Introduction
For entrepreneurs and small businesses, leveraging technology presents a pathway to securing competitive advantages. Currently, prominent technologies such as virtual reality (VR), artificial intelligence (AI), business analytics, and electric vehicles (Leadem, 2017) offer significant potential. The COVID-19 pandemic has disrupted traditional modes of interpersonal communication, leading to heightened reliance on Internet-based conferencing platforms like Zoom or Microsoft Teams, as well as virtual reality technology, to facilitate remote interactions.
This study undertakes the development of a Virtual Reality Marketing and Training System (VRMTS) tailored for real estate and hospitality management/marketing. Its core objective is to examine the impact of virtual reality (VR) systems on these sectors. The paper delineates two primary aims. Firstly, it utilizes VR headsets to enable students/customers to view videos captured by a 360-degree camera. Secondly, it discusses the potential use of AI and virtual reality in the real estate and hospitality industries, aiming to determine its potential applications in other entrepreneurial endeavors. In this study’s context, “real estate” encompasses companies engaged in the sale, rental, and promotion of residential and commercial properties to customers, while “hospitality” pertains to the management of motel, hotel, tourism, and cruise businesses.
Significance of the Study
Virtual reality (VR) is defined as a “simulated experience that can be similar to or completely different from the real world.” Through wearing a headset, customers are immersed in an environment that feels authentic, enabling them to effectively navigate and explore real estate properties (Boyd & Koles, 2019; Interaction Design Foundation - IxDF, 2016). Moreover, by incorporating gaming-like features, customers can interact with the simulated environment to gain a clearer understanding of what to expect. Augmented reality, also known as mixed reality, plays a pivotal role in creating this interactive experience. By blending real video with gaming features, it enhances the VR package, offering interactive elements for education. training, and product development (De Silva et al., 2018; Nayyar et al., 2008; Wei, 2019). As AI continues to advance rapidly, VR is poised to play a significant role not only in marketing but also in employee training initiatives.
Literature Review
Considerations surrounding virtual reality (VR) extend across various domains, including education and training, with applications spanning the medical, military, and law enforcement sectors. Furthermore, discussions on VR’s efficacy in employee training are prevalent within the real estate and hospitality industries. Recently, van der Meulen and deVries (2023) conducted a comprehensive literature review on the integration of VR into hospitality education, highlighting its evolving role beyond the confines of the United States. Leung et al. (2022) scrutinized the effectiveness of VR game-based training for hospitality employees, contributing to the ongoing discourse on optimal training methodologies. While still emerging, VR’s potential in marketing within real estate and hospitality management is subject to moderate discourse. Muwandeniya and Eranda (2022) explored VR’s utilization for experiential marketing within the Sri Lankan hospitality industry, shedding light on its practical applications. Additionally, Wong (2021) elucidated strategies for harnessing VR in marketing initiatives within the hospitality and tourism sectors, further enriching the discourse on VR’s multifaceted role in business promotion.
As stated by Gasparini (2021), the integration of artificial intelligence (AI) with virtual reality (VR) represents a burgeoning frontier in both training methodologies and business applications. Vlasceanu et al. (2023) delved into this intersection, particularly within the cruise industry and global hospitality organizations, highlighting AI’s pivotal role in fostering augmented reality (AR) experiences. Nayyar et al. (2018) expanded on the applications of VR and augmented reality (AR) technologies within the tourism and hospitality sectors, providing intricate descriptions and illustrative examples to elucidate the potential of augmented reality in enhancing customer experiences.
The scholarly discourse also scrutinizes the efficacy of virtual reality (VR) within the realm of hospitality management, evaluating its utility and impact. Lo and Cheng (2020) for example, embarked on an investigation into whether VR serves as a magnet for visitors, yielding results that underscore its capacity to engender a palpable sense of presence among audiences.
While there exists a moderate level of discourse surrounding VR’s application in training and marketing within hospitality management, the majority of these discussions fall short of offering comprehensive insights into the development and experimental validation of VR systems. Hence, the impetus behind this research is to elucidate our experiential journey in crafting and empirically testing the perceptions of users in VR systems.
Additionally, artificial intelligence (AI) has ushered in a transformative era in the real estate industry. Srivastava’s (2024) research outlines the top applications of AI in real estate, such as (1) listing descriptions generations, (2) virtual tours, (3) lead generation, (4) property management, (5) fraud and compliance detection, (6) property search, (7) automating due diligence (8) property analysis, (9) customer support and (10) intelligent data processing. The Motley Fool website further underscores AI’s role in home management through the integration of smart home devices such as thermostats and monitoring systems. Yao Morin, the Chief Technology Officer of JLLT, underscores the importance of viewing AI as a means to enhance human capabilities rather than replace them entirely, offering a nuanced viewpoint on its incorporation into the industry.
Artificial intelligence (AI) has become instrumental in the realm of real estate price prediction as well. Alzain et al. (2022) employ the artificial neural network (ANN) technique to meticulously estimate sales prices for residential properties in Saudi Arabia, yielding results that underscore the model’s high accuracy and the remarkable proximity between experimental and predicted prices. Further, Kang et al. (2020) harness AI in conjunction with statistical methodologies to fashion forecasting models for estimating apartment auction prices in Seoul, Korea, spanning the period from 2013 to 2017. Their exploration reveals that the genetic algorithm model emerges as the pinnacle of predictive accuracy. Pinter et al. (2020) innovate by leveraging call detail records (CDR) to prognosticate real estate prices through a novel machine-learning approach in Hungary. The incorporation of CDR data, replete with informative mobility insights such as travel speed and time, elucidates the intricate dynamics shaping real estate valuations, with findings elucidating the direct impact of workers’ entropy and dwellers’ work distance on property prices.
Experimental Design
Development of VR systems
In this study, the development of virtual reality (VR) systems tailored for the real estate and hospitality sectors entailed the integration of sophisticated technological components. Utilizing a 3D camera, capable of capturing images and videos with panoramic precision, alongside a computer equipped with an advanced graphics card, and a VR headset (refer to Figure 1), facilitated the creation of immersive VR experiences. Specifically, our methodology involved employing the 3D camera to meticulously document various aspects of a medium-sized motel located in Indiana, capturing both conventional images and videos as well as immersive 360-degree footage. Figure 2 showcases a motel’s entry door, illustrating the standard visual representation, while Figure 3 depicts the same entryway with the added enhancement of 360-degree capabilities, rendered through specialized computer processing. Subsequently, the captured media underwent meticulous editing procedures on a computer endowed with a specialized graphics card, ensuring seamless integration with the VR device. Notably, the Meta Quest2 VR headsets were selected for this experiment, with detailed instructions meticulously prepared to guide end-users in navigating the VR experience. It is worth noting that the immersive 360-degree videos necessitate viewing through VR headsets, as they cannot be accessed via conventional computer displays.
Hypotheses
Based on the literature review, we have developed the following hypotheses, and an experiment was conducted to test each.
H1: There is no difference in the perceptions of end users among different visual presentation modes: (a) picture, (b) video, and (c) 360-degree video (H0: μp = μv = μv360).
If there is a difference among all three presentation methods, the following hypotheses will be tested:
H2: There is no difference in the perceptions of end users between the traditional marketing modes (picture) and VR with non-360-degree video (H0: μp = μv).
H3: There is no difference in the perceptions of end users between VR with 360-degree Video and traditional marketing modes (picture) systems (H0: μv360 = μp).
H4: There is no difference in the perceptions of end users between video with 360-degree in VR and Video without 360-degree in VR (H0: μv360 = μv).
To examine the hypothesis stated above, 43 students with diverse academic backgrounds, including graduate and undergraduate students in Computer Information Systems, Enterprise Resources Planning, Business Process Management, Web Applications Development, and Data Warehouses, were enlisted for the experiment. Equipped with VR devices, the students immersed themselves in experiences that involved watching both standard videos and 360-degree videos for 25 minutes. Subsequently, participants were tasked with completing questionnaires outlined in Appendix B, aimed at assessing their subjective experiences and perceptions regarding VR technology. Additionally, post-experimental surveys were conducted to gather insights into participants’ sentiments and attitudes toward VR devices after their involvement in the experimental sessions.
Study Results and Analysis
Below in Table 1 are the means and standard deviations for each question in the questionnaire. While the majority of students major in information systems, most of them have limited or no prior experience with VR devices.
In investigating Hypothesis 1 (H1), we applied the one-way ANOVA test to discern perception differences among pictures, videos, and 360-degree video modes. The findings (refer to Table 3) reveal an F-value of 43.3, resulting in the rejection of Hypothesis 1. This outcome indicates notable differences among pictures, videos, and 360-degree video modes. Consequently, additional t-tests are warranted to elucidate these variations further. Table 3 presents a summary of the hypotheses and corresponding t-values.
For Hypotheses (H2), (H3), and (H4), t-tests were conducted to evaluate the differences in perception between pictures and videos, between pictures and 360-degree videos, and between videos and 360-degree videos (refer to Table 2). The t-value for the comparison between pictures and videos is -2.0755, with a corresponding p-value of 0.0441. Given a significance level of 0.05, the hypothesis is marginally rejected. The t-value for the comparison between pictures and 360-degree videos is -8.4148, and for videos and 360-degree videos is -7.7446. These results indicate significant differences in perceptions in each pair comparison at a 95% confidence interval.
Conclusions
The results of this study showed that the utilization of 360-degree video unequivocally emerges as the premier option among available alternatives, followed by standard video content, with static images trailing as the least preferred choice. It is imperative for businesses operating within the realms of real estate and hospitality management, encompassing entities such as motels, travel agencies, and cruise operators, to incorporate video content into their online platforms, where feasible. However, while VR equipped with 360-degree video garners superior preference, there remains ample room for refinement in its implementation to enhance user-friendliness.
In this era, Generative Artificial Intelligence (GenAI or GAI) stands out as a transformative technology with promising prospects for the future. By harnessing AI generative models, Generative AI enables the creation of diverse content types, spanning text, images, and videos, thereby opening up new avenues for the advancement of Virtual Reality (VR) systems. This is especially relevant in augmented reality settings, where gaming elements seamlessly merge with real-world visuals and videos, promising significant shifts in user experiences.
Commercial solutions like Generative Fill and Generative Expand, integrated into Adobe Photoshop (Boehman, 2023), exemplify tangible progress in leveraging AI to enhance image and photo quality, demonstrating the potential for rapid development of VR systems tailored to meet user demands. For instance, within the real estate sector, these advancements empower agents to provide potential buyers with immersive representations of unfinished projects, such as showcasing the conversion of vacant land into vibrant shopping centers within VR environments.
Thus, the intersection of AI applications and virtual reality marks a crucial frontier for future exploration, offering opportunities to drive innovation and reshape various industries.
Recommendations for Future Studies
While the experimental outcomes underscore the affirmative potential of VR adoption within hospitality management, there exist avenues for enhancement and recommendations for VR integration stemming from this investigation. Primarily, attention must be directed towards addressing the challenges faced by individuals wearing eyeglasses when using VR devices, necessitating future research to explore alternative devices tailored to accommodate this demographic. Furthermore, prolonged engagement with VR devices may yield discomfort, prompting contemplation of alternative solutions such as constructing immersive rooms equipped with video projections on multiple walls. However, it’s crucial to acknowledge that such endeavors may incur higher costs compared to traditional VR devices.
Although the sample size of 43 participants meets the requisites for conducting ANOVA tests and t-tests, it remains relatively modest, warranting consideration for future studies to employ larger cohorts. Additionally, it’s noteworthy that the participants in this study predominantly comprise students; thus, there exists a necessity to broaden the scope of future research endeavors to encompass non-student demographics.
Author Note
Authors would like to thank reviewers for their comments and suggestions, especially the second reviewer.
Authors would like to thank the Center for Entrepreneurship & Innovation at Eastern Michigan University for their support for this research.
