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Vol. 8. Issue 2.
(April - June 2023)
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Vol. 8. Issue 2.
(April - June 2023)
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Travel intention determinants during COVID-19: The role of trust in government performance
Huan Minh Nguyena,
Corresponding author

Corresponding author.
, Hung Nguyen Phucb, Duong Thien Tamc
a Faculty of Social Sciences and Humanities, Ton Duc Thang University, Ho Chi Minh City, Vietnam
b Faculty of Tourism and Culinary Arts, Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam
c Department of Tourism, Sport and Hotel Management, Griffith University, Australia
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Figures (2)
Tables (5)
Table 1. Profile of respondents.
Table 2. Scale reliability test results.
Table 3. Exploratory factor analysis result.
Table 4. Correlation matrix.
Table 5. Coefficients from SEM Model.
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The Coronavirus disease 2019 (COVID-19) has compelled tourism experts to devise efficient ways to attract visitors. Based on the theory of planned behavior (TPB) model, this study created a comprehensive model of residents’ trust in government performance, perceived risk, and travel intention. The model was tested on 469 Vietnamese residents using confirmatory factor analysis and structural equation modeling. The findings reveal that perceived risk negatively impacts attitudes toward travel, perceived behavioral control, and subjective norms, while trust in government performance positively impacts these categories. Furthermore, attitude toward travel, perceived behavioral control, and subjective norms positively influence a tourist's travel intention. This study offers theoretical and practical insights into how to build safe tourism practices and design appropriate COVID-19 recovery programs in the tourism industry.

Perceived risks
Trust in government performance
Travel intention
Theory of the planned behavior
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The COVID-19 outbreak is considered a significant event in world history (Fong, Law & Ye, 2020). As the disease spread swiftly, governments worldwide implemented suppression and mitigation efforts to control the pandemic (Muragu, Nyadera & Mbugua, 2021). The first confirmed case of COVID-19 in Vietnam was discovered in late January 2020. The Vietnamese government quickly implemented strategies and measures to effectively control the outbreak. However, the impact of this outbreak on Vietnam's tourism industry was significant. Human contact and movement restrictions during the outbreak severely affected tourism (Fong et al., 2020), which is considered among the most sensitive industries to epidemics (Chen et al., 2007). Notably, although Vietnam shares a boundary with China, it has thus far weathered the pandemic with a sum of infected cases and deaths that are minimal compared to other national numbers worldwide. In 2020, Vietnam ranked third out of over 100 countries in the COVID Performance Index for its successful handling of the COVID-19 outbreak (Pandey, 2021). Moreover, during the first wave of the pandemic, Vietnam was mentioned as one of the top-performing countries controlling the outbreak by public service broadcasters worldwide, including global information and news providers (i.e., BBC and Reuters). Vietnam has set a fine example regarding a nations’ fight against COVID-19 (Tedros Adhanom, 2020); it has gained a high reputation regionally and internationally, with specific results in many fields to reasonably control the COVID-19 pandemic (Alexander Korolev, 2021). Owing to successful government policies, the people of Vietnam have more trust in national leaders’ decisions, which has also contributed to controlling the COVID-19 outbreak.

Many researchers have focused on behavioral intention in the tourism industry, and prior studies have mentioned the importance of perceived risk of the COVID-19 pandemic on travel intention. For example, Zhu and Deng (2020) utilized structural equation model (SEM) to assess risk knowledge, perceived risk, risk aversion attitudes, and rural tourism intention in their research with selected Chinese citizens. Bae and Chang (2021) investigated the impact of COVID-19 risk perception on behavioral intention toward “untact” tourism in South Korea. However, some studies have reported different results on cognitive-behavioral control factors (Bae & Chang, 2021; Seow, Choong, Moorthy & Chan, 2017), which indicates the need for further testing. However, there is a lack of research on trust in government performance to measure whether travelers’ intentions increase in recognition of the government's response to an epidemic. In the current context of uncertainty, tourism marketers must be aware of all the important aspects that promote and restrict travel to design strategies that attract visitors during and after the COVID-19 pandemic.

Widely used to evaluate tourism choices, the theory of planned behavior (TPB) is relevant in the context of a pandemic (Shin, Nicolau, Kang, Sharma & Lee, 2022). However, it is necessary to extend the TPB to explain the impact of the COVID-19 pandemic on tourists’ intentions. Presumably, a government that performs effectively during an epidemic will enjoy increased public trust in its efforts to restore the economy. Residents’ positive assessment of the government's response to the outbreak leads to their expectation of speedy tourist revival (Fong et al., 2020).

Few studies have employed TPB to explore issues, such as residents’ trust in government performance and its influence on travel intentions. Based on the literature review, this study offers a new theoretical perspective exploring the effects of residents’ trust in government performance and perceived risks that affect travel intentions. The findings of this study may substantially impact tourism governance and management. Policymakers may benefit from a comprehensive knowledge of citizens’ trust in government performance regarding tourism, and their support for the sector's growth during and after the pandemic.

Literature review and hypothesis developmentPerceived risk

The subjective judgement of the risks presented by COVID-19 to people's health is referred to as perceived risk of COVID-19. Most prior investigations have found that perceived risk can influence attitudes, which in turn influences behavioral intention (Quintal et al., 2010; Seow et al., 2017; Bae & Chang, 2021). Several authors have assessed the influence of risk perception as positive or negative regarding research objectives. Bae and Chang's (2020) study focused on the intention of limited-contact travel as a behavior to reduce risks stemming from the awareness of the COVID-19 pandemic, indicating that if people are aware of the risks, they will have a positive attitude toward contact-limiting travel behavior. Another recent study found that perceived risk affects subjective norms (Zhang et al., 2019). Perceived risk is a critical component that positively influences subjective norms (Bae and Chang, 2020; Xu et al., 2021). When visitors’ families, friends, and surrounding social interactions demonstrate a favorable attitude when engaging in vacation during the awareness of health risks caused by the COVID-19 pandemic, tourists are more likely to gain trust, which leads to the intention to engage in that behavior if exposure is limited. In recent research on the pandemic, several studies have examined the influence of perceived risk on perceived behavioral control (Zhang et al., 2019; Bae and Chang, 2020). According to Zhang et al. (2019), perceived risk reduces perceived behavioral control; however, this study was conducted considering different circumstances because it studied the behavioral intention to purchase poultry in the face of perceived danger during the earlier H7N9 epidemic. According to Xu et al. (2020), perceived risk influences perceived behavioral control and has been examined in the context of contact-limiting travel as a health-promoting behavior. Tourists who perceive a relatively lower concentration of infection risk will be more likely to expand their awareness of their potential, financial resources, expected duration, and available knowledge to prioritize active participation in tourist behavior accommodating their requirements, as suggested by Bae and Chang (2020).

In light of these arguments, we propose the following hypotheses.

  • H1: High perceived risk negatively affects tourists’ attitudes toward travel.

  • H2: High perceived risk positively affects perceived behavioral control of tourists.

  • H3: High perceived risk positively affects tourist's subjective travelling norm.

Trust in government performance

Government trust refers to a person's belief that the political system and government policy will generate the desired outcomes (Miller & Listhaug, 1990). Nunkoo (2015) defined government trust as the determination of how individuals assess the effectiveness and performance of state policies. The level of government trust influences individual behavior. For example, people who have a high level of confidence in government policy are more inclined to support and adhere to policy requirements. People are more likely to travel if they trust the governments’ general strategies in response to the COVID-19 pandemic because they feel safer and believe the issue is under control (Shin et al., 2022). The concept of trust was used in this study to better understand tourists’ trust in government performance. Trust manifests itself in the implementation of government performance, where it aids community protection and strengthens national unity. If people trust the government to hasten the revival of the economy, particularly the tourism industry, they will respond more effectively to recovery initiatives. In the domestic tourism industry, people will travel if they trust appropriate epidemic management and information about the places they intend to visit. Therefore, we propose the following hypothesis:

  • H4: High trust in government performance positively affects tourists’ attitude toward travel.

  • H5: High trust in government performance positively affects perceived behavioral control of tourists.

  • H6: High trust in government performance positively affects tourists’ subjective travelling norm.

The extended theory of planned behavior

The TPB framework can be used to explain travel behavior (Ajzen, 1985). The constructs of attitude, perceived behavioral control, and subjective norms comprise the TPB. TPB is founded on the theory of reasoned action (TRA), which states that attitudes and subjective norms predict behavior. Various fields utilize TPB, including psychology, health, marketing, physical education, and tourism (De Groot & Steg, 2007; Ferdous, 2010). However, researchers have sought to add more factors to the TPB to boost its explanatory power and improve its behavior prediction accuracy. Owing to the lack of explanatory power regarding behaviors influenced by several determinants, Ajzen (1985) later included a new variable, perceived behavioral control, in TPB. To better understand tourist behavior, other researchers have incorporated elements such as travel motivation, electronic word of mouth, destination image, destination familiarity (Bianchi, Milberg & Cúneo, 2017; Soliman, 2021), and climate change (Juschten, Jiricka-Pürrer, Unbehaun & Hössinger, 2019). For example, Bae and Chang (2021) recently introduced risk-related factors, indicating that cognitive and affective risk perceptions significantly impact behavioral intention. Bae and Chang investigated the impact of COVID-19 risk perception on untact tourist behavior in South Korea during the first wave of the pandemic (March 2020). The COVID-19 pandemic wreaked havoc, hurled current norms into disarray, and necessitated the development of a more robust model for predicting future visitor behavior (Bae & Chang, 2021). This study proposes an extended TPB model incorporating constructs, including perceived risk and trust in government performance, to predict tourists’ intentions. We propose the following hypotheses:

  • H7: High attitudes toward travel positively affect tourists’ intention to travel.

  • H8: High perceived behavioral control positively affects tourists’ intention to travel.

  • H9: High subjective norms of travelling positively affect tourists’ intention to travel.

MethodologyResearch space

The survey was conducted from October 2021 to May 2022. This study employed quantitative research methods and was conducted through a questionnaire shared as a Google form that surveyed tourists. The results were used to test the theoretical model. The pre-test was a try-out of the questionnaire to assess and resolve design shortcomings before the actual survey was conducted. A group of 50 respondents, whose characteristics were reasonably similar to the survey population, were selected for the pretest. The questionnaire was then revised and finalized based on the pre-test results.

To maximize the value of the analysis and assess the complexity in the responses, we used two distinct forms of surveys in this study. The author used Google Forms to administer an online questionnaire. The author received an email for each questionnaire filled to eliminate the possibility of a participant filling the form more than once. To conclude, 469 responses were recorded.

Data collection

A self-completed seven-point Likert scale questionnaire was used in this study. Its contents focused on six constructs including: (1) perceived risk (six items) adapted from Sebastian Zenker, Erik Braun, Szilvia Gyimóthy (2021); (2) trust in government performance (six items) borrowed from Lawrence Hoc Nang Fong, Rob Lawc, Ben Haobin Yed (2021); (3) Attitude to travel (three items) borrowed from Li, Thi Hong Hai Nguyen, J. Andres Coca-Stefaniak (2020); (4) Subjective norm (four items); (5) Perceived behavioral control (four items); Travel intentions (four items) adapted from So Young Bae & Po-Ju Chang (2020).

Data analysis

In this study, the investigated scales were first tested for reliability using Cronbach's alpha coefficients. A scale is considered reliable if the Cronbach's alpha coefficient exceeds 0.7, and the corrected total-item correlation of each item is greater than 0.3 (Nunnally & Bernstein, 1994). Then, the reliable scales were further analyzed by employing Exploratory Factor Analysis (EFA), which required: (1) eigenvalues ≥1; (2) total variance explained ≥50%; (3) KMO ≥ 0.5; (4) Sig. coefficient of the KMO test ≥0.05; (5) Factor loadings of all observed variables ≥ 0.5; and (6) Weight difference between the loadings of two factors > 0.3 (Hair, Black, Babin & Anderson, 2010).

Next, the data were analyzed using Confirmatory Factor Analysis (CFA) to further confirm the results obtained from the EFA. Finally, SEM was used to test the proposed model, which is considered appropriate if the significance value of the Chi-square test is no more than 5%, CMIN/df ≤ 2 (but in some cases, CMIN/df ≤ 3 is also acceptable), and GFI, TLI, CFI ≥ 0.9. Besides these criteria, recent researchers suggest that GFI should be greater than 0.8, RMSEA ≤ 0.08, with composite reliability > 0.6, and extracted variance > 0.5 (Hair, Black, Babin & Anderson, 2010).

Empirical resultsDescriptive statistics

In this study, 469 questionnaires were analyzed as valid responses. The results revealed gender differences among respondents, with 45.2% being male and 54.8% being female. In addition, there was a significant disparity in marital status, with 59% being single and 40.3% being married. The group accounting for the highest proportion of responses was between 20 and 39 years of age. The training level was mainly undergraduate or graduate. In terms of income, respondents earned VND10Mil - VND20Mil. One to three times was recorded as the highest number of travel time in 2019, at 56.7%. The demographic characteristics of the respondents are shown in Table 1.

Table 1.

Profile of respondents.

Characteristics  Frequency  %
Gender  MaleFemale  212257  45.254.8 
Age group  <2020–2930–3940–4950–59>60  7416214458274  15.834.530.712.45.80.9 
Level of Education  High schoolCollegeUndergraduatePostgraduate  1108418491  23.517.939.219.4 
Income (VND/ month)  VND 10 Mil and belowVND 10 Mil- VND 20 MilVND 20 Mil- VND 30 MilAbove VND30Mil  1112181319  23.746.527.91.9 
Status  SingleMarried  280189  59.740.3 
Frequency of travelling in 2019  NoneFrom 1 to 3 timesFrom 4 to 6 timesOver 6 times  1062666532  22.656.713.96.8 

Note: Exchange rate is approximately 23,000 VND/USD.

Scale reliability tests

Table 2 presents the results of the scale reliability tests, in which Cronbach's alpha coefficients of the scales were all greater than 0.7, and their corrected item-total correlations were all greater than 0.3. Therefore, the scales were deemed appropriate and acceptable.

Table 2.

Scale reliability test results.

Observed variables  Scale mean if item deleted  Scale variance if item deleted  Corrected- item total correlation  Cronbach's Alpha if item deleted 
Perceived Risk (PR): Cronbach's Alpha = 0.915
PR1  21.86  27.596  0.756  0.902 
PR2  21.97  26.337  0.819  0.889 
PR3  21.77  26.195  0.842  0.884 
PR4  22.17  26.105  0.775  0.899 
PR5  22.03  27.741  0.729  0.907 
Trust (TR): Cronbach's Alpha = 0.892
TR1  31.34  16.947  0.700  0.875 
TR2  31.47  16.108  0.705  0.875 
TR3  31.44  16.482  0.728  0.871 
TR4  31.51  16.823  0.706  0.874 
TR5  31.31  17.312  0.725  0.872 
TR6  31.33  16.740  0.717  0.872 
Attitude to travel (ATT): Cronbach's Alpha = 0.807
ATT1  11.49  5.118  0.632  0.765 
ATT2  11.44  4.478  0.749  0.644 
ATT3  11.71  4.098  0.614  0.802 
Perceived Behavioral Control (PBC): Cronbach's Alpha = 0.850
PBC1  16.15  10.036  0.702  0.805 
PBC2  16.00  10.280  0.702  0.806 
PBC3  16.21  8.916  0.767  0.775 
PBC4  16.33  10.077  0.601  0.848 
Subjective norm (SN): Cronbach's Alpha = 0.827
SN1  14.83  11.845  0.657  0.780 
SN2  14.87  11.830  0.654  0.781 
SN3  14.88  11.450  0.635  0.789 
SN4  15.04  10.582  0.670  0.775 
Intention to travel (TI): Cronbach's Alpha = 0.874
TI1  22.43  18.301  0.714  0.846 
TI2  22.53  17.506  0.733  0.840 
TI3  22.57  18.225  0.722  0.844 
TI4  22.77  16.588  0.743  0.838 
TI5  22.67  18.606  0.612  0.869 
Exploratory factor analysis

The results of the EFA are briefly presented in Table 3, which shows that the analysis result of the four different factors was 61.480%. When evaluating the results of the factors, the value of each item was greater than 0.4, which is suitable for EFA (Hair, Black, Babin & Anderson, 2010). The results of Cronbach's alpha values of factors show that all values are satisfactory and meet the condition of being greater than 0.6 (Nunnally & Bernstein, 1994). The core items fully reflect the content of the factors. Most factor loading values were greater than 0.6, and significant at p <0.001 (Tabachnick & Fidell, 2013). However, one perceived risk item and one perceived behavioral control item were removed because of low loadings. The study model was appropriate for KMO = 0.863, chi-square = 7259.888, df = 351, and Sig = 0.000.

Table 3.

Exploratory factor analysis result.

Factors/items  Factor loading  Eigenvalue  % Of variance explained  Cronbach's alpha  Overall mean 
TRUST (TR)    7.115  24.935  0.892  6.28 
TR3  0.809        6.24 
TR5  0.769        6.37 
TR1  0.767        6.34 
TR4  0.760        6.17 
TR2  0.752        6.21 
TR6  0.734        6.35 
Perceived Risk (PR)    3.898  38.182  0.915  5.49 
PR3  0.897        5.68 
PR2  0.879        5.48 
PR4  0.823        5.28 
PR1  0.785        5.59 
PR5  0.760        5.42 
Intention to travel (TI)    2.905  47.473  0.874  5.65 
TI2  0.810        5.71 
TI3  0.769        5.68 
TI1  0.769        5.81 
TI4  0.766        5.47 
TI5  0.680        5.57 
Subjective norm (SN)    2.151  53.827  0.827  4.97 
SN4  0.768        4.84 
SN2  0.740        5.00 
SN1  0.725        5.04 
SN3  0.718        4.99 
Perceived Behavioral Control (PBC)    1.472  57.948  0.850  5.39 
PBC3  0.887        4.84 
PBC1  0.799        5.00 
PBC4  0.643        5.04 
PBC2  0.638        4.99 
Attitude to travel (ATT)    1.310  61.480  0.807  5.77 
ATT2  0.940        5.88 
ATT1  0.707        5.83 
ATT3  0.628        5.61 
Bartlett's Test  Chi-SquaredfSig.7259.888351.000 

Notes: *p < 0.001.

Confirmatory factor analysis

Fig. 1 demonstrates the results of the CFA model, with the aim of reconfirming the relevance of the research model compared to the data collected. Specific results are shown through Chi-square = 845.530, df = 309, p-value = 0.000, CMIN/df = 2.736 <3, GFI = 0.882, TLI = 0.914, CFI = 0.924, and RMSEA = 0.061, which are satisfactory considering the required criteria.

Fig. 1.

CFA results of the saturated model.


Fig. 1 confirms the one-way relationship that complies with the conditions related to the convergence value and unique value. Discriminant validity was used to evaluate the AVE values with squared correlations between coupling structures. All AVE estimates were higher than the squared interconstruct correlations. These structures are different from other factors (Fornell & Larcker, 1981; Hair, Black, Babin & Anderson, 2010). The results are presented in Table 4.

Table 4.

Correlation matrix.

TR  0.893  0.582  0.763           
PR  0.916  0.687  −0.001  0.829         
TI  0.876  0.588  0.297***  −0.103*  0.766       
SN  0.828  0.547  0.120*  −0.203***  0.305***  0.739     
PBC  0.855  0.598  0.407***  −0.093†  0.622***  0.309***  0.773   
ATT  0.825  0.614  0.404***  −0.154**  0.451***  0.303***  0.516***  0.784 

Notes: aAverage variance extracted; bInter-construct squared correlations.

Structural equation modelingTests for model goodness of fit and hypotheses

Fig. 2 briefly shows the results of SEM analysis regarding its suitability to the study data. With the values of Chi-square = 934.514; df = 314; p-values = 0.000; CMIN/df = 2.976<3; GFI = 0.871; TLI = 0.902; CFI = 0.912; and RMSEA = 0.065, the proposed model is considered satisfactory to the required criteria. Thus, it is affirmed that the research model is reliable.

Fig. 2.

Standardized SEM Model.

Hypotheses tests using SEM model

Table 5 confirms the significance of the study hypotheses, indicating that the nine hypotheses established are all significant with p-values less than 0.05; thus all hypotheses (H1, H2, H3, H4, H5, H6, H7, H8, and H9) are supported by the data.

Table 5.

Coefficients from SEM Model.

Hypothesized relationshipCoefficient  Standardized Coefficient  S.E  C.R.  Conclusion 
H1  PR  →  ATT  −0.122  −0.155  0.037  −3.283  0.001  Supported 
H2  PR  →  PBC  −0.086  −0.101  0.041  −2.128  0.033  Supported 
H3  PR  →  SN  −0.176  −0.208  0.044  −3.975  ***  Supported 
H4  TR  →  ATT  0.544  0.419  0.066  8.250  ***  Supported 
H5  TR  →  PBC  0.603  0.427  0.073  8.254  ***  Supported 
H6  TR  →  SN  0.202  0.144  0.074  2.724  0.006  Supported 
H7  ATT  →  TI  0.181  0.188  0.045  4.034  ***  Supported 
H8  PBC  →  TI  0.463  0.523  0.045  10.183  ***  Supported 
H9  SN  →  TI  0.109  0.122  0.041  2.631  0.009  Supported 

Note: *** p<0.01.

Discussion and managerial implicationsDiscussion

In terms of perceived risk, first, the results indicate that the higher the perceived risk due to COVID-19, the more negative the effects on tourists’ attitude toward travel because the results show coefficient β = −0.155 and significant p-value = 0.001 (Hypothesis 1). The results demonstrate that during the COVID-19 pandemic, travelers were concerned about being infected, which means that the greater the perception of risk when travelling, the more adversely it impacts their trip; fear of danger due to traveling, anxiety, and tension all rise sharply. However, when risk awareness is low and tourists feel safe travelling through the outbreak, this positively affects their travel attitude. This observation has been verified in previous studies such as Chew and Jahari (2014), Shim and You (2015), and Bea and Chang (2020).

When individuals are aware that they can control their behavior, it is easy for them to develop their intentions. However, perceiving a high risk of infection while travelling also greatly influences travel behavior control. Therefore, hypothesis 2 is supported owing to the result of coefficient β = −0.101 and significant p-value = 0.033, which show that the higher the perceived risk of COVID-19, the more negatively it will affect perceived behavioral control of tourists. This result is in accordance with the study of Sánchez-Cañizares et al. (2020).

Another finding indicates that risk perception is influenced by relationships with families, acquaintances, and colleagues. Individuals associated with the community tend to be anxious and nervous about infected relatives when travelling. Consequently, with coefficient β = −0.208 and significant p-value <0.01 (hypothesis 3), the higher the perceived risk from COVID-19, the more negatively it will affect the subjective norm of travelling, and similar results were reported by Murray and Schaller (2012) and Bea & Chang (2020).

During the COVID-19 outbreak in Vietnam, the tourism industry was severely and adversely affected, which influenced visitors’ perceptions. Following the outbreak, when the Vietnamese government successfully controlled it, Vietnamese people demonstrated a high level of trust in government decisions. The Vietnamese people supported government policies on quarantine periods and social exclusion, taking the government's prevention measures seriously. Consequently, the government's success affects tourists’ attitudes toward tourism, implying that the more they trust the government, the more likely they are to travel. This hypothesis is accepted because the result of Hypothesis 4 is coefficient β = 0.419 and significant p-value <0.01.

In addition, when the government makes the right decision regarding handling a pandemic and successfully prevents the spread of diseases, every citizen becomes cognizant of their ability to check their behavior. To comply with the government's disease prevention policies, each person should be fully aware of and check their behavior. Additionally, it contributes to successful disease prevention because of strong knowledge of executive control. This supports hypothesis 5 because higher levels of trust in government positively influence visitors’ perceived behavioral control, with a coefficient β = 0.427 and a significant p-value < 0.01.

In addition to the government's effective and correct actions, national solidarity is critical for assessing the effectiveness of the COVID-19 pandemic management in Vietnam (Sen Nguyen, 2020). This solidarity demonstrates that each person trusts and engages with the government in implementing healthy disease prevention measures. It further helps to promote same political goals and values that can be shared among friends, colleagues, and family members. Consequently, it will spread a positive attitude toward travel in the society. Therefore, Hypothesis 6 is accepted when as the results show that high trust in government performance positively affects the subjective norm of travelling among tourists, with the test result coefficient β = 0.144 and a significant p-value = 0.006.

Regarding travel intentions, Hypothesis 7 is accepted, which stated that attitude toward travel more positively affects intention to travel among tourists because the result is supported by coefficient β = 0.188 and a significant p-value <0.01. When a person has a positive attitude toward travel, tourists will have a positive intention to travel. Visitors are most likely to choose to travel as soon as the pandemic is under control and when they have the financial resources and time to do so. Other visitors believe that they would spend additional time ensuring healthcare protection.

Furthermore, as each person can choose the number of days they want to travel and is aware of considerations such as finances and health, this increases travel intention. People have control of their behaviors shows that Hypothesis 8 is accepted, which stated that high decisive perceived behavioral influence positively impacts visitors’ desire to visit. This result is support by coefficient β = 0.523 and a significant p-value <0.01.

Moreover, relationships with travelers who have a positive attitude toward travel can positively affect an individual's travel intention. This suggests that people are more likely to travel if their friends, families, and relatives help them, and can support the view that travelling at this time is safe and supportive for them. With a coefficient of β = 0.122 and a positive p-value <0.009, Hypothesis 9 was accepted. Consequently, the higher the subjective travel norm, the more favorable the impact on tourists’ intentions to travel. Furthermore, the results reported through Hypotheses 7, 8, and 9 have been found in previous research, such as Huang et al. (2020), Lam and Hsu (2006), Bea and Chang (2020), and Sandra M. Sánchez-Cañizares et al. (2020).

ImplicationsTheoretical implications

This study investigated the relationship between perceived risk, government performance, and travel intention through travel attitude, perceived behavioral control, and subjective norm during the COVID-19 pandemic using Ajzen's TPB module. Some tourism researchers have used TPB in the context of diseases (Huang et al., 2020) such as SARS, the Avian flu, and MERS to indicate the critical effects of the pandemic on tourism and their effects on people's travel intentions (Floyed et al., 2004; Lee et al., 2012). In addition, it is predicted that it will take a long time to control the pandemic (Lew et al., 2020). Many epidemiologists have forecasted subsequent pandemics similar to COVID-19 (Contreras, 2020). This study's findings contribute to the reference resources relating to COVID-19′s influences on the tourism sector and enrich social science resources for future research on tourists’ perceptions of tourism or tourism amidst a pandemic.

Government performance has been studied in diverse social sectors and in the development of the national economy and strategies (Boix & Posner, 1998). Residents’ belief in government trust is that the political process generates favorable outcomes in tourist development (Miller & Listhaug, 1990). Some previous studies have used this construct to develop strategies for different countries and local levels based on political decisions for the tourism sector (Nunkoo & Smith, 2013). A well-functioning regulatory framework leads to long-term development of a nation's tourism industry (Shojaei et al., 2007). However, studies on trust in government performance to investigate tourists’ perceptions and intentions are lacking. In particular, in the COVID-19 context, understanding tourists’ trust in government performance is essential for potential tourism recovery. This finding contributes to the creation of pose testing models, the tourist industry, and public administration literature by examining how a president's initial reaction affects residents’ tourism restoration possibilities. Furthermore, the findings of this report will act as a valuable benchmark for in-depth follow-up research on visitors and improvements in their intentions during the disease outbreak.

Practical implications

The findings suggest that trust in government performance is critical for the robust potential recovery of the tourism industry, significantly boosting tourists’ travel intentions. In contrast, perceived risk negatively impacts travel intention because of the fear of infection. Therefore, these two factors are essential in the design of successful tourism restoration actions. They help tourism management and travel companies to understand tourists’ demands and trends by effectively stimulating tourism.

Perceived risk arises from tourists’ incorrect perception of COVID-19 information, such as knowing who is infectious or which destinations are safe (Quintal et al., 2010). There is an association between the awareness of risks related to COVID-19 and an enhancement in searching for information on media platforms (Huynh, 2020). When there is a high-risk perception, tourists tend to find more information before making travel decisions. Therefore, government leaders and stakeholders should provide reliable information for tourists through different networks, such as news, materials, viral clips, social media, and websites to increase tourists’ awareness of safety destination information about the disease during their information-seeking process. Furthermore, having particular boundaries between tourist zones and residential areas helps tourists reduce their fear (Joo et al., 2021). During the post-COVID-19 period, to ensure the safety of visitors and residents, it is necessary to set boundaries to limit direct contact among visitors and destinations need to formulate specific regulations to house a limited number of tourists. Additionally, tourism forums are an effective way for organizations to access and make provisions for tourists to share their adventures and impressions of a destination. These forums can be considered as a method of directly interacting with visitors to better understand their psychology and minimize their anxiety about the disease.

Regarding trust in government performance, administration promotes tourism growth, including infrastructure investment, creating popular attractions, and implementing destination marketing arrangements (Nunkoo, 2015). More specifically, trust in government performance during the pandemic is essential for disease control and determines the potential for successful tourism stimulus. First, by increasing the trust of inhabitants and tourists in the government, it should take measures and actions to protect people's health through controlling the spread of the disease. Governments will be critical in mitigating the adverse effects of crises on the economy by offering financial stimulus packages to tourism organizations, rescinding taxation for tourism businesses, and subsidization and reduced lines of credit (Ritchie & Jiang, 2019). Third, the government needs to provide continuous and accurate information about COVID-19′s situation to increase people's confidence and provide adequate and accurate information about the disease. In addition, information about safe destinations should also be emphasized because it helps potential visitors to prepare properly by gaining an in-depth understanding of the destination. According to VNAT's report, Con Dao Island is a safe destination in Vietnam because herd immunity has been achieved, with more than 70% of the population receiving two doses of the vaccine. Finally, there is the issue of expanding international tourism because it is a significant source of revenue for countries (Schubert et al., 2011). The government must develop a passport campaign vaccine strategy to make it easier for locals and foreigners with vaccination certificates that meet internationally recognized standards to travel to these attractions. This action is also a factor in managing the disease to protect the health of tourists and the local people. For instance, Phu Quoc Island, in particular, will be the first tourist attraction to welcome international visitors as part of a trial vaccine passport campaign that will subsequently be expanded to other well-known tourist attractions.

In terms of attitude toward travel, it plays an influential role in explaining tourists’ travel intentions. Therefore, tourism managers should maintain a positive attitude toward tourists during an epidemic. First, tour operators should focus on a positive impact strategy related to tourist attitudes and product strategies that can leverage this invisible power to enhance travel after the COVID-19 pandemic. Next, businesses should concentrate on the visitors’ perspectives. Companies should first analyze existing opinions on their services. They can better understand travelers’ perceptions of the risks of contracting COVID-19 and their travel plans after the pandemic. Travel strategists can use this to devise methods to overcome their fears and increase their desire to travel. In addition, during a pandemic, the customer service department should assist the company's communication department in creating safe travel information for visitors and stay up-to-date with safe destination information based on government information. Consequently, this action will help maintain tourists’ travel attitudes, thereby improving their travel attitudes after the pandemic and quickly motivating them to travel.

Subjective norms are social determinants that reflect social pressure on behavior. These subjective normative influences could be cross-cultural and societal influences on one's beliefs about the idea of vacation once the outbreak has been managed. Therefore, to increase travel choices after the Covid pandemic, tour operators should enhance strategies to influence surrounding relationships. To create more significant influences on society, tourism managers should work with celebrities and KOLs to associate with tourism stimulus campaigns, because influencers impact behavioral trends and affect their followers’ travel-related intentions (Magno & Cassia, 2018). Next, the travel managers should develop a travel forum where people can share their trip information. This forum will allow travelers to share their experiences regarding trips after the translation is controlled for those who are interested. In addition, this will also be a valuable channel for destinations and enterprises to understand tourists’ behaviors (Wenger, 2008). Furthermore, it is necessary to focus on stimulating the demand of young tourists. As they tend to experience new forms and have a significant impact on the surrounding community and their relationships, various promotions, gifts, and other forms of promotion should be introduced to encourage them to travel. This group also has a high demand for social networks, creating a new tourism trend and spreading vigorously to the surrounding community.

Perceived control behavior. Tourists can easily make travel intentions when they feel protected and find it straightforward to travel within their capabilities (Thao & Trang, 2018). However, the choice to travel in the post-COVID-19 era may be influenced by unmanageable situations controlled by external factors, such as communication with someone COVID-19 infected or infected person. Therefore, for tourists to control this pandemic factor, tourism managers should project and equip tourists with consciousness and measures to prevent the disease from feeling safe during their trip. Next, finance affects tourists’ behavioral control and reduces their intention to travel (Liu et al., 2018). Remarkably, when the pandemic spread to all countries worldwide, it seriously affected the economy at the national and individual levels. Therefore, a sufficient budget from the government will strongly stimulate travel intentions. Develop cooperative strategies with banking institutions, provide appropriate application tools such as credit card payments, raise awareness of tourists’ spending ability, and encourage large purchases without increasing costs. By developing promotional programs to encourage visitors to engage, tourism businesses can coordinate with organizations and individuals inside and outside the industry to develop service incentive programs and gift incentives for tourists such as discounts, preferential sightseeing tickets, and travel discounts for guests taking public transport such as taxis, preferential packages for medical examination, and care. Perceived behavioral control also positively influenced the intention to travel to limit exposure. When limited-contact tourism activities are designed to suit tourists’ time and financial capacity, are convenient and easy to engage, tourists will increase their intention to travel with less contact than The travel programs offered are not suitable for their ability.

Limitations and research directions

This study has several shortcomings in addition to its contributions. First, although the COVID-19 outbreak has been effectively controlled in Vietnam, it continues to spread dangerously across the world, necessitating the author's removal of the definition of “actual behavior” from the TPB study model (Ajzen, 1991) to balance the current situation; it is difficult to measure the factor regarding “actual behavior.” Second, because this questionnaire was administered through online and direct surveys, selecting a diverse group of respondents was challenging. People over the age of sixty, in particular, had trouble accessing technologies to engage in the survey. Consequently, this age group accounted for only 0.9 percent of the 469 respondents. Third, since this survey was conducted when Vietnam had been taking measures to prevent pandemic, the findings might not be universally applicable to other countries, especially those where epidemics are complicated and difficult to manage.

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