## 5 Discussion
### Limitations and Future Research  
Our study acknowledges certain limitations, particularly the relatively small dataset (\(n=84\)), which may affect the robustness and generalizability of our conclusions due to the high number of features compared to data points, known as the \"curse of dimensionality\" [97]. While our dataset is sufficient for initial conclusions, expanding it would enhance the depth and validity of our analysis, mitigate individual group variability, and improve the generalizability of our findings. Future research should focus on increasing the dataset size and refining the treatment approach. The general nature of the information provided in our experiment suggests that more tailored and actionable feedback, possibly utilizing advanced large language models (LLMs), could better prompt behavioral modifications in participants. Another factor of un-certainty is our metric for measuring team performance. Despite efforts to create an objective assessment, the inherent variance in the simulation game outputs diluted the performance evaluation. We attempted to counteract this by examining performance trends over time and using a specific quantile range to reduce outlier impact. However, this approach does not entirely eliminate potential biases and may overlook instances of exceptional performance or significant failure. Additionally, regression analysis assumes consistency across iterations, which might miss complex, non-linear interactions influencing team performance. The limitations of our facial and voice emotion recognition software could have impacted the accuracy of our results. While we utilized established tools for this analysis (FAS [68] and AAS [65]), the inherent limitations of such software, compounded by real-world experimental conditions like partial facial visibility and suboptimal lighting, may have introduced inaccuracies in emotion detection. Additionally, our reliance on slightly outdated software versions could have further contributed to potential discrepancies in emotion recognition. Future research could benefit from employing the latest advancementsin emotion recognition technology to mitigate these limitations and enhance the precision of emotional data analysis. Although we accounted for several control variables, such as gender composition, time of day, emotional intelligence, and prior acquaintance among team members, additional factors, such as participants' prior experience with virtual teamwork and familiarity with the simulation software, could influence the results and enhance our understanding. Future research should consider these factors to provide a more nuanced understanding of the impact of emotional feedback on diverse virtual team settings. Feature selection was another challenge, as examining a wide range of metrics required working with many features relative to our dataset size. While this aimed to capture different aspects of team dynamics, it was necessary to exclude some features to improve interpretability, potentially losing explanatory power. Our results showed significant differences between the experimental and control groups, but the extent of these differences was small and un-certain, warranting cautious interpretation. Our experimental design did not account for the potential impact of feedback presence alone. Introducing a third group receiving non-specific randomized feedback in future studies could help determine whether observed effects stem from targeted feedback or the feedback mechanism itself. This approach would clarify the direct effects of emotional feedback versus general monitoring effects and investigate long-term impacts on team cohesion and trust. This study's design limits the examination of the sustained effects of real-time emotional feedback on team dynamics and performance over an extended period. Further research employing a longitudinal approach would provide more comprehensive insights into how feedback influences collaboration and outcomes beyond a single task [98].