As virtual assistants become more intelligent, these applications are dispersed in our everyday life. However, virtual assistants are still used in very routine tasks (Wingfield, 2018). People are expected to speak to virtual assistants in a conversational manner; nevertheless, there has been little research looking at people's mental models for what kinds of interactions they think the devices are capable of. I conducted a qualitative study to describe people’s strategies to seek a wide range of information when interacting with Google Home, which is a type of Home Virtual Assistant (HVA), and how their understandings of HVAs may change their interaction. I found that people believed applying human-to-human communication skills may facilitate a conversation with Google Home (i.e., common-sense models). Also, people expected Google Home performs like Google search or an artificial intelligence system that is able to become more intelligent if people provide more information (i.e., machine-oriented models). I present implications for the design of HVAs to encourage inexperienced people to be involved in the interaction with HVAs based on these results.
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