Using Random Forests to Predict Team Creativity from Psychological Diversity and Emotional Intelligence
Keywords:
Team Creativity, Emotional Intelligence, Psychological Diversity, Machine Learning, Personality PsychologyAbstract
Objective: The objective of this study was to deploy a Random Forest machine learning algorithm to evaluate the predictive power and complex non-linear interactions of psychological diversity and team emotional intelligence on collective team creativity.
Methods and Materials: A cross-sectional quantitative research design was employed, collecting data from employees nested within established work teams in the Greek corporate sector. Team creativity was assessed via supervisor ratings, while emotional intelligence and psychological diversity (measured as the standard deviation of Big Five personality traits within teams) were self-reported and statistically aggregated to the team level. Data analysis utilized Random Forest regression, comparing its predictive performance against traditional Multiple Linear Regression using an training set ( teams) and a testing set ( teams), with hyperparameters optimized via grid search cross-validation.
Findings: The Random Forest model significantly outperformed Multiple Linear Regression in predicting team creativity on the testing set ( , , versus , , ). Variable importance analysis revealed that Team Emotional Intelligence was the paramount predictor (Importance Score , , ). This was followed by psychological diversity in Openness (Importance Score , , ) and Extraversion (Importance Score , , ), which both positively correlated with creative output. Conversely, diversity in Conscientiousness (Importance Score , , ) demonstrated a negative impact on team creativity.
Conclusion: High aggregate emotional intelligence and specific deep-level personality diversities interact in highly non-linear patterns to drive team innovation, underscoring the necessity of advanced ensemble learning techniques for accurate organizational and behavioral modeling.
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