| Literature DB >> 33803023 |
Aleksandar Radic1, Bonhak Koo2, Eloy Gil-Cordero3, Juan Pedro Cabrera-Sánchez3, Heesup Han4.
Abstract
The COVID-19 pandemic is a serious threat to human health, the global economy, and the social fabrics of contemporary societies as many aspects of modern everyday life, including travel and leisure, have been shattered to pieces. Hence, a COVID-19 mandatory vaccination as a precondition for international travel is being debated in many countries. Thus, the present research aimed to study the intention to take the COVID-19 vaccine as a precondition for international travel using an extended Norm-Activation Model. The study model integrates a new construct, namely mass media coverage on COVID-19 vaccination as additional predictor of intention to take the COVID-19 vaccine. The survey data were collected from 1221 international travelers. Structural equation modelling shows a very good fit of the final model to the data; the conceptual model based on extended Norm-Activation Model was strongly supported. Awareness of consequences related to the COVID-19 pandemic on individuals' health has shown a positive effect on individuals' ascribed responsibility to adopt emotionally driven (anticipated pride and anticipated guilt) pro-social behaviors that activate a personal norm towards altruistic and pro-mandatory vaccination-friendly behavior. Theoretical and practical implications are discussed.Entities:
Keywords: COVID-19 mandatory vaccination; Norm-Activation Model; behavioral intention; mass media coverage
Mesh:
Substances:
Year: 2021 PMID: 33803023 PMCID: PMC8002605 DOI: 10.3390/ijerph18063104
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1The conceptual model based on extended Norm-Activation Model (NAM).
List of social media groups from where participants were recruited.
| Social Media Group | Domain |
|---|---|
| China Travel Group |
|
| Tourists |
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| Tripadvisor Travel Forum |
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| Thorn Tree forum |
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| Fodor’s Travel Talk Forums |
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| Travel and Tourism |
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| Worldwide Travel |
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| Travellers Around The World |
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| Travellers point |
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| South Asian Tourism & Travelers Group |
|
Demographic characteristic of sample (n = 1221).
| Variable |
| % |
|---|---|---|
| Gender | ||
| Male | 590 | 48.3 |
| Female | 631 | 51.7 |
| Age group | ||
| 20–29 | 485 | 39.7 |
| 30–40 | 374 | 30.6 |
| 41–50 | 277 | 22.7 |
| 51–60 | 61 | 5.0 |
| 60 and older | 24 | 2.0 |
| Education level | ||
| High school | 274 | 22.4 |
| Associate degree | 252 | 20.6 |
| Bachelor’s degree | 492 | 40.3 |
| Master’s or doctoral degree | 203 | 16.7 |
| Place of residence | ||
| North America | 137 | 11.2 |
| Central/South America | 122 | 10.0 |
| Europe | 125 | 10.2 |
| China | 215 | 17.6 |
| South Asia | 170 | 13.9 |
| South East Asia | 232 | 19.0 |
| Africa | 209 | 17.1 |
| Australia and New Zealand | 11 | 1.0 |
Measurement model assessment (n = 1221).
| Variable | Mean | SD | Skewness | Kurtosis | Standardized Factor Loading | Composite Reliability | Cronbach’s α |
|---|---|---|---|---|---|---|---|
| MAC | 0.857 | 0.847 | |||||
| MAC1 | 3.24 | 1.239 | −0.374 | −0.856 | 0.771 | ||
| MAC2 | 3.24 | 1.177 | −0.325 | −0.726 | 0.954 | ||
| AWC | 0.848 | 0.846 | |||||
| AWC1 | 3.74 | 1.034 | −0.651 | 0.008 | 0.813 | ||
| AWC2 | 3.92 | 0.909 | −0.751 | 0.524 | 0.808 | ||
| AWC3 | 3.8 | 0.956 | −0.615 | 0.048 | 0.797 | ||
| ACR | 0.898 | 0.898 | |||||
| ACR1 | 3.73 | 1.002 | −0.624 | 0.069 | 0.875 | ||
| ACR2 | 3.73 | 0.994 | −0.635 | 0.109 | 0.845 | ||
| ACR3 | 3.8 | 1.021 | −0.655 | −0.011 | 0.87 | ||
| PSN | 0.916 | 0.916 | |||||
| PSN1 | 3.18 | 1.275 | −0.23 | −0.98 | 0.914 | ||
| PSN2 | 3.19 | 1.277 | −0.248 | −0.965 | 0.924 | ||
| ANP | 0.945 | 0.945 | |||||
| ANP1 | 3.14 | 1.29 | −0.174 | −1.015 | 0.918 | ||
| ANP2 | 3.06 | 1.273 | −0.125 | −1.013 | 0.927 | ||
| ANP3 | 3.17 | 1.297 | −0.225 | −1.038 | 0.925 | ||
| ANG | 0.925 | 0.925 | |||||
| ANG1 | 2.33 | 1.09 | 0.515 | −0.421 | 0.882 | ||
| ANG2 | 2.31 | 1.07 | 0.5 | −0.409 | 0.919 | ||
| ANG3 | 2.33 | 1.109 | 0.548 | −0.425 | 0.89 | ||
| BHI | 0.941 | 0.94 | |||||
| BHI1 | 3.17 | 1.268 | −0.224 | −1.002 | 0.919 | ||
| BHI2 | 3.17 | 1.271 | −0.224 | −0.986 | 0.905 | ||
| BHI3 | 3.13 | 1.267 | −0.162 | −0.986 | 0.927 |
Note 1. All factor loadings statistically significant at p < 0.001; Note 2. MAC = Mass media coverage; AWC = Awareness of consequences; ACR = Ascribed responsibility; PSN = Personal norm; ANP = Anticipated pride; ANG = Anticipated guilt; BHI = Behavioral intention.
The results of the CFA model and factor correlations (n = 1221).
| Variable | 1 | 2 | 3 | 4 | 5 | 6 | 7 | AVE | (√AVE) |
|---|---|---|---|---|---|---|---|---|---|
| 1. MAC | 1.000 | 0.752 | (0.867) | ||||||
| 2. AWC | 0.185 | 1.000 | 0.650 | (0.806) | |||||
| 3. ACR | 0.171 | 0.821 | 1.000 | 0.746 | (0.864) | ||||
| 4. PSN | 0.326 | −0.024 | 0.027 | 1.000 | 0.845 | (0.919) | |||
| 5. ANP | 0.345 | −0.019 | 0.004 | 0.936 | 1.000 | 0.853 | (0.924) | ||
| 6. ANG | −0.087 | −0.506 | −0.411 | 0.193 | 0.136 | 1.000 | 0.805 | (0.897) | |
| 7. BHI | 0.345 | −0.044 | −0.043 | 0.942 | 0.897 | 0.179 | 1.000 | 0.841 | (0.917) |
|
| 3.24 | 3.82 | 3.75 | 3.37 | 3.12 | 2.32 | 3.16 | ||
|
| 1.125 | 0.846 | 0.916 | 0.948 | 1.222 | 1.016 | 1.199 |
Note 1. Goodness-of-fit indices: χ2 (131) = 625.377, p < 0.001, RMSEA = 0.056 [0.051, 0.060], CFI = 0.977, TLI = 0.970, SRMR = 0.019. Note 2. MAC = Mass media coverage; AWC = Awareness of consequences; ACR = Ascribed responsibility; PSN = Personal norm; ANP = Anticipated pride; ANG = Anticipated guilt; BHI = Behavioral intention; CFA = confirmatory factor analysis.
The results of fully latent structural regression model (n = 1221).
| Relationships | Estimate | S.E. | Est./S.E. | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| H1: | AWC | → | ACR | 0.869 | 0.032 | 27.344 *** | ||||
| H2: | ACR | → | PSN | 0.062 | 0.022 | 2.760 ** | ||||
| H3: | PSN | → | BHI | 0.929 | 0.020 | 45.412 *** | ||||
| H4: | ACR | → | ANP | −0.006 | 0.042 | −0.153 | ||||
| H5: | ANP | → | PSN | 0.913 | 0.020 | 46.087 *** | ||||
| H6: | ACR | → | ANG | −0.481 | 0.033 | −14.546 *** | ||||
| H7: | ANG | → | PSN | 0.107 | 0.020 | 5.459 *** | ||||
| H8: | MAC | → | AWC | 0.164 | 0.028 | 5.819 *** | ||||
| H9: | MAC | → | BHI | 0.072 | 0.018 | 3.976 *** | ||||
| Direct effect: | AWC | → | BHI | −0.064 | 0.021 | −2.990 ** | ||||
| Indirect effect: | AWC | → | ACR | → | PSN | → | BHI | 0.050 | 0.018 | 2.754 ** |
Note 1. * p < 0.05 (This data means that test was done even though there is no result), ** p < 0.01, *** p < 0.001. Note 2. Standardized values are in parentheses; Bias-corrected bootstrap sample size = 5000. Note 3. Goodness-of-fit indices: χ2 (141) = 888.142, p < 0.001, RMSEA = 0.066 [0.062, 0.070], CFI = 0.965, TLI = 0.958, SRMR = 0.084. Note 4. MAC = Mass media coverage; AWC = Awareness of consequences; ACR = Ascribed responsibility; PSN = Personal norm; ANP = Anticipated pride; ANG = Anticipated guilt; BHI = Behavioral intention; Note 5. Total variance explained (R2): AWC = 0.038; ACR = 0.694; PSN = 0.887; ANP = 0.000; ANG = 0.193; BHI = 0.893.
Figure 2Results of the structural model estimation (n = 1221). Note. Goodness-of-fit indices: χ2 (141) = 888.142, p < 0.001, RMSEA = 0.066 [0.062, 0.070], CFI = 0.965, TLI = 0.958, SRMR = 0.084. ** p < 0.01, *** p < 0.001.
Constructs and Measurement Items.
| Constructs and Items |
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