Explanatory and predictive model of the adoptionof 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 andservices, such as mobile payments, are still used relatively infrequently. In this paper, we analyseco...

ver descrição completa

Detalhes bibliográficos
Autores: Lara Rubio, J., Villarejo Ramos, Ángel Francisco, Liébana-Cabanillas, Francisco
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2020
País:España
Recursos:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/104534
Acesso em linha:https://hdl.handle.net/11441/104534
https://doi.org/10.1080/0144929X.2019.1706637
Access Level:acceso abierto
Palavra-chave:Mobile payment
Adoption
P2P
Intention to use
Neural networks
id ES_bc0cd582d4e41c40b9cc53481178baae
oai_identifier_str oai:idus.us.es:11441/104534
network_acronym_str ES
network_name_str España
repository_id_str
spelling Explanatory and predictive model of the adoptionof P2P payment systemsLara Rubio, J.Villarejo Ramos, Ángel FranciscoLiébana-Cabanillas, FranciscoMobile paymentAdoptionP2PIntention to useNeural networksThe 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 andservices, such as mobile payments, are still used relatively infrequently. In this paper, we analyseconsumers’adoption of P2P mobile payment services. Following a review of previous literaturein thisfield, we identify the main factors that determine the adoption of mobile payments, andthen perform a logistic regression (LR) analysis and propose a neural network to predict thisadoption. From the logistic regression results obtained we conclude that six variablessignificantly influence intentions to use P2P payment: ease of use, perceived risk, personalinnovativeness, perceived usefulness, subjective norms and perceived enjoyment. With respectto the nonparametric technique, wefind that the multilayer perceptrons (MLP) prediction modelfor the use of P2P payment obtains higher AUC values, and thus is more accurate, than the LRmodel. This paper is a pioneer study of intention to use with mobile payment using thesemethodologies. The outcome of this research has important implications for the theory andpractice of the adoption of P2P mobile payment services.Ministerio de Ciencia e Innovación B-SEJ-209-UGR18Taylor and FrancisAdministración de Empresas y MarketingMinisterio de Ciencia e Innovación (MICIN). España2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/104534https://doi.org/10.1080/0144929X.2019.1706637reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésBehaviour & Information TechnologyB-SEJ-209-UGR18https://doi.org/10.1080/0144929X.2019.1706637info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1045342026-06-17T12:51:07Z
dc.title.none.fl_str_mv Explanatory and predictive model of the adoptionof P2P payment systems
title Explanatory and predictive model of the adoptionof P2P payment systems
spellingShingle Explanatory and predictive model of the adoptionof P2P payment systems
Lara Rubio, J.
Mobile payment
Adoption
P2P
Intention to use
Neural networks
title_short Explanatory and predictive model of the adoptionof P2P payment systems
title_full Explanatory and predictive model of the adoptionof P2P payment systems
title_fullStr Explanatory and predictive model of the adoptionof P2P payment systems
title_full_unstemmed Explanatory and predictive model of the adoptionof P2P payment systems
title_sort Explanatory and predictive model of the adoptionof P2P payment systems
dc.creator.none.fl_str_mv Lara Rubio, J.
Villarejo Ramos, Ángel Francisco
Liébana-Cabanillas, Francisco
author Lara Rubio, J.
author_facet Lara Rubio, J.
Villarejo Ramos, Ángel Francisco
Liébana-Cabanillas, Francisco
author_role author
author2 Villarejo Ramos, Ángel Francisco
Liébana-Cabanillas, Francisco
author2_role author
author
dc.contributor.none.fl_str_mv Administración de Empresas y Marketing
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Mobile payment
Adoption
P2P
Intention to use
Neural networks
topic Mobile payment
Adoption
P2P
Intention to use
Neural networks
description 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 andservices, such as mobile payments, are still used relatively infrequently. In this paper, we analyseconsumers’adoption of P2P mobile payment services. Following a review of previous literaturein thisfield, we identify the main factors that determine the adoption of mobile payments, andthen perform a logistic regression (LR) analysis and propose a neural network to predict thisadoption. From the logistic regression results obtained we conclude that six variablessignificantly influence intentions to use P2P payment: ease of use, perceived risk, personalinnovativeness, perceived usefulness, subjective norms and perceived enjoyment. With respectto the nonparametric technique, wefind that the multilayer perceptrons (MLP) prediction modelfor the use of P2P payment obtains higher AUC values, and thus is more accurate, than the LRmodel. This paper is a pioneer study of intention to use with mobile payment using thesemethodologies. The outcome of this research has important implications for the theory andpractice of the adoption of P2P mobile payment services.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/104534
https://doi.org/10.1080/0144929X.2019.1706637
url https://hdl.handle.net/11441/104534
https://doi.org/10.1080/0144929X.2019.1706637
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Behaviour & Information Technology
B-SEJ-209-UGR18
https://doi.org/10.1080/0144929X.2019.1706637
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Taylor and Francis
publisher.none.fl_str_mv Taylor and Francis
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869418079897780224
score 15,301629

Registos relacionados