Introduction

Intangible cultural heritage (ICH) represents the living practices, expressions, and knowledge that define the cultural identity of communities1. Unlike tangible artifacts, ICH is dynami and continuously reshaped through interactions with nature, history, and society. This evolving character not only sustains cultural diversity but also strengthens a sense of identity and continuity in an increasingly globalized world.

Fig. 1
figure 1

An older adult under a pine tree was created by Wuhu artisans using the iron painting method (The authors took the picture at the Wuhu Iron Painting Museum. Image reproduced with permission from the Wuhu Iron Painting Museum).

Full size image

Among the many forms of ICH, Wuhu iron painting (e.g., Fig. 1) stands out as a unique craft with over 340 years of history. Originating in China’s Song Dynasty, it symbolizes the ingenuity and artistic excellence of Wuhu artisans, who use hammers as brushes and iron as ink to produce expressive artworks2. Iron paintings come in three distinct types. The first type features small scenes, typically depicting themes such as pine, plum, orchid, bamboo, chrysanthemum, and eagle. These artworks are framed with backing boards and displayed against pink walls, characterized by their black and white palette, bold, straight lines, and clear structures, lending them a dignified and striking appearance. The second type includes lanterns, usually made up of four to six iron paintings. These are adorned with paper or plain silk and designed to encase candles, creating a dazzling and captivating display. The third type comprises screens, often showcasing landscape scenes that are both simple and elegant, yet visually stunning.

Despite its historical and cultural significance, Wuhu iron painting faces increasing threats of decline. Existing efforts to preserve Wuhu iron painting primarily rely on museum exhibitions and hands-on workshops, which face challenges in engaging younger generations. The pressures of globalization, urbanization, and modernization have left this art form vulnerable, with declining interest from younger generations and a limited market presence3,4. The intricate, labor-intensive nature of this craft, has made it less attractive to younger generations. Although local governments and artisans have introduced programs to revitalize interest, such as educational programs and partnerships with universities, the craft still faces difficulties in capturing the interest of young people.

Virtual reality (VR) has emerged as a promising tool for cultural heritage conservation, providing immersive and interactive experiences that enhance audience engagement. However, despite VR’s growing role in heritage preservation, little is known about how users perceive and accept its use for skill-intensive crafts like Wuhu iron painting. Recent studies highlight the effectiveness of VR in education, demonstrating its ability to enhance engagement, improve conceptual understanding, and bridge theoretical and practical learning. A review on cultivating effective learning suggests that VR, when combined with educational theories, can significantly improve student motivation and retention rates5. Furthermore, research in engineering education has shown that VR provides a more immersive, hands-on approach, reducing conceptual misunderstandings and improving learning outcomes6,7. These insights support the integration of VR into cultural heritage education to enhance deeper engagement and understanding. These findings support the application of VR in cultural heritage education, though its value in skill-based artistic practices requires further exploration.

This study addresses that gap by investigating how users perceive and accept VR when applied to the preservation of Wuhu iron painting. Rather than focusing solely on technical feasibility, we adopt the UTAUT model to analyze user behavior and identify key factors that influence adoption. Our goal is to inform the design and deployment of more effective digital heritage tools that resonate with audiences.

In particular, the contributions of this study are listed below.

  1. (1)

    We design a VR system built using Unity3D to simulate the Wuhu iron painting process with real-time rendering, which integrates advanced shading techniques and physics-based rendering to enhance realism, providing an immersive and interactive user experience.

  2. (2)

    We evaluate user acceptance of the proposed VR system through the UTAUT. A structured user study is conducted to analyze key acceptance factors.

  3. (3)

    Our findings highlight that performance expectancy and social influence play significant roles in user adoption. Users perceive the VR system as an effective tool for understanding and appreciating Wuhu iron painting, while social endorsement further enhances their willingness to engage with the technology.

  4. (4)

    This study offers practical recommendations to improve the effectiveness of VR in cultural heritage preservation. Emphasizing user-centered design and strategic marketing approaches, we suggest optimizing accessibility, incorporating interactive storytelling elements, and enhancing collaborations with cultural and educational institutions to maximize impact.

The structure of this paper is organized as follows: Section “Literature review” reviews the existing literature on the integration of VR in cultural heritage preservation. Section “VR Iron painting system design” describes the design and development of the VR system for Wuhu iron painting, including technical specifications and user interface considerations. Section “UTAUT model and research hypotheses” details the UTAUT model applied to evaluate user acceptance and outlines the research hypotheses. Section “Research methodology” explains the research methodology, including survey design and statistical analysis techniques. Section “Results and findings” presents the results and findings from the survey, analyzing the impact of various factors on user acceptance. Sections “Discussions” and “Limitations” discuss the implications of these findings for cultural heritage preservation and the potential of VR technology in this field. Finally, Section “Conclusion” concludes the paper with a summary of the findings and Section “Future directions” provides suggestions for future research directions. The abbreviations are listed in Table 1.

Table 1 The terminology and abbreviations.
Full size table

Literature review

Virtual heritage

Virtual reality (VR) has emerged as a transformative tool in the field of cultural heritage, reshaping how we preserve, present, and engage with both tangible and intangible cultural assets. The integration of VR offers a range of possibilities for safeguarding historical and cultural knowledge while providing immersive, interactive experiences that transcend traditional methods of learning and engagement. The interplay between tangible and ICH underpins VR’s unique potential, with each aspect benefiting from its unique capabilities.

Research indicates that immersive learning environments can significantly enhance educational outcomes. For instance, a study by PwC found that employees trained using VR retained knowledge at a rate of up to 80% one year after training, compared to 20% just one week after traditional training methods8. Similarly, Tuli et al.9 demonstrated that AR-based learning experiences led to higher academic achievement and improved attitudes toward the subject matter among engineering students.

For tangible cultural heritage (e.g., monuments, artifacts, and sites), VR contributes notably to preservation. By employing technologies such as 3D scanning, photogrammetry, and advanced visualizations, VR enables the creation of highly accurate digital replicas10,11,12. These digital models act both as preservation backups and as interactive tools for detailed exploration, reducing the need for physical handling. Furthermore, VR allows global accessibility to remote or fragile sites, overcoming geographic or conservation-related restrictions13,14. Users can explore ancient ruins or rare artifacts virtually, enhancing cultural accessibility15,16,17.

Beyond preservation, VR enhances the educational and engagement potential of tangible heritage. Traditional museums may limit interaction due to spatial or display constraints, whereas VR enables users to manipulate detailed 3D artifacts, encouraging active exploration18,19. This hands-on approach creates a more dynamic learning environment, making cultural heritage more engaging, particularly for younger audiences who are accustomed to interactive experiences20.

For ICH, (e.g., traditions, rituals, music, and oral histories), VR is equally transformative. The intangible nature of these cultural elements makes them inherently difficult to preserve using traditional methods21,22. However, VR provides a unique platform to simulate these cultural expressions. By creating immersive environments, VR enables users to participate in virtual reenactments of cultural practices, festivals, or ceremonies23. This interactive preservation method ensures the transmission of cultural knowledge, even when physical participation in these traditions is no longer feasible.

Moreover, VR enables experiential learning24a key component in the transmission of intangible heritage. Users can not only observe but also actively engage with cultural practices in a simulated environment. Whether it is through recreating ancient rituals, experiencing traditional music performances, or learning indigenous crafts25,26,27VR makes it possible to interact with and understand cultural practices in a way that static documentation, such as videos or texts, cannot.

VR projects such as “Virtual Lion Dance” (Japan) and “Dunhuang Digital Tourism” (China) exemplify their role in revitalizing cultural practices through engaging and immersive storytelling28. Other projects like the Mostar Bridge diving tradition use 360° video and interactive quizzes to combine education with entertainment23. In addition, initiatives like the Xiuyan Jade Carving VR experience demonstrate how technology can enhance public understanding and emotional connection to cultural crafts29.

UTAUT applications

Understanding user acceptance is critical for the sustainability of digital heritage initiatives. The UTAUT integrates constructs from eight prior models to better predict behavioral intentions and usage. It provides a comprehensive lens to examine factors that influence VR adoption in cultural heritage30.

For example, the UTAUT model has been employed to evaluate the acceptance of AR technology in the preservation of cultural monuments, such as the old Jami Mosque of Palopo31which underscored the model’s ability to reveal the key drivers of user adoption in heritage contexts, showcasing its relevance in understanding the complex dynamics behind technology acceptance.

Similarly, research on virtual museums32which gained popularity during the COVID-19 pandemic, used an extended version of the technology acceptance model (TAM), which served as a precursor to UTAUT, was adapted to include factors specific to virtual museum experiences, such as tour quality, multi-sensory engagement, and access to detailed information.

Further, in the tourism industry, an AR acceptance model, derived from TAM, was customized to incorporate additional factors like enjoyment, personal innovativeness, perceived benefits, information quality, and the costs of use33demonstrating the importance of understanding user motivations when adopting emerging technologies like AR and VR, particularly in enhancing user engagement.

Given the complexities of user behavior in adopting VR for cultural heritage preservation, UTAUT provides a comprehensive framework to explore these dynamics. By focusing on factors like perceived usefulness and ease of use, UTAUT offers valuable insights into how digital heritage initiatives can be optimized to meet user expectations and ensure long-term sustainability. This is particularly relevant in the context of VR, where user engagement and satisfaction are critical for the success of preservation efforts. The adaptability of the UTAUT model allows for the inclusion of moderators such as age, gender, and experience, which are pivotal in examining how diverse groups perceive and interact with VR in ICH protection. Compared to TAM and TPB, UTAUT offers several distinct advantages. Firstly, while TAM focuses primarily on perceived usefulness and TPB adds the element of behavioral intentions, UTAUT introduces additional constructs like social influence, acknowledging the role of social context and infrastructural support in technology adoption. Furthermore, UTAUT’s empirical validation across various fields demonstrates its robustness in predicting technology acceptance, which makes UTAUT a preferred theoretical framework for decision-makers aiming to implement new technologies effectively across diverse user bases.

In our study, performance expectancy primarily concerns the efficacy of VR technology in enhancing user engagement, moving beyond mere task efficiency. Social influence, in this context, stems not from peer or managerial input, as commonly seen in corporate environments, but rather from the perspectives of cultural experts and community leaders. Additionally, this research integrates specific moderating variables, such as prior VR experience and extensive knowledge of Wuhu iron painting, acknowledging that these factors significantly shape user interactions with the technology.

Moreover, a keen sensitivity to cultural nuances is critical, as VR technology is utilized not solely for its functional capabilities but as a tool for preserving, interpreting, and communicating cultural values and historical narratives. This requires a robust integration of technology with the subject matter, ensuring that the VR experience not only respects but also accurately represents the cultural essence of Wuhu iron painting. Consequently, the outcomes of our research are evaluated not just by user adoption rates but also through their impact on cultural engagement and educational enrichment, thus broadening the scope beyond traditional metrics of efficiency and satisfaction commonly used in other domains.

In applying UTAUT to VR in cultural heritage preservation, we address both technological aspects and socio-cultural dimensions, providing a comprehensive view of the factors impacting user acceptance and aiding in the development of effective strategies for technology implementation. By focusing on factors like perceived usefulness and ease of use, UTAUT offers valuable insights into how digital heritage initiatives can be optimized to meet user expectations and ensure long-term sustainability.

VR Iron painting system design

The VR iron painting system was designed to digitally preserve and promote the traditional art of iron painting through immersive and interactive experiences. The system aims to simulate not only the aesthetic presentation of iron artworks but also the craftsmanship process, thereby offering users both educational and experiential value. By integrating visual storytelling, craft simulation, and user interaction, the system seeks to bridge cultural heritage with modern technology.

To achieve these goals, the system comprises several functional modules, including a virtual museum, a simulated workshop environment, and detailed representations of traditional artworks. These components are implemented within a VR environment developed using Unity3D. An overview of the core visual and interactive elements is provided in Fig. 2.

Fig. 2
figure 2

Iron painting experience using VR designed by authors.

Full size image

To support the implementation of these components, Fig. 3 illustrates the systematic design flow of the VR iron painting system, consisting of six interconnected stages: observation, modeling, rendering, optimization, implementation, and interaction. Each stage builds upon the previous one, ensuring a progressive transition from conceptualization to user experience in the virtual environment.

The process begins with observation, where on-site research at the Wuhu Iron Painting Museum was conducted to document crafting techniques, museum layout, and the artisan’s workflow. This phase provided foundational data for digital reconstruction and ensured authentic representation in the VR environment. The gathered insights were translated into digital assets in the modeling phase, where Cinema 4D was used to construct high-fidelity 3D models of iron painting tools, traditional artwork, and the museum setting.

Once the 3D models were built, the rendering phase enhanced their visual realism by applying detailed textures, lighting effects, and material properties. To ensure an immersive experience, the optimization phase was introduced, where polygon counts were reduced, rendering efficiency was improved, and real-time interaction features were refined using 3ds Max. These optimizations balanced performance and realism, allowing the VR system to run efficiently on consumer hardware.

Following optimization, the implementation phase integrated these assets into Unity3D, where the interactive components were scripted using C#. This phase transformed static models into an interactive VR environment, enabling users to engage with the iron painting process through gesture-based inputs, brushstroke simulations, and real-time object manipulation. Finally, the interaction phase ensured that users could seamlessly navigate the VR museum, interact with digital iron painting tools, and experience the craftsmanship intuitively and engagingly.

This structured workflow demonstrates a tightly connected pipeline, where each phase directly contributes to the next—from real-world observation to 3D modeling, then enhancing realism through rendering, followed by performance optimization, VR system implementation, and ultimately, user interaction. This cohesive approach not only ensures technical efficiency but also preserves the artistic integrity of Wuhu iron painting, bridging traditional heritage and modern digital engagement.

The study utilized a VR-based simulation of Wuhu iron painting, developed using Unity3D, to replicate the intricate process of this traditional art form. The virtual environment was specifically designed to provide guided and interactive experiences, structured into two distinct phases: an initial tutorial phase, where participants learned fundamental iron painting techniques through instructional prompts, followed by an interactive phase in which they independently practiced creating digital iron paintings utilizing VR tools. Participants individually engaged with the VR environment for approximately 15–20 min, allowing sufficient exposure to fully explore the system’s features. Prior to the experiment, each participant received a 5-min briefing that introduced the VR controls, navigation mechanics, and the historical significance of the iron painting, ensuring they were adequately prepared for the simulation. The VR system featured three categories of iron paintings-historical designs, contemporary adaptations, and instructional patterns selected based on artistic complexity, historical significance, and user accessibility to foster meaningful participant engagement. For the experiment, the PICO4 VR headset with motion controllers was employed due to its high-resolution display and precise hand-tracking capabilities, essential for accurately simulating the fine motor skills inherent in iron painting. To maintain uniformity in data collection, participants engaged individually during the VR sessions, while qualitative insights were subsequently gathered through small-group discussions following the experimental activities.

Fig. 3
figure 3

The system design flow of Wuhu Iron Painting VR system.

Full size image

UTAUT model and research hypotheses

To fully address the users’ acceptance of VR technology in preserving the Wuhu iron painting heritage, the UTAUT model is adopted, and an associated questionnaire is designed. The UTAUT model was designed by Venkatesh et al.34 by integrating eight technology acceptance models. In UTAUT mode, there exist four independent variables, namely, performance expectancy (PE), effort expectancy (EE), and social influence (SI), facilitating conditions (FC) which affect the dependent variable, i.e., users’ behavioral intention to use, namely, behavioral intention (BI). It should be emphasized here that the other dependent variable, “use behavior,” is not considered in this work, which is left as a future study. Our primary focus was on user adoption intention rather than long-term engagement patterns. Since the VR system is still in its initial deployment phase, empirical data on sustained use behavior was not available. Future research should incorporate longitudinal studies to track how users engage with the system over time and evaluate actual usage trends. In addition, variables like gender, age, and experience act as moderators, influencing the relationship between the independent and dependent variables, as illustrated in Fig. 4.

UTAUT model has been applied to the cultural heritage domain in various studies30,31,33,35,36,37. According to Venkatesh et al.34 and the related work, the definitions of dependent and independent variables in this work, as well as the hypotheses are summarized as follows.

Fig. 4
figure 4

Research model based on the UTAUT framework, illustrating the hypothesized relationships between performance expectancy, effort expectancy, social influence, and facilitating conditions with behavioral intention to use VR technology. Moderating variables include gender (a), age (b), VR experience (c), and iron painting familiarity level (d).

Full size image

Performance expectancy (PE)

Performance expectancy refers to the user’s belief in the effectiveness of a certain technology or system in aiding them to accomplish a specific task or reach a desired objective. It assesses the users’ belief that using the technology will enhance their performance, make tasks more accessible, or lead to better outcomes than the case without VR technology. In simpler terms, it is about whether users expect the technology to be beneficial and improve their effectiveness in a given context34. Studies have consistently shown that PE is a significant predictor of behavioral intention. For instance, research on mobile banking adoption revealed that users are more likely to adopt the technology if they perceive it as beneficial to their financial management38. Similarly, in the context of e-government services, PE positively influenced citizens’ intentions to engage with online platforms39.

In this work, the PE factor refers to users’ beliefs regarding the ability of the technology, which creates immersive, interactive experiences that preserve the authenticity of cultural expressions and make them accessible worldwide, enhance their visual experience, facilitate rapid access to historical information about iron painting, provide quick knowledge acquisition related to iron painting, offer insights into the cultural value of iron painting, and whether it surpasses traditional methods. PE captures users’ expectations of how well the VR iron painting (VRiP) system can improve their experience and understanding of iron painting, encompassing its educational and experiential aspects. It is contingent on the users’ perceptions of VRiP’s potential benefits in terms of visual immersion, educational efficiency, and cultural enrichment compared to conventional preserving approaches. Users’ agreement with these statements will indicate the overall PE in this context, which can significantly influence their acceptance and adoption of VRiP system for exploring iron painting.

In the context of the UTAUT model, PE is a key factor that positively affects behavioral intention. Hence, we formulate the following hypothesis on PE:

H1

Performance expectancy positively affects users’ behavioral intention to adopt the VR iron painting system.

Effort expectancy (EE)

Effort expectancy pertains to users’ perceptions of ease of use of a particular technology or system. It evaluates whether users believe that interacting with the technology requires minimal effort and is straightforward. In the context of this study, EE assesses users’ expectations regarding the ease of using the VRiP system. This encompasses their beliefs about the system’s user-friendliness, intuitiveness, and the effort required to navigate and engage with the virtual environment. Essentially, EE reflects users’ anticipated ease of interacting with VR iron for exploring the world of iron painting. Higher EE indicates that users expect the technology to be user-friendly and not overly demanding regarding mental or physical effort. When users find a system user-friendly and free of complexity, their intention to use it increases. For example, a study examining the adoption of fingerprint authentication for ATMs found that Effort Expectancy significantly impacted users’ behavioral intention, highlighting the importance of intuitive design in technology acceptance40. In the healthcare sector, nurses’ willingness to adopt mobile learning platforms was significantly influenced by their perceptions of the system’s ease of use41.

EE is a crucial factor within the UTAUT model, as it influences users’ perceptions of the usability of a technology. Therefore, we formulate the following hypothesis on EE:

H2

Effort expectancy positively affects users’ behavioral intention of the VR iron painting system.

Social influence (SI)

Social influence relates to the impact of external factors, such as opinions, recommendations, or pressures from social networks, on the intention to use a technology. It assesses whether users’ decisions to adopt the VR iron painting system are influenced by the attitudes, encouragement, or endorsements from their social circles, including friends, family, colleagues, or peers in the field of iron painting. SI explores the extent to which users are swayed by the perceptions and behaviors of those around them when considering adopting the VRiP system. This construct has been identified as a strong determinant of technology adoption. For instance, in the context of mobile banking, social influence significantly affected users’ intentions to adopt the service. Similarly, nurses’ adoption of mobile learning systems was positively influenced by the expectations and opinions of their peers and supervisors38.

In the context of the UTAUT model, SI plays a significant role in shaping users’ behavioral intentions. Therefore, we formulate the following hypothesis on SI:

H3

Social influence positively affects users’ behavioral intention to adopt the VR iron painting system.

Facilitated conditions (FC)

Facilitating conditions encompass users’ perceptions of the support, resources, and infrastructure for effectively using technology. It evaluates whether users think they have the necessary resources, access, and technical support to adopt the VR iron painting system. FC reflects users’ expectations regarding the availability of adequate tools, training, and assistance to navigate and make the most of the technology. While FC often directly influences actual usage behavior, its impact on behavioral intention has also been observed. For example, research on the adoption of community-based online reporting systems found that facilitating conditions significantly affected users’ intentions to engage with the platform. In the context of e-government services, the availability of resources and support systems was crucial for citizen engagement42.

Within the UTAUT model, FC is critical in facilitating or hindering technology adoption. Therefore, we formulate the following hypothesis on FC:

H4

Facilitating conditions positively affect users’ behavioral intention to adopt the VR iron painting system.

Effect of moderator variables

In addition to examining the direct relationships between the UTAUT constructs and users’ behavioral intentions to adopt the VR Iron painting system, it is essential to investigate how specific moderator variables influence these relationships. We then explore the potential moderating effects of four variables: gender, age, VR experience, and iron painting knowledge. Here, studies43,44,45 indicate that different age groups and genders may respond to technological innovations in distinct ways due to varying levels of comfort, familiarity, and proficiency. VR experience and iron painting knowledge represent whether or not participants know and/or use VR and iron painting, respectively. To understand whether these demographic and experiential factors moderate the impact of UTAUT constructs on users’ behavioral intentions, we formulate the following hypotheses in the categories of four moderators (gender, age, VR experience, and iron painting knowledge level), respectively. We list the hypotheses related to the moderating effect in Table 2. In the following sections, we use the designed research method to test these hypotheses from the collected data.

Table 2 The research hypotheses regarding the moderators.
Full size table

Research methodology

In this research study, a comprehensive quantitative research methodology was employed to investigate and analyze the relationships and factors influencing the adoption of technology, specifically in the context of VR technology in preserving iron painting. To achieve this, a combination of statistical techniques and structural equation modeling (SEM) was utilized, which is preferred for handling latent variables—those that are not directly measurable but inferred through observable indicators—and for testing hypotheses about the relationships between these latent constructs in a comprehensive framework46. SEM provided a robust framework to assess the complex interplay of variables and hypotheses, enabling the examination of direct and indirect effects among key constructs.

Furthermore, correlation analysis was employed to explore the associations between variables, shedding light on potential relationships that underlie the theoretical framework. In addition to these techniques, a word cloud analysis was implemented to extract the viewpoints and suggestions from the open questions. This multifaceted research methodology facilitated a comprehensive examination of technology acceptance and usage, providing valuable insights into the factors and dynamics shaping individuals’ attitudes and behaviors towards VR technology in the context of ICH preservation.

Ethical approval for this study was obtained from the Research Department of Anhui Normal University. Informed consent was obtained from all participants, who agreed to participate after reading an information statement provided at the beginning of the questionnaire, in accordance with data privacy regulations and ethical standards. All methods were performed by the relevant guidelines and regulations.

Participants and sampling

The sample size of 668 participants was chosen to ensure statistical robustness and a diverse representation of potential users, as SEM requires a large sample for reliable model estimation. Additionally, we aimed for a balanced demographic distribution, ensuring an equal representation of gender and a well-distributed age range. This diversity allows for a more comprehensive understanding of VR adoption in cultural heritage preservation.

Initially, we aimed to recruit individuals who were directly connected to the field of iron painting, either through study or work in Wuhu city. To achieve this, we employed convenience sampling techniques, leveraging our networks and contacts within the local community associated with iron painting. This decision was guided by prior research emphasizing the importance of domain-specific expertise in technology acceptance studies related to cultural heritage30,46. Participants with prior exposure to iron painting were expected to provide more informed feedback on the effectiveness of VR applications in preserving this craft. Studies on digital heritage initiatives indicate that users with domain knowledge exhibit more nuanced perspectives on the authenticity and effectiveness of digital simulations, leading to more reliable assessments7.

Table 3 The distribution of the ages of participants.
Full size table

Additionally, we utilized online platforms and social media channels to disseminate information about the survey to a wider audience within Wuhu city and its province Anhui. Furthermore, the recruitment strategy was refined to include individuals with an interest in or prior experience with iron painting rather than the general public. This adjustment aligns with research demonstrating that individuals with even limited exposure to a cultural practice can offer valuable insights into engagement strategies and digital intervention efficacy3. Without some degree of familiarity with the craft, participants may have lacked the contextual understanding necessary to critically evaluate the VR system. Through targeted advertisements and posts, we reached out to individuals who were likely to have an interest or involvement in iron painting. This approach helped us to engage a diverse range of participants from different backgrounds and professions within the city. Finally, the combined use of convenience sampling and targeted recruitment strategies allowed us to gather a sample of 668 participants, comprising 330 males and 338 females, who were willing to participate in our survey on iron painting in Wuhu city. This study was approved by the research department of our university for the ethics issue.

Questionnaire design

To ensure questionnaire reliability, a pre-test was conducted with 49 participants, leading to minor refinements. Reliability analysis using Cronbach’s alpha yielded a score of 0.80, confirming strong internal consistency. Additionally, confirmatory factor analysis (CFA) validated the measurement model.

The questionnaire was designed according to the UTAUT model to collect quantitative data to evaluate the user attitude towards using VR iron painting. The questionnaire mainly has two parts: demographics information and UTAUT factors (i.e., PE, EE, SI, FC, and BI). A total of 17 Likert-scale questions are divided into five sections. Also, the questionnaire contains general information about the participants, including their gender, age, VR experience, and iron painting-related knowledge level. The detailed questionnaire is included in Appendix “1”.

The participants should rate their agreement with statements on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree) and respond to multiple-choice questions.

Data analysis

Quantitative data from the questionnaire were analyzed using descriptive statistics (e.g., means, frequencies) to provide an overview of participants’ information. Factor statistics, such as SEM analysis, were used to explore relationships between variables.

Qualitative data from the open questions in the questionnaire were analyzed using the word cloud figure to show the viewpoints of participants. However, the open questions are optional in the settings. As shown in Fig. 5, prominent suggestions include enhancing “advertisement” strategies, facilitating “simple interaction,” and fostering collaboration with “local government.” There is also a focus on the need to “update contents regularly” and drive “innovation,” highlighting areas where participants feel improvements could enhance the VR system’s effectiveness and appeal. Figure 6 illustrates the recognized advantages of the VR iron painting system, including its visual and immersive experiences, innovation, convenience, and accessibility. It also emphasizes the system’s capacity for cultural transmission and interaction with traditional culture, highlighting areas where participants see significant benefits.

Results and findings

Moving forward, we demonstrate the results and findings derived from our analysis within the framework of SEM, aiming to provide a clear understanding of the relationships and impacts observed.

Participant demographics and descriptive statistics

We first carried out a pre-test with 49 participants from two universities in Wuhu City, which is the place of iron paintings. According to these participants’ feedback, the questions’ presentations are slightly modified to avoid misunderstanding. Also, we evaluated the average time of answering the entire questionnaire to check whether the length was suitable.

After that, we distributed the questionnaire online and collected 668 valid samples from August to September 2023. The participants are 49.4% male and 50.6% female (330 males and 338 females). Based on Tables 2 and 36.5% of them are aged from 18 to 25 years old, 19.6% of them are between 26 and 30 years old, 18.6% of them are between 31 and 40 years old, 18.7% of them are between 41 and 50 years old, and 16.6% between 51 and 60, respectively. The age group are chosen to be almost balanced.

Since Wuhu iron painting exhibits strong ties to the local community, we then gathered data regarding participants’ familiarity with Wuhu city. Among the participants, 63.9% are either local residents or have previously lived in Wuhu, while 36.1% have no experience in Wuhu, based on the data in Table 4.

Table 4 The distribution of the gender and local community of participants.
Full size table

Analysis of UTAUT constructs

To ensure the robustness of our findings, several statistical tests were conducted. Reliability testing, using Cronbach’s alpha and composite reliability (CR), confirmed internal consistency. Confirmatory Factor Analysis (CFA) verified construct validity and measurement accuracy. SEM examined the relationships between performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention. Correlation analysis assessed interdependencies among variables, while moderation analysis evaluated the impact of demographic factors such as gender, age, and VR experience on adoption behavior.

Reliability and validity analysis

Based on the results shown in Table 5, the fitting indices of the conducted SEM analysis indicate that the measurement model is reliable and valid. Factor loadings demonstrate the strength and direction of the relationship between observed variables and latent factors in the SEM analysis. All factor loadings in Table 5 are above 0.64, which shows that the items are relatively reliable as an indicator of the intended construct. In addition, the overall Cronbach’s alpha of 0.80 indicates high internal consistency across all constructs, and a Kaiser-Meyer-Olkin (KMO) value of 0.81 suggests that the obtained data is reliable for factor analysis. Furthermore, the composite reliability (CR) values are consistent with Cronbach’s alpha values for each construct, ranging from 0.774 to 0.891, which assesses excellent internal consistency reliability. Also, the SEM measurement model (\(\:{{\upchi\:}}^{2}\)=198.901, \(\:\text{d}\text{f}\)=125, p = 0.000) achieves acceptable values of fit indicators, as shown in Table 6, where all the indices are within the acceptable criteria47.

Table 5 Obtained convergent results (factor loading, cronbach’s alpha, CR and AVE).
Full size table
Table 6 Model fitting indices and their corresponding criteria.
Full size table

Relations between UTAUT factors

Analyzing the relations between UTAUT factors helps identify critical drivers of acceptance, allowing for the development of targeted strategies tailored to specific contexts. Insights derived from these relationships facilitate enhancements in technology design, focused on user-centric features and ease of use.

Table 7 presents a comprehensive summary of the findings about the relationships between different constructs in the research model. The diagonal elements, representing the square roots of the average variance extracted (AVE) for each construct, demonstrate that all constructs capture an essential amount of variance relative to the measurement error, indicating a strong convergent validity. Specifically, the constructs, namely PE, EE, SI, FC, and BI, indicate robust convergent validity with square root values of the AVE ranging from approximately 0.74 to 0.85. It implies that these constructs effectively measure the underlying latent factors they represent. Additionally, the other results (i.e., the off-diagonal elements) in.

Table 7 Correlation and squared root of ave’s matrix. Correlation is significant at the 0.01 level (2-tailed). The diagonal values are the squared root of AVE.
Full size table

Table 7 shows strong relationships between these constructs, which sheds light on their interplay.

Fig. 5
figure 5

Path coefficients from independent variables (PE, EE, SI, FC) to the dependent variable BI using SEM (Note that * indicates p \(\:\le\:\:\)0.05 and *** indicates p \(\:\le\:\:\)0.001).

Full size image

Particularly, PE has positive correlations with EE (0.56), SI (0.39), FC (0.27), and BI (0.64). Moreover, PE and BI exhibit a notably strong positive correlation (0.64), emphasizing the impact of performance expectancy on behavioral intentions. These statistical correlations provide valuable insights into the complicated dynamics among the constructs and help us understand how they interact. In a nutshell,

Table 7 confirms the robustness of the measurement model, affirming convergent validity across constructs and highlighting the significance of specific construct interrelationships, which are essential for comprehending the broader theoretical implications of the study.

Structural relationships between PE, EE, SI, FC and BI

Our analysis employed SEM to assess the relationships between various factors. The results of this analysis are depicted in Fig. 5. The analysis used R with the lavvan51 and semTools packages52. According to Fig. 5, we found statistically significant effects on BI of using the VRiP system from the PE, and SI factors (p < 0.05). However, it is worth noting that EE and FC did not significantly affect the behavioral intention of using the VRiP system (p > 0.05). Our findings support hypotheses H1, and H3, while hypotheses H2 and H4 are rejected. In addition, it is found that EE has a negative (no statistically significant) path coefficient to the dependent variable BI.

The standardized factor loadings and \(\:{R}^{2}\) values are demonstrated in Fig. 5. Based on \(\:{R}^{2}\), we determined that independent variables PE, EE, SI, as well as FC jointly explain 72.5% of the variance in the dependent variable BI. In conclusion, our SEM analysis provides strong evidence supporting our hypotheses, confirming that the factors PE, SI, and FC jointly influence the intentions of users to adopt the VRiP system, except EE.

Effect of moderator variables

The results of examining the moderating effect on the independent variables to the dependent variable are summarized in Table 8 using SEM in R.

Table 8 Path coefficient results using the structural model with moderator effects.
Full size table

Gender plays a critical role in moderating the relationship between key factors and BI in adopting VR technology. Our analysis indicates that gender significantly influences the effects of PE and FC on BI. Specifically, while the path coefficient between PE and BI was statistically significant, it was inversely related, leading to the rejection of hypothesis H1a. For EE to BI, it observed the statistically significant path coefficient with a positive value, which leads to the support of hypothesis H2a. However, for the social influence factor, gender does not significantly impact the relationship (H3a rejected). Gender also moderates the relationship between FC and BI, supporting H4a.

Age moderates the relationship between factors and BI. There is a statistically significant negative influence of age on the impact of PE, EE, and FC on the dependent variable BI, leading to the rejection of hypotheses H1b, H2b, and H4b. However, even the hypotheses are rejected, they accept the alternative hypotheses, i.e., as age increases, the effect of PE, EE, and FC on BI decreases. Moreover, age affects the relationship between FC and BI positively, which means that H3b is supported.

VR experience moderates the relationship between factors and behavioral intention. PE and EE are significantly affected by VR experience (H1c and H2c supported). Social influence is strongly influenced by VR experience (H3c supported). Moreover, the effect of facilitated conditions on behavioral intention is impacted by VR experience (H4c supported).

Iron painting knowledge level also moderates the relationship between factors and behavioral intention. All four hypotheses (H1d, H2d, H3d, and H4d) are supported, demonstrating a clear and substantial impact of knowledge level on the dynamics between performance expectancy, effort expectancy, social influence, and facilitating conditions in shaping behavioral intention.

Fig. 6
figure 6

Word cloud of the suggestions for VR iron painting system from participants.

Full size image

Discussions

With a specific focus on Wuhu iron painting, a traditional Chinese art form, this study explores the potential of VR technology in preserving ICH. Employing the UTAUT model, we aimed to assess various factors influencing the adoption of VR for cultural preservation. The study’s findings provide valuable insights into user acceptance of VR for ICH preservation. PE emerged as the strongest predictor of adoption, indicating that users are more likely to engage with VR if they perceive it as enhancing their experience and understanding of Wuhu iron painting. Additionally, SI played a significant role, reinforcing the importance of cultural endorsement and peer recommendations in shaping adoption behavior. Contrary to expectations, EE and FC did not significantly affect behavioral intention, which suggests that VR’s increasing prevalence in entertainment and education may have familiarized users with its operation, reducing concerns about usability.

Prior research suggests that a more comprehensive justification is necessary. According to Venkatesh et al.34EE is often influenced by users’ prior exposure to similar technologies, reducing concerns about ease of use in technologically proficient groups. Additionally, Ch’ng et al.46 found that digital heritage users who had previous interactions with immersive technologies were less likely to consider usability as a barrier. Regarding FC, previous studies indicate that as VR technology becomes more accessible, users increasingly rely on their existing technological infrastructure and digital literacy, diminishing the importance of external support systems74. This phenomenon aligns with research in digital learning environments, which suggests that as users become more self-sufficient in technology adoption, institutional support plays a smaller role in influencing behavioral intention5. Moreover, in the context of VR-based cultural heritage studies, Wen et al.30 observed that participants accustomed to digital interaction were less reliant on structured facilitation and external resources.

Over the last several years, adopting digital and smart technologies in preserving cultural heritage has gained significant momentum. Many applications utilizing various technologies have been developed for cultural heritage; however, it is crucial to determine the key factors driving user intention to adopt these applications. Our study, focusing on Wuhu iron painting as an ICH, sheds light on the impact of VR on promoting its preservation.

The findings offer valuable insights into user acceptance of VR technology for preserving Wuhu iron painting within the UTAUT model framework. The PE and SI factors emerge as the key factors driving user intention to engage with VR in the context of iron painting. This finding is consistent with the works30,53which found a positive but not significant effect of the social influence factor on user behavioral intention to use AR/VR in cultural domains.

As shown in Fig. 6, simple interaction, visual experience and advertisement are the main suggestions from the participants. It suggests that that we need to improve the quality of the VRiP system to have a notable performance for demonstrating iron painting. Aligning with previous research36,54participants acknowledge that the VRiP system enhances the visual experience and facilitates technical innovation, as illustrated in Fig. 7.

Fig. 7
figure 7

Word cloud of the advantages of VR iron painting system which participants recognized.

Full size image

Therefore, it is advisable to delve deeper into the information related to iron painting and enhance the demonstration to maximize the impact of performance expectancy. Facilitating conditions does not play a crucial role, which is not consistent with previous study54. This underscores the need to provide comprehensive support for using the VRiP system. Furthermore, we need to work with the local government to improve the promotion of VRiP system.

Contrary to expectations, effort expectancy, however, does not significantly impact the behavioral intention of users to adopt the VR iron painting system. This aligns with previous studies30,37 and can be attributed to the widespread application of VR technology across various fields, leading to greater user familiarity and ease of use. Also, facilitating conditions do not affect the behavioral intention. The possible reason is that VR is now popular and support can be obtained easily, which can be attributed to its growing affordability and accessibility, with a wide range of hardware options now available, from low-cost devices like Google Cardboard to high-end systems such as Oculus Rift and HTC ViVe. Industry initiatives like the VR industry forum have also standardized technical guidelines, making it easier to create and distribute high-quality VR content across various sectors.

In terms of practical implications, our study findings offer practical insights for the design and implementation of VR systems aimed at preserving ICH, not only Wuhu iron painting. The following recommendations are based on our research findings and aim to enhance the effectiveness and user experience of VR cultural heritage systems.

  1. (1)

    Experience. Enhancing interactivity through gesture recognition, object manipulation, and spatial navigation may increase user engagement with cultural artifacts. By facilitating interaction with virtual objects and environments, VR systems could simulate a more immersive cultural experience55. Furthermore, advanced VR technologies, such as haptic feedback devices and spatial audio, are utilized to create a multi-sensory experience that enhances user immersion and realism. VR allows users to closely observe intricate details of the artwork in 3D, providing a level of visual engagement that surpasses physical exhibitions. Haptic feedback can simulate the tactile experience of forging iron, allowing users to interact with the virtual creation process, and enhancing learning and engagement. Additionally, immersive soundscapes, such as traditional music or the sounds of metalwork, deepen emotional connections with the craft. By engaging multiple senses, VR systems provide a more authentic and memorable cultural heritage experience56.

  2. (2)

    Personalization. Tailoring content to individual preferences and backgrounds may improve the relevance and appeal of VR cultural heritage experiences. By leveraging user data and preferences, VR systems could help tailor content and experiences to better meet the diverse needs and expectations of user groups57due to the fact that age, VR experience and iron knowledge affect the adoption of VR in cultural heritage domain.

  3. (3)

    Accessibility. Ensuring that VR systems are accessible to users with diverse needs and abilities by incorporating features such as voice commands, text-to-speech functionality, and adjustable user interfaces. By prioritizing accessibility, VR systems can provide equal access to cultural heritage experiences for all users58.

  4. (4)

    Education. Integrating educational content into VR experiences suggests the potential to promote cultural literacy and historical understanding. By incorporating elements such as guided tours, historical narratives, and interactive quizzes, VR systems can become valuable educational tools for schools, museums, and cultural institutions with more knowledge on cultural heritage59since iron knowledge also affects the adoption of VR in our study.

Furthermore, to enhance the effectiveness and user experience of VR cultural heritage systems, it is suggested that practical implementation could focus on interactive engagement, tailored content, institutional support, accessibility, and hybrid technological integration.

Implementing advanced interaction techniques such as gesture recognition and object manipulation allows users to engage directly with digital cultural artifacts, while spatial navigation enables movement within virtual heritage spaces. Haptic feedback devices, including motion controllers with resistance simulation, could help replicate the tactile sensation of forging iron, while spatial audio technology could be integrated to reproduce authentic workshop sounds. For improved adaptability, VR systems are suggested to provide structured learning modules with guided tutorials for beginners, alongside an expert mode featuring precision tools and customizable brush settings for experienced users. Gamification elements, such as task-based challenges and skill progression tracking, need to be considered for incorporation to maintain user motivation and engagement.

Government agencies and cultural institutions are suggested to establish funding programs to support VR development and facilitate partnerships with museums, universities, and tourism boards for content integration. Museums may consider the potential of incorporating VR exhibits where visitors experience traditional craftsmanship, while schools could explore the use of VR modules as supplementary learning tools in art and history courses. Collaborative development with cultural historians and artisans could be prioritized to ensure historical accuracy and artistic authenticity in VR representations. Accessibility measures could include developing lightweight VR applications compatible with mobile devices. Additionally, voice-guided navigation and simplified user interfaces may help accommodate older users and those unfamiliar with VR technology. Providing downloadable offline content or AR alternatives may also enhance accessibility, particularly in regions with limited technological infrastructure.

Taken together, these findings underscore the importance of designing VR cultural heritage systems that are not only technologically robust but also responsive to users’ expectations and culturally situated engagement needs. By aligning system features with these considerations, such applications may better support the sustainable preservation of intangible heritage.

Limitations

The primary limitations include sample diversity and size, reliance on self-reported data, and the cross-sectional design. These limitations underscore areas for future research, such as expanding the sample to encompass different geographic locations and cultural backgrounds, incorporating more objective measures of user interaction with VR technology, and adopting longitudinal study designs to track changes in user acceptance over time.

One of the primary limitations of VR adoption in cultural heritage education is accessibility. The high cost of VR hardware remains a significant barrier for institutions and users in lower-income regions. Additionally, usability concerns arise for older users and those unfamiliar with interactive digital environments. Prolonged exposure to VR can also lead to motion sickness and user fatigue, limiting engagement. Future improvements should focus on affordable VR solutions, adaptive interface designs for different user demographics, and the integration of alternative formats such as mobile-based AR applications.

Several significant limitations must be considered to ensure the reliability and applicability of research findings. One major limitation is technological accessibility, as VR technology requires specific hardware and software that may not be available or affordable for all users, which can limit the generalizability of the applications. The user experience with VR can also vary significantly due to individual differences in sensitivity to VR-induced symptoms like motion sickness, which can affect engagement levels and interaction quality. Additionally, the accuracy of cultural representation in VR is heavily dependent on the developers’ understanding and interpretation of the cultural content, posing risks of cultural misrepresentation if not overseen by cultural experts or the communities involved.

Moreover, creating immersive VR environments requires substantial technical expertise, which could be a barrier for cultural organizations with limited technical resources. Many VR studies may also face statistical limitations due to small sample sizes, which can lead to statistical uncertainty and limited generalizability across broader populations. VR’s ability to replicate the temporal dynamics and spatial complexities of real-world settings is not perfect, which might impact the authenticity and educational quality of cultural heritage representations. The long-term engagement and efficacy of VR applications in cultural heritage are not well-documented, raising questions about their sustained use and impact.

Ethical considerations also play a crucial role, particularly in managing the ownership and portrayal of cultural content to avoid exploitation or misrepresentation of vulnerable or indigenous cultures. Future research should focus on creating more accessible VR solutions, improving user interfaces to accommodate diverse users, ensuring culturally accurate and ethically responsible content, and conducting larger and more diverse studies to enhance our understanding of VR’s long-term impacts on cultural heritage preservation.

By addressing the limitations and leveraging the practical implications of our findings, further research can continue to examine and enhance the use of VR technology for preserving intangible cultural heritage.

Conclusion

This study illuminated the integral roles of social influence, performance expectancy, effort expectancy, and facilitating conditions in accepting VR for preserving Wuhu iron painting. The research has highlighted the significance of demographic and contextual factors, such as gender, age, VR experience, and cultural heritage knowledge, in shaping users’ behavioral intentions. The findings have confirmed the importance of ensuring the reliability and validity of measurement models in technology acceptance research.

Future directions

While this study provides evidence supporting the value of VR in preserving intangible cultural heritage such as Wuhu iron painting, several areas warrant further exploration.

First, future research should address the limitations of this study, including the reliance on self-reported data, the absence of long-term usage behavior analysis, and the relatively homogeneous participant demographics. Longitudinal studies could track how users continue to engage with VR systems over time, offering deeper insights into sustained acceptance and behavioral patterns.

Second, theoretical extensions may enrich the understanding of VR adoption in cultural contexts. For instance, integrating models such as the Technology Readiness Index or the Theory of Planned Behavior may offer a more comprehensive framework that accounts for motivational or emotional factors. Additionally, examining cultural-specific moderators may provide insights into regional adoption differences.

Third, future studies should focus on advancing the technological and methodological aspects of VR implementation. Emerging technologies such as artificial intelligence and multimodal interaction could improve personalization and adaptivity in VR experiences. Investigating multi-sensory engagement can help simulate more realistic craft-making experiences. Moreover, developing cost-effective and accessible VR solutions remains a critical challenge, especially for institutions or regions with limited resources. Addressing motion sickness, interface complexity, and device dependency can further reduce user barriers.

Finally, AR-VR integration models should be explored to extend the reach and flexibility of cultural heritage experiences across platforms. By combining mobile AR with immersive VR, users could access learning experiences in diverse environments, potentially increasing the inclusiveness of cultural preservation efforts.