Limited Information and Quick Decisions: Consumer Privacy Calculus for Mobile Applications

Abstract

Mobile Applications (a.k.a. apps) have rapidly grown into a multi-billion-dollar industry. Because they are available through devices that are “always on” and often with the user, mobile apps are often adopted “on the fly” as needed. As a result, the related adoption and disclosure decisions are frequently only based on the information provided through the mobile app delivery platform (e.g., the Apple App Store™ or Google Play™). The fact that mobile app use often requires the disclosure of an unprecedented combination of personal information (e.g., location data, preferences, contacts, calendars, browsing history, music library) means that a very complex risk/benefit tradeoff decision is based on only the small amount of information provided by the mobile app delivery platform — and all in a short period of time. Hence, this process is much shorter and much riskier than traditional software adoption. Through two experiments involving 1,588 mobile app users, we manipulated three primary sources of information provided by the platform (app quality ratings, network size, and privacy assurances) to understand their effect on perceptions of privacy risks and benefits, and in turn, how they influence consumer adoption intentions and willingness-to-pay (WTP). The results indicate that network size influences not only perceived benefits but also the perceived risks of apps in the absence of perfect information. In addition, we find that integrating a third-party privacy assurance system into the app platform has a significant influence on app adoption and information disclosure. LBS privacy risk perceptions can also be reduced by network size, thus confirming our information cascade hypothesis. We further discuss the implications of these findings for research and practice.

Keywords: Mobile applications, location-based services, network effects, privacy assurance, electronic commerce, information cascades, privacy seals, privacy calculus, information privacy