2014
DOI: 10.5539/ijms.v6n2p153
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Applying Logistic Regression to E-Banking Usage in Kumasi Metropolis, Ghana

Abstract: Purpose: The main focus of this paper is to ascertain whether customer's decision to use e-banking is influenced by socioeconomic classifications. It further seeks to identify the kinds of e-banking services provided to customers, and also examines the most frequently patronized e-banking service. Methodology/approach:In all, 241customers of three state-owned retail banks from Kumasi Metropolis, one of the most urbanized metropolis in Ghana, were used as sample for the survey. A well-structured questionnaire w… Show more

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Cited by 5 publications
(6 citation statements)
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“…The model contains categorical variables, and therefore methods for processing categorical data were used to determine the consumer profile of organic food buyers, i.e., logistic regression, analysis of contingency tables and correspondence analysis. These methods are used in the analysis of customer preferences and consumer behavior research [ 60 , 61 , 62 , 63 ]. The results were presented by means of correspondence maps.…”
Section: Methodsmentioning
confidence: 99%
“…The model contains categorical variables, and therefore methods for processing categorical data were used to determine the consumer profile of organic food buyers, i.e., logistic regression, analysis of contingency tables and correspondence analysis. These methods are used in the analysis of customer preferences and consumer behavior research [ 60 , 61 , 62 , 63 ]. The results were presented by means of correspondence maps.…”
Section: Methodsmentioning
confidence: 99%
“…After the PCA, a logistic regression with categorical variables was conducted to provide the best fit to a linear regression model on the factors causing the highest possible variability on the level of financial inclusion (as proposed by Archer et al [ 4 ]). The advantage of a logistic regression is that it accounts for more than one category of both the dependent and independent variables through the maximum likelihood estimation and works well with interval and categorical data [ 2 ].…”
Section: Methodological Approach: Twofold Approachmentioning
confidence: 99%
“…The mean difference between mobile banking adopter and non adopter merchant customers shows statistically significant at 10 % significance level (t= -5.4181), and indicating that as customers income increases a probability of customer to be mobile banking adopter also increases. High income clients and those who have current account and computer and internet literate are more likely to use Mobile-banking services (Poon, 2008;Annin K, Adjepong OM, Senya SS, 2013). The result shows that the mean educational level of adopter and non adopter merchant customers is approximately grade 10.…”
Section: Descripitive and Inferential Analysismentioning
confidence: 98%