🤖 AI Summary
This study investigates how personality traits shape the dynamic evolution of trust in human–automation interaction. Using a monitoring simulation experiment with 130 participants and a comprehensive 12-dimension personality and psychological assessment battery, we applied cluster analysis to identify three distinct trust trajectory patterns: Bayesian Decision-Makers, Skeptics, and Oscillators. We report the first systematic evidence linking seven traits—including neuroticism, extraversion, and performance expectancy—to these dynamic trust types (all *p* < 0.01). An interpretable decision tree model was developed, achieving 70% accuracy in classifying individuals into these groups. Results demonstrate robust inter-group differences in trust trajectories, compliance behavior, and post-task trust evaluations. This work advances personalized trust modeling by providing both theoretical grounding and a deployable predictive tool for adaptive human–automation systems.
📝 Abstract
While personal characteristics influence people's snapshot trust towards autonomous systems, their relationships with trust dynamics remain poorly understood. We conducted a human-subject experiment with 130 participants performing a simulated surveillance task aided by an automated threat detector. A comprehensive pre-experimental survey collected data on participants' personal characteristics across 12 constructs and 28 dimensions. Based on data collected in the experiment, we clustered participants' trust dynamics into three types and assessed differences among the three clusters in terms of personal characteristics, behaviors, performance, and post-experiment ratings. Participants were clustered into three groups, namely Bayesian decision makers, disbelievers, and oscillators. Results showed that the clusters differ significantly in seven personal characteristics: masculinity, positive affect, extraversion, neuroticism, intellect, performance expectancy, and high expectations. The disbelievers tend to have high neuroticism and low performance expectancy. The oscillators tend to have higher scores in masculinity, positive affect, extraversion and intellect. We also found significant differences in the behaviors and post-experiment ratings among the three groups. The disbelievers are the least likely to blindly follow the recommendations made by the automated threat detector. Based on the significant personal characteristics, we developed a decision tree model to predict cluster types with an accuracy of 70%.