Exploring User Experience in Virtual Industrial Heritage Platforms: Impact of Cultural Identity, Functional Clarity, Scene Interactivity, and Narrative Quality

Abstract

This study aims to investigate the key factors influencing user experience in the design of virtual interactive platforms for the preservation of industrial heritage within the context of digitalization. In the literature review, this paper introduces a theoretical model comprising five latent variables: cultural identity, functional clarity, scenario interactivity, narrative quality, and user experience. To construct and validate the model, the author employed structural equation modeling (SEM) to analyze data from 323 valid questionnaires collected in China. The validation of the SEM model led to conclusions regarding the impact of each latent variable on user experience. The results indicate that cultural identity, functional clarity, scene interactivity, and narrative quality significantly affect user experience and play a critical role in enhancing user experience on virtual platforms. To validate this theoretical framework, the study employs the virtual interactive platform of Hanyang Ironworks as a case study, examining how these factors manifest in practical scenarios and their specific impact on platform design. The findings reveal that a strong sense of cultural identity, clear functional design, interactive scenes, and a well-structured narrative are the core factors that enhance user experience on virtual industrial heritage platforms. This research provides both theoretical support for the digital preservation of industrial heritage and practical insights for the design of virtual platforms. The study improves virtual interactions with industrial heritage and informs future research and applications.

1. Introduction

The protection and utilization of industrial heritage are gaining increasing global attention. As industrialization advances worldwide, significant amounts of industrial heritage with historical, cultural, and social value are at risk of gradual erosion or destruction. In this context, digital technology has emerged as a crucial tool for the preservation and dissemination of industrial heritage on a global scale [1]. Industrial heritage encompasses both material and immaterial remnants of the industrialization process, which carry significant historical, cultural, and technological value [2]. These remnants serve as repositories of collective memory and embody the legacy of human industrial development.

Virtual platforms have become increasingly integral to protecting cultural heritage, providing immersive environments that enhance learning and communication while creating dynamic educational spaces [3]. In particular, the integration of industrial heritage with virtual reality and interactive technologies has become a focal point of scholarly research. Such platforms allow users to interact with and experience the evolution of industrial history, fostering a deeper engagement with industrial culture [4]. Several studies have examined the influence of education, visitor experience, and environmental exploration on user engagement with industrial heritage in virtual settings, contributing to the development of interactive experience models [5]. Despite these advances, challenges remain in enhancing the user experience [6,7,8].

This study seeks to address these challenges by developing a model that analyzes how various factors—specifically cultural identity, functional clarity, scene interactivity, and narrative quality—affect user experience during virtual interactions with industrial heritage. The study combines a comprehensive literature review with the design of a questionnaire, which was administered to collect 373 valid responses. Using structural equation modeling (SEM), the study analyzes the relationships between the identified factors and their influence on user experience. A Hanyang Ironworks industrial heritage case study further validates the proposed model. The findings of this study contribute to filling gaps in the academic literature by offering a framework for enhancing virtual heritage experiences through the integration of cultural identity, clarity, interactivity, and narrative quality.

Contemporary research increasingly focuses on the construction of virtual environments and the exploration of emotional engagement within cultural heritage contexts. Immersion, interactivity, and the effective transmission of educational content are central to the design of these virtual environments [9]. However, existing virtual platforms for industrial heritage often fail to adequately combine the cultural identity of industrial sites with functional clarity and narrative richness in their interactive scene.

This study developed a framework and model to analyze the impact of cultural identity, functional clarity, scene interactivity, and narrative quality on user experience in virtual interactions with industrial heritage. A questionnaire, based on a comprehensive literature survey, was designed and distributed to 373 participants. The data were analyzed using structural equation modeling (SEM), and the results confirm that these factors significantly influence user experience. A case study of the Hanyang Ironworks industrial heritage was used to verify the model, and the analysis provides design guidance for enhancing virtual interactions with industrial heritage.

2. Literature Review and Hypothesis

Before conducting this study, a detailed review of the existing literature was carried out, along with a comprehensive analysis of the theoretical background in related fields. To ensure a systematic and thorough review, a systematic literature review (SLR) methodology was employed. This approach involved multiple steps of retrieval and screening, ensuring the comprehensiveness and diversity of the sources. During the literature selection process, specific criteria regarding the publication date, language, subject matter, and research methods were established, ensuring that only the most relevant and representative studies were included. Subsequently, thematic and quantitative analyses were used to examine the literature. Key concepts and factors, such as cultural identity, functional clarity, context interactivity, and narrative quality, were identified as significant determinants influencing user experience. These factors, widely discussed in the literature, were considered to have a substantial impact on user engagement with industrial heritage. To further enhance the reliability of the study, the identified methods not only highlighted the current research landscape but also revealed existing gaps. Building upon this, an improved theoretical framework and research hypotheses were proposed. This systematic literature classification and analysis lay a solid foundation for the development of research hypotheses and provide clear guidance for selecting appropriate research methods and data analysis approaches.

2.1. Industrial Heritage

Industrial heritage represents the material manifestation of industrial change in urban development, serving as an industrial remnant with significant cultural and social value. The Nizhny Tagil Charter was the first to define industrial heritage, emphasizing its importance and the need for identification, documentation, and research. Industrial heritage encompasses buildings, transportation infrastructure, machinery, equipment, technical methods, and processes, as well as structures constructed with materials produced through industrial activities [10]. The conservation and utilization of industrial heritage today necessitate a comprehensive consideration of historical, cultural, technological, and socio-economic factors, aiming to foster urban development, cultural heritage preservation, and sustainable growth [11]. Research indicates that as industrial processes have evolved, the protection of industrial heritage has transitioned from traditional museum exhibitions to digital platforms [12]. In the cultural domain, the application of virtual technologies enhances the user experience, thereby amplifying the dissemination of the heritage’s cultural value [13]. Consequently, examining the multi-dimensional factors of user experience in the context of industrial heritage can provide valuable insights for the design of virtual platforms, facilitating the innovative application of industrial heritage in its digital transformation.

2.2. Virtual Interactive User Experience of Industrial Heritage

In recent years, there has been growing attention towards the application of augmented reality (AR) and virtual reality (VR) technologies in industrial heritage conservation [14]. Aromaa et al. conducted an in-depth study on user experience and acceptance of an AR-based knowledge-sharing system within the field of industrial heritage, underscoring the importance of assessing user experience in this context [15]. Hain et al. expanded the user experience model for AR applications in urban heritage tourism, enhancing the tourist experience by building upon the existing theoretical framework [16]. Additionally, Hulusic et al. developed tangible user interfaces for VR-based cultural heritage applications, aiming to enhance the user experience in educational environments through participatory design methods [17]. Li et al. performed a bibliometric analysis of immersive technologies in museum exhibitions, emphasizing the role of VR in enriching the visitor experience. Industrial heritage, often displayed through virtual interaction, helps preserve industrial scenes that have been destroyed or cannot be restored through the industrial process. Ghani et al. proposed that “presence” is a crucial factor in the user experience of virtual interactions, suggesting that effective heritage education and learning occur only when the user is fully immersed in the virtual space [18,19]. Marto A emphasized the integration of audio-visual functional elements in the development of virtual cultural heritage platforms, which enriches the overall user experience [20]. As interest in the sustainable development of industrial heritage continues to grow, there is a rising focus on the integration of virtual technologies with industrial heritage tourism, development, and reuse. However, there remains a lack of research on the factors affecting the user experience of individuals visiting industrial heritage sites [21,22,23,24].

Moreover, many researchers have identified cultural identity and spatial interactivity as key factors influencing user experience in virtual platforms. A comprehensive review of current research on virtual interactions within cultural heritage contexts reveals that cultural identity and spatial interactivity coexist as essential components of cultural expression. Additionally, a clear articulation of the interactive platform’s functions within its context, along with a coherent narrative presentation, are equally important for effective user engagement. Therefore, based on a human-centered design approach, this study examines user experience as a critical factor in the virtual interaction of industrial heritage. Cultural identity, functional clarity, scene interactivity, and narrative quality are identified as key impression factors, which are scientifically validated through a structural equation model to guide the protection and reuse of industrial heritage (Figure 1).

2.3. Cultural Identity (CI)

Cultural identity refers to the sense of belonging an individual or group experiences in relation to their own culture, particularly within the context of their cultural background [25]. This concept is frequently applied in the virtual preservation of cultural heritage. In multicultural contexts, cultural identity can foster a sense of belonging for users, enhancing their overall experience [26]. Previous studies have demonstrated that cultural identity is crucial in strengthening users’ sense of belonging and connection [27]. When users encounter elements related to their own culture in a service or product, their satisfaction with the experience is significantly enhanced [28]. Cultural identity can also strengthen the emotional bond between users and brands or products by providing a familiar and intimate connection, thereby improving the user experience [29]. In constructing virtual interactions with industrial heritage, it is essential to account for not only immersive experiences, such as emotional connections and identification but also the uniqueness of industrial processes across different countries represented by industrial heritage. Moreover, while experiencing the virtual industrial heritage scene, it is necessary to convey the diverse cultural backgrounds and industrial histories of these sites [30]. Therefore, cultural identity directly influences the user experience within virtual industrial heritage settings, as supported by the findings of Yang et al. Based on this, we propose the following hypothesis:

Hypothesis 1 (H1).

Cultural identity positively impacts user experience.

2.4. Functional Clarity (FC)

The relationship between functional clarity and user experience can be traced to the Expectation Confirmation Theory, which posits that user satisfaction and overall experience improve significantly when the functions of a product are clearly perceived by the user [31]. In the specific context of industrial heritage, the interaction between users and various elements of the experience plays a critical role [32]. The clarity of functional elements influences the user’s motivation, thereby contributing to creating an immersive experience [33]. User satisfaction and experience are substantially enhanced when the functions within virtual interactive platforms are clearly and explicitly designed to guide users through tasks intuitively, almost subconsciously [34]. In the virtual interaction of industrial heritage, elements such as scene interaction, education and learning, and social engagement must be coordinated to ensure a seamless and fulfilling user experience [35]. Given that the clarity of functional elements directly impacts the user experience, we propose the following hypothesis:

Hypothesis 2 (H2).

Functional clarity positively impacts user experience.

2.5. Scene Interactivity (SI)

The impact of scenario interactivity on user experience is supported by scenario theory (Schank, 1990) [36], which posits that interactive scenarios can create immersive experiences, fostering a heightened sense of participation and engagement. In the context of the digital economy and services, scenario interactivity can cater to users’ diverse needs through personalized interaction methods and dynamic contextual designs, ultimately enhancing user satisfaction [37]. Specifically, in industrial heritage applications, the replication of context and environment plays a pivotal role in shaping the user experience within the virtual scene [38]. The concept of user experience in scenario interactivity is crucial for designing engaging and immersive interfaces, as it enables users to interact with various elements in the virtual environment, enhancing their overall experience [39]. Consequently, both the transformation of industrial heritage forms across different time scales and user interaction within the system environment influence the user’s experience. Based on this theoretical framework, the following hypothesis is proposed:

Hypothesis 3 (H3).

Scene interactivity has a positive impact on user experience.

2.6. Narrative Quality (NQ)

As a multi-dimensional experience, spatial narrative provides users with a more intuitive and vivid engagement through elements such as spatial layout and narrative design. According to Experience Economy Theory, enhancing narrative quality can drive the story’s development, immerse users in a specific context, and strengthen their perception and memory of the experience [40]. Previous research has demonstrated that narrative quality plays a significant emotional and behavioral role in shaping user experience [41]. In the context of industrial heritage, the interactivity of the narrative creates a more distinct historical context for users, enabling them to perceive deeper meanings within the narrative space [42]. The quality of the narrative influences the user experience by allowing users to engage with the industrial story through a dynamic, movable perspective, enabling them to observe symbolic elements in the scene and actively participate in the process of industrial evolution rather than merely passively observing the industrial scene [43,44]. Therefore, we propose the following hypothesis:

Hypothesis 4 (H4).

Narrative quality positively impacts user experience.

3. Research Methods and Hypothesis

3.1. Research Framework and Process

The present methodology delineates the development of a conceptual framework (Figure 2). Through a comprehensive review of the existing literature, the definitions and measurement indicators for each variable were established, and a questionnaire was designed to collect and validate data, thereby informing subsequent design practices. In alignment with current industrial heritage preservation policies, the questionnaire was utilized to gather data. Structural equation modeling (SEM) was then employed to analyze the valid responses and verify the impact paths and relationship strengths of each factor on user experience. Finally, based on the SEM analysis results, an optimization model for user experience in the design of a virtual platform for industrial heritage was constructed and validated, offering practical design recommendations and actionable guidance.

3.2. Structural Equation Modeling (SEM)

Structural equation modeling (SEM) has been widely applied across various research fields, including marketing, management, counseling psychology, family businesses, information systems, and ecological studies [45]. Recent advancements in SEM have focused on hypothesis testing, statistical power, new methods for model fit assessment, and the utilization of different SEM algorithms. SEM is particularly advantageous because it can simultaneously handle multiple dependent variables and account for errors in both independent and dependent variables. As a result, SEM is well suited for exploring complex relationships that may be difficult to address using traditional methods in sociology and psychology. Albahri A. et al. demonstrated that SEM effectively quantifies the relationship between user perception and the factors influencing it [46]. Additionally, SEM has been used to analyze user satisfaction concerning the industrial value of heritage, identifying key factors that influence user satisfaction during visits to industrial heritage sites [47]. In this paper, user satisfaction is treated as the dependent variable, and the hypotheses derived from the previous analysis—cultural identity, functional clarity, scene interactivity, and narrative quality—are tested using SEM.

3.3. Sample and Data Collection

To ensure the scientific rigor and validity of the questionnaire content, this study emphasizes integrating popular science with the concept of industrial heritage and the development of measurement indicators during the questionnaire design phase. The concept of industrial heritage, as the foundation of the study, is defined as industrial sites, equipment, and remains with historical, social, cultural, or scientific value, including but not limited to factories, machinery, and transportation facilities [48]. In the introductory section of the questionnaire, a systematic overview of the basic definition, development status, and significance of industrial heritage in cultural preservation is provided. Additionally, the concept is further illustrated through pictures and videos to enhance the respondents’ understanding and mitigate potential cognitive biases. Several preparatory steps were undertaken to ensure the questionnaire’s validity and evidence-based design. Drawing from prior research on design factors in industrial heritage scenarios, the questionnaire was structured around four key dimensions: cultural identity, functional clarity, scene interactivity, and narrative quality. An initial version of the questionnaire was tested with 50 participants using a 7-point Likert scale to assess its clarity and reliability. Feedback was also obtained from three industry experts specializing in industrial heritage. Based on the feedback, the final questionnaire was revised and optimized, as depicted in

Table 1. Measurement items.

Code Number	Research Question Demand Indicator	Reference
A1	Industrial heritage embodies the region’s distinctive historical and cultural identity.	[49,50]
A2	Industrial heritage is an important part of our local cultural identity
A3	I can feel the continuity of the history of our region in the industrial heritage
A4	I believe that protecting and passing on industrial heritage helps to strengthen a region’s cultural identity
A5	I have an emotional connection with industrial heritage and feel that it is part of our culture
B1	My interactive experience within the industrial heritage site was fluid and natural	[51]
B2	The design of the space encourages people to interact and communicate with each other
B3	Various spatial settings can effectively guide people’s actions and communication
B4	The interactive scenes in the industrial heritage allow me to better understand the cultural context
B5	The layout of the venue makes me feel comfortable and easy to participate in the interactive activities
C1	The spatial layout of industrial heritage helps to understand its historical context and cultural story	[52,53]
C2	The historical and cultural significance of the industrial heritage is effectively communicated through the spatial design
C3	Design elements can help me better understand the cultural value of heritage
C4	I can feel the historical atmosphere of industrial heritage from the details and installations in the space
C5	The spatial narrative is clear and precise, which helps me to understand the cultural background of the place
D1	The spatial layout of industrial heritage helps to understand its historical context and cultural story.	[54]
D2	The spatial design effectively conveys the historical and cultural significance of the industrial heritage.
D3	Design elements (such as exhibition panels, signs, objects, etc.) can help me better understand the cultural value of heritage.
D4	Design elements (such as exhibition panels, signs, objects, etc.) can help me better understand the cultural value of heritage.
D5	The narrative approach to space is clear and accurate, which helps me understand the cultural background of the place.
E1	My overall experience at the industrial heritage site was pleasant.	[55,56]
E2	The design of the place makes me feel comfortable
E3	I am satisfied with the functional design of the industrial heritage site
E4	My behavior and activities in industrial heritage sites are a natural experience
E5	The design of the industrial heritage makes me feel relaxed and happy, and I can enjoy the atmosphere

below.

The survey was conducted to examine the influence of observed variables on latent variables within the SEM model [46]. A total of 350 questionnaires were distributed, and 323 valid responses were collected after eliminating incomplete or invalid submissions. Frequency analysis of the data revealed balanced demographic representation in terms of gender, age, educational background, and prior visitation experience. Male respondents accounted for 171 participants (52.94%), while female respondents comprised 152 participants (47.06%), indicating a relatively even gender distribution. Furthermore, the sample covered diverse ages and educational levels, ensuring comprehensive representation. This balanced demographic distribution provides robust support for the reliability and generalizability of the findings. Additionally, the diversity of demographic groups allows for meaningful subgroup comparisons and in-depth analyses (see Figure 3).

3.4. Reliability Test

The reliability analysis results indicate that the measurement tools for each dimension have high internal consistency and reliability [47]. The Cronbach’s a coefficients for all dimensions exceed the recommended threshold of 0.70. Specifically, the dimensions of cultural identity, functional clarity, scenario interactivity, narrative quality, and user experience have Cronbach’s a values of 0.912, 0.889, 0.881, 0.885, and 0.886, respectively, as shown in

Table 2. Reliability and validity test of the measurement model.

Dimension	Item	CITC	Item Deleted α Coefficient	Cronbach α Coefficient	CR	AVE
CI	A1	0.754	0.855	0.886	0.886	0.609
	A2	0.745	0.857
	A3	0.69	0.869
	A4	0.729	0.86
	A5	0.704	0.866
FC	B1	0.747	0.861	0.889	0.889	0.617
	B2	0.722	0.867
	B3	0.747	0.861
	B4	0.705	0.871
	B5	0.729	0.865
SI	C1	0.702	0.858	0.881	0.861	0.598
	C2	0.686	0.862
	C3	0.715	0.856
	C4	0.718	0.855
	C5	0.753	0.846
NQ	D1	0.729	0.859	0.885	0.885	0.607
	D2	0.716	0.862
	D3	0.692	0.867
	D4	0.716	0.862
	D5	0.76	0.852
UE	E1	0.751	0.855	0.886	0.886	0.610
	E2	0.731	0.86
	E3	0.734	0.859
	E4	0.731	0.86
	E5	0.676	0.872

.

Additionally, after the deletion of individual items, the corrected item–total correlation (CITC) values and the α coefficients further support the consistency between items and their respective dimensions. These findings confirm that the questionnaire demonstrates high reliability and is a dependable measurement tool for this research.

3.5. Validity Test

The results of the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s sphericity test confirm the suitability of the data for factor analysis. The KMO value is 0.907, exceeding the excellent threshold of 0.9, indicating strong correlations among variables. The Bartlett test yields a chi-square value of 4510.397 with 300 degrees of freedom and a significance level of p < 0.001, confirming that the correlation matrix significantly deviates from the identity matrix. These results, detailed in

Table 3. KMO and Bartlett.

KMO Price		0.907
Bartlett sphericity test	Approximate california	4510.397
	df	300
	p price	0.000

, validate the appropriateness of the data for factor extraction and structural analysis.

3.6. Factor Analysis

As shown in

Table 4. Variance interpretation rate.

Factor Number	Characteristic Root			Rate of Variance Interpretation Before Rotation			Rate of Variance Interpretation After Rotation
	Characteristic Root	% Variance Interpretation Rate	Accumulate %	Characteristic Root	% Variance Interpretation Rate	Accumulate %	Characteristic Root	% Variance Interpretation Rate	Accumulate %
1	8.105	32.418	32.418	8.105	32.418	32.418	3.513	14.051	14.051
2	2.687	10.749	43.167	2.687	10.749	43.167	3.472	13.889	27.940
3	2.398	9.592	52.760	2.398	9.592	52.760	3.461	13.845	41.785
4	2.260	9.042	61.802	2.260	9.042	61.802	3.423	13.694	55.479
5	1.807	7.226	69.028	1.807	7.226	69.028	3.387	13.549	69.028

below, the results of the variance interpretation rate of the factor analysis show that the factor structure of the data is well explained. Among the five factors extracted, the eigenvalues are all greater than 1, and the total variance explained before rotation is 69.028%, indicating that the five factors can explain 69.028% of the total information of the original variables. Specifically, the first factor has an eigenvalue of 8.105 and alone explains 32.418% of the variance; the second factor has an eigenvalue of 2.687 and explains 10.749% of the variance; the third factor has an eigenvalue of 2.39 8, which explains 9.592% of the variance; the fourth factor has an eigenvalue of 2.260, which explains 9.042% of the variance; and the fifth factor has an eigenvalue 1.807, which explains 7.226% of the variance.

As shown in

Table 5. The factor load coefficient after rotation.

Name	Factor Load Factor					Common Degree (Common Factor Variance)
	FQ	CI	NQ	SI	UE
A1	0.127	0.807	0.134	0.097	0.169	0.723
A2	0.130	0.800	0.138	0.130	0.128	0.709
A3	0.074	0.748	0.191	0.121	0.156	0.641
A4	0.072	0.819	0.091	0.072	0.125	0.706
A5	0.087	0.790	0.058	0.105	0.152	0.669
B1	0.814	0.040	0.123	0.073	0.173	0.715
B2	0.789	0.118	0.089	0.120	0.146	0.680
B3	0.834	0.122	0.057	0.054	0.085	0.724
B4	0.802	0.100	0.054	0.093	0.078	0.671
B5	0.795	0.091	0.066	0.100	0.191	0.692
C1	0.110	0.084	0.181	0.763	0.181	0.666
C2	0.158	0.098	0.121	0.766	0.100	0.647
C3	0.076	0.164	0.083	0.798	0.098	0.686
C4	−0.007	0.095	0.061	0.804	0.200	0.699
C5	0.116	0.077	0.107	0.823	0.132	0.726
D1	0.084	0.173	0.782	0.163	0.133	0.693
D2	0.112	0.133	0.780	0.170	0.104	0.679
D3	0.083	0.051	0.773	0.073	0.223	0.663
D4	0.093	0.082	0.807	0.105	0.097	0.688
D5	0.024	0.174	0.824	0.046	0.166	0.740
E1	0.163	0.119	0.176	0.169	0.789	0.722
E2	0.174	0.178	0.146	0.159	0.759	0.684
E3	0.154	0.155	0.118	0.117	0.794	0.705
E4	0.146	0.202	0.178	0.149	0.762	0.696
E5	0.104	0.143	0.158	0.175	0.738	0.632

Rotation method: the maximum variance method is Varimax.

, the cultural identity dimension (A1–A5) has a load factor of 0.748–0.819 and a common degree of 0.641–0.723 on the second factor, indicating a strong explanatory power of the factor connotation; the functional clarity dimension (B1–B5) has a load factor of 0.789–0.834, and the degree of commonality is 0.671–0.724, which reflects the dimensional stability; the dimension of scenario interactivity (C1–C5) has a load factor of 0.763–0.823 on the fourth factor, and the degree of commonality is 0.647–0.726, indicating good factor performance; the dimension of narrative quality (D1–D5) has a load factor is 0.773–0.824, and the common degree is 0.663–0.740, which has strong discriminant and explanatory power; and the user experience dimension (E1–E5) has a load factor of 0.738–0.794 on the fifth factor, and a common degree of 0.632–0.722, which further verifies the stability of its structure. The load factor of each item on its corresponding factor is significantly higher than that of other factors, indicating that the factor structure has strong convergent validity and discriminant validity. In addition, the commonality of all items exceeds 0.4, which further supports the goodness of fit of the factor model and the explanatory power of the variables. The rotated factor loadings’ distribution verifies the questionnaire design’s scientific nature and lays a solid theoretical and empirical foundation for follow-up research.

3.7. Structural Modeling and Validity Analysis

The results of the discriminant validity analysis show that the model’s latent variables exhibit strong discriminant validity between constructs (

Table 6. Regional validity: Pearson correlation with AVE square root value.

	CI	FQ	SI	NQ	UE
CI	0.781
FQ	0.279	0.785
SI	0.306	0.263	0.773
NQ	0.343	0.243	0.318	0.779
UE	0.419	0.382	0.407	0.412	0.781

Note: bold font is the square root of AVE.

). Specifically, the square root of the AVE value of each latent variable exceeds its correlation coefficient with other latent variables, thereby confirming their independence. For instance, the square root of the AVE value for cultural identity is 0.781, significantly higher than the correlation coefficients between it and functional clarity (0.279), scene interactivity (0.306), narrative quality (0.343), and user experience (0.419), reflecting its high discriminant validity. Similarly, the AVE square root value of functional clarity is 0.785, surpassing its correlation coefficients with other latent variables, demonstrating its clear distinction from different dimensions. Scenario interactivity (AVE square root value of 0.773) also demonstrates distinct characteristics, as does narrative quality (0.779) and user experience (0.781), with none of their correlation coefficients exceeding the respective AVE square root values.

Overall, the square root of the AVE value of all latent variables is greater than the correlation coefficient between them. This further supports the structural rationality of the measurement model and provides a statistical basis for theoretical verification and model analysis. This study constructs a framework of user experience influencing factors, as shown in Figure 4, and uses SEM model analysis to explore the positive influence of cultural identity, functional clarity, scene interactivity, and narrative quality on the user experience of virtual interaction with industrial heritage.

3.8. Simulated Fitting Index

The results of the model fit index analysis (

Table 7. Model fit metrics.

Commonly Used Indicators	χ2	df	p	Chi-Square Degrees of Freedom Compared to χ2/df	GFI	RMSEA	RMR	CFI	NFI	NNFI
Criterion for judgment	-	-	>0.05	<3	>0.9	<0.10	<0.05	>0.9	>0.9	>0.9
Price	307.602	265	0.037	1.161	0.931	0.022	0.035	0.990	0.934	0.989
Other indicators	TLI	AGFI	IFI	PGFI	PNFI	PCFI	SRMR	RMSEA 90% CI
Criterion for judgment	>0.9	>0.9	>0.9	>0.5	>0.5	>0.5	<0.1	-
Price	0.989	0.916	0.990	0.759	0.825	0.875	0.034	0.006–0.032

Note: in the default model, χ² (300) = 4656.979; p = 1.000.

) show that the constructed model generally fits well and meets the assessment criteria for most commonly used fit indices, supporting the rationality of the model and the degree of data fit.

Among the basic fit indicators, the chi-squared value (χ²) is 307.602, the degree of freedom (df) is 265, and the p-value is 0.037. Although the p-value is slightly lower than 0.05, the chi-square test is sensitive to large samples so that it can be combined with other appropriate indicators for a comprehensive assessment. The chi-squared degree of freedom ratio (χ²/df) is 1.161, well below the recommended standard of 3, indicating that the model has a good fit. Furthermore, the GFI (0.931), CFI (0.990), NFI (0.934), and NNFI (0.989) are all significantly higher than 0.9, indicating that the model has a good overall fit. Among the residual correlation indicators, the RMSEA value is 0.022, the RMR value is 0.035, and the SRMR value is 0.034, all of which are lower than the recommended thresholds (<0.10 or <0.05), indicating that the model has small residuals and that the data fit the model well. In addition, the 90% confidence interval of the RMSEA is 0.006~0.032, further confirming the reasonable range of the model residuals. Among the auxiliary fit indices, TLI (0.989), AGFI (0.916), and IFI (0.990) are all significantly higher than 0.9, and PGFI (0.759), PNFI (0.825) and PCFI (0.875) all exceed the recommended standard of 0.5, indicating that the model structure is simple and has strong explanatory power. These results reflect a good balance between model fit and parsimony.

In summary, the model constructed in this study meets or significantly exceeds the recommended evaluation criteria in terms of goodness of fit, residual correlation, and additional fit indicators, supporting the scientific and reasonable structure of the model. This result provides a sound statistical basis for subsequent theoretical verification and practical application.

The regression analysis results indicate that cultural identity, functional clarity, scenario interactivity, and narrative quality all significantly impact user experience. The standardized regression coefficients for all paths are similar, demonstrating that each latent variable contributes relatively equally to user experience. Specifically, the standardized regression coefficient for cultural identity is 0.230, with a significant p-value of 0.000, highlighting its importance in enhancing user experience. Functional clarity also shows a standardized regression coefficient of 0.230 and a significant p-value of 0.000, confirming its key role. Scenario interactivity (standardized coefficient: 0.229) and narrative quality (standardized coefficient: 0.230) similarly exhibit strong explanatory power and statistical significance in improving user experience (

Table 8. Summary table lattice of the model regression coefficients.

X	→	Y	Non-Standardized Regression Coefficients	SE	z (CR Price)	p	Standardized Regression Coefficient
CI	→	UE	0.224	0.059	3.806	0	0.230
FC	→	UE	0.238	0.059	4.019	0	0.230
SI	→	UE	0.249	0.065	3.842	0	0.229
NQ	→	UE	0.236	0.062	3.825	0	0.230

).

In conclusion, these four latent variables—cultural identity, functional clarity, scenario interactivity, and narrative quality—have comparable and statistically significant contributions to user experience. These findings validate the rationality of the model structure and provide empirical evidence for refining the theoretical framework and supporting its practical applications.

4. Results

4.1. Design Case

4.1.1. Design Research Framework

According to the research, cultural identity, functional clarity, scene interactivity, and narrative quality are key user experience factors influencing the design of virtual interactive scenes. By leveraging these factors, the interactivity design of industrial heritage scenes can be enhanced, improving user satisfaction during contextual interactions. The Unity platform serves as a foundation for constructing virtual industrial heritage scenes, as illustrated in Figure 5.

A virtual platform for industrial heritage construction was developed using Unity, with Hanyang Ironworks serving as a case study to validate the design. Recognized as one of China’s earliest industrial heritage sites, Hanyang Ironworks was meticulously reconstructed by combining insights from literature review and findings from field investigations. This reconstruction allows users to engage with the process of industrial heritage protection by navigating between scenarios representing the site before and after its transformation. The research framework, illustrated in Figure 5, lays the foundation for an exploratory approach to designing virtual industrial heritage scenes.

4.1.2. Design Process

Building on the aforementioned user experience model, an interactive system flowchart is developed in Figure 6 to structure the design process. The primary goal is to enhance user experience by identifying opportunities for engagement with industrial heritage, as depicted in Figure 7. When users interact with the virtual platform for industrial heritage, they are guided through a clearly defined set of platform functions designed to provide an immersive experience. Central to this design is an emphasis on fostering a strong sense of industrial cultural identity.

This project focuses on the industrial heritage of Hanyang Ironworks, delving into its industrial development history and tracing the transformations brought about by industrialization. Interactive features are integrated into the virtual environment, allowing the architectural elements to engage dynamically with users. Furthermore, the platform facilitates cross-cultural communication, enabling users from diverse national and cultural backgrounds to interact within the scene, thereby enhancing cultural connectivity. During the development process, attention is given to enhancing the interactivity and narrative quality of the scene. This ensures that users remain focused on the evolution of industrial heritage throughout their experience. The design incorporates task-based prompts that guide users through scenario updates and functional upgrades, ultimately culminating in creating a modernized representation of industrial heritage.

4.1.3. Design Output

As one of Wuhan’s first industrial heritage sites, Hanyang Ironworks is located on the north side of Guishan Mountain, with over a century of industrial development history. The research team conducted field surveys and sampling and reviewed relevant literature to create a preliminary draft for modeling and rendering. The basic architectural modeling was carried out in Blender, and the scene was constructed based on the appearance of the Hanyang Ironworks factory area as described in historical documents. Subsequently, the terrain was created, and the spatial layout of Hanyang Ironworks was simulated using the Unity platform to support the requirements of the interactive scene. Integrating materials and textures enhances spatial richness and delivers a more realistic user experience. During user interaction, operations such as clicking, running, and jumping trigger task points in the virtual Hanyang Ironworks scene, which is dynamically updated under the guidance of these tasks. The industrial development history of Hanyang Ironworks is embedded within the virtual platform, encouraging users to contribute to the preservation and enhancement of its industrial heritage. Furthermore, users on the virtual platform can communicate and interact with participants from different countries by completing scenarios set within various industrial heritage maps. This facilitates in-depth learning of industrial heritage and allows users to experience industrial development processes across diverse cultural contexts, as illustrated in Figure 8.

• Technical support: Basic modeling and rendering were performed using Blender and Rhino, combined with multi-dimensional architectural and environmental modeling based on background research and on-site investigations of the initial factory buildings of Hanyang Ironworks. The Unity platform was utilized to construct an interactive platform for the “Hanyang Ironworks”, while Python scripts were employed to manage commands and handle environmental switching code.
• Cultural Identity: When users enter the virtual industrial heritage scene, their initial impression is of the factory’s original appearance. The classic red brick factory buildings and old-fashioned boiler rooms immerse users in a 1990s-era atmosphere. As users explore the scene, they trigger explanations related to the ironmaking plant and boiler equipment, allowing them to learn about the plant’s historical transformations and social context through background knowledge cards. After gaining an initial understanding of Hanyang Ironworks, users proceed to improve the factory under the guidance of task points. Through interactive elements, they renovate equipment and engage with the industrial development process. From the birth of the first molten iron in Blast Furnace No. 1 in New China to the modern production of rails, users appreciate industry evolution by interacting with the buildings and equipment.
• Functional Clarity: An intuitive and user-friendly main menu and navigation bar ensure that users can quickly locate the desired function module. Once inside each module, the interface layout remains simple, clear, and logical to prevent users from becoming disoriented in complex sub-menus or nested hierarchies. Interactive functions throughout the operation process are well defined, with button logic aligning with users’ natural habits. Basic operations are performed using mouse clicks, dragging, and keyboard shortcuts for rapid navigation. Python integration facilitates the creation of trigger task points, simplifying user interactions. The UI design is clear and incorporates color and dynamic elements to guide users toward task points and progress through the main objectives. In presenting historical stories, the core message of “The Founding of Hanyang Ironworks” is emphasized as the primary focus, with background information and further reading positioned as secondary content or accessible via expandable sections. Upon entering the platform, users are introduced to a brief guided tutorial to familiarize them with its functionality.
• Scene Interactivity: Scene construction is based on three dimensions: the user, the environment, and their experience. It allows users to engage in two key stages of industrial development: the initial and subsequent stages of improvement and preservation. Users can explore the historical evolution of industrial heritage, enriching their overall experience. The platform recreates detailed historical scenes to provide users with an immersive historical context, embedding key historical events or tasks to allow users to experience or relive significant historical moments actively. With industrial heritage regeneration as the central theme, interactive stories are embedded to enrich the virtual scenarios. For instance, when passing by Blast Furnace No. 1, users can “choose materials and processes”, deciding between various materials or production methods available during that era and observing the impact of their choices on the production process. This interactive decision-making process helps users better understand the complexities of historical development.
• Narrative Quality: The narrative structure should follow a clear timeline and logical progression, highlighting the key historical events and figures associated with Hanyang Ironworks while avoiding fragmentation of information. The virtual platform employs an interactive timeline or key historical nodes to display different Hanyang Ironworks’ development stages. Each stage is presented through interactive simulations and multimedia displays to help users understand the historical context. Historical events are arranged chronologically or by significance, enabling users to progressively comprehend Hanyang Ironworks’ transformation over time. By assuming the role of industrial builders, users actively preserve industrial heritage through exploration, construction, and improvement. They are not passive recipients of information but shape the renewal of industrial heritage through spontaneous exploration, creating a cohesive and immersive spatial narrative. Within the virtual platform, users act not only as observers but also as active participants. Interactive tasks, decision making, and role playing shape the narrative’s progression and can even influence its conclusion.

4.2. Design Verification

To evaluate whether the interactivity of the “Hanyang Ironworks” scene satisfies user needs, 30 users were randomly selected to test and experience the scene, culminating in a questionnaire survey. The questionnaire results were compared against a control group from the “How Steel is Made in VR Exhibition Hall” at the WISCOM Museum. The survey utilized the UEQ (User Experience Questionnaire) scale to assess attributes such as attractiveness, clarity, efficiency, dependability, stimulation, and novelty. These metrics were evaluated with four factors influencing user experience: cultural identity, functional clarity, scene interactivity, and narrative quality.

The scoring system followed a Likert scale with five levels ranging from “very satisfied” to “very dissatisfied”, corresponding to scores from 1 to 5 [49]. Data analysis indicates that the virtual interactive experience of Hanyang Ironworks surpasses that of the VR display at the WISCOM Museum. User evaluations reveal significant enhancements in the Hanyang Ironworks Virtual Interactive Platform across the dimensions of cultural identity, functional clarity, scene interactivity, and narrative quality, validating the prior assumptions and structural equation model calculations (

Table 9. Paired samples t-test analysis of significant differences.

Variant	WISCOM Museum VR		Hanyang Ironworks		T	p
	AVE	SD	AVE	SD
attractiveness	3.2	0.761	0.785	4.07	−4.342	0
clarity	3.1	0.923	0.947	4	−3.728	0
efficiency	3.03	0.809	0.89	3.97	−4.251	0
dependability	3	0.886	0.885	4.1	−4.77	0
stimulation	3.07	0.868	0.944	4.07	−4.269	0
novelty	3	0.947	1.017	4	−3.942	0

).

This design practice underscores the significance of cultural identity in virtual interactive platforms. A comparative analysis of the scores between the two virtual platforms reveals that the Hanyang Ironworks platform is more engaging and demonstrates superior functional clarity. In this platform, the enhancement of scene interactivity and the redesign of the original industrial heritage scene notably improve the user experience. These findings align with previous research [35]. Furthermore, the narrative element of the scene, particularly the depiction of industrial events within the virtual platform, effectively guides users through the experience, enhancing their sense of immersion. In the comparative case study, the Hanyang Ironworks platform outperforms similar platforms in terms of efficiency and novelty. In conclusion, virtual interactive platforms designed according to the user experience model provide users with a deeper understanding of industrial heritage.

It is worth noting that four of the users analyzed in the questionnaire gave feedback that they still needed help understanding the reward mechanisms in the Industrial Heritage interaction and that they could not find all the Industrial Heritage update points when exploring the Industrial Heritage scenarios. The feedback from the questionnaire suggests that the Industrial Heritage virtual interaction platform still needs to be improved, for example, by updating the reward panel and the visual navigation of the Industrial Heritage transitions.

5. Discussion

This study fills a gap in the literature regarding the factors that affect user experience in virtual interactive platforms for industrial heritage. Specifically, this paper proposes a user experience model that incorporates cultural identity, contextual interactivity, functional clarity, and narrative quality, and validates it through real-world examples. The empirical results indicate that the data collected from users support the hypotheses outlined in the paper. In summary, the study results demonstrate that cultural identity, scene interaction, functional clarity, and narrative quality significantly influence user experience in virtual interactive platforms for industrial heritage. The following section provides a more detailed discussion.

5.1. Discussion of the Results

First, the study shows that cultural identity significantly impacts user experience in interactive platforms for industrial heritage. Previous research has shown that cultural identity enhances user immersion in the experience [26,27,28,29,30]. As expected, when users interact with industrial heritage scenes, their empathy with historical value facilitates a clearer understanding of the cultural connotation of industrial heritage. Cultural identity thus has the greatest impact on user experience [32,33,34,35]. Additionally, regarding functional clarity, this paper proposes clear visual guidance and interface interactions, which enable users to quickly immerse themselves in and actively explore the scene, thereby enhancing the user experience, consistent with previous research [37,38,39]. However, it is important to note that in practice, balancing the clarity of functions with the richness of information in industrial heritage interactions remains necessary. In scene interaction, our findings show that narrative quality effectively enhances users’ sense of connection with the virtual scene, thereby improving the user experience [40,41,42,43,44]. Overall, designing a virtual interactive platform for industrial heritage requires a comprehensive consideration of the factors mentioned above to enhance user experience.

5.2. Theoretical and Practical Significance

First, this paper establishes a user experience model to guide the design of virtual interactive platforms for industrial heritage. Previous studies have focused on individual user experience factors, with relatively few models addressing the impact of multiple factors on user experience. Based on a literature review, this paper proposes a new user experience model and combines it with the SEM model to validate the design. Our results indicate that cultural identity, scene interactivity, functional clarity, and narrative quality should be considered key factors for guiding user experience design.

6. Conclusions

6.1. Research Conclusions

This study explores the factors influencing the user experience of virtual digital heritage platforms by integrating industrial heritage with virtual interactive technologies. Building upon prior research and employing structural equation modeling, this study develops a user experience model. From a user experience perspective, it comprehensively evaluates cultural identity, functional elements, scene interactivity, and narrative quality within the virtual platform. The findings reveal that cultural identity, functional elements, scene interactivity, and narrative quality significantly impact the user experience. The Hanyang Ironworks virtual interactive platform served as a case study. The systematic design process incorporated these four variables and compared the platform with existing virtual experience cases, strengthening the scientific validity of the theoretical user experience model. This research enriches the theory of virtual interactive design and the cultural experience of industrial heritage, offering a novel perspective for interdisciplinary studies.

6.2. Limitations of the Study

The sample of industrial heritage scenarios in this paper requires expansion to include a broader range of industrial heritage sites across diverse cultural regions, thereby enhancing the credibility and generalizability of the model samples. The current research primarily relies on cross-sectional data to analyze users’ perceptions and behaviors concerning virtual industrial heritage experiences. However, user acceptance and preferences may evolve. As the promotion and popularization of industrial heritage and conservation efforts progress, users’ expectations and needs for interactive design may shift, necessitating ongoing scholarly engagement with this research. Additionally, there is some bias in the virtual experience of users from varying cultural backgrounds. Future studies should incorporate user experience feedback from different countries to enhance the representativeness of user experience data in industrial heritage research.