Privacy vs. Pandemic: An Exploratory Study on Drivers and Barriers to Download and Share Data with an Infection Tracking App

Abstract

On the 12th of March 2020, the Norwegian government implemented a national lock-down and the most vigorous and intrusive measures in peacetime due to the outbreak of the Covid-19 virus. A month later, Norway launched an infection tracking app called Smittestopp which goal was to mitigate the virus's spread. However, due to poor data management, the app was withdrawn shortly after. In December 2020, an improved version was launched. Yet, half a year later, only 20 percent of the population have downloaded the app. This empirical study aims to discover why the app's adoption rate is so low by uncovering drivers and barriers towards downloading and sharing data with the infection tracking app. By employing an abductive methodological approach, the case of Smittestopp is used to explore this novel research area of privacy during a pandemic. In order to achieve its purpose, a multi-method data collection was obtained from interviewees with different demographics, divided into individual and focus group interviews. To help answer the research question, two main theories were applied; privacy calculus and the theory of reasoned action. Through our empirical findings, it became clear that the drivers for using Smittestopp can be divided into two categories; direct effects improving infection tracking and indirect effects on the economy and well-being of the population. The barriers can also be divided into two categories; privacy-related barriers and social influence barriers. We also uncovered findings contradicting the theories and arguments that did not stem from current literature, hence broadening existing knowledge of this novel area of research. Overall, we found that the barriers of Smittestopp exceed the drivers, explaining the app’s currently low user base. Fear of surveillance and processing of data causes many potential users to become sceptical towards the app. Moreover, we discovered a lack of network effect and word of mouth as the most essential factors, leading to low app functionality and a negative reputation.