Examining user behavior with machine learning for efective mobile peer-to-peer payment adoption

Abstract

Disruptive innovations caused by FinTech (i.e., technology-assisted customized fnancial services) have brought digital peer-to-peer (P2P) payments to the fore. In this chal lenging environment and based on theories about customer behavior in response to technological innovations, this paper identifes the drivers of consumer adoption of mobile P2P payments and develops a machine learning model to predict the use of this thriving payment option. To do so, we use a unique data set with information from 701 participants (observations) who completed a questionnaire about the adop tion of Bizum, a leading mobile P2P platform worldwide. The respondent profle was the average Spanish citizen within the framework of European culture and lifestyle. We document (in this order of priority) the usefulness of mobile P2P payments, infu ence of peers and other social groups such as friends, family, and colleagues on indi vidual behavior (that is, subjective norms), perceived trust, and enjoyment of the user experience within the digital context and how those attributes better classify (poten tial) users of mobile P2P payments. We also fnd that nonparametric approaches based on machine learning algorithms outperform traditional parametric methods. Finally, our results show that feature selection based on random forest, such as the Boruta procedure, as a preprocessing technique substantially increases prediction perfor mance while reducing noise, redundancy of the resulting model, and computational costs. The main limitation of this research is that it only has a place within the socio cultural and institutional framework of the Spanish population. It is therefore desirable to replicate this study by surveying people from other countries to analyze the efects of the institutional environment on the adoption of mobile P2P payments