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

Mobile applications (also known as “apps”) have rapidly grown into a multibillion-dollar industry. Because they are available through devices that are “always on” and often with the user, users often adopt mobile apps “on the fly” as they need them. As a result, users often base their adoption and disclosure decisions only on the information provided through the mobile app delivery platform (e.g., the Apple App StoreTM or Google PlayTM). The fact that using a mobile app often requires one to disclose an unprecedented combination of personal information (e.g., location data, preferences, contacts, calendars, browsing history, music library) means that one makes a complex risk/benefit tradeoff decision based on only the small amount of information that the mobile app delivery platform provides—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 a 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). We found that network size influenced not only perceived benefits but also the perceived risks of apps in the absence of perfect information. In addition, we found that integrating a third party privacy assurance system into the app platform had a significant influence on app adoption and information disclosure. We also found that a larger network size reduces LBS privacy risk perceptions, which confirms our information cascade hypothesis. We discuss the implications of these findings for research and practice.