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

Author

Listed:
  • Faheem Aslam

    (COMSATS University Islamabad)

  • Tahir Mumtaz Awan

    (COMSATS University Islamabad)

  • Tayyba Fatima

    (COMSATS University Islamabad)

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.

Suggested Citation

  • Faheem Aslam & Tahir Mumtaz Awan & Tayyba Fatima, 2022. "Classification of m-payment users’ behavior using machine learning models," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(3), pages 264-275, September.
    • Handle: RePEc:pal:jofsma:v:27:y:2022:i:3:d:10.1057_s41264-021-00114-z
    DOI: 10.1057/s41264-021-00114-z
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    References listed on IDEAS

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    1. Merhi, Mohamed & Hone, Kate & Tarhini, Ali, 2019. "A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust," Technology in Society, Elsevier, vol. 59(C).
    2. YoungJin Choi & YooKyung Boo, 2020. "Comparing Logistic Regression Models with Alternative Machine Learning Methods to Predict the Risk of Drug Intoxication Mortality," IJERPH, MDPI, vol. 17(3), pages 1-10, January.
    3. Shaw, Norman & Sergueeva, Ksenia, 2019. "The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value," International Journal of Information Management, Elsevier, vol. 45(C), pages 44-55.
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    Cited by:

    1. L. Vimal Raj & S. Amilan & K. Aparna & Karthick Swaminathan, 2024. "Factors influencing the adoption of cashless transactions during COVID-19: an extension of enhanced UTAUT with pandemic precautionary measures," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 29(2), pages 488-507, June.

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