## 4 Results
### Factor and Correlation Analysis
#### 4.2.2 EFA, CFA, and Pearson's Correlation Analysis
Results of the statistical factor analysis show the existence of five-factor groups, but only three of them are directly related to the digital behavior and purchase decision-making process of Baltic insurance customers: the first-factor group, the second-factor group, and the fourth-factor group. Additionally, calculation of the total rank-sum was conducted to identify the most influential factor groups; the first-factor group with 6648 points. The second-factor and the fourth-factor groups have rank-sums of 4482 and 4352 points, respectively. The Pearson correlations between the five-factor groups were calculated in a parallel way. Details of the Pearson's correlation calculation are provided in Table 6.  
It should be outlined that three factors groups are identified as having a strong positive Pearson correlation: the first-factor group, the second-factor group, and the fourth-factor group. This result has a two-fold outcome. First, it supports the previous finding of these three factor groups being predominant factors within insurance purchase-decision-making processes at Baltics online insurance platforms. Second, it grounds the theoretical assumption about the digital environment and technological factor influences to end-users and extends the foundation of the theoretical insurance decision-making framework, with new components in the pre-purchase and purchase stages. \begin{table}
\begin{tabular}{c c} \hline
**Indices** & **Result** \\ \hline
**SRMR** & 0.063 \\ \hline
**RMSEA** & 0.065 \\ \hline
**CFI** & 0.921 \\ \hline
**TLI-NNFI** & 0.903 \\ \hline \end{tabular}
\end{table} 
Table 5: Results of CFA. Source: composed by the authors, using IBM SPSS Statistics 26 (Armonk, NY: IBM Corp.). Finally, the aim was to analyze the views of insurance-related specialists regarding their sociodemographic characteristics, i.e. age, country, and gender. As the normal distribution assumptions were not met, Mann-Whitney U and Kruskal-Wallis tests were applied to identify whether there were any statistically significant differences between different groups of respondents in the three most influential factor groups. Results of the Kruskal-Wallis test are presented in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Factor Group**} & \multicolumn{4}{c}{**Results of Kruskal–Wallis H Tests**} \\ \cline{3-6}  & & **Kruskal–Wallis H** & **Df** & **Asymptotic Significance** \\ \hline \multirow{3}{*}{**Age group**} & F1 & 3.453 & 4 & 0.485 \\ \cline{2-6}  & F2 & 15.209 & 4 & 0.004 \\ \cline{2-6}  & F4 & 2.736 & 4 & 0.603 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of Kruskal–Wallis H test. Source: composed by the authors by using IBM SPSS Statistics 26 (Armonk, NY: IBM Corp.). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Factor Group**} & \multicolumn{4}{c}{**Results of Kruskal–Wallis H Tests**} \\ \cline{3-6}  & & **Kruskal–Wallis H** & **Df** & **Asymptotic Significance** \\ \hline \multirow{3}{*}{**Age group**} & F1 & 3.453 & 4 & 0.485 \\ \cline{2-6}  & F2 & 15.209 & 4 & 0.004 \\ \cline{2-6}  & F4 & 2.736 & 4 & 0.603 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The calculation of [PERSON]’s correlation. Source: composed by authors by using using IBM SPSS Statistics 26 (Armonk, NY: IBM Corp.). The Kruskal-Wallis identified that the difference between the mean values of the second-factor group for age groups was statistically significant (_p_-value = 0.004 is less than the significance level 0.05). It was identified that the highest mean rank of 103.53 was in the age group 46-55, compared to the lowest mean rank of 59.22 in the age group 18-25. Moreover, it was noticed that the value of a mean rank increases constantly within age groups. Several conclusions can be formulated in regards to the above-defined observations:  
1.