Information Disclosure in Location-based Services: An Extended Privacy Calculus Model
Dana Naous, Vaibhav Kulkarni, Christine Legner, Benoît Garbinato · Journal of the Association for Information Systems · 2019
In today’s interconnected world, disclosing information on location-based services (LBS) has several privacy implications. In line with the general privacy studies, the rationale behind individual’s disclosure motivations has been studied in information systems (IS) through the lens of privacy calculus. However, existing work investigates location- information sharing as uni-dimensional user behavior under highly contextual settings. In this study, we propose an extended privacy calculus model that views location disclosure across three dimensions; (1) extent, (2) location sensitivity and (3) sharing parties. We also introduce amendments to account for data privacy regulations, data streaming economy and interdependent privacy risks. We thus provide a more nuanced conceptualization of location disclosure along with empirical insights from a large-scale empirical study (n=1050). We find that: (1) there is a need for transparent control settings, (2) users are willing to disclose for monetary incentives, and (3) they are not cognizant about interdependent privacy risks.