
MODELING THE IMPACTS OF E-GOVERNMENT SERVICES ON CORRUPTION REDUCTION IN RWANDA: A CASE EVIDENCE FROM NYAMASHEKE DISTRICT, RWANDA
Author(s) -
Mweruli Fidèle Tubanambazi,
Eric Ruvuna
Publication year - 2021
Publication title -
european journal of social sciences studies
Language(s) - English
Resource type - Journals
ISSN - 2501-8590
DOI - 10.46827/ejsss.v6i2.1036
Subject(s) - multinomial logistic regression , variables , government (linguistics) , regression analysis , language change , sample (material) , outcome (game theory) , population , logistic regression , local government , econometrics , business , statistics , actuarial science , economics , geography , medicine , mathematics , environmental health , art , philosophy , linguistics , chemistry , literature , mathematical economics , chromatography , archaeology
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.
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