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

Blanco-Oliver Antonio (), Lara-Rubio Juan (), Irimia-Diéguez Ana () and Liébana-Cabanillas Francisco ()
Additional contact information
Blanco-Oliver Antonio: University of Seville, Seville
Lara-Rubio Juan: University of Granada
Irimia-Diéguez Ana: University of Seville, Seville
Liébana-Cabanillas Francisco: University of Granada

Financial Innovation, 2024, vol. 10, issue 1, 1-30

Abstract: Abstract Disruptive innovations caused by FinTech (i.e., technology-assisted customized financial services) have brought digital peer-to-peer (P2P) payments to the fore. In this challenging environment and based on theories about customer behavior in response to technological innovations, this paper identifies 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 adoption of Bizum, a leading mobile P2P platform worldwide. The respondent profile 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, influence of peers and other social groups such as friends, family, and colleagues on individual behavior (that is, subjective norms), perceived trust, and enjoyment of the user experience within the digital context and how those attributes better classify (potential) users of mobile P2P payments. We also find 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 performance 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 sociocultural and institutional framework of the Spanish population. It is therefore desirable to replicate this study by surveying people from other countries to analyze the effects of the institutional environment on the adoption of mobile P2P payments.

Keywords: Boruta; Feature selection; Mobile; P2P; Payment; Random forest (search for similar items in EconPapers)
JEL-codes: C45 C53 F65 G17 G28 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1186/s40854-024-00625-3 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00625-3

Ordering information: This journal article can be ordered from
http://www.springer. ... nomics/journal/40589

DOI: 10.1186/s40854-024-00625-3

Access Statistics for this article

Financial Innovation is currently edited by J. Leon Zhao and Zongyi

More articles in Financial Innovation from Springer, Southwestern University of Finance and Economics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().