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Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach

机译：预测移动支付接受的决定因素：混合SEM神经网络方法

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摘要

As a modern alternative to cash, check or credit cards, the interest in mobile payments is growing in our society, from consumers to merchants. The present study develops a new research model used for the prediction of the most significant factors influencing the decision to use m-payment. To this end, the authors have carried out a study through an online survey of a national panel of Spanish users of smartphones. Two techniques were used: first, structural equation modeling (SEM) was used to determine which variables had significant influence on mobile payment adoption; in a second phase, the neural network model was used to rank the relative influence of significant predictors obtained by SEM. This research found that the most significant variables impacting the intention to use were perceived usefulness and perceived security variables. On the other side, the results of neural network analysis confirmed many SEM findings, but also gave slightly different order of influence of significant predictors. The conclusions and implications for management provide companies with alternatives to consolidate this new business opportunity under the new technological developments.
机译：作为现金，支票或信用卡的现代替代品，从消费者到商人，对移动支付的兴趣在我们的社会中日益增长。本研究开发了一种新的研究模型，用于预测影响使用m-pay决策的最重要因素。为此，作者通过对西班牙智能手机用户全国小组的在线调查进行了一项研究。使用了两种技术：首先，使用结构方程模型（SEM）来确定哪些变量对移动支付的采用具有重大影响；在第二阶段，使用神经网络模型对SEM获得的重要预测变量的相对影响进行排名。这项研究发现，影响使用意图的最重要变量是感知的有用性和感知的安全性变量。另一方面，神经网络分析的结果证实了许多SEM结果，但也给出了重要预测变量影响的顺序略有不同。这些结论和对管理的启示为公司提供了在新技术发展下巩固这一新商机的替代方法。

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