## 5 Discussion and Limitations
The personal insurance experience factor has the highest rank (7.4) among all influential factors, supporting the findings by the structural model of attitude towards insurance (ATI) by [PERSON] and [PERSON] (2018) [63], where factors of perception of risk in relation to the good/asset and personal concern with finances are indicated as having the highest positive influence towards consumers’ willingness to pay for insurance. Additionally, the high rank (7.2) and influence of the insurance service provider brand factor confirm marketing \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Factor Group**} & \multicolumn{3}{c}{**Results of Kruskal–Wallis H Tests**} \\ \cline{3-5}  & & **Kruskal–Wallis H** & **Df** & **Asymptotic Significance** \\ \hline \multirow{3}{*}{**Country**} & F1 & 0.986 & 2 & 0.611 \\ \cline{2-5}  & F2 & 2.317 & 2 & 0.314 \\ \cline{1-1} \cline{2-5}  & F4 & 1.732 & 2 & 0.421 \\ \hline \hline \end{tabular}  
* Results of the Mann–Whitney U test are presented in Table 8.  
\end{table} 
Table 8: Results of Mann–Whitney U test. Source: composed by the authors by using IBM SPSS Statistics 26 (Armonk, NY: IBM Corp.). domain-oriented findings of [PERSON] and [PERSON] (2013) [30], where marketing mixes are recognized as an outlying component of the final financial decision-making frameworks, and the structural ATI model of [PERSON] and [PERSON] (2018) [63], where the factor of trust in the industry is also outlined among three of the most influential factors. From the perspective of the Baltic insurance market research, the empirical investigation and results of marketing-oriented factors extend the finding by [PERSON] and [PERSON] (2014) [11], where no significant statistical relationship has been identified between the insurance purchase intention and the discount factor. Moreover, this contradiction of evaluation results in insurance research on marketing-oriented factors will reveal a possible scope of the marketing domain inclusion in further studies on digital insurance decision-making. Second, the findings on the insurance literacy factor have a two-fold meaning and scientific contribution. The lower average evaluation rank (6.9) on the insurance literacy factor partially supports the findings by [PERSON] and [PERSON] [18], [PERSON] et al. [64], and [PERSON] et al. [17], where insurance literacy was identified as a potential mitigation action and an influential factor to reduce the problem of underinsurance and low-level insurance knowledge. Otherwise, recognition of the insurance literacy factor in non-standard combination, with the factors product terms and conditions acceptability, customization, and personalization foster an additional scientific discussion and contribute, as a standpoint, to further conceptual modeling and empirical analysis. The research also has methodological and empirical limitations. First, in methodological terms, the validity and reliability of CFA outcomes within the selected sample of 157 respondents are questionable. It contradicts the [PERSON]'s critical N (C.N.) recommendation, where the acceptable size of the sample (N) should be above 200 to accept a model at the 0.05 level [65; 66]. Nevertheless, authors follow the methodological position by [PERSON] and [PERSON] (1988) [62], claiming that a sample's size, as a function of the number of variables, is not an important factor to determine the sample pattern as being a stable and approximate population pattern. It is outlined that a research sample size between 100 and 200 observations is valid, and under this sample, \"a correlation coefficient becomes an adequate estimator of the population correlation coefficient\" [62]. Additionally, the value of the KMO of 0.839 confirms the adequacy of sampling. Additionally, the scope of the research instrument and assessment scale, by including more than 10 items, is considered to bring some negative effects on the reliability of measurement results and affect the perception of respondents [67].