial for governments, project implementation groups, and external stakeholders to optimize management strategies.