ABSTRACT
In response to the coronavirus disease 2019 pandemic, the Chinese government implemented blockade measures in Hubei, which largely affected the emission of pollutants. This work is aimed to explore the effects of epidemics on pollutants at different temperatures in Hubei, China. We applied for a panel nonlinear model with autonomous search thresholds to explore this, using daily average temperature as a threshold variable, and PM2.5 set as the explained variable, and the cumulative number of confirmed coronavirus disease 2019 cases set as the explanatory variable. An empirical analysis was conducted by running the proposed model and using nine cities in China most impacted by the pandemic. The results show that there was a non-linear negative relationship between the cumulative number of confirmed coronavirus disease 2019 cases and PM2.5. A more detailed non-linear relationship between the two was uncovered by the proposed panel threshold regression model. When the temperature crosses the threshold value (12.5 degrees C and 20.5 degrees C) in sequence, the estimated value was -0.0688, -0.0934, and -0.1520 in that order. This means that this negative non-linear relationship increased with increasing temperature. This work helps to explore the effect of coronavirus disease 2019 on pollutions at different temperatures and provides a methodological reference to study their nonlinear relationship.
ABSTRACT
Since the COVID-19 outbreak, the use of mobile easy payment services that minimize human contact has rapidly increased. Several studies have explored the relationship between the COVID-19 pandemic and the intention to use mobile easy payment services, assuming that the relationship between both variables is simply linear. However, actual complex relationships between variables cannot be fully analyzed in a linear fashion, as most relationships between variables of social phenomena are non-linear. Therefore, this study attempted to analyze the non-linear relationships between factors influencing the intention to use mobile easy payment services, especially since the COVID-19 outbreak, by applying the extended technology acceptance model (TAM2). Online and offline surveys were conducted with users who have used mobile easy payment services since the COVID-19 outbreak; 227 samples were secured for analysis. In addition, an empirical analysis was conducted using PLS-SEM to determine the linearity of relationships between variables. The results showed that subjective norms, perceived ease of use, and perceived usefulness had significant effects on the intention to use mobile easy payment services. Moreover, the COVID-19 pandemic had a significant moderating effect, also implying non-linear relationships between variables. Based on these results, the study proposes that the pandemic is a factor influencing the intention to use mobile easy payment services, and recommends that providers adopt marketing strategies, such as improving the usefulness of these services.
ABSTRACT
As one of the five largest industries in China, the automotive industry may well become a prosperous market of production and a large consumer market, but with the 2019 novel coronavirus (COVID-19) outbreak, automotive companies have suffered great losses. How to maintain financial competitiveness (FC) through innovation and knowledge after this calamity has become an area of focus for researchers and practitioners. By analyzing listed Chinese automotive companies over the period 2013-2018, the research focus is to determine the non-linear effect of intellectual capital (IC) on FC. IC is measured by the modified Value Added Intellectual Coefficient (MVAIC) model, and FC is measured through a comprehensive index system. The results reveal a cubic relationship between IC and FC. In addition, physical, innovation, and relational capitals have an S-shaped relationship with FC, whereas human capital has an inverted S-shaped curve. The non-linear effect of SC on FC is not significant. It is recommended that managers optimize investment in IC to drive FC in organizations.
ABSTRACT
BACKGROUND: To explore the relationship between peripheral lymphocyte counts (PLCs) and the mortality risk of coronavirus disease 2019 (COVID-19), as well as the potential of PLC for predicting COVID-19 hospitalized patients death. METHODS: Baseline characteristics, laboratory tests, imaging examinations, and outcomes of 134 consecutive COVID-19 hospitalized patients were collected from a tertiary hospital in Wuhan city from January 25 to February 24, 2020. Multiple regression analysis was used to analyze the relationship between the PLC at admission and mortality risk in COVID-19 patients and to establish a model for predicting death in COVID-19 hospitalized patients based on PLC. RESULTS: After adjusting for potential confounding factors, we found a non-linear relationship and threshold saturation effect between PLC and mortality risk in COVID-19 patients (infection point of PLC: 0.95 × 109/L). Multiple regression analysis showed that when PLCs of COVID-19 patients were lower than 0.95 × 109/L, the patients had a significantly higher mortality risk as compared to COVID-19 patient with PLCs > 0.95 × 109/L (OR 7.27; 95% CI 1.10-48.25). The predictive power of PLC for death in COVID-19 patients (presented as area under the curve) was 0.78. The decision curve analysis showed that PLC had clinical utility for the prediction of death in COVID-19 inpatients. CONCLUSIONS: PLC had a non-linear relationship with mortality risk in COVID-19 inpatients. Reduced PLCs (< 0.95 × 109/L) were associated with an increased mortality risk in COVID-19 inpatients. PLCs also had a potential predictive value for the death of COVID-19 inpatients.