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To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies, and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic. © 2023 SPIE.
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The cryptocurrency market has been growing rapidly in recent years. The volume of transactions and the number of participants in the cryptocurrency market makes it huge enough that we cannot ignore it. At the same time, the global stock market has also reached a new height in the past two years. However, due to the COVID epidemic and other political and economic-related factors in the last two years, the uncertainty in the capital market remains high, and short-term large fluctuations occur frequently;thus, many investors have suffered substantial losses. Pairs trading, an advanced statistical arbitrage method, is believed to hedge the risk and profit off the market regardless of market condition. Amongst the vast literature on pairs trading, there have been investors trading a pair of cryptocurrencies or a pair of stocks using machine learning or empirical methods. This research probes the boundary of utilizing machine learning methods to do pairs trading with one stock asset and another cryptocurrency. Briefly, we built an assets pool with both stocks and cryptocurrencies to find the best trading pair. In addition, we applied mainstream machine learning models to the trading strategy. We finally evaluated the accuracy of the proposed method in prediction and compared their returns based on the actual U.S. Stock and Cryptocurrency Market data. The test results show that our method outperforms other state-of-the-art methods. © 2022 IEEE.
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As a precious metal and investment commodity, gold has been signified to be important for risk management, diversification, and hedging. The gold market has undergone considerable structural changes in the facet of the pandemic and other geopolitical developments, attracting the interest of investors. Thus, it is crucial to look into how these structural changes affect the efficiency of the market. Accordingly, the study examines and compares the evolution of the gold market efficiency in three major economies from January 1, 2018, to August 31, 2022: India, USA, and Brazil. For this, we first decompose the time series using Loess Smoother's Seasonal and Trend Decomposition and then employ a multifractal detrended fluctuation analysis approach. The estimates are strengthened by the alternative approach of the rolling window method of wild bootstrap automatic variance ratio. The findings indicate a considerable decline in the efficiency of the gold returns across three economies, with the highest decline in India, followed by USA and Brazil. Notably, during covid and post covid periods, India and USA show persistence in small fluctuations, while Brazil displays persistent behavior in large fluctuations. Thereby, the market panic makes the gold market unstable, and its use as a safe haven is "erratic”. © 2023 Elsevier Ltd
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This study used bibliometric analysis to investigate global research trends regarding the effect of COVID-19 risks in sustainable facility management fields. Between 2019 and 2021, the Scopus database published 208 studies regarding the effect of COVID-19 risks on sustainable facility control fields. VOSviewer software was used to analyse the co-occurrence of all keywords, and Biblioshiny software allowed getting the most relevant affiliation using the three-field plot. The results show the contribution by authors from 51 countries, and 73 keywords were identified and organised into six clusters, such as the effect of COVID-19 risks on human health, supply chain in construction projects and industry, disaster risk management in a changing climate, sustainable supply chain benchmarking, facility management and quality control, and, finally, sensitivity analysis & decision-making. © 2023 Khaled Jameel Aladayleh et al., published by Sciendo.
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In the article, there are considered decision aid digital instrument development results for arctic maritime logistics during polar night under climate change and COVID-19 pandemic within Industry 4.0 era. In research, there are used risk management, situational analysis, web technologies and building database methods in distributed networks. As the research result, it is proposed the using of geodata from the active remote sensing and supercomputer modelling to increase the efficiency and reliability of arctic maritime logistics during polar night within the global environmental economics. As the decision aid digital instrument, it is proposed to use modular decision aid system, which integrate the heterogeneous hardware and the software resources in distributed networks. As the research results, there are demonstrated examples for arctic maritime logistics in central and eastern Russian Arctic during polar night. Presented in this paper study results have scientific novelty and can be used by the different players, including energy export sector, insurance business and institutional investors. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This paper studies the equilibrium decision-making problem of product service supply chain (PSSC) network under the impact of COVID-19 related risks. The PSSC is composed of service-oriented transformation of manufacturing enterprises to sell product service systems (PSSs) to customers. So, under the impact of COVID-19, the network faces dual risks of products and services. This paper constructs the PSSC network of raw material suppliers, service providers, manufacturing service integrators and demand markets. Through variational inequalities, a network equilibrium model of PSSC considering risk management was established, and their decision-making problems were discussed. Three numerical examples were used to analyse the impact of risk management on the supply chain network at various levels. The results show that the risk management of upstream and downstream enterprises will have mutual in uence, and the cost input of service risk management will benefit the entire PSSC network. Therefore, through the diversified development and improvement of services, the market demand for PSSs can be increased. © 2023,Journal of Industrial and Management Optimization. All Rights Reserved.
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Purpose: This research seeks to understand, for the first time, what motivates knowledge-intensive organizations (KIOs) to initiate knowledge management (KM) activities in times of routine and emergency. The COVID-19 pandemic was placed at the center as a case study of an extreme crisis. Design/methodology/approach: Based on the adoption of the qualitative-constructivist paradigm, the study was conducted among 52 KM professionals through in-depth interviews and focus groups. The data were analyzed using a thematic analysis method, according to the principles of the grounded theory approach. Findings: The findings reveal that opportunities and risks are two types of catalysts which accelerate KM efforts in times of routine and emergency respectively. Due to KM's support of the transition to flexible employment during COVID-19, the authors show that this field experienced real growth and prosperity in the "new normal.” KM initiatives were promoted during the COVID-19 crisis in light of gaps in retention, sharing, accessibility and development of knowledge. Originality/value: Given that knowledge risks are a field with fragmented understanding, the results contribute to understanding the importance of risk management related to knowledge in times of crises and turmoil. The authors call for incorporating this niche into the overall risk management of the organization, while adopting a holistic and long-term perspective of KM. Furthermore, the authors uncover KM's position in KIOs during the global pandemic. The paper proposes food for thought regarding informal knowledge sharing in virtual environments typical of the "Corona routine”. © 2023, Emerald Publishing Limited.
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The largest and most significant disruption to the world's supply chain networks recently was brought on by the COVID-19 pandemic. Supply chain networks are facing unprecedented pressure to reassess their resilience although risks and disasters occur more frequently. Recent years have seen significant research on supply chain management and its importance to firm performance. Resilient supply chain management has been analyzed and explained using a variety of managerial theories. Thus, we can lay the groundwork for future supply chain resilience research by identifying trends in previous studies. Although a sizable amount of literature on resilient supply networks, only a small number of studies have gone in-depth. This article analyses supply chain management research. Based on a literature study, the amount of research on SCM theory and practice has dramatically expanded during the previous ten years. The current analysis identified the major themes, significant literature, and significant authors in supply chain resilience studies. Additional research in other sectors connected to supply chain resilience is anticipated to benefit from these findings. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Supply chain is a cornerstone of the eCommerce industry and is a key component in its growth. Supply chain data analytics and risk management in the eCommerce space have picked up steam in recent times. With the availability of suitable & capable resources for big data and artificial intelligence, predictive analytics has become a significant area of interest to achieve organizational excellence by exploiting data available and developing data-driven support systems. The existing literature in supply chain risk management explain various methods assisting to identify & mitigate risks using big data and machine learning (ML) techniques across industries. Although ML techniques are used in various industries, not many aspects of eCommerce had utilized predictive analytics to their benefit. In the eCommerce industry, delivery is paramount for the business. During COVID-19 pandemic, needs changed. Reliable delivery services are preferred to speedy delivery. Multiple parameters involve delivering the product to a customer as per promised due date. This research will try to predict the risks of late deliveries to online shopping customers by analyzing the historical data using machine learning techniques and comparing them by multiple performance metrics. As a part of this comparative study, a new hybrid technique which is a combination of Logistic Regression, XGBoost, Light GBM, and Random Forest is built which has outperformed all the other ensemble and individual algorithms with respect to accuracy, specificity, precision, and F1-score. This study will benefit the eCommerce companies to improve their customer satisfaction by predicting late deliveries accurately and early. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The goal of this paper is to demonstrate how Wastewater Based Epidemiology (WBE) can be used after COVID-19 in both Municipal and Industrial wastewater systems to proactively monitor, manage, and avoid risks that could negatively impact the business continuity and resiliency of an organization. The history of WBE will first be reviewed to show how it has been used to maximize public health protection and social well-being while minimizing economic impacts and unintended consequences in public and private settings. The design of a WBE monitoring program for Closed, Semi-Closed, and Open Municipal and Industrial wastewater systems will be evaluated through a couple of case studies. Alignment between WBE programs and an organizations' risk management programs, sustainability goals, and ethical considerations will also be explored. Copyright © 2022 Water Environment Federation.
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The year 2020-21 has shown us that the likelihood of extreme events is greater than we would have expected. When organizational resources are stretched to their limits due to extreme events, they are also more vulnerable to cyber-attacks and knowledge risks. Based on the events that took place during the 2020-21 period, we identify five knowledge risks and categorize them as technical, behavioral, and legal risks. We identify possible controls to mitigate these knowledge risks: proper knowledge identification, guidelines for employee knowledge behavior, identification and evaluation of online communication channels, and risk re-assessment to knowledge. © 2022 IEEE Computer Society. All rights reserved.
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Exposure notification applications are developed to increase the scale and speed of disease contact tracing. Indeed, by taking advantage of Bluetooth technology, they track the infected population's mobility and then inform close contacts to get tested. In this paper, we ask whether these applications can extend from reactive to preemptive risk management tools? To this end, we propose a new framework that utilizes graph neural networks (GNN) and real-world Foursquare mobility data to predict high risk locations on an hourly basis. As a proof of concept, we then simulate a risk-informed Foursquare population of over 36,000 people in Austin TX after the peak of an outbreak. We find that even after 50% of the population has been infected with COVID-19, they can still maintain their mobility, while reducing the new infections by 13%. Consequently, these results are a first step towards achieving what we call Quarantine in Motion. © 2022 IEEE.
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Globally, COVID-19 pandemic has influenced and changed norms and common health cultures. Different countries have implemented risk management and dealt with the condition based on the applicability of the international measures and some uniquely to their situations. As technology has become a key tool in daily lives and smart phones and connectivity has become a common necessity for most of the world's population, these can be used to help face the pandemic and the new normal it brings. Using one of the widely used software platforms, the research intends to design a framework for a health monitoring application for private institutions. © 2022 IEEE.
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There is a fresh interest in the application of digital technologies and analytics in managing supply chain risks. This is a result of recent events such as COVID-19 and the geo political tension between Ukraine and Russia. The resurgence of such events triggered a shortage of raw materials and disruption of supply chain networks. Several studies have explored the application of digital technologies in various settings using mathematical models. Machine learning a module under Artificial Intelligence (AI) received more attention. However, this study brings a new perspective by assessing ways in which manufacturing companies can use digital technology and analytics for holistic supply chain risk management. The study collected data from 14 Fast Moving Consumer Goods (FMCG) manufacturing firms in Mauritius using closed end questionnaire method. The key findings indicated that 29% of the firms are utilising digital technology to predict and create visibility for their supply chain. In addition, 8% of the firms are still within the emerging level for supply risk capabilities. To minimise risks, a workflow is developed to enhance visibility across FMCG supply chain. The workflow enables proactiveness approach instead of reactiveness when disruption occurs. © 2023 Elsevier Ltd. All rights reserved.
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Purpose: The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends, hotspots, and directions for future research. Design/methodology/approach: The data source for this paper is the Web of Science Core Collection, and 7,154 publications and related information have been derived. We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools (VOS Viewer and CiteSpace). Findings: The analysis results show that China is the most productive and influential country/region. East Asia countries have strong cooperation with each other and also have cooperation with other countries. The study shows that risk management has been involved in various fields such as credit, supply chain, health emergency and disaster especially in the background of COVID-19. We also found that machine learning, especially deep learning, has been playing an increasingly important role in risk management due to its excellent performance. Originality/value: This paper focuses on studying risk management in East Asia, exploring its publication's fundamental information, citation and cooperation networks, hotspots, and research trends. It provides some reference value for scholars who are interested or further research in this field. © 2023, Emerald Publishing Limited.
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China crude oil futures market launched in 2018 has become the third-largest global crude oil exchange, indicating its important role in global crude oil markets. Understanding the time-varying jumps of the newly RMB denominated crude oil futures market is not only for the benefit of the market participants in risk management and hedging, but also for the reference of policy-makers in formulating regulation policies, market pricing and financializing energy markets. However, the literature on time-varying jumps in China crude oil futures market is quite scarce compared with existed literature. In this regard, we attempt to study time-varying jumping behaviors of China crude oil futures market impacted by discrete random events, and analyze the sensitivity of jump intensity, jump size and its variance to market volatility and historical volatility, applying the constant and time-varying intensity jump models, based on the daily returns of China crude oil futures market from March 26, 2018 to August 31, 2021. Further, we compare the differential jumps of China crude oil futures market impacted by COVID-19 pandemic. The empirical results have shown that significant time-varying jumping behaviors appear in China crude oil futures market and take on discrete jumping form. The jump intensity is persistent and sensitive to historical volatility of the market. Meanwhile, jump intensity and jump size increase suffered by great public health emergency, and negative jump size arises with high probability. However, the variance of jump size is little sensitive to historical volatility of the market. These findings suggest that the time-varying jumps, especially negative jumps, should be considered for decision-makers and market participants associated with China crude oil futures market. © 2023 Elsevier Ltd
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Online mail order and online retail purchases have increased rapidly in recent years worldwide, with Covid-19 forcing almost all non-grocery shopping to move online. These practices have facilitated the availability of new data sources, such as web behavioural variables providing scope for innovation in credit risk analysis and decision practices. This paper examines new web browsing variables and incorporates them into survival analysis as predictors of probability of default (PD). Using a large sample of purchase and repayment credit accounts from a major digital retailer and financial services provider, we show that these new variables enhance the predictive accuracy of probability of default (PD) models at account level. This also holds in the absence of credit bureau data, therefore, the new information can help people who may not have a credit history (thin file) who cannot be assessed using traditional variables. Moreover, we leverage on the dynamic nature of these new web variables and explore their predictive value in short and long- term horizons. By adding macroeconomic variables, the possibility for stress-testing is provided. Our empirical findings provide insights into web browsing behaviour, highlight how the inclusion of non-standard variables can improve credit risk scoring models and lending decisions and may provide a solution to the thin files problem. Our results also suggest a direct value added to the online retail credit industry as firms should leverage the increasing trend of consumers embracing the digital environment. © 2022 The Authors
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The COVID-19 pandemic affected all industries and presented manufacturing firms with enormous challenges, with considerable changes in consumer demand for goods and services. Supply chain management disruption caused by the COVID-19 outbreak resulted in several socio-economic roadblocks. The slow propagation of disruption risk results in a ripple effect along the entire chain. The lack of resilience and risk management capability is the prime cause, attributed to the unavailability of digital resources, skills, and knowledge. The main objective of this article is to develop supply chain capability for disruption risk management and supply chain resilience for competitive gain in terms of controlling the ripple effect. The resource-based view approach was used to develop the theoretical structure in this article. Supply chain digitalization and viability provide necessary resources to develop the capability for managing risk and resilience to tackle the impact of disruptions due to pandemics, war, recession, and other such massive challenges on the supply chain. Seven hypotheses were proposed and evaluated for relevance using structural equation modeling (SEM). In total, 199 valid responses to a survey on SEM were gathered and examined using the AMOS V-21 software. Our research findings supported all the proposed hypotheses, thereby generating positive theoretical evidence for practitioners to digitalize their supply chain for enhanced supply chain capabilities and effective control of the ripple effect. IEEE
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Risk management in software engineering projects describes an integrated design to prevent project failure with methods, processes, and artifacts that continually identify, analyze, control, and monitor risks. For example, changes in people's lifestyles during the COVID-19 pandemic pose unexpected risks to the information technology industry. Agile is known as a methodology that is responsive and adapts quickly to change. Scrum is the most frequently used method based on the 2016 Agile development survey results. Many studies have produced a risk management framework for Scrum in recent years. However, repeating the risk analysis process and selecting a response to risk becomes a burden for stakeholders, so a framework is needed that can become a support system to help make decisions. This paper used a comparative study of risk management framework literature and literature that utilizes risk management tools and a case study of risk classification using 34k GitHub Issues for data mining. This study proposed a new framework that integrates datasets and machine learning into a risk management framework. The novelty in this paper is that the risk priority scheme is carried out using Long Short-Term Memory (LSTM) and Multinomial Naive Bayes (MNB). Further analysis can be carried out to test the overall effectiveness of the framework. © 2022 IEEE.
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In this paper, we focus on the management of risks in the maritime transport sector after the spread of the pandemic Covid-19. We propose an integrated strategy to determine the best action decision to manage the most important risks. This strategy is based on two stages: the first stage consists on detecting the most serious risks using the AMDEC method and proposing a set of corrective actions for each of these risks;the second stage is responsible to determine the most appropriate action about the set of proposed alternatives using the AHP multi-criteria decision-making approach. Finally, to validate the proposed strategy, real data are collected from a Tunisian maritime transport company and the obtained results show the effectiveness of the proposed integrated method. © 2022 IEEE.