مدیریت ریسک

In the digital age and today’s complex, fast-paced world, policymaking in science, technology, and innovation faces numerous challenges. Rapid technological advancements, diverse societal needs, and the vast volume of data have made policymaking processes increasingly intricate. In this context, data mining has emerged as a novel and powerful tool, offering innovative solutions for analyzing, forecasting, and evaluating policies. By extracting valuable insights from big data, data mining enables policymakers to make more effective decisions grounded in precise analysis and evidence-based forecasts. These capabilities are particularly vital in the realm of science and technology, where the complexity of information and the presence of nonlinear relationships are prominent.This study primarily aims to identify the roles and functions of data mining in science and technology policymaking. It endeavors to provide a framework for the practical utilization of data mining throughout the policymaking process, from problem identification and definition to policy impact evaluation.Methodologically, the study employs a systematic literature review to gather data, thematic analysis to identify patterns and key concepts, and constant comparative analysis to develop a comprehensive framework. The analyzed sources include scientific articles, policy reports, and case studies focusing specifically on data mining applications in science and technology policymaking. Through a structured analysis process, the study extracts and categorizes the roles and functions of data mining across various aspects of policymaking.Key findings indicate that data mining serves five pivotal functions in science and technology policymaking:Trend analysis and future forecasting: Utilizing machine learning algorithms and historical data analysis, data mining uncovers hidden patterns and predicts shifts in science and technology priorities. This capability prepares policymakers for an uncertain future.Policy impact assessment and effectiveness evaluation: By analyzing the outcomes of implemented policies, data mining provides precise feedback to enhance and refine policymaking strategies.Risk prediction and management: Data mining identifies risk factors and offers preventive analyses, enabling policymakers to manage potential risks and mitigate adverse consequences.Resource prioritization and optimization: Through big data analysis, data mining identifies high-impact areas and optimizes resource allocation to these domains.Decision support through scenario evaluation: Data mining assists policymakers in evaluating alternative scenarios and selecting the most effective options based on detailed data-driven insights.The findings underscore the transformative role of data mining in science and technology policymaking. With advanced data mining tools, policymakers can make informed, evidence-based decisions in complex and dynamic environments. Analyzing extensive data from diverse sources facilitates the identification of emerging challenges and opportunities, contributing to the development of flexible and efficient policies. Moreover, the study demonstrates that data mining not only enhances decision-making processes but also fosters transparency and accountability in policymaking.Practical and academic implications of this study include:Facilitating evidence-based decision-making across all stages of policymaking.Identifying emerging technologies and formulating supportive policies to promote innovation.Improving resource management and minimizing wastage through optimized allocations.Enhancing transparency and accountability in policymaking through precise data analysis.Providing an operational framework for leveraging data mining to evaluate and improve policy strategies.This study provides a new perspective on the application of data mining in science and technology policymaking by identifying and analyzing its roles and functions. Data mining enables policymakers to navigate uncertainties, achieving more precise and effective outcomes. The adoption of data mining not only improves policymaking in science and technology but also serves as a model for other policy domains.

Internal controls are an integral part of risk management Very important in realizing the goals of companies has it. The stronger the internal controls are, the risk related to economic enterprises will decrease and their efficiency will increase due to the more correct use of available resources. On the other hand, weak internal control increases the risk of economic enterprises and leads to bankruptcy of economic enterprises. Risk management is a process that the board of directors, management It is designed to formulate the strategies of companies enterprises and identifying potential events and to be able to identify risks in order to provide a reasonable assurance of achieving the goals of an effective economic enterprise. This article, relying on the framework of Kosovo, which every organization should take steps to establish according to its own conditions, examines the role of management on risks and applying optimal internal controls to predict the bankruptcy of economic enterprises using financial ratios. The research sample consists of 71 bankrupt economic enterprises and 71 non-bankrupt economic enterprises as a control sample during the time period of 2011-2014. The results of the investigations showed that with the establishment of effective internal controls and risk management, the value of economic enterprises increases and the risk of bankruptcy decreases. Also, the results of univariate tests showed that there is a significant difference between the lack of risk management and the lack of effective internal control system in bankrupt companies and the control sample before bankruptcy. This difference reaches its maximum value in the period of five years before bankruptcy. The bankruptcy prediction model, which was built using logistic regression, showed that increased risk and lack of internal controls can be used as predictors of bankruptcy, and this variable significantly improves the performance of the model.

Iran's oil industry plays a strategic role in the country's economy in the field of selling oil and gas and their products and providing domestic and foreign financial resources, and in this regard, it has serious challenges in the processes of doing work, which can be considered as one of the instance of this industry facing the phenomenon of corruption and since Iran's economy is a single product based on the sale of oil and gas, the occurrence of corruption in this industry will always be conceivable. This study was conducted with the aim of preventing corruption by identifying corruption points with a process approach based on risk management in one of the subsidiaries of Iran's gas industry.

The research method was a mixed type and combination of qualitative and quantitative methods, and in the qualitative part, to count the points of corruption, while examining the main and secondary processes of each management and Staff unit, using the interview tool, and in the quantitative part, the risk management approach based on a questionnaire used.

Based on the findings of the research, 58 points were in the low-risk range (green area), 63 points were in the medium risk range (yellow area) and 7 points were in the high-risk range (red area).

The findings can help the studied organization to control corruption points as a driver for improving the integrity of the administrative system, and this is possible through a review of the financial, administrative, legal and gas supply processes.

Supply chain risk management is a proactive approach to prevent potential and unexpected consequences. This research aims to develop a mathematical model to reduce the risk within the blood supply chain during pandemics. Specifically, a robust, multi-objective, scenario-based model has been proposed to mitigate the risk of the blood supply chain under critical conditions.

The COVID-19 pandemic disrupted the blood supply from donors, leading to a crisis in the blood supply chain. Unlike previous research that focused on increased demand due to crises like earthquakes or wars, this study addresses the disruption in supply. To overcome this uncertainty, a three-level model with two objectives was developed. The first objective is to minimize total cost, and the second is to maximize the reliability of the blood supply chain. The model is then made robust by considering uncertainty in blood supply. The novelty of this research lies in presenting a mathematical model that simultaneously optimizes the two conflicting objectives of cost and reliability while considering supply uncertainty. The weighted sum method was used to convert the multi-objective model into a single-objective one, and the model was solved using GAMS software and the BARON solver.

To validate the model, the problem was tested under various scenarios using real-world data, and a sensitivity analysis was conducted to assess the model's stability against parameter changes. The total cost minimization in the robust model was calculated, and it was observed that as the weight of the cost minimization objective function increased, this objective function moved towards minimization and optimization, stabilizing at a weight of 0.1. By increasing the weight in the reliability maximization objective function, the value of this objective function stabilized at 0.5 and moved towards maximization, reaching its maximum at a weight of 1. The Pareto solutions for changes in the cost function and stable reliability are presented, showing that as the stability of the cost objective function increases, the stable reliability function decreases significantly, and vice versa. Additionally, the relationship between reliability and the number of blood collection facilities was directly proportional. However, the reliability of the system did not increase beyond a certain point (15 facilities). Consequently, constructing more than 15 blood collection facilities is not cost-effective, indicating increased efficiency in the supply chain at the level of blood collection facilities when using the proposed model. The findings show that the presented model can determine the optimal amount of blood collected from donors, the number of collection centers, the blood inventory level at blood centers and hospitals, as well as the units of blood sent from blood centers to hospitals, aiming to reduce risk and manage the blood supply chain effectively during critical blood supply conditions like the COVID-19 pandemic.

The COVID-19 pandemic highlighted the importance of blood supply chain risk management. Since the blood supply chain is vital for public health, organizations and institutions involved in this field should implement robust plans and strategies to manage risks and enhance the stability of the blood supply chain during crises like pandemics. Therefore, implementing a robust risk management model for the blood supply chain in the context of the COVID-19 pandemic will help organizations ensure their stability and performance, effectively addressing society's blood supply needs.

The productivity of skilled human resources is one of the key factors influencing the performance of construction projects, and neglecting this issue can result in irreparable damages to the projects. Low labor productivity is one of the fundamental challenges in the construction industry, as it has consistently been a cause of excessive increases in time and cost in construction projects. Research conducted on the risk factors affecting human resource productivity in projects has shortcomings, such as a lack of comprehensiveness in the risk identification process, failure to consider the interactions between risks, and the failure to account for risk factors and their impacts on productivity. Therefore, the purpose of this research is to identify the risk factors and the resulting risks, analyze, and prioritize them to manage critical risks more effectively.

This research is a case study, and the data collection tools include structured interviews and questionnaires. The first step of this research is to identify the productivity indicators of skilled human resources and the risks arising from these indicators. The process for completing this step includes a comprehensive review of the literature to identify the indicators and risks, followed by interviews with 10 experts in the field to verify the identified risks. In the second step, to analyze the risks, we first employ the fuzzy DEMATEL method to determine the causal relationships between system variables. Using the information obtained from this step, we apply the system dynamics approach to model the risks. After creating the stock-flow diagram and conducting simulations, we identify the sensitive and critical points of the system.

The results show that the risks of site execution interference, falling from height, unsafe operations, electrocution, non-compliance with plans and specifications, surface defects during execution, rework, machinery and equipment efficiency, communication and coordination, and material wastage are among the 10 key risks that contribute to decreased productivity in high-rise building projects.The results of the risk analysis indicate that the risks of 1) site execution interference, 2) rework, 3) communication and coordination, and 4) falling from height have the most significant impact on construction projects. The findings also reveal that the risk of site execution interference, if it occurs, could reduce productivity to 190 units of work completed per month, while rework could reduce productivity to 300 units. On the other hand, the risk of falling from height reduces productivity to 380 units, whereas communication-related risks maintain productivity at around 300 units. Therefore, as the greatest reduction in productivity is related to the risk of site execution interference, this risk is identified as the most critical in the system.

The model developed in this research has significant potential to assist construction managers in systematically evaluating the negative impacts of productivity risks. Project managers can use the findings of this research to focus more on key risks, such as site execution interference, rework, communication and coordination, and falling from height, managing these risks more effectively. By doing so, they can optimize the productivity of human resources, a critical issue in the construction industry. The advantage of this research for the construction industry is that it helps managers better understand the dynamics of productivity risks and, by restoring the connections between risks and breaking key interactions in feedback loops, stop the escalating impact of risks on one another.

The aim of this study is to identify the components of financing through factoring with a risk management approach. 

The study is applied in nature, cross-sectional in data collection, and descriptive-survey in its execution. The statistical population consisted of experts in financial management and accounting with relevant specializations and at least five years of executive experience in banks. The sampling method was purposive and chain sampling, with a sample size of 15 individuals. The data were analyzed using the qualitative method of thematic analysis. The validity of the research tool was ensured through thematic analysis, and reliability was confirmed using the test-retest method and dual coders. 

The results indicated the identification of 14 basic themes, 5 organizing themes, and 1 overarching theme for the model. The organizing themes include financial risk management, selection of factoring as a financing method, economic and financial factors, relationships with factoring companies, and interaction with business partners. Each of these themes emphasizes various processes essential to managing financial risks and decision-making in the use of factoring within organizations. 

The findings suggest that factoring, as a financing method, can contribute to improving liquidity and profitability when financial risks are managed effectively. Moreover, establishing effective relationships with factoring companies and setting terms of interaction with business partners are key factors for the successful implementation of this financing method.

Risk management is one of the solutions of organizations to deal with uncertainty in the business environment, which has an effective role in using resources, as well as maintaining and promoting efficiency and effectiveness, and as a result,improving the organization's performance. The purpose of this research is to analyze the components of resource utilization with a risk management approach using the evolutionary theory (foundation data). In order to collect data in the qualitative part,semi-structured interviews were used, and the interviewees included managers and experts of one of the companies active in the copper industry and were selected purposefully. In order to model, the version of Strauss and Corbin was used in the foundation data approach.In this process, 60 concepts and 21 categories were created and they were grouped in the main 6 axis of Grounded theory. In the quantitative validation part of the model, structural equation modeling method was used.At first, questionnaires were compiled and their validity and reliability were examined.Content validity, divergent and convergent validity were used to measure validity, and Cronbach's alpha method was used for reliability and all were confirmed.Then it was distributed among the statistical sample. The statistical sample of managers and experts of the studied company was 82 people, and based on this, sampling was not done and the total number was counted.Based on the analysis of the questionnaire data, considering the non-normality of the data distribution,the research model was tested and confirmed by the partial least squares method with the help of SmartPLS3 software.

Risk management in the supply chain is one of the important challenges in the business world that has a great impact on the performance of organizations. In this research, an intelligent model of risk management in the supply chain is designed using machine learning techniques. The purpose of this model is to identify and assess risks, predict their effects, and provide strategies for optimizing strategies based on large and complex data. By using machine learning algorithms such as decision trees, neural networks, and support vector machines, the proposed model is able to simulate and predict risks more accurately and quickly. The results of this research show that this intelligent model can be effective in improving decision-making and increasing the resilience of the supply chain. Ultimately, this model helps organizations to effectively manage risks and reduce the costs of crises. This study can be used as a basis for further research on the application of machine learning in supply chain risk management.

The Summer 1403 issue of this journal aims to provide novel and practical perspectives in strategic management and decision-making, gathering a collection of interdisciplinary and pioneering articles. This issue features six key papers addressing topics such as “Strategic Decision-Making Based on Sentiment Analysis in Social Media,” “Strategic Modeling of Organizational Resilience to Complex Crises,” “Designing a Customer Behavior Prediction Model Using Artificial Intelligence Algorithms and Neural Networks,” “Developing an Intelligent Risk Management Model in Supply Chains through Machine Learning Techniques,” “Organizational Performance Evaluation Using Business Intelligence (BI) Tools,” and “A Localized Strategic Sustainability Management Model in Iranian Industries.” Each paper offers a precise theoretical framework and methodology, striving to provide practical solutions for contemporary organizational and industrial challenges. The integration of Big Data analytics, artificial intelligence, machine learning approaches, and multi-criteria decision-making techniques represents a turning point that can enhance strategic processes and increase organizational resilience and sustainability. We hope that this issue serves as an inspiring resource for researchers, managers

The aim of this study is to accurately estimate the cost of capital and manage risk in energy projects based on public-private partnerships (PPP) in emerging markets.
 
To explore the mechanism of determining capital costs and evaluating capital budgeting, Dynamic Conditional Correlation (DCC) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models were employed. Additionally, the pure-play method was used as a tool for measuring systematic risk in energy-related projects in emerging markets.
 
The results showed that the DCC-GARCH model is capable of more accurately predicting volatility and capital costs in emerging markets. Moreover, the use of the pure-play method for calculating systematic risk led to reduced volatility and improved capital cost estimation. The findings also indicated that market volatility and risks associated with economic and financial policies in emerging markets have a significant impact on capital costs.
 
This study demonstrates that the application of advanced financial models such as DCC-GARCH and the pure-play method can lead to more accurate capital cost estimations in PPP projects and provide a better understanding of risks in emerging markets. These models offer valuable tools for risk management in the dynamic economic conditions of emerging markets.

The purpose of this research is analyzing the supply shain risk management based on resilience capabilities with TISM approach in Iran's National Oil Products Distribution Company and the gasoline supply chain.
Variables were identified through a multi-grounded theory.Then, the relationships between the dimensions of the structural model was determined through TISM.
The results showed that, inherent risks, socio-political risks and environmental risks are at the top of the model and have a greater impact on other risks. The occurrence of supply, demand and processes - operational risks lead to the emergence of organizational risks. A high level of risk in the organization causes disruptions in the supply chain.Therefore, in order to respond to risks and control them, resilience capabilities have been provided. The dimensions of resilience are affected by risks and react to control them. This issue can help the supply chain achieve its goals.

The purpose of this research is to explain and validate the coordination model of the marketing and sales department in the insurance industry in Iran. The present research is applicable in terms of purpose. The statistical population of the research includes experienced managers in insurance marketing and sales, senior experts of insurance companies and university professors; and the statistical sample was considered to be 181 people using Cochran's formula. Sampling in this research is in the form of random clusters. The collection tool in this research includes a researcher-made questionnaire derived from the qualitative method. Data analysis was done by SPSS software. The findings showed that the coordination model of the marketing and sales department in the insurance industry is affected by various factors directly and indirectly, and managers' understanding of the complexity of the relationship between these two units is of great importance in the growth of insurance companies and creating a competitive advantage for them. In order to ensure the achievement of the goals, managers must pay attention to the causal conditions such as the implementation of the risk management dashboard, structural integration and coordination in the business, creating synergy in organizational decisions, recognizing the prerequisites for coordination between the two departments of marketing and sales in the form of background conditions or coordination challenges such as "partial prejudices, non-specialist human capital, conflict of interests, managers' taste, CEO's attitude, ethical considerations, unfavorable customer support when receiving damages and fulfilling the organization's obligations, as well as interfering conditions such as upstream documents, market conditions (market stakeholders), legal conditions, and competitive conditions.

Investment in the construction industry can only cause economic growth and achieve economic results when capital injection and housing production are targeted and ultimately lead to housing consumption; because if the imported funds are not directed to the building sector, the final price of housing will increase and will make real consumers less able to buy. Therefore, the entry of liquidity into the construction industry requires formulation of strategy and policy in order to increase the growth rate of housing. Also, risk management and knowing its key components lead to reducing construction costs and creating a more appropriate price in the construction industry. The aim of the current research is to know the dimensions and components of risk management in Iran's construction industry with a fuzzy Delphi approach with interpretive structural modeling and DEMATEL technique.

The current research method is exploratory and the goal is to answer the research questions. In terms of the type of research, it is practical because its results are used for risk management in Iran's construction industry. The method of the current research is mixed (qualitative-quantitative). In the qualitative dimension, using the fuzzy Delphi method, it has identified the dimensions and components of risk management in the construction industry in Iran. In the quantitative dimension, by using interpretative structural modeling technique and DEMATEL, by using surveys of experts and university professors, it has been done to stratify and examine the intensity of relationships between dimensions.

The results show that at the lowest level of the model (level three), the risk dimension of political factors is placed, which acts as the cornerstone of the model and to achieve risk management in projects. Iran's construction should start from this factor. Also, the results of examining the intensity of relationships between dimensions with Dimetal show that the social risk dimension is the most effective dimension, has the highest numerical value based on the row sum (D), and thus, with the most influential dimension among the investigated dimensions of risk management in Iran's construction industry has been identified. Also, according to (D-R), the lowest value, the risk dimension of laws and regulations, is the most effective dimension of risk management in Iran's construction industry.

Originality/Value:

 The current research is new in terms of the subject area, i.e. in the field of the construction industry of Iran, and in terms of the location area, i.e. companies active in the field of the construction industry of New Tehran. The research gap that creates the space for innovation in the current research, according to the perspective of soft operations research, which is a tool for creating complex and new problems, is the use of Interpretive Structural Modeling (ISM) and DEMATEL. Less research has been done on leveling and examining the intensity of impact and effectiveness of the components of risks in the construction industry.

Commercial credit insurance as an insurance that insures the credit sellers of goods and services as well as creditors, including banks and credit institutions, against non-repayment of paid credits; It plays an important role in today's business, but it has not been well received by insurers in Iran. The reluctance of insurers to provide these insurances is more evident, especially in the case of export credit insurance coverage and also the credit insurance coverage of banks and financial institutions. One of the most important reasons for this situation is the very high risk of credit insurance coverage, which can lead the insurance company to bankruptcy. Although reinsurance is a solution for insurance companies to transfer and manage risk; But it faces limitations. By converting such risk into securities and transferring it to the capital market, risk management becomes possible for insurers, and it becomes possible to offer credit insurance. A solution that is also inserted in the note of Article 5 of the Budget Law of 1399. In this article, with a descriptive-analytical approach and using library resources, we discuss the special features of credit insurance and the effect the securitization of that risk will have on increasing its popularity

The aim of this study is to identify and prioritize risks and to provide appropriate control strategies for management contract of projects of the IIES. By examining the research background, Delphi method and questioning from experts were identified the risk and in order to correct, complete and confirm them, they entered different Delphi method, then the probability of occurrence and effect of each risk was determined by using the opinion of experts. After combining the cost, time, quality and range effects of each risk and multiplying the overall effect by the probability value of that risk number was obtained and control strategies in relation to high risks were presented. A total of 68 risks were identified at different stages of the project life cycle, of which 48 risks were included in the evaluation and prioritization calculations. The risks of the contract and the implementation stage were the most important risks.

One of the most effective services provided by the armed forces during social crises (such as war, etc.) is the construction of military field hospitals. The location and construction of these hospitals have potential risks that need to be identified, prioritized, and find appropriate solutions to contain them. The purpose of the current research is to identify the risks of building military field hospitals during war and crisis and to prioritize them using the FMEA approach. The research is based on the method of data collection, descriptive-case study, and based on the type of data, it is mixed. The research expert team includes 17 experts (all of whom were familiar with the basics of risk management, field hospitals and the FMEA approach and had experience or academic records of more than 20 years, related to one of the mentioned subjects) using the available method and until reaching the theoretical saturation stage was selected. In order to survey the experts, a semi-structured interview was used. The first researcher-made questionnaire was used by the expert team to calculate the CVI index, and the second researcher-made questionnaire was used by 60 experts in the research field for the FMEA approach. The validity of the questionnaires was confirmed using content validity and its reliability using Cronbach's alpha and SPSS software. Based on the results, 60 priority risks (including 16 high-risk cases, 26 medium-risk cases, and 18 low-risk cases) were identified in the establishment of military field hospitals. Also, the results show that the risks of lack of drug storage, blood supply bank, operating room equipment, engine house, water tanks and facilities, health and energy infrastructure, land quality, waste disposal system, structure strength, camouflage and security of transmission routes are the most important risks.

One of the effective solutions in managing the crisis and improving the resilience of the affected areas is to benefit from military field hospitals. This is despite the fact that there are various risks during the establishment of such hospitals, and the development of effective management strategies for such risks can lead to the improvement of the resilience of areas affected by the crisis. The present research is based on the method survey-descriptive, and based on the nature of the data, it is mixed. It is also practical. For this purpose, in the first stage, after calculating the risks, they were screened using the content validity index. In the second step, the screened risks were classified into three categories of low risk, medium risk and high risk using the combined approach of "FMEA and AHP". Comparing the results with the results of risk assessment using the classic FMEA, indicates the high efficiency of the proposed method. Based on the results of these two steps, the risks related to the lack of access to energy infrastructure, patient transfer route, change of construction site, lack of operating room equipment and lack of access to the waste disposal system are the five most important risks identified with high weight. In the third step, using the combined approach of "AHP and SWAT", the strengths and weaknesses of military organizations in the construction and establishment Military Field Hospital and based on its results, nine strategies and eleven implementation solutions was proposed.