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This study aims to develop a software risk prioritization model using analytic hierarchy process (AHP) and further validate the usability attributes of the model in prioritizing operational, technical, technological, strategic and environmental software risks.

Questionnaire was used to collect data from software practitioners to evaluate the usability attributes of the AHP-software risk prioritization model. Accordingly, partial least square-structural equation modeling was used to analyze the collected data.

Results reveal that the developed AHP-software risk prioritization model is efficient and effective in facilitating software risk factor prioritization. In addition, results suggest that the experts are satisfied with the learnability, accessibility and navigation capability of the model. Besides, results indicate that the model provides a useable interface and system design for content availability of information needed by software practitioners in evaluating and prioritizing operational, technical, technological, strategic and environmental risk. Furthermore, results show that the experts intend to adopt the model to prioritize identified software risk in their firm.

Methodologically, the developed AHP-software risk prioritization model is faced with issues such as inconsistency in judgments, weakness of confronting ambiguities and uncertainties of high complexity. Empirically, data were collected from software practitioners in Malaysia to validate the AHP-software risk prioritization model. Hence, results from this study cannot be generalized to other software practitioners in different countries.

This study developed a software risk prioritization model to evaluate and prioritize software risks that occur in software organizations by deploying AHP to carryout risk factor priority selection. Moreover, the model provides risk knowledge as guidelines for evaluating software risks in software organizations.

The developed AHP-software risk prioritization model computes risk prioritization factor priority selection and further supports software practitioners and evaluates risks and associated risk factors. Besides, this study develops an instrument that can be used in project risk management to validate the usability attributes of software risk approaches.

This research designs use case and class diagram to show how the AHP-software risk prioritization model evaluates and prioritizes software risks factors by using risk evaluation questions. Additionally, the AHP-software risk prioritization model computes, evaluates and prioritizes software risk factors using risk factor priority selection for software project management.

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 research is so that project managers can use a systemic approach to prioritizing the risks of outsourcing including an understanding of the external factors that could affect the prioritization.

A survey was designed by the authors and distributed to 2,500 outsourcing professionals from different organizations and the hypotheses were tested using the data from these surveys. The logic for forming the hypothesis is discussed for each hypothesis and so are the demographics of the respondents

External factors such as the type of technology involved, type of market targeted, location of outsourcing and the amount of experience the outsourcing professional had, did affect the prioritization of the outsourcing risks. Furthermore, trends were identified among the ranking of the outsourcing risks.

One of the constraining factors of this research, as in the majority of empirical research initiatives, was the limited sample size, which could potentially affect the rating. An increased sample size could have also provided the researchers with a more detailed insight into the interrelationships between the various outsourcing risks.

For the first time, the outsourcing risks have been prioritized using a systemic approach. The systemic approach has been used in the financial industry while analyzing risk but the authors have applied it to prioritization of outsourcing risks. This includes understanding the interrelationships between the risks and also the effect that external factors can have on the prioritization of those risks.

Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

This study aims to develop a risk prioritization process using failure mode and effect analysis (FMEA) in association with composition of probabilistic preferences (CPP) and weighting the risk analysis criteria. It seeks to develop decision-making considering the fast response necessary to achieve project objectives in complex scenarios, such as the pandemic of COrona VIrus Disease 19 (COVID-19).

After identifying the risks, the prioritization process was applied to a project in the oil and gas area, in which a focus group assessed these risks. This evaluation took place employing traditional FMEA, FMEA with CPP by axes considering four points of view and FMEA with CPP by weighted sum with the use of a multicriteria method to weight the criteria. These approaches were compared to understand their differences and benefits, with a flow chart being developed, consolidating the procedure.

The methodologies that showed the greatest benefits were FMEA with CPP by axes PO (progressive-optimistic) and by weighted sum. Essentially, this was mainly related to the interrelationship between risks and to the importance of prioritization.

This procedure can consider company's views on what is critical and the interrelationship between risks. It provides a clear segmentation of what should and should not be prioritized. It was also developed in a practical case, showing a possible alternative to support fast responses in decision-making.

International projects very commonly experience failure due to various factors at the global level. Especially, large projects at the international level virtually have no chance of meeting scope, time, cost and quality. This fact has been underlined by most of the international surveys and published literature. Effective risk management plays a vital role in preventing projects from failure by implementing appropriate risk response strategies. The success of risk management will be based on the understanding of various risk categories which specifically affect international projects, analysis of their interdependence, prioritize them according to their importance and develop strategies for risk management based on the prioritization. The paper aims to discuss these issues.

This paper represents typical eight risk categories frequently observed in the international projects through literature survey and feedback from project professionals. Interpretive structural modeling (ISM) and Matrice d’Impacts croises-multiplication applique´ an classment (MICMAC) analysis have been used to analyze the interactions among the risk categories and prioritize them. The strategy management tool threats, opportunities, weaknesses and strengths (TOWS) matrix has been used to develop the strategies for effective project risk management.

The analysis represents political risks, contractual and legal risks, cultural risks, and financial and economic risks as the highest priority risk categories, the mitigation of which should be paid the highest attention. The strengths-threats strategy has been applied to develop the strategies by identifying the various internal strengths of project organization to overcome the various threats caused by the eight risk categories observed in international projects.

This paper tries to represent the prioritization of international project risk categories which are generic in nature. For any specific international project, the risk categories as well as their prioritization may be slightly varying. The tool used for prioritization; Interpretive structural modeling (ISM) is more suitable for few numbers of variables as it becomes complex as the number of variables increases. The strengths and threats considered for developing strategies using TOWS matrix are based on the feedback from project professionals and may vary according to the nature of project.

This paper uses ISM and MICMAC for risk prioritization in international projects and TOWS matrix for developing risk management strategies. This may trigger new opportunities for in-depth research in the risk management strategy development for international projects.

Today, methods and risk analysis tools are used to argue decisions concerning issuance of operating licenses, risk management and development of action and emergency plans. In practice, a company is looking for a tool to help in decision-making and in the development of action plans. Thus, the creation of a safe working environment is essential to a company’s success and is one of the best ways to attract/retain staff and increase productivity. It should then set up a kind of funnel sieve that allows understanding all risks to keep only the most significant, which should be given priority. Thus, global risk management should be based on an accurate diagnosis of all company risks. This diagnosis can be formalized in the form of a risk map. The latter is crucial because it raises the overall risk identification, assessment, prioritization and prioritization of action. It is an essential component of any company’s risk management processes. The objective is to have an overall inventory (entity, system, process, activity, etc.) of the vulnerabilities of all fields of activity. It offers a simple and didactic presentation, giving an overview to decision-makers to guide their strategic choices of action. This mapping is then used to monitor the effectiveness of strategies implemented, forming, in a very relevant tool for communication, the objective assessment of this work. So, the purpose of this study is to describe a methodology for comprehensive risk analysis, called risk map. The purpose of this work is to provide a decision support tool to identify areas of weaknesses to allow setting priorities for action to be undertaken by the company to improve the working conditions by organizational, technical and human solutions while engaging in a process of continuous improvement.

The proposed approach for the development of a risk map comprises five stages: risk identification, where the starting point is the identification of the main company processes or, alternatively, the main company activities; exhibition evaluation, the company counts then evaluate the most important risks that affect their processes or activities and persons; evaluation and prioritization of risks; action plan, the company implements the action plan developed on the basis of the prioritization of risks and the company identifies the solutions implemented to reduce the risk and evaluates; and actions control.

This study allows making an image of overall places with vulnerabilities inside the company by a simple and didactic representation, facilitating the promotion of a culture of communication regarding the company vulnerabilities. The development of this risk mapping allowed the development of a very useful tool of communication on the inventory for formulating a risk appetite and determining the risk profile. It is a tool for decision and action whose purpose is the improvement of working conditions by organizational solutions, technical and human resources for the health and safety of employees while engaging in a process of continuous improvement.

The limitations of this study include the lack of competence in risk concepts. It is enough to have a pludisciplinary team to highlight the various dangers and move to the priorities and engage in a process of continuous improvement.

The approach detailed in this study can be used in any entity. One just needs to have skills.

Low level of work accidents, a pleasant working environment, a very good motivation, a good productivity at work.

This work intends to reflect the concrete situation in which companies face the risks. It is interested in the relations between scientific expertise and the precautionary principle. It is a tool that guarantees the success of any preventive approach in a company. It is a decision-making tool for identifying areas of weakness to establish priorities for action by a company whose aim is to improve the working conditions through organizational, technical and human resources while engaging in a process of continuous improvement.

Construction projects involve with various risks during all phases of project lifecycle. Failure mode and effective analysis (FMEA) is a useful tool for identifying and eliminating possible risk of failure modes (FMs) and improving the reliability and safety of systems in a broad range of industries. The traditional FMEA method applies risk priority number method (RPN) to calculate risk of FMs. RPN method cannot consider the direct and indirect interdependencies between the FMs and is not appropriate for complex system with numerous components. The purpose of this study is to propose an approach to consider interdependencies between FMs and also using fuzzy theory to consider uncertainties in experts' judgments.

The proposed approach consist of three stages: the first stage of hybrid model used fuzzy FMEA method to identify the failure mode risks and derive the RPN values. The second stage applied Fuzzy Decision-Making Trial and Evaluation Laboratory (FDEMATEL) method to determine the interdependencies between the FMs which are defined through fuzzy FMEA. Then, analytic network process (ANP) is applied in the third stage to calculate the weights of FMs based on the interdependencies that are generated through FDEMATEL method. Finally, weight of FMs through fuzzy FMEA and FDEMATEL–ANP are multiplied to generate the final weights for prioritization. Afterward, a case study for a commercial building project is introduced to illustrate proficiency of model.

The results showed that the suggested approach could reveal the important FMs and specify the interdependencies between them successfully. Overall, the suggested model can be considered as an efficient hybrid FMEA approach for risk prioritization.

The originality of approach comes from its ability to consider interdependencies between FMs and uncertainties of experts' judgments.

The paper aims to propose a multi-criteria model for risk prioritisation associated to supply chain management involving multiple decision-makers.

The model integrates the composition of probabilistic preferences (CPP) on the failure modes analysis and its effects (FMEA) criteria. First, the authors carried out a probabilistic transformation of the numerical evaluations of the multiple decision-makers on the FMEA criteria regarding the internal risks that affect the chain of clothing pole in the Agreste region of Pernambuco. Then, the authors proposed the use of the Kendall's concordance coefficient W to aggregate these evaluations.

Contrary to expectations, the two main risks to be investigated as a model suggestion was related to the context of supply chain suppliers and not related to the raw material costs. Besides, a simulation with the traditional FMEA was carried out, and comparing with the model result, the simulation is worth highlighting seven consistent differences along the two rankings.

The focus was restricted to the use of only internal chain risks.

The proposed model can contribute to the improvement of the decisions within organisations that make up the chains, thus guaranteeing a better quality in risk management.

Establishing a more effective representation of uncertain information related to traditional FMEA treatment involving multiple decision-makers means identifying in advance the potential risks, providing a better supply chain control.

In current times, organizations operating in emerging economies are providing digital services to its citizen the internet. Literature indicates that digital services are facing major challenges with respect to its adoption among users groups due to the perceived risks.

With the use of generalized analytic network process (GANP), prioritization of different dimensions of risk has been done on the basis of an empirical survey among user groups in India.

The result indicates that dimensions like privacy risk, performance risk and financial risk are more important risks across digital services models. However, physical risk, social risk, psychological risk and time risk are comparatively less important risks across digital services. This research also finds out that the end users are reluctant to provide their personal information.

The sample size is relatively small which limits generalizability of results beyond India. However, an application of GANP has been showcased for empirical research.

The research outcome can help managers in deciding which dimensions of risk are more important for digital service delivery and thus facilitate adoption.

This paper focused on the different facets of risk perceived by consumers, toward the digital services available in smart cities. Perceived risk dimensions such as privacy risk, performance risk, financial risk, physical risk, social risk, psychological risk and time risk have shown that there is a need to prioritize these risks to the digital services which is offered to the residents of the smart cities.