ABSTRACT
Research has highlighted the impact of COVID-19 on firms without elaborating on how the epidemic effect sharing economy and business models from both the short and long-run perspectives. Drawing on the literature-based view and the vector error-correction model, this study attempts to examine the effects of COVID-19 related factors on companies that provide or share access to goods and services that are facilitated by a community-based online platform. We argue that the government response, and the testing policy and contact tracing will promote managers to adjust their business model. In the long term, economic support, such as income support and debt relief, will reduce stuffs' motivation to work, leading to less achievements. On the other hand, due to the strictness of policies, people will increase online activities and stimulate the sharing economy. Using Indxx data and the Oxford COVID-19 Government Response Tracker database, the analysis of time series data from 75 U.S.-listed companies provides supports for both the short-run effects of the lockdown restrictions and closures with measures, and the government response, and the long-run effects of economic support, and the strictness of lockdown-style policies. This study contributes to business management literature by elaborating upon the causality relationships of how COVID 19 related factors effect sharing economy and business models in the short and long terms. The findings benefit scholars, managers, and policymakers of modernized firms.
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Research background: The globalization trend has inevitably enhanced the connectivity of global financial markets, making the cyclicality of financial activities and the spread of market imbalances have received widespread attention, especially after the global financial crisis. Purpose of the article: To reduce the negative effects of the contagiousness of the financial cycles, it is necessary to study the persistence of financial cycles and carve out the total connectedness, spillover paths, and sources of risks on a global scale. In addition, understanding the relationship between the financial cycle and economic development is an important way to prevent financial crises. Methods: This paper adopts the nonlinear smoothing transition autoregressive (STAR) model to extract cyclical and phase characteristics of financial cycles based on 24 countries during 1971Q1?2015Q4, covering developed and developing countries, the Americas, Europe, and Asia regions. In addition, the frequency connectedness approach is used to measure the connectedness of financial cycles and the relationship between the global financial cycle and the global economy. Findings & value added: The analysis reveals that aggregate financial cycles persist for 13.3 years for smoothed and 8.7 years for unsmoothed on average. The national financial cycles are asynchronous and exhibit more prolonged expansions and faster contractions. The connectedness of financial cycles is highly correlated with systemic crises and contributes to the persistence and harmfulness of shocks. It is mainly driven by short-term components and exhibits more pronounced interconnectedness within regions than across regions. During the financial crisis, the global financial cycle movements precede and are longer than the business fluctuations. Based on the study, some policy implications are presented. This paper emphasizes the impact of systemic crises on the persistence of financial cycles and their connectedness, which contributes to refining research related to the coping mechanisms of financial crises.
ABSTRACT
The outbreak of epidemic has had a big impact on the investment market of China. Facing the turbulence in the investment market, many enterprises find it difficult to judge the development prospects of investment projects and make the right investment decisions. The three-way decisions offer a novel study perspective to solve this problem. Then the developed model is applied to select the investment projects. Firstly, some relevant attributes of the project are described with the double hierarchy hesitant fuzzy linguistic term sets. And a double hierarchy hesitant fuzzy linguistic information system is constructed for each project. Secondly, the weights of attributes are determined with the Choquet integral method. And the closeness degree calculated by Choquet-based bi-projection method is taken as the conditional probability that the project will be profitable. Next, considering the influence of the bounded rationality of decision makers, the threshold parameters are calculated based on prospect theory. Finally, the decision results about investment projects during four stages are deduced based on the principle of maximum-utility, which demonstrates the practicability and effectiveness of the proposed model.
ABSTRACT
In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to "COVID-19” and some comparisons are provided to verify the feasibility and advantages of the proposed method.
ABSTRACT
In consideration of the different importance degrees that may be assigned to all possible linguistic terms, this paper investigates a novel three-way group decision-making method based on the probabilistic linguistic term set (PLTS) information systems. We first construct PLTS information systems based on multiple attributes. Considering the reliabilities of the experts, we determine the weights of the experts by the similarities of the information provided by the expert with regard to other experts. Subsequently, using the evidential reasoning (ER) method, we aggregate the information provided by all experts and obtain the conditional probability of each object. The introduction of the ER rules and the weights of experts successfully solve the problem of conflict between the evaluation information. Then an approach is presented to calculate loss functions and thresholds, which reduces the subjectivity of the decision-making process. Next, the decision result of each object is deduced based on the minimum-loss principle. Finally, a case study about the selection of mask foundries during the COVID-19 is used to demonstrate the effectiveness of our proposed method. And the superiority of our proposed method are proved by comparative analysis.
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This paper aims to help practitioners and researchers understand the impact of COVID-19 on the service business industry through bibliometric analysis. For this purpose, our study collects 671 publications from Web of Science and Scopus. The bibliometric choices in this paper rely on two techniques: performance analysis and science mapping. The performance analysis is organized by the contribution analysis of research constituents. The science mapping uncovers the cooperative network between research constituents, as well as the co-occurrence analysis of keywords. This paper further explores the research topic with content analysis to summarize some findings and discussions. We find that most service business industries have been negatively affected by COVID-19, especially the aviation and tourism industry. Information technology services are a response driver to the negative pandemic impact. Given the current research status of COVID-19 impact on the service business industry, this paper finally concludes the potential directions for future research.
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As the COVID-19 pandemic rages, the changing trends and emerging areas of total quality management (TQM) research before and after the COVID-19 pandemic are spotlighted, while the links between TQM and environmental social governance (ESG) are deeply discussed in this study. To explore the impact of the pandemic on TQM research, a comprehensive bibliometric analysis is conducted by collecting 1465 pre-pandemic and 76 post-pandemic publications from the WoS database. Further, the fundamental characteristics, conceptual structure and intellectual and social structure of TQM research are statistically analysed through bibliometric tools. Consequently, this article methodically sorts out the evolution, new research areas, primary sources, national collaboration networks and influential themes within an intricate and large TQM research system. The linkages between ESG and TQM are explored by focussing on some emerging topics after the outbreak such as sustainability and environmental management, which advances the innovative attempt towards the goal of cooperating sustainability. Finally, we offer some enlightening new perspectives on economic construction and social life during the pandemic to better cope with the negative effects of the pandemic.
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The onset of the COVID-19 pandemic has changed consumer usage behavior towards mobile payment (m-payment) services. Consumer usage behavior towards m-payment services continues to increase due to access to usage experiences shared through online consumer reviews (OCRs). The proliferation of massive OCRs, coupled with quick and effective decisions concerning the evaluation and selection of m-payment services, is a practical issue for research. This paper develops a novel decision evaluation model that integrates OCRs and multi-attribute decision-making (MADM) with probabilistic linguistic information to identify m-payment usage attributes and utilize these attributes to evaluate and rank m-payment services. First and foremost, the attributes of m-payment usage discussed by consumers in OCRs are extracted using the Latent Dirichlet Allocation (LDA) topic modeling approach. These key attributes are used as the evaluation scales in the MADM. Based on an unsupervised sentiment algorithm, the sentiment scores of the text reviews regarding the attributes are calculated. We convert the sentiment scores into probabilistic linguistic elements based on the probabilistic linguistic term set (PLTS) theory and statistical analysis. Furthermore, we construct a novel technique known as probabilistic linguistic indifference threshold-based attribute ratio analysis (PL-ITARA) to discover the weight importance of the usage attributes. Subsequently, the positive and negative ideal-based PL-ELECTRE I methodology is proposed to evaluate and rank m-payment services. Finally, a case study on selecting appropriate m-payment services in Ghana is examined to authenticate the validity and applicability of our proposed decision evaluation methodology.
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Research on the correlation analysis between COVID-19 and air pollution has attracted increasing attention since the COVID-19 pandemic. While many relevant issues have been widely studied, research into ambient air pollutant concentration prediction (APCP) during COVID-19 is still in its infancy. Most of the existing study on APCP is based on machine learning methods, which are not suitable for APCP during COVID-19 due to the different distribution of historical observations before and after the pandemic. Therefore, to fulfill the predictive task based on the historical observations with a different distribution, this paper proposes an improved transfer learning model combined with machine learning for APCP during COVID-19. Specifically, this paper employs the Gaussian mixture method and an optimization algorithm to obtain a new source domain similar to the target domain for further transfer learning. Then, several commonly used machine learning models are trained in the new source domain, and these well-trained models are transferred to the target domain to obtain APCP results. Based on the real-world dataset, the experimental results suggest that, by using the improved machine learning methods based on transfer learning, our method can achieve the prediction with significantly high accuracy. In terms of managerial insights, the effects of influential factors are analyzed according to the relationship between these influential factors and prediction results, while their importance is ranked through their average marginal contribution and partial dependence plots.
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This study explores the problems related to the development of innovation research in the field of business and economics and the change in their characteristics following the coronavirus disease 2019 (COVID-19) pandemic. We compile a comprehensive bibliometric analysis of 17,277 pre-epidemic publications and 4,240 post-epidemic publications from the Web of Science. Using bibliometric methods and visualization tools, we present the changes in these publications following the COVID-19 pandemic, and identify the influential countries and regions, sources, and references, and obtain features of keywords over time. The results show that innovation research is rich in content, and involves a wide range; it has been focusing on emerging topics, such as those concerning low-carbon, innovation forms, and epidemic environments, following the COVID-19 pandemic. This study contributes to the body of knowledge on innovation, and helps to understand the features and structures of innovation research in business and economics.
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Large-scale group decision-making process has received an increasing attention in recent years. After making the general survey of the existing large-scale group decision-making methods, we have found that: 1) consistency threshold value of hesitant fuzzy linguistic preference relation is fixed in traditional consistency measures;2) the clustering process of LSGDM does not consider the similar relationship between different evaluation information and the information quality simultaneously. Thus, in order to tackle the above issues and describe the hesitancy of experts in the decision-making process, the paper proposes a hesitant fuzzy linguistic bi-objective clustering method considering consensus and information entropy for tackling large-scale group decision-making problems. Firstly, a selection procedure for preference information is developed to quickly select suitable experts who meet the consistency requirements. Then, a bi-objective clustering method based on the group consensus degree indicator and group information entropy indicator is proposed to divide the experts into different clusters, considering the similar relationship and the quality of evaluation information simultaneously. After that, comprehensive preference information and the overall ranking of alternatives can be obtained. In the end, an illustrative example of choosing the optimal way to protect the personal information while defending against COVID-19 and some comparative study show that the proposed method is valid for large-scale group decision-making problems and has good performance and strong robustness.
ABSTRACT
Emergency medical services during the COVID-19 epidemic have become the focus of worldwide attention, and how to effectively respond to urban epidemic situation during a complex environment has become a global challenge. Emergency decision-making can be considered as a multicriteria decision-making (MCDM) problem, which involves multiple criteria or attributes about qualitative and quantitative aspects. So, in this paper, based on the TODIM method, a hybrid TODIM method with crisp number and probability linguistic term set is first provided to evaluate the severity of urban COVID-19 epidemic situation during a complex humanitarian crisis environment. In this hybrid method, the quantitative aspects are evaluated on the basis of precise numerical values, and the qualitative aspects are evaluated by means of probability linguistic term set, which can not only express their judgments or linguistic preference with multiple linguistic terms but also reflect different importance degrees or probability degrees of all the possible linguistic information or preference information. In addition, the concept of entropy and probability linguistic entropy is applied to induce hybrid criteria weight information. Furthermore, sensitivity analysis of the parameter about attenuation factor of the losses in the hybrid TODIM method, which considers the psychology factors and cognitive behavior of the DMs, is further conducted on a case study, to verify the effectiveness and stability of the proposed method for urban epidemic situation evaluation according to the results of this study.