This study empirically analyzes the impacts of open data on behavioral change by investigating the case of Taiwan. In Taiwan, each retailer’s mask stock level was made publicly open, enabling citizens to check the availability of masks at nearby stores from their smartphones in near real-time. This study analyzes citizens’ mask purchase behaviors by using data on the number of masks sold at 1658 stores in 55 regions in Taiwan and the usage of the mask map at the area level. This study found that the average of daily sold mask numbers per store per household among stores located in mask map use areas was fewer than those of other stores by 2.079. These results indicate a reduced panic buying behavior as a consequence of the openly accessible information in the form of an online mask map. Furthermore, the results also suggested that such open-data-based countermeasures did not equally impact every citizen and rather varied among socioeconomic conditions, particularly the education level (average effect of mask map usage: -14.514).
Shibuya, Y., Lai, CM., Hamm, A. et al. Do open data impact citizens’ behavior? Assessing face mask panic buying behaviors during the Covid-19 pandemic. Sci Rep 12, 17607 (2022). https://doi.org/10.1038/s41598-022-22471-y
Figure 1. Average sold face mask numbers per area before and after the government loosened the mask purchase policy (Panel (a), the red line indicates when the policy was changed). The launch date of the mask map is outside of Panel (a) (the launch was in February 2020). The target mask selling stores’ locations are shown in Panels (b) and (c). In Panel (b), the stores are plotted in red if they locate in the mask map use areas (more than 1 % of mask map use), otherwise blue. In Panel (c), stores are further categorized according to
the areas’ college graduate rates. Several areas do not show any stores in the panels because there were no corresponding mask map usage data in those areas. Thus, they were excluded from the analysis (see “Methods” section). See Table S2 in Supplementary Materials for area information. The maps of Panels (b) and (c) were created by the authors with Python 3.10.2. and GADM (https://gadm.org).