Customers? acceptance of artificially intelligent service robots: The influence of trust and culture

This study addresses two critical research gaps in human-robot interaction (HRI): the limited systematic research on the role of trust in customers' acceptance of artificially intelligent (AI) robots; and the lack of understanding of robot acceptance under different cultural backgrounds. Drawing on the AIDUA framework, this study examines the impacts of trust and moderating effects of both national (the U.S. and China) and individual culture on customers' intentions to use AI robots in hospitality services by developing a theoretical model. The model is tested on data collected using online data collection platforms from 491 U.S. and 495 Chinese respondents. PLS-SEM and the bootstrapping method were used to test the hypothesized relationships and analyze the moderating effects of culture, respectively. The findings suggest that trust in interaction with AI robots is a significant higher -order construct that influences the intention of use. Furthermore, uncertainty avoidance, long-term orientation, and power distance have been found to exhibit significant moderation effects. The results of this study extend the theoretical frameworks in HRI and provide detailed guidance to promote AI robot applications across different cultures.

Automated summary

This study fills two critical research gaps in human-robot interaction (HRI): the lack of systematic research on the role of trust in customers' acceptance of AI robots, and the lack of understanding of robot acceptance across different cultural backgrounds. Using the AIDUA framework, the study develops a theoretical model to examine the impacts of trust and moderating effects of national and individual culture on customers' intentions to use AI robots in hospitality services. The model is tested on data collected from 491 U.S. and 495 Chinese respondents. The findings show that trust in interaction with AI robots significantly influences the intention of use, and cultural factors (uncertainty avoidance, long-term orientation, and power distance) moderate this relationship. This study extends the theoretical frameworks in HRI and provides guidance for promoting AI robot applications in different cultural contexts.