Applying the extended theory of planned behavior to understand domestic tourists’ behaviors in post COVID-19 era

Abstract The study aims to develop a new framework that explains domestic travelers’ behavior to Ho Chi Minh City by applying the extended TPB to evaluate and validate the influence of significant variables on travel intention and lead to increasing tourist behavior in post COVID-19 era. This study empirically examines the factors impacting domestic tourist behavior by using the partial least squares structural equation modeling (PLS-SEM) method. The data were collected from 473 domestic travellers who visit Ho Chi Minh City in the post COVID-19 period. The empirical finding is that the extended TPB with two additional constructs variables consisting of trust and risk perception has a significantand positive impact on travel intention and in turn leads to increasing tourist behavior in post COVID-19 period. The primary value of this paper is that it tested the extended theory of planned behavior to prominently explain and understand tourist behavior in post COVID-19 period.

important role in the travel market in Vietnam due to the impact of the COVID-19 pandemic and related travel restrictions, the international tourism market needs longer time to recover even when the epidemic ends. Entertainment facilities, shopping activities are activated; travel businesses are re-opened to serve domestic tourists, thereby creating jobs for a certain number of tourism service employees. Although the revival of tourism may not be enough to boost the operation of the whole country but it can keep small businesses active and stimulate the local economy, reducing negative impacts of the epidemic on the economy until international tourism is active again (General Statistics Office, 2021a).
According to reports of localities, revenue from tourism and travel services in the first 2 months of the year of Ho Chi Minh City decreased by 69.2% over the same period last year. Particularly during the Lunar New Year holiday in 2021, although many incentive programs, promotions, discounts are launched but occupancy of accommodation establishments in Ho Chi Minh City was only less than 10%; the number of tourists to Ho Chi Minh City has dropped dramatically 37% compared with last year (General Statistics Office, 2021b). When the COVID-19 crisis was controlled in first 5 months of the year 2021, the revenue increased 23,3% compared with the first 5 months of last year (Vienamnews, 2021). All above figures are reflected in changes in the domestic tourist's behavior and travel intention as they perceived all information risks arising from the pandemic. Pahrudin et al. (2021) concluded that COVID-19 pandemic has reduced both the number of tourists and has reduced the income of tourism industry and has changed travel behavior of tourist. Hashim et al. (2018) claimed that little attempt has been made to understand how tourists' perceived risk influences tourist intention to travel a destination. Bae and Chang (2020), Quintal et al. (2010), and Sánchez-Cañizares et al. (2020) pointed out that perceived risk influenced attitudes toward visiting destination, which in turn influences behavioral intention. However, Amaro and Duarte (2015, p. 73) found that perceived risks do not affect attitude, and risk perceptions negative direct effect on intention. Perić et al. (2021, p. 1) also indicated that the risk perception negatively affects travel intentions during the COVID-19 pandemic as previously suggested by Bae and Chang (2020). Therefore, the negative effect of COVID-19 perception seems to be temporary on people's travel intentions (Li & Ito, 2020). However, Falahuddin et al. (2020) claimed that a positive association between risk perception and travel intention in the context of post-pandemic which opposite with the finding of Amaro and Duarte (2015); Perić et al. (2021). This indicated a research gap in the previous study between travel intention and risk perception. Thus, the current study attempted to evaluate and validate the causal relationship between tourist intention and risk perception. Bae and Chang (2020) also argued that behavioral intention during the pandemic may not remain the same after the end of COVID-19. Martín-Azami and Ramos-Real (2019, p. 925) supported that the perceptions of risk may vary depending on the characteristics of the traveller and the destination. Perceived risk differs from people to people, varies from time to time (Falahuddin et al., 2020), from one geographical region to another (Kozak et al., 2007). Ivanova et al. (2020) analyzed the travel intentions of tourists in the post-pandemic world and the findings of research shown that most of the tourist are ready to travel within two months after travel is allowed in the country. Thus, different stages of COVID-19 control could have varying influenced on travel intention (Li & Ito, 2020, p. 491). It is necessary here to clarify exactly what is meant by an insight understanding of post-crisis travel intentions for the tourism industry to respond effectively to crises. However, there has been little interest in the domestic tourist intention in post COVID-19 pandemic. Isaac and Keijzer (2020) clairified that several factors that drive and limit travel intentions have been studied separately and limitedless. Li et al. (2020Li et al. ( , 2020 claimed that the theory of planned behavior (TPB) was useful for modeling tourist behavior in terms of the likelihood to travel during the situation created by COVID-19. Most recent tourism researches (Meng & Choi, 2019;Meng & Cui, 2020;Pahrudin et al., 2021) extended the TPB model to have a better predictive power than original TPB model; also an insight into explaining of domestic tourist behavioral intention. Therefore, the current research extended the original TPB model by inserting perceived risk and trust constructs to validate and evaluate the travel intention and tourist behavior in post Covid-19 period.
Overall, the current study aims to develop a new model that evaluates and validates domestic tourist behavior to Ho Chi Minh City by using the extended of Theory Planned Behavioral in postpandemic situation to close the existing gap in the existant literature and to provide the valuable knowledge of tourist behavior. Therefore, this study proposed the new theoretical framework that predicts the domestic tourist behavior intention to visit Ho Chi Minh city in post-pandemic COVID-19 based on the extended of TPB model. The current study also contributes to an insight into understanding of the factors impacting the domestic behavior intention in the post-outbreak COVID-19.
In addition, the study has investigated how trust and risk perception could influence domestic tourist behavior by expanding TPB. The current study aims 1) to extend the Theory of Planned Behavior by inserting trust and risk percpetion in tourist behavior in the post-pandemic, 2) to explore the factors impact on the domestic tourist behavior intention to visit a Ho Chi Minh city in post-pandemic COVID-19, 3) to evaluate and validate the role of trust and risk perception in the new proposed conceptual framework of research. Understanding tourist behavior intention in the context of risk such as the post-pandemic COVID-19 is a crucial component in developing destination recovery strategies (Golets et al., 2020).

The theory of planned behavior (TPB)
The TPB is an extended model of the theory of reasoned action (TRA;1991;1991), having four main constructs: attitude toward behavior, subjective norm, intention, and actual behavior. However, the TRA could not fully explain behavior that is not entirely under volitional control (Ajzen, 1991). Therefore, TPB was designed to predict better behaviors not entirely under volitional control by including measures of perceived behavioral control (Armitage & Conner, 1999, p. 36). The addition of perceived behavioral control should become increasingly useful as volitional control over the behavior declines (1991; see Figure A1).
The new framework in the current study are developed form Theory Planned Behavior, because the theory of TPB has been considered to the strength to an insight into understanding of the likelihood to visit a destination in the post pandemic (Pahrudin et al., 1991). Moreover, the relative importance of attitude, subjective norm, and perceived behavioral control in the prediction of intention is expected to vary across behaviors and situations (Ajzen, 1991). Behaviors are more likely to result from intention when people believe they have the resources to perform the behavior and are likely to be successful at doing so (Wiethoff, 2004, p. 225). Travel intention, as a kind of behavioral intention, can be understood in the same theoretical context. Tourist's behavior can usually be predicted by intention. Intention is sometimes considered more effective than behavior to comprehend the human mind (Jang et al., 2009, p. 52). Intention to travel refers to the willingness to visit a tourist destination (Chen et al., 2014, p. 793). Thus, travel intention refers to visitors' perceived likelihood of visiting a specific place within a specific period (Hashim et al., 2018, pp. 97, Noh, 2016. In conclusion, travel intention is an outcome of a mental process that leads to an action and transforming motivation into behavior (Makhdoomi & Baba, 2019, p. 38).

The extended theory of planned behavior
The TPB model has been successfully applied in explaining tourist behavior because studies on travel mode choice have used different target behaviors and target groups (De Groot & Steg, 2007, p. 1831. Meng and Choi (2015) claimed that the new variables introduced to the original model should be entered only in line with the following principles: (1) They should be imperative factors that affect the decision-making process; (2) they should be conceptually independent from existing factors in the theory; a (3) they should be potentially appropriate for a specific behavior. In tourism, new variables such as authenticity (Girish & Lee, 2019); desire and environmentally friendly tourism (Song et al., 2012); destination image (Abbasi et al., 2021;Park et al., 2016) and travel constraint (Park et al., 2016); perceived risk (Quintal et al., 2010;Sánchez-Cañizares et al., 2020); trust (Rasoolimanesh et al., 2021) were added to enhance the prominent understanding of tourist behaviors and empirically established that the extended TPB model has an insight into predicting power than original TPB model (Abbasi et al., 2021). Therefore, using the extended theory of planned behavior is useful to prominent predict tourist behavior to visit a destination in the post pandemic covid-19.

Proposed model and hypotheses
The intention to visit a destination can be formed based on various factors, which can both drive and limit travel intention (Makhdoomi & Baba, 2019). Travel intention can be seen as a form of behavioral intention (Isaac & Keijzer, 2020) which emphasises a person's intent to travel to a certain destination. Travel intention is an outcome of a mental process that leads to an action and transforming motivation into behavior (Jang et al., 2009). Ajzen (1991); Lam and Hsu (2006) confirmed that TPB model has been widely used to predict tourist behavior in different contexts and claimed that subjective norm, attitude toward behavior and perceived behavioral control are prominent factors to be impacting travel intention. Perceived behavioral control (PBC) is held to influence both intention and actual behavior (Armitage & Conner, 1999). An individual has the intention of a behavior, he or she is likely to perform that actual behavior (Ajzen, 1991). Sánchez-Cañizares et al. (2020) defined the intention as the individual's inclination to travel in the short term, which implies assuming the risk from COVID-19. Liu et al. (2021); Pahrudin et al. (2021) confirmed that attitude toward behavior, subjective norms and perceived behavioral control all have a significantly and positively impact on travel intentions in post-pandemic. The current study hypothesized the mediating effect of attitude toward behavior, subjective norms, perceived behavioral control between travel intention and tourist behavior as the following:

Risk perception theory and risk perception
Tourism risk perception theory involves psychology, sociology, culture, economics and many other disciplines (Cui et al., 2016). Bayramov (2021) indicated that risk perception plays a prominent role in understanding of the tourists' expectations, motivations, experiences of visiting the conflict-ridden areas. Wang et al. (2020) conclued that the risk perception theory has has been widely applied and expanded, thereby this theory has become one of the viral theories for evaluating tourists' travel decisions by explaining the interaction between tourists and destinations and effectively predicting tourists' travel attitudes and choice behaviors to tourist destinations when they encounter a major crisis. In tourism, risk is defined as what is perceived and experienced during the process of a group package tour and at the destination (Tsaur et al., 1997, p. 796). The risks may include physical, psychological, financial, and health risks from injuries, accidents, terrorism, natural disasters, political instability, and epidemics (Bae & Chang, 2020, p. 1019). Perceived risk is defined as tourist perception of the probability that an action may expose them to danger that can influence travel decisions if the perceived danger is deemed to be beyond an acceptable level (Chew & Jahari, 2014). Fuchs and Reichel (2006, p. 87) found that perceived risk could be used in part as a variable in explaining decision-making processes of tourists. The higher the perceived risk, the more information tourists seem to seek and the more rational the decision process becomes. Tavitiyaman and Qu (2013, p. 182) provided that safety information while traveling will also reduce perceived risks and increase the possibility to visit, in the mean time, reducing risk perception of travelers can also increase tourist behavioral intention. Perceived risk has directly influenced tourist's intention and also been referred to as a successful indicator in predicting actual behavior (Awang et al., 2021;Nik Hashim et al., 2017, p. 80). Perceived risk is one of the factors possibly explaining the tourist intentions to visit, or not to visit, a destination (Noh, 2006). Risk perception has a significant influenced on travelers' intention (Chew & Jahari, 2014). Falahuddin et al. (2020) highlighted the influence of risk perception as the determinant factor of travel intention; intention to travel is also influenced by risk factors (Wachyuni & Kusumaningrum, 2020, p. 69). Makhdoomi and Baba (2019) concluded that the perceptions of risk positively and significantly influence the intentions to travel. Thereby, the current study focuses on such research hypothesis also as: H 6 : Risk perception will positively influence on travel intention in post pandemic. Morgan and Hunt (1994, p. 23) defined trust as existing when one party has confidence in an exchange partner's reliability and integrity. Trust is defined as belief, confidence or an expectation on the trusted partner in providing the services to customers (Ratnasingam, 2012, p. 193). Trust is also the behavioral intention of "willingness" to rely on that partner, trust is limited (Morgan & Hunt, 1994). Trust can be also defined as a willingness to rely on the tourist destination in which one has confidence, or the belief that the tourist activities in the destination are reliable; and trust could also affect behavioral intention directly (Hsiao & Yang, 2010, p. 281). Trust has been viewed as one of the essential factors affecting tourist intention (Rasoolimanesh et al., 2021) and trust has also been viewed as an effective tool to minimize uncertainty (Han & Hyun, 2015, p. 22). Moreover, Lobb et al. (2007, p. 387) also found that the direct impacts of trust on the intention. Hence, the research hypothesis also imposed as: H 7 : Trust will positively influence on travel intention in post pandemic.

Trust
The purpose of this study was to examine the causal relationship among the risk perception, trust and travel intention towards tourist behavior, based on the extended of TPB model to predict tourist behavior in post COVID-19 period and developed the new research model ( Figure A2). This research model will be tested with the primary responses collected from the sample population.

Scales of the study
This study has developed a survey questionnaire to acquire responses from domestic travellers to Ho Chi Minh City. The questions were divided into two parts, including demographic information (3 items), The scales of Attitude (4 items) was adapted from Abbasi et al.  Rasoolimanesh et al. (2021), and tourist behavior (4 items) was adapted from Lapteva (2021) in this study. Demographic variables used in the current study include age group, gender, educational background level. The questionnaire was designed in Vietnamese language because the respondents in the current study are Vietnamese tourist who visit Ho Chi Minh city post-pandemic covid-19. All of the items were measured on a 5-point Likert scale. Respondents were asked to indicate their level of agreement toward each statement, from 1 = strongly disagree to 5 = strongly agree (see table A1). All of the questions were tested to ensure the reliability and validity of the constructs. In this study, the data analysis was made using the SmartPLS 3.3.3 which allowed the measurement model and structural model to prove the hypothesis, positive influence of construct.

Research design
This study was conducted in three phases. The first phase was for a qualitative research to understand the situation and explain the indicative findings (Dawadi et al., 2021) in which the author interviewed 8 tourism experts from the five tourism and travel services company in Ho Chi Minh City and then discussing in groups with 25 domestic tourists to improve the factor scales as well to design a survey questionnaire and to ensure high reliability of all constructs. Secondly, the author limited the research location within Ho Chi Minh City (HCMC) as the COVID-19 crisis has been controlled and tourists can travel from February and April 2021, this is also the period time the most domestic tourists come to HCMC. The self-completed questionnaire officially used in this study consisted of items as shown in Table A1. In the third phase, the collected data were coded, screened and analyzed with SmartPLS 3.3.3. The PLS-SEM approach was adopted because the study is prediction-oriented research which aims to predict tourist behavior of prospective tourists to support and travel to a destination in post COVID-19. The sample respondents of the study are domestic travellers who is older than 18 years old and the questionnaire survey was sent randomly to domestic travelers who were visiting some tourist sites in Ho Chi Minh city based on the convenience sampling method.
According to J. Hair et al. (1995), in order to have reliable representation of the population, sample size must be at least m x 5, where m is the number of independent variables. As there are 28 variables used in this study, the sample size must be at least 140. However, to ensure a high reliability, this study conducted an official survey with 570 respondents who were traveling to Ho Chi Minh City during February-April 2021. There were totally 530 completed questionnaires collected (rate of return: 92.98%); among which there were 57 invalids. As a result, only 473 valid responses are used for the data analysis, representing 89,24%, is sufficient for Partial Least Squares Structural Equation Modeling analysis as suggested by Hair et al. (2017)

Descriptive analysis
Descriptive statistics was used to analyze the characteristic of respondents with SPSS software tools. There are three questions asked about the characteristic of respondents consisting of gender, age groups, and educational background. Demographic data obtained from the survey are shown in table A2.
Based on this survey, the sample size consists of 250 male respondents (52.9 percent of the total respondents) and 223 female respondents (47.1 percent of total respondents). In terms of the age group, the majority of respondents fall into the age of below 25 years old (15.09 percent), 26-35 years old (34.5 percent), 36-45 years old (23.3 percent), 46-55 years old (11.2 percent) and above 55 years old (15.4 percent). The majority of the respondents graduated from various universities with a bachelor's degree (64.3 percent), followed by college certificate (20.7 percent), Post-graduation (5.5 percent), and other degree holders (9.5 percent).

Analysis of results
The information provided in table A3 stands for the statistical values of the composite reliability more significant than the cut-off point that complies with the necessary conditions to get accepted. The composite reliability (CR)and average variance extracted (AVE) should be greater than 0.7, and 0.5 respectively to establish reliability and convergent validity (Hair et al., 2019). The value of the reliability statistics using Cronbach's alpha was above 0.845; all the calculated values of the composite reliability (more than 0.896) are acceptable (Henseler et al., 2016). The average variance extracted AVE values were above the minimum required level of 0.684 more than 0.500 (Henseler et al., 2015). tTable A4 signifies the discriminant validity using the PLS approach. Fornell-Larcker criterion is used commonly to evaluate the degree of shared variance between latent variables of the model. Fornell-Lacker criterion can be achieved when the square root of AVE values (bold numbers in diagonal) was higher than their corresponding correlation coefficients among the other construct's loadings (off-diagonal elements) in the same column and row (Fornell & Larcker, 1981), indicating that the scales had a good discriminant validity.
Also, to examine the discriminant validity using the PLS approach, the values of Heterotrait-Monotrait correlations less than 0.900 will be acceptable (Henseler et al., 2015). The calculated values are less than the Heterotrait-Monotrait correlations discriminant validity, so the discriminant validity was accepted. The results proved that the measurement scales are reliable and valid (Henseler et al., 2015), thus, the validity of the new conceptual model is established. Additionally, both the model's predictive power and the causal relationships between the variables constructs were statistically significant ( Figure A3). Figure A3 represents that the R 2 value for the estimated equation is 0.567, which is significant at a 1 percent level of probability. The R 2 adjusted shows that 0.566 (56.6) percent of the variation in tourist behavior is described by Attitude, Subjective norms, Perceived behavioral control, Risk Perception, Trust, Travel intention. This result indicated the better predictive power than the original TPB for the new proposed model.
The shreds of evidence revealed in table A5 indicates the detailed results of bootstrapping for the testing of the hypothesis. In testing the hypothesis, the analytical bootstrapping technique describes the level of significance of the path between the variables, 5000 re-sampling bootstrapping procedure utilized while calculating SmartPLS. The results indicate that the Attitude, Subjective norms, Perceived behavioral control, Risk Perception, Trust, Travel intention have a positively and significantly influence on tourist behavior (p < 0.05) which is consistent with that of Dang and Tran (2020). Additionally, the seven path coefficients identified in the proposed model are found to be significantly validity. These path coefficients reflect the influence of e tourist attitude behavior on travel intention (β = 0.358, p = 0.000), subjective norms on travel intention (β = 0.130, p = 0.004), perceived behavioral control on travel intention (β = 0.194, p = 0.000), perceived behavioral control on tourist behavior (β = 0.112, p = 0.003), travel intention on tourist behavior (β = 0.706, p = 0.000), risk perception on travel intention (β = 0.126, p = 0.015), trust on travel intention (β = 0.120, p = 0.024), travel intention on actual tourist behavior (β = 0.697, p = 0.000). The results demonstrated that all proposed hypotheses in this regard were accepted, and were found to have a significantly effect on tourist behavior.
Multicollinearity is calculated by either variance inflation factors (VIF) or tolerance. If the values of VIF exceeds 4.0 or less than 0.2 reflects the problems with multicollinearity (Hair et al., 2014, p. 197). The Collinearity Statistics (inner VIF values) of Attitude 1.191; Perceived behavioral control with tourist behavior 1.149 and with travel intention 1.137; Risk perception 1.170; Subjective norms 1,064, Trust 1.238 were less than 4.0 represents that there is no multicollinearity effect among the variables (Table A6). Furthermore, the R 2 values of 0.75, 0.50 and 0.25 for endogenous latent variables can may be referred to substantial, strong, moderate and weak, respectively (Hair et al., 2017). The values of the coefficient of determination (R 2 ) of the tourist behavior in post pandemic are respectively 0.567. Thus, the value of R 2 is considered moderate in the current study. Results of this study indicated that the utility of the extended TPB model as a new conceptual framework for a novel insight into understanding of tourist behavior.

Discussion, managerial implications, conclusions, research limitations
The current study confirmed the tourist behavior by extending of the TPB original model with the additional contructs with risk perception and trust were generally significant. The findings of the study revealed that among all the constructs used in this study had a positive relationship. Morever, travel intention had the strongest effect tourist behavior with path coefficient = 0.706, at 0.05 level of significance. Specifically, this study contributes an insight into understanding tourist behaviors by extending the TPB framework by adding the risk perception and trust in the context of post-pandemic. The current study is also to bridge the above research gap, and to propose a new model to contribute theorical and managerial implications for local tourism authorities. The following significant points are discussed according to the analysis results above.
The first, the results of this study provide theoretical and practical contributions to the local tourism authorities. Theoretically, this study attempted to propose the new model based on the extending of the TPB model because the extend TPB has provided an insight into explaining the intention behavior than the original TPB model which is consistent with the study of Pahrudin et al. (2021).
The second, the managerial and empirical implications indicate that the study provides the new conceptual framework for local authorities to insight into understanding of tourist behavior, and can increase tourist behavior in the context of post-pandemic thereby, the local authority and the tour operator could provide information about Covid during the trip because the domestic tourists trust the information and communication provided by the government of this city about the infection and mortality rate of COVID-19 and they will make an effort to travel a less wellknown destination, where there will be less tourist crowds for safe trip in the near future.
The third, the new finding of this study has a different impact on the intention to travel at different stages of COVID-19 control; this confirms that risk perception exerted a significantly and positively influence on travel intention in post pandemic as opposed with the finding of Amaro and Duarte (2015), Bae andChang (2020), andPerić et al. (2021). However, the result of this study is in agreement with the findings of Pahrudin et al. (2021) who claimed that the risk perception of covid of domestic tourist had a significant impact on travel intention and domestic tourist will be considering the safety destinations while traveling and changing the behavior during the post pandemic covid-19. Thus, the local destination authorities and the tour operator, and other tourism service providers still keep the safety guideline for tourist during the trip. On the other hand, the local government could open the destination with the guideline for safety such as keep social distancing, wear mask to protect the tourist from the pandemic covid-19; and should make effort to change tourist behavior intention is as the marketing strategy to recover the tourism industry quickly when Covid-19 is over. Empirically, the findings of this study can contribute to understand how domestics tourist behavior changes when the COVID-19 is over.
Although the study has some limitations as well. This study is insufficient data and survey in a random selected of the domestic tourists who were traveling to Ho Chi Minh City only and primary data was collected from February-April 2021, in Ho Chi Minh city, thereby, the results may not generalized to all cities in Viet Nam as well may not gain different view of domestic tourist behavior in post pandemice period; thus, future research may try the application of the extended TPB model with a different survey technique and should replicate the studied model in other cities in Viet Nam and a larger population to understand more domestic tourists' behaviors. There still have other factors could also affect tourist behavior. Further research would also consider other latent variables constructs to provide an insight into understanding of tourist behavior in postpandemic period.
Furthermore, around 34.2 % of tourists are between the age group of 26-35 years old with a bachelor's degree. In conclusion, the findings related to the extended TPB with additional variables as risk perception, trust influence tourist behavior are very useful. This finding helps local government could develop the marketing strategies focus on safety trip and raise the probability of future tourist behaviors for the age group of 26-35 years old. Additionally, this study has constructed the new model for an insight into understanding of domestic tourist behavior. The new model consisting of the TPB, risk perception and trust variables is confirmed respectively. Moreover, the research method and the major findings of the current study were acceptable and were valuable to tourist behavior in post-pandemic period.

Funding
The author received no direct funding for this research.

Disclosure statement
No potential conflict of interest was reported by the author(s).

Citation information
Cite this article as: Applying the extended theory of planned behavior to understand domestic tourists' behaviors in post COVID-19 era, T. T. B Bui, Cogent Social Sciences (2023), 9: 2166450.