The Impact of Covid-19 lockdown on Intention to Follow Preventive Measures in Vietnam: Integrated Protection Motivation Theory and Theory Planed Behavior

Figures & data

Figure 1. Research Framework.

Table 1. Measurement items

Items	Measures	Source
Understand of COVID 19
UC1	I do understand the transmission of COVID-19	Liu (2020)
UC2	I do understand the incubation period of COVID-19	Coccia (2020)
UC3	I do understand the symptoms of COVID-19	Li et al. (2020)
UC4	I do understand the protocol if I have symptoms that might lead to COVID-19	Nicola et al. (2020)
Perceived Risk of COVID-19
PRC1	I am worried the COVID-19 pandemic will last long	Beirman (2006)
PRC2	I think although I am healthy, I still have a chance to spread COVID-19 to others	Beirman (2006)
PRC3	I think the risk of death caused by COVID-19 infection is great	Beirman (2006)
PRC4	I think COVID-19 is highly dangerous	Beirman (2006)
Perceived Behavior Control
PBC1	The preventive protocols are completely up to me	Sato (2018)
PBC2	I think preventive protocols are easy to be implemented	Shen et al. (2020)
PBC3	I am confident that I can prevent getting infected by COVID-19	Veldhuijzen et al., 2006)
PBC4	I am confident that I enough knowledge about COVID-19	Barati et al. (2020)
Subjective Norm
SN1	Most people I know are following the preventive protocols given by the government	Centers for Disease Control and Prevention, (2020))
SN2	Most people I know are wearing face masks outside	Coner (2020)2021
SN3	Most people I know are staying at home and work from home.	Yancey-Bragg and Bravo (2020)
SN4	Most people I know are using hand sanitizer	USA Food & Drug Administration (2020)
SN5	Most people I know, are doing physical distancing	Adam (2020)
Attitude
ATT1	I worry about the number of people infected by COVID-19	Sarkodie and Owusu (2020)
ATT2	I feel stressed during the COVID-19 outbreak	Shader (2020)
ATT3	I am afraid that one of my family members will get infected.	Dariya and Nagaraju (2020)
ATT4	I feel anxious during COVID-19 outbreak.	Roy et al. (2020)
ATT5	I feel insecure if someone stand too close to me during the COVID-19 outbreak	Roy et al. (2020)
ATT6	I feel insecure if someone is not wearing a mask during the COVID-19 outbreak	D’Amours (2020)
ATT7	I feel insecure if someone sneezes or coughs next to me	Centers for Disease Control and Prevention, (2020))
Intention to follow
ITF1	I am willing to follow the recommended precautions until the end of the COVID-19 outbreak	Barati (2020);
ITF2	I am willing to stay at home during COVID-19 outbreak	Sen-Crowe et al. (2020)
ITF3	I am willing to follow every rule made by my government during COVID-19 outbreak	Oyeniran and Chia (2020)
ITF4	I am willing to reschedule my travel plans	Lee et al (2020)
ITF5	I am willing to follow my government to lock down the country, city, and community.	University of Michigan School of Public Health (2020)

Table 2. Demographics; Characteristics and online shopping behavior of samples

Category	Number of respondents	Percentage
Gender
Male	128	41%
Female	193	59%
Age
Less than 18 years old	11	4%
19–29 years old	141	45%%
30–39 years old	69	22%
40–49 years old	48	15%
50–60 years old	34	11%
Above 60 years old	8	3%
Occupation
Student	140	45%
Financial	72	23%
Education	63	20%
Government	26	9%
Others	10	3%
Education
Senior High Diploma	41	13%
Associate Bachelor Degree	140	45%
Bachelor Degree	91	29%
Master Degree	39	13%

Table 3. Confirmatory factor analysis

Measure	Threshold	Study results	Source
Chi-square/df (cmin/df)	≤ 2 good; ≤ sometimes permissible	2.035	Hair et al. (2010)
Goodness of fit index (GFI)	≥ 0.9: acceptable; ≥ 0.8: marginal	0.857	Hair et al. (2010)
Adjusted goodness of fit index (AGFI)	≥ 0.8	0.829	Hair et al. (2010)
Comparative fit index (CFI)	≥0.95 great; ≥0.90 traditional;	0.940	Hair et al. (2010)
Root mean squared error of approximation (RMSEMA)	≥0.80 sometimes permissible	0.058	Hair et al. (2010)
Tucker Lewis Index (TLI)	≥0.9great	0.933	Hair et al. (2010)

Table 4. Confirmatory factor analysis (CFA) fitting Indices

Items	Estimate	Cronbach	CR	AVE
Understand of COVID 19		0.915	0.918	0.739
UC1	0.798
UC2	0.855
UC3	0.920
UC4	0.862
Perceived Risk of COVID-19		0.821	0.831	0.553
PRC1	0.643
PRC2	0.837
PRC3	0.733
PRC4	0.751
Perceived Behavior Control		0.846	0.850	0.589
PBC1	0.813
PBC2	0.765
PBC3	0.837
PBC4	0.641
Subjective Norm		0.899	0.902	0.649
SN1	0.683
SN2	0.866
SN3	0.812
SN4	0.882
SN5	0.772
Attitude		0.910	0.917	0.618
ATT1	0.709
ATT2	0.671
ATT3	0.668
ATT4	0.784
ATT5	0.868
ATT6	0.882
ATT7	0.885
Intention to follow		0.944	0.943	0.769
ITF1	0.797
ITF2	0.887
ITF3	0.933
ITF4	0.942
ITF5	0.817

Table 5. Discriminant validity

	UC	PRC	PBC	SN	ATT	ITF
UC	0.739
PRC	0.385	0.553
PBC	0.126	0.264	0.589
SN	0.286	0.339	0.237	0.649
ATT	0.207	0.237	0.430	0.291	0.618
ITF	0.222	0.283	0.542	0.170	0.440	0.769

Table 6. Hypothesis testing and result

Hypotheses	Standardized path coefficient	Result
H1: Understanding COVID 19 has a positive influence on perceived risk of COVID 19	0.438***	Supported
H2: Perceived risk of COVID 19 has a positive impact on perceived control behavior	0.351***	Supported
H3: Perceived risk of COVID 19 has a positive impact on subjective norm	0.065	Unsupported
H4: Perceived risk of COVID 19 has a positive impact on Attitude	0.378**	Supported
H5: Perceived control behavior has a positive impact on subjective norm	0.404***	Supported
H6: Attitude has a positive impact on subject norm	0.206	Supported
H7: Perceived control behavior has a positive impact on intention to follow the preventive measures of COVID-19	0.516***	Supported
H8: Subject norm has a positive impact on intention to follow the preventive measures of COVID-19.	0.220**	Supported
H9: Attitude has a positive impact on intention to follow the preventive measures of COVID-19.	−0.44	Unsupported

N = 249, *p < .05; **p < .01; ***p < .001

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