IDEAS home Printed from https://ideas.repec.org/a/bao/ijimes/v1y2021i1p83-93id11.html
   My bibliography  Save this article

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

Author

Listed:
  • Saeid Salam

Abstract

Purpose: 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.

Suggested Citation

  • Saeid Salam, 2021. "Evaluating Customer Trust in E-Commerce with a Combined Approach to Structural Equations and Artificial Neural Networks," International Journal of Innovation in Management, Economics and Social Sciences, International Scientific Network (ISNet), vol. 1(1), pages 83-93.
    • Handle: RePEc:bao:ijimes:v:1:y:2021:i:1:p:83-93:id:11
    as

    Download full text from publisher

    File URL: https://ijimes.ir/index.php/ijimes/article/view/11/8
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bao:ijimes:v:1:y:2021:i:1:p:83-93:id:11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: International Scientific Network (ISNet) (email available below). General contact details of provider: https://ijimes.ir/index.php/ijimes/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.