# Impact of Latency on QoE, Performance, and Collaboration in Interactive Multi-User Virtual Reality
## 4 Results and Discussion
### Subjective Results
#### 4.4.2 Completion Time and Subjective Time Perception  
_(1) Completion time:_ Figure 15 shows the distribution of completion times for each of the four network configurations. No significant differences were found (Repeated Measures Analysis of Variance (ANOVA): \(p=0.136\)). This is an interesting finding as it indicates that, despite the clear differences in perception between scenarios as discussed in the previous section, this is not reflected in objective player performance. As such, it shows that participants have the ability to overcome the perceived distortions in order to complete the given task. Furthermore, it is also worth mentioning that the distribution of scenario C (latency) shows a strong positive skewness, in contrast to the more or less symmetrical distributions of the other scenarios. This indicates that, while the majority of the pairs were able to overcome the latency effects, performance was heavily affected by latency effects for a minority of them. This can be explained by the observed desynchronization between collaborators in the case of latency, as discussed earlier, for which one can assume that a minority of participants had severe issues handling these.  
Figure 14: Overview of the perceived task difficulty for each of the four network configurations, on a 1-to-5 Likert scale, where a higher score indicates a higher perceived level of difficulty. Significant differences are indicated as * (\(p<0.05\)), and *** (\(p<0.001\)), respectively, as obtained from Friedman tests followed by Dunn-Bonferroni post hoc tests. The full orange line indicates the median, the green dotted line the mean. The bullets indicate outliers, which are defined as samples laying outside the interval \([Q1-1.5\cdot\text{IQR},Q3+1.5\cdot\text{IQR}]\), with IQR the Interquartile Range defined as \(\text{IQR}=Q3-Q1\).  
Figure 15: Overview of the task completion time distributions for each of the four scenarios. The full orange line indicates the median, the green dotted line the mean. The bullets indicate outliers, which are defined as samples laying outside the interval \([Q1-1.5\cdot\text{IQR},Q3+1.5\cdot\text{IQR}]\), with IQR the Interquartile Range defined as \(\text{IQR}=Q3-Q1\).  
(2) Subjective time perception: Figure 16 shows the distribution of the difference between the estimated and the actual duration of the playthrough. No significant differences were found between the scenarios (Repeated Measures ANOVA: \(p=0.368\)). However, it is worth mentioning that scenario A (no distortions) is the only network configuration for which the deviation from zero approaches significance (One-sample t-test: \(p=0.055\)). In other words, there is weakly significant evidence that participants are consistently overestimating the duration of the playthrough when immersed in scenario A (no distortions). This is a counter-intuitive observation. On the one hand, over-estimation of time duration has been shown to relate to a lack of immersion into the environment or engagement with the task at hand [28; 29]. Increasing levels of distortion, on the other hand, are also known to decrease the degree of immersion and/or engagement. As such, one would expect this effect to be observed in scenario D (burst + latency) rather than scenario A (no distortions). The longer time perception could also be increased by a higher level of cybersickness [30]. However, no significant difference in cybersickness occurrence could be found in this study, as will be further discussed in Section 4.4.3. Research with respect to other influence factors in this regard, such as the multi-user context, is currently scarce in the literature. As such, deriving a coherent explanation for this observation is an interesting direction for further research.