User preferences for privacy features in digital assistants

Abstract

Digital assistants (DA) perform routine tasks for users by interacting with the Internet of Things (IoT) devices and digital services. To do so, such assistants rely heavily on personal data, e.g. to provide personalized responses. This leads to privacy concerns for users and makes privacy features an important component of digital assistants.This study examines user preferences for three attributes of the design of privacy features in digital assistants, namely (1) the amount of information on personal data that is shown to the user, (2) explainability of the DA’s decision, and (3) the degree of gamification of the user interface (UI). In addition, it estimates users’ willingness to pay (WTP) for different versions of privacy features.The results for the full sample show that users prefer to understand the rationale behind the DA’s decisions based on the personal information involved, while being given information about the potential impacts of disclosing specific data. Further, the results indicate that users prefer to interact with the DA’s privacy features in a serious game. For this product, users are willing to pay €21.39 per month. In general, a playful design of privacy features is strongly preferred, as users are willing to pay 23.8% more compared to an option without any gamified elements. A detailed analysis identifies two customer clusters “Best Agers” and “DA Advocates”, which differ mainly in their average age and willingness to pay. Further, “DA Advocates” are mainly male and more privacy sensitive, whereas “Best Agers” show a higher affinity for a playful design of privacy features.

A Contextualized Acceptance Model for Proactive Smart Services

“…The paradox is that customers are most sensitive to personal data, and yet customers are most willing to share personal data if they expect to receive value-adding service in return (Gimpel et al 2018;Bertoncello et al 2016;Morey et al 2015). This phenomenon is called privacy paradox and can be explained by trust in the service provider, lack of risk awareness, lack of knowledge about privacy-friendly behaviors, or social benefits of self-disclosure (Ebbers et al 2021;Hargittai and Marwick 2016;Kokolakis 2017). In particular, 'digital natives' are more accustomed to providing personal data (Prensky 2001).…”

Improving customer satisfaction in proactive service design

“…This phenomenon is known as the privacy paradox. This may be explained by trust in the service provider, a lack of risk awareness, a lack of knowledge about privacy-protective behaviors, or the social advantages of self-disclosure (Ebbers et al, 2021;Hargittai & Marwick, 2016;Kokolakis, 2017). The same data which brings significant advantages for service providers also increases customer privacy concerns (Hauff et al, 2015;Zhan & Rajamani, 2008).…”

Attitudes and preferences toward the adoption of voice-controlled intelligent personal assistants: evidence from Croatia

“…While many users claim to care about their privacy and have a positive attitude toward privacy-protection behaviour, this rarely translates into actual protective conduct, and this discrepancy between the claimed concern and actual behaviour is a phenomenon known as the privacy paradox (Brown, 2001;Barth and De Jong, 2017). Generally, meeting consumer privacy expectations increases the likelihood to adopt devices such as intelligent personal assistant (Cases et al, 2010;Eastlick et al, 2006;Ebbers et al, 2021;Liao et al, 2019) but also increases the level of trust related to the company or manufacturer (McCole et al, 2010). Furthermore, a violation of privacy expectations will undoubtedly result in adverse reactions from consumers including nonadoption, usage withdrawal or rejection, and associated negative perception (Liao et al, 2019;Miyazaki, 2009).…”

“…Lastly, intelligent personal assistants use text-tospeech technology to respond to users (Yang and Lee, 2019). It should be noted that several different names and associated terminology have been utilised in available studies for this artificial intelligence-driven conversational software, such as voice-based digital assistants, smart speakers, smart voice assistants, personal intelligent agents, intelligent voice assistants, artificial intelligence personal assistants, speech-based intelligent personal assistants, voice assistants, digital personal assistants and others (Arnold et al, 2019;Budzinski et al, 2019;Ebbers et al, 2021;Mishra et al, 2021;Moussawi et al, 2021;Nallam et al, 2020;Pal et al, Sun et al, 2021;Vimalkumar, 2021;Zwakman et al, 2020). With some minor exceptions, these device names generally designate the same thing, so the term intelligent personal assistants will be used throughout the paper.…”