Developing and validating a Chinese human-automation trust scale: Advancing trust measurement of emerging automation in sustainable ergonomics

自动化 人为因素与人体工程学 比例(比率) 工程类 风险分析(工程) 计算机科学 工程管理 毒物控制 知识管理 业务 医学 医疗急救 机械工程 地理 地图学
作者
Zixin Cui,Xiangling Zhuang,Seul Chan Lee,Jieun Lee,Xintong Li,Makoto Itoh
出处
期刊:Applied Ergonomics [Elsevier BV]
卷期号:125: 104477-104477 被引量:3
标识
DOI:10.1016/j.apergo.2025.104477
摘要

Measuring humans' learned trust in emerging automation systems across different trust development stages is important for fostering a sustainable and human-centered human-automation interaction. Given the notable differences in human-automation trust between Chinese culture and other cultures, particularly Western cultures, the development of an effective measurement tool for human-automation trust within Chinese cultural context is indispensable. This study aimed to develop a Chinese version of the Human-Automation Trust Scale (C-HATS) with reasonable reliability and validity, based on several existing theories and scales related to human-automation trust. Following three phases of assessments, including exploratory factor analysis, item analysis, and confirmatory factor analysis, the scale demonstrated reasonable reliability and validity for both initial and post-task trust assessments. However, certain items of our C-HATS should be separately applied when assessing initial and post-task trust. Furthermore, it is crucial to acknowledge the structural differences between initial and post-task trust. Post-task trust consists of three factors: performance, process, and purpose-based trust, whereas initial trust consists of only two dimensions: cognition-based and affect-based trust. These distinctions should be considered when evaluating the subfacets of initial and post-task trust. Although further validation is required, the developed C-HATS has the potential to assess initial and post-task human-automation trust within Chinese cultural context across various automation systems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
黎至完成签到 ,获得积分10
刚刚
刚刚
刚刚
酷波er应助卜乌采纳,获得30
1秒前
junge应助隐居采纳,获得10
1秒前
闪闪的忆枫应助言溪采纳,获得10
1秒前
2秒前
科研通AI6.4应助LIANG采纳,获得10
2秒前
小蘑菇应助清风采纳,获得10
2秒前
sea发布了新的文献求助30
2秒前
3秒前
3秒前
林夕君发布了新的文献求助10
3秒前
不想看文献的大学生关注了科研通微信公众号
3秒前
3秒前
3秒前
卓涵柏完成签到,获得积分10
4秒前
4秒前
小宇子发布了新的文献求助10
4秒前
wanci应助清秀诗珊采纳,获得10
4秒前
5秒前
wwy发布了新的文献求助10
5秒前
娃哈哈完成签到,获得积分10
5秒前
csz发布了新的文献求助10
5秒前
5秒前
LY发布了新的文献求助10
5秒前
6秒前
6秒前
好人一生平安完成签到,获得积分10
7秒前
俊逸的晓蕾完成签到,获得积分10
7秒前
茂密的头发完成签到,获得积分10
7秒前
8秒前
NexusExplorer应助铃铛采纳,获得30
8秒前
Akim应助神勇的女孩采纳,获得10
8秒前
8秒前
动人的珩发布了新的文献求助10
8秒前
liam发布了新的文献求助10
8秒前
8秒前
8秒前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Molecular Mechanisms of Photosynthesis, 4th Edition 1000
Organic Reactions, Volume 116 1000
Matrix Methods in Data Mining and Pattern Recognition 510
Social Skills Improvement System-Rating Scales--Chinese Version 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7254969
求助须知:如何正确求助?哪些是违规求助? 8876880
关于积分的说明 18744380
捐赠科研通 6935366
什么是DOI,文献DOI怎么找? 3200266
关于科研通互助平台的介绍 2374871
邀请新用户注册赠送积分活动 2175232