{"title":"自动驾驶中情绪对信任的中介作用:风险感知和系统绩效","authors":"Lilit Avetisyan , Emmanuel Abolarin , Vanik Zakarian , X. Jessie Yang , Feng Zhou","doi":"10.1016/j.ijhcs.2025.103642","DOIUrl":null,"url":null,"abstract":"<div><div>Trust in automated vehicles (AVs) has traditionally been explored through a cognitive lens, but growing evidence highlights the significant role emotions play in shaping trust. This study moves beyond correlation to formally test the mechanisms through which emotions mediate the relationship between real-time AV performance and trust. We conducted an experimental study with 70 participants (42 male, 28 female) who viewed real-life AV recordings operating with or without errors, coupled with varying levels of risk information (high, low, or none). Participants reported their anticipated emotional responses using 19 discrete emotion items, while trust was assessed through dispositional, learned, and situational trust measures. Through factor analysis, 4 key emotional components were extracted, namely hostility, confidence, anxiety, and loneliness, that were influenced by risk perception and AV performance. Using mediation analysis, the extent to which four emotional factors explain the effect of AV performance on trust was quantified. The results show that real-time AV behavior is more influential on trust than pre-existing risk perceptions, indicating trust in AVs might be more experience-based than shaped by prior beliefs. The mediation analysis revealed major asymmetry in the power of emotional mediators: confidence emerged as the primary psychological pathway to trust, mediating 46.7% of the performance–trust effect. In contrast, negative emotions showed substantially weaker mediating effects. Hostility (11.3%) and anxiety (17.7%) were significant but substantially weaker negative mediators, while loneliness did not significantly mediate the relationship between AV performance and trust. Linear mixed modeling supported these patterns, confirming that unlike risk perception, AV performance and individual differences serve as the primary predictors of trust. These findings quantify trust’s emotional architecture, revealing that fostering positive emotional responses is more powerful than mitigating negative ones. AV development should therefore prioritize performance reliability and confidence building over safety communication or anxiety reduction.</div></div>","PeriodicalId":54955,"journal":{"name":"International Journal of Human-Computer Studies","volume":"205 ","pages":"Article 103642"},"PeriodicalIF":5.1000,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The mediating effects of emotions on trust through risk perception and system performance in automated driving\",\"authors\":\"Lilit Avetisyan , Emmanuel Abolarin , Vanik Zakarian , X. Jessie Yang , Feng Zhou\",\"doi\":\"10.1016/j.ijhcs.2025.103642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Trust in automated vehicles (AVs) has traditionally been explored through a cognitive lens, but growing evidence highlights the significant role emotions play in shaping trust. This study moves beyond correlation to formally test the mechanisms through which emotions mediate the relationship between real-time AV performance and trust. We conducted an experimental study with 70 participants (42 male, 28 female) who viewed real-life AV recordings operating with or without errors, coupled with varying levels of risk information (high, low, or none). Participants reported their anticipated emotional responses using 19 discrete emotion items, while trust was assessed through dispositional, learned, and situational trust measures. Through factor analysis, 4 key emotional components were extracted, namely hostility, confidence, anxiety, and loneliness, that were influenced by risk perception and AV performance. Using mediation analysis, the extent to which four emotional factors explain the effect of AV performance on trust was quantified. The results show that real-time AV behavior is more influential on trust than pre-existing risk perceptions, indicating trust in AVs might be more experience-based than shaped by prior beliefs. The mediation analysis revealed major asymmetry in the power of emotional mediators: confidence emerged as the primary psychological pathway to trust, mediating 46.7% of the performance–trust effect. In contrast, negative emotions showed substantially weaker mediating effects. Hostility (11.3%) and anxiety (17.7%) were significant but substantially weaker negative mediators, while loneliness did not significantly mediate the relationship between AV performance and trust. Linear mixed modeling supported these patterns, confirming that unlike risk perception, AV performance and individual differences serve as the primary predictors of trust. These findings quantify trust’s emotional architecture, revealing that fostering positive emotional responses is more powerful than mitigating negative ones. AV development should therefore prioritize performance reliability and confidence building over safety communication or anxiety reduction.</div></div>\",\"PeriodicalId\":54955,\"journal\":{\"name\":\"International Journal of Human-Computer Studies\",\"volume\":\"205 \",\"pages\":\"Article 103642\"},\"PeriodicalIF\":5.1000,\"publicationDate\":\"2025-09-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Human-Computer Studies\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1071581925001995\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Human-Computer Studies","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1071581925001995","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
The mediating effects of emotions on trust through risk perception and system performance in automated driving
Trust in automated vehicles (AVs) has traditionally been explored through a cognitive lens, but growing evidence highlights the significant role emotions play in shaping trust. This study moves beyond correlation to formally test the mechanisms through which emotions mediate the relationship between real-time AV performance and trust. We conducted an experimental study with 70 participants (42 male, 28 female) who viewed real-life AV recordings operating with or without errors, coupled with varying levels of risk information (high, low, or none). Participants reported their anticipated emotional responses using 19 discrete emotion items, while trust was assessed through dispositional, learned, and situational trust measures. Through factor analysis, 4 key emotional components were extracted, namely hostility, confidence, anxiety, and loneliness, that were influenced by risk perception and AV performance. Using mediation analysis, the extent to which four emotional factors explain the effect of AV performance on trust was quantified. The results show that real-time AV behavior is more influential on trust than pre-existing risk perceptions, indicating trust in AVs might be more experience-based than shaped by prior beliefs. The mediation analysis revealed major asymmetry in the power of emotional mediators: confidence emerged as the primary psychological pathway to trust, mediating 46.7% of the performance–trust effect. In contrast, negative emotions showed substantially weaker mediating effects. Hostility (11.3%) and anxiety (17.7%) were significant but substantially weaker negative mediators, while loneliness did not significantly mediate the relationship between AV performance and trust. Linear mixed modeling supported these patterns, confirming that unlike risk perception, AV performance and individual differences serve as the primary predictors of trust. These findings quantify trust’s emotional architecture, revealing that fostering positive emotional responses is more powerful than mitigating negative ones. AV development should therefore prioritize performance reliability and confidence building over safety communication or anxiety reduction.
期刊介绍:
The International Journal of Human-Computer Studies publishes original research over the whole spectrum of work relevant to the theory and practice of innovative interactive systems. The journal is inherently interdisciplinary, covering research in computing, artificial intelligence, psychology, linguistics, communication, design, engineering, and social organization, which is relevant to the design, analysis, evaluation and application of innovative interactive systems. Papers at the boundaries of these disciplines are especially welcome, as it is our view that interdisciplinary approaches are needed for producing theoretical insights in this complex area and for effective deployment of innovative technologies in concrete user communities.
Research areas relevant to the journal include, but are not limited to:
• Innovative interaction techniques
• Multimodal interaction
• Speech interaction
• Graphic interaction
• Natural language interaction
• Interaction in mobile and embedded systems
• Interface design and evaluation methodologies
• Design and evaluation of innovative interactive systems
• User interface prototyping and management systems
• Ubiquitous computing
• Wearable computers
• Pervasive computing
• Affective computing
• Empirical studies of user behaviour
• Empirical studies of programming and software engineering
• Computer supported cooperative work
• Computer mediated communication
• Virtual reality
• Mixed and augmented Reality
• Intelligent user interfaces
• Presence
...