H13: Positive attitudes towards the services offered by online
healthcare have a positive effect on satisfaction with current e-health
services.
Numerous studies have shown the relationship between positive attitudes
and the adoption/experience of new technologies [27, 36, 55]. We
expect a similar effect between positive attitudes and intention to try
and willingness to use telediagnostics, and we hypothesise that it will
also have a positive effect on satisfaction.
RESULTS
Based on the previous chapter, we created the database. We used the
average score method to create the constructs we wanted to test (based
on the responses we received), and we included demographic variables.
The dependent variables were as follows:
- Willingness to try (the higher the value, the more likely to
try telediagnostics)
- Intention to use (the higher the value, the more likely you are
to use telediagnostics regularly)
- Usage of e-health: Measured by the question “Have you
ever used an e-health service?” (Yes-No - dummy)
- Satisfaction (the higher the value, the more likely the user
was satisfied)
And the explanatory variables are:
- Positive attitude (the higher the value, the more open to
e-health)
- Technological readiness (the higher the value, the more
proficient in modern technologies)
- Fear (the higher the value, the more he/she is afraid of
e-health)
- Social benefits (the higher the value, the more likely you are
to consider e-health useful for society)
- Individual benefits (the higher the value, the more he/she
considers e-health to be beneficial for him/herself)
- COVID-19 anxiety (the higher the value, the more likely he/she
is to think that e-health reduces the chance of infection)
- Demographic variables: Age , Gender, Child, Residence,
Education, Income 11Average gross income in the analysed
country in the summer of 2021: ~1.100 euros (KSH
2021.
The analysis of the results obtained was divided into three parts. In
the first, we used an OLS model to investigate how the explanatory
variables listed affect willingness to try and intention to
use. In the second, a logit model was used to examine which factors
played a key role for those who have already used an e-health service,
while in the third, an OLS model was used to examine the effect of
constructs and demographical variables on satisfaction . The
basic regression model was as follows (robust standard error was used
for OLS regression models):
\begin{equation}
DependentVariable=\beta_{0}+\beta_{1}Attitude+\beta_{2}TechnologicalReadiness+\nonumber \\
\end{equation}\begin{equation}
+\beta_{3}Fear+\ \beta_{4}Ind.Benefits+\beta_{5}SocialBenefits+\ \beta_{6}CovidAnxiety+\beta_{7}Gender+\nonumber \\
\end{equation}\begin{equation}
+\ \beta_{8}Age+\beta_{9}Child\ +\beta_{10}\text{Residence}+\beta_{11}\text{Education\ }+\beta_{12}\text{Income}\nonumber \\
\end{equation}