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Article

Effects of User Experience on Satisfaction and Behavioral Intentions in Metaverse Model Houses

by
Haewon Lim
1,
Yoojin Han
1,
Dowon Lee
2 and
Ji-Hyoun Hwang
3,*
1
Department of Interior Architecture and Build Environment, Yonsei University, Seoul 03722, Republic of Korea
2
Human-Centered Design Department, Cornell University, Ithaca, NY 14850, USA
3
Department of Housing and Interior Design, Chungbuk National University, Cheongju 28644, Republic of Korea
*
Author to whom correspondence should be addressed.
Buildings 2026, 16(2), 268; https://doi.org/10.3390/buildings16020268
Submission received: 14 November 2025 / Revised: 5 January 2026 / Accepted: 5 January 2026 / Published: 8 January 2026
(This article belongs to the Section Architectural Design, Urban Science, and Real Estate)
Browse Figure
Versions Notes

Abstract

Although metaverse model houses have recently emerged as an interactive alternative to traditional housing marketing tools, empirical research addressing users’ experiences within these environments remains limited. This study aimed to examine how three dimensions of user experience (UX)—operational, sensory, and exploratory—affect user satisfaction and behavioral intentions in metaverse model houses. A total of 83 participants explored a metaverse model house using a tablet PC and completed a questionnaire. Multiple linear regression analysis revealed that exploratory experience significantly influenced user satisfaction, while sensory experience was positively associated with all behavioral intentions, including the intention to revisit, recommend, reside, and purchase. These findings advance our understanding of UX in virtual housing environments and highlight the importance of immersive and exploratory elements in designing effective metaverse model houses. The results offer practical implications for improving digital housing marketing strategies and guiding the future development of metaverse-based architectural platforms.

1. Introduction

The metaverse has expanded rapidly, with advances in digital infrastructure and immersive technologies, particularly during the COVID-19 pandemic, which intensified the need for virtual environments supporting business operations, social interaction, and commercial transactions [1,2,3,4]. In the real estate sector, these developments have enabled virtual property exploration and consultations, transforming housing marketing practices by allowing users to remotely navigate residential spaces, interact with consultants, and make informed decisions without physical visits [2,5]. As a result, online model houses have evolved into metaverse-based model houses that integrate AI, VR, and AR technologies, providing interactive, customizable environments where users can use avatars to explore spaces and receive real-time information [6].
The metaverse is a persistent, avatar-driven virtual world that blends digital and physical elements, enabling users to experience activities beyond spatial and temporal constraints [7,8,9,10,11,12]. Its applications now extend across culture, healthcare, design, and marketing [13,14,15,16], and recent technological advances have shifted virtual model houses from static, information-oriented tours toward dynamic platforms offering multisensory engagement and enhanced spatial presence. Unlike traditional online model houses, metaverse-based environments allow users to actively navigate and receive contextual information.
In virtual residential settings, user experience (UX) plays a crucial role in how users evaluate spaces and form attitudes toward potential housing options. Three UX dimensions are particularly relevant: sensory experience, which shapes emotional responses and perceived presence [17]; operational experience, which influences usability, system fluency, and perceived functionality in digital architectural environments [18]; and exploratory experience, which enables users to understand spatial layouts, information structures, and functional affordances necessary for housing-related decision-making [19]. These UX dimensions are therefore particularly relevant in metaverse-based model houses, where users must rely on mediated cues rather than physical immersion to assess livability and residential quality.
However, empirical research on UX in metaverse-based housing contexts remains limited. Prior studies have mainly explored technological feasibility, platform development, or metaverse investment potential rather than examining how users cognitively, behaviorally, and sensorially interact with virtual residential spaces [8,20,21,22,23]. Although UX theory asserts that user perceptions significantly shape satisfaction and behavioral intentions [24,25,26], little is known about how different UX dimensions operate within housing decision processes—a notable gap given the role of model houses in stimulating onsite visits and influencing purchase behavior [27,28]. As metaverse model houses remain in an early developmental stage, establishing UX-based design strategies that support meaningful user engagement is increasingly necessary [29]. Recent studies have further demonstrated the growing architectural relevance of immersive VR systems in enhancing design evaluation and user interaction [30,31,32,33,34]. Nonetheless, these investigations have primarily focused on professional or instructional applications rather than on users’ housing decisions within metaverse-based residential settings.
To address this gap, this study empirically examines how three UX dimensions—sensory, operational, and exploratory experiences—affect user satisfaction and behavioral intentions in metaverse model houses, including the intention to revisit, recommend, reside, purchase, and substitute offline model houses. By identifying which UX components most significantly influence users’ evaluations and behaviors, this study contributes theoretical insights into virtual housing experience and offers practical guidance for leveraging metaverse environments as an emerging real estate marketing strategy.

2. Research Background and Hypothesis Development

2.1. Metaverse and Real Estate Industry

2.1.1. Metaverse Concept

The first metaverse can be traced back to CitySpace, which was operational from 1993 to 1996 [35]. The concept was popularized in the early 2000s with the rise of the VR game “Second Life.” The term “metaverse,” combining the prefix “meta” (in the sense of transcendence) with “universe,” was first used in Neal Stephenson’s 1992 novel Snow Crash. This concept describes a 3D virtual world in which people interact without the physical limitations of the real world; its evolving meaning has continued to expand. Matthew Ball, metaverse expert and CEO of venture capital firm EpyllionCo, defined it as a massively scaled and interoperable network of real-time rendered 3D virtual worlds that can be experienced synchronously and persistently by an effectively unlimited number of users with an individual sense of presence, and with continuity of data, such as identity, history, entitlements, objects, communications, and payments [36].
When the metaverse concept initially emerged, technology was incapable of implementing 3D virtual worlds. Since then, rapid advancements in AI, 5G, VR, and AR have led to the development of various metaverses centered around the gaming industry, such as Roblox, the Sandbox, and Fortnite [37]. Additionally, the COVID-19 pandemic has increased the need for metaverse tools as an alternative to social interactions in offline spaces, accelerating the development of various platforms. Currently, two main strands of metaverse research exist. The first strand has focused on the technology behind the metaverse. Researchers examined the configuration of virtual worlds in, for example, metaverse games [38,39,40]. Another strand of research has examined how the metaverse can function as a broad medium for disseminating content and facilitating social interactions [41], leading to increased attention on user perception and experience alongside the focus on metaverse technology [42,43,44]. However, to date, research on the metaverse has mostly addressed aspects of how technologically advanced it is [8,20]. Therefore, there is a gap between our understanding of the development of metaverse platforms and user behavior within them. As a result, the usage of these platforms remains unclear [21].

2.1.2. The Emergence of the Metaverse in the Real Estate Industry

Globally, model houses emerged as a promotional tool for the booming housing supply in the late 1940s and 1950s after World War II. By presenting ideal living spaces and lifestyles, they played a role in meeting and shaping people’s expectations for transforming traditional housing, and they provided purchase-related information and promoted the application of modern technology [45]. In the 1960s, South Korea experienced an increase in housing demand as the economy grew. As a solution to urban overcrowding and housing shortages, the supply of apartments rapidly increased, and a “pre-sale system” was implemented, which allowed customers to pay part of the price upfront, reducing the construction cost burden for builders [46]. Model houses were constructed as a reference for customers considering pre-sales. In addition, online model houses emerged in the mid-1990s owing to rapid digital technological development and the adoption of new marketing methods [47]. Initially, online model houses primarily provided information on PCs in a panoramic VR format based on photos of offline model houses [48]. These online model houses evolved to enable users to easily obtain information unilaterally provided by suppliers and navigate spaces with simple clicks. However, the online model houses could not actively communicate with users and lacked the sense of presence that VR can provide, thus failing to meet the diverse needs of consumers [6].
The emergence and advance of the metaverse and VR technologies have elevated online model houses to a new level. For example, in metaverse model houses, users can explore unit layouts and change interiors by navigating virtual spaces with avatars appropriate to their age group, and obtain information through real-time consultations with non-player characters (NPCs) and chatbots. Additionally, they can gather location information from maps represented in aerial views and acquire other types of information through videos, images, and other media [49,50]. Thus, with technological advances, metaverse model houses have moved beyond the traditional one-way information provision and passive environment of previous virtual model houses, and offer an environment centered around user interaction, communication, and maximized realism.
As metaverse technology spreads widely in the real estate industry, metaverse model houses offer various potential benefits to both customers and construction companies. Metaverse model houses increase accessibility for customers by reducing spatiotemporal constraints and provide rich information about as yet unbuilt units and nearby community facilities, as well as unlimited design options to help customers make purchase decisions [5]. According to a Matterport report, more than 90% of potential homebuyers are more likely to purchase a property if the listing includes an immersive 3D tour [51]. For construction companies, metaverse model homes increase ongoing exposure and positive reputation for their home brand [51]. They also reduce the costs and risks associated with leasing land, building, managing, and demolishing offline model homes, and they create a reusable platform that combines a variety of marketing activities and consulting capabilities.
Previous virtual housing studies in real estate primarily focused on enhancing visualization or marketing effects—such as virtual tours, staging, and presence—without examining how distinct UX dimensions influence users’ housing decisions or satisfaction [52,53]. By contrast, this study advances the field by empirically identifying which UX components drive user satisfaction and behavioral intentions in metaverse-based residential settings, thereby addressing a critical gap between platform development and user decision-making.

2.2. User Experience (UX) in the Metaverse

Within the metaverse, the physical and virtual worlds converge to create an enhanced version of reality [11]. As a result, the metaverse functions as an online social platform that integrates various innovative technologies with the goal of providing an immersive UX [54]. To achieve this immersive experience, familiar physical customer interactions must be adapted and extended into more advanced technological environments. These extended experiences allow users to engage in activities that closely resemble real-life experiences, such as social interactions, and even participate in creative and innovative endeavors that would be impractical or impossible in the physical world [14,55]. Consequently, the experiences within the metaverse offer users high-quality content, diverse value, and opportunities for personal growth [56].
User experience (UX) encompasses all direct and indirect experiences that arise when users interact with a product or service [57,58]. With the growth of AR- and VR-based platforms, UX research has commonly classified experience into three dimensions—sensory, interactive (operational), and informative (exploratory)—each capturing a distinct aspect of how users perceive and engage with virtual environments [59,60,61]. Sensory experience refers to visual, auditory, tactile, and other perceptual stimuli that enhance immersion and presence [59]. Sensory richness has been shown to improve satisfaction and engagement [62,63,64,65], particularly in environments that require spatial awareness and environmental interpretation [64]. Operational experience reflects how effectively users can navigate and control a system, including usability aspects such as navigation, feedback, and overall system fluency [59,61,66]. High operational usability reduces cognitive load and supports seamless interaction in VR environments [67]. Exploratory experience relates to how information is presented and organized [59]. Clear orientation and feedback mechanisms facilitate learning and user comprehension, with a well-structured informational framework guiding users through complex tasks [68].
In virtual residential environments, these three UX dimensions become particularly critical because users must rely entirely on mediated cues—rather than physical inspection—to assess livability, spatial comfort, and functional suitability [17,69]. Accordingly, sensory realism enhances emotional connection, operational usability supports efficient exploration, and exploratory affordances strengthen spatial understanding and decision confidence. Reflecting this multidimensional perspective, prior studies have classified UX attributes along these three dimensions and developed corresponding evaluation frameworks for metaverse services (Table 1), providing a foundation for linking UX to user perception and behavioral responses in digital housing contexts [3,32]. Building on this work, the present study adopts the sensory–operational–exploratory experiences framework to examine how these dimensions influence user satisfaction and behavioral intentions in metaverse model houses. Based on these theoretical premises, each UX dimension is hypothesized to exert a positive effect on satisfaction and subsequent housing-related behavioral intentions.

2.3. Satisfaction and Behavioral Intention in the Metaverse

Satisfaction and behavioral intention has been widely applied to diverse consumer behaviors, elucidating the influence of personal attitudes and beliefs [75], service quality [76], and the reinforcement of environmental factors [77] on consumer behavior, thereby offering valuable insights for the development of effective marketing strategies. Numerous studies in the field of virtual reality and the metaverse have shown a significant relationship between UX and behavioral intention, which can have a positive impact on purchase intention. For example, UX enhances consumer attention, and strengthens information power [78,79,80], enjoyment [70,71], and satisfaction [70,81]. Furthermore, it has been proven to lead to continuous use [82], word-of-mouth [3], intention to use [83], purchase intention [3,8,28], and the intention to visit the actual house [28].
Building on these prior findings, the present study further distinguishes how three specific UX dimensions—sensory, operational, and exploratory experiences—operate through different psychological mechanisms in virtual residential spaces.
First, sensory experience enhances emotional engagement and perceived presence, which are known to strengthen satisfaction and approach behaviors in immersive environments [70,71]. Sensory realism (e.g., visual depth, lighting, spatial fidelity) contributes to users’ affective responses, increasing positive evaluations of virtual spaces. Second, operational experience reduces cognitive effort and supports smooth interaction by providing intuitive navigation, clear system feedback, and functional stability. This aligns with established models of technology adoption, which argue that perceived ease of use and system usability positively influence attitudes and behavioral intentions [66,67]. Third, exploratory experience facilitates users’ ability to obtain and interpret spatial and informational cues, enhancing decision confidence. Prior studies emphasize that spatial understanding, information availability, and environmental affordances are critical to housing evaluation, especially when users rely entirely on mediated cues rather than physical inspection [17,68]. Therefore, exploratory affordances are expected to be particularly influential in metaverse housing contexts.
Based on these theoretical perspectives, each UX dimension is expected to contribute positively to satisfaction and behavioral intentions. Accordingly, the following hypotheses are proposed:
H1. 
Sensory, operational, and exploratory experiences will positively influence user satisfaction.
H2. 
Sensory, operational, and exploratory experiences will positively influence the perceived substitutability of offline with metaverse model houses.
H3. 
Sensory, operational, and exploratory experiences will positively influence revisit intention.
H4. 
Sensory, operational, and exploratory experiences will positively influence recommendation intention.
H5. 
Sensory, operational, and exploratory experiences will positively influence intention to reside.
H6. 
Sensory, operational, and exploratory experiences will positively influence purchase intention.

3. Research Methods

3.1. Questionnaire Design

To test the hypotheses, we developed a questionnaire examining the UX of a metaverse model house. The questionnaire was divided into two sections assessing respondents’ general characteristics and evaluating their UX. General characteristics included demographic information, such as sex, age, current housing type, current house size, and number of cohabiting family members, as well as awareness of metaverse model houses and experience with offline model houses. In the UX evaluation, respondents answered questions about sensory, operational, and exploratory experience on 5-point Likert scales. Overall satisfaction and behavioral intentions (users’ belief that offline model houses can be replaced and the intention to revisit, recommend, reside, and purchase) were also evaluated on 5-point Likert scales. Further opinions were collected through interviews after the evaluation to aid data analysis and interpretation.
To validate the collected and classified evaluation items, interviews were conducted with seven experts who had 8 to 20 years of experience in the fields of VR, housing product development and marketing, or UX/user value creation. Through these interviews, we grouped and categorized the UX evaluation items for the metaverse model house, removed redundant or ambiguous items, and incorporated additional items as needed to enhance the reliability and validity of the experiment. Therefore, a final set of eight UX attributes were ultimately derived, as shown in Table 2.
The sensory experience section comprised seven items evaluating enjoyment and sense of presence, that is, resemblance to reality. Operational experience (asking if interactions within the metaverse model house were smooth) consisted of 11 items evaluating functionality, navigability, and learnability. Exploratory experience comprised 12 items, assessing information availability, persistence, and affordance.

3.2. Experimental Design

The experimental procedure and survey, including tools, data collection, and analysis methods, were approved by the Chungbuk National University Institutional Review Board (CBNU-202208-HR-0192). The study was conducted in South Korea, a country located in the temperate climate zone. South Korea is characterized by a temperate climate with four distinct seasons and a high prevalence of high-density apartment housing, providing a representative context for examining digital housing evaluation and decision-making behaviors. The experiment was conducted in commonly used communal indoor environments such as seminar rooms, informal study lounges, and café-style spaces. These settings were chosen to minimize environmental novelty effects and to reflect real-life contexts in which users typically engage with digital platforms. Participants were recruited through online advertisements and stratified into four age groups (20s to 50s) to reflect typical household decision-making demographics in the Korean housing market. This age-based stratification captures population groups that are actively involved in residential evaluation and purchasing decisions. Participants were regarded as representative household members, including potential homebuyers, renters, and cohabiting adults who commonly participate in housing-related decision-making processes. As recruitment was conducted online, a potential self-selection bias may exist, given that individuals with higher digital literacy or greater interest in virtual platforms may have been more likely to participate. To address this limitation, age-based stratification and screening procedures were applied to ensure balanced representation across demographic segments relevant to housing decisions. The target number of participants was calculated using G*Power 3.1, considering an effect size of 0.15, a significance level of 0.05, a power (1 − β) of 0.80, and three independent variables (sensory, operational, and exploratory experience), resulting in a minimum required sample size of 77. We recruited 83 participants; all 83 responses were used in the analysis after we confirmed that there were no problems with missing responses, outliers, or duplicate responses.
The experiment was conducted from September 2022 to August 2023, a period corresponding to the rapid expansion and early stabilization of metaverse-based housing applications in the Korean real-estate sector following the COVID-19 pandemic. This timeframe is particularly relevant for observing user responses to emerging virtual and hybrid housing marketing platforms. Trained researchers supervised all sessions to ensure standardized administration. Every participant used the same tablet PC model to minimize device-related variance and to maintain consistency during the metaverse model house exploration. Participants engaged with a metaverse model house application developed by a major South Korean construction company. They navigated the virtual residential space for at least 30 min, simulating typical household evaluation behaviors such as examining spatial layout, furniture arrangement, circulation, and environmental cues. After completing the scenario-based exploration, participants responded to the questionnaire (Figure 1). They then took part in semi-structured interviews designed to deepen the interpretation of the findings by capturing subjective perspectives, including aspects they were satisfied or dissatisfied with, elements they found awkward, and suggestions for improvement.

3.3. Variables and Analytical Model

Based on the hypotheses, user satisfaction and behavioral intentions were modeled as dependent variables. Behavioral intentions included five sub-dimensions: substitutability of offline model houses, revisit intention, recommendation intention, residence intention, and purchase intention. The independent variables were operational experience (OE), sensory experience (SE), and exploratory experience (EE), each measured through multi-item 5-point Likert scales validated in previous UX and VR literature. No control variables were included, as the experimental design restricted device type, exposure time, and exploration environment to minimize extraneous influences.
The regression model used to test the hypotheses was expressed as follows:
Y = β0 + β1OE + β2SE + β3EE + ε
where Y represents either user satisfaction or each behavioral intention variable, OE denotes operational experience, SE denotes sensory experience, and EE denotes exploratory experience.
All statistical analyses were performed using SPSS 22.0. Multiple regression analysis was selected because it enables examination of the independent contribution of each UX dimension while controlling for shared variance among predictors.

4. Results

4.1. Preliminary Analysis

4.1.1. Sample

A total of 83 people participated in the survey; their general characteristics are shown in Table 3.
There were 37 men (44.6%) and 46 women (55.4%), with 26 aged in their 20s (31.3%), 20 in their 30s (24.1%), 20 in their 40s (24.1%), and 17 in their 50s (20.5%). Regarding current housing type, 58 respondents (69.9%) lived in apartments, followed by 13 in detached houses (15.7%), 9 in villas/multi-family houses (10.8%), 2 in mixed-use buildings (2.4%), and 1 in a quasi-residential space (1.2%). The most common house size was 99–132 m2, with 36 respondents (43.3%) in that category, followed by 17 (20.5%) each in the 66–99 m2 and 132 m2 or over categories. Smaller sizes included eight (9.6%) and five (6%) respondents in the 33–66 m2 and under 33 m2 categories, respectively. The most common family size was four members, accounting for 31.3% (26 respondents). This was followed by three members at 22.9% (19 respondents), two members at 19.3% (16 respondents), five or more members at 18% (15 respondents), and one member at 8.4% (7 respondents).
The study also surveyed awareness of metaverse model houses and experience with offline model houses. Thirty-five respondents (42.2%) were aware of metaverse model houses, while forty-eight (57.8%) were not, with more than half unaware. Only 3 respondents (3.6%) had experience using metaverse model houses, while 63 (75.9%) had visited offline model houses, with 25 (39.7%) visiting two to three times, 16 (25.4%) four to five times, 13 (20.6%) six or more times, and 9 (14.3%) once. When asked about the most important factor during model house visits, more than half of the respondents (47; 56.6%) prioritized checking the actual space and options of the house. Others responded with just looking around (15; 18.1%), gathering housing information (8; 9.6%), participating in events or others (3; 3.6%), and considering housing subscriptions or contracts (1; 1.2%).

4.1.2. Reliability and Validity

We verified the reliability and validity of our proposed UX evaluation items (Table 4). We measured reliability using Cronbach’s alpha to assess the stability, consistency, and predictability of the tool. In exploratory studies, a Cronbach’s alpha coefficient of 0.7 or higher is considered acceptable [84,85]. All our items showed a Cronbach’s alpha above 0.8, indicating good consistency and reliability. To test construct validity, we conducted factor analysis, which involves clustering various items and determining if they measure the same dimension. Kaiser–Meyer–Olkin (KMO) values were in the range of 0.75–0.865 (KMO values between 0.8 and 1 indicate adequate sampling, and values between 0.70 and 0.79 are considered middling [86]).

4.2. Hypothesis Testing

We conducted a multiple linear regression analysis to investigate how users’ experiences with metaverse model houses impacted their satisfaction and behavioral intention (Table 5). We analyzed satisfaction as the dependent variable and operational, sensory, and exploratory experiences as independent variables. The resulting regression model had an R-squared value of 0.498, which was significant (F = 26.141, p = 0.000).
In the regression model testing the dimensions of UX as separate independent variables, exploratory experience showed a significant positive effect (t = 3.392, p = 0.01), sensory experience was marginally significant (t = 1.921, p = 0.058), and operational experience was not significant (t = 1.314, p = 0.192). Thus, H1 was partially supported, as only exploratory experience significantly enhanced user satisfaction. This suggests that the extent to which the system affords users the ability to obtain relevant information continuously and perform desired tasks effectively is the primary determinant of satisfaction in virtual residential environments. Although sensory experience had a smaller effect, its marginal significance implies that improvements in visual and auditory realism may also contribute meaningfully to perceived satisfaction.
H2–H6 predict that operational, sensory, and exploratory experiences in metaverse model houses significantly affect users’ behavioral intention, including whether offline model houses could be replaced by metaverse houses and intention to revisit, recommend, reside, and purchase. Table 6 presents the results of the linear regression analysis with the experience dimensions as independent variables and participants’ behavioral intention as the dependent variable.
The linear regression analysis with the possibility of replacing offline with metaverse model houses as the dependent variable and operational, sensory, and exploratory experiences in the metaverse model house as independent variables resulted in a significant regression model (R-squared = 0.370, F = 15.449, p = 0.000). Among the independent variables in the regression model, sensory experience showed a significant positive effect (t = 3.976, p = 0.000), but operational and exploratory experiences were not significant. As for intention to revisit, the regression model was significant (R-squared = 0.449, F = 21.471, p = 0.000). Individually, sensory experience was significantly positive (t = 3.312, p = 0.001), operational experience was marginally significant (t = 1.850, p = 0.068), and exploratory experience was not significant (t = −0.677, p = 0.500). Similar patterns were found for intention to recommend, reside, and purchase, where sensory experience consistently showed a significant positive effect. Therefore, hypotheses H2–H6 were partially supported, as sensory experience emerged as the only UX dimension consistently associated with behavioral intentions across all models. This pattern indicates that visual immersion, realistic sensory cues, and enjoyment are central determinants of users’ willingness to revisit, recommend, reside in, or purchase units presented in metaverse model houses, as well as their perceptions of the virtual environment as a potential substitute for physical model houses. The magnitude of these effects suggests that enhancing sensory realism—through improvements in texture quality, lighting, spatial fidelity, and engaging visual or enjoyable elements—may yield meaningful marketing benefits by strengthening user engagement and supporting decision-making processes in virtual housing contexts.

5. Discussion

This study examined user experience (UX) in metaverse model houses, a rapidly emerging form of virtual housing marketing. Although prior research has explored virtual tours and VR-based housing tools, most studies have emphasized visual fidelity or technological feasibility rather than empirically assessing multidimensional UX in residential metaverse environments. Existing work often treats UX as a single construct, overlooking how specific experience components shape satisfaction and housing-related behavioral intentions. Addressing this gap, the present study identifies three distinct UX dimensions—sensory, operational, and exploratory experiences—and demonstrates that each contributes differently to users’ evaluations of metaverse model houses.
The first contribution lies in categorizing UX into sensory, operational, and exploratory dimensions with concrete evaluation items. This approach builds on foundational UX frameworks that differentiate sensory, operational, and exploratory interactions in digital environments [59,61]. Sensory experience relates to pleasure, enjoyment, and perceived presence—an aspect linked to immersive media research showing that visual and auditory realism enhances spatial engagement [17]. Operational experience reflects usability, functionality, and navigability, aligning with prior findings that intuitive interfaces reduce cognitive load and increase adoption intention in VR housing platforms [87]. Exploratory experience—defined by information availability, persistence, and affordance—has rarely been examined independently, even though a model house’s core function is to deliver housing information, introduce new options and technologies, and present ideal living environments [45]. Thus, this study differs from existing metaverse and VR housing research by operationalizing UX as a multi-element construct that allows differentiated statistical examination of each experiential component.
Second, the results demonstrate that UX significantly affects user satisfaction. Notably, exploratory experience emerged as the most influential dimension. This finding contributes to theory by showing that satisfaction is not merely triggered by sensory immersion (as argued in VR presence research) but by users’ ability to gather and understand spatial information autonomously. This aligns with embodied cognition perspectives, which emphasize that spatial knowledge acquired through active exploration is fundamental to environmental evaluation in virtual environments [88], and extends earlier housing-tour studies suggesting that consumers prioritize understanding the physical layout, material configurations, and dwelling affordances when choosing residences. The quantitative regression results and qualitative interview findings indicate that exploratory affordances enhance satisfaction by enabling users to continuously access relevant information, verify spatial configurations, and perform evaluative tasks with greater confidence. The interviews further reveal that unlimited virtual visits—unlike the time-bound nature of offline showrooms—enable users to repeatedly assess layout and décor, reinforcing exploratory freedom as a psychological mechanism driving satisfaction. Conversely, operational experience did not significantly influence satisfaction, suggesting that basic system usability may function as a threshold condition—necessary for interaction but insufficient to elevate satisfaction once a minimum level of operability is met. Given users’ familiarity with contemporary digital interfaces, incremental improvements in usability may exert weaker effects than experiential elements that directly support spatial understanding and informed evaluation in virtual housing contexts.
Third, UX significantly influenced behavioral intentions, including substitutability of offline with online model houses, revisit, recommendation, residence, and purchase intentions. Here, sensory experience exhibited the strongest effect. This corroborates research indicating that emotional engagement fosters approach behaviors in digital environments [89,90]. The interviews also contextualize this effect; game-like interactions, avatar movement, and NPC encounters created novelty and hedonic value, especially among first-time metaverse users. These findings advance existing VR marketing studies by confirming that hedonic elements trigger behavioral commitment, not just momentary enjoyment. Compared to prior VR housing research that primarily emphasized visual realism or presence as isolated factors, this study shows that sensory experience is most effective when integrated with exploratory functions that support informed decision-making rather than serving solely as a source of entertainment. However, the relatively weak explanatory power regarding the replacement of offline model houses highlights an unresolved gap: despite technological advancements, sensory realism cannot fully emulate tactile or embodied physical-space experiences, echoing critiques that virtual architectural environments still lack multisensory grounding [91]. From a human-metric perspective, these findings suggest that metaverse model houses currently function most effectively as hybrid decision-support platforms that enhance satisfaction, immersion, and decision confidence, rather than as complete substitutes for physical model houses.

6. Conclusions

Metaverse model houses, which offer a VR experience of living spaces through the use of avatars, represent a user-customized service facilitating indirect experience of a house before a potential purchase. As metaverse model houses are in an introductory stage, these findings contribute to research by highlighting the marketing potential of such model houses and their implications for the real estate market through a theoretical investigation of UX. Based on the findings, we suggest several possibilities for enhancing the utilization of metaverse model houses.
First, since exploratory experience elements related to information search contribute to user satisfaction, such experiences should be targeted and improved. Although metaverse model houses provided various kinds of information through 360° images, videos, NPCs, etc., users still felt that they did not receive sufficient information. Therefore, metaverse model houses should be designed to allow users to easily find basic information about exhibited items, options, and housing prices. Users expressed dissatisfaction with not receiving the desired information immediately, indicating a need for customer service improvements. Synchronous user interactions in the metaverse are particularly valued for the way in which they facilitate information acquisition [92], and thus, metaverse model houses should focus on enabling interactive information search. Hence, in addition to videos and NPCs, various methods should be considered to allow users to obtain information immediately when needed, such as through real-time AI chatbots or connections to consultants. Another useful feature would be to allow families or friends to enter and interact together within the metaverse model house, facilitating the exchange of opinions and enhancing the overall experience.
Second, sensory experience elements related to enjoyment and sense of presence (i.e., resemblance to reality) positively impact users’ behavioral intentions. Thus, future metaverse model houses, which may serve as a supplementary service to offline model houses, should focus on creating content that features graphics closely resembling real spaces and provides detailed information on specific furniture and interior items. Additionally, there is a need to enhance the sense of presence when implementing spaces related to the complex and location, as well as the community facilities. Improvements such as naturalistic graphics, realistic views, and free space exploration through avatars are needed to enhance the sense of presence. Some users reported dizziness resulting from the 360° image rotations, and there were differing opinions on avatar movement speed. Therefore, users should be able to control the rotation speed of 360° images and VR screens, as well as their avatar’s movement.
This study is significant as it analyzes UX in metaverse model houses used in the real estate industry, which has relied more on remote interactions since the COVID-19 pandemic. The study contributes theoretical findings about UX in virtual architectural spaces, adding to the broad field of virtual housing. In practice, this study provides a basis for improving metaverse model houses through empirical research data. However, it has some limitations. As metaverse technologies remain in an early developmental stage, the experiment was conducted within a single platform and inevitably involved a relatively small sample. Therefore, caution is required when interpreting and extending these findings. Future studies should incorporate larger and more diverse user groups, multiple platforms, and various interaction devices to validate and refine the applicability of UX effects across different virtual housing contexts. Additionally, examining a broader range of residential virtual building types will enable comparative analyses of how UX influences perception and decision-making in different architectural configurations and spatial affordances. As metaverse technologies continue to evolve, integrating physical building data, occupancy patterns, environmental variables, and cross-cultural user behaviors will be essential for advancing UX-driven approaches in digital architecture. Such efforts will contribute to the development of evidence-based design strategies that inform future building environments and strengthen the role of metaverse model houses as a meaningful architectural tool within the built environment.

Author Contributions

Conceptualization, J.-H.H.; Methodology, H.L. and J.-H.H.; Data Curation, Y.H. and D.L.; Formal Analysis, Y.H. and D.L.; Supervision, H.L.; Validation, J.-H.H.; Visualization, Y.H. and D.L.; Writing—Original Draft, H.L.; Writing—Review and Editing, J.-H.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (RS-2025-24422978, RS-2024-00360680, RS-2023-00244419).

Institutional Review Board Statement

The study was approved by the Chungbuk National University Institutional Review Board Committee (CBNU-202208-HR-0192).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data supporting the findings of this study are not publicly available due to privacy and ethical restrictions associated with human subject research. However, anonymized datasets may be obtained from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest. The graphical illustration presented in this article was created by the authors exclusively for this research and does not involve any external commercial or proprietary interests.

References

  1. Kye, B.; Han, N.; Kim, E.; Park, Y.; Jo, S. Educational applications of metaverse: Possibilities and limitations. J. Educ. Eval. Health Prof. 2021, 18, 1149230. [Google Scholar] [CrossRef] [Scilit]
  2. Nalbant, K.G.; Uyanık, Ş. Computer Vision in the Metaverse. J. Metaverse 2021, 1, 9–12. [Google Scholar]
  3. Araiqat, D.M.; Al-Sartawi, A. The introduction of interior design and architecture into the metaverse and their interrelation to the reality-enhancing technologies. In Artificial Intelligence, Internet of Things, and Society 5.0; Springer: Berlin/Heidelberg, Germany, 2023; pp. 107–117. [Google Scholar]
  4. Hadavi, A.; Alizadehsalehi, S. Metaverse for AEC: A SWOT Analysis. In Computing in Civil Engineering 2023; American Society of Civil Engineers: Washington, DC, USA, 2023; pp. 10–18. [Google Scholar]
  5. HLB. The Impact of the Metaverse on the Real Estate Industry. Available online: https://www.hlb.global/the-impact-of-the-metaverse-on-the-real-estate-industry/ (accessed on 31 October 2025).
  6. Hwang, B.Y.; Lee, T.H. A study on the application flow of virtual reality in the apartment model house. J. Korea Acad. Ind. Coop. Soc. 2019, 20, 585–590. [Google Scholar]
  7. Greene, B. A Physicist Explains Why Parallel Universes May Exist. Available online: https://www.npr.org/2011/01/24/132932268/a-physicist-explains-why-parallel-universes-may-exist (accessed on 31 October 2025).
  8. Park, J.; Kim, N. Examining self-congruence between user and avatar in purchasing behavior from the metaverse to the real world. J. Glob. Fash. Mark. 2024, 15, 23–38. [Google Scholar] [CrossRef] [Scilit]
  9. Papagiannidis, S.; Bourlakis, M.; Li, F. Making real money in virtual worlds: MMORPGs and emerging business opportunities, challenges and ethical implications in metaverses. Technol. Forecast. Soc. Change 2008, 75, 610–622. [Google Scholar] [CrossRef] [Scilit]
  10. Díaz, J.; Saldaña, C.; Avila, C. Virtual world as a resource for hybrid education. Int. J. Emerg. Technol. Learn. (IJET) 2020, 15, 94–109. [Google Scholar] [CrossRef] [Scilit]
  11. Golf-Papez, M.; Heller, J.; Hilken, T.; Chylinski, M.; de Ruyter, K.; Keeling, D.I.; Mahr, D. Embracing falsity through the metaverse: The case of synthetic customer experiences. Bus. Horiz. 2022, 65, 739–749. [Google Scholar] [CrossRef] [Scilit]
  12. Samarnggoon, K.; Grudpan, S.; Wongta, N.; Klaynak, K. Developing a Virtual World for an Open-House Event: A Metaverse Approach. Future Internet 2023, 15, 124. [Google Scholar] [CrossRef] [Scilit]
  13. McKinsey. Innovative and Practical Applications of the Metaverse. Available online: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/innovative-and-practical-applications-of-the-metaverse (accessed on 5 November 2025).
  14. Prieto, J.d.l.F.; Lacasa, P.; Martínez-Borda, R. Approaching metaverses: Mixed reality interfaces in youth media platforms. New Techno Humanit. 2022, 2, 136–145. [Google Scholar] [CrossRef] [Scilit]
  15. Mutlu Avinç, G.; Yıldız, A. A bibliometric and systematic review of scientific publications on metaverse research in architecture: Web of science (WoS). Int. J. Technol. Des. Educ. 2025, 35, 825–849. [Google Scholar] [CrossRef] [Scilit]
  16. Rosário, A.T.; Lopes, P.R.; Rosário, F.S. Metaverse in Marketing: Challenges and Opportunities. In Handbook of Research on AI-Based Technologies and Applications in the Era of the Metaverse; Khang, A., Shah, V., Rani, S., Eds.; IGI Global Scientific Publishing: Hershey, PA, USA, 2023; pp. 204–227. [Google Scholar]
  17. Slater, M.; Sanchez-Vives, M.V. Enhancing our lives with immersive virtual reality. Front. Robot. AI 2016, 3, 74. [Google Scholar] [CrossRef] [Scilit]
  18. Mastrolembo Ventura, S.; Castronovo, F.; Ciribini, A.L.C. A design review session protocol for the implementation of immersive virtual reality in usability-focused analysis. J. Inf. Technol. Constr. 2020, 25, 233–253. [Google Scholar] [CrossRef] [Scilit]
  19. Agirachman, F.A.; Shinozaki, M.; Koerniawan, M.D.; Indraprastha, A. Implementing affordance-based design review method using virtual reality in architectural design studio. Buildings 2022, 12, 1296. [Google Scholar] [CrossRef] [Scilit]
  20. Fernandes, F.; Werner, C. A systematic literature review of the metaverse for software engineering education: Overview, challenges and opportunities. PRESENCE 2022. [Google Scholar] [CrossRef] [Scilit]
  21. Lee, H.J.; Gu, H.H. Empirical research on the metaverse user experience of digital natives. Sustainability 2022, 14, 14747. [Google Scholar] [CrossRef] [Scilit]
  22. Joo, S.; Chung, H.; Nah, K. A study on the user experience in Metaverse: Focused on technology trends and literature research. In Proceedings of the 2021 KSDS Fall International Conference, Seoul, Republic of Korea, 13 November 2021; pp. 122–127. [Google Scholar]
  23. Lee, J.-H.; Rhee, B.-A. A Study on User Experience on Metaverse: Focusing on the ZEPETO platform. J. Digit. Contents Soc. 2022, 23, 995–1011. [Google Scholar] [CrossRef] [Scilit]
  24. Aziz, N.S.; Kamaludin, A.; Sulaiman, N. Assessing web site usability measurement. IJRET Int. J. Res. Eng. Technol. 2013, 2, 386–392. [Google Scholar]
  25. Gao, J.; Jia, W.; Yin, J. Exploring Smartphone User Interface Experience-Sharing Behavior: Design Perception and Motivation-Driven Mechanisms through the SOR Model. Sustainability 2024, 16, 6670. [Google Scholar] [CrossRef] [Scilit]
  26. Papagiannidis, S.; Pantano, E.; See-To, E.W.K.; Bourlakis, M. Modelling the determinants of a simulated experience in a virtual retail store and users’ product purchasing intentions. J. Mark. Manag. 2013, 29, 1462–1492. [Google Scholar] [CrossRef] [Scilit]
  27. Ha, J.-M.; Park, S.-B. A study on the user’s evaluation for the visual types of virtual model house. Korean Inst. Inter. Des. J. 2011, 20, 160–169. [Google Scholar]
  28. Mauri, M.; Rancati, G.; Riva, G.; Gaggioli, A. Comparing the effects of immersive and non-immersive real estate experience on behavioral intentions. Comput. Hum. Behav. 2024, 150, 107996. [Google Scholar] [CrossRef] [Scilit]
  29. Hwang, J.-H.; Han, Y.; Lim, H. Research trend analysis on metaverse applications in South Korea: A keyword network analysis and topic modeling approach. J. Archit. Inst. Korea 2023, 39, 55–65. [Google Scholar]
  30. Shehadeh, A.; Alshboul, O. Enhancing engineering and architectural design through virtual reality and machine learning integration. Buildings 2025, 15, 328. [Google Scholar] [CrossRef] [Scilit]
  31. Sami Ur Rehman, M.; Abouelkhier, N.; Shafiq, M.T. Exploring the effectiveness of immersive virtual reality for project scheduling in construction education. Buildings 2023, 13, 1123. [Google Scholar] [CrossRef] [Scilit]
  32. Zhou, Q.; Wang, S.; Wang, J. Exploring user experience in virtual industrial heritage platforms: Impact of cultural identity, functional clarity, scene interactivity, and narrative quality. Buildings 2025, 15, 253. [Google Scholar] [CrossRef] [Scilit]
  33. Liu, Y.; Konduri, S.; Park, C. A Study on the Factors Influencing Willingness to Use Virtual Reality Systems for External Evaluation of Buildings. Buildings 2024, 14, 3714. [Google Scholar] [CrossRef] [Scilit]
  34. Rodrigo, G.-A.; Mariela, G.-R.; Pedro, S.-R.; Hernán, B.-C.; Mauricio, L.; Patricia, L. A Virtual Reality-Based Exploration of Chilean Apartment Models with Features from the Surrealist Illustrations of Roberto Matta. Buildings 2025, 15, 3380. [Google Scholar] [CrossRef] [Scilit]
  35. Benedikt, M.L. Cityspace, cyberspace, and the spatiology of information. J. Virtual Worlds Res. 2008, 1–22. [Google Scholar] [CrossRef] [Scilit]
  36. Ball, M. Framework for the Metaverse. Available online: https://www.matthewball.vc/all/forwardtothemetaverseprimer (accessed on 31 October 2025).
  37. Lin, Z.; Duan, H.; Li, J.; Sun, X.; Cai, W. MetaCast: A self-driven metaverse announcer architecture based on quality of experience evaluation model. In Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada, 29 October–3 November 2023; pp. 6756–6764. [Google Scholar]
  38. Hudson, J. Virtual immersive shopping experiences in metaverse environments: Predictive customer analytics, data visualization algorithms, and smart retailing technologies. Linguist. Philos. Investig. 2022, 21, 236–251. [Google Scholar]
  39. Jenkins, T. Immersive virtual shopping experiences in the retail metaverse: Consumer-driven E-commerce, blockchain-based digital assets, and data visualization tools. Linguist. Philos. Investig. 2022, 21, 154–169. [Google Scholar]
  40. Kliestik, T.; Novak, A.; Lăzăroiu, G. Live shopping in the metaverse: Visual and spatial analytics, cognitive artificial intelligence techniques and algorithms, and immersive digital simulations. Linguist. Philos. Investig. 2022, 21, 187–202. [Google Scholar]
  41. Park, S.-M.; Kim, Y.-G. A metaverse: Taxonomy, components, applications, and open challenges. IEEE Access 2022, 10, 4209–4251. [Google Scholar] [CrossRef] [Scilit]
  42. Oh, H.J.; Kim, J.; Chang, J.J.C.; Park, N.; Lee, S. Social benefits of living in the metaverse: The relationships among social presence, supportive interaction, social self-efficacy, and feelings of loneliness. Comput. Hum. Behav. 2023, 139, 107498. [Google Scholar] [CrossRef] [Scilit]
  43. Wongkitrungrueng, A.; Suprawan, L. Metaverse Meets Branding: Examining Consumer Responses to Immersive Brand Experiences. Int. J. Hum. Comput. Interact. 2024, 40, 2905–2924. [Google Scholar] [CrossRef] [Scilit]
  44. Xi, N.; Chen, J.; Gama, F.; Riar, M.; Hamari, J. The challenges of entering the metaverse: An experiment on the effect of extended reality on workload. Inf. Syst. Front. 2023, 25, 659–680. [Google Scholar] [CrossRef] [Scilit]
  45. Wallack, C. Dream home: Remodeling American expectations with model houses. J. Am. Cult. 2009, 32, 332. [Google Scholar] [CrossRef] [Scilit]
  46. Juan, Y.-K.; Chen, H.-H.; Chi, H.-Y. Developing and evaluating a virtual reality-based navigation system for pre-sale housing sales. Appl. Sci. 2018, 8, 952. [Google Scholar] [CrossRef] [Scilit]
  47. Lee, S.; Lee, W.-C.; Lee, J.-S.; Park, E. A study of the realistic media of smart glass in the public relations hall of a small model house less than three pyeong. J. Korean Soc. Imaging 2022, 20, 75–87. [Google Scholar]
  48. Choi, M.B.; Kim, Y.M.; Jin, B.; Ku, S.; Kim, K.; Kim, G.-W.; Yun, M.H. Application possibility of web based model house to real model house. In Proceedings of the HCI Korea, Seoul, Republic of Korea, 27–29 January 2016; pp. 240–244. [Google Scholar]
  49. Olimplanet. Expanding Experiences, Creating a New World. Available online: https://www.olimplanet.com/ (accessed on 5 November 2025).
  50. Vrapoint. Where Imagination Meets Immersion. Available online: https://vrapoint.com/ (accessed on 31 October 2025).
  51. Matterport. New Study Shows Property Buyers and Sellers Overwhelmingly Prefer Listings with 3D Tours. Available online: https://matterport.com/ko/news/new-study-shows-property-buyers-and-sellers-overwhelmingly-prefer-listings-3d-tours (accessed on 1 October 2025).
  52. Xiong, C.; Cheung, K.S.; Levy, D.S.; Allen, M. The effect of virtual reality on the marketing of residential property. Hous. Stud. 2024, 39, 671–694. [Google Scholar] [CrossRef] [Scilit]
  53. Deep, S.; Vishnoi, S.; Malhotra, R.; Mathur, S.; Yawale, H.; Kumar, A.; Singla, A. Influence of augmented reality and virtual reality on real estate investment decisions: Understand consumer perspective in Indian AEC industry. Eng. Constr. Archit. Manag. 2025, 32, 1122–1140. [Google Scholar] [CrossRef] [Scilit]
  54. Weinberger, M. What Is Metaverse?—A Definition Based on Qualitative Meta-Synthesis. Future Internet 2022, 14, 310. [Google Scholar] [CrossRef] [Scilit]
  55. Duan, H.; Li, J.; Fan, S.; Lin, Z.; Wu, X.; Cai, W. Metaverse for social good: A university campus prototype. In Proceedings of the 29th ACM International Conference on Multimedia, Chengdu, China, 20–24 October 2021; pp. 153–161. [Google Scholar]
  56. Kim, J. Advertising in the metaverse: Research agenda. J. Interact. Advert. 2021, 21, 141–144. [Google Scholar] [CrossRef] [Scilit]
  57. Alben, L. Quality of experience: Defining the criteria for effective interaction design. Interactions 1996, 3, 11–15. [Google Scholar] [CrossRef] [Scilit]
  58. Desmet, P.; Hekkert, P. Framework of product experience. Int. J. Des. 2007, 1, 57–66. [Google Scholar]
  59. Shedroff, N. 11 Information Interaction Design: A Unified Field Theory of Design. In Information Design; MIT Press: Cambridge, MA, USA, 2000; p. 267. [Google Scholar]
  60. Jun, H.; Kim, S.-Y. Digital Restoration research using GPS-based mobile augmented reality—Focusing on Seosomun Park. J. Digit. Contents Soc. 2021, 22, 1755–1763. [Google Scholar] [CrossRef] [Scilit]
  61. Hartson, R. Cognitive, physical, sensory, and functional affordances in interaction design. Behav. Inf. Technol. 2003, 22, 315–338. [Google Scholar] [CrossRef] [Scilit]
  62. Deliyannis, I.; Panagopoulos, I.; Panagiotakopoulos, D.; Marentakis, G.; Metzitakos, R. The sensory enrichment and interactivity of immersive user experiences in the public sector: The Ionian film office metaverse. In Augmented and Virtual Reality in the Metaverse; Springer: Berlin/Heidelberg, Germany, 2024; pp. 149–177. [Google Scholar]
  63. Ramalho, J.; Chambel, T. Sensing immersive 360 mobile interactive video. In Proceedings of the 2014 International Conference on Computer Graphics Theory and Applications (GRAPP), Lisbon, Portugal, 5–8 January 2014; pp. 1–10. [Google Scholar]
  64. Waller, D.; Hodgson, E. Sensory Contributions to Spatial Knowledge of Real and Virtual Environments. In Human Walking in Virtual Environments: Perception, Technology, and Applications; Steinicke, F., Visell, Y., Campos, J., Lécuyer, A., Eds.; Springer: New York, NY, USA, 2013; pp. 3–26. [Google Scholar]
  65. Hepola, J.; Karjaluoto, H.; Hintikka, A. The effect of sensory brand experience and involvement on brand equity directly and indirectly through consumer brand engagement. J. Prod. Brand Manag. 2017, 26, 282–293. [Google Scholar] [CrossRef] [Scilit]
  66. Marques, A.B.; Barbosa, S.D.J.; Conte, T.U. Defining a notation for usability-oriented interaction and navigation modeling for interactive systems. J. Interact. Syst. 2017, 8, 35–49. [Google Scholar] [CrossRef] [Scilit]
  67. O’Brien, H.L.; Toms, E.G. What is user engagement? A conceptual framework for defining user engagement with technology. J. Am. Soc. Inf. Sci. Technol. 2008, 59, 938–955. [Google Scholar] [CrossRef] [Scilit]
  68. Beauregard, R.; Corriveau, P. User experience quality: A conceptual framework for goal setting and measurement. In Proceedings of the International Conference on Digital Human Modeling, Beijing, China, 22–27 July 2007; pp. 325–332. [Google Scholar]
  69. Lyu, K.; Brambilla, A.; Globa, A.; de Dear, R. An immersive multisensory virtual reality approach to the study of human-built environment interactions. Autom. Constr. 2023, 150, 104836. [Google Scholar] [CrossRef] [Scilit]
  70. Lee, K.C.; Chung, N. Empirical analysis of consumer reaction to the virtual reality shopping mall. Comput. Hum. Behav. 2008, 24, 88–104. [Google Scholar] [CrossRef] [Scilit]
  71. Xu, J.; Liu, S.; Yang, W.; Fang, M.; Pan, Y. Beyond Reality: Exploring User Experiences in the Metaverse Art Exhibition Platform from an Integrated Perspective. Electronics 2024, 13, 1023. [Google Scholar] [CrossRef] [Scilit]
  72. Efthimiadis, E.N. Interactive query expansion: A user-based evaluation in a relevance feedback environment. J. Am. Soc. Inf. Sci. 2000, 51, 989–1003. [Google Scholar] [CrossRef] [Scilit]
  73. Laugwitz, B.; Held, T.; Schrepp, M. Construction and evaluation of a user experience questionnaire. In Proceedings of the Symposium of the Austrian HCI and Usability Engineering Group, Graz, Austria, 20–21 November 2008; pp. 63–76. [Google Scholar]
  74. Darin, T.; Andrade, R.; Sánchez, J. Usability evaluation of multimodal interactive virtual environments for learners who are blind: An empirical investigation. Int. J. Hum. Comput. Stud. 2022, 158, 102732. [Google Scholar] [CrossRef] [Scilit]
  75. Ajzen, I. The theory of planned behaviour: Reactions and reflections. Psychol. Health 2011, 26, 1113–1127. [Google Scholar] [CrossRef] [Scilit]
  76. Lin, J.-H.; Lee, T.-R.; Jen, W. Assessing asymmetric response effect of behavioral intention to service quality in an integrated psychological decision-making process model of intercity bus passengers: A case of Taiwan. Transportation 2008, 35, 129–144. [Google Scholar] [CrossRef] [Scilit]
  77. Foxall, G.R. The behavioral perspective model of purchase and consumption: From consumer theory to marketing practice. J. Acad. Mark. Sci. 1992, 20, 189–198. [Google Scholar] [CrossRef]
  78. Kang, H.J.; Shin, J.-H.; Ponto, K. How 3D virtual reality stores can shape consumer purchase decisions: The roles of informativeness and playfulness. J. Interact. Mark. 2020, 49, 70–85. [Google Scholar] [CrossRef] [Scilit]
  79. Skard, S.; Knudsen, E.S.; Sjåstad, H.; Thorbjørnsen, H. How virtual reality influences travel intentions: The role of mental imagery and happiness forecasting. Tour. Manag. 2021, 87, 104360. [Google Scholar] [CrossRef] [Scilit]
  80. Yung, R.; Khoo-Lattimore, C.; Prayag, G.; Surovaya, E. Around the world in less than a day: Virtual reality, destination image and perceived destination choice risk in family tourism. Tour. Recreat. Res. 2021, 46, 3–18. [Google Scholar] [CrossRef] [Scilit]
  81. Sujud, H.; Hachem, B. Effect of the quality of the accounting information system outputs on customer satisfaction in Lebanese commercial banks. Int. Res. J. Financ. Econ. 2019, 176, 26–40. [Google Scholar]
  82. Cabrera-Sánchez, J.-P.; Villarejo-Ramos, Á.F.; Liébana-Cabanillas, F.; Shaikh, A.A. Identifying relevant segments of AI applications adopters–Expanding the UTAUT2’s variables. Telemat. Inform. 2021, 58, 101529. [Google Scholar] [CrossRef] [Scilit]
  83. Alshurafat, H.; Arabiat, O.; Shehadeh, M. The intention to adopt metaverse in Islamic banks: An integrated theoretical framework of TAM and religiosity intention model. J. Islam. Mark. 2024. [Google Scholar] [CrossRef] [Scilit]
  84. George, D.; Mallery, P. SPSS for Windows Step by Step: A Simple Guide and Reference; Allyn & Bacon: Boston, MA, USA, 2003. [Google Scholar]
  85. Nunnally, J.C. An Overview of Psychological Measurement. In Clinical Diagnosis of Mental Disorders: A Handbook; Wolman, B.B., Ed.; Springer: Boston, MA, USA, 1978; pp. 97–146. [Google Scholar]
  86. Beavers, A.S.; Lounsbury, J.W.; Richards, J.K.; Huck, S.W.; Skolits, G.J.; Esquivel, S.L. Practical considerations for using exploratory factor analysis in educational research. Pract. Assess. Res. Eval. 2013, 18, n6. [Google Scholar]
  87. Xie, B.; Liu, H.; Alghofaili, R.; Zhang, Y.; Jiang, Y.; Lobo, F.D.; Li, C.; Li, W.; Huang, H.; Akdere, M. A review on virtual reality skill training applications. Front. Virtual Real. 2021, 2, 645153. [Google Scholar] [CrossRef] [Scilit]
  88. König, S.U.; Keshava, A.; Clay, V.; Rittershofer, K.; Kuske, N.; König, P. Embodied spatial knowledge acquisition in immersive virtual reality: Comparison to map exploration. Front. Virtual Real. 2021, 2, 625548. [Google Scholar] [CrossRef] [Scilit]
  89. Jo, H.; Park, D.-H. Affordance, usefulness, enjoyment, and aesthetics in sustaining virtual reality engagement. Sci. Rep. 2023, 13, 15097. [Google Scholar] [CrossRef] [Scilit]
  90. Avila-Garzon, C.; Bacca-Acosta, J.; Chaves-Rodríguez, J. Predictors of Engagement in Virtual Reality Storytelling Environments about Migration. Appl. Sci. 2023, 13, 10915. [Google Scholar] [CrossRef] [Scilit]
  91. Bhowmik, A.K. Virtual and augmented reality: Human sensory-perceptual requirements and trends for immersive spatial computing experiences. J. Soc. Inf. Disp. 2024, 32, 605–646. [Google Scholar] [CrossRef] [Scilit]
  92. Cho, H. A study on the analysis of affordance design elements in the metaverse environments. J. Korean Soc. Des. Cult. 2021, 27, 441–453. [Google Scholar] [CrossRef] [Scilit]
Buildings 16 00268 g001
Figure 1. Experimental procedure.
Figure 1. Experimental procedure.
Buildings 16 00268 g001
Table 1. UX attributes by three UX dimensions.
Table 1. UX attributes by three UX dimensions.
AttributesArticle
Sensory Experience
    Sensory enrichment (visual, auditory, haptic, scent sensory)[59,62,63,64]
    Sense of presence (familiarity)
    Enjoyment[67,70,71]
Operational Experience
    Functionality[21,66]
    Navigability[21]
    Relevance[66]
    Efficiency[24,72]
    Learnability (controllability)[21,66,67,73]
    Effectiveness[24,74]
Exploratory Experience
    User control[67]
    Endurability (persistence)[67]
    Affordance[21]
Table 2. UX attributes for metaverse model house.
Table 2. UX attributes for metaverse model house.
Attribute DetailQuestionnaire
Sensory
experience (SE)		Sense of presenceIs it similar to the real environment (e.g., views, finishing materials, lighting)?SE 1–4
EnjoymentIs controlling your character (avatar) fun?SE 5–7
Operational experience (OE)FunctionalityIs the system running smoothly (e.g., space movement, screen transition)?OE 2, 5
NavigabilityIs it possible to use method intuitively to navigate?OE 7–11
LearnabilityCan users learn how to operate the service naturally and perform tasks?OE 1, 3, 4, 6
Exploratory experience (EE)Informative Are users able to find sufficient information they want (e.g., interior options, exhibits, finishes, area of space, detailed structure, and features)?EE 1–6
PersistenceDoes it provide continuous information (e.g., live Q&A)?EE 7
AffordanceDoes its functionality allow users to do what they want (e.g., open and close furniture doors, view 360° space, change the interior, adjust sound volume)?EE 8–12
Note: Informative attribute was added based on expert interviews.
Table 3. General characteristics.
Table 3. General characteristics.
CategoryItemFrequencyPercentage
SexMale3744.6%
Female4655.4%
Age (years)20s2631.3%
30s2024.1%
40s2024.1%
50s1720.5%
Current residence typeApartment5869.9%
Detached house (including multi-unit)1315.7%
Villas/townhouses910.8%
Mixed-use apartment building22.4%
Semi-residential house11.2%
Current residence sizeBelow 33 m256%
33–66 m289.6%
66–99 m21720.5%
99–132 m23643.4%
Larger than 132 m21720.5%
Family members1 person78.4%
2 people1619.3%
3 people1922.9%
4 people2631.3%
5 or more people1518%
Being aware of metaverse model housesYes3542.2%
No4857.8%
Having experience of metaverse model housesYes33.6%
No8096.4%
Number of visits to offline model houses1914.3%
2–32539.7%
4–51625.4%
≥61320.6%
Important factors when visiting a model houseGathering housing information89.6%
Housing subscription and contract11.2%
Just looking around1518.1%
Inspecting actual space and options4756.6%
Participating in events and other33.6%
Future house purchase plansYes4857.8%
No3542.2%
Table 4. Reliability and validity of the evaluation tool.
Table 4. Reliability and validity of the evaluation tool.
DimensionCronbach’s AlphaKMONo. of Items
SE0.8490.7507
OE0.9160.86511
EE0.8460.80412
Note: OE = operational experience; SE = sensory experience; EE = exploratory experience.
Table 5. Regression analysis: user satisfaction.
Table 5. Regression analysis: user satisfaction.
BSEBetat-ValueR2 (Adj. R2)F-Value
(Constant)−0.3090.507 −0.6100.498 (0.479)26.141 ***
OE  0.2400.1830.165  1.314
SE  0.3620.1880.282  1.921
EE  0.5490.1620.360      3.392 **
Note: OE = operational experience; SE = sensory experience; EE = exploratory experience. ** p < 0.01, *** p < 0.001.
Table 6. Regression analysis: participants’ behavioral intentions.
Table 6. Regression analysis: participants’ behavioral intentions.
Dependent VariableIndependent Variablet-ValueR2 (Adj. R2)F-Value
Substitutability of Offline with Online Model Houses(Constant)−0.1310.370 (0.346)15.449 ***
OE−1.635
SE  3.976 ***
EE  1.288
Intention to Revisit(Constant)  1.5110.449 (0.428)21.471 ***
OE  1.850
SE  3.312 **
EE−0.677
Intention to Recommend(Constant)  1.1430.454 (0.433)21.877 ***
OE  0.598
SE  3.838 ***
EE  0.312
Intention to Reside(Constant)  0.5440.478 (0.458)24.100 ***
OE−1.816
SE  4.868 ***
EE  1.573
Intention to Purchase(Constant)−0.2370.489 (0.469)25.172 ***
OE−1.079
SE  4.783 ***
EE  1.182
Note: OE = operational experience; SE = sensory experience; EE = exploratory experience. ** p < 0.01, *** p < 0.001.
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Lim, H.; Han, Y.; Lee, D.; Hwang, J.-H. Effects of User Experience on Satisfaction and Behavioral Intentions in Metaverse Model Houses. Buildings 2026, 16, 268. https://doi.org/10.3390/buildings16020268

AMA Style

Lim H, Han Y, Lee D, Hwang J-H. Effects of User Experience on Satisfaction and Behavioral Intentions in Metaverse Model Houses. Buildings. 2026; 16(2):268. https://doi.org/10.3390/buildings16020268

Chicago/Turabian Style

Lim, Haewon, Yoojin Han, Dowon Lee, and Ji-Hyoun Hwang. 2026. "Effects of User Experience on Satisfaction and Behavioral Intentions in Metaverse Model Houses" Buildings 16, no. 2: 268. https://doi.org/10.3390/buildings16020268

APA Style

Lim, H., Han, Y., Lee, D., & Hwang, J.-H. (2026). Effects of User Experience on Satisfaction and Behavioral Intentions in Metaverse Model Houses. Buildings, 16(2), 268. https://doi.org/10.3390/buildings16020268

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