Dataset for: How identification with the social environment and with the government guide the use of the official COVID-19 contact tracing app: Three quantitative survey studies. (Scholl, A., & Sassenberg, K.)

DOI

Official contact tracing apps have been implemented and recommended for use across nations to track and contain the spread of COVID-19. Such apps can be effective if people are willing to use them. Accordingly, many attempts are being made to motivate citizens to make use of the officially recommended apps. The present research sought to contribute to an understanding of the preconditions under which people are willing to use this app (i.e., their use intentions and actual use). To go beyond personal motives in favor of app use, it takes people’s social relationships into account; doing so, it argues that the more people identify with the beneficiaries of app use (i.e., people living close by in their social environment) and with the source recommending the app (i.e., members of the government), the more likely they will be to accept the officially recommended contact tracing app. Before, right after, and five months after the official contact tracing app was launched in Germany, a total of 1044 people participated in three separate studies. Structural equation modeling tested and supported the hypotheses, examining the same model in all studies at these critical points in time.

Dataset for: Scholl, A., & Sassenberg, K. (in press). How identification with the social environment and with the government guide the use of the official COVID-19 contact tracing app: Three quantitative survey studies. JMIR mHealth and uHealth. https://dx.doi.org/10.2196/28146

Identifier
DOI https://doi.org/10.23668/psycharchives.5120
Metadata Access https://api.datacite.org/dois/10.23668/psycharchives.5120
Provenance
Creator Scholl, Annika
Publisher PsychArchives
Contributor Leibniz Institut Für Psychologie (ZPID); Sassenberg, Kai
Publication Year 2021
Rights CC-BY 4.0; openAccess; Creative Commons Attribution 4.0 International
OpenAccess true
Representation
Language English
Resource Type Dataset
Discipline Social Sciences