IDEAS home Printed from https://ideas.repec.org/a/ibn/ibrjnl/v17y2024i6p1.html
   My bibliography  Save this article

Pandemic-Era Trends in US Automatic Payment Adoption: A 2022 Behavioral Analysis

Author

Listed:
  • Florent Nkouaga

Abstract

Introduction- This study analyses the adoption trends of automated payment methods among different racial groups in the United States during the COVID-19 pandemic in 2022. It investigates the correlations among financial literacy, risk attitudes, customer sentiment, and the adoption of automated payment mechanisms for bill settlements. Method- An analysis of responses from a diverse demographic is conducted using logistic regression on data from the 2022 Survey of Consumer Finance. This analysis investigates the trends associated with subjective and objective financial literacy, consumer sentiment as evaluated by economic perceptions, and risk attitudes among several generations, taking into account race as well. Results- Initial results suggest disparities in the degrees to which financial literacy, risk attitudes, and consumer sentiment are connected with adoption rates among various racial groups. The statistical analysis highlights disparities in the adoption of automated payments among different racial and ethnic groups. Discussion- The findings underscore the complex interplay among socio-economic status, behavioral variables, and technology adoption in the period after the pandemic. This methodology aligns with current scholarly works on behavioral finance, which emphasize the need to consider both individual psychological aspects and wider social impacts in the process of making financial decisions. By incorporating racial diversity into the research, the study provides a valuable understanding of how cultural and demographic factors interact with behavioral aspects in the particular setting of financial technology adoption during a time of substantial economic transformation.

Suggested Citation

  • Florent Nkouaga, 2024. "Pandemic-Era Trends in US Automatic Payment Adoption: A 2022 Behavioral Analysis," International Business Research, Canadian Center of Science and Education, vol. 17(6), pages 1-1, December.
  • Handle: RePEc:ibn:ibrjnl:v:17:y:2024:i:6:p:1
    as

    Download full text from publisher

    File URL: https://ccsenet.org/journal/index.php/ibr/article/download/0/0/50941/55222
    Download Restriction: no

    File URL: https://ccsenet.org/journal/index.php/ibr/article/view/0/50941
    Download Restriction: no
    ---><---

    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    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:ibn:ibrjnl:v:17:y:2024:i:6:p:1. 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: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    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.