Classification of m-payment users’ behavior using machine learning models

Faheem Aslam, Tahir Mumtaz Awan () and Tayyba Fatima
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Faheem Aslam: COMSATS University Islamabad
Tahir Mumtaz Awan: COMSATS University Islamabad
Tayyba Fatima: COMSATS University Islamabad

Journal of Financial Services Marketing, 2022, vol. 27, issue 3, No 8, 264-275

Abstract: Abstract The purpose of this study is to classify mobile payment (m-payment) users’ behavior and determine the relative importance of influencing factors by using support vector machine and logistic regression. By using survey data of 426 users who had transferred payments frequently in previous one year, classification of users and non-user is estimated by using machine learning classifiers. The findings of the confusion matrix confirm that the accuracy of support vector machine is better than the logistic regression. The research confirms perceived value as the most important predictor of usage behavior through both the models, while other predictors as told by Theory of Acceptance and Use of Technology 2 (UTAUT2) varied slightly in each model. This manuscript provides insights for technology managers who are designing services involving m-payments which ultimately help them with a strategy to better address the users’ forfeiture and switching to other brands.

Keywords: M-payments; Perceived value; UTAUT2; Machine learning; Logistic regression; Support vector machine; Classification (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/s41264-021-00114-z

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