Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions

Piçarra, Nuno; Giger, Jean‐Christophe

doi:10.1016/j.chb.2018.04.026

Cited by 89 publications

(64 citation statements)

References 95 publications

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“…This study aimed to test whether the social responses add predictive power to the TAM specifically for the persuasive robot. Using the same method as in [97], we compared the R 2 for Attitude and Intention from TAM (presented in Objective 1) and PRAM (inclusion of social responses presented in Objective 2) by calculating the F-ratio and its significance. Results of these analyses confirmed that adding social responses as determinants to TAM significantly increased the predictive power in explaining the acceptance of the persuasive robot (demonstrated by PRAM).…”

Section: Objectivementioning

confidence: 99%

“…If the model fits the data 100%, or in other words, the model explains all of the variations in the endogenous variable, then the R 2 for such variable is equal to 1.00. The R 2 value was used in the earlier study [99] especially in human-robot interaction applications [97,100] as a model-fit measure [101]. According to Cohen [96], R 2 of 0.02, 0.13 and 0.26 are considered as small, medium and large effects respectively in the field of social and behavioural science.…”

Section: Objectivementioning

confidence: 99%

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Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Ghazali

Ham

Barakova

et al. 2020

Int J of Soc Robotics

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In the last years, there have been rapid developments in social robotics, which bring about the prospect of their application as persuasive robots to support behavior change. In order to guide related developments and pave the way for their adoption, it is important to understand the factors that influence the acceptance of social robots as persuasive agents. This study extends the technology acceptance model by including measures of social responses. The social responses include trusting belief, compliance, liking, and psychological reactance. Using the Wizard of Oz method, a laboratory experiment was conducted to evaluate user acceptance and social responses towards a social robot called SociBot. This robot was used as a persuasive agent in making decisions in donating to charities. Using partial least squares method, results showed that trusting beliefs and liking towards the robot significantly add the predictive power of the acceptance model of persuasive robots. However, due to the limitations of the study design, psychological reactance and compliance were not found to contribute to the prediction of persuasive robots' acceptance. Implications for the development of persuasive robots are discussed.

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Section: Objectivementioning

confidence: 99%

Section: Objectivementioning

confidence: 99%

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Ghazali

Ham

Barakova

et al. 2020

Int J of Soc Robotics

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show abstract

“…Social robots are becoming attractive to many of us. There are attempts to involve robots in more intense interactions with humans, more collaboration on certain specific goals (Picarra and Giger, 2018), tasks such as preparing medication or serving food, teaching children foreign languages, and assisting the elderly with their daily activities (Simon, 2015;McDaniel, 2010;Wada et al, 2005). Robots are also used as walking assistants for children with multiple disabilities.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement learning for an intelligent pediatrician robot

Basori

2020

FRL

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The staff shortage in the medical world needs to be addressed. In this era of mobile communication, where online consultancy or telemedicine has become one way to overcome this problem, an innovative solution is needed for addressing tasks usually dealt with by a doctor. But doctors are limited by their physical fitness, tight schedules and even by their mental stability. Therefore, the involvement of Artificial Intelligence and robotics in the medical field is quite a normal development. There is a demand for robots that can imitate the personality of a friendly pediatric doctor, even though such robots cannot yet fully substitute for human pediatricians. In this paper, we discuss several possibilities as to what type of robot behavior is suitable as a substitute for a pediatrician, and the role of reinforcement learning, training them to be as friendly as possible with children, and enabling them to mimic human doctors. This learning process will not only teach robots to impersonate the personality of a doctor, but also to provide diagnosis capabilities-one of the core skills needed by a pediatrician. This paper highlights promising directions for potential applications and research. Our simulation showed that machine learning algorithms such as neural networks could support reinforcement learning in the field of pediatric medicine, reaching a diagnostic recognition rate of 96.6% for children with a particular disability. Future work might focus on increased integration between sensor devices in a humanoid robot and the medical Internet-of-Things, with more intense training enabling such a robot to be adaptable to any change of environment.

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“…此外, 人与机器人交互之中的一些具体行为也会对接受度产生影响. 例如, 研究发现参与机器人的制作 [96] 或者组装过程 [97] [99] , 它能够预测使用者特定的情绪反应(如钦佩、嫉妒、轻蔑和怜悯等), 并进而预测特定的行为倾向(帮助或伤害) [100] . 对机器人社会存在的感知也是影响机器人接受度的一个重要因素.…”

Section: 拟人化是在机器人外观研究中最为热点的一个问题unclassified

Factors that influence robot acceptance

Xu¹,

Ye²

2019

Chin. Sci. Bull.

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Predicting intention to work with social robots at anticipation stage: Assessing the role of behavioral desire and anticipated emotions

Cited by 89 publications

References 95 publications

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Persuasive Robots Acceptance Model (PRAM): Roles of Social Responses Within the Acceptance Model of Persuasive Robots

Reinforcement learning for an intelligent pediatrician robot

Factors that influence robot acceptance

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