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Robotic moment: The influence mechanism of frequency of socializing on willingness to interact with ai robots
International Journal of Human-Computer Interaction ; : No Pagination Specified, 2023.
Article in English | APA PsycInfo | ID: covidwho-20244492
ABSTRACT
Past research has discovered that the shape design and interaction process design of AI robots, as well as the users' constant features, are the major factors that affect users' willingness to interact with AI robots. Currently, AI robots that play a vital part in the daily activities of our society are becoming increasingly prevalent, thus things about AI robots have gone from mythic to prosaic. But when and where people are more likely to adopt AI robots remains an important research topic. With the development of online technology and the long-term impact of COVID-19, there has been a recent trend in the lower frequency of socializing. To investigate whether a state of low socializing frequency is a robotic moment and whether it affects people's willingness to interact with AI robots, we conducted two-wave questionnaire surveys to collect data from 300 participants from 23 provinces in China. The results showed that the frequency of socializing had a significant negative correlation with the willingness to interact with the AI robots via the mediation role of social compensation. Furthermore, the relationship between social compensation and willingness to interact with the AI robots was demonstrated to be stronger, when participants had a lower anthropomorphic tendency. These findings have theoretical implications for the human-computer interaction literature and managerial implications for the robotics industry. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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Full text: Available Collection: Databases of international organizations Database: APA PsycInfo Type of study: Observational study Topics: Long Covid Language: English Journal: International Journal of Human-Computer Interaction Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: APA PsycInfo Type of study: Observational study Topics: Long Covid Language: English Journal: International Journal of Human-Computer Interaction Year: 2023 Document Type: Article