Explanatory and predictive model of the adoption of P2P payment systems

The purpose of this paper is to identify the factors affecting the intention to use peer-to-peer (P2P) mobile payment. Although mobile technology has become part of everyday life, certain actions and services, such as mobile payments, are still used relatively infrequently. In this paper, we analyse consumers' adoption of P2P mobile payment services. Following a review of previous literature in this field, we identify the main factors that determine the adoption of mobile payments, and then perform a logistic regression (LR) analysis and propose a neural network to predict this adoption. From the logistic regression results obtained we conclude that six variables significantly influence intentions to use P2P payment: ease of use, perceived risk, personal innovativeness, perceived usefulness, subjective norms and perceived enjoyment. With respect to the nonparametric technique, we find that the multilayer perceptrons (MLP) prediction model for the use of P2P payment obtains higher AUC values, and thus is more accurate, than the LR model. This paper is a pioneer study of intention to use with mobile payment using these methodologies. The outcome of this research has important implications for the theory and practice of the adoption of P2P mobile payment services.

Automated summary

This paper identifies the factors affecting the intention to use peer-to-peer mobile payment, including ease of use, perceived risk, personal innovativeness, perceived usefulness, subjective norms, and perceived enjoyment. The study uses logistic regression analysis and neural network to predict adoption, finding that the multilayer perceptrons prediction model is more accurate than the LR model. The research has important implications for the theory and practice of the adoption of P2P mobile payment services.