Title: Predicting the acceptance of UPI-based mobile payment system by using logistic regression and artificial neural networks: a study of Indian engineering students

Authors: Ajay Kumar; Gaurav Agrawal; Amandeep Singh

Addresses: Department of Applied Sciences, ABV-IIITM Gwalior, Gwalior-474015, MP, India ' Department of Management Studies, ABV-IIITM Gwalior, Gwalior-474015, MP, India ' Department of Management Studies, ABV-IIITM Gwalior, Gwalior-474015, MP, India

Abstract: The article develops a predictive model of acceptance of the UPI-based payment system by utilising primary datasets collected from India's young engineering students. Initially, we identified some prominent factors affecting the mobile payment from literature, and then by using the chi-squared test, some relevant factors are extracted. Here, the output variable is binary, so the logistic regression model has been used to predict UPI-based mobile payment system acceptance. Finally, we employed the artificial neural network (ANN) to overcome the limitations of logistic regression as ANN works better when the data are nonlinear. The final model consists of nine factors, and the ultimate predictive ability was 82.7%. We also ranked the factors as per their usefulness. The study may help the service providers to make a convenient system that may be trustworthy and easy to use for the end-users.

Keywords: mobile payment system; artificial neural network; ANN; logistic regression; multilayer perceptron; MLP; electronic commerce.

DOI: 10.1504/IJBISE.2021.122748

International Journal of Business Intelligence and Systems Engineering, 2021 Vol.1 No.4, pp.300 - 316

Received: 02 Mar 2020
Accepted: 26 Jun 2020
Published online: 10 May 2022 *

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