1. Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior, Englewood Cliffs, NJ: Pren-tice-Hall.
2. Bank for International Settlements. (1996). Implication for central banks of the development of electronic money.
3. Bollen, K.A. (1989). Structural equations with latent variables. New York: Wiley.
4. Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
5. Downing, R. E., Moore, J. L., & Brown, S. W. (2005). The effects and interaction of spatial visualization and domain expertise on information seeking. Computers in Human Behavior, 21(4), 195–209. https://doi.org/10.1016/j.chb.2004.03.040
6. Gefen, D., Karahanna, E., & Straub, D.W. (2003). Trust and TAM in online shopping: an integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
7. Hu, L.T., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
8. Igolkina, A. A., & Meshcheryakov, G. (2020). semopy: A Python Package for Structural Equation Modeling. Structural Equation Modeling: A Multidisciplinary Journal, 27(6), 952-963. https://doi.org/10.1080/10705511.2019.1704289
9. Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
10. Nunnally, J.C. (1978). Psychometric Theory”, New York: McGraw Hill.
11. Peter, P.J., & Olson, J.C. (1990). Consumer Behavior and Marketing Strategy (2nd ed.). R.R.Donnelley, Chicago, IL, 1990.
12. Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1). 85-92. https://doi.org/10.1287/mnsc.42.1.85
13. Thompson, R.L., Higgins, C.A., & Howell, J.M. (1994). Personal computing: towards a conceptual model of utilization. MIS Quarterly, 15(1), 125-142. https://www.jstor.org/stable/249443