The Impact of Perceived Quality of Government Subsidies on Residents’ Travel Intention Post-COVID-19 in Wuhan, China

Abstract

To stimulate tourism demand and promote internal circulation, government subsidy policies have been introduced in many places in recent years due to public health concerns. In this paper, we propose a mechanistic model of the impact of the perceived quality of government subsidies on the travel intentions of residents based on consumer perception. Data were collected from Wuhan, China for empirical analysis. The results of this study showed that the perceived quality of government subsidies influenced travel intentions through the perceived behavioral control of travel consumers. Government subsidies were more likely to promote perceived behavioral control among residents with higher public health concerns, and their willingness to travel was stronger. Predicted risks undermine the positive effect of the perceived quality of government subsidies on travel intentions. The analysis suggested that a precise implementation of subsidy policies can enhance the efficiency of effects. We are keen to help China’s tourism market sustain growth in the post-COVID-19 period.

1. Introduction

Tourism is a highly volatile industry, and any occurrence involving public safety may have a noticeable effect on demand for travel. Following the COVID-19 pandemic, China’s tourism industry has witnessed an economic re-growth. The government can play a top-level design, policy guidance and macro-control role in tourism recovery [1]. The country has proposed an abundance of rules and regulations on both the supply and demand sides of the industry to help the industry recover due to the epidemic’s long-lasting and significant effects on the travel and tourism industry. Tourism is a cross-industry activity that involves “food, accommodation, public transportation, travel, shopping, and entertainment”. The State Ministry of Culture and Tourism originally unveiled a tax subsidy program for the four primary sectors of transportation, hospitality, lodging, and tourism in February 2020. This policy is focused on ensuring the survival of supply-side businesses. The “14th Five-Year Plan” for tourism development, which the State Council issued in 2021, provided more encouragement for local governments to create policies that would benefit the general population and boost domestic demand in order to strengthen economic circulation. When the pandemic was under control and steady, China began to allocate external stimulants on the supply side to increase tourist spending. The state’s macro-policy acts as the basis for the subsidy policy, which is carried out by the provinces and municipalities. As of now, governments in China frequently adopt steps, such as offering coupons for discounts on tickets and cultural tourism consumption. The “government–business cooperation” strategy, or the government-led organization’s collaborative distribution, is typically used to fund subsidy programs. Both consumers and businesses are benefited by the policy. Various subsidy policies are developed by each region in accordance with its distinctive economic, social, and other considerations.

It has been shown that public health issues negatively affected the national economy, and the decline of the population’s income has led to a relative reduction in the spending budget and a lower willingness to travel [2]. For the purpose of assisting the tourism industry’s recovery, numerous research have been carried out both domestically and abroad. Numerous international academics have researched traveler behavior intentions in the context of the outbreak in addition to the impact on tourism in multiple countries and recovery strategies. For example, tourists with different personality traits have different levels of risk perception [3,4,5]. The perception of risk [6] and anxiety [7,8] leads to a change in typical travel behavior. The contribution of the policy to the revival of the tourism industry has received increased attention from domestic practitioners.

Sheng et al. [9] believed that the severity of the shock depends on the strength of the policy hedge. He proposed that measures such as boosting travelers’ purchasing potential and issuing electronic consumption vouchers can help stimulate market dynamics. Peng [10] used network text analysis to classify policies and found that the top three policy types were security, a combination of security and feasibility, and a combination of security and economy. Safety and feasibility measures adopted by the government are preconditions for travel, and economic measures play a facilitating role. All of the above studies consider subsidy policies as an effective way for market economy recovery. The coordination of supply and demand in the market contributes to the functional adjustment of the tourism system and restores the resilience of tourism [11]. Empirical evidence on the effects of government subsidies and the mechanisms of their effects focus on the supply side of the enterprise. For example, Wang et al. [12] analyzed data on listed companies and found that organizational resilience capacity strengthens the role of government subsidies in promoting firm survival. Shan et al. [13] found that the best method of cooperation between enterprises and the government is in jointly issuing consumption vouchers. The majority of recent research on government subsidies has concentrated on how they affect the supply-side economy’s output efficiency, although there are distinctions between the ways that enterprises and travelers are impacted and the mechanisms by which they are acted upon. The effects of consumption vouchers for cultural tourism have not been extensively examined in studies on the demand side because they have mostly focused on the field of consumption vouchers. These studies have not taken into account the analysis of consumer-focused subsidy schemes or other forms of subsidies like ticket discounts. Furthermore, few scholars examined tourists’ true perceptions based on primary data, preferring to use secondary data to determine the economic impact of subsidies. While confirming that attitudes toward tourism policies can influence travel intentions, Zhang et al. [14] focused on describing tourists’ willingness to accept various policies during their travels, resulting in a lack of an evaluation of the various aspects of subsidized policies and making it difficult to improve policy quality based on research. The impact of consumer-oriented government subsidies on travel intentions warrants additional investigation. In conclusion, this paper combined the effect of shocks brought on by public health issues and the psychological risk faced by travelers, and introduced perceived behavior control, predicted risk, and public health concern to explore the impact of the perceived quality of government subsidies on travel intention. The objective of this paper is to explain the micro-penetration mechanism of government subsidies and travel intention in the context of the pandemic. From a theoretical standpoint, it enhances the understanding of the role of government travel grants under the umbrella of public health worries; from an economic perspective, it provides a direction for the enhancement of the growth of domestic demand in the tourism market under the new situation and helps the tourism economy recover sustainably.

The organizational structure of this article is as follows. Following the introduction, the next section discusses the pertinent theoretical underpinnings and the literature evidence for the study variables and models. The third section initially provides a brief overview of the questionnaire by investigating its reliability, validity, and the responses received for each question. To determine whether the mediating moderating effect is still present, the fourth portion analyzes the empirical model. The “Discussion” and “Conclusions” are covered in the Section 5 and Section 6.

2. Literature Review and Hypothesis Research

2.1. Perceived Quality of Government Subsidies

Government subsidies act on consumers and producers to produce various consumer and producer surpluses, and the macro-policy is an “invisible hand” that controls the equilibrium of the markets. As a result, it is important to investigate the policy’s target, impact mechanism, and effect. According to Liu et al. [15], based on an analysis of the central and local governments’ tourism policy documents from 2008 to 2019, the current tourist policies are mostly focused on the innovation and reform of the tourism industry. These regulations primarily standardize the operation of the travel industry and the prevention and control of travel security from the supply side, but they lack regulations that encourage demand-side trip spending. Wang et al. [16] concluded that although some preferential policies, such as distributing consumption vouchers, appear crucial for encouraging tourism, policies that are specifically targeted toward tourists’ needs are the most successful. The majority of the research that has been conducted on state strategies involving taxes, levies, and loan subsidies for tourism businesses focused on the results of their deployment. It is reasonable to assume that the effect of the subsidy policy on consumers is also related to its acceptance, but Liu [17] argued that the effectiveness of the government’s tax incentives and financial subsidies depends on the acceptance of the policy by enterprises and the intensity of its implementation. It is vital to determine the objective of government subsidies from a subjective standpoint by using the definition of “perceived quality” because there are discrepancies between how the policy is actually being implemented and what tourists actually perceive. Consumers’ subjective evaluations of the product were used to determine perceived quality by both Steenkamp [18] and Wang Peng [19]. The perceived quality of government subsidies in the study was defined as follows, with reference to Fan et al.’s [20] definition of the term “quality of public policy”: “From the subjective perception of consumers, whether the various government subsidies for tourism are meeting their need, they will make an assessment.” Garvin [21] measured the perceived quality in terms of three dimensions: usability, safety, and reliability. Wang et al. [22], in their investigation of the PV subsidy policy, identified three criteria: advocacy, benefit, and stability. As a result, the perceived benefit and perceived stability of government subsidies have been split into two aspects in this study.

2.2. Perceived Quality of Government Subsidies and Travel Intention

Since the COVID-19 pandemic, national and local governments have implemented a number of subsidy schemes to assist tourism businesses in overcoming challenges and have also conducted a number of actions to increase traveler demand. However, less research has examined how government subsidies affect consumers. Qiu [23,24] argued that in the supply chain, consumers prefer subsidies to act directly on them and believe such subsidies benefit from them. Tourism subsidy policy is also a supply chain, with government and business working together to achieve the policy implemented. And the effect is different whether the policy is implemented by consumers or companies. There were even less studies of consumer-oriented subsidy policies in the tourism industry, as existing studies have focused on the segmentation of consumption vouchers. Most domestic scholars conducted empirical studies on the effect of consumption vouchers based on macro-data, and have confirmed that the government providing subsidies to residents by issuing consumption vouchers is significant in stimulating tourism consumption. Lin et al. [25] projected that the issuing of consumption vouchers could increase the number of industry transactions by 26.26% compared to areas where no vouchers were issued. Li [26] believed that consumption vouchers have boosted the boom in tourism and its related industries. Sun et al. [27] believed that even if the government issued consumer vouchers several times over a long period of time, it will still be effective in stimulating consumption. Wang [28] argued that consumption vouchers are effective in boosting consumers’ confidence and promoting consumption through an analysis of Weibo data. These studies discussed the consumption vouchers of various industries as a whole, but there were no studies that analyzed the effects of the implementation of domestic cultural tourism consumption vouchers. Foreign scholars’ studies on consumer-oriented subsidy policies have focused on Japan’s “go-to-travel” policy, such as Tagashira Takumi [29], who found that individuals who did not qualify for the subsidy traveled more after the subsidy was implemented. Matsuura T et al. [30] confirmed the effectiveness of the price-discounting strategy in mitigating the economic losses caused by the pandemic to the Japanese accommodation industry. Therefore, it is conceivable that a variety of government subsidies, such as consumption vouchers, may be useful in promoting travel and the rebirth of the tourism industry. The following assumptions were made based on the consumer’s perspective that the perceived quality of government subsidies can affect their desire to travel:

H1. 

A perceived quality of government subsidies positively influences travel intention.

H1a. 

A perceived benefit of government subsidies positively influences travel intention.

H1b. 

A perceived stability of government subsidies positively influences travel intention.

2.3. The Mediating Role of Perceived Behavioral Control

According to the S-O-R theory, psychological reactions to environmental stimuli could affect how people behave. Government subsidies are external impulses for the general population. Potential travelers’ assessments of their capacity to learn about policies and take advantage of their benefits when given policy knowledge as a stimulant affect their motivation to proceed further.

Huang Chunhui [31], based on MGB theory in major public health events, researched that perceived behavioral control can have a positive effect on the travel intention. In corporate investment behavior, the strength of a firm’s financing constraints can influence the role between unstable economic policies and investment behavior [32]. Individuals’ compliance with the policy aim may be influenced by how consistently the policy is being implemented. The firm’s financial limitations are comparable to personal behavior controls in terms of its transitory nature. Hye-Kyung Bae [33] revealed that the relationship between cosmetic quality and buying intent can be mediated by perceived behavioral control. The following idea was then put forth by extending the aforementioned mechanism to the perception of the quality of government subsidies:

H2. 

Perceived behavioral control mediates between a perceived quality of government subsidies and travel intention.

H2a. 

Perceived behavioral control mediates between a perceived benefit of government subsidies and travel intention.

H2b. 

Perceived behavioral control mediates between a perceived stability of government subsidies and travel intention.

2.4. The Moderating Role of Public Health Concerns

Potential visitors must travel across regions because of the property of the tourism supply, and the movement and gathering of people can easily result in the spread of infectious diseases. The potential for tourism consumption in China at present is enormous, and the country’s citizens are eager to travel. However, people’s perceptions of the importance of public health vary, which affects how difficult it is to plan trips. Before engaging in tourism activities, hesitant or weak potential travelers are less sensitive to external cues. In the instance of COVID-19, the disease has given rise to a phenomena known as “anti-globalization”, in which travel destinations have not only failed to work together but have also become further dispersed from one another [34]. From initial ignorance and psychological panic to an effective management of the outbreak, the public’s overall concern about the outbreak has decreased from the initial level. They became more positive overall and more optimistic in future expectations, but there were differences in the level of concern among different groups, resulting in different levels of desired effects [35]. Different sources of information regarding the pandemic and varying degrees of public knowledge of the outbreak’s risks would also influence how the public copes [36]. Bai Lan [37] suggested that stock markets are unpredictable because investors’ anxieties about the pandemic change over time, affecting their investing decisions. At the onset of a public health outbreak, investor attention had a significant “early warning” effect on the performance of the stock market in our sector. This means that the level of attention only at the outbreak’s onset will cause investors to behave with greater caution. This indicated that various levels of public health concern can play a role in the event and its psychological impact on the population, but this effect is only negative in the early stages.

When the epidemic was under control, residents were generally optimistic about the government’s subsidy policy and the likelihood of receiving subsidies in the context of current public health concerns. Concerned groups vary in their degree of optimism regarding the “government subsidies access to government subsidies” approach. In other words, public health concern moderates the relationship between the perceived quality of government subsidies and perceived behavioral control. Hence, the hypothesis is stated:

H3. 

Public health concerns play a moderating role in the perceived quality of government subsidies and perceived behavioral control.

2.5. The Moderating Effect of Predicted Risk

Risk has been a hot topic of discussion in tourism research. In Flynn’s [38] study, it was noted that individuals’ behavior intentions are influenced by subjective perceptions of risk. Riittichainuwat [39], on the other hand, found that tourists do not cancel their plans in the face of sudden illness or terrorist events, but change their original plans to travel to areas with low risk. In the process of foreign investment, Wang et al. [40] confirmed the moderating effect of aggregate risk on trade effects, the higher the comprehensive risk level of the host country, the stronger the trade creation effect of China’s OFDI in countries along the “Belt and Road”. In the act of travel, the risk is always to weaken the intention to travel. In tourism activities, Zhang et al. [41] verified that tourists’ perceived risk is an important antecedent of tourists’ behavior intentions and suggested that incentives should be taken to attract a portion of tourists first. Gou et al. [42] further considered the impact of perceived risk on travel under different periods and argued that the stage of risk leads to variability in behavior outcomes. Caber et al. [43] verified that risk perception has a negative moderating effect on the relationship between travel motivation, destination image, and travel intention. Through focusing on the potential risks of visitors’ prediction, such as disapproval and differential treatment by businesses, and referring to this mental process as “predicted risks”, subsidy policy as an external stimulus influences the propensity to travel via perceived behavioral control, and anticipating risk moderates this effect. The following hypotheses were therefore proposed:

H4. 

Predicted risks moderate between perceived behavioral control and travel intention.

H5. 

Predicted risks moderate the relationship between the perceived quality of government subsidies and travel intention.

2.6. Model Construction

In a sense, the act of traveling is also a consumer act, and travelers’ purchases of tour route products and tickets from travel agencies are comparable to purchases made in shopping malls. In consumer behavior, the S-O-R theory and the theory of planned behavior are frequently utilized to model purchase decisions, and the two theories share similarities. S-O-R, or stimulus–organism–behavior response, is a theory that describes a succession of psychological motivations that cause individuals to make behavioral decisions in response to external stimuli. In the theory of planned behavior, there is also a path of “Facilitator–Perceived–Behavior Control–Behavioral Intention”, and the combination of the two can be thought of as the individual perceiving the facilitator (stimulus), the perceived behavioral control (organism), and the behavioral intention to produce changes (behavioral response).

Guan Rongwei [44] mentioned that the stimuli recognized by domestic and foreign scholars include perceived quality and value. The notion of perceived quality of subsidy policy is developed in this study through the concept of product quality and measuring government subsidies, as was already described in Section 2.1. Combining these two theories results in a model where the perceived behavioral control serves as the mediating variable, the willingness to travel serves as the dependent variable, and the perceived quality of the subsidy program serves as the independent variable. Additionally, as stated in the background and discussion in Section 2.4, one of the goals of the government’s subsidy policy is to lessen the pandemic’s negative effects on the tourism industry. Accordingly, external stimuli have varying psychological effects on the populace, depending on their level of concern. As a result, the concern for public health was added as a moderating factor. Furthermore, it is well-known that studies on epidemics have focused on risk, and Section 2.5 elaborates on the idea that different degrees of predicted risk can eventually have varied effects on the intensity of travel intentions.

The analysis presented above leads to the model that is suggested: Figure 1.

3. Materials and Methods

3.1. Data Collection and Questionnaire Design

Data were collected through a well-designed data collection instrument from 1 September to 30 October 2022. A mixed approach via on-site and online questionnaire surveys was employed for Wuhan residents. The link to the online survey was shared on different tourism-related pages on Weibo and WeChat. On-site survey was distributed in the Wuhan community.

We employed a no-touch strategy to erratically drop questionnaires on paper with gifts of sanitizers at homeowners’ doorsteps because China was still strictly in a pandemic prevention and control zone when the surveys were given out. We gathered the questionnaires the following day. The offline survey was conducted in Wuhan since it was the first place of the new crown pandemic outbreak in China. Residents of Wuhan also experienced the earliest public health event in the most intuitive manner, as well as the most intense levels of psychological panic and frustration. It is more realistic to choose the locals as the research popup group because the subsidy program is intended to rebound from the new crown pandemic’s negative effects on the region’s tourism industry. A total of 219 full responses were received, of which 182 were from legitimate surveys.

The data-gathering tool is divided into several sections. Questions about the visitors’ backgrounds were asked in the first part of the information collection. The second section related to the tourists’ perceptions of government subsidies, with reference to the studies of Wang et al. [22] and Zheng [45]. The third section related to the tourists’ perceptions of predicted risks referring to Xu Hui [46] and Ai et al. [47] in their study of perceived risk. The fourth section related to public health concerns, whose questions were developed with expert opinion. The fifth section related to travel intention, referencing Zheng [45]. All of the questions, with the exception of those in Section 1, were graded on a 5-point Likert scale. Additionally, numerous illustrations of subsidy policies were provided.

The demographic information about the respondents is shown in

Table 1. Basic characteristics statistics.

Features	Category	Percentage Share/%	Features	Category	Percentage Share/%
Age	18–25 years	12.8	Gender	Man	48.4
	26–30 years	29.1		Woman	51.6
	31–40 years	22.4	Occupation	Student	11.2
	41–50 years	26.5		Company staff	45.2
	51–60 years	5.2		Individual household	22.3
	Over 60 years	4.0
Monthly income	Under CNY 3000	11.2		Employee of the government and public institutions	5.0
	CNY 3000–5000	34.8
	CNY 5000–7000	28.1
	CNY 7000–9000	18.3		Retiree	8.3
	Over CNY 9000	7.6		Other	8.0

. More than half of the respondents were between the ages of 26 and 40. There were roughly equal numbers of men and women, with women making up 51.6% of the total. By profession, 45.2% of respondents were workers for a corporation. Over half of the respondents received salaries of more than CNY 3000. The average monthly income of the respondents is calculated according to monthly living expenses; campus prices are low; and this amount is more than enough to cover the living costs of college students’ children who are still in school. More than 88% of the respondents have salaries that are higher than CNY 3000; the minimum wage in Wuhan is CNY 2600; and about 12% of the respondents have average monthly incomes that are below CNY 3000 because they are primarily students.

3.2. Credibility Analysis and Description of Current Situation

3.2.1. Exploratory Factor Analysis

We used an exploratory factor analysis (EFA) by PASW statistics 27.0 to ensure that the survey responses and dimensions were satisfactory. The factors were rotated using the maximum variance approach, and the principal components were chosen using the eigenvalues larger than 1 rule. Under each primary component, the items with factor loading larger than 0.5 and comparable values were chosen. The initially developed questionnaire’s EI4, EI5, FI1, FI4, HI2, OI2, and OI3 items were eliminated after many filtering iterations.

The remaining questions are then examined. We started by running a reliability analysis. The scale’s overall Cronbach’s value was 0.850, and the individual values for each dimension were 0.869, 0.867, 0.828, 0.759, 0.784, and 0.773 (

Table 2. Confidence validity and exploratory factor loading tables.

Variables	Title	Factor Loading	Cronbach’s α	Sum of Squared Rotating Loads
Travel intention	OI4	0.818	0.869	16.198
	OI5	0.781
	OI7	0.775
	OI6	0.733
	OI1	0.71
Predicted risks	KI4	0.862	0.867	30.027
	KI3	0.861
	KI1	0.827
	KI2	0.822
Perceived stability of government subsidies	FI2	0.855	0.828	40.977
	FI3	0.852
	FI5	0.758
Perceived behavioral control	HI4	0.853	0.759	51.309
	HI1	0.723
	HI3	0.714
Perceived benefits of government subsidies	EI1	0.802	0.784	61.445
	EI2	0.765
	EI3	0.741
Public health concerns	HI6	0.877	0.773	71.563
	HI5	0.765
	HI7	0.758

), all of which were greater than 0.7, indicating that the scale was reliable.

We then conducted a validity analysis. In Bartlett’s spherical test, the findings revealed KMO values was 0.825, greater than 0.8 and p < 0.01 (

Table 3. KMO and Bartlett’s test.

Variables		Value
Kaiser–Meyer–Olkin Measure of Sampling Adequacy		0.825
Bartlett’s Test of Sphericity	Approx. Chi-Square	1780.030
	df	210
	Sig.	0.000

), demonstrating the scale’s high validity. Using an exploratory factor analysis (EFA), the remaining question items can be extracted as six common factors, named travel intention (OI1, OI4, OI5, OI6, OI7), predicted risk (KI1, KI2, KI3, KI4), perceived stability of government subsidies (FI2, FI3, FI5), perceived behavioral control (HI1, HI3, HI4), perceived benefits of government subsidies (EI1, EI2, EI3), and public health concern (HI5, HI6, HI7). The overall contribution from variance was 71.563%, as shown in Table 2.

3.2.2. Validation Factor Analysis

Validation factor analysis of the scale was conducted by AMOS 24.0 and yielded CMIN/DF values greater than 1 and less than 3, RMSEA values less than 0.08 and IFI, TLI and CFI values greater than 0.9, indicating that the model values all met the standard and the model fit well. In addition, the inclusion of univariate and covariate factor models on top of the six variables in this study was checked to prevent potential issues of homology bias.

As can be seen in

Table 4. Table of overall fit coefficients.

	CMIN/DF	RMSEA	IFI	TLI	CFI
One-factor model	5.623	0.6	0.477	0.412	0.471
Six-factor model	1.557	0.055	0.943	0.929	0.921
Inclusion of a common methodology factor model	1.330	0.043	0.970	0.985	0.969

, the single-factor model did not fulfill the standards and considerably differed from the six-factor model, but the addition of the common variance component did not result in a significant rise in TLI or CFI, and the change in RMSEA value was also not greater than 0.05. Therefore, the six-factor model does not suffer from the homology bias problem.

The convergent validity (AVE) and combined reliability (CR) of each dimension were further examined under the assumption that the model’s overall fit was good, and the standardized factor loading for each dimension was calculated to obtain AVE values greater than 0.5 and CR values greater than 0.7 for each dimension (see

Table 5. Convergent validity and combined reliability tests for each dimension of the scale.

Variables	Title	Estimate	AVE	CR
Perceived Benefits of Government Subsidies	EI1: I feel that the travel subsidy policy has made my trips more affordable	0.795	0.5628	0.7937
	EI2: I feel that the travel subsidy policy has reduced the cost of my trips	0.769
	EI3: I feel that the subsidy policy is beneficial to me	0.682
Perceived Stability of Government Subsidies	FI2: I think the amount of government subsidies will not change randomly	0.821	0.6218	0.831
	FI3: I think the duration of government subsidies will not change randomly	0.815
	FI5: In general, I feel that the government’s subsidy policy is stable	0.726
Perceived behavioral control	HI1: I think I understand travel policy subsidies	0.787	0.5343	0.7742
	HI3: I think I can get a travel-related government subsidy	0.719
	HI4: I have used government subsidies for trips before	0.683
Public Health Concerns	HI5: I think I know about public health in the places I travel	0.781	0.5431	0.7788
	HI6: I consider myself to be aware of the seriousness of the risks to public health	0.8
	HI7: Before I go on a trip, I will find out about the public health situation in the destination	0.616
Predicted Risks	KI1: I am concerned about the poor awareness of epidemic prevention and poor service in local hotels that can use government subsidies	0.774	0.6216	0.8678
	KI2: I am concerned that the arrangements for food, accommodation and entertainment will not meet my expectations when I use the government subsidy	0.754
	KI3: I am concerned that the tourist destination using government subsidies may be temporarily changed or substituted for the tour	0.822
	KI4: I am concerned that the services provided by the tourist attraction will not be satisfactory after using the government subsidy	0.802
Travel Intention	OI1: I am interested in tourism-related government subsidies	0.761	0.5739	0.8706
	OI4: I am willing to travel with government subsidies	0.779
	OI5: I recommend others to travel with government subsidies	0.762
	OI6: I would share my experience of using it with others	0.712
	OI7: The government subsidy makes me feel better about going on a trip	0.772

). These values showed that the scale had good convergent validity and a combined reliability. A discriminant validity study was performed to see if each dimension could be distinguished from the others.

Table 6. Differential validity scale.

	Perceived Benefits	Perceived Stability	Perceived Behavioral Control	Public Health Concerns	Predicted Risks	Travel Intention
Perceivedbenefits	0.5628
Perceivedstability	0.378	0.6218
Perceivedbehavior control	0.422	0.511	0.5343
Public healthconcerns	0.433	0.196	0.349	0.5431
Predicted risks	0.074	0.008	−0.001	0.074	0.6216
Travel intention	0.628	0.438	0.543	0.273	0.135	0.5739
Square root of AVE value	0.750	0.789	0.731	0.737	0.788	0.758

shows that there was a good discriminant validity between the dimensions because the correlation coefficients for the dimensions of perceived benefit, perceived stability, perceived behavioral control, public health concern, predicted risk, and travel intention were all smaller than the square root of the convergent validity value of each dimension.

3.2.3. Current Situation Analysis

Descriptive analyses of travel intention, predicted risk, the perceived stability of government subsidies, the perceived benefit of government subsidies, public health concerns, and perceived behavioral control are shown in

Table 7. Statistics for the key variable description.

	N	Minimum Value	Maximum Value	Average	Standard Deviation
Travel intention	182	1.4	5	4.0264	0.63713
Perceived benefits	182	2	5	4.1282	0.62439
Perceived stability	182	1.33	5	3.8608	0.71649
Perceived behavioral control	182	1	5	3.2198	0.96051
Public health concerns	182	2.33	5	4.152	0.61451
Predicted risks	182	1	5	3.8008	0.79785
Number of active cases	182

. The general public, according to the average value, thinks that the advantages of government subsidy schemes outweigh their stability. Additionally, the public’s perception of their ability to influence whether they can receive government subsidies is at a medium level, meaning that they are unsure if they can. The public perceives a high risk while using a subsidy, even if they do receive one, in terms of the quality of the services. In addition, there was a high degree of public anxiety regarding public health and safety, which may be connected to incidents involving contagious diseases.

To investigate the differences between each variable and age, gender, mean monthly income, and occupation, we continued to conduct Pearson’s chi-squared tests for each of the variables. The findings revealed that there were significant differences in predetermined risk among age groups (p = 0.06), perceived behavioral control among visitors of different genders (p = 0.06), and public health concerns among occupational groups (p = 0.001).

4. Result

4.1. Main Effects Test

The study’s independent variable was the perceived quality of government subsidies, which took into account both their perceived value and their perceived stability. To ascertain whether the independent variables significantly influenced the dependent variable, travel intention, a regression analysis was performed.

Table 8. Table of regression equations.

M Model		Unstandardized Coefficient		Standardized Coefficient	t	Significance
		B	Standard Errors	Beta
Model 1	constants	1.335	0.292		4.578	0
R2 = 0.328	X1	0.461	0.067	0.452	6.928	0
	X2	0.204	0.058	0.23	3.52	0.001
Model 2	constants	1.792	0.269		6.658	0
R2 = 0.281	X1	0.541	0.064	0.53	8.392	0
Model 3	constants	2.708	0.24		11.269	0
R2 = 0.148	X2	0.342	0.061	0.384	5.581	0
a. Dependent variable: Y

presents the outcomes. Model 1 was a regression study with travel intention (Y) as the dependent variable, and the two independent variables being the perceived stability of government subsidies (X2) and perceived benefit of government subsidies (X1). The standardized coefficient value was positive, the p-values were all less than 0.01, and there was a significant positive effect of the independent variable on the dependent variable. A single-factor regression analysis was conducted to avoid the interaction effect between the two variables of perceived benefit and stability of government subsidies. Model 2 was a regression study that focused on the relationship between the willingness to travel (Y) and the perceived value of government subsidies (X1). It revealed that the perceived value of government subsidies contributes by 28.1% to travel intention. Regression analysis in Model 3 of the relationship between the perceived stability of government subsidies (X2) and the intention to travel (Y) revealed that the perceived stability of government subsidies accounts for 14.8% of the intention to travel. When compared to Model 3, where the standardized coefficient value for the perceived stability of government subsidies was 0.384, Model 2’s standardized coefficient value for the perceived affordability of government subsidies was 0.53, which is higher. This suggests that the former has a larger impact on travel intention than the latter. The H1, H1a, and H1b hypotheses are thus true.

4.2. Testing for Mediating Effects of Perceived Behavioral Control

To fit the variable perceived quality of government subsidies (X), the perceived benefit of government subsidies (X1) and perceived stability of government subsidies (X2) were averaged. The bootstrap approach process was used to examine the mediation effect of perceived behavioral control (M) between the three independent variables and travel intention (Y).

Table 9. Table of tests for mediating effects of Perceived Behavioral Control.

Behavior Control		Effect	Se	t	p	LLCI	ULCI
X–M–Y	Total Effect	0.6266	0.0736	8.5133	0	0.4814	0.7719
	Direct effects	0.5234	0.0798	6.5611	0	0.3659	0.6808
	Indirect effects	0.1033	0.0464	/	/	0.0275	0.2119
X1–M–Y	Total effect	0.4702	0.0678	6.9381	0	0.3364	0.6039
	Direct effects	0.4214	0.0679	6.2029	0	0.2873	0.5554
	Indirect effects	0.0488	0.0263	/	/	0.0086	0.1129
X2–M–Y	Total effect	0.1849	0.0592	3.1208	0.0021	0.068	0.3018
	Direct effects	0.129	0.0605	2.1313	0.0345	0.0095	0.2484
	Indirect effects	0.0559	0.0269	/	/	0.0139	0.1188

displays the results. In path X-M-Y, the upper and lower limits of CI values for the total, direct and indirect effects did not include 0 and p < 0.01. This suggests that perceived behavioral control has a role in mediating the path. And, since all the effect values are positive, it follows that the higher the quality of the public’s perception of government subsidies, the more they are able to control their conduct to believe that they can obtain government subsidies, thereby increasing travel intentions. H2 is thus appropriate. The upper and lower ranges of the CI values of the total, direct, and indirect effects in pathways X1–M–Y and X2–M–Y do not contain 0 and p0.01, and the effect values are all positive. This suggests that perceived behavioral control has a partial mediation influence on the perceived affordability of government subsidies and travel path intention, as well as the perceived stability of government subsidies and travel path intention. H2a and H2b are thus correct.

4.3. A Test of the Moderating Effect of Predicted Risks and Concern

Table 10. Test of the moderating influence on risk and pandemic concern.

	Dependent Variable: Perceived Behavior Control			Dependent Variable: Willingness to Travel
	Coeff	p	95%CI	Coeff	p	95%CI
Gender	−0.4282	0.0009	[−0.6792, −0.1773]	−0.0615	0.4639	[−0.2269, 0.1039]
Age	−0.1519	0.2091	[−0.3898, 0.0859]	−0.0461	0.5474	[−0.1971, 0.1049]
Occupation	0.0039	0.9533	[−0.1261, 0.1338]	−0.0245	0.5553	[−0.1065, 0.0574]
Monthly income	0.1731	0.0396	[0.0083, 0.3380]	0.0188	0.7255	[−0.0865, 0.1240]
X	0.6036	0	[0.3688, 0.8384]	0.4978	0	[0.3416, 0.6540]
W	0.2227	0.0359	[0.0149, 0.4305]			[0.0445, 0.2329]
M				0.1387	0.0041
X*W1	0.4849	0.0098	[0.1183, 0.8514]
W2				0.1054	0.0411	[0.0043, 0.2065]
X*W2				−0.2791	0.0142	[−0.5014, −0.0567]
M*W2				0.0783	0.2011	[−0.0421, 0.1987]

shows a significant moderating effect between public health concerns (W1), the perceived quality of government subsidies (X), and perceived behavioral control (M); the coefficient values for all variables were greater than 0. The 95% CI interval for the interaction term X* W1 does not contain 0, p = 0.0098, in the moderating path between these variables, demonstrating that the favorable relationship between the perceived quality of government subsidies and perceived behavioral control was positively attenuated by worries about public health. The correlation between H3 and H2 was positive. The 95% CI interval of the interaction term M* W2 on perceived behavioral control (M) and travel intention (Y) in the moderating path of predicted risk (W2) contained zero, and the p-value of 0.2011 was greater than 0.05. The moderating impact of predicted risks did not hold along this route, and hypothesis H4 was disproved. The 95% CI interval in this path did not contain zero, the regression coefficient of the interaction term X*W2 was negative, and the p-value was less than 0.05. Prejudgment risk had a negative moderating effect on this path, hence H5 was true.

With a regression coefficient of 0.3057 and a 95% confidence interval (CI) of [−0.0480, 0.6593], further analysis of the moderating effects of the aforementioned moderating variables at high and low levels revealed that for those with low levels of public health concern, the beneficial influence of the perceived quality of government subsidies on perceived behavioral modification was not crucial. For those with higher levels of public health concerns, the perceived quality of government subsidies had a substantial beneficial impact on perceived behavioral control, with a regression coefficient of 0.9016 and a 95% confidence interval (CI) of [0.6071, 1.1960]. Figure 2 demonstrates that the positive association between the perceived quality of government subsidies and perceived behavioral control is more strongly moderated by high levels of public health concerns. For the public with a low level of perceived predicted risk, the regression coefficient was 0.7204 with 95% CI of [0.4938, 0.9470]. With a 95% confidence interval of [0.0285, 0.5209], the regression coefficient for the public with a modest level of predicted risk perception was 0.2751. The strong connection between the perceived quality of government subsidies and willingness to travel is moderated positively by high levels of predicted risk perception, as shown in Figure 3.

5. Discussion

Residents of Wuhan were used as the research subject in this study, and it covered how they felt about government assistance and how it affected their desire to travel. This served as a resource on how regulations might enhance the development of the tourism industry during the post-pandemic period. The empirical study of subsidies can help the current system of policy study to some extent and confirm the sustainability of subsidies in China’s public health events. The results showed that subsidy programs can positively affect travelers’ intentions to travel, with perceived behavioral control acting as a mediating factor, and public health worries and risk aversion as moderating factors.

In comparison with previous studies, there are similarities and differences between the findings of this study. Scholars confirmed that duty-free policies have a strong positive impact on travel intentions and that the severity of the epidemic moderates the effect [48]. Adventurous people found the subsidy policy to be a better incentive to travel than the policy to curb the pandemic [49]. This paper expanded the scope of the subsidy policy to take into account the actual situation in China and arrived at a view similar to that of the abovementioned scholars. In food waste reduction policies, the mediating role that perceived control could play in the policy and household intentions to reduce waste has been demonstrated [50]. This paper confirmed that China’s subsidy policy can influence travel intentions through perceived behavioral control. The difference lies in the fact that the abovementioned scholars’ studies were about perceived external governmental control, whereas this paper was about the perceived control of the internal self-behavior. Perceived control at both the individual and policy levels was positively associated with preventive behavior during the COVID-19 pandemic [51]. It is therefore reasonable to assume that after the implementation of the policy, residents will judge whether they are likely to receive the relevant subsidies through their own knowledge of the policy, which will have an impact on their travel intention. Zhou et al. [52] introduced the moderating role of haze concerns and risks in congestion charging policies, and proposed that the public perception of policy effectiveness can enhance policy acceptance. The findings of this paper are similar, exploring whether residents are traveling in line with policy intentions by measuring the affordability and effectiveness of government subsidies. People with different levels of concerns about public health events and different perceptions of risk will respond differently to these policy intentions.

The purpose of this research philosophical contribution is to review the literature on government subsidies, create the variable of perceived quality of subsidy policy, blend planned behavior theory and S-O-R theory, create a structural equation model in accordance with empirical analysis, and examine and reveal the mediating role of perceived behavioral control (organism) in the perceived quality of subsidy policy (stimulus) and travel intention (behavioral response). In order to enrich the research findings of S-O-R theory and planned behavior theory and to some extent provide a relatively integrated analytical framework for tourism government subsidies research, this investigation uses the intermediary effect created by the combination of S-O-R theory and planned behavior theory as its main framework, expanding the moderating role of public health concern and predicted risk.

6. Conclusions and Recommendations

6.1. Conclusions

A guarantee for the steady growth of tourism is public health and safety. The increase in domestic demand is seen as a key task for the growth of tourism in the new environment.

The government has implemented a number of steps to subsidize tourists in an effort to support the recovery of the tourism industry. According to theory, the study enhanced the stimulant effect of government subsidies based on the viewpoint of public health issues from the perspective of tourists. Providing guidance for promoting the tourism economy from the perspective of domestic demand is a practical consideration. We combined public assistance with perceived quality to assess how they rank with consumers by developing a theoretical model and examining the micro-action course of the Chinese government’s subsidy policy in light of tourists’ opinions.

The findings led to the following conclusions:

(1)

Tourists find that the current government subsidy policies are effective, which indicates that their perception of government subsidies is of a high caliber. Visitors’ intentions to travel are significantly favorably influenced by both the perceived benefit and stability of government subsidies, with the positive effect of perceived benefit being larger.

(2)

The perceived quality of government subsidies affects both the travel intention directly and also through perceived behavioral control, which plays a mediating role. In other words, based on their assessment of the perceived value of government subsidies, tourists’ travel intentions are influenced by the perceived ease of access to government subsidies.

(3)

Public health concerns reinforce the positive effect between the perceived quality of government subsidies and perceived behavioral control. The more concerned the public is about public health safety, the more knowledgeable they are about the disease, and hence, they are less likely to exaggerate the consequences of infection. When the government subsidies are published, visitors feel that they can access and use them successfully and are not prevented from using them due to illness.

(4)

Predicted risks prevent the perceived quality of government subsidies from having a favorable impact on travel intention. Visitors will be aware of the hazards associated with using travel subsidies from the government, such as receiving unfavorable treatment from merchants. Government subsidies are less successful in encouraging willingness to travel when the risks that travelers anticipate before traveling are higher.

(5)

Additionally, there are significant differences in predicted risks among age groups (p = 0.001), perceived behavioral control among visitors of various genders (p = 0.06), and public health concerns among professional categories (p = 0.001).

6.2. Study Limitations

Additionally, there are some further problems with the article.

This survey does not differentiate between urban and rural inhabitants because it is based solely on Wuhan locals, lacking a study comparing the two groups.

The sample size was also controlled due to the fact that the questionnaire was distributed while China was still employing stringent controls against the pandemic. In the future, additional factors like policy trust can be included to examine how government travel subsidies affect willingness to travel by combining facts from both first- and second-hand data sources to offer suggestions for improving the effectiveness of our policy execution and our policy formulation.

6.3. Recommendation

On the basis of the findings of the research, the following recommendations are made. First, prolonging the policy’s use length to improve stability, a high frequency, and a brief duration are features of the current subsidy regimes. When the general public utilizes them, unexpected events or negligence can trigger conflicts with the anticipated trip time or cause them to miss the expiration date. To guarantee that the policy is implemented well, the government can make it clear how long the policy will last in order to increase its credibility in the public’s eyes. In addition, it can address unforeseen occurrences by offering more explanations or extending the deadline. Second, the government should minimize the chances of negative public opinion and increase publicity. As government subsidies may be used differently by businesses, it is important to improve the regulatory framework governing their use. This will ensure that residents can utilize the subsidies without difficulty and that those who do so will receive the same high-quality goods and services as those who do not. In addition, more information must be made widely available on the usage of government subsidies so that individuals of all ages can access it so as to help the public feel less uncertain and concerned about government subsidies by providing an increased awareness of the techniques and scope of use.

Third, the government should develop an environment for sharing knowledge about public health. Tourism activities facilitate the spread of both domestic and international infectious diseases, and the public’s concern for public health and safety has grown to be a significant determinant of their travel choices. A platform such as this might describe recent epidemics, common illnesses that affect travelers, and treatments and preventative methods. Increasing public knowledge of illnesses and lowering fear levels consequently lowers the chances of health risks when traveling, assuring traveler happiness.