Evaluating Customer Trust in E-Commerce with a Combined Approach to Structural Equations and Artificial Neural Networks

Abstract

Due to the increasing familiarity of people with the Internet and considering the many benefits of online shopping, the fields of its use have grown rapidly. At such a stage, in case of mistrust among customers, this new phenomenon will fail in the first steps of progress. Organizing and supervising online stores is one of the essential indicators of e-commerce development, so that the electronic receipt and payment system for providing goods must be done in a safe and correct environment. Methodology: In this paper, various descriptive and inferential methods as well as neural network method for data analysis and PLS intelligent software are used to establish causal relationships of independent variables with dependent variables and MATLAB software is used to measure and predict dependent variables. Findings: The proposed model showed the priority and importance of key parameters and indicators affecting online trust. Originality/Value: The most important purpose of this study is to examine the dimensions and components of trust in the context of online shopping. Structural equations as well as neural networks have been used for this analysis.