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Top1. Introduction
The AI virtual assistants are a series of applications or platforms based on AI technology. They use natural language in both written and oral forms to help people find information and make decisions. The AI virtual assistants (for example, Siri for Apple, Alexa for Amazon Echo, Cortana for Microsoft, etc.) can provide users with convenience and more efficient services (Doorn et al., 2017). Consequently, the frequent use of the AI virtual assistants gradually increases. Among these, AVA as a key variable reflects the extent to which the AI virtual assistants are accepted by users (Fernandes & Oliveira, 2020). Therefore, it is important to examine the factors affecting the AVA and their differences. There is also much value in making recommendations for product development and private investors.
Currently, the research on the AI virtual assistants is rising, but there are still some gaps. The gaps consist primarily of the following three areas. First, the AI virtual assistants pay little attention to the driver of AI technology from the perspective of user perception. Past advances have focused on the technology itself, changing or replacing everyday manual tasks (Ostrom et al., 2019). Second, previous studies rarely explore the unique empathy characteristics of AI technology (Lin et al., 2019), and lack the different studies on AVA at the level of user emotional cognition (Fernandes & Oliveira, 2020). Third, previous studies have added social factors as the driving factors of AVA into the service robot acceptance model (Wirtz, 2018), but less attention has been paid to the interaction between functional cognition, emotional cognition, and social factors (Fernandes & Oliveira, 2020). Exploring the interaction effects of different factors is key to broaden application boundary of the AVA model.
On this basis, this research performs three tasks. First, we explore the drivers for AVA from a user perception perspective. We consider functional cognition and emotional cognition as the major factors which influences AVA, in the hope of bridging the gap on user perception. Second, we investigate the empathy traits of artificial intelligence technology. Referring to the service robot acceptance model (Wirtz et al., 2018), we divide emotional cognition into three dimensions (perceived social presence, perceived social interactions and perceived humanness) to explore the influence of user emotional cognition based on empathy. Lastly, we present external social norms as factors in social relations. In fact, these social factors are important, especially the spread of innovative technological products such as the AI virtual assistants (Yoo et al., 2021). We explore the interaction of social norms between two types of user perception and AVA, and establish a social regulatory framework.