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Aviation has multi-cultural business environment in all aspects as operational and management. Managing aviation requires high awareness on human factor risk which includes organizational behavior-related topics. The greatest risk to an enterprise’s ability to achieve its strategic goals and objectives is the human factor. Both organizational behavior and corporate culture behavior with social psychology are the most vital aspects of management and strategy in terms of human resources. Related risks, including organizational behavior and culture, have the potential to directly impact on both business performance and corporate sustainability. Therefore, in this paper, the most prominent risks were determined in accordance with social psychology, and after identification of human factor-based risks, these have prioritized and prepared risk mapping with fresh approach. For this reason, this study aims to develop risk mapping model for human factors that takes into account interrelations among risk factors three dimensional based new approach. This approach includes both identification of human factor based risks, prioritization them and setting risk mapping according to corporate based qualifications via tailoring risk list. Developed risk map in this paper will help to manage corporate risks to achieve improved performance and sustainability.

This new organizational behavior- and culture-focused risk mapping model developed in this study has the potential to make significant contribution to the management of the human factor for modern management and strategy. In enterprise risk management system, risk mapping is both strong and effective strategic methodology to manage ergonomics issue with strategic approach. Human factor is both determinative and also strategic element to both continuity and performance of business operations with safely and sound. In view of management and strategy, vitally, the human factor determines the outcome in both every business and every decision-making.

It is assumed that, if managers manage human risk you may get advantages to achieving corporate strategies in timely manner. Aviation is sensitive sector for its ingredients: airports, airlines, air traffic management, aircraft maintenance, pilotage and ground handling. Aim of this paper is to present risk management approach to optimize human performance while minimizing both failures and errors by aircraft maintenance technician (AMT). This model may apply all human factors in other departments of aviation such as pilots and traffic controllers. AMT is key component of aircraft maintenance. Thus, errors made by AMTs will cause aircraft accidents or incidents or near miss incidents. In this study, new taxonomy model for human risk factors in aircraft maintenance organizations has been designed, and also new qualitative risk assessment as three dimensions is carried out by considering the factors affecting the AMT’s error obtained from extensive literature review and expert opinions in the field of aviation. Human error risks are first categorized into two main groups and sub three groups and then prioritized using the risk matrix via triple dimension as probability, severity and interrelations ratio between risks.

Risk mapping is established to decide which risk management option they will apply for managers when they will look at this map. Managers may use risk map to both identify their managerial priorities and share sources to managing risks, and make decisions on risk handling options. This new model may be a useful new tool to manage ergonomic human factor-based risks in developing strategy in aviation business management. In addition, this paper will contribute to department of management and strategy and related literature.

This study has originality via new modeling of risk matrix. In this study, dimension of risk analysis has been improved as three dimensions. This study has new approach and new assessment of risk with likelihood (probability), impact (severity) and interrelations ratio. This new model may be a useful new tool to both assess and prioritize mapping of ergonomic-based risks in business management. In addition, this research will contribute to aviation management and strategy literature and also enterprise risk management literature.

Consumers increasingly rely on organisations for online services and data storage while these same institutions seek to digitise the information assets they hold to create economic value. Cybersecurity failures arising from malicious or accidental actions can lead to significant reputational and financial loss which organisations must guard against. Despite having some critical weaknesses, qualitative cybersecurity risk analysis is widely used in developing cybersecurity plans. This research explores these weaknesses, considers how quantitative methods might address the constraints and seeks the insights and recommendations of leading cybersecurity practitioners on the use of qualitative and quantitative cyber risk assessment methods.

The study is based upon a literature review and thematic analysis of in-depth qualitative interviews with 16 senior cybersecurity practitioners representing financial services and advisory companies from across the world.

While most organisations continue to rely on qualitative methods for cybersecurity risk assessment, some are also actively using quantitative approaches to enhance their cybersecurity planning efforts. The primary recommendation of this paper is that organisations should adopt both a qualitative and quantitative cyber risk assessment approach.

This work provides the first insight into how senior practitioners are using and combining qualitative and quantitative cybersecurity risk assessment, and highlights the need for in-depth comparisons of these two different approaches.

The purpose of this paper is to construct a two-stage risk management framework for international construction projects based on the meta-network analysis (MNA) approach. A plethora of international construction studies seems to assume risks as independent and therefore, risk intervention strategies are usually critiqued as ineffective.

In the risk assessment stage, a multi-tiered risk network structure was developed with the project objectives, risk events, risk factors and stakeholders, and critical risk factors were selected based on a series of calculations. In the risk intervention stage, targeted risk intervention strategies were proposed for stakeholders based on the results of the first stage. A highway construction project in Eastern Europe was selected as a case study.

The results showed that 17 risk factors in three categories – external, stakeholder-related and internal – are critical, and the project manager, construction management department, supplier and contract department are the most critical stakeholders that affect the entire project performance. Based on the critical risk factors and project stakeholders, targeted risk intervention strategies were proposed. The risk assessment results of MNA were found to be more reliable and consistent with the project conditions than the risk matrix method; the risk intervention strategies of MNA can effectively address project objectives.

This study modeled risk priorities based on risk associations and put forward a new method for risk management, supplementing the body of knowledge of international construction. The results of this study are of critical importance in management practices.

This paper aims to develop a process for prioritizing project risks that integrates the decision-maker's risk attitude, uncertainty about risks both in terms of the associated probability and impact ratings, and correlations across risk assessments.

This paper adopts a Monte Carlo Simulation-based approach to capture the uncertainty associated with project risks. Risks are prioritized based on their relative expected utility values. The proposed process is operationalized through a real application in the construction industry.

The proposed process helped in identifying low-probability, high-impact risks that were overlooked in the conventional risk matrix-based prioritization scheme. While considering the expected risk exposure of individual risks, none of the risks were located in the high-risk exposure zone; however, the proposed Monte Carlo Simulation-based approach revealed risks with a high probability of occurrence in the high-risk exposure zone. Using the expected utility-based approach alone in prioritizing risks may lead to ignoring few critical risks, which can only be captured through a rigorous simulation-based approach.

Monte Carlo Simulation has been used to aggregate the risk matrix-based data and disaggregate and map the resulting risk profiles with underlying distributions. The proposed process supported risk prioritization based on the decision-maker's risk attitude and identified low-probability, high-impact risks and high-probability, high-impact risks.

Traditional risk assessment (RA) methodologies cannot model vagueness in risk and cannot prioritize corrective-preventive measures (CPMs) by considering effectiveness of those on risk types (RTs). These cannot combine and reflect accurately different subjective opinions and cannot be used in a linguistic manner. Risk factors (RFs) are assumed to have the same importance and interrelations between RFs are not considered. This study aims to overcome these disadvantages by combining fuzzy logic with multi-criteria decision-making in a dynamic manner.

This study proposes a novel three-stage fuzzy risk matrix-based RA integrating fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) and fuzzy multi-attributive border approximation area comparison (F-MABAC). At the first stage, importance weights of RFs are computed by F-DEMATEL. At the second stage, risk degrees of RTs are computed via using fuzzy risk matrix. At the third stage, CPMs are ranked by F-MABAC. Finally, a numerical example for RA in a warehouse is given.

Results show that developing instructions for material loading or unloading is the most important CPM and severity is the most important RF for the warehouse.

This study has originality in terms of having fuzzy dynamic structure. At first, RFs are assumed to be criteria sets then, RTs are assumed to be criteria set considering their risk degrees to rank CPMs in a fuzzy manner. Risk degrees of RTs are used for weights of RTs and effectiveness of CPMs are used for performance values of CPMs.

The purpose of this paper is to improve risk assessment processes in airline flight operations by introducing a dynamic risk assessment method.

Fuzzy logic and Bayesian network are used together to form a dynamic structure in the analysis. One of the most challenging factors of the analyses in aviation is to get quantitative data. In this study, the fuzzy data quantification technique is used to perform dynamic risk assessment. Dynamic structure in the analysis is obtained by transforming the bow-tie model into a Bayesian network equivalent.

In this study, the probability of top-event from fault tree analysis is calculated as 1.51 × 10⁻⁶. Effectiveness of the model is measured by comparing the analysis with the safety performance indicator data that reflects past performance of the airlines. If two data are compared with each other, they are at the same order of value, with small difference (0.6 × 10⁻⁷).

This study proposes a dynamic model to be used in risk assessment processes in airline flight operations. A dynamic model for safety analysis provides real-time, autonomous and faster risk assessment. Moreover, it can help in the decision-making process and reduce airline response time to undesired states, which means that the proposed model can contribute to the efficiency of the risk management process in airline flight operations.

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 purpose of this paper is to present a comprehensive framework for quality-related performance measures linked to supply chain risk (SCR) by analyzing and framing them into a hierarchical structure.

In this paper, quality-related performance measures (QM) are identified on the basis of literature survey and expert opinion. The quality measures are formulated as hierarchy structure and fuzzy AHP as a multi attribute decision-making tool is applied to judge the viable candidates.

Based on a fuzzy AHP approach, a revised risk matrix with a continuous scale was proposed to assess the QMs’ classes. The result classifies the QMs in different categories (extreme, high, medium and low). Based on this result, some management implications and suggestions are proposed.

The present work proposes an assessment methodology for quality-related performance measures linked to SCR. The revised risk matrix with continuous scale for risk assessment in this field is a novel approach. This study contributes to the supply chain management and quality management literature, and provides suggestions for managers to adopt different strategies for different risk classes.

The purpose of this paper is to review the methods for assessing food safety risk within a food safety plan.

The research involved analysis of both qualitative and quantitative methods of risk assessment.

Risk assessment is a key element of the HACCP approach to food safety. It requires food business operators and those on HACCP teams to determine both the acceptable level of contamination and the risk for the food business, and ultimately the consumer. The choice of food safety risk assessment model is crucial to an organisation. The mechanisms to determine what is acceptable can be a combination of scientific based and value based criteria and utilise qualitative or semi‐quantitative approaches. Whilst fuzzy logic has a place in making risk assessment more quantitative; specific software tools are required to enable quantitative risk assessment especially where what is acceptable at one point could, subject to other factors later in the supply chain, change to an unacceptable level of risk to the consumer. Quantitative mechanisms are required to make these decisions at organisational, or indeed at policy level, fully transparent.

This research is of academic value and of value to policy makers and practitioners in the food supply chain.

In the evolution process of building construction accidents, there are key nodes of risk change. This paper aims to quickly identify the key nodes and quantitatively assess the node risk. Furthermore, it is essential to propose risk accumulation assessment method of building construction.

Authors analyzed 419 accidents investigation reports on building construction. In total, 39 risk factors were identified by accidents analysis. These risk factors were combined with 245 risk evolution chains. Based on those, Gephi software was used to draw the risk evolution network model for building construction. Topological parameters were applied to interpret the risk evolution network characteristic.

Combining complex network with risk matrix, the standard of quantitative classification of node risk level is formulated. After quantitative analysis of node risk, 7 items of medium-risk node, 3 items of high-risk node and 2 items of higher-risk nodes are determined. The application results show that the system risk of the project is 44.67%, which is the high risk level. It can reflect the actual safety conditions of the project in a more comprehensive way.

This paper determined the level of node risk only using the node degree and risk matrix. In future research, more node topological parameters that could be applied to node risk, such as clustering coefficients, mesoscopic numbers, centrality, PageRank, etc.

This article can quantitatively assess the risk accumulation of building construction. It would help safety managers could clarify the system risk status. Moreover, it also contributes to reveal the correspondence between risk accumulation and accident evolution.

This study comprehensively considers the likelihood, consequences and correlation to assess node risk. Based on this, single-node risk and system risk assessment methods of building construction systems were proposed. It provided a promising method and idea for the risk accumulation assessment method of building construction. Moreover, evolution process of node risk is explained from the perspective of risk accumulation.