DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Understanding the Impact of COVID-19 on Agriculture and Food Supply Chains: System Dynamics Modeling for the Resilience of Smallholder Farmers

COVID-19 has caused severe agriculture and food supply chain disruptions; significantly affecting smallholder farmers who supply most of the world’s food; specifically their changes in vulnerability; resilience; and food loss and waste. Therefore; the objective of this study was to understand the complex causal and feedback relationships for this system by developing a dynamic hypothesis and causal loop diagrams utilizing the System Dynamics methodology. Results provide a roadmap for dialogue and a framework for case-specific model development and help to guide policy decisions for smallholder farmers’ survival during health crises

Machine learning shows that the Covid-19 pandemic is impacting U.S. public companies unequally by changing risk structures

Covid-19 has impacted the U.S. economy and business organizations in multiple ways, yet its influence on company fundamentals and risk structures have not been fully elucidated. In this paper, we apply LDA, a mainstream topic model, to analyze the risk factor section from SEC filings (10-K and 10-Q), and describe risk structure change over the past two years. The results show that Covid-19 has transformed the risk structures U.S. companies face in the short run, exerting excessive stress on international interactions, operations, and supply chains. However, this shock has been waning since the second quarter of 2020. Our model shows that risk structure change (measured by topic distribution) from Covid-19 is a significant predictor of lower performance, but smaller companies tend to be stricken harder

Impact of COVID-19 on transportation and logistics: a case of China

Recent research has shown that the COVID-19 pandemic has holistically affected the financial and industrial sectors of China, in a considerably adverse manner. This paper aims to examine the impact of the COVID-19 virus on the transportation and logistics sector in China. In this regard, the study opted for a quantitative method of research design, along with primary data collection. The explanatory variable here is the COVID-19 virus, whereas, the dependent variables are air freight, ocean freight, and land freight. The data analysis technique used is the Structural Equation Modelling (SEM) analysis, comprising of the Confirmatory Factor Analysis (CFA) and the path assessment method. A structured survey questionnaire was also used, and the survey questionnaire scale was set from strongly agree to disagree strongly. The findings revealed that the effect of COVID-19 on air freight is statistically negative and significant. Moreover, the impact of COVID-19 on land freight is statistically negative and significant, whereas the effect of COVID-19 is statistically insignificant in the context of ocean freight, particularly during the period of the COVID-19 pandemic. Therefore, keeping these results in consideration, policymakers can enhance their support, so as to increase the performance of the logistics and transportation sector in China

Supply chain dynamics after the Covid-19 pandemic and stock market performance: Evidence from the US

Supply chain risk is a strategic issue for managing multinational companies, and Covid-19 has shown the relevance of this type of risk for the firm's survival probability. The market may perceive the choice of replacing some of the main customers or suppliers as an increase or a decrease of the risk based on the features of the new supply chain members, and markets tend to penalize companies that increase their exposure to unaffordable events. During the pandemic, many supply chains suffered from glitches and companies were obliged to redefine their network by selecting their new strategic customers and/or suppliers. The paper evaluates the supply chain composition strategies of a set of multinational companies based in the US during the last decade. It highlights the differences in supply chain management behavior before and during the pandemic. Data collected allow testing the impact on the stock market performance of modifying the supply chain network by adding new members that may have a different level of risk. Results show that the market reaction to supply chain updates changed after the Covid-19, and nowadays there is greater attention on the credit risk of the new companies entering the supply chain

Strategic, Legal, Financial, and Operational Risks for Businesses During COVID-19 Pandemic

The economic effect of the COVID-19 pandemic is as significant as the threat it poses to public health. Companies are negotiating a new world dealing with new risks. Organizations seek confidence in their risk management and control frameworks and reaction strategies for the short, medium, and long term as COVID-19 grows to threaten commercial life. As a result, it is critical for firms to be proactive in analyzing their risk and susceptibility from many aspects. This exploratory research seeks to discuss some major risks faced by many organizations during the pandemic, namely, the strategic, compliance, regulatory, financial, and operational risks. To thrive in this new business climate, organizations must take immediate action to limit risks and prepare for both fast and gradual recovery.  Businesses that properly manage risk not only endure but also strengthen their endurance and equip themselves to capitalize on new possibilities. &nbsp

How do green financing and green logistics affect the circular economy in the pandemic situation: key mediating role of sustainable production

Emerging economies are striving to realize their potential for sustainable production in achieving zero-carbon agenda. Due to natural resource constraints, businesses must focus on green production resources to develop the circular economy. Therefore, this study aims to identify the key role of green financing and logistics in adopting sustainable production and circular economy. We have collected the data from 240 respondents from the Chinese manufacturing sector following the COVID-19 peak in late 2020 and analyzed using structural equation modeling. As per research findings, green financing and green logistics have a significant and positive effect on sustainable production and the circular economy. Second, sustainable production has a significant positive influence on the circular economy. Manifestly, sustainable production was discovered to play an important mediating role among these variables. Besides, the novel Importance-performance map analysis shows each constructs performance and importance value towards the circular economy. This paper contributed to the literature and highlighted the importance of each construct. Moreover, the study findings implied that green financing and green logistics should be integrated into organizational procuring and financing strategies for manufacturing green and sustainable goods, and advancing the circular economy goals

Impact of investment behaviour on financial markets during COVID-19: a case of UK

This study aims to determine the impact of investment behavior on financial markets during COVID-19 with respect to the UK. This study is quantitative, where the data has been gathered from the primary sources of information, i.e., through a survey questionnaire. The researcher adopted the non-probability convenience sampling through which 337 responses were gathered. The questionnaire was self-administered, which was based on 7 points Likert scale. Concerning the analysis, the SEM technique has been adopted in which CFA and path analysis were carried out to determine the impact of variables. The study’s analysis determined significant moderation of COVID-19 uncertainty over the relationship of risk perception and general risk to tolerance. Similarly, the moderation of COVID-19 uncertainty over the relationship of risk perception and financial risk to tolerance was also determined. Additionally, the profitability rate’s effect was determined by the financial risk tolerance and general risk tolerance. Moreover, the effect of risk perception was also determined over the financial risk to tolerance. Lastly, the effect of satisfaction was determined to be significant over the general risk to tolerance