# Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
## 4 Results  
### Model Assessment  
The PLS-SEM path analysis algorithm estimates standardized partial regression coefficients in the structural model after approximating the measurement model parameters [61]. Thus, a two-stage evaluation of the psychometric features of the introduced conceptual model was undertaken. The quality of the measurement model was tested by examining the indicator reliability, internal consistency, convergent validity, and discriminant validity. Indicator reliability was assessed by exploring the standardized loadings of items with their respective construct. According to Hulland's purification guidelines [62], items should be retained in the measurement model only if their standardized loadings are equal to or greater than 0.708. Since loadings of items USE3, EFF2, EFF4, and EASE1 were below the recommended threshold value, they were removed from the measurement model and further analysis. The outcome of the confirmatory factor analysis (CFA) shown in Table 1 indicates that standardized loadings of all remaining items in the measurement model were above the acceptable cut-off level. Standardized loadings of items that constitute the measurement model are in the range from 0.708 to 0.937, which means that constructs accounted for between 50.13% and 87.80% of their items' variance. Figure 2: Login screens of evaluated mobile banking applications (from left to right: PBZ Mobile banking, Erste George, m-zaba, OTP m-banking). Internal consistency of constructs was tested using three indices: [PERSON]'s alpha, the composite reliability (rho_C), and the consistent reliability coefficient (rho_A). [PERSON]'s alpha [63] is a lower bound estimate of construct reliability based on equal weightings of items. By considering the actual item loadings, composite reliability [64] offers a more accurate estimate of internal consistency than [PERSON]'s alpha. The consistent reliability coefficient proposed by [PERSON] and [PERSON] [65] is an approximately exact measure of construct reliability and, as such, represents a compromise between [PERSON]'s alpha and composite reliability [66]. For all three indices, values between 0.60 and 0.70 are acceptable in exploratory studies, values between 0.70 and 0.95 represent good internal consistency, while values above 0.95 indicate item redundancy that plagues content validity [67]. Considering that item SEC3 was poorly worded, it was removed from the measurement model, which resulted in values that were in the acceptable range for all three internal consistency indices of the associated security of personal data construct. As shown in Table 2, estimated \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & **BEH** & **EASE** & **EFF** & **FEDQ** & **RES** & **SAT** & **SEC** & **USE** \\ \hline  
\hline \end{tabular} Note that bold values on the diagonal represent standardized factor loadings.  
\end{table} 
Table 1: Standardized factor loadings and cross-loadings of items. values for all three set forth indices were in a range from 0.740 to 0.949, thus implying good internal consistency of all eight constructs in the research framework. Convergent validity was examined with average variance extracted (AVE). An AVE value of 0.50 and higher is considered acceptable because it indicates that the shared variance between a construct and its items exceeds the variance of the measurement error [66]. Study findings presented in Table 2 imply that all constructs in the research model have met the requirements of this criterion. Discriminant validity refers to the degree to which a particular construct is unique when compared with the remaining ones in the model. It was examined with three measures: the cross-loadings, the Cornell-Larcker criterion, and the Heterotrait-Monotrait ratio of correlations (HTMT). The cross-loadings measure suggests that the outer loading of each item on the associated construct should be greater than its loadings on the remaining constructs in the model.