# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 4 Results
### Contributions of Video Features to UES-SF
#### 4.3.5 Feature Contributions and Model Shapes  
Overall, a series of 25 combinations of video features and their respective engagement dimensions including its total score were tested. Each of the five low-level video features was tested for four engagement dimensions and for a total engagement score (see Tables 5-9), using both linear and quadratic models. Based on the statistical significance of the coefficients of determination \(R^{2}\) (null hypothesis \(H_{0}=[R^{2}=0]\)), the low-level video features were identified as significant in two cases for the linear models and in six cases for the quadratic models.  
As indicated in Section 4.2, the polynomial models were restricted to quadratic order. Only the models that were significant in terms of the coefficient of determination were considered, despite the fact that also some other plots exhibit convincing model shapes.  
The observed shapes are presented in Table 10. To summarize the relationships between the video features and the predicted engagement dimensions, the relations of the Color Variance video feature towards engagement score subdimensions of Focused Attention (FA), Aesthetic Appeal (AE) and Reward (RW), as well as towards overall User Engagement Score (UES), are U-shaped. The same observations are valid for relations of video features Motion Mean and Motion Standard Deviation with Perceived Usability (PU) and for the video feature Shot Length towards Aesthetic Appeal (AE). We identified no case of a prevailing linear model shape. Typical representations of these shapes are shown in Figure 3. The interpretation of these observations is weak because very little theoretical knowledge is available from the existing studies.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**Quadratic**} \\ \cline{2-5}  & \(R^{2}\) & \(p\) & \(R^{2}\) & \(p\) \\ \hline FA & 0.001 & 0.749 & 0.01 & 0.476 \\ PU & 0.004 & 0.452 & 0.062 & \textless{}0.01 \\ AE & 0.01 & 0.225 & 0.015 & 0.326 \\ RW & 0.01 & 0.232 & 0.01 & 0.464 \\ UES & 0.005 & 0.409 & 0.012 & 0.407 \\ \hline \end{tabular}
\end{table}
Table 8: Mean motion in relation to user engagement scale (short form) for a linear and quadratic regression model.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Linear**} & \multicolumn{2}{c}{**Quadratic**} \\ \cline{2-5}  & \(R^{2}\) & \(p\) & \(R^{2}\) & \(p\) \\ \hline FA & 0.0 & 0.859 & 0.001 & 0.931 \\ PU & 0.017 & 0.107 & 0.072 & \textless{}0.01 \\ AE & 0.01 & 0.214 & 0.031 & 0.099 \\ RW & 0.007 & 0.3 & 0.014 & 0.349 \\ UES & 0.0 & 0.793 & 0.001 & 0.964 \\ \hline \end{tabular}
\end{table}
Table 9: Standard deviation of motion in relation to user engagement scale (short form) for a linear and quadratic regression model.  
In general, the tests yielded low \(R^{2}\) values, suggesting that the models explained little of the variability in the UES-SF dimensions. These results were expected and can be seen visually in the widely dispersed score values shown in Figure 3 and in the other scatter plots under evaluation. Further discussion of unexplained variability in the models is provided in Section 4.4.  
Regarding the multiple-testing problem [34], the expected number of false positives is \(25\times 0.05=1.2\), which is one or possibly two false positives.