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Article

Impacting Elements of Metaverse Platforms’ Intentional Use in Cultural Education: Empirical Data Drawn from UTAUT, TTF, and Flow Theory

by
Shan Hu
,
Geqi Xing
* and
Jing Xin
School of Industrial Design, Hubei University of Technology, Wuhan 430068, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(21), 9984; https://doi.org/10.3390/app14219984
Submission received: 25 September 2024 / Revised: 16 October 2024 / Accepted: 20 October 2024 / Published: 31 October 2024
Browse Figures
Figure 1
<p>Refinement of user perception factors (coding results).</p> "> Figure 2
<p>Research model of users’ willingness to use the cultural education meta-universe system.</p> "> Figure 3
<p>Flow of the research methodology.</p> "> Figure 4
<p>Descriptive statistical information of the official questionnaire.</p> "> Figure 5
<p>Analysis of the path coefficients of the research model.</p> "> Figure 6
<p>Design strategy of cultural heritage education meta-universe system.</p> "> Figure 7
<p>Cross-Music lost instrument meta-universe system interface design.</p> ">
Versions Notes

Abstract

:
This study aims to address the need for design guidelines in developing a cultural-heritage-based metaverse educational system. Using the UTAUT, the TTF model, and Flow Theory, a theoretical framework is constructed. Through qualitative research based on the GT, three user perception factors—presence, interactivity, and narrativity—are introduced as external variables to explore the relationship between these factors and users’ willingness to adopt the cultural heritage metaverse system. The study examines this relationship from the dual perspectives of user perception and technology acceptance. A scale was designed to test the theoretical model empirically, and 298 valid responses were collected through a structured process involving GT coding, pre-testing, and formal surveys. The findings indicate that interactivity, narrativity, and presence significantly enhance the flow experience, while factors such as performance expectancy, effort expectancy, social influence, facilitating conditions, technology–task fit, and flow positively influence users’ intention to adopt the system. Among these, technology–task fit emerged as the most influential factor. This integrated approach reduces subjectivity and bias in criteria determination, enhancing the objectivity and precision of cultural heritage metaverse system assessments and making the system more responsive to user needs.

1. Introduction

In the new era, China places significant emphasis on cultivating cultural self-confidence, enhancing public cultural literacy, and advancing the digital development of the cultural industry. These priorities have collectively propelled the dissemination and promotion of cultural heritage into a deeper stage of development [1]. Therefore, it is necessary to study how cultural heritage education can be effectively integrated with new technological tools. Integrating digital technologies with cultural heritage has revolutionized cultural asset management, preservation, and dissemination. This integration has also reshaped the structure of cultural industries, influencing consumption patterns, production methods, and channels of artistic communication [2]. Implementing advanced technologies such as virtual reality, augmented reality, and artificial intelligence can revolutionize how people experience, interact with, and study cultural heritage [3]. By creating immersive, personalized, and engaging virtual environments, metaverses can enhance the accessibility, interpretation, and preservation of cultural sites, artifacts, and assets [4]. Research on metaverses in the cultural heritage field primarily focuses on exploring technical applications related to cultural heritage and preservation [5]. However, users’ willingness to engage with cultural-heritage-based educational systems needs more analysis from a user-centric perspective. With in-depth research and a thorough understanding of users’ willingness to adopt new technologies and the factors influencing this willingness, it will be easier to fully realize the potential of these technologies in cultural heritage education and communication. This gap could impede the effectiveness of metaverse technologies in fostering user engagement, potentially leading to the allocation of resources to ineffective products and solutions [6]. This situation contradicts the goal of leveraging scientific and technological innovation to promote the innovative development of China’s cultural heritage, especially within the country’s current supply-side reform efforts [1]. Therefore, conducting in-depth research to encourage users to accept and engage with cultural-heritage-based metaverse educational products is crucial. This study explores the potential of meta-universes to facilitate cultural and academic promotion, increase users’ willingness to use these products, and close the gap between products and effective user engagement, filling a gap in the design field that lacks the involvement of user-perceived factors in design and providing more scientific design guidelines for the field. This study integrates user-perceived factors of cultural-heritage-based metaverse educational systems into the UTAUT from a user-centric perspective. These factors are combined with the TTF model and Flow Theory to analyze the determinants influencing users’ willingness to engage with cultural-heritage-based metaverse educational systems.

2. Theoretical Foundations

2.1. Unified Theory of Acceptance and Use of Technology (UTAUT)

The UTAUT model, proposed by Venkatesh et al. in 2003, is one of the seminal models for understanding information technology acceptance. It effectively explains individuals’ behavioral intentions to adopt emerging technologies [7]. The UTAUT model has been demonstrated to explain up to 70% of user acceptance intentions, highlighting its significant influence in information technology applications. The core components of the model include four key variables: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) [8]. Of these, PE, EE, and SI are vital factors influencing behavioral intentions to use technology. In addition, behavioral intentions, in combination with FC, determine the actual use of the technology. Venkatesh [9] also found that different combinations of the four moderators—age, gender, experience, and voluntariness of use—moderate these relationships, influencing how each factor affects the intentions and behaviors associated with technology adoption. Nowadays, UTAUT models are widely used in the fields of information technology [10,11,12], e-commerce [13,14,15], sociocultural studies [16,17,18], and educational technology [19,20,21] for evaluating and predicting users’ acceptance and usage behaviors toward new technologies.

2.2. Technical Task Matching Model (TTF)

The TTF model is widely utilized to explain how new technologies can lead to performance improvements. It assesses the impact of technology adoption and evaluates the alignment between task requirements and technology characteristics [22]. Developed by Goodhue and Thompson in 1995, the TTF model explains how the alignment between task characteristics and technology attributes affects the degree of fit, influencing end-user performance and technology utilization. According to this model, users are more likely to adopt and consistently use information technology when it is perceived to be well suited to their tasks and significantly enhances their productivity or performance [13]. This implies that if a technology effectively supports communication and educational tasks related to cultural heritage while enhancing user experience and learning outcomes, it is more likely to gain widespread acceptance and usage. For instance, Wu et al. [22] proposed a TTF integration model to examine the ongoing intention to use MOOCs; Vanduhe et al. [23] applied a TTF model to evaluate the utility of gamification in sustaining the use of gamified training in higher education; and Sun et al. [24] explored visitors’ intention to use digital museums by integrating a new TTF model within the UTAUT2 framework, considering social distance. This underscores the importance of successfully applying new technologies in cultural heritage contexts. When combined, the UTAUT and TTF models create an integrated framework for elucidating and predicting end-user behaviors and intentions regarding the acceptance and adoption of new technologies [19]. This study aims to identify the influential factors for learning using metaverse platforms in cultural heritage education based on the combination of the UTAUT and TTF models. By providing a clear and logical explanation of the current situation, the study seeks to pinpoint the factors that significantly influence users’ engagement with metaverse technologies for educational purposes in cultural heritage.

2.3. The Grounded Theory (GT)

GT is a qualitative research method involving the systematic collection and in-depth analysis of data to uncover core concepts that capture the essence of objects and phenomena. It is a bottom-up approach to theory building [25]. The theory development in GT typically involves three key stages: open coding, axial coding, and selective coding. Further data collection and analysis are necessary to ensure validity when the theory has yet to reach saturation according to established criteria. Foley et al. [25] demonstrated that GT enabled researchers to capture and understand healthcare experiences effectively. Similarly, Li et al. [1] applied GT to introduce variables for constructing a more comprehensive model of willingness to use AR, while Hu et al. [26] employed GT to identify the main factors contributing to older users’ difficulties with smartphones, facilitating the development of more user-friendly apps tailored to this demographic. This study employed the GT method to explore and define new variables, addressing the limitations of the combined UTAUT and TTF models. This mixed research approach leverages the strengths of both qualitative and quantitative methods, resulting in a more scientifically robust research model. This, in turn, contributes to a more comprehensive understanding and explanation of users’ willingness to engage with the cultural heritage metaverse education system.

2.4. Current Status of Research on Users’ Willingness to Use

A metaverse is an envisioned domain that provides immersive digital spaces to support highly interactive environments in educational contexts. Alfaisal et al. [27] explored the main predictors of students’ intention to engage with a metaverse, providing deeper insights into the drivers behind young learners’ adoption of metaverse platforms. Willingness to use refers to a user’s attitude toward utilizing a particular technology or system, which subsequently influences their subjective thinking and behavior [28]. Existing research has increasingly focused on the impact of metaverse technology within the cultural heritage domain, analyzing how this technology affects users’ willingness to engage in cultural-heritage-related industries and identifying the factors that influence this willingness from various perspectives. Willingness to use is recognized as a prerequisite for user behavior and satisfaction [29]. Therefore, exploring the factors influencing user intention on metaverse platforms can help address the mismatch between the supply of culture and education technology and user demand, effectively integrating users into the metaverse education ecosystem. Compared to domestic research, foreign studies are relatively more extensive, primarily focusing on applying metaverse technology in developing and implementing world-renowned cultural heritage tourism products. At the technological level, Higuera-Trujillo et al. [30] investigated the impact of different virtual environment displays and formats on user perceptions, demonstrating that the authenticity and usability of a virtual cultural tourism experience may be highly dependent on the specific technology employed. Ahmed et al. [31] and Siddiqui et al. [32] explored the impact of visual realism on user experience in virtual heritage tourism environments, finding that higher levels of authenticity enhanced presence, enjoyment, and knowledge acquisition. He et al. [33] emphasized the importance of users’ willingness to engage with the cultural heritage metaverse as a catalyst for offline experiences, arguing that the cultural heritage metaverse is a crucial medium for presenting cultural heritage. At the cognitive level, Sun et al. [24] demonstrated how visitors’ intentions to use digital museums and actual usage behaviors are influenced by epidemic anxiety, which facilitates the conversion of intention into actual use by integrating the new UTAUT2 model with the TTF model. Kalınkara et al. [34] investigated the level of acceptance and use of metacosmic technology by students in anatomy education in a metacosmic environment within the framework of the UTAUT2 model. Qi-Teng Zhuo et al. [35] employed structural equation modeling (SEM) to validate that learners’ perceived risks negatively affect their intention to continue using metaverse technology. On the social level, Xie et al. [36] examined tourism virtual communities and confirmed that social needs are the core elements influencing the interactions among members of these communities. Bashar Dayoub et al. [4] explored the relationship between metaverse characteristics, tourists’ attributions, satisfaction, and behavioral intentions using a mixed-methods approach grounded in attribution theory. Their study aimed to optimize the potential of cultural tourism along the Silk Road within the metaverse. Nguyen et al. [37] used the Theory of Planned Behaviour (TPB) as a grounded theory framework to investigate the adoption of meta-universe education in Ho Chi Minh City, Vietnam. However, from the learners’ perspective, the factors influencing technology acceptance and adoption in metaverse-based cultural education remain unclear [35]. Additionally, the complexity of user intention is often overlooked in the existing studies. The mechanisms underlying the relationships between influencing factors and user intention have yet to be thoroughly investigated. Therefore, employing multiple models and research methods to empirically study the factors affecting user intention from a demand-side perspective is essential.
The main objective of this study is to present a comprehensive and objective methodology specifically designed to provide design principles for a cultural education metaverse system. As highlighted in the previous discussion, existing research often relies on subjective methods such as expert interviews and questionnaires, which can introduce bias and result in subjective assessments. This study aims to bridge the divide between subjectivity and precision by integrating qualitative and quantitative research methods to address this gap. The study aims to utilize GT to uncover hidden patterns and connections within the rich interview data of users of the cultural education metaverse system. This inductive approach ensures that the assessment indicator system is comprehensive and reflects the actual experiences and perspectives of the target user groups. Additionally, the study intends to elucidate and predict end-user behaviors and intentions to accept and adopt new technologies by combining the UTAUT and TTF models. The methodology will construct a model of users’ intention to use the cultural education metaverse system from two dimensions: user perception and technology acceptance. It will also explore the relationships between relevant factors, ultimately proposing correlations and their influence on users’ intention to engage with the cultural education metaverse system.

3. Model Construction and Research Assumptions

3.1. Extraction of User-Perceived Factors of Cultural Education Meta-Universe Systems

To explore the factors influencing users’ willingness to use metaverse technology in cultural heritage, it is essential to consider the complexity of the user experience beyond just reviewing the relevant literature. This requires more in-depth investigation and analysis. Consequently, we have borrowed psychological research methods to extract users’ perceptual factors—the “expressive features” of the cultural education metaverse system—through feature recognition pathways. This approach aims to provide targeted research variables for the study. To ensure the comprehensiveness of these research variables, data were collected through offline in-depth interviews, and original data were directly gathered from the user evaluation system.
The GT approach requires an in-depth analysis of original data to summarize hidden inner patterns and connections, and it must be ensured that the selected interviewees are representative and undisturbed during the process [26]. This study interviewed 62 users with experience using the cultural education metaverse system, including 30 males and 32 females. The interviews were conducted in a semi-structured, one-on-one, open-ended format with participants aged 19 to 45. The duration of the interviews varied from 15 to 35 min. With the interviewees’ consent, the entire interview was recorded using audio-accelerated recording, which was later transcribed into text to serve as the essential information for coding and analysis.
Based on the logical coding framework of grounded theory for measuring and identifying research variables [38], this study employed open coding and axial coding to analyze and summarize the logical relationships between perceptual factor features in user evaluations at the conceptual level, ultimately extracting core concepts. The NVivo 11 software was used to facilitate the analysis and design of coding nodes. During the open coding stage, raw data were analyzed sentence by sentence, with a focus on the research theme and purpose. Specific nodes related to feature perception dimensions were refined, resulting in the identification of 18 initial concepts. These concepts were then integrated based on similarities, with repetitive and intersecting concepts summarized. Initial concepts with a frequency of less than two and contradictory concepts were discarded. This process formed nine sub-categories, including user control, social interaction, immersive experience, and feedback mechanisms. Each sub-category was defined by a representative original statement to encapsulate the phenomenon. In the axial coding stage, the nine sub-categories were re-categorized according to the characteristics of augmented reality, allowing for the extraction of three higher-level main categories: interactivity, sense of presence, and narrative, which were identified as the critical user perception factors in the cultural education metaverse system. Finally, the six pieces of initial information reserved beforehand were coded and analyzed, and no new conceptual categories emerged. This indicated that the theory had reached saturation, allowing for the next step of summarization and generalization. The coding process is detailed in Figure 1.
Based on the research findings, three critical performance characteristic factors—presence, interactivity, and narrative—have been identified from the user perspective as crucial in the cultural education metaverse system. However, how these factors are applied within the system and whether they play a significant influential role remains unclear. This uncertainty necessitates further in-depth analysis and research to understand better their impact and effectiveness in the cultural education metaverse system.

3.2. Model Construction

After Li et al. [1] highlighted the significance of user experience in behavioral intention research, there was a noticeable increase in interest in the flow experience. The notion of flow is the psychological theory that explains why people are intensely concentrated when participating in behavioral activities. According to the theory, during the experience, users tend to lose track of unimportant perceptual details and experience a state of “flow”, or the flow experience. This is a crucial psychological parameter frequently used with behavioral theoretical models to examine how the user’s psychological experience affects behavioral motivation [39]. When users are in a state of flow, they may overlook the disadvantages of the experience. Some studies have demonstrated that the flow experience is a crucial factor influencing users’ acceptance of information technology, which affects their behavioral decisions [40]. By combining the UTAUT and TTF models, this study constructs a theoretical model that includes the core elements of both models as independent variables, with the willingness to continue using the system as the dependent variable. Additionally, external variables are identified as factors affecting user perception in the cultural education metaverse system—such as sense of presence, interactivity, and narrative. The construction of the research model is illustrated in Figure 2.

3.3. Research Hypotheses

Task–technology fit is influenced by task characteristics and technology, according to Zhou et al. [13]. Furthermore, Wu et al. [22] reported that task–technology fit influences end-user adoption in a definite and noteworthy way. Based on the findings of earlier research, the following hypotheses, H1 through H3, were developed:
H1. 
TEC has a positive effect on TTF.
H2. 
TAS has a positive effect on TTF.
H3. 
TTF positively affects users’ willingness to use the cultural education meta-universe system.
According to research by Abdekhoda et al. [41], Tan [21], Nassuora [42], and Zhou et al. [13], EE directly and significantly impacts end users’ propensity to adopt e-learning. The direct and considerable influence of PE has been described by Abdekhoda et al. [43] and Echeng et al. [44]. The study defines EE as an individual’s perceived ease of use of a meta-universe system or the effort they put into using it. Users have a firm intention to use the system and are willing to utilize it for an extended amount of time when they believe it to be easy to use and that the learning process is straightforward. In this study, PE, or the person’s subjective assessment of the system’s value, refers to the person’s opinion of how the metacosmic system can enhance their traditional cultural cognition. Users’ intention to use the app will be weakened if they believe it has no benefit. Still, if they think the system helps enhance their cognitive abilities, they will be more motivated to use it. Furthermore, users’ perceptions of the technology’s benefits are directly impacted by its ease of use. Therefore, effort expectancy is also a prior factor for PE. Consequently, the following theories are put forth:
H4. 
EE has a positive effect on the willingness of users of the cultural education meta-universe system to use it.
H5. 
EE has a positive effect on PE.
H6. 
PE has a positive effect on the willingness of users to use the cultural education meta-universe system.
According to research by Abdekhoda et al. [41,43] and Tan [21], SI significantly and directly influences the uptake of new technology. SI is described in the study as the degree to which an individual is influenced by the surrounding group and, as a result, is willing to use the meta-universe educational system. It is the degree to which individuals are influenced by the surrounding group when utilizing the system. Positive attitudes increase users’ propensity to use the educational meta-universe system when those around them support, encourage, or suggest it. Furthermore, SIs have the power to positively reinforce users’ opinions about how convenient the cultural education meta-universe system is. Based on this, the following hypotheses are proposed:
H7. 
SI has a positive effect on the willingness of users of the cultural education meta-universe system to use the system.
H8. 
SI has a positive effect on FC.
FC consists of the services and technical assistance that users need. When presented with obstacles to use, users are more likely to stick with a meta-universe system if they receive prompt assistance. This is because users’ system use is typically dependent on service support. Considering this, the following hypothesis is proposed:
H9. 
FC has a positive effect on the willingness of users of the cultural education meta-universe system to use it.
The ability of a user to react rapidly, switch roles, and alter relationships while utilizing a medium is referred to as interactivity (IN). According to Sundar et al. [45], interaction is the users’ active participation in the shape and content of the mediated environment, which the computer medium controls in real time. According to Liu et al. [46], interaction significantly brings users and cultural heritage closer together. Zhao et al. [47] found that interactivity is crucial in online learning, especially within virtual communities. It enhances learners’ sense of community, perceived usefulness, and sustained engagement, positively influencing satisfaction, learning outcomes, and experience flow. Therefore, interactivity is crucial in user behavior and significantly affects the flow experience. Based on this, the following hypothesis is proposed:
H10. 
IN has a positive effect on the flow experience.
The concept of presence (PR), initially proposed by Parker et al. [48], refers to the subjective experience where a user feels the presence of another entity while interacting with a medium. In virtual environments, presence is recognized as a complex psychological phenomenon. Nunez [49] developed a framework integrating presence, interaction, and flow, highlighting that presence and mindfulness are essential for a positive VE experience. Sylaiou et al. [50] found a positive correlation between presence and enjoyment in virtual museum settings, indicating that a heightened sense of presence enhances engagement. Furthermore, Ji et al. [51] confirmed that presence positively influences user behavior, emphasizing its significance in improving the enjoyment of virtual environments, particularly in cultural heritage meta-universe experiences. Based on these findings, the following hypothesis is proposed:
H11. 
PR has a positive effect on the experience of flow.
According to Irshad’s [52] research, things and environments are viewed as more natural, and the narrative experience is more thrilling and captivating when people are in immersive and narrative states. The study indicates that story improves mediating presence and participants’ cognitive abilities. Participants remembered more details and wanted the experience to continue when asked to complete tasks in contextualized virtual reality situations. To see how narrativity plays a constructive and active part in users’ usage patterns and affects each person’s flow experience, the following hypothesis is proposed in this paper:
H12. 
Narrativity (NA) has a positive effect on the experience of flow.
More studies have revealed that users’ usage behavior during moments of flow is especially noticeable in virtual environments. Users’ propensity to use virtual environments is influenced by their flow experience, according to research by Change et al. [53]. Furthermore, virtual cultural heritage excursions involve a mediation role in the flow experience [54]. Based on this, this paper proposes the following hypothesis:
H13. 
Flow experience has a positive effect on the willingness of users to use the cultural education meta-universe system.

4. Research Design

4.1. Research Methodology

This study integrates the UTAUT and TTF models with external variables to construct a model that identifies the factors influencing users’ willingness to use the cultural education metaverse system. Relevant research hypotheses are proposed based on this model. Sample data were collected from 298 users, and structural equation modeling (SEM) was introduced to analyze the data and test the research hypotheses. This process helped determine the key factors influencing users’ willingness to use the system, and the linkages and commonalities between these factors were refined. The resulting insights were used to develop a corresponding design strategy to guide design practices. The flow of the research methodology is illustrated in Figure 3.

4.2. Questionnaire Design

Measurement scales for the concepts covered in this work were created based on pre-existing research scales to collect data and test the research above hypotheses. Ten independent factors and one dependent variable from the study model and the person’s routine data make up the scale. The measurement scale is presented in Table 1. To ensure the questionnaire’s efficiency and reliability while balancing simplicity, neutrality, differentiation, and broad acceptance, a 5-point Likert scale was used. A score of 1 represents “completely disagree”, and 5 means “completely agree”.

4.3. Pre-Questionnaire

To ensure the quality of the questionnaire, a small-scale pre-test survey was conducted before the official questionnaire was distributed, resulting in 52 valid responses. The questionnaire targeted users currently or previously using the cultural education metaverse system. The collected data underwent reliability and validity testing, and the questionnaire was adjusted accordingly. Based on the refined scale (Table 2), a formal survey was conducted online and offline. For the online survey, museum staff invited target users to complete the questionnaire through an online platform. Meanwhile, the offline survey involved personally visiting local museums, where users were guided through face-to-face interactions to fill out the questionnaire. Ultimately, 298 responses were collected—232 from the online questionnaire and 66 from the offline survey.

5. Data Analysis and Results

5.1. Descriptive Statistical Analysis

According to the results from the questionnaire, the percentage of female participants (55.8% of the total) was higher than that of male participants (44.2% of the total). The age range of all the samples was 19–38, with the most significant proportion of participants (48.6%) falling between the 21–25 age range. A bachelor’s degree, the most frequently reported level of educational achievement, was held by 47.3% of the population. About 34.4% of respondents, or the majority, reported having three to five usage experiences. Figure 4 provides specific details regarding the sample’s descriptive statistics.

5.2. Reliability and Validity Tests

5.2.1. Reliability Tests

The internal consistency reliability Cronbach’s Alpha of each variable was calculated using SPSS 26.0 software to ensure the reliability of the sample data. The results indicated that the Cronbach’s Alpha and CR values were above 0.7, indicating that the research sample data had good reliability. The analysis’s findings are displayed in Table 2.

5.2.2. Validity Tests

The validity of the sample data was examined using KMO and Bartlett’s spherical test. Table 3 demonstrates that the data may be factor analyzed, the statistical test results are significant, the significance level is less than 0.001, the KMO value is more important than 0.7, and Barlett’s spherical test chi-square value is 6079.531.
Principal component analysis was used for exploratory factor analysis of the sample data. The results are displayed in Table 4 and show that the sample data can be adequately represented by the cumulative variance contribution rate, which reached 70.505%. Finally, 12 components were recovered, and the factor components’ makeup agreed with the model’s suppositions, suggesting structural solid validity.

5.3. Analysis of Model Fit Tests

5.3.1. Structural Validity

The exploratory factor analysis results were used to construct a factor model for investigating factors influencing the propensity to use the cultural heritage education meta-universe system. The model’s fit indices all satisfied the evaluation criteria after a validated factor analysis using AMOS 26.0, and the outcomes are displayed in Table 5.

5.3.2. Convergent Validity

The convergent validity of the scale was demonstrated by the fact that all of the observables met the test’s basic requirements, the average variance extracted (AVE) values of the scale were more outstanding than 0.5, the factor loadings of the measurement items of each latent variable were more significant than the theoretical value of 0.50, and the CR values were more important than 0.80. Table 6 presents the findings.

5.3.3. Distinguishing Validity

The data in Table 7 shows that all observed variables have correlation coefficients less than 0.9 and that all of the square roots of the construct AVEs on the diagonal have stronger correlations than their correlations with other variables. This suggests that the scale has strong discriminant validity because there is some association between the observed variables and a relatively high degree of differentiation.

5.4. Path Analysis and Hypothesis Testing

According to the path analysis results presented in Figure 5, all proposed paths are statistically significant. Among the factors influencing IU, EE exhibits a highly significant impact (β = 0.525, p < 0.001), confirming hypothesis H4. Additionally, TTF (β = 0.417, p < 0.001), PE (β = 0.408, p < 0.001), SI (β = 0.043, p < 0.05), and FC (β = 0.128, p < 0.05) also positively influence IU, supporting hypotheses H3, H6, H7, and H9, respectively. IN (β = 0.275, p < 0.05), PR (β = 0.311, p < 0.001), and NA (β = 0.255, p < 0.05) significantly affect FL, validating hypotheses H10, H11, and H12. Moreover, FL strongly affects IU (β = 0.401, p < 0.001), supporting hypothesis H13. Furthermore, TEC (β = 0.260, p < 0.001) and TAS (β = 0.336, p < 0.001) significantly influence TTF, confirming hypotheses H1 and H2. EE also significantly affects PE (β = 0.325, p < 0.01), and SI has a positive effect on FC (β = 0.025, p < 0.01), supporting hypotheses H5 and H8. The hypothesis testing results are summarized in Table 8.

6. Design Strategies and Case Studies

6.1. Design Strategy

The study’s findings support the validity of the research hypothesis by demonstrating that presence, interactivity, narrativity, EE, PE, FC, SI, and mind-flow experience are the main elements influencing users’ willingness to use the cultural heritage education meta-universe system. The study demonstrates that users’ propensity to use the cultural heritage education meta-universe system is significantly impacted by presence, interactivity, and narrativity based on user perception dimensions. This aligns with the justification presented in Lin et al.’s [56] study for integrating individual characteristic dimensions with technological acceptance models. This expands on Li et al.’s [1] design innovation component to improve user happiness with cultural heritage education meta-universe products. A total of 15 experts in user experience, cultural heritage education, meta-universe system user research, and interaction design were asked to list the factors influencing users’ willingness to use it and their coefficient sizes as the initial materials. They then used the Delphi method to summarize the connections and commonalities between the factors to form a series of design strategies that would guide the design of the meta-universe system. The goal was to create a cultural heritage education meta-universe system that would satisfy users and encourage them to use it. The four layers of technical assistance, emotional experience, cognitive dimension, and social influence are represented in Figure 6.

6.1.1. Technology Support

To give users an immersive experience, the technological support level of the system should incorporate cutting-edge 3D modeling and virtual reality technology to create incredibly realistic reconstructions of historical scenes and cultural treasures. It should ensure that various input techniques, including gesture, speech, and touch, are supported so that users can naturally engage with the virtual environment, increasing the presence sensation. Simultaneously, the system’s technological stability and response speed should be optimized to guarantee seamless user interaction, minimizing lag and delays and offering a seamless and uninterrupted experience. Different users with varying task challenges are also required for pairing to make the task and technology compatible.

6.1.2. Emotional Experience

To enhance the emotional richness of the discovery process, the system ought to craft compelling narratives that enable users to identify with historical personalities. You may raise their sense of accomplishment and engagement by offering personalized learning routes and content that are modified based on the user’s interests and progress. Creating an online community also enables users to collaborate on tasks, share experiences and insights, and strengthen social ties—all of which contribute to increased emotional engagement and pleasure.

6.1.3. Cognitive Dimension

To allow users to naturally learn and internalize cultural heritage knowledge points through exploration and engagement, the system should include them in storylines and tasks at the cognitive dimension level. To encourage users to explore and learn, provide a set of functions about cultural heritage, such as repairing artifacts and picking up traditional skills. Once the tasks are completed, users can access new information and features. Concurrently, a system of rapid feedback and rewards, such as point rankings and achievement badges, should be implemented to motivate users to keep participating and advance, enhancing and advancing their cognitive abilities.

6.1.4. Social Factor

Regarding social impact, the system makes it simple for users to share their experiences and learning results on social media, increasing cultural heritage education’s influence on society. A user community is created through discussion boards and activity planning to foster user involvement, support, and cooperative learning and development. Furthermore, creating cross-cultural content that promotes understanding and communication between users from various cultural backgrounds increases the influence of cultural heritage education globally, which benefits society on a social level.

6.2. Design Case

With the aid of meta-universe technology, the “Cross-Music” project aims to preserve the traditional Chinese musical instrument culture that is in danger of disappearing while also assisting users in understanding the history and performance of lost musical instruments. As seen in Figure 7, the project “Cross-Music” is a real-world example of this paper’s practical case study.
  • Technical Support: The system employs advanced virtual reality technology to create a hyper-realistic virtual environment. Through 3D modeling, dynamic lighting, and shadow effects, ancient musical instruments’ visual and acoustic properties are meticulously recreated, providing users with a deeply immersive experience. The system also supports multiple interaction methods, including gesture recognition and voice commands, enabling users to interact with the virtual environment naturally and intuitively. For instance, users can simulate playing a Shakuhachi flute through gesture recognition, allowing them to experience the nuances of playing the instrument. To ensure a seamless experience, the system has been optimized for fast response times and technical stability, minimizing delays and lags for smooth, uninterrupted interaction.
  • Emotional Experience: The system creates a rich emotional experience through a well-crafted storyline centered around the transmission of musical instruments and an engaging role-playing mechanism. As users explore the virtual environment, they encounter various historical figures, engage in meaningful dialogues, and complete interactive tasks that deepen their connection with the lost instruments’ historical context and cultural significance. These interactions foster emotional resonance, allowing users to appreciate the charm of ancient music and cultivate a more profound interest in and affection for traditional culture. Additionally, the project offers a personalized experience path, dynamically adjusting the difficulty and content of tasks based on the user’s progress and preferences, further enhancing the user’s sense of participation and accomplishment.
  • Cognitive Dimension: Users are directed to actively investigate and learn about forgotten musical instruments using a task-driven learning technique. To improve their comprehension of the instruments through practice, users must finish several musical instrument-related chores, such as repairing the instruments and learning how to play them. Simultaneously, the system incorporates real-time feedback and incentive features, such as point rankings and achievement badges, to promote ongoing engagement and advancement. Furthermore, the narrative includes the background, manufacturing method, and associated cultural information of musical instruments, enabling users to acquire and engage with them organically through interaction, thus enhancing and advancing their cognitive abilities.
  • Social Factors: By making it simple for users to share their experiences and learning outcomes on social media, cultural heritage education has a more significant societal influence. Through establishing discussion forums and event planning, the project has created a user community that fosters user support and interaction as well as collaborative learning and development. Furthermore, a cross-cultural communication element has been formulated to encourage communication and comprehension amongst users from diverse cultural backgrounds and augment the global influence of cultural heritage education, thereby serving a constructive social function.

6.3. Design Case Validation

The seven elements of the gaming experience—presence, mind flow, competence, positive and negative impact, tension, and challenge—were scored using the Game Experience Questionnaire (GEQ) [52] for the design case. Thirty-two users who met the study’s criteria were randomly chosen, and the questionnaires were graded on a 7-point Likert scale. The current cultural heritage educational meta-universe systems Dunhuang Meta-universe, Music Future—Sound Project, and “Cross-Music”, which have extensive user coverage and high ratings, were chosen for evaluation and comparison. A 7-point Likert scale was used to score the questionnaire, with 1 denoting extreme dissatisfaction and 7 denoting high satisfaction. According to the questionnaire results, Cross-Music had a higher total GEQ score than the two educational metaverse systems now in use. This is because users may feel negatively after an evil mind-streaming experience, which lowers their motivation to use the system. Additionally, the current cultural education metaverse systems are made to appeal to a larger audience. Second, users’ willingness to use the current cultural education meta-universe system is significantly reduced since it needs more service assistance and cannot offer prompt and convenient service support when challenging issues are encountered. Users’ ratings of “Cross-Music” are much better in the same dimension. This suggests that the cultural heritage education meta-universe system built on this study’s design strategy has higher user satisfaction and a more vital willingness to use.

7. Conclusions and Discussion

7.1. Research Conclusion

This study combines metaverse technology with education to explore users’ willingness to engage with a metaverse education platform. By integrating the UTAUT, the TTF model, and Flow Theory, the research constructs an indicator system to identify factors influencing user willingness. Through GT coding, pre-testing, and formal survey data collection, the study validates the indicator system’s scientific rigor and the hypotheses’ feasibility using SEM. The specific analysis results are as follows:
The empirical results of SEM indicate that interactivity, narrative, and presence significantly positively impact the flow experience, with presence having the most significant influence coefficient. Factors such as PE, EE, SI, FC, TTF, and flow positively affect users’ willingness to use the platform. Among these, TTF has the most decisive influence, while EE positively affects PE, and SI positively influences FC. Additionally, within TTF, both TEC and TAS have a significant positive impact on TTF.
Second, the research model confirms that PE and EE significantly positively affect users’ intention to use the metaverse education platform, aligning with previous studies [21,41,42,43]. Notably, PE has a higher influence coefficient. Additionally, SI and FC also significantly impact users’ intention to use. This finding contrasts with Sun et al. [24], who concluded that FC and SI do not significantly affect people’s intention and behavior in using digital museums.
Finally, the study developed a more detailed research framework and hierarchical integration model by incorporating the UTAUT, TTF model, and three external factors (interactivity, narrative, and presence). This integrated model is beneficial for identifying the key factors influencing users’ intention to adopt the cultural-heritage-based metaverse education system. It also helps deduce the primary antecedent configurations that drive user intention. Furthermore, the model offers valuable insights and guidance for policymakers, businesses, and designers in developing strategies and practices related to metaverse educational systems.

7.2. Theoretical Contribution and Practical Enlightenment

In terms of theoretical contributions, this study first develops a model for understanding user willingness to engage with cultural heritage metaverse educational systems, drawing on the UTAUT, the TTF model, and Flow Theory. By incorporating presence, interactivity, and narrativity as external variables, the research extends the existing theoretical framework and offers new insights into user behavior in these environments. Secondly, it systematically explores for the first time how presence, interactivity, and narrativity impact the flow experience and how these factors indirectly influence user intention through flow. This provides a robust theoretical basis and empirical support for future research. Finally, the study highlights the critical role of technology–task fit as the most significant factor in shaping user willingness, enriching the technology acceptance model and underscoring the importance of aligning technology with task requirements.
In terms of practical contributions, this study offers valuable design guidelines for developers of cultural education metaverse systems. It provides concrete strategies for enhancing the user’s flow experience by improving presence, interactivity, and narrative, thereby increasing user willingness to engage with the platform. At the user level, the findings help developers prioritize user acceptance by optimizing technology–task fit, improving performance expectations, and considering social influences. These measures are crucial in creating a highly interactive and immersive metaverse educational experience that can spark learners’ curiosity, motivation, and initiative. On a societal level, the study suggests that increasing user satisfaction and engagement with metaverse educational systems can enhance learning outcomes and promote the transmission and preservation of cultural heritage. The metaverse allows students from different regions to transcend geographical boundaries and engage in cultural exchange, fostering mutual understanding and respect. The integration of research and practice supports the long-term preservation of cultural heritage. It cultivates a sense of responsibility in younger generations, contributing to achieving the Sustainable Development Goals.

7.3. Limitations and Future Outlook

This study has several limitations. First, the sample is primarily drawn from specific regions and cultural backgrounds, which may limit its representativeness. Future research could expand the sample to include more diverse cultural groups to improve the generalizability of the findings. Second, although qualitative methods (GT) and quantitative surveys were used, subjectivity and bias may persist in data collection and analysis. Future studies could employ a mixed-methods approach to validate the findings. Additionally, this research needed to account for how user acceptance and willingness to use the metaverse education system may evolve. A longitudinal design could track these changes in future studies, offering more profound insights into shifting user behaviors.

Author Contributions

Concepts, G.X. and S.H.; Methods, G.X.; Software, G.X.; Validation, J.X. and G.X.; Formal Analysis, J.X.; Investigation, J.X.; Resources, S.H.; Data Curation, G.X.; Writing—Original Draft Preparation, G.X.; Writing—Review and Editing, S.H.; Visualization, G.X.; Supervision, S.H.; Project Management, G.X.; Funding Acquisition, S.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The National Health and Health Commission, Ministry of Education, Ministry of Science and Technology, and the State Administration of Traditional Chinese Medicine publicly released the ‘Life Science and Medical Research Involving Human Beings’ in February 2023, which stipulates that ‘under the premise of using information data or biological samples of human beings, not causing harm to human beings, and not involving sensitive personal information or commercial interests ‘Ethical review may be waived for life sciences and medical research involving human beings in some cases. Ethical approval was not necessary as the data for this study did not include any information that would identify individuals.

Informed Consent Statement

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

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Acknowledgments

For the successful completion of the experiment, I am grateful to the Institute of Information and Interaction Design for providing the necessary materials and setting. Furthermore, I thank the Lost Instrument Metacosmic System project team for their invaluable help, without which this research would not have been feasible.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Refinement of user perception factors (coding results).
Figure 1. Refinement of user perception factors (coding results).
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Figure 2. Research model of users’ willingness to use the cultural education meta-universe system.
Figure 2. Research model of users’ willingness to use the cultural education meta-universe system.
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Figure 3. Flow of the research methodology.
Figure 3. Flow of the research methodology.
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Figure 4. Descriptive statistical information of the official questionnaire.
Figure 4. Descriptive statistical information of the official questionnaire.
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Figure 5. Analysis of the path coefficients of the research model.
Figure 5. Analysis of the path coefficients of the research model.
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Figure 6. Design strategy of cultural heritage education meta-universe system.
Figure 6. Design strategy of cultural heritage education meta-universe system.
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Figure 7. Cross-Music lost instrument meta-universe system interface design.
Figure 7. Cross-Music lost instrument meta-universe system interface design.
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Table 1. Refinement of user perception factors (coding results).
Table 1. Refinement of user perception factors (coding results).
VariableQuestionnaire CodeContent of the QuestionnaireSource
Performance expectationsPE1Using the educational metaverse system can help with cultural learning[19]
PE2Using the Educational metaverse system has significantly improved my learning efficiency
PE3Immersive learning environments in the educational meta-universe system are important to me
Effort expectationsEE1For me the educational meta-universe system was very easy to get started with[35]
EE2I can easily master the operation of the educational meta-universe system
EE3The interaction of the educational meta-universe system is clear and easy to understand
EE4I don’t think it takes much effort to learn to use the educational metaverse system
Social impactSI1People around me recommended that I use the Educational Metaverse System[19]
SI2People around me think I should use the educational meta-universe system to learn knowledge
SI3Influenced by the people around me, I thought I should use the Metaverse system to study
Facilitating conditionsFC1I have the resources needed to use the educational metaverse system[55]
FC2When I had trouble with the Educational Metaverse system, it was easy to ask for help
Technical characteristicsTEC1These techniques help me avoid unnecessary social contact[24]
Task characteristicsTAS1I need to be able to experience these cultures alone at home
Technical task matchingTTF1These technical features were enough for me to experience this cultural heritage knowledge
PresencePR1During the experience, I felt like I was in the middle of it[1]
PR2During the experience, my body was in reality, but my mind was taken to the world created by the system
PR3At the end of the experience, I felt like I had just gone through a real travel experience
InteractivityIN1I have very little time between operation and system response[1]
IN2I think I have a high degree of operability with the system
IN3I was able to easily switch scenes in the system
NarrativeNA1I wish there was a story line.[52]
NA2I want this me to be a part of the story that can dictate where the plot goes
NA3I want the knowledge to be presented in a compelling storyline
FlowFL1I’ll be so intensely drawn into the meta-universe experience that nothing will bother me[1]
FL2I’ll be so focused on the experience that I won’t pay much attention to what’s going on around me
FL3I would focus on the meta-universe experience and it felt like the time went by so quickly
Intention to useIU1I love learning historical information in this way[55]
IU2I would recommend people around me to use this type of meta-universe system for learning and communication
IU3I would prioritise learning experiences that use cultural heritage presented in the metaverse
IU4I’m going to try to experience more legacy-based meta-universe systems in the future
Table 2. Reliability test analysis.
Table 2. Reliability test analysis.
Measured VariablesScale ItemsCronbach’s AlphaCR
PE30.8350.842
EE40.8120.824
SI30.8030.825
FC20.7320.739
TEC10.8230.806
TAS10.8310.832
TTF10.8450.835
PR30.8670.871
IN30.8730.862
NA30.9070.925
FL30.9260.955
IU40.8640.866
Table 3. KMO and Bartlett’s test of sphericity.
Table 3. KMO and Bartlett’s test of sphericity.
Testing IndicatorsCR
The Kaiser–Meyer–Olkin metric0.723
Bartlett’s test of sphericityApproximate chi-square6079.531
df445
Significance0.000
Table 4. Total variance explained.
Table 4. Total variance explained.
IngredientInitial EigenvaluesExtracting the Sum of Squared LoadsRotating Load Sum of Squares
TotalPercentage of VarianceCumulative PercentTotalPercentage of VarianceCumulative PercentTotalPercentage of VarianceCumulative Percent
13.1599.6777.6303.1599.6777.6302.9438.6696.424
23.0258.9849.3323.0258.9849.3322.7928.5739.683
32.9817.66314.4022.9817.66314.4022.6407.98513.512
42.6087.41428.6052.6087.41428.6052.6087.73325.259
52.5426.59636.7422.5426.59636.7422.5257.36932.159
62.3746.43338.4652.3746.43338.4652.2286.97438.463
72.2586.20942.7492.2586.20942.7492.2406.70944.374
82.2305.99855.3452.2305.99855.3452.0656.54155.690
92.0655.74259.0822.0655.74259.0822.0245.88460.002
101.9305.46964.2961.9305.46964.2961.9255.75064.790
111.8325.50566.7411.8325.50566.7411.7725.42069.385
121.7094.33970.5051.7094.33970.5051.7104.21970.505
130.6922.09672.315
…………
330.1140.30999.774
340.0450.225100.000
Table 5. Results of model fit analysis.
Table 5. Results of model fit analysis.
Fitness IndexCriteria for JudgmentMetricFitting Situation
CMIN/DF<31.122Ideal
RMSEA<0.080.032Ideal
GFI>0.9 (Ideal)/>0.8 (Acceptable)0.859Acceptable
AGFI>0.80.877Ideal
IFI>0.90.985Ideal
TLI>0.90.989Ideal
CFI>0.90.996Ideal
PCFI>0.50.973Ideal
PNFI>0.50.967Ideal
Table 6. Convergent validity results.
Table 6. Convergent validity results.
VariablesSubject MatterStandardized LoadingsCRAVEVariablesSubject MatterStandardized LoadingsCRAVE
PEPE10.8740.8420.772PresencePR10.8650.8710.758
PE20.897PR20.874
PE30.743PR30.911
EEEE10.8740.8240.705InteractivityIN10.9040.8620.790
EE20.863IN20.901
EE30.832IN30.872
EE40.814NarrativeNA10.8690.9250.799
SISI10.7430.8250.724NA20.884
SI20.899NA30.906
SI30.864Flow ExperienceFL10.9130.9550.783
FCFC10.7740.7390.692FL20.894
FC20.829FL30.902
TECTEC10.8730.8060.742Intention to useAT10.8950.8660.795
TASTAS10.9020.8320.779AT20.933
TTFTTF10.8860.8350.767AT30.874
Table 7. Discriminant validity of measurement scales.
Table 7. Discriminant validity of measurement scales.
PEEESIFCTECTASTTFTRINNAFLIU
PE0.772
EE0.6920.705
SI0.4490.6780.724
FC0.4560.5770.5970.692
TEC0.2410.3490.5790.3360.742
TAS0.4580.3690.4710.6540.5330.779
TTF0.3360.4720.3450.5790.6010.6750.767
TR0.3340.4060.4450.3920.4080.6790.2450.758
IN0.2970.3340.3590.4070.5720.4180.4490.3070.790
NA0.3350.3970.3580.3660.4860.6420.2450.4350.2430.799
FL0.3460.5350.4310.1450.5560.3320.5350.3120.3550.2150.783
IU0.4140.5340.3860.4380.5210.5850.6830.4590.6760.5740.5430.795
AVE square root0.8720.8840.8530.7420.8330.8940.8650.8570.8910.8970.8710.872
Table 8. Research hypotheses and path coefficient results.
Table 8. Research hypotheses and path coefficient results.
HypotheticalPath FactorS.E.C.R.PResults
H1: TEC➡TTF0.2600.0653.5440.006Yes
H2: TAS➡TTF0.3360.0434.6720.000Yes
H3: TTF➡IU0.4170.0925.6780.000Yes
H4: EE➡IU0.5250.0636.1430.000Yes
H5: EE➡PE0.3250.1144.5290.000Yes
H6: PE➡IU0.4080.0525.3460.000Yes
H7: SI➡IU0.0430.0792.1420.024Yes
H8: SI➡FC0.2050.0953.0650.002Yes
H9: FC➡IU0.1280.0612.1920.011Yes
H10: IN➡FL0.2750.0493.9030.014Yes
H11: PR➡FL0.3110.0864.0050.000Yes
H12: NA➡FL0.2550.0623.5140.005Yes
H13: FL➡IU0.4010.0895.0120.000Yes
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Hu, S.; Xing, G.; Xin, J. Impacting Elements of Metaverse Platforms’ Intentional Use in Cultural Education: Empirical Data Drawn from UTAUT, TTF, and Flow Theory. Appl. Sci. 2024, 14, 9984. https://doi.org/10.3390/app14219984

AMA Style

Hu S, Xing G, Xin J. Impacting Elements of Metaverse Platforms’ Intentional Use in Cultural Education: Empirical Data Drawn from UTAUT, TTF, and Flow Theory. Applied Sciences. 2024; 14(21):9984. https://doi.org/10.3390/app14219984

Chicago/Turabian Style

Hu, Shan, Geqi Xing, and Jing Xin. 2024. "Impacting Elements of Metaverse Platforms’ Intentional Use in Cultural Education: Empirical Data Drawn from UTAUT, TTF, and Flow Theory" Applied Sciences 14, no. 21: 9984. https://doi.org/10.3390/app14219984

APA Style

Hu, S., Xing, G., & Xin, J. (2024). Impacting Elements of Metaverse Platforms’ Intentional Use in Cultural Education: Empirical Data Drawn from UTAUT, TTF, and Flow Theory. Applied Sciences, 14(21), 9984. https://doi.org/10.3390/app14219984

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