TAM has proven useful for broadly examining emerging technology adoption and VR adoption more specifically. In essence, TAM examines how potential users’ perceptions that new technology is both useful and easy to use ultimately influence their intention to use (ITU) a new technology (Davis, Bagozzi, & Warshaw, 1989). Specifically, the perceived usefulness (PU) of a technology is related to a user’s belief that a new technology could improve their life in a meaningful way. Furthermore, a technology’s perceived ease of use (PEOU) is related to a user’s belief that using a new technology will not require too much effort. Perceived ease of use can also enhance the perceived usefulness of technology, as technologies that are easier to use are often perceived as more beneficial. Both perceptions (usefulness and ease of use) subsequently impact potential users’ willingness to adopt (intent to use), which is theoretically linked to technology adoption (purchase behaviour).

TAM has been used successfully for over a decade to examine VR acceptance and adoption (Fagan, Kilmon, & Pandey, 2012). This body of scholarship continues to grow as new scales are developed, and different contexts and populations are examined (Bunz, Seibert, & Hendrickse, 2021; Jang et al., 2021). As a result, scholars have built upon the TAM framework by examining key external variables that also impact VR acceptance and adoption (Castiblanco Jimenez, Cepeda García, Violante, Marcolin, & Vezzetti, 2020; Sagnier, Loup-Escande, Lourdeaux, Thouvenin, & Valléry, 2020). A notable advancement in this area is the creation of a VR-specific version of TAM, known as the ‘virtual reality hardware acceptance model’ (VR-HAM) (Manis & Choi, 2019). VR-HAM includes a series of external variables that form a complex model predicting VR use and purchase intentions. For instance, VR-HAM considers user variables such as curiosity and its impact on perceived ease of use. However, while the TAM literature shows promise, the results are still mixed. For example, in one study, while the perceived usefulness of VR predicted participants’ intention to use VR, the perceived ease of use did not (Sagnier et al., 2020). This study also explored external factors that reduce intentions to use VR, such as cybersickness (Sagnier et al., 2020). Therefore, further research is still required to explore the factors that encourage and discourage VR perceptions and to continue testing the efficacy of TAM in a VR context.

Another crucial external user variable that has emerged in the literature is material access to the hardware or technology (Van Deursen & Van Dijk, 2019). Specifically, the digital divide literature suggests that having access to VR can influence perceptions of that technology as users gain first-hand experience, whether positive or negative (Huang, Ball, Cotten, & O’Neal, 2020). Furthermore, VR ownership does not necessarily guarantee intention to use VR, as reports have indicated that 69% of VR owners report using the hardware less than once a month (AR Insider, 2020). In other words, if people purchase VR, but their experience is negative, then they may have less intentions to use VR in the future. Thus, our study will explore how material access, or ownership of VR hardware, affects people’s perceptions of VR and, ultimately, their intention to use it.

While user-level external variables are potentially important influencers of VR acceptance and adoption, contextual factors also play an important role. Specifically, the onset of the COVID-19 pandemic, as an external variable, presents a unique context to study VR technology adoption (Ball et al., 2021). In March 2020, most of the world instituted mandatory stay-at-home orders (i.e., lockdown procedures) to curb the spread of COVID-19 (Goodman & Schulkin, 2020). During this time, people’s lives became increasingly disrupted as they are confined to their homes for extended periods. As a result, some industries experienced a ‘boom’ as the demand for home-based technology and entertainment suddenly and dramatically increased. For instance, the video game industry saw a significant increase, with a 12% rise in global sales (Clement, 2021), indicating a similar potential for growth in VR usage.

The appeal of VR, including its applications in gaming and virtual travel, naturally increased as people sought new forms of entertainment and escapism during lockdowns (Ball et al., 2021). While concrete figures are challenging to obtain, some popular press articles estimate that spending on extended reality technologies increased by 50% in 2020, fuelled at least in part by the pandemic (Vardomatski, 2021). Interestingly, online surveys indicated a disparity in VR usage: while 26% of teens in the US owned a VR device, nearly half of these users seldom utilized them (Lin, 2022). This raises questions about the long-term engagement with VR technologies post-adoption.

Three years post-COVID-19, the pandemic’s impact persists, though signs indicate a shift towards a more stable and predictable situation, potentially moving from a pandemic to an endemic (Grennan, 2019). By early 2023, 68.5% of the global population had been vaccinated against COVID- 19 (Our World in Data, 2022), leading to a relaxation of strict containment measures. Consequently, industries that flourished during the pandemic, like video games, began to experience a downturn (Monaco-Vavrik, 2022). This fluctuating landscape underscores the need to continue examining technology adoption in a post-pandemic world.

Prior VR adoption research found that COVID-19-related factors, such as financial and health worries, are related to VR acceptance and adoption. For example, the perceived impact of COVID- 19 improved the perceived usefulness of VR as the pandemic provided a potential use case for the technology (Huang, Ball, & Francis, 2023). Moreover, the pandemic influenced how people used VR, with some turning to it for work and others for coping with mental health challenges (Ball et al., 2021). In this study, we aim to extend this research by exploring the long-term impacts of COVID- 19 on VR technology acceptance and adoption.

To explore the influence of the pandemic on VR adoption and further our understanding of how VR is perceived over time, we have developed a conceptual model based on the Technology Acceptance Model (TAM) and its extensions. This model serves as the foundation for our hypotheses and research questions, which investigate both direct and indirect effects of VR ownership and the perceived impacts of COVID-19 on VR use (see Figure 1).

We propose the following six hypotheses based on our conceptual model:

H1: VR Ownership has a direct effect on (a) Perceived Ease of Using VR, (b) Perceived Usefulness of VR, and (c) Intention to Use VR.

The current study includes a three-wave cross-sectional survey that began in 2020. All three waves of data collection were conducted through Amazon Mechanical Turk (MTurk), an online crowdsourcing platform widely used for research purposes (Goodman et al., 2013). MTurk participants, known as ‘Turkers’, receive monetary compensation for completing Human Intelligence Tasks (HITs) on the MTurk platform (Paolacci & Chandler, 2014). Compensation varies based on task complexity, with Turkers earning from 1 cent to several dollars per HIT (Ross, Irani, Silberman, Zaldivar, & Tomlinson, 2010). The data quality from MTurk is generally considered equal to or better than data collected from undergraduate research pools (Miller, Crowe, Weiss, Maples- Keller, & Lynam, 2017).

The researchers posted three HITs on MTurk during the Fall of 2020, the Summer of 2021, and the Fall of 2022, detailing the survey purposes and associated monetary compensation. Each wave of HITs was open to participants within the United States until approximately 315 responses were received, typically over 72 hours (3 consecutive days). The target of 315 responses per wave aimed for 300 complete datasets with an additional 5% to account for potential missing or incomplete data. The sample size for each wave is in line with other studies that have examined VR adoption (Bunz et al., 2021; Jang et al., 2021; Lee et al., 2019). Respondents were not required to own a VR headset to participate in the study. A total of 928 participants were recruited for this study. Twenty-six participants with incomplete surveys or missing data were excluded, reducing the usable sample to 902. All participants received one US dollar upon submitting their survey codes to MTurk. All study procedures were approved by the Institutional Review Board at a US university, adhering to ethical standards. Respondent identifiers were removed post-incentive distribution, and results are reported in aggregate.

This three-wave study uses a custom-created cross-sectional survey to investigate the impacts of COVID-19 on people’s attitudes toward virtual reality. The questionnaires across all three years were composed of questions related to (1) technology acceptance and adoption (TAM), (2) COVID- 19 impacts, and (3) demographics.

We utilized TAM items related to VR adoption developed by Manis and Choi (2019). These items, slightly modified from Davis’s (1989) traditional TAM measures, form three scales: perceived ease of using VR (PEOU, Cronbach’s α = 0.853), perceived usefulness of VR (PU, Cronbach’s α = 0.876), and intention to use VR (ITU, Cronbach’s α = 0.857). Each scale comprises four equally weighted items, with overall scale scores ranging from 1 (lowest) to 7 (highest). Sample items include ‘I believe using VR hardware would help me be more productive (PU)’ and ‘I believe using VR hardware would be easy for me (PEOU)’.

The COVID-19 measures employed in this study were adopted verbatim from a recent study that examined the perceived impacts of COVID-19 on psychological outcomes (Tull et al., 2020). We asked participants to report how much the pandemic impacts them in the following domains: financial worry, social support, health anxiety, and loneliness. The response categories ranged from 1(no impact at all) to 5 (impacted my life a great deal), and the items were averaged to attain an overall scale score ranging from 1 (respondent marked each of the four items as 1) to 5 (respondent marked each of the four items as 5).

We also included several VR-related and demographic control variables, including device ownership (i.e., material access) and types of devices participants owned. Lastly, we asked participants about their demographic information, including sex, age, income, education level, and race. The order of the sections presented in the questionnaire was as follows: technology acceptance variables, VR-related variables, the perceived impacts of COVID-19, and control variables.

We employed descriptive analyses, ordinary least squares regressions (OLS), and mediation-moderation analyses. Descriptive analyses provided basic variable information, while OLS tested the four hypotheses. Mediation-moderation analyses, conducted using Hayes’s (2018) PROCESS macro in SPSS v29, explored the two research questions regarding VR technology adoption.

The descriptive analysis included (1) demographic characteristics of participants and their VR ownership and (2) means and standardized deviations of the variables of interest. For demographic information, 61.6% of participants identified themselves as males, 69.2% were white, and more than half of the respondents were 25-34 years old. Regarding the respondents’ household income, more than one-third of the participants were between $40,000 and $59,999. For the education levels, almost 80% of participants possessed a 4-year degree or higher, while less than 10% did not have some level of higher education. This participant profile is consistent with the demographic trends typically seen in the U.S. MTurk population, as noted in other studies exploring MTurk user demographics (Moss, Rosenzweig, Robinson, & Litman, 2020).

Regarding VR hardware ownership, close to 75% of respondents reported owning some form of VR hardware. This rate of VR hardware ownership in our study appears higher than the figures reported in previous VR adoption studies, such as 50% in Manis & Choi (2019) and 69.6% in Huang, Ball, and Francis (2023). However, it’s noteworthy that most respondents indicated ownership of more accessible, lower-cost VR hardware, like Google Cardboard (19.7%) and Samsung Gear VR (51.2%). In contrast, only a smaller fraction, about 15%, reported possessing higher-end VR head-mounted displays (HMDs) such as the HTC Vive or the Valve Index. In other words, while VR adoption appears high in our sample (74.1%), it is essential to note that there is a great deal of variability in the type and quality of VR devices owned, and most (77%) devices our respondents reported owning are low-cost headsets with limited immersive characteristics and features such as Google Cardboard. Table 1 provides a detailed breakdown of these demographic characteristics and the specifics of VR hardware ownership among the participants.

The variables of interest include VR hardware ownership, the perceived impacts of COVID-19, and TAM variables. Almost 75% of respondents reported owning VR hardware, although most own entry-level devices such as Samsung Gear VR and Google Cardboard. For the perceived impacts of COVID-19, the means were calculated based on the responses of the 902 participants. Participants reported moderate overall perceived impact of COVID-19 (Cronbach’s α = 0.853). Participants also reported a moderate amount of COVID-19 impacts across all four categories. Financial worry and lack of social support are the top two perceived impacts, followed by loneliness and health anxiety. Regarding TAM-related variables, participants reported an average of 5.47 and 5.61 points out of a 7-point Likert-like scale (7 as the highest) when asked about the PEOU and PU of VR hardware. Furthermore, participants also reported a strong intention to use (5.43) on average. Table 2 presents the descriptive analysis of the perceived impacts of COVID-19 and the TAM variables.

A series of OLS regressions were conducted to test our hypotheses, with results detailed in Table 3. In Model 1, the focus was on the direct effects of VR ownership and the perceived impacts of COVID-19 on participants’ perceived ease of using VR (PEOU), addressing Hypotheses H1 and H2. The main predictors were VR ownership and the perceived impacts of COVID-19, with demographic variables such as sex, age, race, educational level, and household income as control variables. Model 2 explored the relationship between PEOU and perceived usefulness (PU), in line with Hypothesis H4. Here, PU was the dependent variable, with PEOU included as a critical predictor along with the variables from Model 1.

Model 3 examined the direct effects of VR ownership and COVID-19 on Intention to Use VR (ITU), relevant to Hypotheses H1 and H2. This model integrated the main variables - VR ownership and COVID-19 impacts - and demographic variables as predictors. Model 4 expanded the analysis to include PEOU and PU as predictors of ITU, crucial for exploring the potential mediating relationships posited in Hypotheses H3, H5, and H6. It compared the differences between Models 3 and 4 to assess mediation. There is no multicollinearity (VIF < 4) in all four models, and the values of skewness (between -2 to +2) and kurtosis (between-7 to +7) were considered acceptable (Hair et al., 2010).

The OLS regression results from Models 1-4 provided insightful findings. In Models 1, 2, and 3, the analyses revealed that VR ownership significantly predicted perceived usefulness (PU, β =.12, p < .001) and intention to use VR (ITU, β =.16, p < .001) but did not significantly predict perceived ease of use (PEOU). This partial support for H1 suggests a complex relationship between VR ownership and its perceived attributes. On the other hand, the perceived impacts of COVID-19 significantly influenced PEOU (β =.26, p < .001), PU (β =.15, p < .001), and ITU (β =.33, p < .001), offering full support for H2 and highlighting the significant role of the pandemic in shaping attitudes towards VR. The findings also showed a positive influence of PEOU on PU (β =.65, p < 0.001), fully supporting H3. This underscores the importance of ease of use in enhancing the perceived usefulness of VR.

In Models 3 and 4, mediation relationships were further explored. Both PEOU (β =.28, p < 0.001) and PU (β =.59, p < 0.01) emerged as significant predictors of ITU in Model 4, fully supporting H4, with PU being the more influential factor. Notably, the effect of VR ownership on ITU was mediated by PEOU and PU, as evidenced by a more than 56% decrease in its effect (from .16 in Model 3 to .07 in Model 4), supporting H5. Similarly, the impact of COVID-19 on ITU also showed a significant decrease (almost 79% from .33 in Model 3 to .07 in Model 4), indicating an indirect effect via PEOU and PU and supporting H6.

Demographic variables played a notable role as well. Household income (β =.07, p < 0.05) was a predictor of PEOU, and education level (β =.08, p < 0.001) influenced PU. These findings emphasize the importance of demographic factors in the adoption and perception of VR technology.

To answer our research questions, we conducted two mediation-moderation analyses using PROCESS model 85 with 5,000 bootstrap samples and a 95% confidence interval. In the first PROCESS analysis, we explored VR ownership as the independent variable and ITU as the dependent variable. The year of data collection was a moderator, and PEOU and PU served as mediators. Demographic variables and COVID-19 were included as covariates.

Our analysis revealed significant interaction effects of VR ownership on PU (F (2,872) =7.5594, p<.001) and ITU (F (2,872) =10.6711, p<.001). However, there were no interaction effects of VR ownership on PEOU. Regarding conditional direct effects, VR ownership positively impacted PU in 2020 (β = .2863, p < .01) and 2022 (β = .3721, p < .001), but not statistically significant in 2021 (β = - .1664, p = .117). The direct effect on ITU was negatively significant in 2021 (β = -.3546, p < .001), with non-significant positive effects in 2020 and 2022.

The conditional indirect effects through PEOU and PU varied annually. The indirect effect through PEOU was not significant across the three years. Through PU, the effect was significant in 2020 (β = .1755, 95% CI [.0305, .3518]) and 2022 (β = .2281, 95% CI [.0670, .4114]), but not significant in 2021. The combined mediators’ effect was not significant across the three-year span.

These findings suggest a complex and fluctuating relationship between VR ownership and user perceptions across different years, especially highlighted by the contrasting results in 2021. Table 4 details VR ownership’s conditional direct and indirect effects on the outcome variables.

In the second PROCESS analysis, the impact of COVID-19 was examined as the independent variable, with intention to use (ITU) VR as the dependent variable. The year of data collection was utilized as a moderator, while perceived ease of use (PEOU) and perceived usefulness (PU) were mediators. Additional covariates included demographic variables and VR ownership.

Our analysis indicated significant interaction effects of the COVID-19 impact on PU (F (2,872) =7.5594, p<.001) and ITU (F (2,872) =10.6711, p<.001), but not PEOU. Specifically, the conditional direct effects revealed a varying influence of the pandemic on users’ perceptions and intentions to use VR across different years. In 2020, the impact of COVID-19 positively influenced PU (β = .2986, p < .001), suggesting an increased perception of VR’s utility amidst the early pandemic. However, in 2021, this effect was not as pronounced, indicating a possible adjustment in perceptions as the pandemic progressed (β = .0958, p < .001). By 2022, the positive impact on PU re-emerged strongly (β = .3140, p < .001), reflecting a sustained or growing appreciation of VR’s usefulness as the pandemic evolved.

Regarding the indirect effects of COVID-19 on users’ intention to use VR (ITU) via perceived ease of use (PEOU) and perceived usefulness (PU), the results are different across three years. In 2020, the impact of COVID-19 on ITU through PEOU was notable (β = .1021, 95% CI [.0579, .1556]), highlighting the initial phase of the pandemic where the perception of VR’s ease of use significantly influenced usage intentions. This pathway remained significant in 2021 (β = .0752, 95% CI [.0429, .1173]) and persisted in 2022 (β = .0654, 95% CI [.0234, .1194]), underscoring the sustained influence of ease-of-use perceptions on VR usage intentions as the pandemic changed.

Similarly, the indirect impact of COVID-19 on ITU via PU was significant across each year, with the effect being considerable in 2020 (β = .1197, 95% CI [.0392, .2037]), continuing in 2021 (β = .0592, 95% CI [.0161, .1094]), and becoming particularly strong in 2022 (β = .1941, 95% CI [.0995, .3046]). This trend indicates a growing recognition of VR’s usefulness throughout the pandemic’s progression.

Additionally, the combined effect of PEOU and PU as mediators was significant. In 2020, their combined influence on the relationship between COVID-19 and ITU was substantial (β = .1413, 95% CI [.0825, .2045]). This effect was again significant in 2021 (β = .1040, 95% CI [.0627, .1492]) and continued into 2022 (β = .0905, 95% CI [.0334, .1577]), highlighting the enduring, but potentially waning, impact of the pandemic on VR technology acceptance. See Table 5 below for the conditional direct and indirect effects of COVID-19 on TAM variables.

These findings demonstrate a complex interplay where both ease of use and usefulness perceptions significantly mediate the relationship between the pandemic and the intention to use VR, emphasizing how external events like COVID-19 can shape technology adoption over time. Table 6 summarizes the findings of the hypotheses and research questions.

The study offers valuable insights into the acceptance and adoption of VR technologies, particularly within the evolving context of the metaverse. Our results confirm the role of perceived ease of use (PEOU) and perceived usefulness (PU) in shaping users’ intentions to use VR (ITU), which is consistent with existing literature (Kim et al., 2020; Lee et al., 2019; Manis & Choi, 2019). However, the results also reveal more nuanced relationships. Specifically, while VR ownership predicts PU and ITU, it does not significantly influence PEOU. This finding challenges the assumption that VR ownership inherently translates into improved usability perception, which suggests that while owners recognize the utility of VR technology, usability remains a persistent barrier.

This study further showed the significant role of COVID-19 as an external factor that positively influenced PEOU, PU, and ITU. The perceived impacts of the pandemic demonstrate its role in accelerating VR adoption and reinforcing its role as a gateway technology to the metaverse. Our mediation analysis further revealed that the impact of VR ownership and COVID-19 on ITU is indirect, which is mediated by PU and PEOU. Notably, the direct effect of VR ownership on ITU was significantly reduced when considering these mediating factors, indicating the importance of both ease and usefulness perceptions in shaping adoption behaviours.

Our focus on the temporal dynamics of VR technology acceptance revealed interesting patterns. The impact of VR ownership on TAM variables varied across the years, with 2021 presenting a unique scenario where VR ownership negatively correlated with intention to use VR devices. This could indicate a shift in user expectations during this period. Similarly, the indirect effects of COVID-19 on ITU via PEOU and PU were consistent across all three years, but the effects showed a gradual decline, suggesting an enduring, yet slowly diminishing, influence of the pandemic on VR acceptance.

In summary, our findings highlight a complex interplay between user perceptions, external influences, and temporal dynamics in shaping the adoption and acceptance of VR technologies (See Figure 2). While VR ownership drives perceived usefulness, usability concerns remain unresolved, signalling a need for enhanced user experiences. The pandemic’s role as a catalyst for VR adoption remains evident but is slowly diminishing. These insights are particularly relevant in understanding VR’s evolving role as a gateway to the metaverse and provide a foundational basis for future research in this rapidly changing field.

The current study has three principal theoretical contributions. First, our study significantly contributes to the theoretical understanding of VR adoption within the technology acceptance model (TAM) framework, particularly in the context of the emerging metaverse. The findings corroborate prior research (Kim et al., 2020; Lee et al., 2019; Manis & Choi, 2019), emphasizing the vital influence of perceived ease of use and usefulness in determining users’ intentions to use VR technology. This reiteration of TAM’s foundational components in a novel context in which public awareness of the metaverse has flowed and ebbed, underlines the model’s robustness and adaptability in explaining technology acceptance across evolving digital landscapes.

Second, our longitudinal approach provides insights into the evolution of VR acceptance, especially during and after the COVID-19 pandemic. This period, marked by substantial shifts in user behaviour and technology interaction, is a crucial backdrop for understanding the dynamic nature of VR acceptance. We extend traditional TAM models by integrating external factors, such as the ramifications of COVID-19, which aligns with recent studies investigating health anxiety and its impact on VR adoption (Ball et al., 2021; Huang et al., 2023). This extension reflects a broader scholarly trend of incorporating external environmental and psychological factors into TAM, offering a more holistic view of technology acceptance dynamics.

Third, our findings also shed light on the nuanced relationship between VR ownership and user perceptions over time. The distinct pattern observed in 2021, where VR ownership correlated with decreased intentions to use VR, challenges and enriches the existing literature. This deviation can be theoretically framed through the concept of hype cycles (Dedehayir & Steinert, 2016; Grundmeyer, 2014; Prinsloo & Van Deventer, 2017). The apparent cycle of heightened acceptance in 2020, followed by disillusionment in 2021 and recovery in 2022, illustrates the complex interplay between technology adoption, societal trends, and marketing dynamics. This nuanced understanding of VR acceptance, influenced by hype dynamics, not only adds depth to TAM’s applicability in new technological domains but also underscores the need for a temporal and contextual lens in technology adoption research.

Our findings offer vital insights for VR technology developers and manufacturers, especially in the context of the evolving metaverse. The fluctuating perceptions of VR’s usefulness and ease of use, particularly noted in 2021, highlight the need for continuous innovation in user interfaces and overall user experience. Developers should focus on enhancing accessibility and providing robust support to address potential user frustrations and facilitate long-term engagement with VR technologies. Marketing professionals can leverage these insights to refine their strategies, focusing on the practicality and versatility of VR applications. During periods of decreased consumer interest, as observed in our study, emphasizing VR’s diverse applications can help sustain user engagement and drive adoption.

The past decade has seen notable advancements in making VR more accessible, with a significant reduction in the price of high-quality VR devices and the simplification of setup processes. Our research confirms the positive impact of these advancements on users’ intention to use VR technology. However, it also highlights the crucial role of usefulness as a consistent predictor of VR acceptance. Thus, while focusing on ease of use is vital, VR developers and designers should not overlook the importance of ensuring that VR applications are perceived as genuinely useful. Developing a “killer app” that showcases a unique and compelling use of VR could be a key driver for broader adoption and acceptance of VR in the mainstream market.

The consistent relevance of VR throughout the pandemic period signals a stable interest in immersive spatial computing technologies, presenting lucrative opportunities for investors and businesses. Strategic investments to bridge the gap between virtual and augmented realities could lead to significant advancements, contributing to a more immersive and cohesive metaverse experience.

Our study contributes significantly to the understanding of VR adoption, yet it has limitations that need addressing in future research. A primary limitation lies in not accounting for the variability of VR devices used by participants. The type and quality of VR devices can greatly impact user experiences and perceptions. Hence, future studies should differentiate between various VR devices to provide a more nuanced understanding of VR adoption.

Second, relying on an MTurk panel sample may not fully represent the general population. MTurk samples tend to overrepresent younger individuals, potentially skewing results (Fleischer, Mead, & Huang, 2015; Huff & Tingley, 2015). Furthermore, this was a panel study, so participants varied over time. Future research should thus employ different methods, such as a cohort panel design or more diverse sampling methods, like probability sampling, to enhance the generalizability of the findings. Third, to explore the impact of hardware access on the intention to use VR, we examined user’s VR ownership. However, other forms of access, such as borrowing a headset, may play a significant role. Future studies may wish to examine the impact of broader forms of access. Lastly, our study’s focus on U.S. residents limits its global applicability. Given the diverse ways VR is used and perceived worldwide, future studies should explore VR acceptance across different cultures and geographical contexts to gain a more global perspective.