Does virtual tourism influence tourist visit intention on actual attraction? A study from tourist behavior in Indonesia

Abstract

The COVID-19 outbreak has altered how visitors live, including in the tourism sector. The current study investigates tourist behavior in virtual tourism during the COVID-19 pandemic using the framework of the theory of planned behavior (TPB) and behavior intention. The study included 217 people who utilized virtual tourism for tourists’ during the COVID-19 pandemic. The data are analyzed using Structural Equation Modelling Partial Least Squares (SEM-PLS). According to the findings, the TPB framework, such as attitude toward virtual tourism, subjective norms, and perceived behavior control, have a favorable and substantial influence on satisfaction. Satisfaction with virtual tourism significantly influences attitude change and willingness to visit the actual tourist destination. The study’s implications relate to marketers and stakeholders to provide virtual tourism as a promotion tool of a destination in Indonesia for tourists during the pandemic or crises.

Keywords:

• pandemic COVID-19
• virtual tourism
• TPB framework
• satisfaction
• visit intention

PUBLIC INTEREST STATEMENT

Virtual tourism has gained attention from tourists and residents during the COVID-19 pandemic. However, the stakeholders and researchers need to examine tourists’ behavior toward virtual tourism during the COVID-19 pandemic. Thus, this study tried to examine tourist behavior by extending the theory of planned behavior, satisfaction, and behavior intention. According to the study findings, tourist behavior in virtual tourism contributes to satisfaction. Furthermore, tourist satisfaction in virtual tourism influences visitor intention in actual attraction. It is hoped that the study’s findings can contribute to both scholars and managers in the context of virtual tourism to use virtual tourism as a tool for destination marketing during a disaster or COVID-19 pandemic.

1. Introduction

The COVID-19 outbreak has affected various sectors globally since 2019, including travel and hospitality, notably in Indonesia. Since the COVID-19 pandemic in Indonesia from 2020 to 2022, the Indonesian government has imposed limitations to prevent COVID-19 spread, such as the large-scale social restriction (PSBB) and the small-scale social restriction (PSBK) (Pahrudin et al., 2021a). These initiatives seek to minimize overcrowding and crowding during the COVID-19 epidemic. This limitation, particularly in tourism industries afflicted by the COVID-19 outbreak, reduced the number of tourists and economic losses by around USD 9.7 trillion per month, particularly in Bali, Indonesia (Rosidin, 2020).

Therefore, Indonesia was one of the countries affected by the COVID-19 pandemic in early 2021, with confirmed cases of 4.174.216 and 139.415 deaths (The Task of Covid-19 2020). In tourism and travel industry has declined to around US$245 Million due to the restriction and cancellations from visitors (The Jakarta Post, 2021). For the hospitality industry, such as in Indonesia, hotel industries have decreased occupancy by as much as 19.7% (The Jakarta Post, 2021). All these data indicated that the pandemic had disrupted the tourism and hospitality industry during the COVID-19 pandemic. Therefore, the tourists’ behavior also changed because of the risk perceived from the virus COVID-19.

During the pandemic, tourists can use social media and virtual reality to decide their visit behavior and intention to the risk’s destination, thereby preventing the spread of COVID-19 (Manchanda & Deb, 2022). Virtual reality and social media are tools that make tourists satisfy during the pandemic and as marketing (Lu et al., 2022). Virtual reality is a media communication channel for exploring tourism data integrated with travel resources, artificial intelligence, and data mining to generate the sources of virtual reality for tourist tours (Itani & Hollebeek, 2021).

The role of information, communication, and technology is one of the strategies in tourism development to increase the effectiveness and efficiency in promoting a tourism destination in both the world (Pahrudin, Hsieh, & Liu, 2023, Pahrudin et al.,2023c; Sigala, 2018), including in Indonesia. Information technology has become a transformational tool in the tourism industry and the role of the tourism stakeholders (Sigala, 2018). Furthermore, virtual reality tourism platforms in Indonesia have grown, especially during COVID-19 namely, Wisata Kreatif Jakarta, Atourin, SIPADU, and other live-streaming platforms (Kinseng et al., 2022). These virtual tourism platforms contribute to Indonesian tourists or residents during the COVID-19 pandemic as an alternative or strategy to minimize stress levels while avoiding the spread of the virus.

Research in tourism and hospitality has expanded in the literature, with over a thousand citations (Manchanda & Deb, 2022; Yung et al., 2021) due to technological advancements such as virtual tourism. In the tourism sector, tourist adoption of technology increases customer happiness and a competitive edge in the destination (Zhang et al., 2019). Consequently, analyzing visitors’ behavior and pleasure with virtual reality tourism during the COVID-19 outbreak requires applying the theory of planned behavior in the context of virtual reality tourism. Many studies on virtual reality in tourism have been undertaken. However, few studies have offered empirical evidence of local visitor behavior and satisfaction with virtual reality tourism during the COVID-19 pandemic.

The COVID-19 pandemic has influenced the tourism sector, and the TPB theory is relevant for analyzing people’s propensity to embrace virtual reality travel technology during the COVID-19 outbreak (Lu et al., 2022; Zhang et al., 2022). Nevertheless, virtual reality tourism has long been investigated due to technical limits and guests’ inclination for conventional means of connecting with the natural environment. During the COVID-19 outbreak, travelers’ perceptions and behaviors in virtual tourism differed considerably. As a result, numerous pieces of research examine virtual reality tourism in non-crisis contexts (Ruan et al., 2022) rather than in crisis contexts, such as during the COVID-19 pandemic (Zhang et al., 2022).

Because of the variations in the notion of virtual tourism during crises and non-crises, this study focuses on how the theory of planned behavior explains tourist behavior on visit intention in virtual reality tourism during pandemic crises, especially COVID-19. According to the study (H. Kim et al., 2021), a virtual reality tour is one of the ways to give travel owing to the COVID-19 pandemic when people stay at home due to constraints and physical distance. In the previous studies, there are some studies of TPB generally applied in tourism sectors to know tourists’ behavior during the COVID-19 pandemic (Hamid & Bano, 2021a; Lu et al., 2022; Pahrudin et al., 2021a). It is necessary here to clarify precisely the role of tourists’ behavior during the COVID-19 pandemic in using virtual tourism by applying the theory of planned behavior. However, there needs to be more interest in the studies on domestic tourist behavior and satisfaction in using virtual tourism during the pandemic in Indonesia and explore how the tourists’ satisfaction with virtual tourism can aid in the recovery of the tourism industry during and after the pandemic. To fill out the research gap in the literature and practice, it is vital to investigate how tourists’ behavior in using virtual tours through some platforms to enjoy some destinations in Indonesia influences satisfaction and decision to visit a destination in actual attraction in COVID-19 post-pandemic. Consequently, this study used the theory of planned behavior to explore tourist behavior in the context of Indonesian tourists’ and the role of satisfaction as a mediator in using virtual tourism during COVID-19.

This study attempted to address and extend the research gap using several pieces of literature. First, virtual reality tourism is a technological innovation, and as a tool for promoting a tourism location, it operates to improve consumer connection, interaction, and satisfaction (Manchanda & Deb, 2022). Overall the current study examines tourists’ behavior and satisfaction with using VR tourism toward tourist behavior intention in the tourism industry during the COVID-19 pandemic. Additionally, tourist marketers and managers sought to design a new strategy for marketing tourism destinations while coping with the COVID-19 pandemic. Second, the current study developed a satisfaction mediation analysis for assessing tourist behavior in virtual tourism, which may bring novelty and forecast tourist behavior in terms of propensity to visit genuine tourist sites. Finally, this study attempts to expand the tourism literature from the perspective of virtual tourism and tourist behavior in a way that has yet to be discovered in previous studies as a contribution.

2. Literature review and hypothesis development

2.1 Tourism, hospitality industry and COVID-19

The studies on tourism, hospitality, and the COVID-19 pandemic have gained attention from researchers due to the impact on tourist arrival and behavior (Jun et al., 2020; Shi et al., 2020). However, tourists’ visit intention is limited due to the spread of the virus (Sánchez-Cañizares et al., 2020). Therefore, tourists can visit a destination during the COVID-19 or post-pandemic by using virtual tourism as a solution for the tourism industry (Lu et al., 2022).

Many studies have discussed the tourism and hospitality industry related to the COVID-19 pandemic. Travelers had attention to physical, social, and risk perception during COVID-19 (Pahrudin et al., 2021). A study by (Jafari et al., 2020) examined the impact of COVID-19 on travelers’ behavior in Turkey. They found that the pandemic significantly influenced tourist life and behavior in traveling and leisure trips. Therefore, the by (Chi et al., 2021) found that COVID-19 had impacted hotel’s employee’s behavior. In addition, a study (Neuburger & Egger, 2021) revealed that the risk perception of the COVID-19 pandemic impacted travelers’ behavior in the DACH region. Thus, several studies found that the COVID-19 pandemic has impacted the tourism and hospitality industry.

This study examined tourist behavior toward virtual tourism by extending the theory of planned behavior, satisfaction, and visit intention in the actual attraction during the COVID-19 pandemic. Therefore, it is essential to understand tourists’ behavior toward virtual tourism due to the COVID-19 pandemic as a solution to the time of the outbreak.

2.2 Virtual reality (VR) in tourism

Virtual reality (VR) systems substitute the scenery and sound of the real world with those of a computer-generated environment to give users the sense that they are genuinely in the real world (Sussmann & Vanhegan, 2000). Customized settings and scenes that closely match the real world are generated using 3D computer graphics as technology advances. When that technology is utilized to create virtual settings comprising tourist attractions, landscapes, and locations, tourists may have surreal virtual travel experiences even when not physically visiting specific areas.

The influence of virtual reality on tourism marketing is an essential element of tourist behavior (Gao et al., 2017; M.-H. Huang, 2003; O’Cass & Carlson, 2010; Wang & Hsiao, 2012). Virtual tourism is a way to promote tourism marketing based on the computer-generated image and give the impression of psychology to the tourist’s intention to visit a destination (Merkx & Nawijn, 2021). However, it also promotes a great sensitivity to the presence of tourist destinations. With the growing interest in virtual reality (VR), there has not been much research on how tourists are embracing VR as a form of travel (Talwar et al., 2022); thus, more emphasis needs to be paid to the key variables and theories that have been used to explain how people are embracing VR travel.

During crises such as the endemic, disaster, pandemic, or COVID-19, virtual tours have a role in tourism activity to prevent visitors, create new opportunities, and model business models in the tourism sector (Guha, 2020). Tourists can explore the site and tourist destinations during the COVID-19 pandemic and the restriction while staying safe (UNESCO, 2020). Virtual tourism has great potential as a solution in the tourism industry during the COVID-19 pandemic because the residents are advised to stay at home and avoid traveling outside. Virtual tourism (Lu et al., 2022). The advancement of technology, such as virtual tourism, has contributed to the tourism industry’s response to the spread of the COVID-19 pandemic. In this study, we extended the meaning of virtual tourism, including virtual and augmented reality and live broadcasting and streaming of tourism on some platforms. Furthermore, some countries, such as Indonesia and China, applied virtual tourism during the COVID-19 pandemic.

In Indonesia, they applied virtual tourism to serve the residents a destination online or virtually. Some applications in Indonesia were used in virtual tourism, such as Wisata Kreatif Jakarta, Atourin, and some platforms via live streaming, virtual reality, and augmented reality. Live streaming is a platform that has been popular during the pandemic for virtual tourism. Many agencies and tourism destinations have launched virtual tourism using live streaming via some platforms such as Tiktok, WeChat, Weibo, etc (Lu et al., 2022). Thus, in this study, live streaming is part of virtual tourism as an essential component during the COVID-19 pandemic and successfully impacted the residents to enjoy the destination.

Virtual tourism can contribute to the residents since COVID-19 has been detected in Indonesia and as an aid to the tourism industry in the recovery process. To best examine the role of the virtual tour for the residents or visitors regarding tourist behavior toward virtual tourism, this study explored the role of the theory of planned behavior on virtual tourism during the COVID-19 pandemic.

2.3 Theory of planned behavior

One of the ideas studied in the social-psychological model is the theory of planned behavior (TPB), which proposes that three elements impact an individual’s intention: attitude, subjective norm, and perceived behavioral control (Ajzen, 1985, 1991; Ajzen & Fishbein, 1980). The fields of marketing, psychology, marketing, technology, health, the environment, and other sectors have all benefited from the research of theory-planned behavior (De Leeuw et al., 2015; Liobikienė et al., 2016; Lu et al., 2021). The TPB research has been used in tourism, hospitality, and management (Ulker-Demirel & Ciftci, 2020). The Theory of planned behavior was applied in tourism to explain tourist intention to support tourism (Erul et al., 2020) and anticipate tourist behavior to revisit their intention in Egypt (Soliman, 2021).

As a result, planned behavior has been used to anticipate the intention and behavior. For example (Han & Hyun, 2017), used the theory of planned behavior to forecast the factors influencing a customer’s decision to visit. As a result, the TPB was implemented in tourist technology, such as online trip booking (Ulker-Demirel & Ciftci, 2020) and social media (Joo et al., 2020). Consequently, the three components in the Theory of planned behavior, such as attitude, subjective norm, and perceived behavioral control, may be used to explain visitors’ behavior and adoption of virtual reality during the COVID-19 pandemic.

The current study was designed by extending the TPB model by combining several indicators such as satisfaction, visit intention, and attitude toward virtual tourism during the COVID-19 pandemic. The study by (Pahrudin et al., 2021) applied the concept of TPB, which refers to predicting tourist behavior after the COVID-19 pandemic. The element of TPB, such as attitude, subjective norm, and perceived behavioral control, is a components to predict the behavior and situation (Ajzen, 1991). Thus, this study tried to explore tourists’ behavior using virtual tourism during the COVID-19 pandemic due to the behavioral intention to visit a destination as an actual attraction.

2.4 The extended theory of planned behavior

The TPB has been successfully extended in the tourism sector to explain tourist behavior in the tourism sector to find different targets (De Groot & Steg, 2007). Meng & Choi (2016) stated that the principle of the theory can be extended based on several principles: 1) it has the vital factors that can affect the decision process, 2) the present theory has independence from existing theory, and 3) has potentially suitable for specific behavior. In the tourism sector, some concepts have been extended from the original theory of TPB, such as in research on intention to visit friendly tourism (H. J. Song et al., 2012), destination image (Abbasi et al., 2021), future travel behavior (Rasoolimanesh et al., 2021), tourist behavior and health sector (Pahrudin et al., 2021). These scholars have added the theory of TPB to establish the model to predict tourist behavior. Thus, using the extended theory of planned behavior in the context of virtual tourism helps explore tourist behavior in using virtual tourism during the COVID-19 pandemic on satisfaction and intention to visit a destination.

2.5 Proposed model and hypothesis development

There are two sorts of intentions: (1) attitude toward conduct and (2) subjective norm; nonetheless, the act of recognizing behavior control is voluntary (Ajzen, 1991; Perugini & Bagozzi, 2001). The major proximal of action in the TPB framework is behavior (Eom & Han, 2019; Guerin & Toland, 2020). As a result, the other primary type of process is intention. In TPB theory, the purpose is a component of attitude toward behavior, which comes under subjective norm and perceived behavioral control (Guggenheim et al., 2020). Tourists’ attitudes and behaviors impact their desire or choice to visit a location (Guggenheim et al., 2020; Han & Hyun, 2017; J. J. Kim & Hwang, 2020). Attitude is a personal evaluation of adverse and negative actions (Ajzen, 2001; Perugini & Bagozzi, 2001).

The subjective norm is the second element in the Theory of Planned Behavior. The idea of a subjective norm is critical of personal or individual behavior from the standpoint of social pressure behavior (H. Song et al., 2017; J. M. Wu et al., 2017). Additionally, it may be stated that the subjective norm is an individual’s perspective in the social context of whether or not to do something (Ajzen & Kruglanski, 2019; Guerin & Toland, 2020).

Perceived behavioral control is the third component of the theory of planned behavior. Perceived behavioral control is described as a person’s or individual’s ability to engage in behavior-related activities (Perugini & Bagozzi, 2001). Tourist intention is influenced by perceived behavioral control (J. J. Kim & Hwang, 2020). Additionally, perceived behavioral control is an individual’s sense of how simple or difficult it is to behave, with various elements impacting this view, such as resources, experience, etc (Ajzen, 1991). Moreover (Hamid & Bano, 2021b; Han et al., 2010; Tonglet et al., 2004), discovered that perceived behavioral control could alter an individual’s behavioral intention.

Numerous research employing the TPB framework, such as attitude, subjective norm, and perceived behavioral control in tourism and consumer behavior (Hwang et al., 2020; J. J. Kim & Hwang, 2020; J. M. Wu et al., 2017), revealed that the framework has a favorable impact on visit intention. Additionally, according to the consumer’s purpose, the components of TPB are effectively and positively supported by Ajzen’s research (Ajzen, 1991). The component of TPB has been extended by some scholars in the context of the COVID-19 pandemic such as (Y. Liu et al., 2021; Pahrudin et al., 2021), revealing that the components of TPB, such as attitude, subjective norm, and perceived behavioral control influence visit intention. Moreover, the construct of TPB on satisfaction has extended in the context of virtual tourism (Y. C. Huang, 2023; Liao et al., 2007; Zheng et al., 2022), and in TPB on virtual tourism (Lu et al., 2022). Based on several explanations above, we proposed the hypothesis that TPB significantly and positively influences tourist satisfaction in using virtual tourism during COVID-19.

Hypothesis 1:

Attitude significantly and positively influences tourist satisfaction toward virtual tourism during the COVID-19 pandemic.

Hypothesis 2:

Subjective norm significantly and positively influences tourist satisfaction toward virtual tourism during the COVID-19 pandemic.

Hypothesis 3:

Perceive behavioral control significantly and positively influences tourist satisfaction toward virtual tourism during the COVID-19 pandemic.

2.6 Satisfaction and visit intention

Satisfaction refers to the consumer’s personal experience with services or products. Satisfaction evaluates the experience of using the services or product (Kao et al., 2007). According to the satisfaction theory, consumers will rate based on their experience (H.-C. Wu et al., 2018). Therefore, positive or high satisfaction influences consumer behavior (Mason & Paggiaro, 2012). Many studies on satisfaction have been conducted in various fields, such as purchase intention in marketing and visit intention in tourism and hospitality (Ali, 2016; Anderson & Srinivasan, 2003; Bai et al., 2008; C.-L. Hsu et al., 2012). These studies revealed that high consumer satisfaction increased purchase intentions for travel products.

Additionally (Kuo et al., 2009), discovered that the impression of pleasure with mobile services affected repurchase intention in the marketing industry. Furthermore, a study by (Mason & Paggiaro, 2012) in the field of tourism and hospitality (in the context of wine and food hospitality) found that satisfaction is one of the keys to motivating visitors to revisit their objective. The satisfaction leads to the attitude change of tourists based on their satisfaction with using virtual tourism during the COVID-19 pandemic.

Several studies have examined the relationship between satisfaction and visit intention. The result found that satisfaction significantly influences visit intention (De Nisco et al., 2015; S.-H. Kim et al., 2009; W. Lee et al., 2017). Furthermore, satisfaction with virtual tourism impacts visits intention (An et al., 2021; W. Lee et al., 2017). In addition, using virtual tourism influences the satisfaction of onsite visit intention (Geng et al., 2023; Rahimizhian et al., 2020). Moreover, the study conducted by (Manchanda & Deb, 2022; Suhartanto et al., 2021), revealed that satisfaction with virtual tourism determines tourist intention to revisit during the COVID-19 pandemic. Based on several explanations in those studies, the hypothesis proposed is as follows:

Hypothesis 4:

Satisfaction with virtual tours during the COVID-19 pandemic influences the intention to visit actual tourist attractions.

Hypothesis 5:

Satisfaction with virtual tours during the COVID-19 pandemic influences attitudes to change.

2.7 Attitude change and visit intention

In the literature of social psychology, attitude is a crucial notion, as is consumer behavior to predict individual conduct. However, attitude and behavior may change in different contexts and conditions (Ajzen & Fishbein, 1977; Glasman & Albarracín, 2006; Smith & Swinyard, 1983). According to the behavior hierarchy (belief—attitude—intention), the link between attitude and conduct is mediated by behavior intention (M.-S. Kim & Hunter, 1993). Numerous researchers have already emphasized the significance of attitude and behavioral intention to visit a location (S. Huang & Hsu, 2009; Phillips et al., 2013; Ryu & Han, 2010).

According to (S. Huang & Hsu, 2009), visitors’ intentions to return to Hong Kong are greatly influenced by their attitude regarding visit intention. Similarly (Lam & Hsu, 2004; Phillips et al., 2013), revealed that attitude substantially affected the intention to visit Korea and Korean food. Much research supports the conclusion that virtual reality tourism has altered views regarding the intention to visit genuine tourist places. In this investigation, the following hypothesis was proposed:

Hypothesis 6:

Attitude changes with a virtual tour during the COVID-19 pandemic influence intention to visit actual tourist attractions.

Based on several explanations and hypotheses development, the framework of the study in Figure 1 was proposed.

Figure 1. Proposed research model.

3. Method

3.1 Research design

In this study, the quantitative approach was used to analyze the data, and a cross-sectional survey method was used to collect data from local Indonesian tourists. Furthermore, purposive sampling was applied in this study for those who had experienced using virtual tourism during the COVID-19 pandemic.

3.2 Sample and data collection procedures

Online surveys are one of the methods in the tourism and hospitality industries for collecting data (Han et al., 2009; W. Kim & Ok, 2009). We employed an online survey to collect the data based on these studies. There were several ways to collect the data, reach the potential respondents, and spread the online survey through social media, such as email, WhatsApp, Facebook, etc. The data were collected from August to November 2022. Furthermore, all of the respondents in this study agreed on the ethical and consent aspects.

The screening questions were applied in this study to identify the respondents. Several questions were distributed to respondents, along with instructions and instructions on completing them. Furthermore, in this study, the population of the respondents was local Indonesian tourists who used virtual reality tourism during the pandemic of COVID-19. Purposive sampling was applied to analyze the potential respondents. To analyze the data, Smart-PLS 3.3 was used in this study.

SEM-PLS was applied in this study and can be used for small sample sizes. It was stated that most of the studies in SEM-PLS could be conducted using samples from 30–500 (Roscoe, 1975) cited in (Sekaran & Bougie, 2016). In sum, 285 respondents who had experience using virtual reality tourism were collected from an online survey. After screening the data and deleting some because it did not progress to the following analysis stage, we had 217 usable responses or 77.5 percent rate of participants from the total data 280 responses. Based on this result of the sample size, Structural Equation Modelling based on (Kline, 2015) stated that the median or minimum sample size is 200 samples. Thus, this sample size was more significant than the minimum sample size in this study.

3.3 Questionnaire development

We employed several variables as predictors to build a framework for the study. Several studies were used to develop the variable construct in this study. Most of the study’s constructs were derived from the Theory of planned behavioral constructs. The attitude variable was developed by (Talwar et al., 2022; Toivonen, 2022). The variable of the subjective norm was adopted from (De Canio et al., 2021; Y. C. Huang, 2023). Therefore, the variable of perceived behavioral control was developed from studies (De Canio et al., 2021; Y. C. Huang, 2023). Furthermore, the variable of satisfaction was developed by (Gao et al., 2017; C.-L. Hsu et al., 2012; Lin & Kuo, 2016). The variable of attitude change was developed by (S. Huang & Hsu, 2009; Phillips et al., 2013; Ryu & Han, 2010; Tussyadiah et al., 2018), and the last variable of intention to visit actual tourism attractions were developed by (S. Lee et al., 2018; Pahrudin et al., 2021). We adopted the Likert-type scale with 1: Strongly Disagree and Scale 7: Strongly Agree to investigate these variables.

3.4 Data analysis

This study analyzed the data using structural Equation Modelling-Partial Least Squares (SEM-PLS). SPSS software was used to analyze the descriptive data, which included gender, education, income, and occupation. There are various procedures in structural equation modeling with partial least squares (SEM-PLS) to measure indicators such as composite reliability (CR), average variance extracted (AVE), and value of factor loadings. Moreover, the composite reliability criterion should be 0.7 (Nunnally & Bernstein, 1994), the average variance extracted must be more significant than 0.5 (Fornell & Larcker, 1981), and the factor loading barrier in confirmatory factor analysis must be higher than 0.6 (Nunnally & Bernstein, 1994).

4. Result and discussion

4.1 Respondent demographic characteristics

The respondents of this study were tourists who had experienced using virtual tourism during the COVID-19 pandemic. Several variables or indicators of the respondents are gender, age, education, income, and occupation. The total respondents are 217 (valid respondents) in this study. Table 1 shows the characteristic of the respondents.

Table 1. Characteristic of the respondents

Variable	Responses	Frequency	Percentage
Gender	Male	126	58%
	Female	91	42%
Age (year)	1965–1980	9	4%
	1981–1996	63	29%
	1997–2012	145	67%
Education	Senior High School	88	41%
	Diploma/Bachelor	91	42%
	Master	35	16%
	Doctor	3	1%
Income monthly	More than Rp. 500.000 (32.25 USD)	136	63%
	Rp. 500.001- Rp.2.500.000 (32.25–161.26 USD)	24	11%
	Rp. 2.500.000- Rp. 4.500.000 (161.26–290.27 USD)	26	12%
	Rp.4.500.000-Rp. 6.500.000, (290.27–419.29 USD)	14	6%
	More than Rp. 6.500.000, (more than 419.29 USD)	17	8%
Occupation	Students	105	48%
	Private Sector	22	10%
	Lecturer/teacher	23	11%
	Looking for job	31	14%
	Servant society employee	7	3%
	Households	5	2%
	Others.	24	12%

4.2 Validity and reliability

The test validity and reliability were applied in this study to examine the research instrument. There are three indicators to test reliability: factor loadings of the instrument should be greater than .50 (Hair et al., 1992), and the composite reliability value should be exceeded by .70 (Fornell & Larcker, 1981). The value of Cronbach’s alpha should be more than .70 (Fornell & Larcker, 1981; Nunnally & Bernstein, 1994). The last is the value of the AVE rather than. 50 (Fornell & Larcker, 1981). Moreover, to examine the value of convergent validity, the value of each variable should be more than .50, and the threshold value of discriminant validity should be more excellent than .70 from the square root of AVE.

According to Table 2, the composite reliability value in this study was more than .70, and the value of all constructs in the study was greater than .70. We investigated both convergent and discriminant validity to determine the validity of the latent variable. The first phase, convergent validity, was investigated by comparing the value of the AVE (average value extracted) with the loading factors of the indicators. Confirmatory factor analysis was used in this study to determine the factors loading on the variables. According to Table 2, the AVE value ranged between 0.643 and 0.916, above the threshold value of 0.50. The study’s factor loading result, which was between 0.725 and 0.958, showed that the indicators’ values were supported by convergent validity.

Table 2. The value of Factor loadings, AVE, CR, and Cronbach’s alpha

Indicators	Factor loading	AVE	CR	Cronbach Alpha	Note
Attitude toward VT
During a pandemic, for personal protection, traveling virtually is an excellent alternative to visiting tourist destinations. During a pandemic, virtual travel can be used as entertainment, making tourists happy.	0.928 0.938	0.871	0.931	.852
Subjective Norm toward VT
(1) During the pandemic, I noticed that others around me (such as colleagues, family, and friends) have tried traveling virtually.	0.907
(2) During the pandemic, people who are important to me want me to travel virtually.	0.903	0.721	0.885	.803
(3) During the pandemic, my friends and I plan to visit real tourism destinations as we have seen when traveling virtually.	0.725
Perceived Behavioral Control toward VT
(1) I know and understand how to travel virtually via handphone, computer or other gadgets.	0.846
(2) I think, it is easy to travel virtually.	0.837	0.643	0.878	.817
(3) During the pandemic, I am confident that I can visit real tourism destinations as I have seen them virtually.	0.785
(4) During the pandemic, I was able to virtually visit tourism destinations that I have seen.	0.736
Satisfaction on Virtual Tourism
(1) My virtual travel experience was better than I expected.	0.956
(2) I am happy with my virtual travel experience.	0.958	0.916	0.956	.908
(3) I intend to travel to a real tourism destination during the pandemic.					Deleted
Attitude Change toward VT
(1) After experiencing the experience of traveling virtually during the pandemic, my preference is for tourism destinations that I saw virtually.	0.915
(2) After experiencing the experience of traveling virtually during the pandemic, my preference/priority is for the tourism destinations that I saw virtually.	0.938	0.848	0.944	.911
(3) After experiencing the experience of traveling virtually during the pandemic, my desire is to visit the tourism destination that I saw virtually.	0.909
Visit Intention
(1) I want to repeat my travel experience virtually in the future.	0.866
(2) I would recommend virtual travel activities to my friends and others.	0.936	0.815	0.929	.886
(3) I will tell other people positive things about virtual travel activities.	0.904
(4) I will visit real tourist destinations as I have seen them while traveling virtually.					Deleted

The root of the AVE from each was used in this study to examine the discriminant validity (Chin, 1998). According to some researchers, the value of AVE was greater than .70 (Venkatesh et al., 2012). The value of AVE was at least 0.70. Table 3 shows the result of discriminant validity. Discriminant validity was examined by comparing the square root of the AVE for each construct against the inter-construct correlation. Based on the result of this study, all of the diagonal elements, which are the square of root AVE and the discriminant validity, were accepted in this study using Fornell-Larcker.

Table 3. Discriminant Validity (Fornell-Larcker Criterion)

	1	2	3	4	5	6
(1) Attitude	0.933
(2) Attitude to change	0.686	0.921
(3) Perceived behavioral control	0.625	0.657	0.802
(4) Satisfaction	0.700	0.734	0.633	0.957
(5) Subjective norm	0.719	0.666	0.684	0.706	0.849
(6) Visit intention	0.752	0.804	0.666	0.776	0.690	0.903

4.3 Structure testing model

The structure testing model was applied to examine the potential collinearity among the constructs by evaluating the significant level (path coefficient) and significant level (t-value or p-value). In addition, obtain the bootstrapping with resampling (5000) in smartPLS-3 to obtain the value. Table 4 displays the results of the structure testing model’s hypothesis testing. The research model was supported accordingly in this study.

Table 4. Structure Testing model

	Original Sample	Standard Error	T Statistics	P Values
AC1 <- Attitude to change	.384	0.021	18.510	.000
AC2 <- Attitude to change	.372	0.010	37.152	.000
AC3 <- Attitude to change	.330	0.009	37.659	.000
I1 <- Visit intention	.344	0.010	33.155	.000
I2 <- Visit intention	.373	0.010	35.571	.000
I3 <- Visit intention	.390	0.014	28.190	.000
PBC1 <- Perceived behavioral control	.362	0.031	11.776	.000
PBC2 <- Perceived behavioral control	.353	0.030	11.757	.000
PBC3 <- Perceived behavioral control	.265	0.030	8.797	.000
PBC4 <- Perceived behavioral control	.260	0.032	8.047	.000
S1 <- Satisfaction	.516	0.010	53.235	.000
S2 <- Satisfaction	.529	0.010	53.900	.000
SN1 <- Subjective norm	.435	0.025	17.329	.000
SN2 <- Subjective norm	.417	0.022	19.269	.000
SN3 <- Subjective norm	.316	0.032	9.915	.000
A1 <- Attitude	.516	0.020	25.787	.000
A2 <- Attitude	.556	0.026	21.116	.000

4.4 Structural testing results

The Structural Equations Modelling Partial Least Squares (SEM-PLS) measurement model was examined using several criteria, including the study’s validity and reliability. The result of the outer and inner model measurements was presented in this section. There are two parts to the assessments in the inner model: the path coefficient model to examine the significance of the path coefficient and R (coefficient determination) (Fornell & Larcker, 1981; Hair et al., 2014).

Table 5 presents the result of the path coefficient. The result found that attitude significantly impacted satisfaction (H1: =.343, t = 4.510), indicating that the hypothesis was accepted. In this study, the second hypothesis discovered that subjective norm significantly impacted satisfaction (H2: =.324, t = .4024). Based on the result of the study conclude that the hypotheses were supported. The variable of perceived behavioral control significantly impacted satisfaction (H3: = 0.197, t = 2.631), so the hypothesis was supported. The hypothesis of satisfaction on attitude toward change had a significant coefficient (H4: β = 0.734, t = 17.109) and was supported. The hypothesis was accepted because satisfaction significantly impacted visit intention (H5: = 402, t = 5.209). The final hypothesis tested in the study found that attitude to change significantly impacted visit intention (H6: =.509, t = 6.391). Thus, this hypothesis was accepted. To examine the relationship between variables, the path coefficient in the regression was used in the study. The path coefficient value represents the variable’s strength and orientation. The results found that all variables have positive path coefficients and are significant.

Table 5. Result of Structural Equation Model

	Original sample (Estimated/ β)	T Values	P Values	Decision	R^2
Attitude -> Satisfaction	.343	4.510	.000***	Supported	0.594
Subjective norm -> Satisfaction	.324	4.024	.000***	Supported
Perceived behavioral control -> Satisfaction	.197	2.631	.009***	Supported
Satisfaction -> Attitude to change	.734	17.109	.000***	Supported	0.539
Satisfaction -> Visit intention	.402	5.209	.000***	Supported	0.721
Attitude to change -> Visit intention	.509	6.391	.000***	Supported

*** Significant at the p < 0.05 level (two-tailed).

To predict each variable’s capability to explain the model, the R-value was used to explain the inner variables. R square is a value to explain the model’s capacity or the R square determination model of the study (Fornell & Larcker, 1981; Hair et al., 2014). The result found that the value of R square 0.594, or 59.4 percent, of the variable satisfaction was explained by the variable’s attitude, subjective norm, and perceived behavioral control. Therefore, the variable satisfaction explained the value of R square 0.539, or 53.9 percent, of the variable attitude to change. The last value, R square 0.721 or 72.1 percent, is the variable satisfaction and attitude to change explained visit intention in this study.

Additionally, in this study, we investigated the mediation analysis using a framework of satisfaction and its relationship to attitude to change and visit intention. As indicated in Table 6, the finding revealed that satisfaction is significantly and positively correlated with attitude and visit intention. Moreover, the mediation analysis of satisfaction toward perceived behavioral control and visit intention had significant and positive, as well as between subjective norm and visit intention. Additionally, the mediation of satisfaction had a significant and positive relationship with attitude and attitude to change, as well as between subjective norm and attitude to change. Finally, the mediation of satisfaction demonstrated a significant and positive relationship between perceived behavioral control and attitude change. The complete outcome of the mediation analysis is presented in Table 6.

Table 6. A mediation analysis

	Original Sample (estimated β)	T Values	P Values	Percentile 95% Interval	Bootstrap Confidence
				Lower	Upper
Attitude -> Satisfaction -> Visit intention	0.138	3.403	0.001***	0.064	.219
Satisfaction -> Attitude to change -> Visit intention	0.374	6.001	0.000***	0.264	.501
Perceived behavioral control -> Satisfaction -> Visit intention	0.079	2.186	0.029***	0.020	.164
Attitude -> Satisfaction -> Attitude to change	0.252	4.196	0.000***	0.139	.374
Subjective norm -> Satisfaction -> Visit intention	0.130	3.285	0.001***	0.058	.211
Subjective norm -> Satisfaction -> Attitude to change	0.238	3.944	0.000***	0.125	.351
Perceived behavioral control -> Satisfaction -> Attitude to change	0.145	2.580	0.010***	0.034	.269
Attitude -> Satisfaction -> Attitude to change -> Visit intention	0.128	3.400	0.001***	0.063	.208
Subjective norm -> Satisfaction -> Attitude to change -> Visit intention	0.121	3.210	0.001***	0.052	.199
Perceived behavioral control -> Satisfaction -> Attitude change -> Visit intention	0.074	2.476	0.014***	0.019	.136

*** Significant at the p < 0.05 level (two-tailed).

5. Discussion

In this study, we tried to explore the component of TPB (Ajzen, 1991; Fishbein & Ajzen, 2011), such as attitude, subjective norms, and perceived behavioral control toward tourist satisfaction in using virtual tourism during the COVID-19 pandemic. The following are the key findings of the study:

This study used the theory of planned behavior to explore tourist behavior in virtual tourism during the COVID-19 pandemic. According to the findings in Table 5, the result found that the TPB variables such as attitude, perceived behavioral control and subjective norm significantly impact tourist satisfaction when using virtual tourism. The result of the study implied that virtual tourism is one of the methods for traveling to the destination in Indonesia; seeing or watching it virtually via a handphone, computer, or another gadget toward the destination give impacts tourist satisfaction. The result of this study, supported by (Lu et al., 2022), revealed that virtual tourism has successfully explained the concept of TPB.

Many researchers (Y. Liu et al., 2021; Pahrudin et al., 2021) applied the theory of planned behavior to visit intention during the COVID-19 pandemic and found that it substantially influenced visit intention. Virtual tourism is a category that heavily impacts visitor on-site destination selections and is used as a marketing approach to promoting tourism destinations (Lu et al., 2022). After the influence of the COVID-19 pandemic, meeting customers’ or tourists’ demands connected to information or destination might boost the inclination to engage in virtual tourism, thereby encouraging visitors to visit a location in reality (Alzaidi & Agag, 2022).

As a result, the study’s findings suggest that attitudes can affect tourists’ intentions and pleasure in a virtual reality or digital tourism environment. The results show that because of the rapid advancement of technology and the attitude toward virtual reality travel, tourists quickly look up information about their destinations and post their images and accounts of their experiences on social media (W.-L. Lee et al., 2022). However, social media has developed into a significant component of virtual reality tourism as a means for travelers to share their experiences, images, films, or other content with personal audiences who may be interested in it (Talwar et al., 2022).

The positive attitude has dramatically altered how to persuade clients to adopt new technology and see the value of verbatim information, photos, video recorders, or other virtual products to enhance the person’s experience (Javornik, 2016; Pleyers & Poncin, 2020). Similarly, new technology acceptance attitudes are critical for triggering consumer cognitive or affective entities, which assist tourists in efficiently accessing appropriate destination-related information and influence user experiences and behavior intention, particularly during emergencies such as the pre-and post-COVID-19 periods (H. Kim et al., 2021). Moreover (Tussyadiah et al., 2018), assessed the effects of a more affluent VR tourism attitude on visitors’ enhanced understanding of technological advantages via social media collaborative efforts to pique their interest (Erjavec & Manfreda, 2022).

The result found that subjective norm significantly and positively influences tourist satisfaction toward virtual tourism during the COVID-19 pandemic. The result implied that the subjective norm toward virtual tourism contributes to tourist satisfaction in virtual destinations. In addition, the subjective norm refers to the individual perception of social pressure from someone such as family, friends, and others who engage in the behavior (Belanche et al., 2019). Moreover, tourists’ in Indonesia accepted the suggestion and advice from their family, friends, and colleagues to use virtual tourism during the COVID-19 pandemic, which impacts satisfaction toward a destination. Thus, the contribution of virtual tourism during the COVID-19 pandemic impacted tourist satisfaction based on social pressure such as family, friends, and colleagues to use virtual tourism to avoid the risk of the COVID-19 pandemic.

In addition, it was also found that perceived behavioral control has a significant and positive impact on tourist satisfaction in using virtual tourism during the COVID-19 pandemic. Perceive behavior control in virtual tourism so that tourists can control their behavior when visiting real destinations due to the pandemic. Perceive behavior control refers to the individual’s belief in managing the aspects that can influence the behavior. This result implied that Indonesian tourists could manage their behavior during the pandemic to use virtual tourism to get satisfaction toward their destination at home. In addition, this situation is due to the restriction during the COVID-19 pandemic in Indonesia, and tourists cannot visit a destination. This result is similar to the study from (Pahrudin et al., 2021), which stated that the Indonesian government had policies during the pandemic to avoid spreading the virus.

Moreover, our findings show that tourist satisfaction with virtual tourism promotes attitudes toward change. It underlines the importance of visitor satisfaction in virtual tourism and how it affects shifting tourist attitudes. Their satisfaction with virtual tourism influences tourists’ willingness to visit a destination. After the COVID-19 epidemic, travelers’ virtual tourism experiences are being prioritized as a stimulant to visit intention in reality. The findings of this study are congruent with those of (Manthiou et al., 2014), who discovered that experiences and satisfaction had a favorable influence on tourist intention and attitude change.

Because of the pandemic, virtual tourism allows tourists to visit a destination digitally (Schiopu et al., 2021). Additionally, virtual tourism helped visitors or tourists avoid the COVID-19 virus and raised tourist knowledge of sustainability (Subawa et al., 2021). On the other hand, Virtual tourism is one of the strategies in tourism marketing to promote the destination and the way to reach sustainability. In addition, tourists can receive positive feedback from social media and may have high expectations from the tourism service toward virtual tourism (Erjavec & Manfreda, 2022).

Furthermore, this study discovered that satisfaction with virtual tourism was significantly related to visiting intention. The study’s findings indicate that tourist satisfaction positively encourages tourists to visit their intended destination while using virtual tourism. The result of this study, supported by (An et al., 2021), stated that tourists’ satisfaction influences their visit intention in virtual tourism. Therefore, the study’s findings were consistent with previous research (Y.-C. Huang et al., 2013; M. J. Kim et al., 2020), which revealed that virtual tourism is an effective marketing tool for promoting tourism destinations and increasing potential tourist visit intention. Virtual tourism is a solution for tourists to avoid spreading risk or the virus when tourists prefer to use virtual tours to explore while staying home (An et al., 2021).

The last result of this study is that attitude change significantly influence the intention to visit actual tourist attraction. The study’s findings conclude that virtual reality tourism has changed the attitude of tourists toward the intention to visit actual tourist attractions in the future. This result refers to the attitude of tourists toward virtual tourism that would change the intention to visit actual attractions or a destination in post-disaster. The results of the studies supported the role of attitude and behavioral intention to visit a destination (S. Huang & Hsu, 2009; Phillips et al., 2013; Ryu & Han, 2010). A study (S. Huang & Hsu, 2009) shows that attitude toward visit intention significantly influences tourists’ intentions to return to Hong Kong. Similarly, some studies by (Lam & Hsu, 2004; Phillips et al., 2013) discovered a significant influence of attitude on the intention to visit Korea and Korean cuisine.

The COVID-19 pandemic has impacted the tourism industry in Indonesia since the beginning of the COVID-19 pandemic in 2020 the last two years. However, since 2022 tourism industry in Indonesia has begun to recover from the COVID-19. The tourism industry’s contribution in Indonesia before the COVID-19 pandemic reached USD 16.9 USD billion in 2019 and has slowed to USD 13.6 billion during 2020–2021 (Kinseng et al., 2022).

To ensure the physical distance of social interaction, the government implemented several policies to keep social distancing during the COVID-19 pandemic, such as work from home (WFH) and quarantine policy for those traveling overseas. In addition, the government also implemented a lockdown for the region, which impacted the COVID-19 pandemic. These policies had consequences on the economic activity in Indonesia, such as trade, production, travel and tourism industry, and transportation.

In most countries, including Indonesia, tourism activities had disrupted by the policy to prevent the spread of COVID-19. Consequently, in the tourism sector, applied virtual tourism is a solution for tourists who want to enjoy the destination during the COVID-19 pandemic. Some platforms that triggered the creation of virtual tourism in Indonesia, such as the platform WKJ, JGG, Atourin, and Bersukaria, provided virtual tourism during the COVID-19 pandemic. These platforms virtual tourism provide virtual tourism for tourists related to some destinations in Indonesia. These virtual tourism platforms have the advantage that anyone can join a tour and are more flexible without limiting time, space, or distance. Aligned with the previous studies, virtual tourism strongly influences tourists’ visit intention to a destination on-site or online (Marasco et al., 2018; Rahimizhian et al., 2020).

Moreover, several studies have conducted that the COVID-19 pandemic has changed tourist behavior due to the spread of the illness such as from (Neuburger & Egger, 2021; Pahrudin et al., 2021), stated that COVID-19 had changed tourist travel intention and behavior. The three factors (Ajzen, 1985; Fishbein & Ajzen, 2011), namely attitude toward virtual tourism, subjective norm toward virtual tourism, and perceived behavior control toward virtual tourism, positively impact tourists’ satisfaction with virtual tours in Indonesia. Thus, the result indicated that the constraints of TPB could extend in the context of virtual tourism during the COVID-19 pandemic. Besides the TPB constructs, satisfaction has successfully influenced the attitude to change of tourists and visit intention in actual attractions after the COVID-19 pandemic. This result indicated that the tourists’ satisfaction with virtual tourism during the COVID-19 pandemic could change their attitude and visit behavior toward a destination during the post-COVID-19 pandemic.

6. Contribution and implication of the study

The contribution and implication of the study are discussed in this section. This section explains the contribution of the study, both theoretical and practical contribution, as well as the implication of the study.

6.1 Theoretical contribution

This study contributes to the existing literature on tourist behavior, such as satisfaction and visit intentions. Therefore, the study’s result can contribute by extending the literature on tourism, technology, and psychology through virtual reality tourism during crises. As stated previously, there were limited studies on the constructs of TPB (attitude, subjective norm, and perceived behavioral control) on satisfaction and visit intention using virtual tourism during the COVID-19 pandemic. Hence, applying the TPB construct, the current study comprehensively discussed the factors influencing satisfaction and visit intention using virtual tourism during the COVID-19 pandemic.

The constructs of TPB, such as attitude, subjective norm, and perceived behavior control, have successfully broadened satisfaction with virtual tourism during the COVID-19 pandemic. The tourists’ put their behavior themselves so they could take advantage of virtual tourism during the COVID-19 pandemic as a solution for traveling to a destination. The researchers and academicians can benefit from the study’s findings because the TPB construct significantly influences satisfaction. Hence, the study successfully extended the original construct of TPB by introducing the significant factors from the primer exogenous variable (such as attitude, subjective norm, and perceived behavioral control) on satisfaction and visit intention during the COVID-19 pandemic. The study by (Kurata et al., 2022) stated that the TPB had a fixed point due to insignificant variables and the need to extend the theory.

The theoretical implication of this study implies enriching the theoretical perspective in tourism during the pandemic and post-pandemic. We extended previous research findings by demonstrating that theory of planned behavior (TPB) attributes significantly influence tourist satisfaction and visit intention in actual attractions COVID-19-post pandemic. The result of TPB in the current studies may extend the current literature on virtual tourism by explaining the indicators of theory planned behavior such as attitude, subjective norm, and perceived behavior control while using virtual tourism to influence their behavior to visit actual attractions. Some scholars in virtual tourism have discussed several studies that revealed that virtual tourism is one of the alternatives to avoid the risk of COVID-19 while traveling (M. J. Kim & Hall, 2020; C-H. S. Liu & Dong, 2021).

6.2 Practical contribution

As a result, this study contributes to destination marketers, virtual tour developers, and tourism managers by adding the marketing factor through virtual reality tourism. It is essential for marketers and stakeholders in tourism and destination during the COVID-19 pandemic as a tool for understanding tourist behavior in using virtual reality. Furthermore, this study explores tourist satisfaction with virtual reality tourism. The result of the study gives insight for destination marketers and stakeholders to arrange the promotion of destination marketing through virtual reality tourism during the pandemic or in times of crisis to enhance tourist visit intention in actual attraction.

For virtual developers, this study can contribute to the virtual developer to enhancing the quality of content in virtual tourism. Furthermore, the developer also can create content related to all kinds of destinations in Indonesia, including nature, museums, beaches, mountains, and other destinations. In addition, virtual tours allow the tourist to watch the destination virtually before the natural attraction or actual visit. Moreover, the developer of virtual tourism can increase the quality of visual elements to increase tourist satisfaction while watching virtually.

Furthermore, the practical and managerial implications of this study provide insight for the marketers and stakeholders in the tourism and hospitality sector that virtual tourism can influence the tourist intention on actual attraction during the COVID-19 post-pandemic by applying the theory of the TPB construct. In the time of natural disasters, such as the COVID-19 pandemic, are one of the concerns in influencing tourist visit intention and the need to determine the effectiveness of marketing strategies; however, few studies have discussed which marketing strategies for the tourism sector (Leung et al., 2022). We discovered that virtual tourism could help the tourism and hospitality industries in times of crisis to improve customer satisfaction with new technologies and generate destination attraction (C-H. S. Liu & Dong, 2021). Thus, in terms of managerial implication, the manager and stakeholders in the tourism sector can provide the infrastructure of virtual tourism as a strategy for marketing tourism destinations for the future.

7. Conclusion

In the new generation era, technology brings new concepts to the tourism and hospitality industries, such as virtual tourism. This study extended the concept of virtual tourism and the theory of planned behavior during the COVID-19 pandemic. The result of the study found that tourist behavior toward virtual tourism during the COVID-19 pandemic can influence tourist satisfaction. In addition, tourist satisfaction can change their behavior toward actual attraction during the COVID-19 post-pandemic. The most important aspect of this study is that we extend the perspective of virtual technology or virtual reality and the theory of planned behavior (TPB) to apply in tourism and hospitality with the functions to increase the intention of tourists to visit actual attractions. This study gives theoretical and practical implications in tourism and hospitality to enhance the competitive advantage in COVID-19 post pandemic, such as sustainable and marketing strategies.

8. Limitations of the study and future research agenda

In this study, we recognize some limitations and the need for further attention in future research. Several limitations in this study and need further attention for future study. First, this study examined how tourists used virtual tourism during the COVID-19 pandemic. From the aims of this study, the result cannot be used to compare tourists’ decisions before and after the COVID-19 pandemic. Furthermore, this study used an online survey to reach the respondents due to the COVID-19 pandemic, with the participant’s limited response to the survey and short observation time without directly facing the respondent. Future research should include extending the observation methods to understand tourist behavior better when using virtual reality tourism. Second, this study has limitations in a single country because it does not recognize cultural background as a limitation. Although the concept of virtual reality in tourism has been discussed primarily in most developing countries, it is one of the current issues for virtual reality tours in developing countries such as Indonesia. It can be accepted in the Indonesian tourism sector. A future study should compare tourist behavior in virtual tourism in developed and developing countries.

Acknowledgments

We would like to thank the editor and anonymous reviewers for their comments and suggestions to improve the quality of the manuscript.

Disclosure statement

The authors declare that there is no potential conflict of interest in this study.

Additional information

Funding

There is no funding for this study.

Notes on contributors

Li-Wei Liu

Li-Wei Liu is a Professor at the Department of Leisure and Service Management, Chaoyang University of Technology, Taiwan.

Chia-Chung Wang

Chia-Chun Wang is an assistant professor at department of Leisure, Recreation and Tourism Management, Southern Taiwan University of Science and Technology Taiwan. His research interests in tourism and leisure.

Pahrudin Pahrudin

Pahrudin, Pahrudin is a Postdoctoral researcher at the Department of Leisure and service management, Chaoyang University of Technology Taiwan. He is also an Lecturer at the Faculty of Social Science and Economic, Hamzanwadi University (Lombok, Indonesia). His research interests are tourism economic, tourism marketing and management, disaster/crises in tourism, and tourist behavior in post-disaster.

Achlan Fahlevi Royanow

Achlan Fahlevi Rayanow is a Lecturer at Lombok Tourism Polytechnic, Lombok, Indonesia. His research interests include tourism, social media (electronic) and tourism, etc.

Chien Lu

Chien Lu is a lecturer at General Education center, National Quemoy University, Taiwan.

Irwan Rahadi is a Lecturer at the Department of Tourism, Hamzanwadi University Lombok, Indonesia. His research interests in tourism, statistic and tourism.

Irwan Rahadi

Irwan Rahadi is a Lecturer at the Department of Tourism, Hamzanwadi University Lombok, Indonesia. His research interests in tourism, statistic and tourism.