Table 1 Variable definitions and relationship hypotheses
Variable types and names | Definition of variables (quantitative units or symbols) | Relationship Hypothesesa |
|---|---|---|
Explained variables: | ||
Internet search volume density Yi (i.e., RIi) | The proportion of information demand of Internet users in each province to the total information demand of Internet users nationwide (%) | |
Explanatory variables: | ||
Male to female sex ratio (X1) | Male share of total population (%) | Higher risk perception among men (+) |
Age structure (X2) | Population aged 15–64 as a percentage of total population (%)b | The higher the ratio, the higher the risk perception (+) |
Years of education per capita (X3) | Years of formal education only (Years) | The longer the number of years of education, the higher the risk perception (+) |
Mortality (X4) | Ratio of the number of deaths by province in a year to the average number for the same period (%) | The higher the mortality rate (lower the regional health level), the lower the risk perception (−) |
Per capita GDP (X5) | Final results of production activities of all resident units in the provinces during the year (billion yuan, RMB) | Higher risk perception in provinces with higher GDP (economically developed areas) |
Rate of decline in risk perception (X6) (λ value) | The probability of Internet users searching for the COVID-19 event reflects the extent of Internet information disclosure | The higher the search volume, i.e., the faster the rate of decline, the higher the risk perception (+) |