Table 2.
Predicting citizen engagement through government social media.
| Model 1 |
Model 2 |
|||
|---|---|---|---|---|
| IRR | SE | IRR | SE | |
| (Intercept) | 621.60∗∗∗ | 108.72 | 449.80∗∗∗ | 74.72 |
| Main effect | ||||
| Media Richness | 0.59∗∗∗ | 0.04 | 0.74∗∗∗ | 0.05 |
| Dialogic Loop | 1.35∗∗ | 0.14 | 1.38∗∗∗ | 0.13 |
| Content Type (reference group: appreciation) | ||||
| News | 248.24∗∗ | 42.80 | 87.24∗ | 25.21 |
| Handling | 4.23∗∗∗ | 0.46 | 2.59∗∗ | 0.28 |
| Guidance | 1.11 | 0.14 | 0.97 | 0.12 |
| Interaction effect | ||||
| Emotional Valence | 1.27 | 0.61 | ||
| EV∗ Media Richness | 10.06∗∗ | 3.77 | ||
| EV ∗ Dialogic Loop | 40.39∗∗∗ | 19.50 | ||
| EV∗ Content Type (reference group: appreciation) | ||||
| News | 0.04∗ | 0.05 | ||
| Handling | 0.00 | 0.00 | ||
| Guidance | 0.07 | 0.05 | ||
| Log likelihood | −10,224.83 | −10087.44 | ||
| Pseudo R2 (%) | 7.34 | 8.59 | ||
| N | 1411 | 1411 | ||
Note: IRR: Incident Rate Ratio; SE: Standard Error; EV: Emotional Valence; ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.