利用七项个人特征预测信任动态类型
Predicting Trust Dynamics Type Using Seven Personal Characteristics
AI总结：
本研究通过130人实验结合k-means聚类识别出三类信任动态类型，发现七项个人特征与信任动态显著相关，并构建了准确率70%的决策树预测模型。
摘要：
本研究旨在探究个体在自动化/自主技术中的信任动态与其个人特征之间的关联，并进一步检验个人特征能否用于预测用户的信任动态类型。我们开展了一项包含130名受试者的人类受试者实验，参与者在自动化威胁检测器的协助下执行模拟监视任务。通过一项涵盖12个构念、28个维度的实验前调查，我们收集了参与者的个人特征数据。基于实验数据，我们执行了k-means聚类，识别出三种信任动态类型。随后，我们开展了单因素方差分析，以评估三种信任动态类型在个人特征、行为、表现及实验后评分方面的差异。参与者被聚类为三组，即贝叶斯决策者、怀疑者和波动者。结果显示，这些聚类在七项个人特征上存在显著差异：男子气、积极情感、外向性、神经质、智力、绩效期望和高期望。怀疑者往往神经质程度高、绩效期望低。波动者往往在男子气、积极情感、外向性和智力上得分更高。我们还发现三组在行为、表现和实验后评分上存在显著差异。怀疑者最不可能盲目遵循自动化威胁检测器给出的建议。基于这些显著的个人特征，我们开发了一个决策树模型来预测信任动态类型，准确率达70%。该模型为识别信任动态可能偏离贝叶斯模式的个体提供了有前景的应用方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
