Conference paper Open Access

Data governance for the public acceptability of personalized COVID-19 advice: An experimental study in Hong Kong

Li, Veronica Qin Ting; Yarime, Masaru

As COVID-19 persists and mutates, governments will need to keep citizens updated with the latest information. During this time of high uncertainty, taking a personalised approach to COVID-19 advice could prove valuable for citizens to protect themselves based on their individual circumstances. Although efforts have been made to develop technologies that could make this approach viable, there is a lack of research focusing on the socio-political barriers that could lead to low public acceptance. Here, we present a survey experiment where we gauged the willingness of Hong Kong citizens to use a mobile application for personalised COVID-19 advice based on different data governance concerns and demographic characteristics, such as the sector of the developer or the method of data storage. We conclude that gender has a statistically significant effect on willingness, possibly due to women having greater concerns over the safety risks of sharing personal data than men. We also note that other concerns surrounding data security and access affect users’ willingness to use a personalised advice application where they would need to share health and location data. Finally, we encourage further research on context-specific factors affecting the public acceptability of data tools for crisis management.

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