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.

中文翻译：

---

通过机器学习检查用户行为，以实现有效的移动点对点支付采用

金融科技（即技术辅助的定制金融服务）带来的颠覆性创新使数字点对点（P2P）支付脱颖而出。在这种充满挑战的环境中，基于客户行为应对技术创新的理论，本文确定了消费者采用移动 P2P 支付的驱动因素，并开发了一个机器学习模型来预测这种蓬勃发展的支付选项的使用。为此，我们使用了一个独特的数据集，其中包含来自 701 位参与者（观察）的信息，这些参与者完成了有关 Bizum（全球领先的移动 P2P 平台）采用情况的调查问卷。受访者概况是欧洲文化和生活方式框架内的普通西班牙公民。我们（按优先顺序）记录移动 P2P 支付的有用性、同伴和其他社会群体（如朋友、家人和同事）对个人行为（即主观规范）、感知信任和用户享受的影响数字环境中的体验以及这些属性如何更好地对移动 P2P 支付的（潜在）用户进行分类。我们还发现基于机器学习算法的非参数方法优于传统的参数方法。最后，我们的结果表明，基于随机森林的特征选择（例如 Boruta 过程）作为一种预处理技术可显着提高预测性能，同时减少噪声、结果模型的冗余和计算成本。这项研究的主要局限性在于它只在西班牙人口的社会文化和制度框架内占有一席之地。因此，有必要通过对其他国家的人们进行调查来重复这项研究，以分析制度环境对移动 P2P 支付采用的影响。