QR-code-based payment. Does the consumer intend to adopt a retail buying transaction?

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The Quick Response (QR) code payment is a relatively new means of payment in Indonesia. Even though this strategy offers a great deal of use, not many people are familiar with it or use it. In this regard, it is fascinating to look at what makes people use the QR Code payment method. The quantitative study used a five-point Likert scale questionnaire to sample e-money users through social media groups. In addition, structural Equation Modeling (SEM), employee Smart-PLS 3.0, was used to examine the data. The results show that social factors affect how people feel about QR code payments, and facilitating support and performance expectations affect how likely they are to use it. Also, attitudes, a mediator of social impact, can change the effect of support performance expectations on adoption intentions. Because of this, people accepting QR code-based payments take a positive attitude. Also, the facilities and environment affected a positive attitude, expected performance, ease of operation, and social interactions. Based on the results, the recommendation for financial institutions and innovation is that the facility and social environment are critical to the success of financial innovation. So, if more people want to use QR code-based payments, financial institutions need to make it easy for them.

Acknowledgment
The author would like to thank the Rector, Vice-Rector of the University of Muhammadiyah Malang. Furthermore, the author sincerely thanks the University of Muhammadiyah Malang’s Dean of Business and Economics Faculty. A University of Muhammadiyah Malang’s School of Economics and Business member supported finishing this paper.

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    • Figure 1. Conceptual framework
    • Figure 2. Model of QR code payment adoption
    • Table 1. Variables and indicators
    • Table 2. Gender of people participated
    • Table 3. Grouped age and education level of respondents
    • Table 4. Respondents’ attitude and intention classification
    • Table 5. Indicator values of validity of construct latent variable indicators
    • Table 6. Cross-loadings of indicators with latent variables
    • Table 7. R-Square dependent variable
    • Table 8. Path coefficient of variables in the model
    • Table 9. Mediation variable test
    • Conceptualization
      Widayat Widayat, Marsudi, Ilyas Masudin
    • Formal Analysis
      Widayat Widayat, Ilyas Masudin
    • Funding acquisition
      Widayat Widayat, Marsudi, Ilyas Masudin
    • Investigation
      Widayat Widayat, Marsudi
    • Methodology
      Widayat Widayat, Marsudi
    • Project administration
      Widayat Widayat
    • Resources
      Widayat Widayat
    • Software
      Widayat Widayat, Marsudi, Ilyas Masudin
    • Supervision
      Widayat Widayat, Marsudi, Ilyas Masudin
    • Validation
      Widayat Widayat
    • Visualization
      Widayat Widayat
    • Writing – original draft
      Ilyas Masudin
    • Writing – review & editing
      Ilyas Masudin