## 4 Results
### Factor and Correlation Analysis
#### 4.2.2 EFA, CFA, and Pearson's Correlation Analysis  
EFA and CFA are generally accepted and widely used as a combination of statistical techniques, allowing researchers to reach high effectiveness in both statistical data content analysis and testing of construct and criteria [58]. Firstly, EFA was applied to determine a structure of latent dimensions among the observed variables reflected in the items of an instrument. Construct validity was determined by using the principal component analysis (PCA) extraction method and varimax rotation. In addition to the indicators of EFA, which were presented in the pre-factor analysis, as per Table 2, it is important to \"ground\" the size of the sample. Authors followed the position offered by [PERSON] and [PERSON] (1988), who claim that 150 participants could be interpreted as adequate if factor loads of several items exceed 0.80 [58,62]. The CFA principal procedure was used for a better interpretation of factor structures, to test the validity of the structure obtained after EFA. Here, three groups of model fit indices were used to examine the goodness-of-fit of the model with a given dataset: videlicet the absolute fit indices (coefficients of standardized root mean square residual, SRMR), parsimonious indices (a coefficient of root means square error of approximation, RMSE), and comparative indices (coefficients of comparative fit index, CFI; non-normed fit index, also known as the Tucker-Lewis index, TLI-NNFI) [58]. Results of these indices are provided in Table 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Indices** & **Result** \\ \hline
**Cronbach \(\alpha\)** & 0.875 \\ \hline
**Spearman–Brown** & 0.701 \\ \hline
**Kaiser–Meyer–Olkin (KMO)** & 0.839 \\ \hline
**[PERSON]’s test of sphericity \(\chi^{2}\)** & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of indexes in the pre-factor analysis. It is important to note that the chi-square/df ratio was 1.66, indicating that the model fitted the data well. Overall, the results of fit indices in Table 5 are under the recommended cut-off points for a good model-data fit. The value of SRMR was less than 0.08, the value of RMSE did not exceed the acceptable limit of 0.08, and values of CFI and TLI-NNFI indices were above the recommended value of 0.90. Finally, the following five groups of related factors were identified after completing EFA and CFA:  
1. The first-factor group (F1) consists of six internal factors, which together represent a factor group of personal evaluation and considerations. These six factors represent the evaluation of the influence level within insurance purchases in digital insurance platforms: perception of a need of insurance, financial well-being, potential financial savings, consideration of loss and gains probability, recommendations, and insurance literacy. 2. The second-factor group (F2) was formed of four external factors of technological features and the marketing domain, which influence the insurance purchase process in digital insurance platforms: advertising, the brand of insurance service provider, key technical platform features, and graphical user interface features. 3. The third-factor group (F3) has two factors inside, representing a status regarding evaluation of the digitalization level and level of preparation of insurance service providers to apply digital solutions. 4. The fourth-factor group (F4) composes four combined factors of insurance knowledge and operational features level, which influence the insurance purchase process in digital insurance platforms: insurance literacy, product terms, and conditions acceptability, customization of insurance products, personalization of insurance processes, and services. 5. The fifth-factor group (F5) has two factors, representing an \"as-is\" evaluation status of the service personalization level and product customization level in existing digital insurance platforms.