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Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic

Author

Listed:
  • Samer H. Atawneh
  • Nawaf N. Hamadneh
  • Jamil J. Jaber
  • S. Al Wadi
  • Waqar A. Khan

Abstract

This study explores the impact of electronic payment systems on Saudi Arabia’s customer satisfaction during the COVID‐19 pandemic. Descriptive analytical approach of a sample of 1,025 people living in Saudi Arabia was used to answer the study questions and test its hypotheses. Then, a new hybrid fuzzy inference system (HyFIS) is proposed to predict customer satisfaction during COVID‐19 pandemic. The proposed system contemplates customer resistance (CR), access to technology (AT), privacy (PV), costs (CT), and speed of efficiency (SE) as the input variables and customer satisfaction (CS) as the output variable. Various statistical tests are utilized to determine the efficiency of input variables in the obtained data. The statistical tests are multicollinearity tests, reliability and validity, ordinal least square (OLS), fixed effect, and random development. As a result, we can determine each input variable’s direct and indirect impact on the CS. Under OLS, fixed effect, and unexpected effect, the SE, CT, PV, AT, and CR considerably impact EP. The EP has been shown to have substantial positive indirect implications. Under OLS, fixed effect, and random effect, the CT, PV, and CR are found to have a significant positive impact on CS. In addition, the AT has a substantial impact on CS in a fixed effect indirect effect. The results of HyFIS were compared to those of the adaptive network‐based fuzzy inference system (ANFIS). The results reveal that HyFIS outperforms ANFIS in predicting CS based on the error criterion.

Suggested Citation

  • Samer H. Atawneh & Nawaf N. Hamadneh & Jamil J. Jaber & S. Al Wadi & Waqar A. Khan, 2022. "Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:1599785
    DOI: 10.1155/2022/1599785
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    References listed on IDEAS

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