Robotic Moment: The Influence Mechanism of Frequency of Socializing on Willingness to Interact with AI Robots

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.

Keywords:

• AI robots
• socializing frequency
• social compensation
• robotic moment

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research was funded by the General Program of the National Natural Science Foundation of China (grant number 72072161).

Notes on contributors

Xiaoyi Wang

Xiaoyi Wang is a Professor at the School of Management, Zhejiang University. His main research areas are digital marketing and neuromarketing, using the intersection method of behavioral cognition and big data. His research has appeared in leading journals such as Marketing Science, Journal of Marketing Research, and Management Science.

Xingyi Qiu

Xingyi Qiu is a PhD candidate at the School of Management, Zhejiang University. Her research interest includes digital endorsers, Human–Computer interaction behavior and brand perception. Her research has been published in Behavioral Sciences, etc.