Privacy paradox in mHealth applications: An integrated elaboration likelihood model incorporating privacy calculus and privacy fatigue

Abstract

As people’s health awareness and standard of living improve, mHealth applications are being increasingly used. However, mHealth application services are mainly based on the collection of personal and behavioral data, which conflicts with users’ growing privacy concerns. In that context, this study considers the privacy paradox phenomenon, in which privacy concerns co-exist with disclosure behavior. This study explores the privacy paradox in mHealth applications using an integrated elaboration likelihood model (ELM) from the perspective of privacy calculus and privacy fatigue. Results from the quasi-experiment and partial least squares structural equation modeling reveal that, compared with privacy concerns, perceived benefits have a greater impact on users’ disclosure intention, which further supports the existence of the privacy paradox in the mHealth context; this process is found to originate in users’ privacy calculus. However, privacy fatigue is found to have an insignificant impact on users’ disclosure intention, which may be due to the low sunk cost of users’ investment in mHealth applications. The results indicate that designers of mHealth applications should optimize their interaction functions to enhance benefits to users.