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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Blanco-Oliver Antonio & Lara-Rubio Juan & Irimia-Diéguez Ana & Liébana-Cabanillas Francisco, 2024. "Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
    • Handle: RePEc:spr:fininn:v:10:y:2024:i:1:d:10.1186_s40854-024-00625-3
    DOI: 10.1186/s40854-024-00625-3
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    More about this item

    Keywords

    Boruta; Feature selection; Mobile; P2P; Payment; Random forest;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
    • F65 - International Economics - - Economic Impacts of Globalization - - - Finance

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