# Self-Disclosure to an IoT Conversational Agent: Effects of Space and User Context on Users'
## 1 Introduction  
Internet-of-Things conversational agents (IoT-CAs) marketed as smart speakers, such as Amazon Echo and Alibaba Group's Tmall Genie, are voice-command agents with integrated artificial intelligence and human-like behavior. IoT-CAs embody the underlying concept of virtual assistants and perform various tasks such as controlling IoT devices, setting reminders, and web searching when requested by users. [PERSON] [1] extrapolated that the number of installed IoT-CAs would approach 100 million by the end of 2018 and will eventually reach 225 million by 2020. Thus, IoT-CAs are a rapidly developing consumer technology, and users are moving into an era of human-IoT-CA interaction.  
As the number of interactions between IoT-CAs and users increases, users will increasingly disclose personal information to IoT-CAs for services, e.g., the disclosure of a daily routine to generate a to-do list, eating habits to obtain dining recommendations, and body data for health monitoring. The act of revealing personal information to others is referred to as self-disclosure [2; 3]. Users can receive more accurate and personalized services by disclosing personal information. Because of privacy concerns, this self-disclosure might be selective; that is, users may prefer to disclose some types of information to a personalized service while keeping other types of information private. Therefore, understanding a user's willingness to self-disclose information to IoT-CAs can aid in the provision of personalized services based on their disclosure preferences and privacy concerns.  
Given the growing capabilities of IoT-CAs, the ranges of spaces (e.g., living spaces and workspaces) and user contexts (e.g., a single user and multiple users) in which they are used are expanding. Living spaces and workspaces are two typical social spaces in our daily lives. Recent studies have explored the use of IoT-CAs in workspaces [4] and living spaces [5], but no prior study has compared the use of IoT-CAs in these two spaces. Moreover, IoT-CAs can support collaborative use by more than one user by exploiting voice recognition and synthesis technologies. The two-user case is the simplest case of multiple users, which can aid in the understanding of the interaction between multiple users and an IoT-CA. Therefore, the space and user context are important factors in human-IoT-CA interaction.  
To the best of our knowledge, no prior studies have investigated the willingness of users to disclose different types of information to an IoT-CA and the effects of the space (living space and workspace) and user context (one user or two users) on their willingness to self-disclose. This study utilized the \"Wizard-of-Oz\" technique [6] and built artificial smart living spaces and workspaces where one or two users interacted with an IoT-CA. Two laboratory experiments were designed and conducted to understand a user's willingness to self-disclose to an IoT-CA on the basis of their actual use of it. This study aims to investigate the self-disclosure of one user/two-users to an IoT-CA in both the living space and workplace. The focus of this study was not to compare the differences between one and two users but to understand a user's willingness to disclose different types of information in the living space and workspace when either one user or two users are interacting with an IoT-CA.