1. Introduction

2. Background and Motivation

3. Materials and Methods

3.3. Procedure

The experiment, leveraging facial recognition software for emotion recognition, received approval from MIT’s Committee on the Use of Humans as Experimental Subjects (COUHES). The study employed a Posttest-Only Control Group Design, as outlined by Campbell and Stanley [55], to evaluate the impact of real-time emotional feedback on virtual team performance. Participants engaged in a collaborative problem-solving task, the Mars Colony simulation game [93], which demands effective communication and coordination. The treatment group was provided with real-time feedback on their emotional states using the Moody software. This system employs cameras to analyze participants’ facial expressions [91], and delivers immediate feedback via a virtual mirroring dashboard [92]. The control group did not receive any feedback.

Having explained the IPO model in the field of virtual teams in Section 2.5 and having described our research variables, we contextualized the research design within this framework. Figure 1 integrates the IPO model with the variables relevant to our experiment and provides a schematic representation that illustrates their interplay and importance in our study and is adapted from McGrath (1964) [16].

For the initial setup, the Mars Colony game and a real-time emotional feedback dashboard built into Moody [91,92] (for the treatment group) were arranged on the desktop. Participants were welcomed and instructed to keep their cameras and microphones on throughout the experiment. The facilitator shared the game instructions and allowed five minutes for participants to read them. This reading period was included in the total game duration of 60 minutes. After the initial instructions, the facilitator gave a quick tour of the Mars simulation game and, for the treatment group, explained the Moody dashboard and its metrics.

Participants were given remote control over the desktop, and the facilitator started the Moody and Zoom recordings. The facilitator then muted themselves and turned off their camera to allow participants to focus on the game. The game session lasted for approximately 50 minutes, with the facilitator monitoring progress and intervening only for technical issues. Participants were reminded to finalize their Mars colony configuration five minutes before the end of the session.

After the game, participants completed a post-experiment survey that collected demographic information, assessed emotional intelligence using the “Reading the Mind in the Eyes” test, and gathered data on participants’ familiarity with other team members and simulation games. This survey aimed to control for potential confounding variables that could influence the study’s outcomes.

The data collected from the Zoom recordings, including video and audio, was used to analyze team interactions, emotions, and communication patterns. The Moody software provided real-time feedback based on facial expressions but was not used for final analysis due to the availability of more advanced emotion analysis tools also developed at the CCI [56,57]. The Mars simulation game data, including the history of configuration changes, was used to determine team performance.

Figure 2. Left side shows the Mars Game, shown to both groups. The right side shows the Moody Dashboard [92] only shown to the treatment group.

Figure 2. Left side shows the Mars Game, shown to both groups. The right side shows the Moody Dashboard [92] only shown to the treatment group.

3.4. Measures

3.4.1. Dependent Variable

The primary dependent variable in this study is team performance, assessed through the Mars simulation game. This game requires teams to design an optimal Mars colony configuration, balancing “Energy Efficiency” and “Site Utilization.” Performance is quantified using the Pareto Score, a metric derived from multi-objective optimization principles that ranks solutions based on their efficiency in achieving both objectives. The Pareto Score evaluates team performance in multi-dimensional optimization problems [58,59]. It identifies non-dominated solutions that minimize energy use and maximize site utilization, eliminating subjectivity in assessing team effectiveness. Mathematically, for two solutions A and B, A is non-dominated if its energy use is less than or equal to that of B (Energy(A) ≤ Energy(B)) and its site utilization is greater than or equal to that of B (Site Utilization(A) ≥ Site Utilization(B)). Solutions that do not meet these conditions are deemed dominated by those that do. Non-dominated solutions are assigned the highest rank (Rank 0), and the process of exclusion and re-evaluation continues, assigning subsequent ranks to new sets of non-dominated solutions.

Figure 3 presents a visualization that illustrates the hierarchy of Pareto Ranks across the tradespace of possible solutions i.e., all the possible Mars colony configurations and the resulting Energy Use and Site Utilization values. This graphical representation aids in understanding the trade-offs and relative performance of different team site configurations within the multidimensional optimization framework.

Figure 3. The gray dots represent the space of all possible solutions (tradespace). The colored dots represent the actual simulations of Group12 with the respective Pareto Rank.

Figure 3. The gray dots represent the space of all possible solutions (tradespace). The colored dots represent the actual simulations of Group12 with the respective Pareto Rank.

To provide a robust measure of team performance, several metrics were derived from the Pareto Ranks. One simple approach would be to just take the average of all Pareto scores. However, this approach is susceptible to outliers. To mitigate this, performance improvement was calculated as the difference in average ranks between the first and second halves of the task, indicating whether teams improved over time. Another key metric, the regression slope, is obtained by fitting a regression line to the Pareto ranks over time, focusing on values between the 10th and 85th percentiles to minimize outlier effects. A negative slope in this context indicates an improvement in performance. This regression value is further categorized into low, middle, and high values to explore whether such binning of the slope variable offers a more effective approach for the subsequent analysis.

3.4.2. Independent Variables

The independent variable in this analysis is whether a team received real-time feedback (treatment group) or not (control group). To measure the effect of feedback on team performance, several mediating variables were collected. These include emotional expressions and communication patterns within the team, derived from facial and audio analysis software. For each metric, the mean, median, standard deviation, max/min value, slope, and percentiles were calculated.

The Facial Analysis System (FAS) [62] analyzed video recordings to detect seven discrete emotions (neutral, surprised, happy, fearful, disgusted, angry, sad), along with 3D head poses and brightness levels. This system provided VAD-values (valence, arousal, dominance) and head motion patterns, contributing to a comprehensive understanding of non-verbal cues and interpersonal dynamics within the team, see Table 1. The Audio Analysis System (AAS) [57] evaluated voice data to derive emotional expressions and communication patterns. This analysis included VAD-values, speaking duration, number of utterances, and interruptions, offering a nuanced view of the communication dynamics within the team, see Table 2. Both systems generated extensive datasets, encompassing key emotional and behavioral statistics.

3.4.3. Control Variable

To ensure the robustness of the analysis, several control variables were included to account for potential confounding factors. Team composition was considered, categorizing the gender composition of the teams as male-only or mixed-gender, with no female-only teams. The level of acquaintance among team members was assessed through the post-experiment survey, capturing whether participants knew each other prior to the experiment. Emotional intelligence was measured using the “Reading the Mind in the Eyes” (RMET) test [60], which evaluates participants’ ability to interpret emotions from eye expressions. This inclusion aimed to determine whether the outcomes were influenced by participants’ inherent emotional intelligence or by the effectiveness of our intervention [60]. Additionally, the time of day at which the experiment was conducted was recorded, as performance could vary throughout the day due to factors like fatigue or circadian rhythms [61]. The location of the experiment, whether conducted at MIT, Tecnológico de Monterrey, or the University of Applied Sciences of The Hague, was also considered to account for any site-specific effects.

3.5. Data Analysis Approach

3.5.1. Data Preparation

To ensure the robustness of the analysis, both datasets, which were susceptible to outliers due to their nature of collection, were initially prepared for further processing [62,63]. Gaussian-distributed features were standardized using scikit-learn to ensure they have a mean of zero and a standard deviation of one [64]. Non-Gaussian features underwent robust scaling, which scales the data based on percentiles rather than the mean and standard deviation, making it less sensitive to outliers. Outliers, defined as values with a z-score greater than 3, were replaced with the median value of the respective feature.

Additionally, to facilitate seamless integration of data from facial analysis and audio analysis, a unique mapping was created to match the IDs between the two output files. After this process, columns deemed irrelevant to the analysis, such as those relating to time or IDs, were removed. For the regression of Pareto Scores, only values between the 10th and 85th percentile were considered, aiming to mitigate the influence of outliers in the subsequent analysis.

Figure 4. Visualization of the Performance Slope Calculation (Example).

Figure 4. Visualization of the Performance Slope Calculation (Example).

3.5.2. Descriptive Statistics

Before delving into the multi-level mixed-effects linear regression analysis, we first examined the relationships between the predictor variables. We created a correlation matrix to assess multicollinearity, using both Pearson correlation coefficients and Variance Inflation Factors (VIFs) [65]. Heteroscedasticity was also examined using the Breusch-Pagan test [66,67]. To understand the most suitable variables for our analysis, Pearson correlation was calculated between each predictor variable and the various performance metrics.

In addition, we utilized t-tests and Mann-Whitney U tests to identify statistically significant differences between the control and treatment groups [68,69]. To quantify the magnitude of these differences, we calculated effect sizes using Cohen’s d for normally distributed data and the rank biserial correlation for non-normally distributed data [70,71].

3.5.3. Multi-Level Mixed Effects Linear Regression

Recognizing the hierarchical structure of our data (individuals nested within teams, and teams nested within groups), we employed a mixed-effects model for our regression analysis [72]. This allowed us to account for both fixed effects (e.g., real-time emotional feedback) and random effects, which capture the variations occurring within teams and groups [73].

A null model with only random intercepts was used as a baseline for assessing the Intraclass Correlation Coefficient (ICC), providing insights into the proportion of variance explained by the grouping structure. In our main models, we included several control variables such as team composition, prior acquaintance among team members, and time slots to account for potential confounding effects. The performance of the models was evaluated using the Akaike Information Criterion (AIC), a measure of relative model quality, as well as marginal and conditional R², which quantify the variance explained by the fixed effects and the entire model respectively [74,75,76].

3.5.4. Feature Selection

Feature selection was performed to improve the model’s ability to generalize to new data and prevent overfitting [77]. Subsets of features were generated based on the insights gained from correlation analysis and VIF calculations, and then utilized for further model development. For tree-based models such as Random Forests and XGBoost, an inherent feature selection mechanism is embedded in the training process. For the Support Vector Machine (SVM), a more refined dataset, produced after the VIF calculation, was employed for model training.

3.5.5. Training of Machine Learning Models

The machine learning models, including Support Vector Machine (SVM), Random Forests, XGBoost, and NGBoost, were trained to predict team performance based on the various features extracted from the dataset [78,79,80,81,82,83]. Additionally, K-Means clustering was employed to identify potential hidden patterns or groupings within the data [84]. Due to the continuous nature of our target variable, which represents team performance, this was framed as a regression problem. Hyperparameter tuning for each model was conducted using both randomized and grid search techniques. An 80/20 train-test split strategy was employed to ensure robust model evaluation.

The models were evaluated based on several key performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R². The MAE and RMSE provide measures of the average magnitude of prediction errors, while the R² represents the proportion of variance in the target variable explained by the model [85].

3.5.6. SHAP-Values for Feature Interpretability

SHAP values were calculated to assess the importance of each feature in the model and to understand their contributions to the final prediction [86,87,88]. SHAP values provide a unified approach to explain the output of any machine learning model. In our analysis, SHAP values were used to rank features by their importance, allowing us to identify the most influential factors in determining team performance.

4. Results

5. Discussion

5.2. Effect of Treatment on Team Performance and Dynamics

Our third hypothesis stated that teams receiving the treatment would show improved performance scores. The analysis found a significant, albeit marginal, impact on performance, thus validating our hypothesis. However, regression analysis revealed negligible variance between control and treatment groups, challenging the robustness of this hypothesis. Data collection inaccuracies further caution the generalizability of our findings. This aligns with Tausczik and Pennebaker [89], who found real-time language feedback effective only in well-coordinated teams.

For Hypothesis 4, which examined the effect of real-time emotional feedback on team emotional regulation, results were nuanced. Contrary to the hypothesis predicting reduced variance, we observed increased variability in happiness, indicating that feedback broadened emotional responses. This was accompanied by slight increases in positive expressions and moderate surprise, suggesting a more positive emotional climate. These findings challenge Hypothesis 4, showing that real-time feedback enriches emotional dynamics rather than streamlining them [50].

Hypothesis 5, suggesting a broader emotional spectrum in the treatment group, was only partially supported. Higher levels of happiness and facial valence were observed, but other emotions showed no significant differences. This indicates that the intervention partially broadened the emotional range and elevated team mood. The lack of a pronounced emotional spectrum suggests further analysis is needed. Existing research highlights the importance of feedback in enhancing group emotional intelligence but often does not establish a clear link between feedback and a broader range of emotional expressions [54].

In conversation patterns, significant differences were observed in communication variability and fewer interruptions, highlighting feedback’s potential to balance speaking turns and promote constructive interruptions. Real-time feedback modestly influenced the emotional landscape, resulting in increased emotional variability and uplifted mood. This suggests the intervention facilitated more nuanced emotional engagement, contributing to a richer and more positive team environment, and demonstrating the potential of real-time feedback to enhance emotional adaptability and diversity in teams.

Figure 5. Acceptance and rejection of proposed hypothesis.

Figure 5. Acceptance and rejection of proposed hypothesis.

6. Conclusions

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