The valuation of privacy premium features for smartphone apps: The influence of defaults and expert recommendations

Abstract

This study examines the impact of privacy defaults and expert recommendations on smartphone users' willingness to pay for “privacy-enhanced” features on paid applications using a 2 (privacy premium default/no privacy premium default) x 2 (privacy expert recommendation/non-privacy expert recommendation) experimental design. Participants (N = 309) configured four paid apps with respect to privacy features. Selecting premium privacy features was associated with an increased cost, while removing premium privacy features reduced the cost of the application. Replicating findings from behavioral economics on default modes in decision-making, we found that participants presented with apps with privacy premium default features were more likely to retain the more expensive privacy features. However, the recommendation source did not have a significant effect on this relationship. We discuss how these findings extend existing work on users' decision-making process around privacy and suggest potential avenues for nudging users’ privacy behaviors on mobile devices.