Regression Analysis Approach Research Articles

Pengaruh Pelayanan terhadap Tingkat Kepuasan Nasabah pada Pembiayaan Murabahah:

Murabahah is a popular financing product in Islamic financial institutions, including the Annajah Solution Indonesia Sharia Savings and Loan Cooperative. This study aims to analyze the effect of service on customer satisfaction on murabahah financing at the Annajah Solution Indonesia Cooperative. The research method used is quantitative research with a simple regression analysis approach. The results showed that the service variable had a positive and significant effect on customer satisfaction on murabahah financing with a determination coefficient of 0.372. That is, service affects cooperative customer satisfaction by 37.2%. The rest, 62.8% is influenced by other factors that were not examined.

Abstract P763: A Precision Medicine Approach to Intracerebral Stem Cell Transplantation Utilizing Lesion Topology in a Porcine Model of Ischemic Stroke

Despite the devastating global impact of ischemic stroke, there are few Food and Drug Administration (FDA) approved treatments, none of which are capable of regenerating or replacing infarcted brain tissue. Recently, our group demonstrated that induced pluripotent stem cell derived neural stem cells (iNSCs) are a regenerative and neuroprotective cell replacement therapy in an ischemic stroke porcine model that closely resembles human stroke pathophysiology. Historically, intracerebral stem cell transplantations have been largely guided by the injury site without taking individual neuroanatomical structures and their functions into consideration. Utilizing magnetic resonance imaging (MRI), the current study aims to identify key structures lesioned by a permanent middle cerebral artery occlusion (MCAO) that impact stroke recovery in a preclinical porcine model and determine an ideal transplantation location for future stroke iNSC transplant studies. A porcine MRI brain atlas was registered to identify stroke lesion location, and linear regressions between infarcted brain structures and functional data were completed to evaluate the predictive capacity of individual brain structure lesion on neurological outcome. MCAO resulted in prominent lesion volumes and decreased white matter integrity. Highly lesioned brain structures included the insular cortex, somatosensory cortices, visual cortices, temporal gyri, and putamen. MCAO severely impaired translational gait parameters, decreased voluntary movement in open field testing, and resulted in increased modified Rankin Scale (mRS) scoring. Linear regression analysis determined that lesions in the secondary visual cortex, claustrum, amygdala, and superior temporal gyrus were highly prognostic of overall gait and behavioral outcomes. This regression analysis approach identified neuroanatomical structures that were predictive of stroke outcome, and these structures may be key iNSC transplantation locations to facilitate optimal functional recovery.

Does Corporate Social Responsibility Disclosure Have Relevance To Profit And Book Value?

This research was aimed to examined and analyzed the value relevance of accounting information by disclosured of Corporate Social Responsibility (CSR) as a moderating variabel in banking sector companies. The population of this research was by 11 banking companies registered on the BEI (Bursa Efek Indonesia) in the period of 2015 -2018. From the population, reasearcher found 44 samples by used purposive sampling method. The hypothesis test of this research used multiple linear regression by Moderated Regression Analysis (MRA) approach. The independent variables of this research were earning and book value. CSR as an independen variable and moderating variable. While the dependent variable was the stock price. The result of this research was to indicated that the earning and book value were relevance to the value of accounting information. While CSR disclosure as an independent variable and moderating variable didn’t have relevance to the value of accounting information. The moderating result of CSR disclosure weren’t able to substantiated the earning and book value.

Prediction of Oral Pharmacokinetics Using a Combination of In Silico Descriptors and In Vitro ADME Properties.

Accurate prediction of oral pharmacokinetics remains challenging. This study investigated quantitative approaches for the prediction of the area under the plasma concentration-time curve after oral administration (AUCp,oral) to rats using the in vitro-in vivo extrapolation (IVIVE), in silico model using machine learning approaches and the combination of the in silico model and in vitro data. A set of 595 structurally diverse compounds with determined AUCp,oral at 1 mg/kg, in vitro intrinsic clearance (CLint), an unbound fraction in plasma (fu,p) in rats, and kinetic solubility at pH 6.8 was used for this assessment. Prediction models developed by two different types of machine learning techniques (i.e., random forest regression and Gaussian processes) were evaluated using three validation methods implementing the time and cluster-split training and test set and fivefold cross-validation. The developed machine learning models have a square of correlation coefficient (R2) in the range of 0.381-0.685 with 33-45% of the compounds being predicted within 2-fold of the observed AUCp,oral value. The predictivity was improved by incorporating CLint, fu,p, and solubility as explanatory variables with R2 = 0.554-0.743. In cases where extraction by the liver is the main elimination pathway and intestinal extraction is negligible, AUCp,oral can be expressed by dose, CLint, and fu,p based on a well-stirred model. By using this conventional IVIVE approach, only 1.7-5.0% of compounds were predicted within the 2-fold error with R2 = 0.354-0.487. Two empirical scaling factors (ESFs) determined by linear regression analysis and machine learning approaches improved the predictivity of AUCp,oral with 33-44% predicted within twofold variability. The IVIVE using ESF predicted by random forest regression showed better predictivity of AUCp,oral with R2 = 0.471-0.618, while it still showed lower predictivity than machine learning approaches applied directly to AUCp,oral prediction. This study demonstrated that the combination of in silico and in vitro parameters is useful to improve the predictivity of the machine learning model for rat AUCp,oral and supports consideration for predicting AUCp,oral for human and other non-clinical species in a similar manner.

Adoption of E-commerce in Bangladesh: Probit Regression and Principal Component Analysis Approach

Web based services have turned into an integral part of peoples’ life. Ease of use and several advantages lead to the popularity of adopting electronic commerce (e-commerce). Though a big number of customers visit and register on the commerce sites daily, the selling rate is low for lack of trust and loyalty. Trust is the main connector of companies and customers. Loyal customers are the important assets for the companies. This study aims at identifying the factors that are associated with the trust and risk perception of the customers on e-commerce. A primary dataset is collected on this occasion. Likert scale type questionnaire is involved in the questionnaire and data reliability is checked through Cronbach's alpha. Probit regression and principal component analysis are used as statistical analysis. The study finds the variables age, internet using purpose, market orientation, and technology as significantly related to trustworthiness of the customers on e-commerce, while the variables experienced using the internet, internet using purpose, and market orientation are potential determinants of risk perception. The study results suggest the e-commerce companies emphasize technologies for keeping accurate information, performing the outmost of the customers’ benefit, and collecting customers’ information. In addition, they should be conscious of the customers’ opinion and pleasant experience, along with maintaining the mentioned delivery time.

The Effect of Benefit Perception, Perception of Easy and Service Features on the Interest of Using Electronic Money (e-money) in Jabodetabek

The purpose of this research is: to determine the significant effect of perceived benefit on the interest in using electronic money, to determine the significant effect of perceived ease on the interest in using electronic money, to determine the significant effect of service features on the interest in using electronic money. The object of the research is respondents who already have electronic money in the form of cards and applications. The analytical tool used is SPSS 23 with multiple regression analysis approaches. With Jabodetabek respondents as many as 106 populations and 30 samples.The result of the descriptive test shows; perceived usefulness effect on interest in using electronic money, insignificant influence on perceived ease on interest in using electronic money, significant effect on service feature availability on interest in using electronic money. Therefore, it can be said that someone who feels the benefits of electronic money will certainly have an interest in using electronic money. Meanwhile, people who feel the convenience of electronic money do not necessarily have an interest in using electronic money. Meanwhile, service features are the main support for growing interest in using electronic money.

Understanding residents’ green purchasing behavior from a perspective of the ecological personality traits: the moderating role of gender

ABSTRACT One of the main obstacles to green development is the green motivation-behavior gap of individuals. Within the stimulus-organism-response paradigm framework, this paper establishes a research model based on the big five personality model, aiming to better understand the effect of media exposure on residents’ green purchasing behavior and the narrowing of the motivation-behavior gap. Structural equation modeling and multiple regression analysis approaches were employed using data from 530 samples. We investigated (1) whether an ecological personality mediated the relationship between media exposure and green purchasing behavior, and (2) whether gender also moderated these relationships. Results indicate that media exposure can effectively shape residents’ ecological personality, although not all dimensions of an ecological personality can be effectively transformed into green purchasing behavior. The mediation effects of the five dimensions of ecological personality were significantly different, and there were gender differences in these pathways. Overall, our findings are the first to indicate that media exposure can influence green purchasing behavior by shaping residents’ ecological personality traits, particularly those that are stable and durable in theory, although green practitioners should be aware that their green information strategy should be tailored to match their audience’s ecological personality and gender.

Corporate Governance Mechanisms and Firm Performance in Saudi Arabia

This paper examines the impact of corporate governance mechanisms including board size, independence, and meeting frequency, audit committee size and meeting frequency, CEO duality and ownership concentration on the operational, financial and market performance of Saudi listed firms using a contingent theoretical-based framework drawing on agency theory, stewardship theory and resource dependence theory. This study examines 210 listed Saudi Stock Exchange firms over the timeframe 2017 to 2019. The paper applies both a manual content and regression analysis approach. The results show that firm performance deteriorates with board size and independence, audit committee and meeting frequency, and the presence of CEO role duality, while performance improves with board meeting frequency and ownership concentration. Thus, Saudi firms should respond by maintaining smaller boards and more frequent meetings, keeping the Chair and CEO roles separate, and maintaining smaller audit committees with more focused meetings. Further, the appointment of independent directors only makes a meaningful contribution to firm performance where they are truly independent. Finally, more concentrated ownership tends to encourage better firm performance due to the regime of monitoring and discipline concomitant with more powerful shareholders. The implications of this paper are threefold. First, the implementation by Saudi Arabia of the latest corporate governance regulations and IFRS adoption almost certainly impact firm performance markedly. Second, corporate governance regulations should recognize the role of more frequent board meetings and more concentrated ownership in enhancing corporate performance. Third, stakeholders should apply pressure on investee firms to maintain smaller boards, engage genuinely independent directors, separate the role of Chairman and CEO, and maintain smaller audit committees with fewer and more effective meetings. The results should help corporate boards when deciding on the best corporate governance mechanisms to enhance firm performance. Further, the study should provide policy makers with a better understanding of the corporate governance structures required to promote better performance by drawing on existing theories and the empirical modelling, in an emerging economy setting such as Saudi Arabia, a new and broader data set, thereby informing better future policy and protecting shareholders’ interests.

An investigation of three mathematical models on the COVID-19 pandemic: the case of Turkey

Mathematical modelling plays a major role in assessing, controlling, and forecasting potential outbreaks. This article consists of implementing mathematical forecasting models by Liang [14], Ma [16], and the curve fitting method with the least squares as a standard approach in regression analysis and compares those calculate results obtained from the Covid-19 outbreak data, which is announced by the Turkish Ministry of Health, between March 10, 2020, and May 15, 2020, in Turkey. For this purpose, we presented an overview of three mathematical models and finally, demonstrated their applications using the Ministry of Health of Turkey’s publicly reported data.

Impact of Staff Training on the Performance of the Zanzibar Immigration Services Department

The main objective of this study was to assess the role of staff training on the performance of Zanzibar Immigration Services Department (ZISD). Quantitative approach was adopted in this study and data was analyzed through descriptive (frequencies, mean and standard deviation) and inferential analysis (multiple linear regression analysis) approaches. Simple Random and purposive sampling were used to select 150 Respondents. Data was collected through closed ended questionnaires. The study results showed that, staff training affects the performance of Zanzibar Immigration Services Department where 80 respondents equal to (53.3%) replied that, training had very high effect on workers’ performance, 37 (24.7%) replied that, the effect was high. Then, the study analysed the quality of training being provided, good relationship with superior, good relationship with peers and recognition of the workers have positive impact on the performance of Immigration Services Department in Zanzibar. The study recommended that, the department should determine the training needs suitable for the achievements of organizational goals. Keywords: Staff Training, Organizational Performance, Zanzibar Immigration Services Department DOI: 10.7176/JRDM/72-06 Publication date: January 31 st 2021

Effects of Empowering Leadership on Employee Voice Behavior: The Mediating Role of Psychological Empowerment

Over the past two decades many organizations are moving towards decentralization approach and hence putting in much effort to ensure that employees acquire the necessary leadership skills. Many organizations have adopted managerial attempts that aim to strengthen and empower leaders to boost organizational performance. Several educational researchers have exploited the relationship that exist between empowering leadership, psychological empowerment. The purpose of this research was to analyze the effects that empowering leadership can have on employee voice behavior, with the mediating role of psychological empowerment. The technique used to gather responses from 151 respondents was mainly quantitative research approach with questionnaires as a tool. The study employed random sampling to involve respondents in the study. Multiple operations such as correlation matrix, regression analysis and bootstrap approach were conducted to analyze data collected from the questionnaire. The findings showed a positive correlation between the variables measured. All hypotheses tested revealed a significant positive relationship between empowering leadership and voice behavior, psychological empowerment and voice behavior, and the mediating role of psychological empowerment. This study is useful in the academic, industrial and organizational sector. Organizations can use it to employ empowering leadership and measure the change in the behavior of employees, attitude, motivation, and empowerment. In terms of academia, this research opens more doors to further investigate the relationship between these variables. The limitations of the study include purely quantitative means of analysis and the use of random sampling. Future researchers can get more insights by employing a qualitative technique and adding more variables to further define the relationship and get better insights.

E-commerce Industry’s R&D, Market Operating and Performance: Based on Worldwide Listed Companies’ Empirical Analysis

Based on the characteristics of the e-commerce industry, this paper proposes the conception of operating intensity and explores the relationships between R&D intensity, operating intensity and firms’ performance. Multiple regression analysis approach is adopted based on the unbalanced panel dataset of global e-commerce listed companies in 49 countries in 2001-2015. Our findings suggest that suitable R&D intensity contributes positively to e-commerce firms’ performance, and with a lag. Operating intensity contributes an inverted U shape to e-commerce firms’ performance. We also find that interaction between R&D and operating intensity’s effects on firms’ performance is positively significant in global samples. In Group 7 sample, R&D intensity contributes positively to e-commerce firms’ performance, but BRICS sample’s is negative. Operating intensity contributes positively to firms’ performance both in Group 7 sample and BRICS sample. Marginal utility of operating intensity on firms’ performance in Group 7 sample is bigger than in BRICS. The results imply that R&D investment of e-commerce listed companies in BRIC countries has not converted to benefit, however it drags down the firms’ performance.

The technique for increase of generating system technical and economic indexes evaluation accuracy using machine learning

This paper addresses the problem of thermal power plant generating unit efficiency evaluation accuracy increase. The comparison of classic regression analysis and artificial neural network regression approaches is described. The feasibility and reasonability of application of the machine learning techniques for generating unit performance prediction and selecting most influential parameters on its efficiency evaluation accuracy are considered.

Long-Term Trend in Infant Mortality in India: A Joinpoint Regression Analysis for 1971–2018

Infant mortality rate (IMR) in India remains high by international standards. India accounts for the largest number of global infant deaths. This study analyses the trend in IMR in India over almost four decades beginning 1971 through 2018. The analysis is based on annual estimates of IMR available through India’s official sample registration system and follows the joinpoint regression analysis approach. The analysis reveals that the trend in IMR in India changed three times during 1971–2018 and the pace of decrease has been different in different sub-periods with a considerable deceleration in the decrease during 1992–2006. It is only after the launch of National Rural Health Mission in 2005 that the decrease in IMR in India and selected states accelerated to more than 4 per cent per year.

Dampak Kawasan dan Status Negara Terhadap Pertumbuhan Ekonomi Negara-Negara Terdampak Covid-19


 Faisal Salistia
 IAI Nasional Laa Roiba Bogor
 faisalsalistia@laaroiba.ac.id
 
 
 ABSTRACT
 The COVID-19 pandemic has had multi-sectoral impacts, including disrupting the economic growth of many countries. This study aims to analyze the impact of the COVID-19 pandemic, fiscal capability as well as regional differences and country status on the economic growth of the affected countries. The method used is a quantitative method with a saturated sample of 135 countries, and a regression analysis approach with dummy variables. The results showed that the variables of pandemic cases, exposure time, population, regional differences and country status differences affected the economic growth of the affected countries (R2 0.6373). If the pandemic is under control and there is no disparity in management between regions and between countries, then global economic growth has the potential to be positive at 0.18%. If state spending is increased, it has the potential to reduce the impact of economic contraction by 0.27%. The management of the pandemic in the Asian, American and African regions is significantly different from that in the Australian region. Poor countries are different from developed countries, but not different from middle-income countries, in managing pandemics in their regions. In relative terms, the impact of the pandemic in Asia, America and Africa is heavier than other regions.

Pattern evaluation of operational radar reflectivity–rainfall relationship for Quitandinha River flooding events: Petrópolis, Rio de Janeiro (Brazil)

Real-time rainfall distribution is required for decision-making based on the support of warning systems, especially in situations of imminent floodings and landslides. In this work, six relationships on radar reflectivity–rainfall relationships were calibrated and evaluated for 33 rainfall events associated with Quitandinha River flooding with occurred between 2013 and 2016. These events were chosen with the purpose of finding new relationships that could characterize with better accuracy the properties of storms and precipitation associated with Quitandinha floods. The procedure of applying variations of 5%, 10%, and 20% in reflectivity band-pass filter was used to minimize the different types of uncertainties associated with the measurement of radar reflectivity and rain gauges. Multiple linear regression analysis approach showed that radar reflectivity–rainfall relationships which were calibrated at 20% presented the lowest dimensionless coefficient of variability (CV), systematic error (BIAS), mean absolute deviation MAD), and root-mean-square error (RMSE). The calibrated expression between Marshall–Palmer and Nexrad showed the best performance among the six relationships examined.

The Differences in Evaluation of Flotation Kinetics of Talc Ore Using Statistical Analysis and Response Surface Methodology

This paper investigates the effect of sodium hexametaphosphate (SHMP) depressant/dispersant in the presence of methyl isobutyl carbinol (MIBC) frother and soluble starch (SS) depressant on the flotation kinetics of talc ore. Emphasis is on a comparison between the evaluation of a custom design of experiment (DoE) using the multilinear regression analysis (MRA) and response surface methodology (RSM) approach. Although analysis of variance (ANOVA) is a good first step in the evaluation of the effect of factors on froth flotation processing, it nonetheless only reveals the effects that are the same under all conditions. In the case of SHMP, its effect on separation efficiency is positive; however, if it is used along with SS, the effect is negative. Moreover, if a higher frother dosage is used, the effect of SHMP on separation efficiency is negligible.

Scrutinizing variability in full and partial rainfall time series by different approaches

The main goal of this study was established on the effect of splitting full data in disclosing time-related internal variability in a given time series, as well as classical trend analysis procedure. For this purpose, Mann–Kendall (MK), Spearman's Rho (SR), linear regression (LR), and innovative trend analysis (ITA) approaches were applied to the full part and the split data of the seasonal and annual rainfall data provided from the Kizilirmak Basin in Turkey. In the study, it was determined that the ITA method was more effective than other methods in terms of finding out the statistically significant trend in a significant portion of the rainfall data (approximately 87%), whereas this rate varied between 7 and 13% in other methods. Similar results have been experienced when the full data were divided into three groups. Among the trend analysis approaches applied to the split data groups formed for the analysis of the internal variability that occurs during the observation periods in the rainfall time series, the ITA method was quite effective in detecting the mentioned change in the time series. The ITA procedure revealed that there was a statistically significant decrease in rainfall amounts in the seasons with the highest rainfall of the region. This result could not be presented sufficiently when the data were divided into two groups. The remaining three methods failed to produce a remarkable result in determining the internal variability experienced in the data. In this sense, the consideration of full data in the analysis produces information about the average trend, it is insufficient to reveal the hidden information in the data.

The effect of exploitative leadership on knowledge hiding: a conservation of resources perspective

PurposeDrawing on conservation of resources (COR) theory, this study aims to explore the influencing mechanism of exploitative leadership on knowledge hiding. Specifically, this study focuses on the mediating role of psychological distress and the moderating role of hostile attribution bias in affecting the mediation.Design/methodology/approachData were collected from 324 employees of a high-technology company in China by a three-wave questionnaire survey. Hierarchical regression analysis and bootstrapping approach were employed to test hypotheses.FindingsThis study found that exploitative leadership was positively related to knowledge hiding and that psychological distress mediated this relationship. Moreover, the results revealed that the positive relationship between exploitative leadership and psychological distress and the indirect effect of exploitative leadership on knowledge hiding via psychological distress were stronger when hostile attribution was high rather than low.Practical implicationsThe findings of this study offer guidance for managers to better undermine the negative effects of exploitative leadership.Originality/valueFirst, this study extends the literature on exploitative leadership by verifying the positive effect of exploitative leadership on knowledge hiding. Second, this study enriches one’s understanding of the “black box” underlying the link between exploitative leadership and its consequences by demonstrating the mediating role of psychological distress. Third, by verifying the moderating role of hostile attribution bias, this study provides insights into the boundary conditions of the impact of exploitative leadership.

A fuzzy nonlinear univariate regression model with exact predictors and fuzzy responses

In this paper, a fuzzy nonlinear univariate regression model with nonfuzzy predictors and fuzzy responses is proposed. For this purpose, both nonlinear parametric and nonparametric methods were utilized. The left and right spreads of unknown fuzzy smooth function were estimated via a popular kernel-based curve-fitting method, while the center was estimated using both parametric and nonparametric curve-fitting methods. In fact, two techniques were suggested and compared in terms of estimating the center of fuzzy smooth function: (1) nonparametric method (similar to the left and right spreads) and (2) parametric method adopted with a common nonlinear regression model called truncated spline regression. Each stage was separately estimated the unknown components were addressed via the conventional statistical regression methods. The proposed method managed to provide a simple and fast estimation/prediction approach for the fuzzy univariate regression analysis for any types of LR-fuzzy numbers. Some common goodness-of-fit criteria were also employed to evaluate the performance of the proposed method. The effectiveness of the developed method was further illustrated through three numerical examples including a simulation study based on a common kernel. The proposed method was also compared with several common fuzzy linear/nonlinear regression models. The numerical evaluations indicated that the proposed parametric method for centers exhibited more accurate results as compared with the nonparametric method.