可信赖性
计算机科学
比例(比率)
自动化
领域(数学分析)
度量(数据仓库)
叙述的
自动驾驶
工作(物理)
应用心理学
人机交互
心理学
数据科学
计算机安全
工程类
运输工程
数据挖掘
机械工程
数学分析
语言学
哲学
物理
数学
量子力学
作者
Ian Robertson,Philip Kortum,Claudia Ziegler Acemyan,Frederick L. Oswald
标识
DOI:10.1177/21695067231192515
摘要
Self-driving vehicles (SDVs) are an emerging technology in which consumers have low levels of trust. Researchers/designers can understand and improve consumer trust through research and iterative design, but doing so effectively requires reliable measures. Although general trust-in-automation measures exist, a measure tailored to SDVs may provide a more accurate tool. This study presents work undertaken to create a domain specific trust measure for SDVs. Candidate items were given to 400 participants who rated their trust in an SDV portrayed in a narrative describing a ride in said vehicle. The Trust in Self-driving Vehicles Scale (TSDV) was created by analyzing participants’ responses using psychometric methods. Four factors were extracted from participants’ responses. Five items were retained for each factor to create the TSDV. Initial evidence of the validity of the instrument is presented through the TSDV’s ability to discriminate between a trustworthy and non-trustworthy vehicle, as portrayed in use scenarios.
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