![]() 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. 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. The information disclosure is analyzed from a dual channel benefit/risk perspective through the calculus lens. 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.
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