The Effect of Inventory Turnover, Cash Turnover, and Receivable Turnover on Profitability at Telecommunication Companies during the Covid-19 Pandemic

This study aimed at analyzing three different turnovers of current asset elements: the inventory turnover, the cash turnover, and the receivable turnover on profitability. Data is taken in the form of financial statements from the public companies of the telecommunication sector during four quarters of 2020. The panel data is processed from 9 companies or 36 observations using the Eview9 application and produced the fixed effect model as the best regression. The results indicate that the cash turnover has a significantly positive effect on profitability, whereas the inventory turnover and the receivable turnover have not significantly any effect on profitability. Therefore, the study concluded that the cash turnover was the one of current assets turnover that effected on the profitability of the public company telecommunication sector in Indonesia. It implied to the public companies of telecommunication sectors to choose the appropriate turnover model to achieve the maximum profitability during the Covid-19 pandemic.

How Does Government Policy Improve Green Technology Innovation: An Empirical Study in China

This paper divides government policy according to policy quantity, policy effectiveness and policy executive force so that the government policy can be quantified in more detail. Green patent data is used to represent green technological innovation, and the fixed effect model and panel data analysis from 2010 to 2019 are employed. The empirical results show that government policy has a significant direct promoting effect on green technology innovation. And the positive impact of policy quantity and policy effectiveness on green technology innovation is greater than that of policy executive force. In addition, the government policy will weaken the positive effect of enterprise innovation vitality on green technology innovation. Research conclusions also show that the direct and indirect effects of government policies on green technology innovation are heterogeneous. The government still needs appropriately policies adapted to the local situation, coordinated in policy quantity, policy effectiveness, and executive force, and accelerate the establishment of market-oriented green technology innovation environment. Different regions also should find the right green technology innovation policy scheme for their own regions.

Determinants of Executive Compensation

The object of this research focuses on executive compensation, which is a form of appreciation for the agent’s contribution as the party responsible for the company performance and the improvement of the walfare of the principals. The aims of this study is to determine the effect of profitability and leverage on executive compensation in non financial companies listed in Indonesia Stock Exchange for the period 2017 – 2019. Population of this study is non financial companies listed in Indonesia Stock Exchange for the period 2017 – 2019 with a sample by non financial listed in Kompas100 index to represent the existing population. Independent variables used in this study are profitability which is measured by return on assets (ROA) and leverage which is measured by debt to equity ratio (DER). Dependent variable used in this study is executive compensation which is measured by total remuneration of president director. This study also used control variables such as executive age, executive gender and executive tenure. The sampling method of this study is a purposive sampling. The research model used is panel data with fixed effect model approach. The findings have shown that profitability has a negative significant effect on executive compensation. It is also noted that the lower level of the company’s debt, the larger the amount of executive compensation. These findings shed the light on research on agency theory that compensation on performance is not valid in Indonesia.

Dose-Response Association Between Bilirubin and Cardiovascular Disease: A Systematic Review and Meta-analysis

The association between bilirubin (BIL) and cardiovascular disease (CVD) remains controversial. We performed a meta-analysis of prospective studies to evaluate this association in the general population. We searched PubMed, EMBASE, Web of Science, Cochrane, and Scopus databases through to September 2021. The Newcastle-Ottawa Quality Assessment Scale was used to assess study quality. The pooled effect estimate was calculated by the fixed-effect model or random-effect model. We included 12 prospective studies (368 567 participants). The pooled risk ratio of CVD for the lowest vs highest groups of BIL levels was .75 (95% CI: .58-.97) with high heterogeneity (I2 = 87.5%, P < .001). Similar associations were observed for coronary heart disease and stroke. We further performed a “dose-response” meta-analysis, and a significant U-shaped relationship between circulating (most values were serum bilirubin, but a few were plasma bilirubin) BIL and CVD ( P < .01) was observed. The lowest risk of CVD events was observed in participants with a BIL of 17-20 µmol/L in serum and/or plasma. In conclusion, there was a U-shaped dose-response relationship between BIL and CVD, especially for men. Further studies are needed to confirm our findings and identify the mechanisms involved as well as any prognostic or therapeutic potential.

Does Supply Chain Concentration Affect the Performance of Corporate Environmental Responsibility? The Moderating Effect of Technology Uncertainty

With the development of society and the improvement of environmental consciousness, the performance of corporate environmental responsibility (CER) has elicited increasing attention in recent years. In previous studies, the exploration of the antecedents of CER is far less evident than the exploration of its results, and only few studies have investigated what determines CER engagement from the perspective of supply chain concentration (SCC). Using data from 2413 firms in China from 2013 to 2019, our study uses the fixed effect model and performs multiple robustness tests to examine the impact of SCC on the fulfillment of CER, its transmission mechanism, and the moderating role of technology uncertainty (TU). Empirical results show that SCC has a pivotal negative impact on CER performance, wherein both supplier concentration (SUP) and customer concentration (CUS) are detrimental to CER performance. Further mechanism analysis shows that such negative effect can be explained by the adverse effect of SCC on the operating cash flow (OCF), in which OCF has a partial mediating effect. Moreover, the negative impact of SCC on CER performance is more significant when the uncertainty of firms’ technological environment is stronger. Our study opens the transmission “black box” between SCC and CER performance and incorporates the behaviors of firms, inter-firm relationships, and environmental factors into the same research framework, and provides a theoretical guidance for management practices.

Factors That Impact Acceptance of COVID-19 Vaccination in Different Community-Dwelling Populations in China

(1) Background: It is important to improve vaccination strategies and immunization programs to achieve herd immunity to infectious diseases. (2) Methods: To assess the acceptance of COVID-19 vaccination, we conducted face-to-face surveys and online surveys in Shanghai, Zhejiang, and Qinghai provinces. A fixed-effect model and a random effects model were used to analyze factors associated with the acceptance of COVID-19 vaccination. (3) Findings: We initially recruited 3173 participants, 3172 participants completed the full questionnaire (the response rate was nearly 100%), of which 2169 were valid questionnaires, with an effective rate of 87.3%. The results indicated that 82.6% of participants were willing to receive vaccination when it was available in the community, and 57.2% of deliverymen, 43.3% of medical workers, 78.2% of parents of primary and secondary school children, and 72.2% of parents of preschool children were willing to receive vaccination. The models showed that participants who were male (female vs. male: OR = 1.49, 95% CI (1.12, 1.98)), 60 to 69 years-old (60–69 vs. <30: OR = 0.52, 95% CI (0.29, 0.92)), had less education (medium vs. low: OR = 1.50, 95% CI (1.05, 2.23)), had good health status (good vs. low: OR = 0.36, 95% CI (0.15, 0.88)), and had positive attitudes and trust (OR = 0.14, 95% CI (0.10, 0.20)) in vaccines approved by the National Health Commission were more likely to accept vaccination. Participants also had an increased vaccination acceptance if it was recommended by government sources, doctors, relatives, or friends. Most participants learned about COVID-19 vaccination from television, radio, and newspapers, followed by community or hospital campaigns and the internet. (4) Conclusions: Government sources and doctors could increase the acceptance of vaccination by promoting the efficacy and safety of COVID-19 vaccination by the use of mass media and emphasizing the necessity of vaccination for everyone.

PENGARUH DANA DESA DAN PRODUK DOMESTIK REGIONAL BRUTO (PDRB) TERHADAP TINGKAT KEMISKINAN DI PROVINSI ACEH

This study aims to see the effect of Village Fund and Gross Domestic Regional Product on poverty in districts / cities in Aceh Province during the 2017-2019 period. To analyze the data, the method used is panel data regression analysis with the estimation of model parameters using a fixed effect model (FEM). The results showed that the village funds variable did not have a significant effect on poverty, this happened because most of the village funds were allocated more to the infrastructure development sector, causing village funds to still not have a direct effect on reducing poverty. The Gross Domestic Regional Product variable has a negative but significant effect on poverty in the District / City of Aceh Province, which means that with an increase in Gross Domestic Regional Product it will significantly affect the reduction of poverty levels in Aceh Province.

Panel Regression Models for Paddy (Oryza sativa) Crop Production

The present investigation was carried out to study area production trends of Paddy crop grown in different districts of Tamil Nadu state, India during the period 1998-99 to 2010- 2020 based on Panel Regression Model. The statistically most suited Panel Regression model was selected based on Hausman and Wald test. The study variables namely the area under the Paddy crop (AREA) and the production (PRODN) of Paddy crop were found to be stationary at level. Analysis of variance test indicated that district to district crop productions were highly significant. Highest area under the crops and productions were registered in Tiruvarur, Thanjavur etc., Very lowest were registered in Coimbatore and Nilgiris districts. The fixed effect model was found to be suitable to study the trend and this model explains the 87% of variations in Paddy crop production.

Research on the economic development impact of earthquake disaster in China

As one of the areas with frequent earthquake disasters, China’s social stability and sustained and healthy economic development have been profoundly affected for a long time. Based on the two-way fixed effect model and a series of deterministic indicators of economic development, including education level, economic development, financial development, fixed asset investment and permanent population, this paper designs a standard regression equation for estimating growth and uses time series panel data to analyze the impact of earthquake disasters on China’s economic development. The research results show that the earthquake disaster has an insignificant positive impact on China’s GDP. From the point of view of control variables, education level and financial development level have a positive effect on economic development as a whole, but there are certain differences in the degree of influence. The degree of trade openness has a significant impact on GDP, industry and service industries. Moreover, it has a negative impact on agricultural development.

Studi Longitudinal Pada Analisis Data Gula Darah Pasien Diabetes melalui Principal Component Analysis

Multicollinearity is a relationship or correlation between predictor variables. Multicollinearity can also occur in longitudinal data, which is a combination of cross-section data and time-series data. The impact of multicollinearity causes the influence of the predictor variable on the response variable to be insignificant, the least-squares estimator, and the error to be sensitive to changes in the data. Therefore, the procedure to overcome multicollinearity uses the principal component analysis method. This study aims to model PCA longitudinal data regression with a fixed-effect model that is applied to blood sugar data of diabetic patients with a time span of January 2019 to July 2019 at Ibnu Sina Hospital Makassar City. The results of this study indicate that there are two main components formed from PCA longitudinal data regression modelling with a fixed-effect model. Obtained variable values are systolic blood pressure of -0.007, diastolic blood pressure of -0,016, the body temperature of -0.098, and platelets of 0.005 which affect blood sugar in patients with diabetes.