Table 6.
Demand drivers.
| Demand drivers | Dependent Variable | ||||
|---|---|---|---|---|---|
| Propensity to obtain prescription medicine online (in sd) | Number of pharmacy visits | Duration per visit (in min) | Items bought | ||
| (1) | (2) | (3) | (4) | (5) | |
| Chronic condition | 0.0915*** (0.0293) |
0.126*** (0.0324) |
7.67788 (0.591) |
3.3905*** (0.863) |
0.670*** (0.0424) |
| Caregiver | 0.298*** (0.0307) |
0.14.7*** (0.0334) |
12.06*** (0.700) |
8.913*** (0.929) |
0.424*** (0.0467) |
| Long distance to pharmacy | 0.0395** (0.0167) |
0.0602*** (0.0178) |
|||
| Inconvenience from short opening hours | 0.124*** (0.0173) |
0.0644*** (0.0183) |
|||
| Constant | −0.499*** (0.0404) |
−1.181*** (0.106) |
3.070 (2.268) |
17.53*** (2.680) |
2.123*** (0.124) |
| Controls | No | Yes | Yes | Yes | Yes |
| Observations R2 |
4,673 0.051 |
3,577 0.218 |
3,577 0.244 |
3,577 0.106 |
3,577 0.177 |
The table shows the regression results of five different linear regression models: Models 1 and 2 show effects on the propensity to obtain prescription medicine online. While model 1 does not include control variables, model 2 does. Model 3 shows estimated linear effects on the number of pharmacy visits, model 4 on the duration of visits, and model 5 on the number of purchased items. For all three models, we include control variables. The control variables are country dummies, age, gender, income, education level, city size, and the frequency of general online shopping. We use robust standard errors. *, **, and *** mark significance levels of p < 0.05, p < 0.01, and p < 0.001, respectively.