scholarly journals The Impact of Work Characteristics on Social Distancing: Implications at the Time of COVID-19

2021 ◽  
Vol 18 (10) ◽  
pp. 5074
Author(s):  
Keisuke Kokubun ◽  
Yoshinori Yamakawa
Keyword(s):  
Information Processing ◽  
Multiple Regression ◽  
Explanatory Power ◽  
Work Conditions ◽  
Multiple Regression Model ◽  
Social Characteristics ◽  
Work Characteristics ◽  
Social Distancing ◽  
Using Data ◽  
The Impact

The coronavirus disease (COVID-19) continues to spread globally. While social distancing has attracted attention as a measure to prevent the spread of infection, some occupations find it difficult to implement. Therefore, this study aims to investigate the relationship between work characteristics and social distancing using data available on O*NET, an occupational information site. A total of eight factors were extracted by performing an exploratory factor analysis: work conditions, supervisory work, information processing, response to aggression, specialization, autonomy, interaction outside the organization, and interdependence. A multiple regression analysis showed that interdependence, response to aggression, and interaction outside the organization, which are categorized as ”social characteristics,” and information processing and specialization, which are categorized as “knowledge characteristics,” were associated with physical proximity. Furthermore, we added customer, which represents contact with the customer, and remote working, which represents a small amount of outdoor activity, to our multiple regression model, and confirmed that they increased the explanatory power of the model. This suggests that those who work under interdependence, face aggression, and engage in outside activities, and/or have frequent contact with customers, little interaction outside the organization, and little information processing will have the most difficulty in maintaining social distancing.

scholarly journals Moderating Effect of Mindfulness on the Influence of Stress on Depression According to the Level of Stress among University Students in South Korea

2020 ◽  
Vol 17 (18) ◽  
pp. 6634
Author(s):  
Kwang-Hi Park ◽  
Hyunlye Kim ◽  
Jaehee Kim
Keyword(s):  
Regression Analysis ◽  
University Students ◽  
South Korea ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Life Stress ◽  
Mental Health Problems ◽  
Explanatory Power ◽  
Moderating Effect ◽  
The Impact

Stress and depression are representative of the mental health problems of university students worldwide. This cross-sectional study explored the moderating effect of mindfulness on the influence of stress on depression according to the degree of life stress. The participants were 738 university students in years 2–4 in five 4-year universities in South Korea. Depression was positively correlated with stress and negatively with mindfulness at a statistically significant level. In multiple regression analysis, stress was found to have an effect by increasing depression, and mindfulness by relieving depression. In the moderated multiple regression analysis, mindfulness had a moderating effect on the impact of stress on depression only in low-stress groups, showing that the interaction of stress with mindfulness was significantly negative (β = −0.11, t = −2.52, p = 0.012) and the inclusion of this interaction significantly increased the explanatory power for depression variation (F change 6.36, p = 0.012) in the full model. In conclusion, we suggest considering stress levels in the development of mindfulness-based intervention strategies to effectively manage the depression of university students.

scholarly journals Does Credit Risk Management affect the Financial Performance of Commercial Banks in Kosovo?

2018 ◽  
Vol 7 (2) ◽  
pp. 49-57
Author(s):  
Rrustem Asllanaj
Keyword(s):  
Risk Management ◽  
Credit Risk ◽  
Financial Performance ◽  
Multiple Regression ◽  
Commercial Banks ◽  
Multiple Regression Model ◽  
Capital Adequacy ◽  
Regression Equations ◽  
Credit Risk Management ◽  
The Impact

This study analyses the impact of credit risk management on financial performance of commercial banks in Kosovo, and comparing the relationship between the determinants of credit risk management and financial performance by using CAMEL indicators. Panel data of 85 observations from 2008 to 2012 of ten commercial banks was analysed using multiple regression model. Findings through multiple regression analysis are presented in forms of tables and regression equations. The study also elaborates whether capital adequacy, asset quality, management efficiency, earnings and liquidity have strong or weak relationship with financial performance of commercial banks. The study concludes that CAMEL model can be used as a system of assessment and rating of credit risk management by commercial banks in Kosovo.

scholarly journals Top-down estimate of black carbon emissions for city cluster using ground observations: A case study in southern Jiangsu, China

2018 ◽  
Author(s):  
Xuefen Zhao ◽  
Yu Zhao ◽  
Dong Chen ◽  
Chunyan Li ◽  
Jie Zhang
Keyword(s):  
Regression Model ◽  
Black Carbon ◽  
Multiple Regression ◽  
Wet Deposition ◽  
Multiple Regression Model ◽  
Top Down ◽  
Bottom Up ◽  
Southern Jiangsu ◽  
City Cluster ◽  
The Impact

Abstract. We combined a chemistry transport model (CTM), a multiple regression model and available ground observations, to derive top-down estimate of black carbon (BC) emissions and to reduce deviations between simulations and observations for southern Jiangsu city cluster, a typical developed region of eastern China. Scaled from a high-resolution inventory for 2012 based on changes in activity levels, the BC emissions in southern Jiangsu were calculated at 27.0 Gg/yr for 2015 (JS-prior). The annual mean concentration of BC at Xianlin Campus of Nanjing University (NJU, a suburban site) was simulated at 3.4 μg/m3, 11 % lower than the observed 3.8 μg/m3. In contrast, it was simulated at 3.4 μg/m3 at Jiangsu Provincial Academy of Environmental Science (PAES, an urban site), 36 % higher than the observed 2.5 μg/m3. The discrepancies at the two sites implied the uncertainty of the bottom-up inventory of BC emissions. Assuming a near-linear response of BC concentrations to emission changes, we applied a multiple regression model to fit the hourly surface concentrations of BC at the two sites, based on the detailed source contributions to ambient BC levels from brute-force simulation. Constrained with this top-down method, BC emissions were estimated at 13.4 Gg/yr (JS-posterior), 50 % smaller than the bottom-up estimate, and stronger seasonal variations were found. Biases between simulations and observations were reduced for most months at the two sites when JS-posterior was applied. At PAES, in particular, the simulated annual mean was elevated to 2.6 μg/m3 and the annual normalized mean error (NME) decreased from 72.0 % to 57.6 %. However, application of JS-posterior slightly enhanced NMEs in July and October at NJU where simulated concentrations with JS-prior were lower than observations, implying that reduction in total emissions could not correct CTM underestimation. The effects of numbers and spatial representativeness of observation sites on top-down estimate were further quantified. The best CTM performance was obtained when observations of both sites were used with their difference in spatial functions considered in emission constraining. Given the limited BC observation data in the area, therefore, more measurements with better spatiotemporal coverage were recommended for constraining BC emissions effectively. Top-down estimates derived from JS-prior and the Multi-resolution Emission Inventory for China (MEIC) were compared to test the sensitivity of the method to initial emission input. The differences in emission levels, spatial distributions and CTM performances were largely reduced after constraining, implying that the impact of initial inventory was limited on top-down estimate. Sensitivity analysis proved the rationality of near linearity assumption between emissions and concentrations, and the impact of wet deposition on the multiple regression model was demonstrated moderate through data screening based on simulated wet deposition and satellite-derived precipitation.

scholarly journals Stock returns and liquidity risk in Chile

2019 ◽  
Vol 17 (2) ◽  
Author(s):  
Francisco Javier Vásquez-Tejos ◽  
Hernán Pape-Larre ◽  
Juan Martín Ireta-Sánchez
Keyword(s):  
Stock Market ◽  
Emerging Markets ◽  
Regression Model ◽  
Stock Returns ◽  
Multiple Regression ◽  
Risk Measures ◽  
Liquidity Risk ◽  
Multiple Regression Model ◽  
Developed Markets ◽  
The Impact

This study analyzes the impact of liquidity risk on the return of shares in the Chilean stock market, during the period from January 2000 to July 2018. A large number of studies have focused on measuring this effect in developed markets and few in emerging markets, especially the Chilean one. To do this, we used 6 risk measures in a multiple regression model; four widely used in previous studies and two new proposed measures. We found evidence of the significance of the liquidity risk over the stock return.RESUMENEste estudio analiza el impacto del riesgo de liquidez sobre el retorno de las acciones en el mercado bursátil chileno, durante el periodo de enero de 2000 hasta julio de 2018. Gran cantidad de estudios se han centrado en medir este efecto en los mercados desarrollados y pocos en mercados emergentes, especialmente el chileno. Para ello, se utilizó un modelo de regresión múltiple 6 medidas de riesgo; cuatro utilizadas ampliamente en estudios anteriores y dos medidas nuevas propuestas. Encontramos evidencia de significancia del riesgo de liquidez sobre el retorno accionario.RESUMOEste estudo analisa o impacto do risco de liquidez no retorno das ações no mercado de ações chileno, durante o período de janeiro de 2000 a julho de 2018. Muitos estudos têm se concentrado em medir este efeito em mercados desenvolvidos e poucos nos mercados emergentes, especialmente o chileno. Para isso, utilizamos 6 medidas de risco em um modelo de regressão múltipla; quatro amplamente utilizados em estudos anteriores e duas novas medidas propostas. Encontramos evidências da significância do risco de liquidez sobre o retorno das ações.  

scholarly journals Multiple Regression Analysis used in Analysis of Private Consumption and Public Final Consumption Evolution, case of Albanian Economy

2020 ◽  
Vol 3 (1) ◽  
pp. 48
Author(s):  
Ilva Isa ◽  
Bederiana Shyti ◽  
Kamen Spassov
Keyword(s):  
Regression Model ◽  
Multiple Regression ◽  
Private Consumption ◽  
Influence Factors ◽  
Multiple Regression Model ◽  
Public And Private ◽  
The Public ◽  
Final Consumption ◽  
Public And Private Sector ◽  
The Impact

This paper approaches the evolution of the final consumption recorded at the level of Albanian economy. According to statistical methodology the public and private consumption are two of the components of the final consumption. The main variable of our study is final consumption, which is set to be influenced by at least two independent variables, such as public and private consumption. Lately, Albanian economy has been presented with a new and different macro economic policy, a new form of partnership of investments between public and private sector. We are highly interested in the impact of these changes on final consumption .The correlation between the main parameter and its influence factors is analyzed through a regression model. Eviews is the software that the data will be processed under standard methods. The model and the results are part of the paper. To be emphasized is that the reliability of the multiple regression model does not exclude the possibility to analyze the single correlation between the parameters, in parallel.

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