## 4 Results
### The Evaluation Results for Specific Gesture Actions by Type
To clarify the extent to which GET influenced the UEQ-S outcomes further--namely pragmatic quality, hedonic quality, and overall satisfaction--and to explore potential differences across environments, multiple Ordinary Least Squares (OLS) regression analyses were conducted using the combined dataset (TV + XR), as well as subsets for the TV and XR environments individually (Table 6). Figure 12: Heat map of measured gesture execution time per each gesture type. The regression results indicated that the GET had no statistically significant impact on most of the UEQ-S outcomes, and the explanatory power of the models was generally low. However, hedonic quality showed a significant effect in both the combined dataset and the TV environment, while no statistical difference was observed in the XR environment (Figure 13). These findings suggest that in the TV environment, longer execution times may negatively affect the enjoyment aspect of gesture interactions, whereas in the XR environment, the duration of gesture execution appears to have little to no influence on participants' gesture experiences. The findings suggest a subtle yet negative influence of the GET on the participants' interaction satisfaction within the TV environment, although the strength of this relationship is limited. Conversely, no statistically significant relationship between GET and interaction satisfaction emerged within the XR environment. These outcomes may be interpreted from several perspectives. Firstly, the participants may have perceived the gesture interactions as inherently more intuitive and natural in the XR environments compared to the TV environments, causing them to prioritize intuitiveness and fluidity over the duration of gesture execution. That is, such intuitiveness and instinctiveness may have led the participants not to exclusively concentrate on the functional execution of the gestures but rather may have allowed them to naturally spend more time engaging with content exploration or concurrently performing other activities, such as interacting with the other ambient virtual environments [70]. This interpretation leads to the point that immersive qualities and natural flow appear to outweigh efficiency. Secondly, the participants' established mental models from prior experience with remote control interactions might have led them to expect quicker and simpler gestures in the TV environment, where there is a conventionally perceived physical distance between \begin{table}
\begin{tabular}{c c c c c} \hline
**Environment** & **UEQ-S Scale** & **Coefficient** & **R2** & _p-Value_ \\ \hline \multirow{2}{*}{Whole (TV + XR)} & Pragmatic Quality & \(-\)0.004 & 0.001 & \textgreater{}0.05 ns \\  & Hedonic Quality & \(-\)0.015 & 0.034 & \textless{}0.05 * \\  & Overall Satisfaction & \(-\)0.009 & 0.015 & \textgreater{}0.05 ns \\ \hline \multirow{2}{*}{TV} & Pragmatic Quality & 0.001 & 0.0 & \textgreater{}0.05 ns \\  & Hedonic Quality & \(-\)0.021 & 0.078 & \textless{}0.01 ** \\  & Overall Satisfaction & \(-\)0.001 & 0.019 & \textgreater{}0.05 ns \\ \hline \multirow{2}{*}{XR} & Pragmatic Quality & \(-\)0.009 & 0.009 & \textgreater{}0.05 ns \\  & Hedonic Quality & \(-\)0.008 & 0.010 & \textgreater{}0.05 ns \\ \multicolumn{2}{c}{Overall Satisfaction} & \(-\)0.008 & 0.013 & \textgreater{}0.05 ns \\ \hline \end{tabular} \({}^{\text{th}}p>0.05\); \({}^{\ast}p<0.05\); \({}^{\ast\ast}p<0.01\).  
\end{table} 
Table 6: The results of OLS regression analyses assessing the effect of gesture execution time on the UEQ-S scores across different environments. Figure 13: Scatter plots with red linear regression lines evaluating the relationship between gesture execution time and hedonic quality among the UEQ-S scores. the TV and the remote [71]. This interpretation suggests that task efficiency plays a greater role in shaping user experiences on the TV platform, where users may expect quicker, remote-like interactions.