Personal Information Disclosure via Voice Assistants: The Personalization–Privacy Paradox

Abstract

The purpose of this research is to examine the factors affecting the willingness to disclose personal information based upon the privacy calculus framework and its relation to the continued usage of the Voice Assistant (VA) devices. The information disclosure is analyzed from a dual channel benefit/risk perspective through the calculus lens. Particularly, the joint effects of the two independent paths of benefits and risks that can induce the desire to disclose personal information, along with their various antecedents are proposed and empirically tested. Data are collected from 427 respondents using Amazon Mechanical Turk (MTurk) as the platform and analyzed using a Maximum Likelihood Structural Equation Modelling (ML-SEM) technique. The analysis shows that personalized services, perceived enjoyment and perceived complementarity influence the perceived benefits, which in turn positively affects the personal information disclosure. Perceived severity and perceived control are found to have a positive effect, while perceived trust has a negative effect on the perceived risks, respectively, which in turn is negatively associated with the personal information disclosure. Compared to the perceived risks, the effect of perceived benefits on the information disclosure is found to be stronger, which motivates the users to continuously use the services of the VA devices. This work extends the current literatures on privacy concerns to the users’ intention of disclosing their personal information by proposing and empirically testing six hypotheses as a part of the research model. Finally, the research implications are discussed, and suggestions are provided.