Effort expectancy and social influence factors as main determinants of performance expectancy using electronic banking

  • Received May 13, 2020;
    Accepted April 26, 2021;
    Published April 28, 2021
  • Author(s)
  • DOI
  • Article Info
    Volume 16 2021 , Issue #2, pp. 27-37
  • Cited by
    14 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

This study is aimed at determining the effect of expected effort and social influence factors on expected performance when using internet banking. The study adapts the constructs and definitions from the UTAUT model in the context of the adaptation of online banking technology. With regard to the nature of the variables analyzed, the following statistical tests and methods were used: calculation of average values using descriptive statistics; multiple linear regression analysis – to interpret associations between quantitative variables. Banks, as well as users of these banking services in the online environment, are the subject of research. The survey sample consists of 454 men and women and reflects the profile of online consumers across different countries of the European Union. The results of this study show the impact of the social influence construct on the respondents’ behavior when using electronic banking. The expected effort factor in the study significantly affects the expected performance factor, which can be characterized by original research, which showed that the effect of perceived ease of use on behavioral intent and use is incompatible with the degree of system complexity.

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    • Figure 1. Results for the factor of expected performance
    • Figure 2. Summary of results for the expected effort factor
    • Figure 3. Overview of results for the social influence factor
    • Table 1. Results for the expected performance factor
    • Table 2. Summary of results for the expected effort factor
    • Table 3. Results for the social influence factor
    • Table 4. SEM for the PE factor – Overview of results for latent variables
    • Table 5. SEM for the EE factor – Overview of results for latent variables
    • Table 6. SEM for the SI factor– Overview of results for latent variables
    • Table 7. SEM when estimating ML – Overview of results for a structural regression model
    • Table 8. Overview of statistical evaluation of research hypotheses
    • Conceptualization
      Igor Fedorko, Radovan Bačik
    • Investigation
      Igor Fedorko, Radovan Bačik
    • Methodology
      Igor Fedorko, Radovan Bačik
    • Project administration
      Igor Fedorko, Radovan Bačik, Beata Gavurova
    • Validation
      Igor Fedorko, Radovan Bačik
    • Writing – original draft
      Igor Fedorko
    • Writing – review & editing
      Igor Fedorko
    • Funding acquisition
      Beata Gavurova
    • Software
      Beata Gavurova
    • Supervision
      Beata Gavurova