Table 2.
Predicting number of installs, number of ratings, number of reviews, and rating scores of COVID-19 apps
| Model 1 (Number of installs) | Model 2 (Number of ratings) | |||||
| Variables | IRR (S.E.) | 95%CI | P-value | IRR (S.E.) | 95%CI | P-value |
| (Intercept) | 423312.9 (2760158) | 1.193, 1.50e+11 | .047 | 0.010 (0.077) | 0.000, 31031.920 | .55 |
| Release date | 0.630 (0.186) | 0.353, 1.125 | .12 | 0.971 (0.551) | 0.319, 2.951 | .96 |
| Population (log) | 2.206 (0.792) | 1.091, 4.460 | .03 | 10.354 (6.273) | 3.158, 33.945 | <.001 |
| GDP (log) | 0.564 (0.250) | 0.237, 1.342 | .20 | 0.269 (0.159) | 0.084, 0.859 | .03 |
| Internet user | 1.020 (0.017) | 0.986, 1.054 | .25 | 1.115 (0.031) | 1.056, 1.178 | <.001 |
| Country-level factors | ||||||
| Number of cases (log) | 0.917 (0.152) | 0.663, 1.268 | .60 | 0.716 (0.153) | 0.470, 1.089 | .12 |
| Political system (Democratic=1) | 0.772 (0.421) | 0.264, 2.251 | .64 | 0.925 (0.893) | 0.140, 6.138 | .94 |
| App-level factors | ||||||
| Developer (Government=1) | 8.156 (3.654) | 3.389, 19.626 | <.001 | 26.036 (16.836) | 7.331, 92.468 | <.001 |
| Function (Health information) | 0.732 (0.261) | 0.364, 1.472 | .38 | 1.408 (0.749) | 0.496, 3.995 | .52 |
| Function (Contact tracing) | 4.533 (1.811) | 2.072, 9.918 | <.001 | 11.634 (7.154) | 3.486, 38.827 | <.001 |
| Function (Home monitoring) | 0.732 (0.389) | 0.258, 2.075 | .56 | 0.206 (0.155) | 0.047, 0.896 | .04 |
| Function (Consultation) | 4.885 (2.263) | 1.970, 12.111 | .001 | 17.194 (10.308) | 5.309, 55.680 | <.001 |
| Log likelihood | -2177.551 | -1470.949 | ||||
| LR chi2 | 72.42 | 74.54 | ||||
| Pseudo R2 | 1.64 | 2.47 | ||||
| R-squared | ||||||
| N | 169 | 169 | ||||
| Model 3 (Number of reviews) | Model 4 (Rating score) | |||||
| Variables | IRR (S.E.) | 95%CI | P-value | Coefficient (S.E.) | 95%CI | P-value |
| (Intercept) | 2.919 (19.028) | 0.000, 1031620 | .87 | 6.255 (1.379) | 3.537, 8.974 | <.001 |
| Release date | 0.277 (0.101) | 0.136, 0.565 | <.001 | -0.049 (0.107) | -0.261, 0.163 | .65 |
| Population (log) | 0.258 (0.141) | 0.088, 0.754 | .01 | 0.150 (0.121) | -0.088, 0.388 | .22 |
| GDP (log) | 3.137 (1.562) | 1.182, 8.324 | .02 | -0.215 (0.109) | -0.430, 0.001 | .05 |
| Internet user | 0.951 (0.026) | 0.902, 1.002 | .06 | 0.007 (0.006) | -0.005, 0.019 | .24 |
| Country-level factors | ||||||
| Number of cases (log) | 1.081 (0.193) | 0.763, 1.533 | .66 | 0.079 (0.040) | 0.000, 0.158 | .049 |
| Political system (Democratic=1) | 0.548 (0.427) | 0.119, 2.526 | .44 | -0.220 (0.222) | -0.657, 0.217 | .32 |
| App-level factors | ||||||
| Developer (Government=1) | 12.188 (7.000) | 3.954, 37.568 | <.001 | -0.369 (0.145) | -0.653, -0.083 | .01 |
| Function (Health information) | 1.314 (0.556) | 0.573, 3.013 | .52 | 0.032 (0.126) | -0.216, 0.281 | .80 |
| Function (Contact tracing) | 5.688 (2.959) | 2.052, 15.770 | .001 | -0.072 (0.142) | -0.352, 0.208 | .61 |
| Function (Home monitoring) | 3.871 (2.588) | 1.044, 14.349 | .04 | -0.550 (0.214) | -0.971, -0.129 | .01 |
| Function (Consultation) | 16.718 (9.698) | 5.363, 52.113 | <.001 | 0.292 (0.182) | -0.066, 0.650 | .11 |
| Log likelihood | -938.135 | |||||
| LR chi2 | 58.00 | |||||
| Pseudo R2 | 3.00 | |||||
| R-squared | 13.86 | |||||
| N | 170 | 216 | ||||