Tourism Using Virtual Reality: Media Richness and Information System Successes

Abstract

Due to the COVID-19 pandemic outbreak, borders were closed, cities were blocked, and individuals went into quarantine. The market size of the tourism industry in 2020 declined by more than 70% compared to the previous year, regressing to the size it was 30 years ago. This does not mean that people’s needs for tourism have decreased. People started to use virtual reality technologies to get the experience of sightseeing even if they could not go directly to tourist attractions. Prior studies found that virtual reality technology is effective for online shopping and gaming contexts. However, there are insufficient studies investigating the effect of using virtual reality for tourism content. Therefore, this study attempts to verify how the media richness of virtual reality tourism content elicits various reactions from potential tourists in terms of perceived usefulness, perceived enjoyment, satisfaction, destination visit intention, and positive word-of-mouth intention. The purpose of this study is to verify how virtual reality tourism content increases the destination visit intention after the COVID-19 pandemic. Based on media richness theory and the information system success model, a hypothesis was developed. One hundred and eighty-two data were gathered from potential tourists who were in quarantine by performing an online scenario survey that used quasi-experiment methods. Data were analyzed with a PLS algorithm. The results indicate that media richness of tourism content using virtual reality significantly increased perceived usefulness and perceived enjoyment. It could significantly increase satisfaction, destination visit intention, and positive word-of-mouth intention. The results of this study explain how information technology can be used in the tourism industry, and they provide suggestions on why tourism content using virtual reality can be useful for attracting tourists, and what experiences it can provide tourists.

1. Introduction

Before the outbreak of COVID-19, the tourism industry was growing rapidly. The number of tourists will grow 3.3% annually, from 2010 to 2030, reaching 1.8 billion. By 2030, the tourism industry will reach 9% of global GDP [1]. The tourism industry is also an industry that has great ripple effects on other industries. Each tourist creates 11 new jobs [1]. Therefore, governments and agencies in each country have made great efforts to promote the tourism industry [2].

However, due to the COVID-19 pandemic, the efforts of governments and agencies faced a crisis. COVID-19, which was first discovered in Wuhan, China in December 2019, has expanded to a global area, with 420 million accumulated confirmed cases, 5.8 million deaths, and 3.7 million new confirmed cases every day as of 21 January 2022 [3]. The tourism industry has frozen. Borders were closed, cities were blocked, and individuals went into quarantine [4]. To prevent the spread of the COVID-19, 81% of countries have implemented complete border closures for tourists, and 11% have implemented partial immigration restrictions since the first half of 2020 [5]. Tourists were required to quarantine for 14 days. The market size of the tourism industry in 2020 declined by more than 70% compared to the previous year, regressing to the size it was 30 years ago [5]. Moreover, contrary to initial expectations that the market would recover in the second half of 2020 [5], the market situation is still gloomy in 2022, as the N-th wave continues due to Delta mutations and Omicron mutations [6].

People’s potential need for tourism has not decreased, and people say they want to travel while following quarantine guidelines [5,6]. In 2020, vaccinations began, and as the severity rate has decreased, expectations for permission to travel are growing [7]. However, the time it will take for the tourism industry to recover its growth trend is anticipated to be slower than people expect. UNWTO expects it will be possible after 2024 [5,6]. As an alternative to meeting their need to travel, people are trying to experience sightseeing, even if they do not go directly to tourist destinations, by utilizing new technologies such as virtual reality technology. For those who have entered quarantine, virtual tourism content includes natural landscapes such as The Cliffs of Moher and Victoria Falls, which are made and provided as 360-degree images to enable tourism using virtual reality technology. This type of travel is called ‘LAN-line tourism’ in Korea, and it is generally described as ‘virtual travel’ [8]. Governments and agencies want to create virtual travel content and advertisements to attract tourists after the COVID-19 pandemic [8].

There are insufficient studies investigating the effect of using virtual reality for tourism content. Most of the research on virtual reality in the field of information systems is focused on research that verifies media selection [9,10]. Research has been conducted to measure its effectiveness by introducing virtual reality technology in areas such as e-commerce [11,12] and virtual reality games [13]. There are few studies which investigate the impact of virtual reality technologies in the field of tourism [14,15,16,17]; therefore, this study attempts to verify how the media richness of virtual reality tourism content elicits various reactions from potential tourists as perceived usefulness, perceived enjoyment, satisfaction, destination visit intention, and positive word-of-mouth intention. Based on media richness theory and the information system success model, I developed our hypothesis. The results of this study not only explain how information technology can be used in the tourism industry, but they also suggest why designing tourism content using virtual reality is useful, and what experiences it can provide tourists. The following theoretical background will explain media richness theory, the information system success model, previous studies on potential tourists’ reactions, and establish hypotheses by applying theories to situations using virtual reality tourism content.

2. Theoretical Background

2.1. Media Richness Theory

As it has become difficult to enjoy tourism in the real world due to the COVID-19 pandemic and self-quarantine, people want to meet their need to travel through virtual travel using virtual reality technology. Virtual travel is a series of simulations using continuous photos or video content to help tourists feel as though they have actually gone to a place. For example, potential travelers can experience a variety of virtual experiences by utilizing headsets to experience 360-degree stereoscopic images, or by using PCs to rotate 360-degree video images of travel destinations. In accordance with the definition of virtual travel, media is essential to experience virtual reality tourism content.

Researchers and travel agencies are also paying attention to the potential possibility of introducing virtual reality technology to the tourism industry, but they are hesitant to introduce it to actual cases and business models [15,16]. The reason is that the provision of travel services through virtual reality was recognized as an alternative or auxiliary means of actual tourism that is limited to cases where tourists could not visit actual travel destinations [17]. Only recently, after the COVID-19 pandemic, have a few studies been conducting conceptualization and empirical studies to introduce virtual reality technology to the tourism industry [14,17].

The use of media is essential, so that virtual travel can use virtual reality technology, and potential tourists can feel as if they have gone to a tourist destination. Media richness theory is the most widely known theory that explains which medium users select and use to enrich their experiences among multiple media, and how media should be designed to maximize media users’ work productivity [9,10,11,12]. Media richness is defined as the degree to which a medium can convey various and abundant cues, along with the message itself [9]. Media richness theorists argue that rich media could allow media users to communicate quickly and accurately, immerse themselves in deeper media use experiences, and maximize work performance [10,18]. The communication method that enables communication most smoothly by delivering a lot of cues and situation information is not through media use, but face-to-face communication [9]. However, face-to-face communication methods require communicators to gather at the same time and place, and this incurs higher costs than media use. Some media are effective for communication, and are similar to face-to-face communication, while others are not. The media richness theory describes media that support smooth communication that is similar to face-to-face communication as ‘rich’ media, and media that do not are ‘lean’ media. Researchers traditionally conducted research on media selection to maximize work productivity when using a particular medium [9,10]. When people were required to communicate through media, such as video, audio, and text, media richness was high, with video, audio, and text media [18] ranked in descending order. This is because video is most similar to face-to-face communication, compared with text or audio only [18].

Studies on what characteristics of media cause these results continued, in order to explain the results of rich media maximizing work performance. When the media provides sufficient media richness [9], provides various cues along with messages in the process of communication [10,18], allows vivid messages that stimulate various sensory organs [12,19], or media users can adjust and operate the communication process in the way they want [12], it contributes to the communication process, helping users reach a mutual understanding. It also enhances the output quality from a utilitarian value perspective [18], and maximizes the possibility of creating a more enjoyable media use experience from a hedonic value perspective [12,19]. Comparing virtual reality technology with static pictures, and expressing products with virtual reality technology, makes people understand products better, allowing them to effectively compare products, feel more useful, and want to revisit the website [12]. The result is that high media richness causes attention, flow, and a positive attitude to be directed toward the medium used [20]. In other media use situations, including virtual reality games [13], this finding was verified. Media richness stimulates the emotions of media users, makes them feel interested to use the media, and contributes to the formation of exploratory behavioral intentions [20,21].

Even in the e-business situation, when using virtual reality technology, these results were empirically verified. In the product presentation situation, using virtual reality technology in the online market, virtual reality technology caused recalled knowledge to become increasingly accurate, a positive attitude, and a high purchase intention [11]. These effects were moderated by the product type [11]. Media richness reduces the cost of information searches and increases the number of options that consumers consider when making choices [22]. Media richness has a greater influence on the three early stages of the consumer decision making process, but has a lower impact on the later stages [23].

Studies have been conducted on the effect of media richness on social interaction. In rich media situations, people interact with each other using avatars [24,25,26]. When avatars reflect the real self, it could help shoppers to make more effective shopping decisions [24]. The avatar is effective in cooperative shopping situations [25]. The websites differ by levels of media richness and interactivity, and media richness affects college students’ intention to visit a destination [26].

They argued that the original theory needs to be reinforced for the application of new technologies, as media richness theory shows inconsistent results depending on each situation [27]. In new technology-enabled business models such as Airbnb, media richness could significantly increase satisfaction and purchase intention [28]. As virtual reality technology was introduced in the tourism industry, research was conducted to verify their effects on tourists’ reactions [29,30,31]. Studies verified the effectiveness of media richness in museum exhibition situations [29,30]. Exhibitions using virtual reality technology have been found to satisfy the needs of visitors to escape from daily life [29], and its impact has been verified in the virtual commerce application context [31].

In summary, media richness not only increases the work performance of media users, but also entertains the media use experience.

2.2. DeLone & McLean’s Is Success Model

There are several models that explain how information system users evaluate and accept information systems. They are the technology acceptance model (TAM) [32,33], DeLone and McLean’s information system success model (D&M IS success model) [34,35], and the unified theory of acceptance and use of technology (UTAUT) [36]. D&M IS success model is the most concise and coherent model, providing a concise demonstration of the impact of IS users’ assessments of information and systems, based on satisfaction and behavioral intention with information systems [35,37]. This model has been widely agreed upon, and has been cited in more than 1000 papers since its publication in 1992 [37]. This model directly sets system quality as an independent variable, which is media richness in our study, and explains how it impacts information system use. This model successfully captures both the individual impact and organizational level of information system use. Therefore, the D&M IS success model was selected as an overarching theory in this study.

There are two versions of D&M IS success model. The original version was suggested in 1992 [34], and the updated version was suggested in 2003 [35]. The updated model is extension of the original model, adding service quality as the independent variable by adapting the original one for specific applications, such as knowledge management and e-commerce systems [35,37]. Due to the causal relationships between the two models being similar, the extended variables of service quality and net benefits being mainly concerned with organizational, rather than individual support [35], and because I should control the influence of other exogenous variables based on their contexts, I decided to adopt the original model in this study. The original model of D&M IS success reviewed the communication theory to elect six taxonomies of an IS user’s assessment of information and systems [34]. DeLone and McLean adopted Shannon and Weaver’s model of communications, which identified three levels of communications: the technical level (accuracy and efficiency of the communication system that produces the information), the semantic level (the success of the information in conveying the intended meaning), and the effectiveness level (its impact on the receiver) [35,37,38,39]. DeLone and McLean mentioned that ‘system quality’ reflects technical success, ‘information quality’ reflects semantic success, and ‘use, user satisfaction, individual impact, and organizational impact’ reflects effectiveness success [35]. It means that system quality stands for the capability of the system to make, compose, and deliver the information; information quality stands for the IS user’s recognitive ability, assessment of the information, and the mutual understanding between the communicators using system; and the remaining variables stand for the user’s cognitive and affective reactions and behavioral changes as a result of using the IS. In light of these considerations, DeLone and McLean’s six components of IS success are system quality, information quality, use, user satisfaction, individual impact, and organizational impact [34]. System quality refers to the performance of the IS in terms of reliability, convenience, ease of use, functionality, and other system metrics. Information quality is defined in terms of the characteristics of the output offered by the IS, such as accuracy, timeliness, and completeness. User satisfaction refers to the approval or likeability of an IS and its output. Usage intention refers to the expected future consumption of an IS or its output. Use refers to the real consumption of an IS or its output [37].

The causal model of D&M IS success has been empirically validated in over 1000 studies and the robustness of this model has been confirmed [37]. They argued that higher system quality is expected to lead to higher user satisfaction and greater usage, leading to positive impacts on individual productivity, resulting in organizational product improvement [35]. When the IS is functional, reliable, easy, and convenient to use, IS users can expect a high performance. When IS users succeed to perform tasks, they attribute their high performance to IS usage, and they evaluate the IS being highly qualified. When the information from the IS could accurately, continuously, completely, reliably, and appropriately do the user’s task, IS users go through the same evaluation process as the system evaluation, and meticulously evaluate the information quality. Two assessments could induce the effective success of Shannon and Weaver, the first could assess satisfaction, use, and individual impacts, and the second could assess organizational impacts [35,38].

Though DeLone and McLean argued that system quality and information quality could directly impact on the abovementioned effectiveness, the other assessment components could be considered. Researchers of the theory of reasoned action argue that people first recognize and evaluate external stimuli in their social interactions, and then they form attitudes and behavioral intentions [40,41,42]. Traditionally perceived usefulness, and perceived enjoyment and satisfaction could be considered as beneficial consequences of IS usage [33,43,44]. Perceived usefulness refers to the perception of the enhanced effectiveness achieved by using an IS service [43]. Perceived enjoyment refers to the extent to which the activity is perceived to be enjoyable in its own right [44]. In consumer research, satisfaction means the favorable feelings toward the service in question [45,46,47], as well as the approval or likeability of an IS and its output in D&M IS success model [37]. Satisfaction is the general assessment, concerning all aspects of the service and service providers, such as economic valuation and needs fulfillment in accordance with consumer psychology [46]. When the IS users attribute their high performance to using the IS, they could perceive the IS as useful [33,35]. If the IS medium could provide aesthetic, multi-sensory, and abundant information to IS users, IS users might feel that these stimuli are novel and enjoy them [44]. If the information system is useful and enjoyable, users will accept the output of the information system or evaluate the overall experience of using the information system positively. Eventually, system quality increases satisfaction through perceived usefulness and perceived enjoyment [35,45,46,47].

Satisfaction could induce various behaviors in individuals [42,43,44] such as IS continuous usage intention [42] and service purchase intention [44]. When an IS user is satisfied with the IS’s use, they want to continue to use the IS to maintain a high level of performance. If the user wants to maintain the performance level, the user would be willing to purchase such a service. These favorable responses from the IS user do not remain with the individual, but spread to the people around them. Consumers who are satisfied with some services want to share their experiences with friends [43]. Moreover, the user spreads positive feedback via word-of-mouth to others.

In D&M IS success model, it is not only system quality, but information quality which has a significant influence on the user’s perception, emotion, attitude, and behavioral intention. However, this study focuses on seeing the effects of virtual reality tourism content and performance of media; therefore, the information quality [48] of D&M IS success model [34] will be controlled in this study.

3. Hypotheses Development & Research Model

As the COVID-19 pandemic continued, there were restrictions on people’s movement and travel in the real world [6]. Potential tourists could not meet their travel needs. Governments and agencies intend to provide virtual reality travel services as an alternative way to travel in the real world [8]. Virtual reality travel does not actually go to a tourism destination, but instead uses virtual reality technology and information systems to experience travel [17]; therefore, virtual reality travel essentially requires information systems to support media.

D&M IS success model describes how IS users evaluate their experience using the system, forming attitudes, and how it causes changes to behavioral intentions [34]. This model argues that system quality and information quality lead to satisfaction with the use of information systems, one’s personal impact, and organizational impact. Media richness corresponds to system quality, one of the independent variables of this model; therefore, I set the media richness of virtual reality travel content as an independent variable of this study.

According to the media richness theory, it is argued that the more similar the medium is to face-to-face communication, the higher the media richness, leading media users to have a more positive media use experience [18]. Two salient variables are perceived usefulness and perceived enjoyment [33]. The high media richness enables media users to communicate accurately and quickly, similarly to face-to-face communication situations. When media users understand the information effectively and efficiently, they can recall the information better, and make better decisions; therefore, high media richness makes the IS evaluation useful, as it positively evaluates the media use experience, and increases the user’s work productivity. At the same time, when the medium provides vivid, aesthetic, and varied sensory stimuli, people find these stimuli fresh, they are curious, and more focused. When the media richness is high, people feel that these media experiences are more interesting; therefore, Hypotheses 1 and 2 are set as follows:

Hypothesis 1.

Media richness could positively affect the perceived usefulness.

Hypothesis 2.

Media richness could positively affect the perceived enjoyment.

Perceived usefulness and perceived enjoyment are two values that symbolize the cognitive and affective responses of users; perceived usefulness is the value of the utilitarian perspective, and perceived enjoyment is the value of the hedonic perspective [33]. When information system users feel that the information system is useful and enjoyable, users generally form a positive attitude towards using the information system; therefore, Hypotheses 3 and 4 are set as follows:

Hypothesis 3.

Perceived usefulness could positively effect satisfaction.

Hypothesis 4.

Perceived enjoyment could positively effect satisfaction.

Satisfaction, which measures the overall positive attitude of system users, causes various positive behavioral intentions. D&M IS success model verifies the effectiveness of the information system using two dimensions: personal impact and organizational impact [37]. In this study, reflecting the tourism situation, personal impact is set as individual destination visit intention, and organizational impact is set as word-of-mouth intention, an interaction between family or friend groups.

Among the various behavioral intentions, this study selected two variables as dependent variables. Considering the purpose of this study, which is to determine whether virtual travel content can attract tourists to real world tourist destinations after the COVID-19 pandemic, the destination visit intention was selected as the dependent variable for individual impact. As a result of the opinion stating that tourism brings people together rather than leaving them alone, positive word-of-mouth was selected as the dependent variable for the group impact; therefore, hypotheses 5 and 6 are set as follows.

Hypothesis 5.

Satisfaction could positively affect the destination visit intention.

Hypothesis 6.

Satisfaction could positively affect the word-of-mouth intention.

As information quality and information literacy capabilities could influence the results of the study, the study tried to control the perceived information quality and information overload. Figure 1 shows the research model reflecting my hypothesis.

4. Methodology

4.1. Operationalization of Constructs

This study used scenario survey methods and online survey tools for the respondent in quarantine, one of the quasi-experimental methods used for data gathering. To gather the data smoothly, a careful review of the survey questionnaire is required. The validation of the questionnaire was performed in accordance with the three-step validation guidelines of Churchill (1979) [49]. First, the questionnaire items were kept comparable and consistent with the other studies in tourism research area by referring to the questionnaire items already used in prior studies as much as possible. Second, since all questionnaire items were translated into Korean, with the help of five English and Information System experts, we checked whether the translation was well done by reflecting even the nuances of words. For this process, I added ‘interesting’ as the fourth item of perceived enjoyment, because ‘excited’, the second item of perceived enjoyment, could not reflect the nuances of the original meaning when translated into Korean. Third, ten students were recruited to engage in the pilot test to ensure that all questionnaire respondents understood the meanings easily and clearly. Ten pilot test participants answered that all questionnaires had clear meanings and were easy to be understood. The questionnaire items were confirmed through the review and consideration process. The measurement items that were finally confirmed after the aforementioned validation process are in

Table 1. Measurement items of the survey questionnaire.

Constructs	Measurement Items	Sources
Media richness (MR)	(MR1) This VRTC provides the information about the destination which could be easily understood. (MR2) This VRTC helped me to understand the destination. (MR3) This VRTC did not get in the way of understanding the destination. (MR4) I could easily explain the destination in this VRTC. (MR5) This VRTC helped me understand the destination quickly. (MR6) This VRTC could provide the various cues which helped me to easily understand the destination. (MR7) This VRTC could provide the various cues which helped me to better understand the destination. (MR8) This VRTC could provide the various cues which helped me to quickly understand the destination.	Dennis & Kinney (1998) [10] and Kahai & Cooper (2003) [18]
Perceived usefulness (PU)	This VRTC is... (PU1) valuable. (PU2) informative. (PU3) helpful.	Sussman & Siegal (2003) [50]
Perceived enjoyment (EJ)	This VRTC is... (EJ1) enjoyable. (EJ2) exciting. (EJ3) pleasant. (EJ4) interesting	Parboteeah et al. (2009) [44]
Satisfaction (SAT)	With this VRTC... (SAT1) I am contented. (SAT2) I am satisfied. (SAT3) it meets what I expect for this type of service.	Kim & Son, (2009) [43]
Destination Visit Intention (VI)	If I could visit a destination… (VI1) the probability of visiting the destination in this VRTC would be probable. (VI2) the likelihood that I would visit this destination is highly likely. (VI3) I would be very willing to visit this destination. (VI4) the probability that I would consider visiting this destination is high.	Song & Zahedi (2005) [51]
Word-of-Mouth intention (WM)	(WM1) I will say positive things about the destination in this VRTC to other people. (WM2) I will recommend the destination in this VRTC to anyone who seeks my advice. (WM3) I will refer my acquaintances to the destination in this VRTC.	Kim & Son (2009) [43]
Perceived Information Quality (PIQ)	In my opinion, the given VRTC... (PIQ1) is current enough to meet my needs.(PIQ2) is accurate enough to meet my needs. (PIQ3) is pretty much what I need. (PIQ4) is an actual fit for my needs. (PIQ5) has an appropriate level of detail for my needs. (PIQ6) can be relied upon. (PIQ7) can reflect the real features of the destination without being distorted.	Nicolaou & McKnight(2006) [48]
Information Overload (IO)	(IO1) I need more time to understand this VRTC. (IO2) This VRTC contains too much complex information for me to understand. (IO3) This VRTC contains too much information for me to understand.	Paul & Nazareth (2010) [52]
Fantasization (FN)	(FN1) I daydream a lot. (FN2) When I go to the movies, I find it easy to lose myself in the film. (FN3) I often think of what might have been. (dropped)	Kim & Son (2009) [43]

Cf1. VRTC means virtual reality tourism contents. Cf2. Perceived information quality and information overload are two control variables and fantasization is the marker variable for checking common method bias.

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4.2. Data Collection

The population in this study are potential tourists who feel the need to travel, despite self-quarantine due to COVID-19, and who have never been exposed to virtual reality tourism content before. I recruited survey participants through in-school advertisements. A total of 200 participants were recruited, and a total of 182 valid responses were collected, excluding 5 respondents who did not attend the survey for personal reasons, and 13 respondents who responded insincerely to the questionnaire. The scenario survey was conducted using the following steps. First, the background of virtual tourism was briefly explained. Second, I explained how to use online survey tools, because my research was conducted using ‘Zoom’ and ‘Google survey’ for potential tourists who were in self-quarantine. Third, participants were exposed to the virtual reality tourism content, named ‘virtual travel to Gayasan National Park (https://www.youtube.com/watch?v=3xCPwue_0WM (accessed on 27 February 2022))’, which is 4K-class, high-definition, virtual reality tourism content created by the Korean Tourism Agency. Participants were asked to enjoy the 360-degree scenery using the operation icon at the left top of the screen. After that, participants were asked to respond to the survey questionnaire. Starbucks coffee coupons worth $10 were paid to participants in the experiment. The demographic data of respondents are shown in

Table 2. Demographic data of the respondents.

G	Num	%	W	Num	%	D	Num	%
MN	109	59.9%	0–1	10	5.5%	1	30	16.5%
FE	73	40.1%	1–2	27	14.8%	2	68	37.4%
M	Num	%	2–3	28	15.4%	3	39	21.4%
H	16	8.8%	4–5	53	29.1%	4–5	22	12.1%
B	140	76.9%	6–7	48	26.4%	6–7	12	6.6%
E	10	5.4%	8–10	10	5.5%	8–9	3	1.6%
Etc.	16	8.7%	Over 10	6	3.3%	Over 10	8	4.4%

cf. G: gender; M: major; W: web experience (years); D: travel experience per year (times); Num: frequency, MN: male, FE: female, H: humanities and art, B: social science and business, E: engineering and natural science.

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5. Analysis and Results

Data analysis was conducted to verify the hypotheses. I chose PLS methods for the structural equation models. PLS can proceed with analysis using fewer observations than other models, and clearly verifies the causal relationship between variables [53,54]. Hypothesis verification using the PLS algorithm consists of two steps: verifying the validity of the measurement model and verifying the structural model by calculating the correlation coefficient between the variables [54].

5.1. Measurement Model

To verify the validity of the measurement model, I should confirm the convergent validity, discriminant validity, and reliability of the measurement model. To check the overall reliability of each construct in this study, I calculated the average variable extracted (AVE), composite reliability, and Cronbach’s alpha of each variable [55]. I checked that all the average variance extracted (AVE) values of the variables were greater than the 0.5 cut-off, that all composite reliability values were greater than the 0.7 cut-off, that all Cronbach’s alpha values were greater than the 0.7 cut-off [55,56], and that I could confirm facial validity, convergent validity, and the reliability of my measurement model. To confirm the discriminant validity between variables, I could adopt two ways of verification. The first way is comparing the square root value of the AVE with the correlation value of the other variables.

Table 3. Latent variable correlations.

	AVE	CR	R2	α	MR	PU	EJ	SAT	VI	WM	PIQ	IO
MR	0.741	0.958		0.950	0.861
PU	0.911	0.969	0.529	0.951	0.704	0.955
EJ	0.947	0.986	0.322	0.981	0.565	0.682	0.973
SAT	0.896	0.963	0.856	0.942	0.651	0.747	0.909	0.947
VI	0.871	0.964	0.481	0.951	0.455	0.556	0.632	0.694	0.933
WM	0.934	0.977	0.595	0.965	0.520	0.658	0.714	0.771	0.793	0.966
PIQ	0.677	0.936		0.919	0.830	0.665	0.487	0.600	0.356	0.439	0.823
IO	0.896	0.963		0.943	−0.354	−0.265	−0.198	−0.251	−0.183	−0.132	−0.421	0.946

Cf1. AVE: average variance extracted, CR: composite reliability, α: Cronbach’s alpha. Cf2, Diagonal cells are the square root of the AVE of each construct. Off-diagonal cells are the correlations. The abbreviation of each variable can be referred to Table 1.

shows that the square root values of the AVE of all variables exceed the correlation value. The second way of checking the discriminant validity is to check whether the factor loading value of items in the variable is sufficiently greater than that of items between variables.

Table 4. Factor loading.

	MR	PU	EJ	SAT	VI	WM	PIQ	IO
MR1	0.868	0.589	0.466	0.556	0.362	0.418	0.804	−0.398
MR2	0.857	0.617	0.496	0.581	0.368	0.471	0.760	−0.394
MR3	0.856	0.552	0.494	0.585	0.382	0.438	0.795	−0.377
MR4	0.859	0.657	0.470	0.556	0.351	0.387	0.781	−0.317
MR5	0.884	0.555	0.441	0.502	0.348	0.391	0.693	−0.272
MR6	0.893	0.629	0.516	0.567	0.438	0.489	0.641	−0.249
MR7	0.848	0.621	0.490	0.557	0.428	0.509	0.612	−0.218
MR8	0.822	0.616	0.509	0.573	0.445	0.472	0.635	−0.222
PU1	0.640	0.938	0.584	0.631	0.498	0.602	0.585	−0.198
PU2	0.670	0.959	0.647	0.738	0.536	0.640	0.665	−0.279
PU3	0.703	0.966	0.716	0.761	0.555	0.639	0.649	−0.277
EJ1	0.555	0.674	0.963	0.876	0.613	0.695	0.479	−0.185
EJ2	0.579	0.696	0.966	0.878	0.611	0.718	0.518	−0.207
EJ3	0.532	0.643	0.982	0.892	0.619	0.682	0.449	−0.189
EJ4	0.532	0.643	0.982	0.892	0.619	0.682	0.449	−0.189
SAT1	0.612	0.696	0.925	0.962	0.666	0.733	0.553	−0.198
SAT2	0.611	0.712	0.906	0.973	0.656	0.737	0.561	−0.211
SAT3	0.627	0.715	0.743	0.904	0.649	0.722	0.595	−0.308
VI1	0.418	0.529	0.622	0.657	0.940	0.771	0.324	−0.181
VI2	0.469	0.562	0.636	0.701	0.967	0.782	0.380	−0.205
VI3	0.329	0.418	0.509	0.557	0.897	0.644	0.239	−0.134
VI4	0.467	0.551	0.583	0.661	0.928	0.751	0.370	−0.158
WM1	0.532	0.670	0.733	0.785	0.780	0.957	0.467	−0.175
WM2	0.481	0.607	0.671	0.730	0.769	0.973	0.399	−0.080
WM3	0.492	0.626	0.661	0.717	0.749	0.969	0.403	−0.124
PIQ1	0.600	0.479	0.495	0.583	0.357	0.460	0.656	−0.182
PIQ2	0.688	0.575	0.360	0.482	0.320	0.368	0.870	−0.430
PIQ3	0.725	0.577	0.426	0.502	0.283	0.367	0.847	−0.294
PIQ4	0.664	0.522	0.346	0.460	0.205	0.283	0.855	−0.448
PIQ5	0.691	0.533	0.428	0.530	0.310	0.388	0.885	−0.314
PIQ6	0.682	0.545	0.307	0.392	0.234	0.294	0.816	−0.379
PIQ7	0.704	0.575	0.416	0.483	0.318	0.347	0.807	−0.379
IO1	−0.349	−0.262	−0.234	−0.280	−0.157	−0.150	−0.397	0.948
IO2	−0.363	−0.275	−0.183	−0.242	−0.210	−0.127	−0.412	0.968
IO3	−0.280	−0.205	−0.127	−0.171	−0.150	−0.087	−0.384	0.923

shows that factor loading values within a variable exceed the factor loading values between variables. Confirmatory factor analysis results mean that the factors in each variable are well tied and differentiated from other factors. Due to the above consideration, I judged my measurement model to have acquired sufficient validity and reliability. I proceeded with structural equation model analysis using the PLS algorithm.

As the data in this study were collected using the survey method, I should pay attention to the occurrence of common method bias (CMB). CMB is an error in which the correlation between the variables appears to be more exaggerated than it really is when both independent and dependent variables are measured in the same way and from the same respondent. I used the marker variable technique to check for CMB. The marker variable technique is the method used to calculate the average correlation coefficient between the variables in the hypothesis and the theoretical unrelated marker variable (i.e., fantasization). If the average correlation coefficient is less than 0.10, I can confirm that CMB is not serious. Due to the average correlation coefficient between the variables and marker being close to 0 (r = 0.062, not significant), I could confirm the absence of CMB. I also assessed the variance inflation factors (VIF) for each dependent variable to check the multicollinearity. VIF values should not exceed 10 when confirming the absence of multicollinearity. The VIF of each variable are 2.122 (perceived usefulness), 1.475 (perceived enjoyment), 6.944 (satisfaction), 1.928 (destination visit intention), 2.468 (word-of-mouth intention), and I confirm that the multicollinearity is not serious.

5.2. PLS Analysis Results

After confirming the validity of the measurement model, I conducted structured equation model analysis using a PLS algorithm to verify my research model and hypothesis. Through PLS analysis, I was able to calculate the path coefficient and the significance of each path coefficient. Figure 2 shows the PLS analysis results. Path coefficients between media richness and perceived usefulness (H1, β = 0.503), between media richness and perceived enjoyment (H2, β = 0.521), between perceived usefulness and satisfaction (H3, β = 0.237), between perceived enjoyment and satisfaction (H4, β = 0.747), between satisfaction and destination visit intention (H5, β = 0.694), and between satisfaction and positive word-of-mouth intention (H6, β = 0.595) are significant; therefore, all hypotheses set in this study were significantly supported.

The interpretation of this analysis result is as follows; the more people that feel the media richness of virtual reality tourism content, the faster and more accurately people can communicate with each other, and the more useful this service is because they can increase their work productivity. Moreover, because media richness enables people to dive deeper into the media use experience, people feel that this media use is enjoyable. When people feel that media (information system) use is useful and enjoyable, people are likely to be satisfied with this service. People create positive behavioral intentions corresponding to their positive media use experiences. People have a greater intention to visit a destination after the outbreak of COVID-19 at the individual level, people share their positive experiences via word-of-mouth with other groups of friends rather than keeping it to themselves at the organizational level. Therefore, this study successfully verified how the media richness (cause) of virtual reality tourism content affects changes (results) in people’s perceptions, attitudes, and behavioral intentions, both in terms of cognitive and affective aspects.

An interesting point of the analysis results is that the influence of media richness on perceived usefulness and perceived enjoyment was very high (β > 0.5) and almost equal (perceived usefulness, β = 0.503; perceived enjoyment, β = 0.521). It was found that perceived enjoyment (β = 0.747) elicits greater satisfaction than perceived usefulness (β = 0.237), which is generally known to have the greatest effect on satisfaction in the other IS literature [33,42,43,48]. It may stem from media use in this research context. Further verification is needed in future studies to confirm whether this result shows the same pattern in several different situations.

To understand the impact of media richness felt by survey participants, I also interviewed several participants. Participants responded that virtual reality tourism content was very helpful in reducing their thirst for travel, even when they were quarantined due to COVID-19. Participants responded that virtual reality tourism content felt better than high-definition tourism movie content, and participants said that it was because they could experience it as if they were traveling directly to the destination and listening to a guide’s explanation.

However, participants say that virtual reality tourism content, which is an experimental tool in this study, needs to be supplemented more. They said that this virtual reality tourism content can only rotate 360 degrees, but other virtual reality content generally allows more various manipulation such as zoom-in and zoom-out. They also explained that 4D movie theaters can stimulate various sensory organs of media users, such as sight, hearing, smell, and touch. They said that more advanced virtual reality tourism content could induce a much greater impact on tourists.

In summary, I argue that governments and agencies can use virtual reality tourism content to effectively attract potential tourists when travel in the real-world resumes after the COVID-19 outbreak, as well as to quench the thirst for travel when people are quarantined due to COVID-19.

6. Discussion

The tourism industry is facing an unprecedented slump due to the COVID-19 pandemic. The tourism industry is regarded as a core industry for sustainable economic growth and has a great ripple effect on other industries. As an example of ‘Go to Travel’ campaign by Japanese government, some countries tried to encourage the tourism industry to help economic recovery by taking the risk of spreading COVID-19, but these campaigns failed, as the number of infected people exploded due to the Nth wave of COVID-19, and mutations such as the Delta mutation and Omicron mutation. In situations where travel is difficult in the real world, the introduction of virtual reality technology into the tourism industry was seriously considered. This study successfully explained the effect of virtual reality tourism content through the media richness theory and D&M IS success model by reflecting these situational needs. The media richness of virtual reality tourism content can be used as a means of effectively maintaining tourism demand and attracting tourists by increasing satisfaction, intention to visit a destination after the COVID-19 pandemic, and intent to positively use word-of-mouth. These results provide several discussion topics.

The first to third issues explain why virtual reality technology should be used in the tourism industry. First, virtual reality technology and applications supporting media richness can be a useful means of promoting the tourism industry. Since tourism is mainly interested in the tourist’s behavior and destination choice in the real world, prior studies in the tourism area have been passive in introducing new technologies such as virtual reality technology [14,15,16,17]. However, the COVID-19 pandemic served as an opportunity to introduce new ways of tourism, and new types of tourism advertisements, using virtual reality technology. The findings of this study indicate that virtual reality tourism content can “significantly” increase potential travelers’ destination visit intentions in the real world, and word-of-mouth intention suggests that using virtual reality tourism content could be a means of promoting tourist attractions in the real world. In terms of a cost-benefit consideration, advertising tourist attractions using virtual reality technology could be as effective as promoting tourist attractions through existing newspapers or TV advertisements.

Second, virtual reality tourism content has a value over the mere real world destination advertising method. It is argued that the realm of physical tourism (i.e., visiting tourist destinations in the real world), could be changed to a realm of virtual travel, (i.e., visiting tourist destinations in the ‘metaverse’ world). Jiang & Benbasat (2007) [12] argued that the core elements of media richness are vividness and interactivity, and Hoffman & Novak (1996) [20] argued that these factors can lead to various cognitive and affective reactions such as focused attention, flow, and so on. Virtual reality technology can provide content of increased vividness and interactivity in the metaverse environment. It overcomes the limitations of real-world travel, such as crowds crowding at each destination, high prices, and physical difficulties moving, and the metaverse can provide vivid travel experiences as if users have visited real world destinations. High interactivity in the metaverse allows tourists to manipulate virtual tourism content by themselves. The metaverse overcomes physical and legal limitations and enables even ‘impossible’ services in the real world. There is no limit to travel in metaverse; therefore in subsequent studies, the potential of virtual travel using the metaverse needs to be investigated more using diverse subjects.

Third, the leadership in the tourism industry is moving from guides, hotels, and airlines to tourists and content developers by using virtual reality technology. When choosing virtual reality tourism content, tourists choose their preferred content using self-determination. Virtual reality tourism content should be customized for each tourist. High-quality content which allows consumers to feel a sufficient level of media richness should be developed. The roles of travel planners, who can identify tourists’ potential needs, and virtual tourism content developers, who can develop interesting and aesthetic tourism content, become important; therefore, IT leads the changes to business models in the tourism industry.

Although the abovementioned issues relate to why virtual reality tourism content should be introduced, the following four issues are on what characteristics tourism content should have, and how to develop tourism content. Fourth, virtual reality tourism content should be fun, interesting, and exciting enough to attract potential tourists. You may recall that the path coefficients from media richness to perceived usefulness and perceived enjoyment were very high and almost equal. Especially in the field of tourism and recreation, the hedonic value for enjoying life could be as critical as the utilitarian value of work performance. Uninteresting content cannot persuade tourists to visit the destination.

Fifth, media richness can be sufficiently increased by using high-performance computer resources and broadband communication infrastructure. To increase media richness, it is necessary to be able to synchronously transport a message with abundant cues, and to be able to provide the various organic stimuli used to fascinate media users. Advanced IS and communication networks could be critical for acquiring these capabilities. Mobile networks that use 5G could be an example of a broadband communication network, and it has already been commercialized in several countries. This infrastructure should be actively used to develop virtual reality tourism content.

Sixth, a service environment that can smoothly provide virtual reality tourism services is important. Virtual reality tourism is inevitably provided in the form of web services using information systems. In the extended models of D&M IS success, service quality was added, as well as information quality and system quality. This means that various methods can be used to improve online services, and continuous innovation is needed. For example, this might include the adoption of headsets supporting more realistic media services, or the development of tourism content focused on travel destinations that travelers crave to visit, but are difficult to get to. Environments that allow tourists to use virtual reality tourism services smoothly must be developed, implemented, and improved.

Seventh, virtual reality tourism content developers and offline travel agencies must cooperate for each other’s survival. It is unreasonable to argue that virtual reality tourism content provided online will replace all travel in the real world. Rich media mimics face-to-face communication in real world. Some tourists may prefer travel in the real world, where all stimuli are real, rather than travel in the metaverse. After seeing the virtual reality tourism content, a potential tourist, whose travel needs were embodied by the content, may go on to travel in the real world. Travel agencies are the companies who know tourists’ preferences and have the most information about the destinations in the real world. Offline travel agencies can help plan and design virtual reality tourism content. In this case, virtual reality tourism content, and offline travel products, could be complementary goods.

In summary, IT is leading the change in the tourism industry, and the adoption of virtual reality tourism content can increase various achievements such as tourist satisfaction, destination visit intention, and word-of-mouth intention. To maintain this effect, the application of new ideas and continuous innovation are required, and collaboration between virtual reality tourism content developers and offline travel agencies is required.

7. Conclusions

The findings of this study, described in the analysis results and discussion section, will have the following academic and practical implications.

There are four academic contributions. First, this study has empirically validated the importance of media richness of the virtual reality tourism content, and presented a way of overcoming the COVID-19 pandemic using IS. The results of this study could be a way of explaining how information technology can be used against an unexpected disaster. The results of this study point out that visit intention to real-world travel destinations can be effectively increased by using virtual reality tourism content.

Second, this study examines the issue of the virtualization of tourism using virtual reality tourism content through the perspective of digital transformation (DX). This study examined the impact of not only back-end systems, such as artificial intelligence and big data analytics, but also front-end systems, such as virtual reality and the metaverse in the era of the 4th industrial revolution. Third, this study expanded the incumbent scope of research and conducted interdisciplinary research, combining tourism and information systems. Fourth, this study drew more persuasive conclusions and suggestions by developing my hypothesis through the selection of a comprehensive theory, D&M IS success model, as this study’s overarching theory. This is different to some prior studies that only observed partial aspects of the causal relationship. Recently, the interest in so-called “smart tourism” using information systems is increasing. D&M IS success model could also be adopted in other studies in the tourism research area.

There are three practical contributions. First, the most important contribution of this study, which is that I confirmed that virtual reality tourism content can be used to remedy the desire to travel for people in quarantine, keep the demand for travel, increase destination visit intention, and spread positive words via word-of-mouth in friend groups. Virtual reality tourism content can be used to recover the growth of the tourism industry, and I recommend adopting these ideas when policy making. Second, this study provides suggestions for how virtual reality tourism content should be designed. This study argues that virtual reality tourism content must be fun, interesting, and exciting. Vivid and interactive virtual reality tourism content should be produced using advanced IS and communication networks, and it should be provided smoothly to potential tourists. It justifies investment in 5G mobile communication networks. Third, cooperation with offline travel agencies is necessary to capture people’s needs to travel, in order to reflect them in virtual reality tourism content. We should make the virtuous cycle of a win-win economy.

Despite these academic and practical contributions, this study has the following limitations. First, the virtual reality tourism content used in this study is too functionally narrow. It only supports 360-degree rotation, but other virtual reality contents support various manipulations and interactions. Second, data in this study were collected through scenario survey methods and online questionnaires, which is different to other, traditional studies on computer-mediated communication, which were conducted by using laboratory experiments. This was inevitable because people were quarantined due to COVID-19. It is desirable to conduct a laboratory experiment to more cautiously examine the causal relationship between variables after the end of the COVID-19 outbreak. Third, the sample size in this study is just 182, which is relatively small compared to other studies. The subjects in this study were mainly students majoring in business in their 20s. Fourth, this study was conducted in the second half of 2021, when it was difficult for many people to travel in the real world due to COVID-19. If the COVID-19 pandemic disappears after 2023, it is necessary to re-verify the results of this study. It may be better to gather data from a population with more varied demographics. If these problems have improved in subsequent studies, more valid research results can be derived.

Despite these limitations, this study is expected to contribute to the recovery of the tourism industry after the COVID-19 pandemic, the introduction of virtual reality tourism content in virtual travel, and quenching the thirst for travel among people in quarantine.