Developing an ontology for data science projects to facilitate the design process of a canvas

Data science projects can become very complex, due to the complexity of their content, but also due to the nature and composition of their stakeholders. There are several approaches to remedy this, e.g., canvases, which support ideation and common understanding. However, previous approaches are limited to single details or abstract too much, so that it is difficult to carry out entire projects successfully based on them. This paper describes one part of the design process, namely the derivation of the underlying ontology, of a new canvas that integrates both the overall project and detail steps. The ontology is mainly derived from CRISP-DM, literature review and project work

‘SPREAD THE APP, NOT THE VIRUS’ – AN EXTENSIVE SEM-APPROACH TO UNDERSTAND PANDEMIC TRACING APP USAGE IN GERMANY

The release of the Corona-Warn-App (CWA), a governmental pandemic tracing app to track infection chains related to COVID-19 in Germany, marks an unprecedented situation that offers a unique opportunity for investigating population-wide adoption of novel technology. We develop a conceptual model to investigate the effects and path relationships of multiple constructs related to technology adoption, data security, morality, social influence, trust, and COVID-19 to predict behavioral intentions and actual usage behavior. We use structural equation modelling with the partial least squares method and identify effort expectancy, social influence, prevailing opinions on COVID-19 and the CWA, as well as moral and ethical considerations as the most influential predictors. We are able to explain moderate to high amounts of variance with our model. Our results offer valuable insights for the technology ac- ceptance literature and enable practical recommendations for improving the public communication and elevating user numbers of pandemic tracing apps in Germany