Computers in human behavior reports最新文献_第3页

Positive evaluation of an online intervention that promotes digital health literacy in adolescents as recommended by the German Digital Healthcare Act: Randomized controlled trial 积极评价德国数字医疗法案推荐的促进青少年数字健康素养的在线干预措施：随机对照试验

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-22 DOI: 10.1016/j.chbr.2026.100948

Rebekka Schröder, Tim Hamer, Victoria Kruzewitz, Ellen Busch, Ralf Suhr, Lars König

Digital health literacy is an important asset to navigate the (digital) world and lead a healthier life. However, digital health literacy levels are insufficient in many segments of society, particularly among adolescents. In response to these deficits, policymakers have called for initiatives to promote digital health literacy, for example in the German Digital Healthcare Act. Addressing this need, a new intervention for adolescents focusing on the topic of digital stress was recently developed, consisting of five e-learning modules incorporating animated videos, short texts and interactive tasks. The present web-based randomized-controlled pre-post trial evaluated the effectiveness of this intervention. Participants (14–21 years) were recruited from an online panel and randomized into two groups. After an initial pre-assessment, the participants in the experimental group (N = 253 randomized; N = 201 analyzed) carried out the online intervention while the control group participants (N = 238 randomized; N = 212 analyzed) were in a waiting period. Subsequently, all participants filled out the post-assessment questionnaires. Mixed-mode ANOVAs testing for the statistical interactions between the repeated-measures and the group factors were conducted. All questionnaire scales showed significant interactions indicating increased knowledge, digital health literacy and digital competence in the experimental vs. the control group at post-assessment except for one digital health literacy subscale. These findings suggest that the evaluated intervention is an effective tool for enhancing central aspects of digital health literacy in adolescents. The stability of these findings over time and the applicability of the intervention in real-life settings should be investigated in the future.

数字健康素养是驾驭（数字）世界和过上更健康生活的重要资产。然而，在社会的许多阶层中，特别是在青少年中，数字卫生素养水平不足。为了应对这些赤字，政策制定者呼吁采取措施促进数字健康素养，例如在《德国数字医疗法案》中。针对这一需求，最近制定了一项针对青少年的新干预措施，重点关注数字压力这一主题，包括五个电子学习模块，其中包括动画视频、短文本和互动任务。目前基于网络的随机对照前后试验评估了这种干预措施的有效性。参与者（14-21岁）从一个在线小组中招募，并随机分为两组。实验组（随机253人，分析201人）经初步预评估后进行在线干预，对照组（随机238人，分析212人）处于等待期。随后，所有参与者都填写了评估后问卷。对重复测量与群体因素之间的统计相互作用进行混合模式方差分析。除一个数字健康素养子量表外，所有问卷量表均显示出显著的相互作用，表明实验组与对照组相比在后评估时知识、数字健康素养和数字能力有所增加。这些发现表明，评估的干预措施是提高青少年数字健康素养核心方面的有效工具。随着时间的推移，这些发现的稳定性和干预在现实生活中的适用性应该在未来进行调查。

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引用次数: 0

Spot the difference: Investigating the effects of ageing on change blindness in QR codes with eye tracking 发现差异：用眼动追踪研究年龄对QR码变化盲目性的影响

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-22 DOI: 10.1016/j.chbr.2026.100939

Elena Ball, Lara Warmelink, Sophie Nightingale, Trevor Crawford

Humans struggle to notice changes in visual scenes, a limitation called change blindness. Older adults show heightened susceptibility to change blindness, which can affect both decision making and safety. Despite increasing reliance on technology in our environments, change blindness in human-computer interactions is poorly understood. Here we used two experiments to explore change blindness in quick response (QR) codes. Experiment 1 recruited 40 participants who completed a one-shot change detection task incorporating modifications to QR code images. Overall, 65.1% of modifications were identified, and detection performance improved with larger changes (95.9%) compared to smaller changes (34.4%). Experiment 2 recruited 60 younger and 60 older adults to complete the same task, while their eye movements were tracked to measure visual scanning behaviour. We examined whether modifications to QR codes could be detected, and whether detection performance was influenced by age, age-related cognitive factors such as visual working memory, visual scanning behaviour and prior QR-code experience. In total, 64.8% of modified QR codes were identified, with greater detection for larger changes (92.6%) than smaller changes (37.0%). Overall, QR-code experience, visual scanning behaviour, and age significantly predicted detection performance, as older adults (61.5%) detected fewer modified QR codes than younger adults (67.9%). Although larger visual working-memory capacity was a significant predictor of better detection performance when considered in isolation, its effect was no longer significant once QR-code experience, visual scanning, and age were added to the model. This implies change-detection performance is shaped directly by QR-code experience, age, and visual scanning.

人类很难注意到视觉场景的变化，这种限制被称为变化盲视。老年人对变化盲目性的敏感性更高，这可能会影响决策和安全。尽管在我们的环境中越来越依赖技术，但人们对人机交互中的变化盲目性知之甚少。本文采用两个实验来探讨QR码的变化盲视性。实验1招募了40名参与者，他们完成了一项包含修改二维码图像的一次性变化检测任务。总体而言，65.1%的修改被识别出来，与较小的变化（34.4%）相比，较大的变化（95.9%）提高了检测性能。实验二招募了60名年轻人和60名老年人来完成同样的任务，同时跟踪他们的眼球运动来测量视觉扫描行为。我们研究了二维码的修改是否可以被检测到，以及检测性能是否受到年龄、与年龄相关的认知因素（如视觉工作记忆、视觉扫描行为和先前的二维码经验）的影响。总共有64.8%的修改QR码被识别出来，较大的变化（92.6%）比较小的变化（37.0%）更容易被发现。总体而言，QR码经验、视觉扫描行为和年龄显著地预测了检测性能，因为老年人（61.5%）检测到的修改QR码比年轻人（67.9%）少。虽然在单独考虑时，较大的视觉工作记忆容量是更好的检测性能的重要预测因素，但一旦将qr码经验、视觉扫描和年龄添加到模型中，其影响就不再显著了。这意味着变更检测性能直接由qr码经验、年龄和视觉扫描决定。

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引用次数: 0

Examining sensory interference and adaptive feedback in VR-based learning for cognitive load management 基于vr的认知负荷管理学习中感官干扰和自适应反馈的研究

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-22 DOI: 10.1016/j.chbr.2026.100945

Jiacheng Sun, Katherine Shagalov, Ting Liao

Managing cognitive load is a critical challenge in immersive learning environments, where sensory interference could disrupt attention and information processing. This study developed and evaluated a VR-based adaptive learning system that incorporates real-time physiological monitoring to support cognitive regulation under auditory and visual interference. Cognitive load was assessed using the Cumulative Skin Conductance Response (CSCR), a continuous measure of sustained sympathetic activation, and learning outcomes were evaluated with a post-study quiz. Results showed that auditory interference elicited significantly higher CSCR than visual interference, indicating greater task-evoked cognitive load, although this difference did not directly translate into lower quiz performance. Participants who received real-time feedback exhibited reduced CSCR levels and slightly improved quiz scores, suggesting that feedback helped stabilize cognitive load during the learning task. A significant negative correlation between CSCR and quiz performance further indicated that higher sustained cognitive load was associated with poorer learning efficiency. These findings demonstrate the potential of integrating physiological monitoring into adaptive VR learning systems and motivate future work on multimodal sensing and more individualized feedback strategies. Future research could focus on personalizing feedback mechanisms and integrating multimodal biosignals to further refine these interventions.

在沉浸式学习环境中，管理认知负荷是一个关键的挑战，在沉浸式学习环境中，感官干扰可能会破坏注意力和信息处理。本研究开发并评估了一种基于vr的自适应学习系统，该系统结合了实时生理监测，以支持听觉和视觉干扰下的认知调节。认知负荷通过累积皮肤电导反应（CSCR）来评估，这是一种持续测量持续交感神经激活的方法，学习结果通过学习后测验来评估。结果显示，听觉干扰诱发的CSCR显著高于视觉干扰，表明任务诱发的认知负荷更大，尽管这种差异并不直接转化为较低的测验成绩。接受实时反馈的参与者表现出CSCR水平降低，测验成绩略有提高，这表明反馈有助于稳定学习任务中的认知负荷。CSCR与测验成绩显著负相关进一步表明，持续认知负荷越高，学习效率越低。这些发现证明了将生理监测整合到自适应VR学习系统中的潜力，并激励了未来在多模态传感和更个性化的反馈策略方面的工作。未来的研究可以集中在个性化反馈机制和整合多模态生物信号上，以进一步完善这些干预措施。

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引用次数: 0

Airport self-service technologies: Are we fully engaged? A hybrid SEM-machine learning approach 机场自助服务技术：我们是否充分参与？一种混合sem -机器学习方法

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-22 DOI: 10.1016/j.chbr.2026.100931

Mehdi A. Kamran , Fatma Al Yaarubi , Nooshin Atashfeshan , Reza Babazadeh

Ensuring the sustained use of self-service technologies (SSTs) is essential for companies investing in these innovations. This study investigates key factors shaping human attitudes toward SST adoption in airports by developing a conceptual model that examines the effects of perceived usefulness (PU), perceived ease of use (PEOU), and the need for human interaction (NHI) on utilitarian and hedonic attitudes. Structural Equation Modelling (SEM) is used to test the model, with age and gender assessed as moderating variables. Data were collected from 214 passengers at Muscat International Airport and enriched with interview insights. The model explains 76.2 % of the variance in passengers’ intentions to use SSTs. PU and PEOU significantly influence both hedonic and utilitarian attitudes, while NHI shows no significant effect. Gender moderates the relationship between PU, PEOU, and utilitarian attitudes, while age moderates the link between hedonic attitudes and SST usage intention.

To complement the SEM analysis and address behavioral complexities, five machine learning (ML) models—Decision Tree, Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Gradient Boosting—are employed to predict SST adoption. These models achieve an average prediction accuracy of approximately 96 %. The integration of SEM and ML provides both explanatory depth and predictive strength, enhancing the understanding of behavioral drivers and supporting more informed SST implementation.

Overall, this research offers practical implications for airport authorities and technology developers, emphasizing the importance of designing SSTs that balance functional efficiency with user engagement, while also considering demographic differences in technology acceptance.

确保自助服务技术（SSTs）的持续使用对于投资这些创新的公司至关重要。本研究通过开发一个概念模型，考察感知有用性（PU）、感知易用性（PEOU）和人际互动需求（NHI）对功利主义和享乐主义态度的影响，探讨了影响人们对机场采用SST态度的关键因素。结构方程模型（SEM）被用来测试模型，年龄和性别被评估为调节变量。数据从马斯喀特国际机场的214名乘客中收集，并通过访谈见解进行丰富。该模型解释了76.2%的乘客使用SSTs意愿差异。PU和PEOU显著影响享乐主义和功利主义态度，而NHI没有显著影响。性别调节PU、PEOU和功利态度之间的关系，年龄调节享乐态度和SST使用意向之间的关系。为了补充SEM分析并解决行为复杂性，我们使用了五种机器学习（ML）模型——决策树、随机森林、支持向量机、人工神经网络和极端梯度提升——来预测海表温度的采用。这些模型的平均预测精度约为96%。SEM和ML的集成提供了解释深度和预测强度，增强了对行为驱动因素的理解，并支持更明智的SST实施。总的来说，这项研究为机场当局和技术开发人员提供了实际意义，强调了设计平衡功能效率和用户参与度的SSTs的重要性，同时也考虑了技术接受度的人口统计学差异。

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引用次数: 0

Three faces of social influence: How compliance, identification, and internalization shape smart tourism co-creation and satisfaction 社会影响的三面：遵从性、认同和内化如何塑造智慧旅游的共同创造和满意度

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-22 DOI: 10.1016/j.chbr.2026.100944

Yee-Yann Yap , Siow-Hooi Tan , Booi-Chen Tan , Siow-Kian Tan , Man Seong Chan

Social influence remains a critical yet underexplored factor of tourists' online co-creation experience (OCE) with smart tourism technologies (STTs). While prior research has largely focused on technological affordances and compliance-based norms, limited attention has been paid to how diverse social influence mechanisms affect co-creation behaviors and downstream satisfaction outcomes. This study addresses this gap by integrating the Technology Acceptance Model (TAM) with Kelman's Social Influence Theory to examine how perceived usefulness (PU), perceived ease of use (PEOU), and three distinct dimensions of social influence—compliance (subjective norms), identification (social identity), and internalization (group norms)—influence OCE and, in turn, satisfaction with STTs and the overall trip. Data was collected from 290 tourists who used STTs and analyzed using SmartPLS. Results show that social identity exerts the strongest impact on OCE. Subjective norms and group norms also positively affect OCE, albeit to a lesser extent. Among technological factors, PU has a stronger influence on OCE than PEOU. Crucially, OCE significantly improves satisfaction with the STT and the trip, reinforcing its importance in delivering enriched tourist experiences. OCE mediates the effects of technological and social drivers on satisfaction, and satisfaction with STTs further mediates the relationship between OCE and trip satisfaction. This study advances theoretical understanding by integrating multidimensional social influence within a TAM framework in the context of smart tourism. From a practical viewpoint, the findings offer actionable insights for platform developers and destination marketers to design socially engaging and functionally effective STTs that encourage co-creation and enhance tourist satisfaction.

社会影响是影响游客与智能旅游技术在线共同创造体验的关键因素，但尚未得到充分研究。虽然先前的研究主要集中在技术支持和基于合规性的规范上，但对不同的社会影响机制如何影响共同创造行为和下游满意度结果的关注有限。本研究通过将技术接受模型（TAM）与Kelman的社会影响理论相结合来解决这一差距，以研究感知有用性（PU）、感知易用性（PEOU）和社会影响的三个不同维度-依从性（主观规范）、认同（社会认同）和内化（群体规范）-如何影响OCE，进而影响对stt和整体旅行的满意度。从290名使用stt的游客中收集数据，并使用SmartPLS进行分析。结果表明，社会认同对英语学习能力的影响最大。主观规范和群体规范也对OCE产生积极影响，尽管影响程度较小。在技术因素中，PU对OCE的影响强于PEOU。最重要的是，OCE显著提高了人们对STT和旅行的满意度，加强了它在提供丰富的旅游体验方面的重要性。出行行为在技术驱动因素和社会驱动因素对满意度的影响中起中介作用，出行行为满意度进一步在出行行为与出行满意度之间起中介作用。本研究通过整合智慧旅游背景下TAM框架内的多维社会影响来推进理论认识。从实际的角度来看，研究结果为平台开发商和目的地营销人员提供了可操作的见解，以设计具有社会吸引力和功能有效的stt，鼓励共同创造，提高游客满意度。

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引用次数: 0

Technostress and employee well-being: A systematic review of empirical evidence 技术压力与员工幸福感：对经验证据的系统回顾

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-21 DOI: 10.1016/j.chbr.2026.100941

Ezgi Mansuroğlu, Andrew P. Smith

As technology continues to reshape industries, understanding the effects of technostress on employee well-being becomes imperative. While research on technostress has grown substantially in recent years, existing studies are often fragmented in scope and limited in cross-contextual depth. In this systematic review, we synthesized the findings of 201 (after the double screening) peer-reviewed empirical studies, primarily retrieved from the PubMed, Scopus, and Web of Science databases, to map technostress along the four analytical dimensions: its core components, its impact on well-being, key mediating and moderating variables, and contextual variations. Our findings demonstrated that the relationship between technostress and employee well-being has been most frequently studied in Germany, Italy, and India, with education and healthcare emerging as the most commonly examined sectors. Furthermore, techno-overload and techno-invasion were the most reported technostressors linked to adverse well-being indicators across the studies. Our analysis revealed an underrepresentation of cross-national and cross-cultural comparisons in the existing literature. Drawing on these insights, this review advances the literature by introducing the Demands-Resources-Individual Effects (DRIVE) model as a coherent integrative framework for studying technostress and well-being. The model provides a theoretically grounded explanation of how digital demands, personal resources, and individual differences interact to shape well-being outcomes. Combined with the Well-being Process Questionnaire (WPQ), it also offers a practical, validated approach for assessing these mechanisms in diverse organizational contexts.

随着技术不断重塑行业，了解技术压力对员工幸福感的影响变得势在必行。虽然近年来对技术压力的研究有了很大的发展，但现有的研究往往在范围上是碎片化的，在跨背景的深度上是有限的。在这篇系统综述中，我们综合了201篇（经过双重筛选）同行评议的实证研究的结果，这些研究主要来自PubMed、Scopus和Web of Science数据库，以绘制技术压力在四个分析维度上的图：技术压力的核心成分、对幸福感的影响、关键的中介和调节变量以及环境变化。我们的研究结果表明，技术压力与员工幸福感之间的关系在德国、意大利和印度得到了最频繁的研究，其中教育和医疗保健成为最常见的研究领域。此外，在所有研究中，技术超载和技术入侵是与不良健康指标相关的最常见的技术压力因素。我们的分析显示，在现有文献中，跨国和跨文化比较的代表性不足。基于这些见解，本综述通过引入需求-资源-个体效应（DRIVE）模型作为研究技术压力和幸福感的连贯综合框架来推进文献。该模型从理论上解释了数字需求、个人资源和个体差异如何相互作用，从而形成幸福结果。结合幸福感过程问卷（WPQ），它也提供了一个实用的，有效的方法来评估这些机制在不同的组织环境。

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引用次数: 0

From interaction to impact: Examining the role of chatbots in enhancing social sustainability using SEM-ANN approach 从互动到影响：使用SEM-ANN方法研究聊天机器人在增强社会可持续性方面的作用

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-21 DOI: 10.1016/j.chbr.2026.100942

Abdulla Alsharhan , Mostafa Al-Emran , Khaled Shaalan

Despite the growing integration of Artificial Intelligence (AI) in educational settings, there is a notable gap in the literature regarding the role of chatbots in promoting social sustainability in higher education. This study aims to fill this void by developing a model that combines constructs from Task-Technology Fit (TTF), Source Credibility Theory (SCT), Fogg's Model of Web Credibility, and Social Presence Theory (SPT). This research utilizes a hybrid approach of Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) to evaluate the proposed model based on data collected from 341 students. The results confirmed 13 out of 16 hypotheses, underscoring the pivotal roles of credibility, social presence, and TTF in enhancing chatbot utilization, which, in turn, supports social sustainability. The ANN findings showed that TTF is the most important factor influencing chatbot use, with a normalized importance of 99.1 %. The significance of this research lies in its potential to guide the development of chatbot applications that effectively support universities' educational and social objectives, making a vital contribution to the discourse on technology's role in sustainable educational practices.

尽管人工智能（AI）越来越多地融入教育环境，但关于聊天机器人在促进高等教育中社会可持续性方面的作用，文献中存在显著差距。本研究旨在通过开发一个模型来填补这一空白，该模型结合了任务-技术匹配（TTF）、来源可信度理论（SCT）、福格网络可信度模型和社会存在理论（SPT）的结构。本研究利用结构方程模型（SEM）和人工神经网络（ANN）的混合方法来评估基于341名学生收集的数据提出的模型。结果证实了16个假设中的13个，强调了可信度、社会存在和TTF在提高聊天机器人利用率方面的关键作用，而这反过来又支持了社会的可持续性。人工神经网络的研究结果表明，TTF是影响聊天机器人使用的最重要因素，标准化重要性为99.1%。这项研究的意义在于，它有可能指导聊天机器人应用的发展，有效地支持大学的教育和社会目标，为技术在可持续教育实践中的作用做出重要贡献。

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引用次数: 0

Sparkling water consumption mitigates cognitive fatigue during prolonged esports play 在长时间的电子竞技中，饮用气泡水可以缓解认知疲劳

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-21 DOI: 10.1016/j.chbr.2026.100943

Shion Takahashi , Wataru Kosugi , Seiichi Mizuno , Takashi Matsui

Prolonged esports play induces cognitive fatigue, characterized by diminished executive function with pupil constriction. Players often rely on caffeinated or sugary drinks to combat fatigue, but regular use poses health risks. Sparkling water, a sugar- and caffeine-free beverage, stimulates brainstem and prefrontal activity via sensory pathways potentially mediated by transient receptor potential (TRP) channels in the throat. This study tested the hypothesis that sparkling water mitigates cognitive fatigue during prolonged esports play. Fifteen young adult players participated in a randomized crossover trial, each completing two 3-h sessions of a virtual football game while consuming either sparkling water or plain water. Subjective fatigue, enjoyment, and executive function (via a flanker task) were measured at baseline and hourly, while pupil diameter and heart rate were monitored continuously. Blood glucose and salivary cortisol were assessed periodically. Compared to plain water, sparkling water significantly attenuated increases in subjective fatigue, enhanced enjoyment, and preserved executive function, along with preventing pupil constriction. Heart rate, blood glucose, and salivary cortisol levels did not differ between conditions. Notably, players committed fewer in-game fouls with sparkling water, while offensive and defensive performance remained unchanged. These findings demonstrate that sparkling water contribute to alleviate both subjective and objective signs of cognitive fatigue during prolonged esports play, consistent with our hypothesis. This non-caffeinated intervention may help sustain inhibitory control and promote fair behavior, offering a safe and sustainable strategy for managing mental fatigue in modern life.

长时间玩电子竞技会引起认知疲劳，其特征是执行功能下降，瞳孔收缩。运动员通常依靠含咖啡因或含糖饮料来对抗疲劳，但经常饮用会带来健康风险。气泡水是一种不含糖和咖啡因的饮料，它通过喉咙中的瞬时受体电位（TRP）通道介导的感觉通路刺激脑干和前额叶活动。这项研究测试了一个假设，即在长时间的电子竞技中，气泡水可以缓解认知疲劳。15名年轻的成年球员参加了一项随机交叉试验，每人完成两次3小时的虚拟足球比赛，同时饮用苏打水或普通水。在基线和每小时测量主观疲劳、享受和执行功能（通过侧侧任务），同时持续监测瞳孔直径和心率。定期评估血糖和唾液皮质醇。与普通水相比，气泡水显著减轻了主观疲劳的增加，增强了享受，保持了执行功能，同时防止瞳孔收缩。心率、血糖和唾液皮质醇水平在两种情况下没有差异。值得注意的是，球员在比赛中使用气泡水的犯规次数减少了，而进攻和防守表现保持不变。这些发现表明，气泡水有助于缓解长时间玩电子竞技时的主观和客观认知疲劳迹象，这与我们的假设一致。这种不含咖啡因的干预可能有助于维持抑制控制和促进公平行为，为现代生活中管理精神疲劳提供了一种安全和可持续的策略。

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引用次数: 0

Collaborative robots and user trust: The role of saccadic gaze, anthropomorphic motion, and repetitive training 协作机器人与用户信任：眼球注视、拟人化运动和重复训练的作用

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-20 DOI: 10.1016/j.chbr.2026.100938

Esmeralda Faria , Ana Pinto , Soraia Oliveira , Gustavo Assunção , Carla Carvalho , Paulo Menezes

The transition from Industry 4.0 to 5.0 increasingly emphasizes the importance of human-robot collaboration, where trust plays a critical role in interaction efficacy. This study analyzes the impact of specific robot design characteristics, saccadic gaze and anthropomorphic movement, on user trust in an industrial setting, as well as the effect of repetitive training. A total of 71 participants interacted with the Kinova Gen 3 robot across four distinct conditions: 1) with gaze and robotic movement, 2) without gaze and robotic movement, 3) with gaze and anthropomorphic movement, and 4) without gaze and anthropomorphic movement. The effect of repetitive training was examined through trust scores collected over these four conditions. Trust levels were assessed using the Human-Robot Interaction Trust Scale (HRITS) and analyzed through paired t-tests and repeated measures ANOVA, in accordance with the within-subjects design. The overall effect of repeated training did not reach statistical significance (F = 2.63; p = .058), whereas a significant linear trend was observed (F = 4.41; p = .039), suggesting a gradual increase in trust scores over time. Neither saccadic gaze (t = .06; p = .561) or anthropomorphic movement (t = .73; p = .466) showed relevant effects. These results suggest that, in industrial contexts, the familiarity provided by repetitive training may play a more relevant role in trust calibration than visual or kinematic anthropomorphic features.

从工业4.0到5.0的过渡越来越强调人机协作的重要性，其中信任在交互效能中起着至关重要的作用。本研究分析了特定机器人设计特征、眼球注视和拟人化运动在工业环境下对用户信任的影响，以及重复训练的效果。共有71名参与者在四种不同的条件下与Kinova Gen 3机器人进行互动：1)凝视和机器人运动，2)没有凝视和机器人运动，3)凝视和拟人化运动，4)没有凝视和拟人化运动。通过在这四种情况下收集的信任分数来检验重复训练的效果。使用人机交互信任量表（HRITS）评估信任水平，并根据受试者内设计通过配对t检验和重复测量方差分析进行分析。重复训练的总体效果没有达到统计学意义（F = 2.63; p = 0.058），但观察到显著的线性趋势（F = 4.41; p = 0.039），表明信任得分随着时间的推移逐渐增加。跳眼凝视（t = .06; p = .561）和拟人化运动（t = .73; p = .466）均未显示相关影响。这些结果表明，在工业环境中，重复性训练提供的熟悉度可能比视觉或运动学拟人化特征在信任校准中发挥更相关的作用。

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引用次数: 0

Exploring the impact of patients' risk-benefit and knowledge perceptions on trust and intention to use AI-based medical imaging tools in radiology 探讨患者的风险-收益和知识感知对在放射学中使用基于人工智能的医学成像工具的信任和意愿的影响

IF 5.8 Q1 PSYCHOLOGY, EXPERIMENTAL

Computers in human behavior reports

Pub Date : 2026-01-17 DOI: 10.1016/j.chbr.2026.100936

Hassan Alipanahzadeh , Eli Eikefjord , Max Korbmacher

The integration of artificial intelligence (AI) with medical imaging tools has enabled faster and more accurate diagnostic processes, transforming radiology into a more precise, efficient, and data-driven medical discipline. However, the successful implementation of AI-based medical imaging tools in emotionally sensitive, life-critical domains such as radiology depends heavily on public trust and acceptance. This study examines how value perceptions and trust shape behavioral intentions to adopt AI-based tools in radiology by extending Esmaeilzadeh's Value-Based Model, which is conceptually aligned with Privacy Calculus Theory. To enhance the model's explanatory power, additional variables were incorporated, including perceived knowledge as a predictor and trust as a mediating factor.

A cross-sectional online survey (N = 961) was conducted, and data was analyzed through structural equation modeling. The findings indicate that perceived risk, perceived benefit, and perceived knowledge significantly influence trust perception. Importantly, trust served as a key mediating variable, partially mediating the effects of these factors on the intention to use AI-based medical imaging tools. The inclusion of trust increased the model's explanatory power from R² = 0.68 to R² = 0.74. Multigroup analysis based on gender, age, and education level revealed significant differences in certain pathways; however, the effect sizes were small. These findings highlight the importance of developing inclusive and targeted strategies that address both technical and emotional concerns, enhance perceived benefits, foster public trust, and strengthen the intention to use AI-based tools in radiology.

人工智能（AI）与医学成像工具的集成实现了更快、更准确的诊断过程，将放射学转变为更精确、高效和数据驱动的医学学科。然而，基于人工智能的医学成像工具在情感敏感、生命攸关的领域（如放射学）的成功实施在很大程度上取决于公众的信任和接受。本研究通过扩展esmaiilzadeh的基于价值的模型（在概念上与隐私微积分理论一致），研究了价值感知和信任如何影响在放射学中采用基于人工智能的工具的行为意图。为了增强模型的解释力，我们加入了额外的变量，包括感知知识作为预测因子和信任作为中介因子。采用横断面在线调查（N = 961），通过结构方程建模对数据进行分析。结果表明，感知风险、感知利益和感知知识显著影响信任感知。重要的是，信任是一个关键的中介变量，部分中介这些因素对使用基于人工智能的医学成像工具的意愿的影响。信任的加入使模型的解释能力由R2 = 0.68提高到R2 = 0.74。基于性别、年龄和教育水平的多组分析显示，某些通路存在显著差异；然而，效应量很小。这些发现强调了制定包容性和有针对性的战略的重要性，这些战略应解决技术和情感问题，增强感知利益，培养公众信任，并加强在放射学中使用基于人工智能的工具的意愿。

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