Virtual Reality as a Travel Substitution Tool During COVID-19

The tourism industry has experienced many crises and disasters over the past decade, ranging from terrorist attacks to infectious diseases [1]. An unknown virus, most likely originating from the Chinese city Wuhan and subsequently named COVID-19, was discovered in December 2019 [2]. At the beginning of July 2020, there were 19,381,455 confirmed cases worldwide and 721,409 deaths according to the John Hopkins COVID-19 Resource Center with over 274.000 new COVID cases confirmed every day [3, 4]. Similar to SARS that developed in 2003, COVID-19 is an airborne transmitted illness that is highly contagious [5]. This resulted in major disruptions on the stock market, with many companies going through upheavals and over 35 million jobs at risk, expected to increase to 100 million by the end of 2020. Due to the nature of traveling that facilitates the spread of the pandemic, the World Health Organization (WHO) and national governments imposed the closure of borders resulting in a disruption of tourism activities worldwide [6, 7].

However, the wish to travel and escape from everyday life still prevails, with novel possibilities offered by information and communication technologies (ICTs). Immersive technologies such as Virtual Reality (VR) enable users to travel virtually using computer generated images or videos, simulating real-life experiences and offering a travel alternative [8, 9]. However, there is a paucity of research dealing with the question of whether VR is capable of replacing physical travel [8, 10, 11].

Therefore, the goal of this paper is to investigate the ability of VR to replace physical travel in times when travel possibilities are limited due to travel restrictions caused by the COVID-19 pandemic using structured equation modeling (SEM), building upon the theory of the technology acceptance model by including additional variables such as presence, perceived risk and perceived severity.

Studies have been conducted to analyze the relationship between PEU and PU, showing that both constructs are related to the BI and the consumer acceptance of ICTs [28, 33]. This means that BI is jointly determined by both the PEU as well as the PU. The relationship implies, that everything being equal, people create intentions to perform a specific behavior that they have a positive affinity to [31]. Therefore, these two variables determine the framework for this study and from this, the following two hypotheses can be formulated:

High usability does not only influence the behavioral intention to use VR but also the perception of its usefulness [34]. Several studies have already successfully confirmed the relationship between PU and PEU and in the present context, it is therefore assumed that a person perceives a VR device as more useful if the operating difficulty is low [30, 35].

Perceived risk affects the situation where the probabilities of the outcome are uncertain. As most of the studies used perceived risk as an antecedent of PU, it is expected that RISK directly affects PU and that they are therefore related whereas they are independent of one another [36, 37]. This means, if the potential reward outweighs the potential risk, it can be expected that the system tends to be adopted [38]. As mentioned aforehand, the perceived risk to travel is considered from the perspective of the traveler, in the context of traveling during COVID-19 and its travel behavior. Perceived severity on the other hand measures the extent of individual concern about negative consequences when catching the disease [15, 39]. To include two dimensions of risk, the effect of perceived severity on perceived usefulness was tested separately [40].

Although there exists a large body of literature discussing virtual reality and virtual environments, the classical approach of the technology acceptance model is not readily applicable to interactive technology. It can have a significant influence on the flow effect during the exposure and therefore change the outcome and the way how the technology is perceived [41, 42]. This study therefore includes the presence as an extension to the TAM model [43, 44].

The study was based on a quantitative research design with a self-administered questionnaire, consisting of nine sections. Measurement items were previously validated and structured into four different parts. The first part focuses on demographic attributes, the second part on the perceived risk and perceived severity, the third part focuses on presence while the last section focuses on the TAM. The study employed 44 items for the four different constructs. The items used in the study were then contextualized based on prior research with necessary wording and validation changes. The questionnaire was validated and pre-tested to ensure its validity and reliability.

An indicator reliability analysis was conducted to evaluate the consistency and stability for each of the proposed latent constructs. Based on their corrected item-to-total correlation and their improved alpha values, several items were deleted to justify the model [47]. All items below the value.30 were deleted.

The five items of the latent variable perceived risk showed a Cronbach Alpha coefficient of α = 0.43 with an increase to α = 0.74 by excluding the variable “I would feel very comfortable traveling right now” (M = 3.85, SD = 0.99). Perceived severity had a measured Cronbach Alpha coefficient of 0.71. Even though all the four items had a high coefficient, the removal of one variable resulted in an increased Cronbach Alpha of 0.81 (M = 2.20, SD = 1.05). The nine items of the construct Presence had the highest Cronbach Alpha coefficient of 0.9 with no increase if any of the items were deleted (M = 3,42, SD = 0,99). Looking at the TAM-constructs, the perceived usefulness was reported to have a coefficient of 0.67. With the removal of the variable “The satisfaction provided by VR makes me want to travel again” the value increased to 0.72 and therefore being suitable (M = 3, SD = 1,20). Perceived ease of use had a Cronbach Alpha value of 0.83 by removing one variable (M = 4,12, SD = 0,84). Finally, behavioral intention had a coefficient of 0.85 with no improvements by removing an item. With the proper adjustments, all coefficients exceed the recommendation of 0.7 [48]. The coefficient for all variables reached a value of 0.85 (M = 3,70, SD = 1,14).

After testing the indicator reliability, confirmatory factor analysis was performed in order to confirm the factor loadings of the six constructs (perceived risk, perceived severity, presence, perceived ease of use, perceived usefulness and behavioral intention) as well as the model fit [49]. Extraction method used in the study was the Maximum Likelihood method and the Promax method was chosen for rotation. The Kaiser-Meyer-Olkin measure of sampling adequacy was.85 and therefore above the recommended value of 60. The Bartlett’s test of spherity was highly significant (χ2 (378) = 2836.48, p < 0.00).

In addition, the construct reliability estimates ranged from 0.72 to 0.90, which exceeds the recommended value of 0.7, indicating a satisfactory estimation, seen in Table 1. The AVE of all constructs ranged between 0.47 and 0.61 with two of the constructs between 0.47 and 0.49. According to Fornell and Larcker [50], the AVE may fall short as “On the basis of p_n (composite reliability) alone, the researcher may conclude that the convergent validity of the construct is adequate, even though more than 50% of the variance is due to error” [50].

The SEM was estimated by using a maximum likelihood estimation method as well as a correlation matrix as input data. All fit indices are shown in Table 2.

The model shows an overall significant fit (χ² = 332.474, df = 274, p < 0.00). As χ2 is sensitive to a large sample size, it frequently rejects well-fitted models with an increase in the sample size as it is the case in this study [49, 51]. Therefore, the normed χ2 (i.e. χ2/df) is used to examine the model fit, showing an acceptable fit with χ2 = 1.26 [51]. To increase the indices, two items were removed due to their factor loadings being below 0.4. The other goodness of fit indices are GFI = 0.89, AGFI = 0.85, CFI = 0.97, RMSEA = 0.03, and TLI = 0.97. Comparing the values to their critical corresponding values, the hypothesized model can be assessed as fitting.

Table 3 provides details about the results of the hypotheses tests. Out of six proposed hypotheses, four were supported. PEU has a positive and highly significant effect on PU (β = 0.57, t = 4.25, p < 0.001) but not on BI (β = 0.23, t = 1.62, p < 0.10), therefore supporting H3 and rejecting H1. Perceived usefulness had a positive and highly significant effect on behavioral intention (β = 0.83, t = 5.90, p < 0.001) and thus supporting the second hypothesis H2. Presence has a medium strong but highly significant positive effect on perceived usefulness (β = 0.33, t = 4.30, p < 0.001), supporting H6. Perceived risk has no significant effect on PU (β = -0.00, t = -0.61, p < 0.90) whereas perceived severity loads highly significant on perceived usefulness (β = 0.26, t = 3.41, p < 0.001) therefore rejecting H4 but accepting H5.

The aim of this study was to investigate the potential of VR to replace real travel when physical travel is restricted due to external circumstances, as in the specific case of this study the pandemic outbreak of COVID-19. Another contribution of the study was to validate the use of TAM in the tourism context as a framework to understand the use of VR [24]. The unique addition to the theory was the consideration of the perceived risk to travel and its potential to affect the PU of participants, as neither TAM nor TPB have provided explanations about behavioral predictions [52]. Thus, this research provides new perspective for researchers by identifying several factors that influence the intention to use VR as a substitute for physical travel during COVID-19.

The results suggested that the measured constructs have an adequate reliability and validity. Out of the six proposed hypotheses, four were confirmed as summarized in Table 3. Results revealed that perceived usefulness has a strong direct effect on behavioral intention, indicating the intention of tourists to use VR to travel virtually during and even after a crisis such as the pandemic outbreak of COVID-19. This result contrasts prior findings reported by Sussmann and Vanhegan [10] indicating that tourists will not replace real traveling by using VR. However, the aforementioned study was not conducted based on a situational crisis such as a global pandemic outbreak.

The finding that perceived risk has no significant effect on participants’ perceived usefulness of VR during a crisis can potentially be explained by other factors that were not considered in this study such as hedonic travel motives, positive emotions or the state of flow [24, 53]. VR can convey a sense of escapism to the user, allowing one to travel virtually, dissolving the link between an infectious disease and travelling [54].

Looking at the relation between perceived severity and perceived usefulness, the hypothesis was confirmed showing a significant effect, implying that personal risks are more meaningful than natural ones or risks associated with destinations when making a decision to use VR as a substitute to travel [40, 55]. Furthermore, presence significantly influenced perceived usefulness suggesting that the higher the degree of spatial presence, the higher the perceived use of VR to substitute physical travel. This indicates that it is imperative to use a sophisticated headset and to negate any distractions [25].

The relatively high value of PEU can explain user acceptance of VR systems and its significant effect on PU. Thus, the easier a system is to use, the more likely it is that people use it to travel virtually. The testing of the first hypothesis is also according to their findings, as evidence for a significant effect of PEU on BI was not provided [28]. A reason could be the rather experienced sample size, as all participants owned a personal VR headset, resulting in the general high mean. Besides hypothesis testing, it became apparent that the general urge to travel increased after the exposure as the variable ‘The satisfaction provided by VR makes me want to travel again’ showed a generally high mean.

The study has several limitations that can be addressed in further and future research. Firstly, the study was conducted with a relatively homogenous sample size as all participants have had previous experience with VR headsets before, which limits the generalizability. Future studies can further validate the findings of this study in a different field with participants who have never used VR before. As perceived risk was found to have no significant effect on perceived usefulness, future research should try to discover other factors such as hedonic travel motivation that might explain the allure of using VR to travel virtually. Furthermore, other sensory modalities apart from auditive/auditory and visual feedback can stimulate the imagination. Subsequent studies can therefore include other sensory information and utilize an environment with a higher degree of interactivity, thereby integrating the effect of immersion on perceived usefulness. Finally, another direction that future researchers can undertake is to further investigate the dynamics of social interaction in a VR experience that is being used in a tourism context.

This study examines the ability of VR to substitute real travel during the COVID19 pandemic. By using TAM with additional variables, namely perceived risk, perceived severity and presence, this study identified the role that VR plays as a substitute to real travel in times when travel is restricted due to external and environmental circumstances. Results suggested that perceived usefulness positively influences the behavioral intention. Perceived usefulness on the other hand is positively affected by presence, perceived severity and perceived ease of use. The most important finding of this study is the significant relationship between perceived usefulness and behavioral intention, suggesting that there is an intention to use VR to travel during and even after the COVID-19 pandemic. Additionally, findings suggested that there is no significant correlation between perceived risk and perceived usefulness, indicating that other unobserved influential factors need to be examined. In this study, it appears that the perceived risk to travel is not impactful compared to the individually perceived severity, such as the fear of contracting the disease. Nevertheless, in order to experience real tourism with the help of VR, it is necessary to provide targeted stimuli to other sensory modalities besides auditive/auditory and visual exposure. The aspect of interaction is also crucial for a positive travel experience, a theme that future researchers might wish to pursue.

The findings of this study demonstrate the potential of VR to be used as a virtual imagery tool to induce travel experiences and potentially substitute travel. Political or environmental instabilities may force customers to make new decisions and adapt their traveling style to the circumstances by using VR for instance [10]. In order to enhance its power, it is imperative to add economic value to it. It is up to marketeers to recognize and use the possibility to create virtual reality content and provide a realistic preview of the destination which eventually translates into a purchase intention. A challenge is to plan activities that take advantage of the technology, even though its used to substitute travel while maintaining the attractiveness of actual traveling and encouraging wanderlust [56].