scholarly journals MODELING THE IMPACTS OF E-GOVERNMENT SERVICES ON CORRUPTION REDUCTION IN RWANDA: A CASE EVIDENCE FROM NYAMASHEKE DISTRICT, RWANDA

2021 ◽  
Vol 6 (2) ◽  
Author(s):  
Mweruli Fidele Tubanambazi ◽  
Eric Ruvuna
Keyword(s):  
Outcome Variable ◽  
Parameter Estimates ◽  
Data Set ◽  
Explanatory Variables ◽  
Government Services ◽  
Services Delivery ◽  
Local Services ◽  
The Cost ◽  
The Impact ◽  
Services Use

The study entitled modeling the impacts of e-government services on corruption reduction in Rwanda: Case evidence from Nyamasheke District, Rwanda was about assessing the contribution of e-government services use on reducing corruption in the area under study. The study was guided with the objective of exploring the utilization of multinomial logistic regression (MLR) in modeling the impact of e-government services on reduction status of corruption. In this regard, the MLR model was performed using a maximum likelihood estimation method on the data set collected to find the parameter estimates of the model describing the relationship between the explanatory and the outcome variables and determine the significance of the explanatory variables that contribute significantly to the reduction status of corruption in the area under study. The study adopted both qualitative and quantitative approaches to collect data from 381 respondents from the target population of 8041 using Solvin’s formula for sample size calculation. Data were collected using questionnaire and interview schedule techniques and analyzed using SPSS-23. In this analysis, the results show that on the total of eleven independent variables, the explanatory variables such as age, income, ownership of the devices used in applying for the local government services and the advice types were dropped from the training set of explanatory variables that contribute significantly to the reduction of corruption in the area under study. In model selection that overall fits well the data, the obtained variables that contributed significantly to the outcome variable were education, e-government services’ use status, cost of accessing e-government services and the e-government services types delivery. The parameters estimate of the selected model revealed that the variables that best predicted the probability of reducing corruption once the e-government services are delivered online were education, status of using e-government services, types of e-government services delivery online while the cost of accessing the e-government services decreased the logit (the probability) of reducing corruption. The main challenges faced by users of e-government services were the cost given while applying to these e-government services is high and lack of enough skills to cope with technological usage. Finally the study recommended that local leaders in the area under study should strengthen the online system in delivering local services to people, educate people to be aware about the use of e-government services since the more a person is educated the more is attempting to use e-government services and then reduce the cost of using e-government services while applying to the local services since this has been the only explanatory variable that decreased the logit of reducing corruption in the study area. <p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0790/a.php" alt="Hit counter" /></p>

scholarly journals Decision making by NICE: examining the influences of evidence, process and context

2013 ◽  
Vol 9 (2) ◽  
pp. 119-141 ◽  
Author(s):  
Karin H. Cerri ◽  
Martin Knapp ◽  
Jose-Luis Fernandez
Keyword(s):  
Decision Making ◽  
New Technologies ◽  
Multinomial Logistic Regression ◽  
Clinical Excellence ◽  
Outcome Variable ◽  
Data Set ◽  
Factors Affecting ◽  
Explanatory Variables ◽  
Relative Contribution ◽  
The Impact

AbstractThe National Institute for Health and Clinical Excellence (NICE) provides guidance to the National Health Service (NHS) in England and Wales on funding and use of new technologies. This study examined the impact of evidence, process and context factors on NICE decisions in 2004–2009. A data set of NICE decisions pertaining to pharmaceutical technologies was created, including 32 variables extracted from published information. A three-category outcome variable was used, defined as the decision to ‘recommend’, ‘restrict’ or ‘not recommend’ a technology. With multinomial logistic regression, the relative contribution of explanatory variables on NICE decisions was assessed. A total of 65 technology appraisals (118 technologies) were analysed. Of the technologies, 27% were recommended, 58% were restricted and 14% were not recommended by NICE for NHS funding. The multinomial model showed significant associations (p ⩽ 0.10) between NICE outcome and four variables: (i) demonstration of statistical superiority of the primary endpoint in clinical trials by the appraised technology; (ii) the incremental cost-effectiveness ratio (ICER); (iii) the number of pharmaceuticals appraised within the same appraisal; and (iv) the appraisal year. Results confirm the value of a comprehensive and multivariate approach to understanding NICE decision making. New factors affecting NICE decision making were identified, including the effect of clinical superiority, and the effect of process and socio-economic factors.

Reliability-based covariate analysis for complex systems in heterogeneous environment: Case study of mining equipment

2018 ◽  
Vol 233 (4) ◽  
pp. 593-604
Author(s):  
Amin Moniri-Morad ◽  
Mohammad Pourgol-Mohammad ◽  
Hamid Aghababaei ◽  
Javad Sattarvand
Keyword(s):  
Complex Systems ◽  
Cox Regression ◽  
Reliability Evaluation ◽  
Distribution Functions ◽  
Heterogeneous Environment ◽  
Mining Equipment ◽  
Data Set ◽  
Explanatory Variables ◽  
The Impact

Operational heterogeneity and harsh environment lead to major variations in production system performance and safety. Traditional probabilistic model is dealt with time-to-event data analysis, which does not have the capability of quantifying and simulation of these types of complexities. This research proposes an integrated methodology for analyzing the impact of dominant explanatory variables on the complex system reliability. A flexible parametric proportional hazards model is developed by focusing on standard parametric Cox regression model for reliability evaluation in complex systems. To achieve this, natural cubic splines are utilized to create a smooth and flexible baseline hazards function where the standard parametric distribution functions do not fit into the failure data set. A real case study is considered to evaluate the reliability for multi-component mechanical systems such as mining equipment. Different operational and environmental explanatory variables are chosen for the analysis process. Research findings revealed that precise estimation of the baseline hazards function is a major part of the reliability evaluation in heterogeneous environment. It is concluded that an appropriate maintenance strategy potentially mitigate the equipment failure intensity.

The effect of environmental regulations: a restricted cost function for Korean manufacturing industries

2007 ◽  
Vol 12 (1) ◽  
pp. 91-104 ◽  
Author(s):  
MYUNGHUN LEE
Keyword(s):  
Cost Function ◽  
Raw Materials ◽  
Environmental Regulations ◽  
Environmental Conservation ◽  
Manufacturing Industries ◽  
Parameter Estimates ◽  
Productive Activities ◽  
The Cost ◽  
The Impact ◽  
Percent Decline

Environmental conservation requires society to consider the trade-off between allocating resources to productive activities and pollution control activities. Therefore, it is informative to measure the effect of environmental regulations on firms' productivity. This paper attempts to estimate the impact of environmental regulations on Korean manufacturing industries. Despite being key inputs in the manufacturing process, raw materials have often been excluded from the cost function due to the lack of price data. A restricted cost function is used to improve the reliability of parameter estimates. Empirical results indicate that environmental regulations caused a 12 percent decline in the average annual rate of productivity growth over the period 1982–93.

scholarly journals The Complex Relationship Between Veterinarian Mental Health and Client Satisfaction

2020 ◽  
Vol 7 ◽  
Author(s):  
Jennifer L. Perret ◽  
Colleen O. Best ◽  
Jason B. Coe ◽  
Amy L. Greer ◽  
Deep K. Khosa ◽  
...  
Keyword(s):  
Mental Health ◽  
Client Satisfaction ◽  
Poor Mental Health ◽  
Personal Accomplishment ◽  
Outcome Variable ◽  
Linear Regression Models ◽  
Health States ◽  
Explanatory Variables ◽  
Health Measures ◽  
The Impact

A relatively high risk of poor mental health has been described among Canadian veterinarians, but no published studies have explored the impact that veterinarian mental health may have on veterinary clients and patients. In order to investigate the association between veterinarian mental health and veterinary client satisfaction, veterinarians were randomly sampled and recruited throughout southwestern Ontario, Canada, from November, 2017, through January, 2019. Sixty participating veterinarians completed an enrollment survey that included psychometric scales measuring resilience, perceived stress, anxiety, depression, emotional distress, emotional exhaustion, depersonalization, personal accomplishment, burnout, secondary traumatic stress, and compassion satisfaction. Nine hundred and ninety-five companion animal clients of these veterinarians were recruited in-clinic over 2–3 days and completed a post-appointment survey including the Client Satisfaction Questionnaire. The associations between clients' satisfaction scores (as the outcome variable) and each of the veterinarians' mental health measures (as the explanatory variables) were assessed using separate, multilevel, multivariable linear regression models. The associations between client satisfaction and veterinarian mental health measures were non-linear and complex; in several of the models, relatively higher client satisfaction was unexpectedly associated with poor veterinarian mental health states, while lower client satisfaction was associated with mental health scores suggesting wellness. Given that client satisfaction may impact client adherence to medical recommendations, client loyalty, and business income, the association with veterinarian mental health may have broad implications and warrants further investigation.

Rank and File Employee Satisfaction and the Implied Cost of Equity Capital

2020 ◽  
pp. 0148558X2097194
Author(s):  
Jiajia Fu ◽  
Yuan Ji ◽  
Jiao Jing
Keyword(s):  
Employee Satisfaction ◽  
Market Competition ◽  
Equity Capital ◽  
Cost Of Equity Capital ◽  
Cost Of Equity ◽  
Data Set ◽  
Financing Cost ◽  
The Cost ◽  
Market Benefits ◽  
The Impact

Rank and file employees execute firms’ daily operating activities, but prior research rarely examines their importance due to a lack of employee information. In this article, we use a novel data set—company reviews by rank and file employees—to provide evidence on the impact of employee satisfaction on a firm’s cost of equity capital. We find that firms with higher employee satisfaction have a lower cost of equity. Our results are robust to a variety of endogeneity tests and model specifications. We also find that the effect of employee satisfaction is more pronounced for firms with higher risk, greater financial constraints, and higher labor intensity or product market competition where labor is more critical to firm success. Further analysis shows that the negative association between employee satisfaction and the cost of equity is primarily grounded in reviews from current rather than former employees. Finally, we document that firms with high employee satisfaction experience lower systematic and idiosyncratic risk. Overall, our article presents novel evidence on the capital market benefits of higher employee satisfaction, particularly with regard to financing cost reduction.

scholarly journals The impact of capital structure and certain firm specific variables on the value of the firm: Empirical evidence from Kuwait

2015 ◽  
Vol 13 (1) ◽  
pp. 1191-1200
Author(s):  
Ahmad Mohammad Obeid Gharaibeh ◽  
Adel Mohammed Sarea
Keyword(s):  
Capital Structure ◽  
Stock Exchange ◽  
Influential Factor ◽  
Annual Reports ◽  
Growth Opportunities ◽  
Data Set ◽  
Explanatory Variables ◽  
Size Growth ◽  
One Year ◽  
The Impact

The main objective of this study is to empirically examine the impact of leverage and certain firm-characteristics that are believed to have significant effects on the decision to use debt and on the value of the firm. The sample is composed of 48 companies listed in the Kuwait Stock Exchange (KSE) representing four different sectors. The study uses actual and historical panel data set obtained from the published annual reports of individual firms in addition to the publications of KSE. The study was accomplished using 8 years of data with a total of 239 observations representing the study period 2006-2013. The study uses descriptive statistics, correlation, and multiple-regression analyses to examine the impact of explanatory variables on the value of the firm. The study findings lead to the conclusion that capital structure (leveraging) is the most influential factor on firm’s value. Business risk, previous year’s value (one-year lagged ROA), dividends payout ratio, size, growth opportunities and liquidity of the firm are found to have significant influence on the firm’s value in Model 1 (where ROA is used as a proxy for the value of the firm). In model 2 (i.e., where ROE is used as a proxy of the firm’s value), the findings reveal that capital structure (leveraging); firm’s size, growth opportunities and liquidity of the firm are significant influential of the firm’s value. The study is valuable to academicians, finance managers, policy makers and other stakeholders as it fills the gap of literature by providing up-to-date evidence of the impact of capital structure and other firm specific variables on the value of the firm in Kuwait.

scholarly journals RISK TAKING BEHAVIOR OF INDONESIAN BANKS: ANALAYSIS ON THE IMPACT OF DEPOSIT INSURANCE COOPERATION ESTABLISHMENT

2013 ◽  
Vol 15 (3) ◽  
pp. 3-25
Author(s):  
Moch Doddy Ariefianto ◽  
Soenartomo Soepomo
Keyword(s):  
Risk Taking ◽  
Deposit Insurance ◽  
Interest Rate Risk ◽  
Basel Ii ◽  
Data Set ◽  
Explanatory Variables ◽  
Before And After ◽  
Bank Behavior ◽  
Risk Taking Behavior ◽  
The Impact

This paper studies the risk taking behavior of Indonesian Banking Industry, especially before and after the establishment and the implementation of Deposit Insurance Corporation (IDIC). Using common set of explanatory variables; we test several empirical models to reveal the conduct of risk management by banks. In the spirit of BASEL II Accord, this paper take closer look at three types of risk behaviors namely credit risk, market or interest rate risk and operational risk, prior and post the establishment of IDIC. We tested the hypotheses using panel data set of banks operational in period of 2000-2009. The dataset consists of 121 banks with semiannual frequency (2420 observations). Our findings show that these variables explain well the three type bank risk exposures. The implementation of IDIC alters the bank behavior albeit in somewhat different way than initially hypothesized. The risk taking responses also varies across bank types. We found that State Owned Enterprise banks (SOE) behave differently relative to the rest types of the bank. Related to size, SOE banks behave more conservative after the implementation of IDIC. On the other hand its response on conditioned capital post the IDIC implementation is the opposite; they became more aggressive. We view the public pressure on this state banks has influenced the way they manage the risk.Keywords : Risk taking behavior, BASEL II, Deposit Insurance.JEL Classification: G11, G21, G32, C23

scholarly journals Wellbeing during the first wave of the COVID-19 pandemic among adults in Nigeria: Associations with psychosocial and pandemic-related factors

2021 ◽  
Author(s):  
Olanrewaju Ibikunle Ibigbami ◽  
Olakunle Ayokunmi Oginni ◽  
Ibidunni Olapeju Oloniniyi ◽  
Victor Ugo ◽  
Matthew Ebuka ◽  
...  
Keyword(s):  
Social Support ◽  
Perceived Social Support ◽  
Tertiary Education ◽  
Depression Scale ◽  
Outcome Variable ◽  
Anxiety And Depression ◽  
Physical And Mental Health ◽  
Explanatory Variables ◽  
Perceived Impact ◽  
The Impact

Abstract Background: Psychosocial factors including stress are determinants of wellbeing. However, there is a shortage of information about how these relationships were impacted by the COVID-19 pandemic among adults in Nigeria.Objectives: To determine the associations between wellbeing, and impact of the COVID-19 pandemic, psychological distress (anxiety and depression), and perceived social support among adults in Nigeria during the first wave of the pandemic.Methods: Wellbeing (assessed using the WHO Wellbeing Index) was the outcome variable while the explanatory variables included anxiety and depressive symptoms (assessed using the Hospital Anxiety and Depression Scale), perceived social support (assessed using the Multidimensional Scale of Perceived Social Support) and perceived impact of the pandemic (assessed using perceived disruptions of life-domains). Univariate and multivariate logistic regression models were used to assess the associations between the outcome and explanatory variables. The models were adjusted for sociodemographic profile (highest level of education, employment status and payment status).Results: Low self-perceived impact of the pandemic was significantly associated with higher odds of high wellbeing (AOR: 2.59; 95% CI: 1.69-3.95; p<0.001). Other factors associated with significantly higher odds of high wellbeing were high perceived social support (AOR: 2.40; 95% CI:1.78-3.22; p<0.001) and having tertiary education (AOR: 1.51; 95% CI: 1.07-2.13; p=0.020). In contrast, experiencing anxiety symptoms were significantly associated with lower odds of high wellbeing (AOR: 0.24; 95% CI: 0.14-0.41; p<0.001)).Conclusions: Measures for enhancing the wellbeing of adults resident in Nigeria may include strategies to ameliorate the impact of the pandemic, strengthening social support systems, and promoting optimal physical and mental health.

Effects of Transportation Infrastructure and Location on Residential Real Estate Values: Application of Spatial Autoregressive Techniques

2000 ◽  
Vol 1722 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Murtaza Haider ◽  
Eric J. Miller
Keyword(s):  
Real Estate ◽  
Large Data ◽  
Transportation Infrastructure ◽  
Residential Real Estate ◽  
Housing Values ◽  
Data Set ◽  
Explanatory Variables ◽  
Greater Toronto Area ◽  
Spatial Autoregressive ◽  
The Impact

Proximity to transportation infrastructure (highways and public transit) influences residential real estate values. Housing values also are influenced by propinquity to a shopping facility or a recreational amenity. Spatial autoregressive (SAR) models were used to estimate the impact of locational elements on the price of residential properties sold during 1995 in the Greater Toronto Area. A large data set consisting of 27,400 freehold sales was used in the study. Moran’s I was estimated to determine the effects of spatial autocorrelation that existed in housing values. SAR models, using a combination of locational influences, neighborhood characteristics, and structural attributes, explained 83 percent variance in housing values. Using the “comparable sales approach,” a spatiotemporal lag variable was estimated for every property in the database. This research discovered that SAR models offered a better fit than nonspatial models. This study also discovered that in the presence of other explanatory variables, locational and transportation factors were not strong determinants of housing values. On the other hand, the number of washrooms and the average household income in a neighborhood were found to be significant determinants of housing values. Stepwise regression techniques were used to determine reduced spatial hedonic models.

scholarly journals Transportation System and Trade Flows in Port Cities of China: A Random Coefficient Model

2015 ◽  
Vol 3 (4) ◽  
pp. 289-300
Author(s):  
Lizhi Xu ◽  
Shu-Cherng Fang ◽  
Kin Keung Lai ◽  
Han Qiao ◽  
Shouyang Wang
Keyword(s):  
Transportation System ◽  
Random Coefficients ◽  
Trade Flows ◽  
Parameters Estimation ◽  
Parameter Estimates ◽  
Trade Volume ◽  
Port Cities ◽  
Explanatory Variables ◽  
Trade Competitiveness ◽  
The Impact

AbstractThis paper quantitatively investigates the effect of transportation system on trade flows of four major port cities in China. Due to the significant country-pair heterogeneities in both intercept and slope terms, this paper introduced a random-coefficients model for parameters estimation. The empirical findings imply that the impact of the explanatory variables included in the gravity equation could be inaccurately estimated if the pair-wise heterogeneity biases in both intercept and slope terms are not accounted for during the econometric estimation of the model. In particular, in the presence of this heterogeneity, parameter estimates tend to be underestimated for country-pairs with higher trade volume and overestimated for those country-pairs with lower trade volume. In addition, the empirical results suggest that the improvement transportation system in port cities of China offers greater scope for its trade competitiveness.

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