Daruwala Neil Anthony
School of Psychology, Sport and Health Sciences, University of Portsmouth, Portsmouth, United Kingdom.
Front Robot AI. 2025 May 22;12:1568146. doi: 10.3389/frobt.2025.1568146. eCollection 2025.
Public attitudes toward service robots are critical to their acceptance across various industries. Previous research suggests that human-like features and behaviours perceived as empathetic may reduce negative perceptions and enhance emotional engagement. However, there is limited empirical evidence on how structured multimodal interventions influence these responses.
A partially mixed experimental design was employed, featuring one between-subjects factor (group: experimental vs. control) and one within-subjects factor (time: pre-intervention vs. post-intervention), applied only to the experimental group. Two hundred twenty-eight adults (aged 18-65) were randomly assigned to either the experimental or control condition. The intervention included images, video demonstrations of human-like service robots performing socially meaningful gestures, and a narrative vignette depicting human-robot interaction. The control group completed the same assessment measures without the intervention. Outcomes included negative attitudes toward robots (Negative Attitudes Toward Robots Scale, NARS), affect (Positive and Negative Affect Schedule, PANAS), and perceived interpersonal connection (Inclusion of Other in the Self scale, IOS).
The experimental group demonstrated a significant reduction in negative attitudes (p < 0.001, Cohen's d = 0.37), as well as lower negative affect and a greater perceived interpersonal connection with the robots (both p < 0.001). Age moderated baseline attitudes, with younger participants reporting more positive initial views; gender was not a significant factor.
These findings suggest that multimodal portrayals of human-like service robots can improve attitudes, affective responses, and interpersonal connection, offering practical insights for robot design, marketing, and public engagement strategies.
公众对服务机器人的态度对于它们在各个行业的接受程度至关重要。先前的研究表明，被视为具有同理心的类人特征和行为可能会减少负面看法并增强情感投入。然而，关于结构化多模态干预如何影响这些反应的实证证据有限。
采用部分混合实验设计，有一个组间因素（组：实验组与对照组）和一个组内因素（时间：干预前与干预后），仅应用于实验组。228名成年人（年龄在18至65岁之间）被随机分配到实验组或对照组。干预措施包括类人服务机器人执行具有社会意义手势的图像、视频演示，以及描述人机交互的叙事短文。对照组在没有干预的情况下完成相同的评估措施。结果包括对机器人的负面态度（机器人负面态度量表，NARS）、情感（积极和消极情感量表，PANAS）以及感知到的人际联系（自我中包含他人量表，IOS）。
实验组的负面态度显著降低（p < 0.001，科恩d值 = 0.37），负面情感更低，与机器人的人际联系感知更强（两者p < 0.001）。年龄调节了基线态度，年轻参与者报告的初始观点更积极；性别不是一个显著因素。
这些发现表明，类人服务机器人的多模态描绘可以改善态度、情感反应和人际联系，为机器人设计、营销和公众参与策略提供了实用见解。
