The mobile commerce (m-commerce) industry has rapidly grown in value in recent years, as has the number of m-commerce service providers and interest in it from consumers and academia alike. In order to ensure customer loyalty, providers must determine which factors influence consumer satisfaction in m-commerce. Therefore, the objective of this study is to determine and rank the significant predictors of satisfaction in m-commerce. The paper also develops a procedure for artificial neural network model design and parameter setting in technology acceptance studies. Data was collected from 224 users of m-commerce services. The results presented are based on a combination of structural equation modeling (SEM) and artificial neural network (ANN) analyses. A multi-layer perceptron was used for ANN modeling. The results show that the optimal ANN model has one hidden layer and a sigmoid as an activation function in both layers, while the number of hidden nodes should be determined using a recommended rule-of-thumb. In addition, mobility and trust were found to be the most significant determinants of consumer satisfaction in m-commerce. The results of the study are significant as they have important implications for both academia and companies, due to the fact that some of the factors investigated in the study, such as mobility, have rarely been explored in previous consumer satisfaction studies, but were proved to be very significant. Another important result of the study is the proposal of a detailed procedure of ANN model design and the recommendations made for the selection of ANN model architecture and parameter settings.

近年来，移动商务（m-commerce）行业的价值迅速增长，移动商务服务提供商的数量以及消费者和学术界对它的兴趣也是如此。为了确保客户忠诚度，提供商必须确定哪些因素会影响移动商务中的消费者满意度。因此，本研究的目的是确定和排名移动电子商务中满意度的重要预测因子。本文还开发了一种用于技术验收研究的人工神经网络模型设计和参数设置的程序。从224个移动商务服务用户中收集了数据。给出的结果是基于结构方程模型（SEM）和人工神经网络（ANN）分析的结合。多层感知器用于ANN建模。结果表明，最优的ANN模型具有一个隐藏层，并且在两个层中都有一个S型激活函数，而隐藏节点的数量应使用推荐的经验法则来确定。此外，人们发现移动性和信任度是移动商务中消费者满意度的最重要决定因素。这项研究的结果是有意义的，因为它们对学术界和公司都具有重要意义，因为在先前的消费者满意度研究中很少研究该研究中所调查的某些因素（例如流动性），但是事实证明，非常重要。这项研究的另一个重要结果是提出了ANN模型设计的详细程序的建议，以及有关选择ANN模型架构和参数设置的建议。