Predicting Trust Dynamics Type Using Seven Personal Characteristics

Abstract

This study aims to explore the associations between individuals' trust dynamics in automated/autonomous technologies and their personal characteristics, and to further examine whether personal characteristics can be used to predict a user's trust dynamics type. We conducted a human-subject experiment (N=130) in which participants performed a simulated surveillance task assisted by an automated threat detector. Using a pre-experimental survey covering 12 constructs and 28 dimensions, we collected data on participants' personal characteristics. Based on the experimental data, we performed k-means clustering and identified three trust dynamics types. Subsequently, we conducted one-way Analyses of Variance to evaluate differences among the three trust dynamics types in terms of personal characteristics, behaviors, performance, and post-experimental 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 behaviors, performance, and post-experimental ratings across 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 the trust dynamics type with an accuracy of 70%. This model offers promising implications for identifying individuals whose trust dynamics may deviate from a Bayesian pattern.