## 4 Results
### Survey Results  
As explained above, the survey was launched and structured into three sections (See Appendix A): (i) the first section focused on general vehicle information, (ii) the second section focused on consumption and expense information, and (iii) the third section referred to the perception of drivers regarding vehicle costs. The purpose of the first section was to check whether there were any biases, patterns, or data that might suggest that the sample was not of an adequate size or could contain alterations disrupting the data collection process (e.g., one sample contained vehicles that were much more modern, differences in car age, etc.). As can be seen in Table 5, both samples were similar in terms of types of cars, year of car production, years of car position, and numbers of kilometres per year. The initial step regarding the results of Section 2 was to normalize the data of each sample (connected and non-connected cars) for each question (total of six). To do this, all the values were first translated into an expenditure index. For instance, if we asked about the kilometer per year and the response in the form was \"between 10,001 km and 15,000 km\", we translated that value to an average scalar (i.e., 12,500 km). These normalisations helped us to calculate average costs per year or the average cost per kilometer. Subsequently, we calculated the descriptive statistics for each of the users' data (Tables 6 and 7). The \begin{table}
\begin{tabular}{c c c} \hline \hline
**Question** & **Connected Car** & **Non-Connected Car** \\ \hline Q1-Vehicle Brand & 20 most popular brands in & 20 most popular brands in \\ Q2-Vehicle year (median) & 2014 & 2013 \\ Q3-How long have you had & & \\ the vehicle? & 5.82 & 5.58 \\ Q4-No. of km/year (mean) & 13,103.82 & 12,786.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of data of connected and non-connected cars (Section 1). confidence level for all dimensions (Q1 to Q6) among both populations is sufficiently high (all dimensions in both sets have a 99% confidence level with a maximum \(\pm\)15% error, as seen in Tables 6 and 7). Such high-quality datasets enabled us to proceed to get valid and usable conclusions according to the statistical analysis proposed [37]. Additionally, the total expenditure level of each of the respondents in each of the samples was compared on a yearly basis. Furthermore, these were calculated according to the kilometers travelled by each user per year (see Table 8). Thus, the differences between the two samples are clearly significant, i.e., a difference of EUR 221.80, calculated by subtracting their corresponding expenditures, EUR 2758 and EUR 2979.80, respectively. Finally, we averaged the total cost per year with the confidence level they gave to their responses (as can be observed in the questionnaire and as it has been explained above, after each question related to the estimated costs per year, we asked them to rank from 1 to 10 the level of confidence in the answer they provided). The results of the level of expenses associated to each sample are shown in the third line of Table 8. As can be seen, the number of respondents is smaller than the number presented in the introduction. This is because we removed outliers and uncompleted responses in the questionnaire. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Q1** & **Q2** & **Q3** & **Q4** & **Q5** & & \\  & **(Petrol)** & **(Insurance)** & **(Mainte-nance)** & **(Car Expen-ditures)** & **(Recommendations)** & **(Savings)** & \(\sum Q\) \\ \hline Mean & 43.55 & 398.59 & 291.41 & 1516.61 & 32.97 & 55.43 & 1.18 \\ Standard Error & 0.66 & 8.91 & 7.96 & 33.09 & 2.26 & 3.14 & 0.00 \\ Standard & 11.69 & 157.71 & 140.78 & 585.50 & 39.82 & 55.64 & 0.07 \\ Deviation & 136.55 & 24,872.38 & 19,817.89 & 342,816.06 & 1585.62 & 3095.41 & 0.00 \\ Confidence Level & 1.71 & 23.10 & 20.62 & 85.77 & 5.86 & 8.15 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Connected car statistical data (Section 2 of the questionnaire).