The paper aims are to identify factors that have the influence on FinTech services acceptance in Lithuania. In order to collect the questionnaire data, several models and analyses were used: technology acceptance model (TAM), structural equation modeling, exploratory and confirmatory factor analysis, path analysis, and visualization. The study results state that perceived usefulness and trust in services have a statistically significant effect on consumers' attitudes towards financial technologies.
Dovilė KUIZINIENĖ Neuroninio tinklo metodo tinkamumo trumpo laikotarpio virtualiųjų valiutų kursams prognozuoti tyrimas Tyrimu siekiama įvertinti neuroninio tinklo metodo tinkamumą virtualiųjų valiutų kursui prognozuoti trumpu laikotarpiu. Tyrimo tinkamumas vertinamas MSE ir R koeficientų reikšmėmis, kurios viso tyrimo metu buvo MSE ~ 0, R ~ 1. Tai nurodo šio metodo tinkamumą tolimesniems tyrimams. Reikšminiai žodžiai: virtualiosios valiutos, neuroniniai tinklai, valiutos kurso prognozavimas, bitkoinas. This paper presents a research of a neural network model suitability for short-term crypto currency forecasting. Suitability is calculated by the test results of MSE and R coefficient. The values MSE ~ 0, R ~ 1 indicate suitability of the model.
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