A Teamwork Science Approach to Trust Dynamics in Hybrid Product Development Teams: Modeling Non-Verbal Interactions Through Bayesian Networks

Motivation: In modern organizations where remote and hybrid work has become normalized, fostering trust without frequent face-to-face interaction is a critical management challenge. This study aims to explore how non-verbal digital dynamics associate with trust formation within hybrid product development teams from a teamwork science perspective, integrating Big Five traits and established trust scales. Methods: The empirical study observed twelve product development teams (N = 40) participating in a major innovation competition over an eight-month period. Dynamic behavioral data, including speaking time, nodding, smiling, and silence, were extracted from online workshop recordings using synchronized behavioral coding validated by high inter-rater reliability (Cohen’s Kappa k ≥ 0.78). These were integrated with Big Five personality traits, mutual trust scales, and idea value metrics into a Bayesian Network (BN) to model probabilistic dependencies. The structural model was validated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to ensure predictive robustness. Furthermore, we performed sensitivity analysis on the BN to quantify how specific shifts in non-verbal cues—particularly nodding and the functional categories of silence—disproportionately affect the “Mutual Trust” node. While this exploratory study utilizes a sample of “digital native” student teams, it provides a critical baseline for “high digital fluency” collaboration, which we contextualize against the “asymmetric cues” found in multi-generational corporate environments. Results: Sensitivity analysis identified specific probabilistic associations suggesting that effective role fulfillment is the strongest predictor of idea originality. Crucially, nodding was identified as a behavioral ‘digital reward’ that enhances psychological safety, facilitating divergent thinking. Smiling showed a strong association with feasibility and consensus-building during convergent phases. The model further identifies distinct behavioral ‘fingerprints’: high-trust sequences are characterized by frequent non-verbal backchanneling and deliberate “thinking silences,” whereas low-trust sequences exhibit a disproportionate increase in unproductive lapses (e.g., a 10% increase in lapses correlating with an 18% decrease in trust probability). Furthermore, a probabilistic pathway was identified where teams with highly open members and frequent non-verbal validation exhibit higher mutual support behaviors. Conclusions: This research offers empirical insights into how trust can be modeled in hybrid environments through specific combinations of behavioral and personality traits. Practically, this study proposes “Hybrid Team Protocols”—such as intentional backchanneling and the normalization of deliberative silence—as actionable Organizational Development (OD) interventions. These provide managers with data-driven guidelines to visualize and monitor the quality of digital collaboration while emphasizing the ethical necessity of transparent implementation to prevent “digital performance” and ensure psychological safety across diverse organizational structures.