| Literature DB >> 34675716 |
Yue Zhuang1, Tiantian Zhao1, Xuanrong Shao1.
Abstract
PURPOSE: Taking COVID-19 as an example, this paper explores the mechanism of WeChat's impact on risk information transmission in social media and builds a model of WeChat's impact on public risk perception based on risk communication.Entities:
Keywords: COVID-19; WeChat; pandemic prevention; public risk perception
Year: 2021 PMID: 34675716 PMCID: PMC8519410 DOI: 10.2147/RMHP.S328175
Source DB: PubMed Journal: Risk Manag Healthc Policy ISSN: 1179-1594
Variable Item Description
| Variable | Item Description |
|---|---|
| Public WeChat attention to COVID-19 risk information (WeChat Attention) | Q1:How often do you get pandemic information through WeChat every day? |
| Q2:How long do you spend reading the pandemic information from WeChat every day? | |
| Public WeChat trust of COVID-19 risk information (WeChat Trust) | Q1:How reliable do you think the information from the WeChat Official Accounts is? |
| Q2:What do you think of the reliability of getting pandemic information from WeChat mini programs? | |
| Q3:What do you think of the reliability of obtaining pandemic information from WeChat search function? | |
| Public risk perception (Risk Perception) | Q1:How much do you think COVID-19 has impacted society? |
| Q2:What do you think is the threat of COVID-19 to your life and health? | |
| Q3:What do you think is the negative impact of COVID-19 on your life and your family’s lives? | |
| Q4:How has COVID-19 impacted your work or study with your family? | |
| Public willingness to prevent pandemic risk (Prevention Willingness) | Q1:If necessary, would you like to participate in the pandemic prevention as a volunteer? |
| Q2:If necessary, would you like to donate to a COVID-19-affected area? |
Figure 1Research model Note: H1a, H2a, etc. represent different assumptions in the model.
Descriptive Statistics of Sample Characteristics (n=801)
| Demographic Factors | Classification | Proportion |
|---|---|---|
| Sex | Male | 43.8% |
| Female | 56.2% | |
| Age | <19 | 20.6% |
| 19~35 | 41.6% | |
| 36~59 | 34.2% | |
| >60 | 3.6% | |
| Education | Junior high school and below | 44.9% |
| Senior high school | 12.5% | |
| University | 31.7% | |
| Graduate and above | 10.9% | |
| Occupation | Government/institution staff | 8.7% |
| Student | 40.1% | |
| Workers of enterprise | 27.3% | |
| Other | 23.9% |
Reliability and Validity of the Questionnaire
| Variable | Question | Factor Load Coefficient | Cronbach’s Alpha | ||||
|---|---|---|---|---|---|---|---|
| WeChat Attention | Q1 | 0.071 | 0.047 | 0.085 | 0.085 | 0.646 | |
| Q2 | 0.084 | 0.107 | 0.005 | 0.035 | |||
| WeChat Trust | Q1 | 0.227 | 0.058 | 0.123 | −0.011 | 0.791 | |
| Q2 | 0.146 | −0.017 | −0.018 | 0.031 | |||
| Q3 | 0.82 | 0.037 | 0.074 | 0.085 | |||
| Risk Perception | Q1 | 0.106 | −0.001 | 0.439 | −0.020 | 0.783 | |
| Q2 | 0.113 | 0.087 | −0.020 | 0.157 | |||
| Q3 | 0.188 | −0.030 | 0.064 | 0.057 | |||
| Q4 | 0.094 | 0.026 | 0.163 | 0.001 | |||
| Prevention Willingness | Q1 | 0.036 | 0.102 | 0.077 | 0.058 | 0.793 | |
| Q2 | 0.044 | 0.145 | 0.085 | 0.081 | |||
Note: Bold letters indicate the corresponding relationship between each question and the variable.
Number and Proportion of Main Ways for the Public to Obtain Risk Information (%)
| Main Approaches | Total | Under 19 Years | 19~35 Years | 36~59 Years | Over 60 Years | Rank |
|---|---|---|---|---|---|---|
| 549 (68.54) | 106 (64.24) | 219 (65.77) | 202 (73.72) | 22 (75.86) | 1 | |
| Television and radio | 507 (63.3) | 103 (62.42) | 195 (58.56) | 187 (68.25) | 14 (48.28) | 2 |
| TikTok, short video, etc. | 372 (46.44) | 89 (53.94) | 135 (40.54) | 134 (48.91) | 14 (48.28) | 3 |
| News and information Apps | 329 (41.07) | 69 (41.82) | 126 (37.84) | 124 (45.26) | 10 (34.48) | 4 |
| 225 (28.09) | 34 (20.61) | 169 (50.75) | 20 (7.30) | 2 (6.90) | 5 | |
| Newspapers and other paper medias | 37 (4.62) | 9 (5.45) | 9 (2.70) | 14 (5.11) | 5 (17.24) | 6 |
| Others | 30 (3.75) | 8 (4.85) | 10 (3.00) | 12 (4.38) | 0 (0.00) | 7 |
Variable Statistics and Pearson’s Correlation Matrix
| Variable | Mean | SD | 1 | 2 | 3 |
|---|---|---|---|---|---|
| 1 WeChat Attention | 4.04 | 1.43 | 1 | ||
| 2 WeChat Trust | 3.41 | 0.67 | 0.116** | 1 | |
| 3 Risk Perception | 3.79 | 0.81 | 0.185** | 0.102** | 1 |
| 4 Prevention Willingness | 4.25 | 0.82 | 0.148** | 0.169** | 0.321** |
Notes: Standard errors in parentheses. *p < 0.05; **p < 0.01.
Figure 2Public use of WeChat functions.
Summary of Multiple Linear Regression Models
| Dependent Variable | Risk Perception | Prevention Willingness |
|---|---|---|
| Independent Variable | ||
| Sex | −0.022 | 0.032 |
| Age | 0.065 | 0.101 |
| Education | −0.070 | 0.009 |
| Occupation | 0.075 | 0.062 |
| Wechat Attention | 0.153*** | 0.112** |
| WeChat Trust | 0.080* | 0.161*** |
| R2 | 0.061 | 0.067 |
| F | 8.592*** | 9.548*** |
Notes: Standard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001.
Test Model of Mediating Effect
| Dependent Variable | Prevention Willingness | Risk Perception | Prevention Willingness | Prevention Willingness | Risk Perception | Prevention Willingness |
|---|---|---|---|---|---|---|
| Independent Variable | ||||||
| Sex | 0.035 | −0.021 | 0.041 | 0.021 | −0.037 | 0.032 |
| Age | 0.100* | 0.065 | 0.081 | 0.106* | 0.072 | 0.085 |
| Education | −0.012 | −0.081* | 0.011 | 0.007 | −0.072 | 0.028 |
| Occupation | 0.052 | 0.070 | 0.031 | 0.079 | 0.097* | 0.050 |
| WeChat Attention | ||||||
| WeChat Trust | ||||||
| Risk Perception | ||||||
| R2 | 0.042 | 0.055 | 0.123 | 0.055 | 0.039 | 0.137 |
| F | 7.029*** | 9.224*** | 18.634*** | 9.334*** | 6.396*** | 18.324*** |
Notes: Standard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001. Bold letters indicated the parameters to be valid ated.