The role of Fintech in predicting the spread of COVID-19


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This study aims to investigate the role of Fintech in predicting the spread of COVID-19 based on consumers’ Fintech perceptions and behavior before and after the outbreak of COVID-19. The study used a questionnaire-based survey distributed in different countries of the world using the LinkedIn platform for this purpose to reach the targeted population. The snowball sampling technique was used. The study targeted consumers with Fintech experience, especially in digital payments services. 507 samples were retrieved. For the analysis, the Structural Equation Modeling (SEM) was used. The study revealed novel results in predicting COVID-19 spread; these three variables (Fintech Behavior before COVID-19, Fintech Behavior after COVID-19, and Fintech Perception after COVID-19) could predict 52.5% of the variance in the dependent variable (COVID-19 Spread) (R² = 0.525, p < 0.05). The findings show that Higher Fintech perception and behavior among Fintech users will help in reducing the spread of COVID-19 by avoiding the use of contact payment methods. Contactless payment methods are the main tools in Fintech that might help in avoiding the probability of COVID-19 spread. Consumers’ Fintech perceptions and behavior are the most influencing factors that could predict the spread of COVID-19 in this study, where digital payments are the main concern. It is recommended that consumers adopt digital payment methods and tools, especially contactless payment methods, to fulfill their financial services. Other researchers are also encouraged to use the same model to predict the spread of this virus in the Fintech context.

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    • Figure 1. The study model
    • Figure 2. SEM model
    • Table 1. Study reliability test
    • Table 2. Study validity test
    • Table 3. Study variables
    • Table 4. COVID-19 spread predictors scale
    • Table 5. COVID-19 spread scale
    • Table 6. Respondents’ characteristics
    • Table 7. Regression weight
    • Table 8. Squared multiple correlations
    • Table 9. Covariances
    • Table 10. Correlations
    • Table 11. Standardized regression weights
    • Conceptualization
      Mohannad Abu Daqar, Milan Constantinovits, Samer Arqawi, Ahmad Daragmeh
    • Data curation
      Mohannad Abu Daqar
    • Formal Analysis
      Mohannad Abu Daqar
    • Funding acquisition
      Mohannad Abu Daqar, Milan Constantinovits, Samer Arqawi, Ahmad Daragmeh
    • Investigation
      Mohannad Abu Daqar, Samer Arqawi
    • Methodology
      Mohannad Abu Daqar, Milan Constantinovits, Samer Arqawi, Ahmad Daragmeh
    • Project administration
      Mohannad Abu Daqar, Milan Constantinovits, Ahmad Daragmeh
    • Resources
      Mohannad Abu Daqar, Ahmad Daragmeh
    • Software
      Mohannad Abu Daqar
    • Supervision
      Mohannad Abu Daqar, Milan Constantinovits
    • Validation
      Mohannad Abu Daqar
    • Visualization
      Mohannad Abu Daqar
    • Writing – original draft
      Mohannad Abu Daqar
    • Writing – review & editing
      Mohannad Abu Daqar, Samer Arqawi, Ahmad Daragmeh