Not all data are equal: Personal data, analysis data, and Mobility-as-a-Service features in privacy calculus

Privacy calculus theory states that users evaluate the trade-off between (utilitarian) benefits and the costs of disclosing their personal data to mobile services. In this study, we ask: How do different types of utilitarian benefits and privacy costs relate to user ratings of free urban mobility apps? Utilitarian benefits are measured as both purely digital app features and as Mobility-as-a-Service (MaaS) modalities; privacy costs are split into personal data and analysis data. Exploring a snapshot sample of 117 European urban mobility apps, we find that the number of transport modalities, i.e., MaaS, bundled in an app is systematically related to app ratings, while purely digital app features are not. On the privacy costs side, as expected, the number of personal data has a negative relationship with user ratings. However, the number of analysis data is positively related to app ratings. The study contributes to an exploratory, empirically grounded differentiation of disclosed data categories within a privacy-calculus lens by disambiguating personal data from analysis data and by demonstrating the use of MaaS features in contrast to purely digital app features.