# Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
## 4 Results
### Model Assessment
Study findings also indicate that responsiveness (\(\beta=0.460\), \(p<0.0001\)) is a significant determinant of efficiency and that customer satisfaction (\(\beta=0.634\), \(p<0.0001\)) is a significant antecedent of behavioral intention, which provides support for H1 and H9, respectively. The effect size (\(f^{2}\)) refers to the change in the coefficient of determination of endogenous construct. Values for \(f^{2}\) of 0.02, 0.15, or 0.35 indicate that the exogenous construct has a small, medium, or large impact on the endogenous construct, respectively [72]. Considering the values presented in Table 8, customer satisfaction (\(f^{2}=0.671\)) has a large influence on behavioral intention. Customer satisfaction is strongly affected by usefulness (\(f^{2}=0.348\)), and modestly by both ease of use (\(f^{2}=0.119\)) and security of private data (\(f^{2}=0.115\)). Responsiveness (\(f^{2}=0.277\)) has a medium impact on the ease of use which in turn is affected by feedback quality (\(f^{2}=0.104\)) to a small extent. Finally, it appeared that responsiveness is medium in size (\(f^{2}=0.268\)) predecessors of efficiency while both efficiency (\(f^{2}=0.157\)) and ease of use (\(f^{2}=0.148\)) have a medium impact on usefulness. \begin{table}
\begin{tabular}{l c c} \hline
**Endogenous Constructs** & \(R^{2}\) & \(R^{2}\)**Adjusted** \\ \hline Behavioral Intention (BEH) & 0.401 & 0.395 \\ \hline Ease of Use (EASE) & 0.416 & 0.403 \\ \hline Efficiency (EFF) & 0.212 & 0.203 \\ \hline Customer Satisfaction (SAT) & 0.621 & 0.609 \\ \hline Usefulness (USE) & 0.463 & 0.452 \\ \hline \end{tabular}
\end{table} 
Table 6: Results of testing the explanatory power of the research model. The nonparametric [PERSON]'s [73] and [PERSON]'s [74] cross-validated redundancy measure \(Q^{2}\) that drawing on the blindfolding reuse technique predicts the endogenous construct's items is commonly applied for testing the predictive validity of exogenous constructs. However, since \(Q^{2}\) combines aspects of out-of-sample prediction and in-sample explanatory power [75], it does not represent a measure of out-of-sample prediction [66]. To address the set forth, [PERSON] et al. [75; 76] developed a PLSpredict algorithm as an alternative approach for evaluating the predictive relevance of a model. PLSpredict employs k-fold cross-validation (where a fold is a subgroup of the total sample, and k is the number of subgroups) to determine whether the model outperforms the most naive linear regression benchmark (referred to as \(Q^{2}_{predict}\) and defined as the indicator means from the analysis sample) [66; 67; 76]. PLS path models with \(Q^{2}_{predict}\) values above 0 have lower prediction errors than those given by the most naive benchmark. Given that \(Q^{2}_{predict}\) can be interpreted similarly as \(Q^{2}\), its values greater than 0, 0.25, and 0.5 point to small, medium, and large predictive relevance of the PLS path model [66]. The predictive power of a model is usually examined with the root mean squared error (RMSE), but in the case of highly non-symmetric distribution of prediction errors, the mean absolute error (MAE) should be used as an alternative [76]. The evaluation procedure represents a comparison of the RMSE (or MAE) values with a naive benchmark that generates predictions for items by using a linear regression model (LM). The outcome of the comparison can be one of the following [76]: (a) if prediction errors in terms of RMSE (or MAE) values are higher than those of the naive LM benchmark for all items, the model lacks predictive power; (b) if the majority of endogenous construct items have higher prediction errors when compared to the naive LM benchmark, this indicates that model has a low predictive power; (c) if the minority (or the same number) of construct items have higher prediction errors when compared to the naive LM benchmark.