TABLE 5.
Path analysis results of the structural model.
| Hypothesized relationships | β | S.E. | Significance level | Conclusion |
| H1a: Intentions to seek information related to COVID-19 to perceived COVID-19 information overload | 0.26 | 0.09** | 0.01 | Supported |
| H1b: Intentions to share information related to COVID-19 to perceived COVID-19 information overload | 0.24 | 0.10* | 0.05 | Supported |
| H2: Perceived COVID-19 information overload to fear of COVID-19 | 0.28 | 0.05*** | 0.00 | Supported |
| Moderation hypothesis | ||||
| Fake news related to COVID-19 | 0.03 | 0.05*** | 0.00 | |
| H3: Interaction effect | 0.10 | 0.05* | 0.05 | Supported |
| Control variables: | ||||
| Age to fear of COVID-19 | 0.14 | 0.08 | 0.07 | |
| Gender to fear of COVID-19 | 0.28 | 0.11** | 0.01 | |
| Education level to fear of COVID-19 | 0.06 | 0.08 | 0.42 | |
| Occupation to fear of COVID-19 | −0.08 | 0.05 | 0.14 | |
| Social media usage frequency to fear of COVID-19 | −0.12 | 0.07 | 0.09 |
*p = 0.05, **p = 0.01, and ***p = 0.001. Interaction effect = Fake news related to COVID-19 × perceived COVID-19 information overload.