Factors associated with parental acceptance of influenza vaccination for their children: the evidence from four cities of China

ABSTRACT

Background

It is necessary and urgent to vaccinate 245 million Chinese children against influenza pandemics. The main purpose of this study was to evaluate different psychological and demographic factors that influence parental willingness to vaccinate their children against influenza.

Methods

A hybrid theoretical framework was expanded and verified with 462 sample data collected from four cities in China. Structural equation models were used to test nine theoretical hypotheses, and the non-standardized coefficient method was used to discuss the moderating effects among demographic variables.

Results

Knowledge is considered to be the significant factor of performance expectancy (β = 0.228), effort expectancy (β = 0.227) and perceived risk (β = −0.138), and social influence also has the significant impacts on the above three variables, with β values of 0.437, 0.386, and −0.172. Performance expectancy (β = 0.402), effort expectancy (β = 0.343), and perceived risk (β = −0.244) thus significantly affect parental behavioral intention regarding children’s influenza vaccination. Gender, education, and kids’ gender are demographic variables with significant moderating effects, while age, income, number of kids are not significant.

Conclusion

To improve the acceptability of influenza vaccination among Chinese children, the promoting policies should emphasize on public knowledge and social influence, as well as effectiveness, affordability, and safety of vaccination.

KEYWORDS:

• Influenza vaccination
• parental intention
• psychological factors
• children
• knowledge
• china

1. Introduction

Influenza virus is highly contagious, spreads quickly, and easily causes pandemics, and vaccination can significantly reduce the incidence and symptoms. During six influenza seasons (2010–2011 through 2015–2016) in the United States, the number of influenza-related illnesses that have occurred during influenza season has ranged from 9.2million to 35.6million,¹ and the estimated average annual total economic burden of influenza to the healthcare system and society was 11.2 USD billion ($6.3–$25.3 billion).² The influenza vaccination coverage in many developed countries increased during the last 10 years, e.g., 43.3% in the United States in 2016–2017, ³ 47.5% in Matsumoto City of Japan in 2014–2015,⁴ 44% in South Korea in 2014, ⁵ and the median coverage rate of all EU states being 47.6% for seasons 2007–2008 to 2014–2015.⁶ China is a region at high risk of influenza outbreaks. However, the annual uptake rate of influenza vaccination was extremely low at 2–3% in China in 2018.⁷ With minimal preexisting immunity in children and the limited cross-protective effect from seasonal influenza vaccine, children constitute one of the most susceptible groups to acquire influenza infection.^8,9 It is necessary and urgent to vaccinate 245 million Chinese children against influenza.

Over the last decades, a considerable amount of literature has aimed at identifying factors that may affect the uptake of the seasonal influenza vaccines. In these studies, the research objects are mainly focused on the elderly, ^10,11 pregnant women, ^12,13 children, ¹⁴ and health-care workers since they are one of the at-risk groups of acquiring infection diseases and transmitting to others health-care workers and patients;¹⁵ the theoretical models include theory of planned behavior (TPB), ^10,16 knowledge, attitudes and practices (KAP), ^17,18 and rational action theory (TRA).¹⁹ Perceived risks may significantly affect parental attitudes to children flu vaccination, which has not been fully discussed in previous literature. In 2018, Changsheng Bio-tech was linked to a substandard adsorbed diphtheria-pertussis-tetanus vaccine for infants, and successive vaccine scandals have caused serious concerns about vaccine safety in China.²⁰ Chinese parents have to face a dilemma of children’s influenza vaccination: if their children are not vaccinated, they will be at great risk of being infected; if their children plan to uptake influenza vaccines, they will worry about vaccine safety and possible side effects.⁷ Recent studies have also shown the importance of socioeconomic and demographic variables as influencing factors, and that samples from subgroups show significant differences in willingness to uptake the seasonal influenza vaccines, e.g., participants from Greece who frequent visits to the doctors had a positive influence upon the uptake of the vaccine.¹⁰ It is necessary to construct a comprehensive theoretical model to verify the parental behavioral intention to accept children influenza vaccination, including psychological factors and demographic factors.

The objective of this study was to determine, among the samples of parents who had one or more children under the age of 14 and had heard of flu vaccination from four cities in China, i.e., Changsha, Wuhan, Guangzhou, and Beijing: 1) the comprehensive influencing mechanism between knowledge, social influence, performance expectancy, effort expectancy, perceived risk, and parental behavioral intention to children influenza vaccination; 2) perceived risk among flu vaccination information sources; 3) the moderating effects of demographic variables, including gender, age, education, income (annual household income), number of kids, and kids’ gender.

2. Materials and methods

2.1. Theoretical model

The hybrid theoretical model was modified from TPB (Technology Acceptance Model)²¹ and knowledge, attitudes, and practice (KAP), ²² and it includes six constructs, i.e., knowledge (KN), social influence (SI), performance expectancy (PE), effort expectancy (EE), and perceived risk (PR), and behavioral intention (BI). The structured relationships are shown in Figure 1. Six demographic variables were included in the theoretical model as moderators, and they affected three paths related to behavioral intention, namely performance expectancy, effort expectancy, and perceived risk. The following nine hypotheses were developed in the context of parental acceptance of influenza vaccination for their children in China:

H1. Knowledge is positively related to performance expectancy.

H2. Knowledge is positively related to effort expectancy.

H3. Knowledge is negatively related to perceived risk.

H4. Social influence is positively related to performance expectancy.

H5. Social influence is positively related to effort expectancy.

H6. Social influence is negatively related to perceived risk.

H7. Performance expectancy is positively related to performance expectancy.

H8. Social influence is positively related to effort expectancy.

Figure 1. Theoretical model

H9. Social influence is negatively related to perceived risk.

2.2. Study questionnaire and data collection

We developed an initial questionnaire structure based on the existing studies, and revised it by three senior behavioral psychologists. The questionnaire was first developed in English and translated into Chinese by an English-speaking expert, and then revised it as an actual questionnaire based on Chinese expression habits. The questionnaire is divided into two parts: Socioeconomic Information and Behavioral Intentions Scale. The Socioeconomic Information section mainly surveys the demographic characteristics of the interviewees, including Gender, Age, Education, Annual household income before tax, Number of kids, Gender of Kids. The Behavioral Intentions Scale part has six constructs, i.e., Knowledge (KN), Social Influence (SI), Performance Expectancy (PE), Effort Expectancy (EE), Perceived Risk (PR), and Behavioral Intention (BI). This part including a total of 23 measurement items (variables). The measurement items for each structure were revised on previous analogies, and Likert scales were used to measure consistency with indicators (see Table 1).

Table 1. Summary of construct with measurement items

Constructs	Items Item in questionnaire		Mean (scale range: 1 to 7)	Std. Deviation	Sources
Knowledge (KN)	KN1	The most effective way to prevent influenza is to get a flu shot.	5.10	1.295	(Venkatesh et al., 2003; Venkatesh et al., 2012)
	KN2	New flu vaccines are needed every year to effectively prevent the flu.	5.23	1.282
	KN3	Even with the flu shot, susceptible people should not to go to crowded places.	5.16	1.229
	KN4	The immunity of influenza vaccines from different countries is similarly equivalent.	4.98	1.231
	KN5	Influenza vaccination can greatly reduce the severity of the flu.	5.17	1.317
Social Influence (SI)	SI1	People who are important to me think: My kids should get the flu shot.	5.24	1.397	(Jewer, 2018; Morosan & DeFranco, 2016; Venkatesh et al., 2012)
	SI2	People who influence my behavior believe that my children should be vaccinated against the flu.	4.95	1.375
	SI3	Parents who have vaccinated their children against flu are considered health conscious.	5.06	1.412
Performance Expectancy (PE)	PE1	After flu vaccination, children will be healthier.	4.55	1.439	(Herrero et al., 2017; Kwame et al., 2019)
	PE2	Vaccination against influenza is cost-effective.	4.52	1.458
	PE3	Children will not get the flu easily after the flu vaccination.	4.37	1.412
	PE4	Getting a flu shot will improve children’s quality of life.	4.56	1.574
Effort Expectancy (EE)	EE1	It is convenient to get the flu vaccine.	4.89	1.540	(Venkatesh et al., 2003)
	EE2	The financial burden of influenza vaccination is affordable.	5.05	1.561
	EE3	It is easy to master the knowledge of influenza vaccines.	4.69	1.487
Perceived Risk (PR)	PR1	The safety of the flu vaccine is worrying me.	3.92	1.640	(Venkatesh et al., 2012)
	PR2	The vaccine crisis in China makes me nervous.	3.81	1.878
	PR3	Reactions to the vaccine (such as fever) make me uncomfortable.	4.00	1.664
	PR4	Children need to get flu shots every year, which makes me feel stressed.	3.78	1.617
Behavioral Intention (BI)	BI1	I would like to get my children vaccinated against the flu.	5.21	1.556	(Wang et al., 2016)
	BI2	I plan to get my children vaccinated in the future.	5.03	1.451
	BI3	I would like to recommend flu vaccination to relatives and friends.	4.48	1.624
	BI4	I will introduce the benefits of flu shots to others.	4.75	1.544

We administered a combined data collection plan by online questionnaires and offline surveys. The online questionnaires were conducted in the waiting room of the child health centers in hospitals via tablets or mobile phones. The child’s medical card ID number was used as the basis for random sampling. The offline interviews were implemented in elementary schools via printing questionnaires. The student card ID number (generally the same as the ID number) was used as the basis for random sampling. If the last digit of the child’s medical card ID number/student card ID number and the last digit of the survey data are the same, their parents were recruited as potential respondents. Only parents of children who have heard of flu vaccination are eligible respondents. To ensure the randomness of sample selection, the survey was conducted only once in each hospital or elementary school. The children visiting the child health center are generally under 6 years old, and the primary school students are 6–12 years old, so there is almost no duplication among the respondents in the two survey methods. All participants signed written informed consent before filling the questionnaire.

Before the formal investigation was carried out, the research team conducted a priori survey with a sample size of 50 in Changsha, and revised three measurement items based on the survey data and respondents’ feedback. From October 2018 to January 2019, the team conducted a comprehensive survey in four cities in China: Changsha, Wuhan, Guangzhou, and Beijing. Changsha and Wuhan are cities in central China with populations of 8.16 million and 9.08 million, respectively. Guangzhou is a metropolis with a population of 14.90 million in southern China. Beijing is the capital of China and is located in northern China with a total population of 21.54 million.

“Structural Equation Modeling (SEM) is a statistical method to analyze the relationship among constructs based on the covariance matrix of variables.^23–26 The sample size is an important research issue in the statistical literature on this method, and it relates to the validity and reliability of statistical results. However, previous studies have not reached consensus on the appropriate sample size. In the two mainstream views, one recommended sample size is 15 times or 10 times the number of variables, ^27,28 and the other recommended sample size is not less than 200.²⁹ Considering the above references and 23 variables in the theoretical model, we set the target of valid sample size for the current study is 345.

2.3. Statistical analysis

We used IBM SPSS Statistics 22 and Amos 24 for data analysis in the following stages: Firstly, the demographic comparisons were made based on the socio-economic data. Quantity accumulation, p-value, ratio, mean, and standard deviation are used as indicators. Second, the validity and reliability of the scales of sample data was tested by Cronbach’s alpha, confirmative factor analysis, and average variance extracted values (AVE), and their threshold values are listed below: Cronbach’s alpha>0.7,³⁰ factor loadings>0.5, ^24,28 AVE>0.5.²⁴ The overall fit of the structural equations were tested by criteria are as follows: CHI/DF, p-value, NFI, IFI, RFI, TLI, CFI, GFI, AGFI, RMSEA.^28,31 Bootstrapping method was used to explore the moderating effects of demographic variables, and the statistical tests were based on the two-tailed with 95% confidence intervals (CI).³² Guided by the recommendations of Baron and MacKinnon, ²³ the demographic variables were extracted into two comparison groups. The structural equations of the two sets of data were compared and the results were p-values, and p < .05 is an acceptable criterion.

3. Results

3.1. Sample descriptive

The survey team invited 611 parents to participate, and 462 eligible participants completed the survey (response rate is 75.6%). The sample size of the current study is accordant with the statistical requirements. As shown in Table 2, among the 462 valid samples, more than half of the respondents are classified as mothers of children. Most respondents were aged between 26 and 40 (79.3%), and few participants are younger than 26 years old (6.5%). About three quarters of the respondents (72.5%) have earned a college degree, and almost 80% reported annual household income greater than 10,000 USD. In view of China’s family planning policy and the low fertility desire in recent years, respondents with one child are the mainstream (59.7%). From the perspective of children’s gender, the families of 183 respondents had only boy(s) (39.6%), 205 families had only girl(s), and only 16% of the families had both boy(s) and girl(s).

Table 2. Sample demographics (N = 462)

Items	n	%	Mean	Std. Deviation
Gender			1.52	0.50
male (1)	224	48.5
female (2)	238	51.5
Age			3.22	1.13
20–25 years old (1)	30	6.5
26–30 years old (2)	97	21.0
30–35 years old (3)	143	31.0
36–40 years old (4)	126	27.3
41+ years old (5)	66	14.3
Education			2.06	0.78
High school education or below (1)	127	27.5
Vocational college degree (2)	179	38.7
Bachelor degree and above (3)	156	33.8
Income (Annual household income)			2.46	1.07
Below $ 10,000 (1)	101	21.9
$ 10,000 – $ 20,000 (2)	149	32.3
$ 20,000 – $ 30,000 (3)	109	23.6
Over $ 30,000 (4)	103	22.3
KidsNum (Number of Kids)			1.4	0.49
1kid (1)	276	59.7
2 or more kids (2)	186	40.3
KidsGender (Kids’ Gender)			1.76	0.71
All boy(s) (1)	183	39.6
All girl(s) (2)	205	44.4
Both boy(s) and girl(s) (3)	74	16.0
City (Data collection city)			/	/
Changsha (1)	117	25.3
Guangzhou (2)	131	28.4
Wuhan (3)	100	21.6
Beijing (4)	114	24.7

3.2. Analysis of the measurements and structural model

Composite reliability and convergent validity of the measures are shown in Table 3. As for Cronbach’s Alpha scores, the lowest one is 0.824 and the highest one is 0.923, and these scores reflected the qualified internal reliability of each construct. The measurement scales were evaluated by three criteria suggested by Fornell and Larcker:³³ factor loadings (β) >0.5, composite reliability>0.7, and average variance extracted (AVE)>0.5. In the current study, factor loadings are between 0.753 and 0.933, composite reliabilities of constructs are between 0.824 and 0.926, and the lowest value of AVEs is 0.610. These indicators reflect the good convergent validity of measurements. To measure discriminant validity, the correlation between items in any two constructs should be lower than the square root of the AVE shared by the items in the constructs.³³ The square root of the AVEs are as follows: 0.786 (knowledge), 0.832 (social influence), 0.812 (performance expectancy), 0.781 (effort expectancy), 0.870 (perceived risk), 0.860 (behavioral intention). The maximum absolute value of correlations in the matrix is 0.60, and all diagonal values exceeded the inter-construct correlations. These results confirmed that the current instruments have qualified discriminant validity.

Table 3. Composite reliability and convergent validity of the measurements

Constructs	Variables	Loadings	C.R	Cronbach’s Alpha	AVE
KN	KN1	0.778	0.89	0.890	0.618
	KN2	0.766
	KN3	0.766
	KN4	0.803
	KN5	0.817
SI	SI1	0.846	0.87	0.869	0.692
	SI2	0.863
	SI3	0.784
PE	PE1	0.860	0.89	0.884	0.659
	PE2	0.753
	PE3	0.851
	PE4	0.778
EE	EE1	0.784	0.82	0.824	0.61
	EE2	0.779
	EE3	0.780
PR	PR1	0.837	0.93	0.923	0.757
	PR2	0.875
	PR3	0.832
	PR4	0.933
BI	BI1	0.824	0.92	0.918	0.74
	BI2	0.867
	BI3	0.882
	BI4	0.866

Loading: standardized factor loading; CR: Composite Reliability; AVE: Average Variance Extracted.

Fitting indices, e.g., GFI, AGFI, CFI, NFI, and RFI, in combination with chi-square are used to evaluate the overall goodness-of-fit of structural model. CHI/DF = 1.809 < 3, p-value<0.05, and these two indicators reflected variance and covariance of the data. NFI = 0.944 > 0.9, IFI = 0.974 > 0.9, RFI = 0.936 > 0.9, TLI = 0.970 > 0.9, CFI = 0.974 > 0.9, GFI = 0.928 > 0.9, AGFI = 0.910 > 0.9, RMSEA = 0.042 < 0.05, and these indicators suggested that the model fit is acceptable.

3.3. Hypotheses testing and moderating effects

The theoretical hypotheses were tested by the structural equation method, and the results supported all hypotheses (Table 4). Knowledge was proved to be a significant influencing factor of performance expectancy (β = 0.228; p < .001), effort expectancy (β = 0.227; p < .001), and perceived risk (β = −0.138; p = .016), and H1, H2, and H3 were supported, respectively. Social influence was also confirmed as a significant factor of performance expectancy (β = 0.437; p < .001), effort expectancy (β = 0.386; p < .001), and perceived risk (β = −0.172; p = .003), and H4, H5, and H6 were supported. Behavioral intention in this study was jointly predicted by performance expectancy (H7: β = 0.402; p < .001), effort expectancy (H8: β = 0.343; p < .001), and perceived risk (H9: β = −0.244; p < .001).

Table 4. Hypothesis testing

Hypotheses	Paths			t	β	p	Comments ^h
H1	PE ^b	< –	KN ^a	4.376	0.228	***	Supported
H2	EE ^c	< –	KN	4.012	0.227	***	Supported
H3	PR ^d	< –	KN	−2.418	−0.138	*0.016	Supported
H4	PE	< –	SI ^e	8.034	0.437	***	Supported
H5	EE	< –	SI	6.541	0.386	***	Supported
H6	PR	< –	SI	−2.993	−0.172	**0.003	Supported
H7	BI ^f	< –	PE	8.739	0.402	***	Supported
H8	BI	< –	EE	7.158	0.343	***	Supported
H9	BI	< –	PR	−5.961	−0.244	***	Supported

^a-fKN, knowledge; SI, social influence; PE, performance expectancy; EE, effort expectancy; PR, perceived risk; BI, behavioral intention.

^hN = 4000 bootstrap samples, Maximum Likelihood Estimates, Significant at p < 0.05.

Table 5 shows the result of moderating effects. As a significant control variable, gender moderates the path loading from perceived risk to behavioral intention (Z = −3.178*), and the absolute value of the influence coefficient was significantly higher in men subgroup (β = −0.33) than that in women subgroup (β = −0.13). Educationmoderated the following paths: from performance expectancy to behavioral intention (Z = −3.213*), and from perceived risk to behavioral intention (Z = −3.080*). Kids’ gender played a significant moderator role in all three lines, namely behavioral intention < – performance expectancy (Z = 5.854*), behavioral intention < – effort expectancy (Z = −2.910*), and behavioral intention < – perceived risk (Z = −2.051*). There are three demographic variables, Age, Income (Annual household income), and Kids Num (Number of Kids), without significant moderating effects in the theoretical model.

Table 5. Moderating effects of control variables

Paths Demographic variables	BI < – PE	BI < – EE	BI < – PR
Gender
male (1)	0.29 ^a	0.28	−0.33
female (2)	0.48	0.23	−0.13
Z-value	−1.428	0.981	−3.178*
Age
20–30 years old (1 + 2)	0.34	0.34	−0.21
36+ years old (4 + 5)	0.32	0.34	−0.20
Z-value	0.066	−0.345	−0.061
Education
High school education or below (1)	0.17	0.27	−0.33
Bachelor degree and above (3)	0.53	0.25	−0.11
Z-value	−3.213*	0.898	−3.080*
Income (Annual household income)
Below $ 10,000 (1)	0.23	0.35	−0.33
Over $ 30,000 (4)	0.35	0.32	−0.27
Z-value	−0.887	0.151	−0.526
KidsNum (Number of Kids)
1kid (1)	0.32	0.25	−0.26
2 or more kids (2)	0.41	0.29	−0.18
Z-value	−1.492	−0.849	−0.370
KidsGender (Kids’ Gender)
All boy(s) (1)	0.70	0.11	−0.16
All girl(s) (2)	0.21	0.32	−0.28
Z-value	5.854*	−2.910*	2.051*

^aStandardized Coefficients Beta; ^b: Significant at α level of 0.05.

4. Discussion

In this study, we describe the psychological influence mechanism of parental intention on children influenza vaccination in four cities of China, i.e., knowledge and social influence are significant determinants of performance expectancy, effort expectancy, and perceived risk; performance expectancy and effort expectancy significantly affect behavioral intentions, and perceived risk is a negative determinant of parental hesitation. Gender, education and kids’ gender are demographic variables with significant moderating effects; while age, income, and number of kids were not significant moderators.

Knowledge is a significant factor influencing parents’ intentions about their children’s flu vaccination, and this evidence aligning with recent results. Knowledge has affected acceptance of influenza vaccination, and this conclusion is reflected in different countries. A survey conducted in Nanhai district of China showed that higher influenza knowledge level and previous influenza information were positively correlated with vaccination willingness.⁷ In a large maternity hospital in Greece, researchers conducted educational interventions on influenza vaccination for pregnant women, and significantly increased their compliance with influenza vaccination recommendations.¹³ Given that most Chinese parents have very limited knowledge about influenza vaccination, it is necessary for China CDC to provide adequate and accessible information for them.

The relationship between social influence and behavioral intention has been previously studied in the field of medical behavior, i.e., social influence had a significant influence on physicians’ behavioral intention to adopt the Electronic Health Record (EHR) in Bangladesh, ³⁴ and social influence was also investigated regarding the parental behavioral intention to use the nonprescription drugs (NPDs).³⁵ This study confirmed that social influence is a significant determinant that affects performance expectancy, effort expectancy, and perceived risk. Furthermore, social influence has higher impact loadings than knowledge, although they are all significant variables of the above three constructs. Social influence should be included in policies that promote childhood influenza vaccination, such as “National Strategy for Pandemic Influenza” in the United States, ² and “School Outreach Vaccination Program” in Hong Kong.³⁶

Both performance expectancy and effort expectancy are direct and positive factors that influence parental behavioral intention regarding children’s flu vaccination. The usefulness or effectiveness of the vaccines are important factors influencing parental willingness of children vaccination, i.e., participants are more likely to support a mandatory vaccine policy if they believe the HPV vaccine is effective.³⁷ The Hong Kong Department of Health has implemented a fully funded school outreach vaccination (SOV) program, which exempts any parental payment. In the 2018–2019 flu season, the vaccination rate in these schools was 69.2%, significantly higher than 34.3% in the control schools.³⁶ The current study corroborates the above conclusion that effort expectancy is a direct determinant of parental intention to vaccinate their children against influenza. For the health management authorities in mainland China, it is beneficial to implement policies in developed countries or Hong Kong, i.e., to include influenza vaccines within the national Expanded Program on Immunization (EPI) at no parental payment, to increase the convenience of influenza vaccination for children, and improve the vaccination services quality.¹⁶

Perceived risk was integrated in this theoretical model as an independent construct, and the empirical results confirmed the rationality of this theoretical innovation and expanded literature on parental intention to childhood influenza vaccination. Previous literature discussed the potential risks influence parental behavioral intention of children immunizations, i.e., fear of side effects, concern that vaccines need to be carefully administered, and mistrust of new vaccines were pivotal barriers hindering parents from having their children vaccinated.⁷ In view of China’s current unsatisfactory vaccine safety conditions, the public is generally concerned about the security assurance capabilities of the entire vaccine industry chain, including manufacturing, cold chain transportation, distribution, and injection.²⁰ To promote children’s influenza vaccination coverage, Chinese public health authorities should strengthen the safety of the vaccine supply chain and restore public trust.

There are some interesting findings about the moderating effects of demographic variables in the current study. First, fathers are more likely than mothers to refuse to get their children vaccinated because of perceived risk. There have been no similar reports in previous studies. Second, education is an important variable that affects parental decisions in different groups. Parents with Bachelor and above degrees are more likely to vaccinate their children because of the effectiveness expectancy of the flu vaccines, but groups with the lowest education (high school education or below) are less likely to make an acceptance decision for this reason. In addition, parents from higher education groups are less likely to refuse to vaccinate their children because of the perceived risk of influenza vaccination, but parents from lowest education groups are significantly affected by it. Third, kids’ gender is a significant moderator. If children are boy(s), parents are more likely to accept their children flu vaccination because they think vaccination is an effective prevention; if children are girl(s), then parents are more likely to accept influenza vaccination due to the convenience or cost-effectively, likewise, girls’ parents are more likely to refuse vaccination because of perceived risk.

There are some limitations of this study. First, sample data are not nationally universality as they were collected solely from four cities in China. Although the purpose of selecting the survey sites is to cover as much demographic information and parental intentions regarding children flu vaccination as possible, the sample only includes eligible parents from four cities in China. For the perspective to verify the current theoretical model, the sample size meets the requirements of the statistical literature, ^23,25,27-29 which is more than 20 times the number of variables. However, the sources of the samples do not cover cities in western China and coastal cities in eastern China. Therefore, the nationally generalizable of conclusions needs to be treated with caution. Subsequent studies can conduct comparative studies across regions or countries to enrich conclusions. On the other hand, the behavioral intentions of Chinese parents regarding childhood flu vaccination are dynamically changing, and future study should conduct some longitudinal comparisons based on the time axis. The latest statistics show that during the 2019–2020 epidemic season, the National Influenza Vaccine Program plans to supply over 28 million doses, double the actual use of the 2018–2019 epidemic season. The rapid expansion of children's vaccination scales reflects the positive changes in Chinese parental intentions regarding influenza vaccination. Looking into the near future, if the influenza vaccination rate among Chinese children rises to the level of developed countries, such as 30% −50%, then the conclusions of the current study will undergo worthy changes.

5. Conclusion

Our study developed a hybrid theoretical model and collected 462 valid samples from four cities in China, then provided insights into the psychological and demographic determinants for parental intention to receive children seasonal influenza vaccination. Performance expectancy, effort expectancy and perceived risk are significant factors that influence parental intentions to children flu vaccination, and they are affected by knowledge and social influence. Parents with Bachelor and above degrees, mother, and boy’s parents have a more positive intention toward children's influenza vaccination. To promote influenza vaccination rates among Chinese children, the policy priorities should focus on educational interventions, the effectiveness and safety of vaccines, and convenient vaccination procedures.

Author contributions

All authors contributed to the conception of study, interpreted the data and drafted the article. Study conception and design: Shujuan Qu and Haiyan Liu. Acquisition of data: Shujuan Qu, Haiyan Liu, and Li He. Analysis of the data: Shujuan Qu and Mingyi Zhao. Shujuan Qu and Kathryn S. Campy modified the manuscript and submitted it. All authors gave final approval of the version to be published and agree to be accountable for all aspects of the work.

Disclosure of potential conflicts of interest

No potential conflicts of interest were disclosed.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant number [81970248].