000000346 001__ 346
000000346 005__ 20220311144650.0
000000346 037__ $$aLERSSE-RefConfPaper-2022-001
000000346 100__ $$aYue Huang
000000346 245__ $$aCOVID-19 Information-Tracking Solutions: A Qualitative Investigation of the Factors Influencing People’s Adoption Intention
000000346 260__ $$c2022-03-11
000000346 300__ $$amult. p
000000346 520__ $$aNumerous information-tracking solutions have been implemented worldwide to fight the COVID-19 pandemic. While prior work has heavily explored the factors affecting people’s willingness to adopt contact-tracing solutions, which inform people when they have been exposed to someone positive for COVID-19, numerous countries have implemented other information-tracking solutions that use more data and more sensitive data than these commonly studied contact-tracing apps. In this work, we build on existing work focused on contact-tracing apps to explore adoption and design considerations for six representative information tracking solutions for COVID-19, which differ in their goals and in the types of information they collect. To do so, we conducted semi-structured interviews with 44 participants to investigate the factors that influence their willingness to adopt these solutions. We find four main categories of influences on participants’ willingness to adopt such solutions: individual benefits of the solution, societal benefits of the solution, functionality concern, and digital safety (e.g., security and privacy) concerns. Further, we enumerate the factors that inform participants’ evaluations of these categories. Based on our findings, we make recommendations for the future design of information-tracking solutions and discuss how different factors may balance against benefits in future crisis situations.
000000346 6531_ $$aUsable privacy and security
000000346 6531_ $$ainformation sharing
000000346 6531_ $$adata practices
000000346 700__ $$aBorke Obada-Obieh
000000346 700__ $$aElissa M. Redmiles
000000346 700__ $$aSatya Lokam
000000346 700__ $$aKonstantin Beznosov
000000346 8560_ $$flersse-it@ece.ubc.ca
000000346 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/346/files/Chiir_Information_Tracking_Solutions.pdf
000000346 909C4 $$pYue Huang, Borke Obada-Obieh, Elissa M. Redmiles, Satya Lokam, and Konstantin Beznosov. 2022. COVID 19 Information-Tracking Solutions: A Qualitative Investigation of the Factors Influencing People’s Adoption Intention. In Proceedings of the 2022 ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR ’22), March 14–18, 2022, Regensburg, Germany. ACM, New York, NY, USA, 23 pages. https://doi.org/10.1145/3498366.3505756
000000346 980__ $$aRefConfPaper