Dynamic Risk Management Research Articles

Developing Methods for Assessing the Impact of Digital Innovations on the Economic Efficiency of Enterprises: Methodology for Measuring ROI of Digital Projects

This study aims to develop methodologies for assessing the return on investment (ROI) of digital projects, crucial for understanding the economic impact of digital innovations on enterprises. Traditional ROI metrics often fail to capture the comprehensive value generated by digital projects. The proposed integrative framework employs advanced analytical methods, including cost-benefit analysis, real options analysis, and predictive analytics, to evaluate both tangible and intangible benefits. It incorporates dynamic risk management and scenario planning to address uncertainties and future challenges. Continuous feedback and iterative improvements ensure the framework's accuracy and relevance, enabling enterprises to optimize economic efficiency and strategic decision-making. This holistic approach offers an understanding of the multifaceted value of digital innovations.

Borderline personality disorder: part 2 – psychiatric management

SUMMARY The large volume of seemingly conflicting guidance on the management of borderline personality disorder (BPD), combined with the ongoing shortage of specialised resources, can make the task feel like an exclusive undertaking that the general psychiatrist is underprepared for. In this article, we distil current evidence to submit that sound psychiatric management principles used to treat all serious and enduring mental disorders (diagnostics, comorbidity management, rational pharmacotherapy and dynamic risk management) are readily applicable and particularly therapeutic for BPD. We offer actionable practice guidance that we hope will render the clinical management experience a more lucid and rewarding one for both practitioner and patient.

AI-driven financial risk management systems: Enhancing predictive capabilities and operational efficiency

The integration of artificial intelligence (AI) in financial risk management systems has revolutionized traditional approaches, providing enhanced predictive capabilities and operational efficiency. This paper explores the various applications of AI in credit risk assessment, market risk analysis, operational risk management, and regulatory compliance. AI-driven systems leverage advanced machine learning algorithms to analyze vast datasets, including real-time market data and non-traditional sources, improving risk predictions and enabling proactive risk management. Scenario simulations, predictive modeling, real-time data analysis, and automated decision-making are discussed as core components of AI-driven systems. The paper also highlights the benefits of AI in automating routine tasks, enhancing data analytics, and ensuring regulatory compliance. By continuously learning and adapting to new data, AI systems offer dynamic risk management solutions that address evolving market conditions and regulatory requirements. This comprehensive analysis demonstrates how AI-driven financial risk management systems can significantly reduce the incidence of loan defaults, enhance portfolio quality, and improve the overall resilience of financial institutions.

Hazardous High-Energy Seismic Event Discrimination Method Based on Region Division and Identification of Main Impact Factors: A Case Study

An investigation of risk factors has been identified as a crucial aspect of the routine management of rockburst. However, the identification methods for principal impact factors and the examination of the relationship between seismic energy and other source parameters have not been extensively explored to conduct dynamic risk management. This study aims to quantify impact risk factors and discriminate hazardous high-energy seismic events. The analytic hierarchy process (AHP) and entropy weight method (EWM) are utilized to ascertain the primary control factors based on geotechnical data and nearly two months of seismic data from a longwall panel. Furthermore, the distribution law and correlation relationship among seismic source parameters are systematically analyzed. Results show that the effect of coal depth, coal seam thickness, coal dip, and mining speed covers the entire mining process, while the fault is only prominent in localized areas. There are varying degrees of log-positive correlations between seismic energy and other source parameters, and this positive correlation is more pronounced for hazardous high-energy seismic events. Utilizing the linear logarithmic relationship between seismic energy and other source parameters, along with the impact weights of dynamic risks, the comprehensive energy index for evaluating high-energy seismic events is proposed. The comprehensive energy index identification method proves to be more accurate by comparing with the high-energy seismic events based on energy criteria. The limitations and improvements of this method are also synthesized to obtaining a wide range of applications.

The Impact of Fintech Startups on Financial Institutions’ Performance and Default Risk

Introduction: It is highly important to understand the role of financial technology-based startups in the field of financial support. Thus, the study here aimed to investigate the job of fintech solutions or startups in the performance management of financial institutions and the process they use to mitigate risk in financial matters. Literature Review: The economic contraction was most likely the cause of the decline in the value of investments. Understanding the importance of operational efficiency, traditional financial institutions have embraced technology advancements to automate repetitive tasks, lower errors, and save costs. As a result, overall performance and cost-effectiveness have increased. Methodology: The method of data collection that has been selected to investigate the roles that fintech startups play in reducing financial risk is "primary quantitative." As a result, answers to a questionnaire consisting of 13 questions were gathered from 55 people. Findings and analysis: The statistical analysis revealed that the majority of responses were either in agreement or strongly in agreement. Given that the Pearson Correlation value is greater than 0.8, there is a strong correlation between each variable. Every hypothesis formulated during the initial stage of the study has been approved. Discussion: Fintech, which is a combination of finance and technology, uses cutting-edge methods and instruments to improve the overall effectiveness and resilience of risk management frameworks. However, fintech startups face a variety of obstacles along the way, but they are also presented with opportunities for innovation and growth on their optimization journey Conclusion: The adoption of fintech by financial institutions is expected to create a dynamic risk management landscape that can adjust to new opportunities and challenges in the constantly changing financial sector. The primary quantitative method was selected in order to collect the pertinent data for this investigation. Through statistical analysis, the gathering of quantitative data can assist in projecting real-time information.

Mine the past: How to make better risk-based decisions and improve outcomes with historical threat data

The impact of every crisis has the potential to cascade throughout an organisation’s operations, supply chain and market ecosystem. To properly understand and mitigate this ripple of dynamic risk, business continuity, security and risk management leaders need to know where to focus their attention. Looking at historical threat data provides a clearer picture of the risk landscape, helping leaders better anticipate and plan for the future. To date, however, there have been challenges in this process. As the volume of data about critical events continues to grow at an alarming rate, sifting manually through data puts organisations — and business continuity — in jeopardy. This paper discusses the value of historical threat data and innovations in data-mining technology that can unlock the true power of historical data for informed, strategic decision-making and better outcomes during a crisis.

Use of Risk Management to Support Business Sustainability in the Automotive Industry

Today’s companies operate in a dynamic, constantly evolving, and highly competitive environment. The globalization of markets has significantly changed the economy, where companies operate within increasingly complex supply chains. The ever-increasing expectations of customers and company stakeholders, as well as the need to incorporate a comprehensive approach to the life cycle of manufactured products in corporate strategies, expose companies to a whole range of risks. The research was based on the need of organizations operating in the (automotive) industry to manage the dynamics of the business environment. This was accomplished using an appropriate model that, through its universality, would help to ensure the effective risk management in a holistic approach, protecting their performance and meeting the needs of the relevant stakeholders. The main idea of the research was to create and implement a dynamic model of risk management in the environment of a production organization based on the use of available methods such as SWOT, PESTLE, brainstorming, affinity diagrams, risk matrix, SIPOC diagram, risk, and the results of questionnaire surveys. The research was conducted in two directions: (1) designing the structure of the dynamic risk management model for the strategic management process; and (2) verifying the effectiveness of the proposed model in specific cases and evaluating the technical and economic benefits. To support the dynamics of the model, three basic management tools have been proposed: process review, internal audit, and management review, enriched with features that support the concept of risk-based thinking.

Stackelberg–Cournot dynamic game and risk management of heterogeneous supply chain enterprises under government carbon emission policy

AbstractThis paper presents the development of a model incorporating two manufacturers in a competitive carbon emissions trading market with varying degrees of market competitiveness. Among them, the manufacturer is in a limited market leadership position, and the game process lies between the Stackelberg game and the Cournot game. Additionally, the Pareto solution sets at various model periods are investigated. In addition, the model examined the effect of dynamic government adjustments on carbon emission limitations. The paper conducts an analysis of the fundamental factors contributing to system instability and evaluates the influence of various factors on the model's stability. At the same time, we also discussed the influence of government adjustment strategy on retail decision‐making and stability. The results highlight that the instability of the system is mainly caused by the adjustment strategy of the market leader, whereby the system becomes increasingly unstable as the market leader's position becomes stronger. In addition, the government's implementation of carbon emission limits will promote market stability.

Dynamic selection of risk response strategies with resource allocation for construction project portfolios

To promote the dynamic risk management of construction project portfolios (CPPs) and improve the effect of risk response, a simulation–optimization model is proposed. The model integrates System Dynamics and optimization to dynamically select risk response strategies (RRSs) and facilitate more refined resource allocation. Specifically, a System Dynamics sub-model simulates the risk level by capturing one-stage and cross-stage risk interactions. Second, develop an optimization sub-model to select RRSs with resource allocation, which allows for partially allocated resources for RRSs. Afterward, integrate them into a simulation–optimization model. The closed-loop information flow between the two sub-models enables the assessment of the initial risk at a specific stage, the selection of optimal RRSs, and the updating of the dynamic impact of the risks of that stage on later stages. Finally, the proposed model is validated using a numerical example. The findings accentuate the significance of considering cross-stage risk interactions and optimizing resource allocation in the risk response of CPPs, and validate the feasibility and superiority of the model for solving the dynamic selection of RRSs.

Navigating Risks: How External Environments Shape Non-Performing Loans in Vietnam's Commercial Banks

Objectives: The primary objective of this study is to analyze the factors influencing Non-Performing Loans (NPLs) in Vietnam's banking sector. It aims to shift the focus from traditional perspectives centered on macroeconomic indicators towards considering external environmental factors, such as global economic shifts, digital transformation, and industry-specific changes. Moreover, it seeks to examine the impact of the COVID-19 pandemic on NPL dynamics in Vietnamese commercial banks. Methods: The study employs empirical data collected from various Vietnamese commercial banks. Robust statistical methods are applied to analyze the data and explore the relationships between external environmental factors and NPLs. The research challenges conventional risk assessment models by advocating for a more comprehensive approach that integrates a broader spectrum of external influences beyond internal and macroeconomic variables. Results: The findings of the study suggest that external environmental factors, including global economic shifts, digital transformation, and industry-specific changes, significantly affect NPLs in Vietnam's banking sector. The analysis reveals the intricate dynamics of NPLs, especially under the influence of the COVID-19 pandemic. Contrary to traditional perspectives, the research underscores the importance of considering a wider range of factors in assessing and managing NPL risk. Conclusion: In conclusion, this study contributes significantly to the understanding of NPL dynamics in Vietnam's banking sector. The research highlights the need for banking executives and policymakers to adopt dynamic risk management strategies that account for external environmental factors. By recognizing the influence of global economic shifts, digital transformation, and industry-specific changes, stakeholders can enhance banking resilience and stability in emerging markets like Vietnam. The study underscores the importance of embracing a holistic approach to risk management in an era characterized by rapid global changes and uncertainties.

A q$q$ Theory of Internal Capital Markets

ABSTRACTWe propose a tractable model of dynamic investment, spinoffs, financing, and risk management for a multidivision firm facing costly external finance. Our analysis formalizes the following insights: (i) Within‐firm resource allocation is based not only on divisions' productivity, as in winner‐picking models, but also their risk; (ii) firms may voluntarily spin off productive divisions to increase liquidity; (iii) diversification can reduce firm value in low‐liquidity states, as it increases the spinoff cost and hampers liquidity management; (iv) corporate socialism makes liquidity less valuable; and (v) division investment is determined by the ratio between marginal and marginal value of cash.

Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings

BackgroundApplication of accumulated experience and management measures in the prevention and control of coronavirus disease 2019 (COVID-19) has generally depended on the subjective judgment of epidemic intensity, with the quality of prevention and control management being uneven. The present study was designed to develop a novel risk management system for COVID-19 infection in outpatients, with the ability to provide accurate and hierarchical control based on estimated risk of infection.MethodsInfection risk was estimated using an auto regressive integrated moving average model (ARIMA). Weekly surveillance data on influenza-like-illness (ILI) among outpatients at Xuanwu Hospital Capital Medical University and Baidu search data downloaded from the Baidu Index in 2021 and 22 were used to fit the ARIMA model. The ability of this model to estimate infection risk was evaluated by determining the mean absolute percentage error (MAPE), with a Delphi process used to build consensus on hierarchical infection control measures. COVID-19 control measures were selected by reviewing published regulations, papers and guidelines. Recommendations for surface sterilization and personal protection were determined for low and high risk periods, with these recommendations implemented based on predicted results.ResultsThe ARIMA model produced exact estimates for both the ILI and search engine data. The MAPEs of 20-week rolling forecasts for these datasets were 13.65% and 8.04%, respectively. Based on these two risk levels, the hierarchical infection prevention methods provided guidelines for personal protection and disinfection. Criteria were also established for upgrading or downgrading infection prevention strategies based on ARIMA results.ConclusionThese innovative methods, along with the ARIMA model, showed efficient infection protection for healthcare workers in close contact with COVID-19 infected patients, saving nearly 41% of the cost of maintaining high-level infection prevention measures and enhancing control of respiratory infections.

Optimizing Financial Performance: Deciphering the Interplay of Liquidity and Profitability in Bank Muscat, Sultanate of Oman

This study explores the intricate relationship between liquidity and profitability at Bank Muscat, Oman, examining various financial indicators such as Cash Reserve Ratio (CRR), Total Deposits to Total Assets (TDTA), Capital Adequacy Ratio (CAR), Total Loans to Total Deposits (TLTD), Liquid Assets to Total Assets (LATA), Liquid Assets to Total Deposits (LATD) and Liquidity Risk Exposure (LRE). The findings reveal that Liquid Assets to Total Assets (LATA) significantly correlates with Return on Assets (ROA), while other indicators show varied relationships. Notably, the Capital Adequacy Ratio (CAR) has a substantial negative impact on both ROA and Return on Equity (ROE). Cash Reserve Ratio (CRR), Total Deposits to Total Assets (TDTA), and Total Loans to Total Deposits Ratio (TLTD) have significant impact on ROE. Regression models exhibit exceptional explanatory power, highlighting liquidity and financial indicators' role in explaining profitability variability. Despite some liquidity ratios affecting profitability, others like Liquid Assets to Total Assets (LATA), Liquid Assets to Total Deposit Ratio (LATD), and Liquidity Risk Exposure (LRE) lack conventional significance levels. The recommendations encompass optimizing liquidity management, fortifying capital adequacy, monitoring deposit-to-asset ratios, evaluating liquidity risk exposure, assessing liquid asset allocation, and implementing dynamic risk management frameworks. Future research should consider comparative analyses across banks or regions, advanced statistical methodologies, and exploration of nuanced factors influencing liquidity-profitability dynamics, contributing to comprehensive financial management strategies for banks.

NAVIGATING RISKS: HOW EXTERNAL ENVIRONMENTS SHAPE NON-PERFORMING LOANS IN VIETNAM'S COMMERCIAL BANKS

Objectives: The primary objective of this study is to analyze the factors influencing Non-Performing Loans (NPLs) in Vietnam's banking sector. It aims to shift the focus from traditional perspectives centered on macroeconomic indicators towards considering external environmental factors, such as global economic shifts, digital transformation, and industry-specific changes. Moreover, it seeks to examine the impact of the COVID-19 pandemic on NPL dynamics in Vietnamese commercial banks.
 
 Methods: The study employs empirical data collected from various Vietnamese commercial banks. Robust statistical methods are applied to analyze the data and explore the relationships between external environmental factors and NPLs. The research challenges conventional risk assessment models by advocating for a more comprehensive approach that integrates a broader spectrum of external influences beyond internal and macroeconomic variables.
 
 Results: The findings of the study suggest that external environmental factors, including global economic shifts, digital transformation, and industry-specific changes, significantly affect NPLs in Vietnam's banking sector. The analysis reveals the intricate dynamics of NPLs, especially under the influence of the COVID-19 pandemic. Contrary to traditional perspectives, the research underscores the importance of considering a wider range of factors in assessing and managing NPL risk.
 
 Conclusion: In conclusion, this study contributes significantly to the understanding of NPL dynamics in Vietnam's banking sector. The research highlights the need for banking executives and policymakers to adopt dynamic risk management strategies that account for external environmental factors. By recognizing the influence of global economic shifts, digital transformation, and industry-specific changes, stakeholders can enhance banking resilience and stability in emerging markets like Vietnam. The study underscores the importance of embracing a holistic approach to risk management in an era characterized by rapid global changes and uncertainties.

Adapting to the Unpredictable: Building Resilience for Business Continuity in an Ever-Changing Landscape

In an age characterized by unprecedented socio-financial disruptions, businesses encounter a crucial mandate: to seamlessly incorporate organizational resilience, risk management, and business continuity strategies. This article navigates this complex landscape, offering insights into the essential interplay between resilience and risk mitigation. It explores the dynamic nature of socio-financial disruptions, emphasizing the inherent risks they pose. Drawing from real-world examples, we dissect key components of organizational resilience and delve into strategies that marry resilience with effective risk management. Proactive measures take center stage, guiding organizations to fortify their structures against uncertainties. From embracing technological innovations to fostering a culture of adaptability, it unveils practical tips for cultivating resilience while concurrently managing risks. Leadership's pivotal role in steering organizations through crises is highlighted, emphasizing risk-aware decision-making. Collaborative approaches and partnerships emerge as crucial elements, promoting collective resilience and shared risk management. The article advocates for a continuous cycle of monitoring, adaptation, and dynamic risk management to meet evolving challenges. As research concludes, it distills key takeaways, urging businesses to adopt an integrated approach to fortify their foundations. Proactive Invitation encourages readers to assess and improve organizational resilience, risk management, and business continuity practices, providing a roadmap for navigating tomorrow's uncertainties.

Dynamic Risk Management for Demand Response in Multi-Utility Smart Grids

Dynamic Risk Management for Demand Response in Multi-Utility Smart Grids

Enhancing public sector enterprise risk management through interactive information processing.

Federal agencies are increasingly expected to adopt enterprise risk management (ERM). However, public sector adoption of ERM has typically focused on the economic efficiency of tax-financed activities based on control-based practices. This reflects an emphasis on quantifiable concerns that invariably directs attention to risk, that (by definition) relates to identifiable and measurable events, thereby downplaying uncertain and unknown aspects of public exposures. This is a potentially serious shortcoming as government entities often act as society's risk managers of last resort. When extreme events happen what were previously considered private matters can quickly turn into public obligations. Hence, there is a need for proactive assessments of the evolving public risk landscape to discern unpredictable-even unknowable-developments. The article reviews recent empirical studies on public risk management practices, effects of digitalization in public sector institutions, current strategic management research, and insights uncovered from a recent study of risk management practices in federal agencies. On this basis, the article explains how the ability to generate value from ERM can be enhanced when it intertwines with local responsive initiatives and central strategic risk analyses. It can form a dynamic adaptive risk management process where insights from dispersed actors inform updated risk analyses based on local autonomy and open exchange of information. This approach builds on specific structural features embedded in culture-driven aspirations to generate collaborative solutions. Its functional mode is an interactive control system with open discussions across levels and functions in contrast to conventional diagnostic controls that monitor predetermined key performance indicators (KPIs) and key risk indicators (KRIs). Backed by theoretical rationales and empirical research evidence, it is found that applications of ERM frameworks can produce positive results but is unable to deal with a public risk landscape characterized by uncertain unpredictable conditions with potentially extreme outcome effects. It is shown how interactive exchange of fast local insights and slow integrated strategic risk analyses supported by digitized data processing can form a dynamic adaptive system that enable public sector institutions to deal with emergent high-scale exposures. It is explained how the requirement for conducive organizational structures and supportive values require a new strategic risk leadership approach, which is contrasted to observed practices in federal agencies that are constrained by prevailing public governance requirements. The need to deal with uncertainty and unknown conditions demands a cognitive shift in current thinking from a primary focus on risk to also appraise complexity and prepare for the unexpected where data-driven methods can uncover emergent exposures through dynamic information processing. This requires strategic risk leaders that recognize the significance of complex public exposures with many unknowns and a willingness to facilitate digitalized information processing rooted in a collaborative organizational climate. If handled properly, adoption of ERM in public risk management can consider emergent dimensions in complex public exposures applying interactive information processing as a dynamic adaptive risk management approach incorporating digitized methods to solicit collective intelligence for strategic risk updating.

Resilience effects for household food expenditure and dietary diversity in rural western China

A more resilient livelihood is increasingly recognized as an efficient way to improve vulnerable households’ food security and optimize their dietary decisions. This study quantifies rural household resilience in western China, identifies the three pillars (absorptive capacity, adaptive capacity, and transformative capacity) contribution to resilience, and then establishes the estimated Resilience Capacity Index (RCI) linked with food security and dietary diversity supported by the multiple indicator multiple cause (MIMIC) model. Results show that, despite geographical heterogeneity, the RCI consistently increased from 2015 to 2021. Households with a higher RCI inheriting better capacity to deal with risk and shocks are significantly and positively correlated with increasing food expenditure and diversifying food choices. It can be because resilient households will allocate more money to food expenditure instead of saving for livelihood uncertainty. Thus, policymakers can provide more incentives for rural households to adopt more dynamic and effective risk management strategies. This, in turn, could yield positive spillover effects by preventing human capital loss associated with dietary-related chronic diseases and mortality.

INFORMATION TECHNOLOGY CAPABILITIES AND ENTERPRISE RESILIENCE OF STATE-OWNED ENTERPRISES FOR SUSTAINABLE COMPETITIVENESS: THE MEDIATING ROLE OF LEADERSHIP CAPABILITIES

Background and Purpose: In today's unpredictable, uncertain, complex, ambiguous, and interconnected environment, businesses face various challenges and uncertainties that are anticipated will intensify and become more frequent in the future. In this harsh environment, maintaining sustainable competitiveness is difficult. Information Technology Capabilities (ITCs) are an important element that determines a company's success or failure as well as its Resilience (ER). Given the complexity and uncertainty of today's business world, businesses with good information collection, analysis, and use can anticipate changes and decrease delays. Enterprises should collect high-quality information to meet modern business settings. Business resilience depends on the ability to gather and process resources to handle environmental uncertainty. Enterprises should use information, knowledge, experiences, and other resources to build resilience. The leadership of top management is essential for ensuring the organisation's sustainability and attaining its objectives. This study applies the concept of dynamic capability to assess the impact of ITCs on Indonesian SOEs and their subsidiaries. Additionally, this study investigates leadership capabilities (LCs) as a mediator between ITCs and ER. Due to their vital role in the Indonesian economy and the paucity of research on State-Owned Enterprises (SOEs) resilience and dynamic capabilities, particularly in the emerging market, this research focuses on Indonesian SOEs and their subsidiaries.
 
 Methodology: The study examined 322 valid data from online surveys using questionnaires to Indonesia SOEs and their subsidiaries’ board of directors and senior management. The data is analysed using SmartPLS 3.
 
 Findings: The research demonstrates that ITCs enhance ER. LCs improved the enterprise's resiliency. The relationship between ITCs and ER is mediated through LCs. 
 
 Contributions: The findings may expand the scope of dynamic capability theory to include resilience. The findings extend the methodology by adopting a quantitative and online survey of SOEs in emerging countries and demonstrate to SOE management and regulators that ER can be fostered by strengthening dynamic capabilities and risk management practices. The findings recommend that policymakers develop a road map, scenario, and strategies to ensure the long-term viability of SOEs.
 
 Keywords: Dynamic capabilities, enterprise resilience, Indonesia, information technology capabilities, leadership capabilities, state-owned enterprises.
 
 Cite as: Lisdiono, P., Said, J., Yusoff, H., Hermawan, A. A., & Abdul Manan, D. I. (2023). Information technology capabilities and enterprise resilience of state-owned enterprises for sustainable competitiveness: The mediating role of leadership capabilities. Journal of Nusantara Studies, 8(TI), 382-407. http://dx.doi.org/10.24200/jonus.vol8issTIpp382-407

The dynamic risk profiles and management strategies in supply chain coopetition under altruistic preference

This paper focuses on bilateral coopetition within a two-echelon supply chain consisting of manufacturer and retailer. The objective is to establish a comprehensive stochastic dynamical framework for investigating the dynamic mechanisms of supply chain coopetition (SCC) and to develop a novel method for optimal decision-making of ownership incentive and capital structure under the measurement of dynamic risks in uncertain environment. The study introduces the altruistic preference to characterize the bilateral utility, and establish a stochastic system model within the framework of Langevin dynamics. Considering the inherent uncertainty in SCC, we define and classify the dynamic risks into bilateral cooperation risk and system response risk, based on which we propose a novel indicator to assess the performance in terms of risks. In the simulations, we consider the different scenarios to discuss the influences of various factors, i.e., ownership ratio, capital leverage, altruistic preference, and uncertain environment, on dynamic risk profiles and the performance in SCC, and explore the feasibility of constructing strategies from these dimensions. The results provide valuable insights for risk management and decision-making within the supply chain context.