Organizations and Markets in Emerging Economies ISSN 2029-4581 eISSN 2345-0037
2023, vol. 14, no. 1(27), pp. 171–193 DOI: https://doi.org/10.15388/omee.2023.18.87

Travel Intention and Travel Behaviour in the Post-Pandemic Era: Evidence from Vietnam

Duong Tien Ha My
Faculty of Economics and Public Management, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
my.dth@ou.edu.vn
https://orcid.org/0000-0001-8796-4934

Le Thanh Tung (corresponding author)
Faculty of Economics and Public Management, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam
tung.lt@ou.edu.vn
https://orcid.org/0000-0001-8487-2217

Abstract. Tourism is on the pathway of returning and contributes to the economic development of many countries. Understanding the decision-making process of tourism customers in the post-pandemic context is crucial for a strong recovery of the tourism sector. The paper aims to identify the determinants of travel intention and the link between travel intention and travel behaviour in the post-COVID-19 pandemic period. A total number of 431 questionnaires regarding the individual behaviour of Vietnamese travellers were collected. The empirical results reveal that electronic word of mouth, crisis management, and destination image in COVID have positive relationships with travel intention. Besides, travel intention is positively linked with the customers’ travel behaviour during the post-pandemic era. By contrast, the results do not support the conclusion regarding the relationship between risk communication, the healthcare system, non-pharmaceutical interventions and the travel intention of customers. Finally, the practical implications are included for enhancing a faster recovery process of the tourism sector.

Keywords: travel intention, travel behaviour, post-pandemic, tourism development, tourism recovery, global tourism

Received: 9/11/2022. Accepted: 2/4/2023
Copyright © 2023 Duong Tien Ha My, Le Thanh Tung. Published by Vilnius University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

1. Introduction

Tourism is an important economic industry in many countries and helps to enhance economic growth, increase income per capita, create jobs, and provide business opportunities (Li et al., 2018; Huang & Baker, 2021; Winter & Kim, 2021). In 2021, the World Travel and Tourism Council (WTTC) stated that travel and tourism contributed 6.1% to the global economy’s GDP by creating 5.8 trillion USD (WTTC, 2022). Travel and Tourism GDP decreased by 50.4% (i. e., USD 4,855 billion) compared to that in 2019, which indicated that the outbreak of the COVID-19 pandemic and restrictions on mobility have severely affected travel worldwide (Chan et al., 2021; Williams et al., 2022). Only in 2020, there were approximately 62 million jobs lost, and the remaining 272 million jobs across the industry globally could be at risk if the travel and tourism industry failed to fully recover (WTTC, 2021).

Fortunately, the global tourism industry has recently begun to show more positive signs, despite still facing uncertainties (Sánchez-Cañizares et al., 2021; William et al., 2022). Due to some efficient solutions, e. g., a high rate of people being fully vaccinated, tourism is on the pathway to recovery to the level of the years before the outbreak of the COVID-19 pandemic (Yeh, 2020; Zheng et al., 2021). While COVID-19 is still circulating in communities, barriers for tourists have been totally lifted in a lot of countries worldwide, and global tourism is better day by day (Chan et al., 2021; Chemli et al., 2022; Zheng et al., 2021). In this regard, some questions are raised for further understanding of travel intention and travel behaviour and suitable measures to better recover the tourism sector (Abbasi et al.,2021; Cheng et al., 2022; Pappas, 2021; Shin & Kang, 2020). Because of the important role of the tourism sector, previous studies indicated that tourism intention and tourism behaviour have been hot topics for studying in the context of an economic recovery plan from a social crisis (Avraham, 2015; Cahyanto et al., 2016; Choi et al., 2017; Novelli et al., 2018; Lee et al., 2012; Ritchie et al., 2013; Sano & Sano, 2019). Nevertheless, it remains unclear how these factors affect tourism behaviour as many countries begin to remove their restrictions. This paper thus investigates the impact of some related elements including the communication about COVID-19 (i. e., risk communication and electronic word of mouth), destination image in COVID, and health protection activities of the destinations (i. e., crisis management, non-pharmaceutical interventions, and health care system) on travel intention and travel behaviour under the new normal of the tourism industry.

To address the above empirical research topic through a survey from Vietnam, an emerging tourism market in Southeast Asia (Tung, 2020), the study introduces and empirically tests a proposed model for exploring the determinants of tourist’s travel intention and the relationship between the travel intention and travel behaviour during the post-pandemic period. Findings from the study not only expand the current literature regarding tourist behaviour but also suggest helpful managerial implications for the tourism business in practice for robustly fostering the recovery of this industry.

In summary, the study makes some contributions to the current literature. First, we contribute further evidence on the factors influencing tourists’ travel intentions and travel behaviour under the new normal when restrictions on movement, isolation, and COVID-19 testing requirements have been gradually removed. This contribution is valuable for the management crisis framework because COVID-19 is the hugest pandemic for one hundred years and it hurt global tourism as much as never seen before. The study results help societies to expand the ways for efficient responses to potential pandemics in the future. Second, the research could be the first attempt to combine risk communication about COVID-19, destination image, and protection activities of the destination, which would enrich the study of tourist behaviour. The study results also highlight the important role of communication in business during a social crisis. Third, the study’s findings recommend practical implications for tourism recovery. The suggestions from this current study are promising for businesses not only in tourism but also in other services. Timely assessment of the determinants of tourist behaviour can inform managers and policymakers about appropriate communication and intervention strategies in the new context.

2. Literature Review and Hypotheses Development

2.1 Risk Communication and Travel Intention

A critical factor that can influence the travel intention of individuals is risk communication (Sano & Sano, 2019). The coronavirus pandemic, which began in late 2019, has caused a severe impact on not only the global economy but also human health. Worldometer (2022) estimates that as of November 09, 2022, there were more than 638 million infections and over 6.6 million deaths due to the pandemic worldwide. Risks and uncertainties regarding the pandemic have considerably influenced tourists’ intentions and behaviour (Pappas, 2021; Williams et al., 2022).

Regarding this issue, some researchers have analyzed the role of risk communication during crises such as a pandemic (Sano & Sano, 2019; Thanh & Tung, 2022). For example, Cheng et al. (2022) examine risk communication as a mediator that connects risk perception and health tourism intention. The authors show that individuals can heed risk information and participate in health tourism during the post-pandemic era to improve their health. The results, therefore, suggest that tourism companies should pay attention to risk communication and individuals’ health consciousness to foster tourism. Furthermore, effective risk communication plays a crucial role in the implementation of control measures against the pandemic (Chan et al., 2021), which can be related to people’s daily activities and travel during the pandemic (Wong et al., 2020; Kwok et al., 2020). Based on previous research, we propose the following hypothesis:

H₁: Risk communication is positively related to travel intention in the post-pandemic era.

2.2 Electronic Word of Mouth and Travel Intention

The word of mouth (WOM) refers to “informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services and/or their sellers.” (Westbrook, 1987, p. 261). With the development of Web 2.0 and media channels, WOM has turned to eWOM (Verma & Yadav, 2021). Social media, including eWOM, can significantly affect public opinions (Pauliene & Sedneva, 2019, Zarezadeh et al., 2019; Rouibah et al., 2021; Hoang & Tung, 2022), as well as provide destination reviews for tourists and information for companies to improve their services and goods (Marine-Roig & Huertas, 2020). Such information is necessary since individuals planning to travel may have limited knowledge on the destination and thus, they try to obtain knowledge from media, the internet, and other sources (Tan & Wu, 2016; Hoang & Tung, 2022).

In other words, individuals’ decisions on tourism and travel can be affected by eWOM. For example, Filieri and McLeay (2014) examined online reviews and suggest that many travellers use such reviews for accommodation and other travel-related products. The study results also indicated that both product ranking and information quality could be predictors of travellers’ use of information from online reviews regarding accommodation. Likewise, Filieri (2015) found that individuals increasingly utilize online reviews when learning about product quality. Meanwhile, Abubakar et al. (2017) analyzed the effect of eWOM on revisit intention and destination trust. They reveal that eWOM can significantly influence both intentions to visit and destination trust. Chopra et al. (2022) supposed that the quality and quantity of information are the most important factors affecting tourism-related eWOM decisions. Based on the preceding discussion, we suggest the following hypothesis:

H₂: Electronic word of mouth (eWOM) is positively associated with travel intention during the post-pandemic era.

2.3 Crisis Management and Travel Intention

A crisis in a destination can negatively impact tourism demand and tourist intention (Novelli et al., 2018). Therefore, disaster management policy helps enhance tourism recovery during the post-pandemic period (Yeh, 2020). The media emphasis on the negatives of the crisis could make the tourism industry even harder to control (Schroeder & Pennington-Gray, 2013). Moreover, in some cases, even though the crisis does not affect the destination directly, it may create a spillover effect with adverse outcomes (Ritchie et al., 2013; Cavlek, 2002). Novelli et al. (2018) examined the impact of the Ebola Virus Disease Epidemic (EVDE) on the tourism sector in the Gambia. They found that a health-related crisis affects individuals’ perceptions, leading to a drop in tourism demand. This can cause negative socio-economic impacts, especially for countries that depend on tourism. Similarly, Zheng et al. (2021) suggest the coronavirus disease has created great fear among the public, which may hamper the recovery of tourism during the post-pandemic period. The empirical result supposes that the threat severity and threat susceptibility may lead to “travel fear”, which triggers different protective behaviours and coping strategies when travelling.

Due to the adverse influence of the pandemic on travel intentions, crisis management is essential to promote tourism during the recovery period. Cahyanto et al. (2016) examined whether and how the Ebola outbreak impacts individuals’ travel decisions. They suggest that while travellers are concerned about the Ebola outbreak, the government’s swift response to the potential outbreak may have influenced their decision to travel. Likewise, Avraham (2015) posits that countries need to consider different measures including policies, effective communication, and tourism product expansion to overcome the consequences of crises and promote tourism. Given the preceding discussion, we suggest the following hypothesis:

H₃: Crisis management positively influences travel intention during the post -pandemic era.

2.4 Non-Pharmaceutical Interventions and Travel Intention

Non-pharmaceutical interventions (NPIs) involve “actions, apart from getting vaccinated and taking medicine, that people and communities can take to help slow the spread of illness like pandemic influenza” (Centers for Disease Control and Prevention, 2020, p.1). NPIs are an important practice to obstruct the spread of infectious diseases through individuals’ movement. For instance, Zhang et al. (2019) suggest that personal protective measures, including wearing a mask, can help prevent the risk of getting infected with A/H1N1 influenza. Nicolaides et al. (2020) point out that if travellers are more involved in hand hygiene at airports, the spread of the pandemic can be reduced.

Due to the effectiveness of NPIs in reducing the spread of the pandemic, people who trust in NPIs may be more likely to move and travel. One of the first studies that examined the effect of NPIs in tourism research is that by Lee et al. (2012). Notably, the authors combined the goal-directed behaviour (MGB) model with NPIs and perceptions of 2009 H1N1 to investigate how they influence international travel intentions. They discovered that personal NPIs can enhance such intentions in the H1N1 pandemic. Similarly, Chung et al. (2021) collected data in South Korea to analyse the relationship between social NPIs and travel intention over the pandemic period. They suggest trust in social NPIs can link travel negotiation strategies to travel intention. That is, if people trust more in social NPIs more, they can increase their intentions to travel. Given the research findings, the following hypothesis is proposed below:

H₄: Non-pharmaceutical interventions are positively associated with travel intention during the post-pandemic era.

2.5 Destination Image and Travel Intention

Destination image has attracted the attention of researchers when learning about tourism (Tan & Wu, 2016). Prior research posits that destination image can influence tourist intentions (Chew & Jahari, 2014; Abubakar et al., 2017; Rasoolimanesh et al., 2021). For example, Chew and Jahari (2014) investigated whether destination image can affect individuals’ intention to revisit Japan after a disaster. The authors show that if individuals’ perceptions of socio-psychological risk and financial risk lead to adverse re-formation of destination image, they would be inclined to reduce their intention to return. Likewise, Rasoolimanesh et al. (2021) suggest that the cognitive image of a destination during the pandemic can influence individuals’ post-pandemic travel intentions.

Indeed, during crises or pandemics, such as the COVID-19 era, when individuals are unable to travel as usual due to mobility restrictions and quarantine, their perception of the destination can be significantly affected by mass media (Choi et al., 2017; Almeida-García et al., 2020; Chemli et al., 2022). In detail, Chemli et al. (2022) examined data of 1845 travelers from various countries and continents representing affected areas in the pandemic time. The empirical results report that the media significantly affects a potential tourist perception of a destination during a crisis. This effect on a destination image can influence individuals’ travel decisions (Yang et al., 2021; Rasoolimanesh et al., 2021). Indeed, Yang et al. (2021) point out that the negative impact of media coverage regarding the COVID-19 pandemic on the image of China as a destination has a detrimental impact on potential tourists’ travel intentions. Therefore, the next hypothesis is shown below:

H₅: Destination image in COVID is positively linked with travel intention in the post-pandemic era.

2.6 Healthcare System and Travel Intention

The high risk of infection can greatly influence a person’s decision to choose a destination (Lee et al., 2012; Donohoe et al., 2015). Notably, Donohoe et al. (2015) reviewed the literature regarding Lyme disease and its potential impact on travel. The study suggests that Lyme disease is a threat to tourism demand in different areas of the world. Likewise, Rosselló et al. (2017) examined whether eradicating Malaria, Dengue, Yellow Fever, and Ebola can enhance economic benefits from tourism in destination countries. The result implies that eliminating such diseases in affected countries could lead to a rise in tourist arrivals and tourism spending.

Obviously, the pandemic also has a great influence on human health. Thus, a reliable health care system in the destination can be a driving force behind travel intentions of individuals during the recovery period. Indeed, Shin and Kang (2020) discover that perceived health risks can impact the intention to book a hotel during the coronavirus period. Therefore, the authors suggest managers need to adopt measures to ensure health safety because this can affect customers’ travel decisions. Meanwhile, Moreno-González et al. (2020) surveyed tourists from different countries, and reported that health-related environmental factors and well-being conditions can influence health expectation of individuals intending to visit a destination. Rasoolimanesh et al. (2021) analysed 518 useable questionnaires from potential international tourists and uncovered that the health care system significantly affects the travel intention of the respondents who have no experience of visiting a destination. Given the preceding discussion, we establish the following hypothesis:

H₆: Health care system is positively connected with travel intention in the post-pandemic era.

Figure 1
Proposed Research Model

 

2.7 Travel Intention and Travel Behaviour

Because of the critical role of tourism industry in the world economy (Li et al., 2018), travel behaviour is a key topic in socio economic research field nowadays (Kim et al., 2020). The scientific knowledge about travel behaviour has begun to expand especially recent decades (Tan & Wu, 2016). Based on suggestions from prior literature, we suppose that travel intention is positively related to travel behaviour. Indeed, the theory of reasoned action (TRA), developed by Fishbein and Ajzen (1975), shows that individuals’ behaviour is influenced by their intention to perform the behaviour. Similarly, the theory of planned behaviour (TPB), introduced by Ajzen (1991), states that the intention is a proper predictor of a person’s behaviour. The relationship between these two factors is supported by different authors (Abbasi et al., 2021; Bui, 2023). For example, Bui (2023) investigates 473 domestic tourists arriving in Ho Chi Minh City after the pandemic. The author claims that tourism intention is the driving force behind human travel behaviour. Hence, in the context of post pandemic era, our last hypothesis is:

H₇: Travel intention positively impacts travel behaviour in the post pandemic era.

The research model is described in Figure 1. Besides, the latent variables and their measures are presented in the next sections.

3. Data Collection and Quantitative Analytical Procedure

3.1 Data Collection

We use the convenient sampling method for collecting the study data through an online survey because it is effective and appropriate during the pandemic (Kim et al., 2020; Chung et al., 2021). Before making the official survey, a discussion group of 3 experts was created to convert the questionnaire into the most suitable content for Vietnamese travellers. Besides, a pilot test with 10 travel customers was performed. This step helps to secure the wording, meaning, suitableness, and consistency of items in the questionnaire form (Rastegar et al., 2021).

The period of the survey was from October 01 to October 31, 2021, when Vietnam finished the national strict social distancing policy for combating the outbreak of COVID-19. This time was selected because the government began a national campaign to recover the economy toward a new normal period. Tourism is a key sector in the general plan of the national economic recovery policy. Indeed, before the pandemic, the total revenue from tourism was about 755 trillion VND (or 3.2 billion USD), accounting for about 9.2% of GDP (Ministry of Culture, Sports and Tourism, 2019). Also in this year, Vietnam had 4.9 million employees working in this sector (WTTC, 2021). However, the pandemic severely affected the travel and tourism industry due to policies such as lockdowns and movement restrictions. Therefore, when the country entered the new normal, the government issued many policies to promote the industry. Accordingly, this period is the most appropriate time to conduct the survey for collecting information on travel intention and travel behaviour in the post-pandemic era. No inducements for respondents were offered, as well as their personalities were secured. After sending the questionnaires (google.forms type) though email, there were 437 respondents who voluntarily joined the survey. After cleansing the data, the final sample used for the study is 431 questionnaires.

Table 1
Characteristics of the Study Sample

Characteristic	Number	%
Gender
Male	215	49.9
Female	216	50.1
Age
18-22	236	54.8
23-29	58	13.5
30-39	92	21.3
40-49	32	7.4
Over 50	13	3.0
Education
High School Diploma	18	4.2
Intermediate Degree	13	3.0
College Degree	23	5.3
Bachelor’s Degree	260	60.3
Master’s Degree and above	117	27.1
Monthly income
<213.54 USD	216	50.1
Over 213.54 – 427.08 USD	88	20.4
Over 427.08 – 854.15 USD	80	18.6
Over 854.15 – 1281.23 USD	24	5.6
Over 1281.23 USD	23	5.3
Travel frequency
0–1 time per year	188	43.6
2–3 times per year	181	42.0
4–5 times per year	41	9.5
≥ 6 times per year	21	4.9
Total	431	100.0%

Note. 1 USD = 23,415 VND (Exchange rate on June 31, 2022).

The descriptive statistics of the survey data are performed in Table 1. The gender of people joining the survey is quite balanced with female respondents (50.1%) and male respondents (49.9%). Most of the respondents are within the range of 18–49 years old (89.6%). Around three-fourths of respondents had a bachelor’s or master’s degree (87.4%). Around 70.5% of respondents have a monthly income per capita below 427.08 USD. In the direction of tourism frequency, most of the respondents confirm that they travel up from 0 to 3 times per year (85.6%). The summary statistics indicate that the study sample is quite suitable compared to statistical generalization on a national scale.

3.2 Measures

The questionnaire of the study covers eight variables mentioned in our proposed research model. These variables were employed from previous research and were adjusted to the post-COVID-19 pandemic context. The variables and their items are shown in Table 2. In our survey, before data collection, the questionnaire was translated into Vietnamese. All items were assessed through a five-point Likert scale, with “strongly disagree” at 1 and “strongly agree” at 5.

We designed a two-part questionnaire based on previous research on the determinants of tourism. Specifically, Part 1 collects data on personal information including gender, age, income, and education. Part 2 involves questions to measure respondents’ evaluations of research indicators. We developed Risk communication (RIC) from Cheng et al. (2022). We re-used eWOM of COVID-19 (eWOM) based on Bambauer-Sachse and Mangold (2011). The questionnaires on crisis management (CRM), non-pharmaceutical interventions (NPI), and healthcare system (HCS) were based on Rasoolimanesh et al. (2021). We developed items of destination image in COVID (DEI) from Abbasi et al. (2021). We re-used travel intention (INT) and travel behaviour (TRB) from the questionnaire of Rasoolimanesh et al. (2021) and Neuburger and Egger (2021), respectively.

3.3 Quantitative Analytical Procedure

To test the hypotheses described in the proposed theoretical model, we use three main quantitative steps for analysing the function. In the first step, the principal axis factoring and the promax rotation techniques are identified by the Exploratory Factor Analysis (EFA) to confirm the measurement model. In the second step, we employ the Confirmatory Factor Analysis (CFA) to check whether all items appropriately reflect the constructs and whether the measurement model fits the collected data. The scale reliability and validity are evaluated by the testing of the signals of reliability, convergent validity, and discriminant validity. The commonly used indicators are Cronbach’s Alpha (α>0.7), Factor Loading (Standardized Estimates>0.6), Composite Reliability (CR>0.7), and Average Variance Extracted (AVE>0.50) (Hair et al., 2010). Besides, Fornell and Larcker (1981) suppose that the correlation with other constructs should be lower than the square roots of the AVE of each construct. The measurement model needs to satisfy the conditions of the certain model fit indices, which are Chi-square/df (ꭓ2/df <5, p<0.05), Comparative Fit Index (CFI>0.9), Goodness-of-Fit Index (GFI>0.8), Adjusted Goodness-of-Fit Index (AGFI>0.8), Root Mean Square Error of Approximation (RMSEA<0.08), and Tucker-Lewis Index (TLI>0.9). In the third step, the Structural Equation Modelling (SEM) is applied for the path analysis, which helps test the proposed hypotheses. The result of path analysis is also checked with the model fit indices to ensure that the model has an acceptable fit to the data. Furthermore, the maximum likelihood method is used to construct the structural equation model.

4. Results

4.1 Measurement Model

The descriptive statistics for each construct are exhibited in Table 2. The correlation analysis result confirms that significant positive correlations exist among risk communication, eWOM of COVID, crisis management, non-pharmaceutical interventions, destination image in COVID, healthcare system, and travel intention, as well as between travel intention and travel behaviour.

Table 2
Means, Standard Deviations and Correlations among the Variables

No	Construct	Mean	SD	1	2	3	4	5	6	7	8
1	Risk Communication (RIC)	4.00	0.69	1
2	eWOM COVID (eWOM)	3.77	0.77	.555*	1
3	Crisis Management (CRM)	3.72	0.79	.491*	.559*	1
4	Non-pharmaceutical Interventions (NPI)	3.74	0.81	.491*	.681*	.667*	1
5	Destination Image in COVID (DEI)	3.72	0.80	.435*	.684*	.501*	.579*	1
6	Health Care System (HCS)	3.98	0.71	.606*	.533*	.560*	.570*	.491*	1
7	Travel Intention (INT)	3.57	0.88	.414*	.657*	.520*	.585*	.653*	.469*	1
8	Travel Behaviour (TRB)	4.15	0.70	.599*	.445*	.434*	.384*	.275*	.555*	.276*	1

Note. N=431; SD=Standard Deviation. *Significance at 1%

Following the estimated methodology, in Table 3, all the Cronbach’s alpha (α) values (between 0.852 and 0.916) are higher than the accepted level of 0.7, indicating internal reliability. In the EFA step, all items of variables are acceptable and then extracted to eight factors as the proposed model. In the next quantitative steps, the CFA was used to check the adequacy of the measurement model regarding reliability, discriminant validity, and convergent validity. In detail, composite reliability (CR) values, running from 0.854 to 0.918 and well above the suggested level of 0.7, represent high reliability. In the test, we have the standardized factor loadings of all items higher than 0.6, confirming a good convergent validity of the constructs. Furthermore, the AVE values run from 0.596 to 0.737 and are larger than the minimum threshold of 0.50, performing a convergent validity status.

In Table 3, the result of CFA process indicates that the goodness-of-fit represents an adequate fit to the data: ꭓ2/df=2.786; p<0.001; CFI=0.927; GFI=.857; AGFI=.825; TLI=0.917; RMSEA=0.064. Therefore, all the measurement results are satisfactory, confirming that the proposed model is suitable to proceed with the evaluation of the structural equation model.

Table 3
Results of Confirmatory Factor Analysis

Construct/Items	α	Loading	CR	AVE
Risk Communication (RIC)	0.884		0.888	0.665
RIC1		0.722
RIC2		0.857
RIC3		0.842
RIC4		0.834
Electronic word of mouth of COVID (eWOM)	0.897		0.900	0.694
eWOM1		0.749
eWOM2		0.847
eWOM3		0.891
eWOM4		0.839
Crisis Management (CRM)	0.884		0.885	0.658
CRM1		0.820
CRM2		0.802
CRM3		0.840
CRM4		0.781
Non-pharmaceutical interventions (NPI)	0.916		0.918	0.737
NPI1		0.847
NPI2		0.880
NPI3		0.826
NPI4		0.880
Destination Image in COVID (DEI)	0.858		0.870	0.691
DEI1		0.809
DEI2		0.829
DEI3		0.855
Health Care System (HCS)	0.857		0.859	0.605
HCS1		0.775
HCS2		0.739
HCS3		0.839
HCS4		0.755
Travel Intention (INT)	0.888		0.891	0.671
INT1		0.795
INT2		0.862
INT3		0.818
INT4		0.801
Travel Behaviour (TRB)	0.852		0.854	0.596
TRB1		0.685
TRB2		0.828
TRB3		0.812
TRB4		0.755

Note. Loading=Standardized estimate; α=Cronbach’s Alpha; CR=Composite Reliability; AVE=Average Variance Extracted.

4.2 Path Analysis Result

In the proposed model, seven hypothesized relationships between the variables have been tested. Table 4 presents the findings of the structural model. Three coefficients were found statistically significant at 1%, besides, one is at 10%, and three were insignificant.

In detail, eWOM and destination image in COVID were found to be positively associated with travel intention at 1%. Then, H2 and H5 were supported. Likewise, crisis management is positively connected with travel intention, and the result confirmed H3 at 10%. Travel intention was found to be positively associated with travel behaviour, therefore, H7 was confirmed (p<0.01). However, hypotheses H1, H4, and H6 could not be confirmed because of the insignificance of the coefficients. The goodness-of-fit indices from estimated results meet the acceptable requirements (ꭓ2/df=2.786; p<0.001; GFI=0.857; CFI=0.927; TLI=0.917; AGFI=0.825; RMSEA=0.064), representing satisfactory conditions for the structural model (Table 4).

Table 4
Results of the Structural Model

Independent variable → Dependent variable	Unstandardized coefficient	SE	t-statistics	p-value	Conclusion
RIC -> INT	-0.002	0.067	-0.025	0.980	H1-Not supported
eWOM -> INT	0.304***	0.081	3.751	***	H2-Supported
CRM -> INT	0.108*	0.063	1.707	*	H3-Supported
NPI -> INT	0.092	0.070	1.314	0.189	H4-Not supported
DEI -> INT	0.332***	0.064	5.188	***	H5-Supported
HCS -> INT	0.056	0.077	0.720	0.471	H6-Not supported
INT -> TRB	0.271***	0.042	6.478	***	H7-Supported
Goodness-of-fit indices	ꭓ2/df= 2.786	CFI = 0.927		TLI= 0.917
	p<0.001	GFI= 0.857		AGFI= 0.825
		RMSEA= 0.064

Note. *** p<0.01, ** p<0.05, * p<0.1. SE=Standard Error. Risk Communication (RIC), eWOM of COVID-19 (eWOM), Crisis Management (CRM), Non-pharmaceutical interventions (NPI), Destination Image in COVID (DEI), Health Care System (HCS), Travel Intention (INT), Travel Behaviour (TRB).

5. Discussion

The study examines the determinants of travel intention and travel behaviour under the new normal policy in Vietnam, as the country removes its strict social distancing policy and initiates measures to recover tourism activities. To achieve the research objective, we collected data from 431 individuals and performed the necessary analytical steps. Accordingly, we uncover that eWOM of COVID-19 can influence the tourism intention of travellers in Vietnam. The finding has contributed to the current literature by expanding the previous results (e. g., Filieri & McLeay, 2014; Abubakar et al., 2017), and it concludes that eWOM has been related to individuals’ travel and travel decisions in the context of the post-COVID-19 pandemic period. Another factor that can either promote or inhibit a customer’s travel intentions is crisis management. That is, if local authorities manage the crisis well, they can attract more tourists and vice versa.

The result provides new evidence for crisis management theories when COVID-19 has been considered as the most dangerous situation for global health during this century. Therefore, our empirical evidence can help both academics and business practitioners respond a huge social crisis. Similarly, we discover that there is a statistically significant link between destination image and travel intention. The result broadens the existing literature developed by some previous studies (e. g., Chew & Jahari, 2014; Abubakar et al., 2017). The empirical evidence confirms that travel intention has a significant positive relationship with travel behaviour. This finding implies that the managers in tourism companies should have strategies for enhancing travel intention; as a consequence, the tourists can change their behaviour in travel activities. This result delivers a new approach to dealing with a health crisis compared to recovery plans suggested by other previous crises (Lee et al., 2012; Chew & Jahari, 2014; Rosselló et al., 2017). Besides, the finding of the study concludes that travel intention positively affects travel behaviour, it is supported for the business plans in tourism enterprises and suggests new empirical evidence for the current literature contributed by previous studies (Tan & Wu, 2016; Abubakar et al., 2017). Also, the study indicates that it is vital to properly recognize the role of eWOM in tourism development. Indeed, promoting customers to post their experiences not only helps travel agencies improve the quality of their services, but also attract potential visitors to the destination. In this regard, relevant companies and authorities can consider strategies to promote s-commerce through the adoption of platforms such as Instagram (Rouibah et al., 2021).

Meanwhile, estimated results can’t conclude that risk communication, non-pharmaceutical interventions, and the healthcare system have significant relationships with the travel intention of tourists. These findings posit that when most people are fully vaccinated, the mere fear of being infected with COVID-19 may no longer be large enough to prevent individuals from traveling. Tourism is a good choice for the recovery of the health of every people, infected or not infected (Li et al., 2018). As a result, they reduce non-pharmaceutical precautions, and the local healthcare system is not a driving force hindering their travel decisions. These findings are helpful for business managers in order to well prepare operation plans in their enterprises in the context of the post-pandemic era.

6. Managerial Implications

The coronavirus pandemic has generated severe consequences for the global economy. Thus, in the post-pandemic era, countries are trying to recover from the ravages of COVID-19. In such a context, many governments work to support tourism activities for enhancing economic recovery (Yeh, 2020). The global tourism industry is expected to prosper in 2022 when most people were fully vaccinated, and restrictions were gradually lifted (WTTC, 2021). When the social isolation measures were about to be lifted, the authorities and tourism businesses prepared plans to stimulate tourism demand. This is implemented synchronously, from local authorities through stimulus programs or local festivals promotion to central authorities and the issuance of new context-appropriate regulations. However, for tourism stimulus programs to be successful, it is necessary to grasp the factors that impact the individual’s travel decision. In this regard, the research results suggest some managerial implications.

First, we posit that local authorities and travel agencies should be aware of the risks and benefits of eWOM communication. In fact, they can develop convenient and proper tools to provide more accurate information to tourists. This is because negative online reviews may have significant detrimental effects on individuals’ travelling intentions, especially with the rapid spread of network technology. Furthermore, this study notes that companies should use eWOM as a proper way to know the market demand and a low-cost-effective way to communicate with customers.

Second, stakeholders need to manage the crisis well. A health-related crisis can impede the recovery of tourism in the post-pandemic period. Indeed, responders suppose that if the local authorities and tourism companies respond effectively and quickly to risks caused by the pandemic, they are more inclined to travel there. The managers not only in tourism but also in other business sectors should prepare a crisis response strategy as an annual document in the future.

Third, the research indicates that destination image is a driving force behind tourists’ decisions. Therefore, there should be activities and programs to promote the local image as a safe, attractive, and friendly destination. More importantly, people need to be aware that maintaining a positive destination image requires the cooperation of various stakeholders, such as the government, travel agencies, and local people. In particular, the local public agency needs to have an advertisement campaign for the local tourism services and local destinations.

7. Conclusions, Limitations and Future Research

The COVID-19 pandemic has left heavy negative consequences on the economies of many countries. These consequences, combined with recent economic difficulties and uncertainties in different parts of the world, further limit the resources a country can use to revive its economy. In that context, identifying factors that promote tourism behaviour is essential for the authorities to make appropriate and effective investments. In this study, we reveal significant positive effects of crisis management, electronic word of mouth, destination image on tourists’ decisions. Besides, a significant positive link from travel intention to travel behaviour is confirmed. In other words, to revive tourism in the new context, authorities and tourism companies should understand the risks and benefits of electronic word of mouth, focus on crisis management, and build a positive destination image to robustly attract tourists in the future. By contrast, we find limited evidence to show that risk communication, healthcare systems, and non-pharmaceutical interventions influence individuals’ travel decisions. These findings may imply that the government can focus fewer resources on risk communication, healthcare systems, and non-pharmaceutical interventions when implementing programs or policies to promote tourism.

While the research sheds some light on the determinants of tourism intention and behaviour, it has some limitations. First, young people make up a fairly large proportion of our study. In fact, they are those who can adapt quickly to new circumstances, including post-pandemic changes. Further studies could expand the scope of survey subjects to provide a more general picture. Second, the survey was conducted from October 1 to October 31, 2021, when Vietnam lifted its strict social distancing national policy and began programs to revive its economy under the new normal. Therefore, the study does not consider the impact of economic uncertainties in 2022 on the travel decision of customers. Future studies could investigate whether and how economic uncertainties affect travel intention and travel behaviour during the post pandemic era. Third, the paper focuses on domestic tourists because they are considered an internal force and a foundation for sustainable tourism development in Vietnam. The role of domestic tourism can be demonstrated through programs and policies by the Ministry of Culture, Sports and Tourism such as Plan No. 3228/KH-BVHTTDL on September 7, 2021 and Program No. 4698/BVHTTDL-TCDL on December 16, 2021 regarding launching a program for domestic tourism to adapt safely, flexibly while effectively controlling the COVID-19 pandemic. However, future studies can expand the research objects, such as foreigners who plan to visit Vietnam. Such studies help uncover how foreigners see Vietnam as a potential destination.

Funding: The authors confirm that this study has not received any funding.

Conflict of interest statement: The authors declare that there is no conflict of interest.

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Appendix
Measures and Items

Item	Content	Source
Risk Communication (RIC)
RIC1	People around me comment that having the coronary pneumonia is very dangerous.	Cheng et al. (2022)
RIC2	The media is constantly reporting on the risk of the coronary pandemic.
RIC3	Information about the harmful effects of the COVID pandemic is widely disseminated in society.
RIC4	There are many people in the community who often discuss the risk of the COVID pandemic.
Electronic Word of Mouth of COVID (eWOM)
eWOM1	When making travel plans, I will refer to online information or reviews on social networks about the situation of the COVID pandemic at the tourist destination.	Bambauer-Sachse & Mangold (2011)
eWOM2	Comments or online reviews about the COVID pandemic situation at tourist destinations are a reliable source of reference.
eWOM3	I feel that online reviews about the COVID situation in tourist destinations are truthful.
eWOM4	I trust online travel commentary when choosing tourist destinations under the context of the COVID pandemic.
Crisis Management (CRM)
CRM1	I am satisfied with the timely judgment and handling of the COVID pandemic by the authorities in tourist destinations.	Rasoolimanesh et al. (2021)
CRM2	I appreciate the strong and quick response to the COVID pandemic by tourism businesses at the request of the authorities.
CRM3	I believe that tourist destinations will respond effectively to the risk associated with supply chain disruption (shortage of food and medicine).
CRM4	I feel secure about the capacity of public health care at tourist destinations when sightseeing activities are restarted.
Non-Pharmaceutical Interventions (NPI)
NPI1	I firmly believe that tourist attractions and tourism businesses will take good measures to prevent COVID.	Rasoolimanesh et al. (2021)
NPI2	I firmly believe that the agencies in tourist destinations and tourism companies will adopt good measures to prevent COVID.
NPI3	I firmly believe that tourists at tourist attractions will adhere well to COVID prevention measures.
NPI4	I firmly believe that staff working at tourist attractions will adhere well to COVID prevention measures.
Destination Image in COVID (DEI)
DEI1	I plan the trip because the risk of COVID pandemic in this tourist destination is very small.	Abbasi et al. (2021)
DEI2	I am excited about this tourist destination because it has been through COVID, but it is safe now.
DEI3	I think this tourist destination has interesting and attractive landscapes and is safe from the COVID pandemic.
Health Care System (HCS)
HCS1	If traveling, I plan to visit destinations where I believe that the healthcare system is reliable.	Rasoolimanesh et al. (2021)
HCS2	I am only interested in tourist destinations whose health care system is comfortable enough.
HCS3	If traveling, I will choose a tourist destination when I trust their ability to test for COVID.
HCS4	When choosing a tourist destination, I feel secure about the ability to deal with pandemic situations at this tourist destination.
Travel Intention (INT)
INT1	If the COVID pandemic ends, I plan to travel as soon as possible.	Rasoolimanesh et al. (2021)
INT2	The risk of the COVID pandemic has been minimized, when I want to make a trip, I plan to do it right away.
INT3	If the COVID pandemic ends, I will work hard to travel as soon as possible.
INT4	There is a high probability that I will travel as soon as the COVID pandemic ends.
Travel Behaviour (TRB)
TRB1	My travel behaviour can change because of the impact of the COVID pandemic.	Neuburger & Egger (2021)
TRB2	In the future, I tend to avoid traveling to places where there is information about the high risk of infection from the COVID pandemic.
TRB3	Currently, due to the COVID pandemic, I tend to minimize going to big events at tourist areas.
TRB4	In general, I will not go to tourist attractions in the context of the COVID pandemic.