Promoting the Sustainable Development of Industrial Heritage Tourism Through Digital Intelligence: User Acceptance and Interface Design Research

Abstract

With the deepening integration of cultural heritage preservation and tourism, the application of intelligent and digital technologies in industrial heritage tourism has increasingly become a research focus for promoting cultural sustainability. This study takes the Wuhan Iron and Steel Company industrial heritage in Wuhan, Hubei Province, China, as the research object. Based on the UTAUT2 model, a structural equation model was constructed to examine the factors influencing users’ behavioral intention, with technology anxiety introduced as an additional variable to explore the acceptance mechanism of intelligent and digital tourism platforms for industrial heritage. Through path analysis and mediation effect testing, the study proposes a platform design strategy of “technological adaptation–emotional resonance–behavioral guidance,” develops the platform interface design, and conducts a multidimensional evaluation using the fuzzy comprehensive evaluation method to verify its feasibility. The findings not only extend the applicability of the UTAUT2 model in the field of cultural heritage tourism at the theoretical level, but also provide practical pathways for optimizing user experience and guiding functional iteration of industrial heritage tourism platforms. Moreover, the study offers practical support for the revitalization, digital inheritance, and sustainable preservation of industrial cultural heritage, thereby fostering the integrated development of cultural value, social value, and the tourism industry.

1. Introduction

Against the backdrop of the continuous deepening of global concepts in cultural heritage conservation, industrial heritage—an essential component of modern civilization—has been gradually transforming from functionally abandoned sites into complex spaces with cultural, educational, and tourism value. As a cross-sector product of global industrial heritage conservation and the integration of culture and tourism, industrial heritage tourism not only reconfigures the spatial functions of post-industrial cities but also serves as a vital medium connecting historical memory with modern civilization. It holds strategic significance for supply-side reform in the cultural and tourism industries and contributes to the professional development of niche markets in cultural tourism. The UNESCO Culture|2030 Indicators framework points out that the intelligent and digital transformation of culture and tourism has become a key trend in promoting cultural sustainability. Digital intelligence technologies (DITs) help enhance the accessibility and interactivity of cultural heritage, foster communication and experiential innovation, strengthen industry resilience and social participation, promote the deep integration of culture and tourism, and contribute to the achievement of sustainable development goals [1]. In the process of industrial heritage tourism, DITs enable a more vivid and immersive presentation of the cultural value of industrial heritage, thereby facilitating its effective dissemination and preservation in contemporary society [2].

At present, although a number of scholars have explored the design and application of cultural heritage tourism platforms as well as the role of DITs in empowering cultural heritage development, most studies have focused on the functional or technical aspects of cultural heritage platforms. Relatively few have examined the influencing factors of users’ technology acceptance or addressed the intelligent and digital design and development of industrial heritage tourism platforms. In particular, at the level of applying intelligent technologies, how to better understand and enhance users’ acceptance and intention to use remains a critical bottleneck restricting the promotion and widespread adoption of intelligent and digital platforms for industrial tourism. Therefore, in-depth investigation into the technology acceptance mechanisms of users and interface design strategies within industrial heritage intelligent and digital tourism platforms not only contributes to the theoretical enrichment of technology acceptance research in the cultural heritage tourism domain but also provides practical guidance for the optimization and sustainable development of such platforms.

This study takes the industrial heritage of Wuhan Iron and Steel Company (WISCO) in Hubei, China as its research object and constructs a design framework for an intelligent and digital industrial heritage tourism platform based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. Addressing the limitations of the traditional model in considering emotional experience and individual needs within cultural tourism contexts, the study innovatively introduces the variable of technology anxiety to develop a user acceptance analysis model more aligned with the characteristics of industrial heritage tourism. The model results are then used to guide platform planning, design, and digital intelligence interaction, thereby enhancing the quality of visitor experience and promoting industrial heritage conservation, cultural transmission, and value innovation in the cultural tourism industry. The core research questions of this article are as follows:

• What key factors influence users’ intention to adopt digital intelligence industrial heritage tourism platform in the context of WISCO?
• How does technology anxiety affect users’ acceptance mechanisms within industrial heritage tourism platforms?
• How can the design framework guided by the UTAUT2-based model optimize digital interaction and interface experience, thereby enhancing visitor engagement and promoting industrial heritage conservation and cultural transmission?

The structure of this paper is as follows: Section 2 reviews relevant literature and proposes research hypotheses based on the UTAUT2 model. Section 3 describes the research process. Section 4 presents the research results, including the influence weights of each variable on behavioral intention, and proposes design strategies based on the case of Wuhan, China, completing a design task to verify the feasibility of the proposed approach. Section 5 discusses and summarizes the research findings.

2. Literature Review and Research Hypotheses

2.1. Industrial Heritage Tourism

Industrial heritage tourism has developed on the basis of industrial archaeology, heritage conservation, and cultural tourism, originating from the redevelopment practices of the Ironbridge Gorge in the United Kingdom. Edwards and Coit (1996) [3] categorized its development models into four types—production sites, production processes, means of transportation, and socio-cultural aspects—thereby forming a new thematic tourism system. The rise and development of industrial heritage tourism demonstrate a distinctive pattern of economic growth, as it not only embodies the essence of industrial material culture and regional culture but also records the evolutionary trajectory of urban spaces.

The uniqueness of industrial heritage tourism lies in the three-dimensional coupling of culture, technology, and space [4]. At the level of cultural narrative, industrial heritage embodies both technological rationality and historical sensibility. It reproduces the technological aesthetics of industrial civilization through physical elements such as factories and machinery, as well as dynamic technological processes [5]. This distinguishes it from traditional heritage types such as religious architecture and imperial gardens. From a technological perspective, the intervention of digital technologies further lowers the threshold of knowledge acquisition and enhances visitors’ embodied cognition. This interaction of virtual and real elements differs from the sensory experiences of natural scenic areas. It restores industrial production scenes, historical changes, and cultural memories, thereby presenting distinctive cultural depth and experiential characteristics. At the level of spatial regeneration, industrial heritage not only preserves its original value during urban renewal but also gains new functions. Through structural transformation and multidimensional value coordination, it promotes the integrated development of history, culture, and economy in degraded areas [6,7]. Meanwhile, in its development process, industrial heritage tourism activates heritage value through diversified pathways. It has given rise to models such as museums, heritage parks, cultural and creative industrial zones, and comprehensive developments [8,9,10], By means such as digitally reproducing industrial processes, ecologically transforming blast furnaces, and embedding cultural industries, it reconstructs functions and integrates tourism, commerce, and community models. In doing so, it balances conservation and development, while demonstrating the multidimensional values of technological empowerment and social recognition [11,12].

2.2. Intelligent and Digital Technologies Empowering Cultural Heritage Tourism

DITs refer to the integration of digitalization and intelligentization, encompassing emerging technologies such as big data, artificial intelligence, blockchain, and cloud computing. Their application provides strong support for the full life-cycle management and sustainable development of cultural heritage [13,14,15]. From 2020 to 2023, affected by the COVID-19 pandemic and other factors, the global cultural tourism industry experienced a downturn. Tourists’ risk perception suppressed their willingness to travel, and the tourism industry suffered severe setbacks [16,17,18]. Against this backdrop, tourism activities based on digital technologies have emerged as an important alternative to overcome such obstacles [19,20]. The deep integration of DITs with cultural tourism has created a new ecosystem for optimizing the tourism experience [15] and has driven the digital transformation of cultural tourism. This not only aligns with the development direction of modern industry but also injects new momentum into the sustainable development of heritage tourism [21]. By breaking through the spatiotemporal limitations of traditional cultural tourism, virtual tours enable heritage education, storytelling, and preservation, thereby enhancing heritage accessibility. By integrating multiple DITs, multidimensional digital spaces can be created to activate cultural heritage through immersive experiences. Sustainable technological pathways further deepen visitor participation, foster understanding and recognition, and ultimately form a digital intelligence tourism ecosystem characterized by immersion, interactivity, and cultural value [22,23].

The application of DITs in the field of cultural heritage tourism demonstrates dual empowerment in content digital reconstruction and tourism digital management. On one hand, through technologies such as virtual reality, augmented reality, 3D modeling, and big data analysis, it not only enriches the ways and channels for presenting and disseminating cultural heritage but also optimizes visitor engagement and personalized service experiences, enhancing both the immersion and the depth of cultural cognition as well as the accessibility of digital resources [24,25,26]. On the other hand, DITs reconstructed the tourism ecosystem through media innovation, social network expansion, and immersive content presentation, driving the tourism industry toward a 4.0 paradigm [27,28]. It promoted the refinement and scientific upgrading of cultural heritage tourism management. By collecting and analyzing visitor behavior data [29], DITs enable precise visitor profiling, zoned management, traffic regulation, and public opinion monitoring, effectively alleviating issues related to limited resource carrying capacity and excessive commercialization.

In the process of DITs empowering cultural heritage tourism, virtual reality (VR) and augmented reality (AR) technologies exhibit the highest visitor acceptance. The global market size for these technologies is expected to increase from USD 12.3 billion in 2023 to USD 26.0 billion by 203 [30]. By providing immersive and interactive experiences, DITs enables visitors to transcend physical limitations and explore historical and archaeological sites, enhancing both visitor experience and understanding of cultural heritage [31,32]. At present, although the application of DITs such as AI, AR, and VR requires substantial budgets and professional maintenance, their multi-purpose functionality significantly promotes regional economic and cultural development.

Performance expectancy (PE), as a core variable of the UTAUT2 model, refers to individuals’ anticipation that a technology will improve efficiency. The application of DITs provides tourists with more comprehensive information about attractions, a more immersive guided experience, and more efficient interaction and services. This further enhances tourists’ satisfaction and the overall value of their travel experience, strengthening users’ perceived value of the platform’s functions. Effort expectancy (EE) is defined as the level of ease associated with using a system, encompassing the degree of physical and cognitive effort required from individuals to operate it [33]. In cultural tourism, perceived effort reflects the user-friendliness of the system, including the ease of content access and end-users’ understanding of its technical features [33]. Studies by Nathan and Cheunkamon have demonstrated that effort expectancy has a significant impact on behavioral intention across various tourism contexts and settings [34,35]. Finally, facilitating conditions are defined as individuals’ subjective perception of the ease of using a technology or system. The infrastructure of the digital intelligence industrial heritage tourism platform—such as networks, devices, system compatibility, and the auxiliary services provided by the platform—jointly determines whether users can obtain timely and effective support when encountering difficulties. When tourists perceive these conditions as complete and reliable, their trust in and willingness to use the platform increase significantly, leading to a more positive overall evaluation [36,37]. Therefore, this study proposes the following hypothesis:

H1. 

Performance expectancy significantly influences tourists’ intention to use digital intelligence industrial heritage tourism platforms.

H2. 

Effort expectancy significantly influences tourists’ intention to use digital intelligence industrial heritage tourism platforms.

H3. 

Facilitating conditions significantly affect tourists’ intention to use digital intelligence industrial heritage tourism platforms.

2.3. Application of the UTAUT2 Model in the Field of Cultural Heritage

While DITs continue to enrich the forms of cultural heritage tourism, users’ acceptance and usage behaviors of DITs in industrial heritage tourism have drawn increasing attention. The UTAUT2 model, proposed by Venkatesh et al. as an extension of the original UTAUT model, aims to provide a more comprehensive explanation of individuals’ acceptance and use of technology in consumer contexts [33]. The UTAUT2 model introduces factors such as hedonic motivation, price value, and habit, emphasizing the importance of emotional drive, economic evaluation, and automated behavior in voluntary usage contexts, making it applicable to fields such as smart tourism. Therefore, applying the UTAUT2 theoretical model to industrial heritage tourism platforms is feasible. This approach can clarify and predict usage intentions and potential behaviors, providing a flexible framework for research that can identify new behavioral patterns in different contexts [38,39,40]. In this study, UTAUT2 is selected as the core framework, with the addition of the variable of technology anxiety to capture the factors influencing tourists’ adoption of the intelligent and digital platform for WISCO industrial heritage tourism. The study aims to reveal the factors affecting platform usage intention, with a focus on expected behaviors rather than post-experience evaluations.

In recent years, with the deep penetration of intelligent and digital technologies such as VR and AR into the tourism industry, research on technology acceptance has gradually become a hot topic. Existing studies mostly focus on prototype development and practical validation of technologies, while the theoretical explanation of user behavioral decision-making mechanisms remains insufficient. Scholars have introduced classical theoretical frameworks such as the Theory of Reasoned Action (TRA) [41], the Theory of Planned Behavior (TPB) [42], the Technology Acceptance Model (TAM) [43], and the Unified Theory of Acceptance and Use of Technology (UTAUT) [44] to systematically explore the factors influencing user behavioral intention in virtual tourism contexts. Mosbeh, based on the UTAUT2 model, verified the implementation of virtual reality technology in tourism and identified performance expectancy, social influence, and facilitating conditions as key drivers [30]. SUN et al. integrated an extended UTAUT model, the TTF model, and the PATS (Pandemic Anxiety Travel Scale) to reveal tourists’ usage intention and actual behavior toward digital museums [45]. Li et al., drawing on the UTAUT model, examined the social interaction attributes of mixed reality technology and highlighted the critical role of social influence in enhancing device usage intention [46]. Huang et al., combining the Technology Acceptance Model (TAM) with hedonic theory, explored the impact of 3D virtual worlds on tourism experience. They argued that immersive interaction can strengthen tourists’ behavioral intention, but it requires balancing emotional engagement, entertainment value, and technological ease of use [47]. However, Li and Chen highlighted the dual-edged effect of VR on tourism intention: when the anticipated enjoyment of a destination is low, a high level of perceived VR enjoyment may suppress the actual intention to visit. This suggests the need to balance virtual experiences with the authentic attractiveness of destinations [48].

Social influence (SI) refers to the extent to which individuals are affected by their social environment and surrounding people when adopting new technologies or systems [33]. In the Internet era, recommendations of online tourism applications by social media and opinion leaders (such as internet influencers) can significantly enhance tourists’ intention [49,50]. Currently, the construction of digital intelligence tourism in China is still at an early stage. However, under the Digital China strategy, forms such as cloud tourism and virtual exhibitions are rapidly developing and are expected to become major tourism modes in the future. Moreover, China’s digital intelligence cultural tourism projects are accelerating the integration of gamification design, further strengthening their hedonic attributes. When users perceive that technology applications can provide stronger entertainment and emotional value, their intention to use them usually increases significantly [30]. Therefore, the following hypotheses are proposed:

H4. 

Social influence significantly affects tourists’ intention to use digital intelligence industrial heritage tourism platforms.

H5. 

Hedonic Motivation significantly affects tourists’ intention to use digital intelligence industrial heritage tourism platforms.

Currently, most studies remain at the level of validating the original variables of the model, with limited attention to personalized psychological factors in specific tourism contexts. For example, cultural heritage tourism contains abundant historical narratives and emotional value, yet factors such as visitors’ emotional experiences and technological maladaptation are difficult to fully explain using the original model. Moreover, industrial heritage tourism platforms possess strong professional and symbolic characteristics. Users often experience cognitive load or resistance when facing complex information architectures and emerging interaction methods, posing new challenges for model applicability. To address the limitations of traditional models in cultural heritage tourism contexts, some studies have advanced the UTAUT2 model through theoretical extensions and localization adjustments. For instance, Marto et al. first proposed the Augmented Reality Acceptance Model (ARAM), incorporating trust expectancy and technological innovation variables into UTAUT, and confirmed the direct effects of performance expectancy, facilitating conditions, and hedonic motivation on behavioral intention [51]. Hanji et al. [52] further extended the UTAUT2 model by introducing personal innovativeness and perceived risk dimensions, revealing the complex mechanisms underlying AR technology acceptance in cultural heritage tourism. Technology anxiety (TA) refers to users’ concerns about the operational difficulty, technical reliability, and privacy risks of the digital intelligence platform [53]. Technology anxiety can reduce users’—especially older adults’—willingness to adopt emerging technologies [54]. For example, elderly tourists may doubt whether the platform can effectively enhance their tourism experience if they worry about mastering virtual operation processes, ultimately decreasing their intention to use it. Therefore, the following research hypothesis is proposed:

H6. 

Technology anxiety significantly affects tourists’ intention to use digital intelligence industrial heritage tourism platforms.

2.4. The Mediating Role of Performance Expectation

Within the UTAUT2 framework, performance expectation not only serves as a key antecedent of user behavioral intention but may also play a mediating role between other variables and usage intention [55]. For the digital intelligence industrial heritage tourism platform, performance expectation reflects users’ overall anticipated level of improvement in travel experience, information acquisition efficiency, and interaction perception. Existing studies indicate that variables such as technical anxiety and effort expectation may not only directly affect user intention but also indirectly influence it through performance expectation, thereby providing a more comprehensive explanation of user acceptance and usage behavior of digital platforms. Based on this rationale, the following hypotheses are proposed:

H7. 

Technical anxiety significantly affects visitors’ performance expectation of the digital intelligence industrial heritage tourism platform.

H8. 

Effort expectation significantly affects visitors’ performance expectation of the digital intelligence industrial heritage tourism platform.

In summary, this study integrated selected UTAUT2 variables with the technical anxiety variable to construct an extended UTAUT2 model identifying factors influencing users’ intention to use the digital intelligence industrial heritage tourism platform. The model aims to explore the relationship between technology acceptance and usage intention. Based on this theoretical framework, the relevant research hypotheses were proposed, as shown in Figure 1.

3. Research Methodology

3.1. Research Procedure

Based on the extended UTAUT2 model, eight related research hypotheses were proposed. A total of 293 valid user responses were collected, and the structural equation modeling (SEM) was used to test the hypotheses. This process helped identify the key factors influencing users’ intention to use the platform and clarified the relationships and commonalities among these factors. The resulting findings were then used to develop corresponding design strategies to guide design practice. The research methodology is illustrated in Figure 2.

3.2. Questionnaire Design and Data Collection

The questionnaire consisted of two parts. The first part collected demographic information such as gender, age, income level, and respondents’ familiarity with industrial heritage tourism. These data were used to describe the basic characteristics of the sample. The second part focused on seven variables—performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, immersive experience, and technology anxiety—with a total of 24 items. To enhance contextual relevance, the items were designed based on the specific context of the WISCO Industrial Heritage Park and the characteristics of digital intelligence technologies, and core dimensions were localized and refined from established scales to ensure respondents’ accurate understanding. All items were measured using a five-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”) to assess respondents’ perceptions of the various factors. The detailed scale items are shown in

Table 1. Scale Items and Design Basis.

Variable	Measure Item	Source
Performance Expectancy (PE)	PE1: I believe the intelligent and digital industrial heritage tourism platform will help me acquire knowledge about industrial heritage.	[33,44]
	PE2: I believe using the intelligent and digital industrial heritage tourism platform will make it easier for me to achieve my visiting goals.
	PE3: I believe using the intelligent and digital industrial heritage tourism platform will improve my tourism efficiency.
Effort Expectancy (EE)	EE1: Based on the platform’s feature descriptions, I feel that using this platform will not be difficult.	[33,56,57]
	EE2: I believe I can quickly learn the main operations of this platform.
	EE3: I am confident that I can understand the platform’s usage process and interface design.
Social Influence (SI)	SI1: If people around me use such platforms, I will be more interested in trying them.	[33,56,58]
	SI2: People around me would recommend such intelligent and digital tourism platforms to me.
	SI3: People around me think I should use the intelligent and digital industrial heritage tourism platform for online tourism.
Facilitating Conditions (FC)	FC1: I believe I have the basic knowledge or learning ability required to use this platform.	[33,56]
	FC2: I think using this platform will not conflict with my existing devices or habits.
	FC3: I believe I have the equipment and internet access needed to use this platform.
	FC4: I am confident that if I encounter problems, I can get help or support from the platform or others.
Hedonic Motivation (HM)	HM1: I think the content of the intelligent and digital industrial heritage tourism platform will be interesting.	[33,59]
	HM2: I believe using the intelligent and digital industrial heritage tourism platform will be an enjoyable experience.
	HM3: I feel that using the intelligent and digital industrial heritage tourism platform will give me a sense of achievement.
	HM4: I think using the intelligent and digital industrial heritage tourism platform will make me feel immersed and relaxed.
Technology Anxiety (TA)	TA1: I am concerned that using the platform’s new technological features (such as VR and AR) may be too complex and affect my visiting experience.	[53,60]
	TA2: I feel worried about privacy and security if the platform requires access to my location or camera.
	TA3: I am concerned that technical problems during the use of the platform may negatively affect my visiting experience.
	TA4: I am worried that I may not be able to solve technical problems encountered while using the platform on my own.
Behavioral Intention (BI)	BI1: I look forward to using the intelligent and digital industrial heritage tourism platform.	[33]
	BI2: I expect to use this platform over the long term.
	BI3: I am willing to recommend others to use the industrial heritage tourism platform.

.

This study adopted a mixed approach to data collection. Online questionnaires were distributed through Wenjuanxing and related social media platforms by sharing survey links and QR codes. Offline data collection was conducted at the entrances, visitor centers, and exhibition areas of the Wuhan WISCO Industrial Heritage Park, where tourists engaged in industrial heritage tourism were invited to complete the questionnaire. During the distribution process, the research team informed respondents of the study’s objectives and anonymity policy, and introduced the background and meaning of intelligent and digital tourism to ensure informed consent and data reliability. Data were collected from March to May 2025. Prior to data entry, the research team conducted logical checks and removed missing and abnormal values to ensure data quality. A total of 341 questionnaires were collected, with 293 valid responses retained after screening, resulting in an effective response rate of 85.9%.

Table 2. Summary of Questionnaire Collection and Valid Responses.

Channel	Collected	Valid	Effective Response Rate
Online	130	118	90.7%
On-site	211	175	82.9%
Total	341	293	85.9%

presents the number of questionnaires collected through each distribution channel.

3.3. Descriptive Statistical Analysis

This study collected a total of 293 valid questionnaires, of which 49% were male and 51% female, indicating a relatively balanced gender distribution. In terms of age, the 19–30 age group accounted for the largest proportion (42%), followed by the 31–40 group (19%) anda the 41–50 group (15%), demonstrating a clear trend toward younger participants. Regarding educational background, the sample was mainly composed of individuals with a bachelor’s degree (31%) and those with a graduate degree or above (20%), indicating a relatively high overall educational level. In terms of tourism experience and attitudes, 50.17% of respondents had previously participated in industrial heritage tourism, and 80.20% expressed willingness to adopt new technologies. As for the main motivations for participating in industrial heritage tourism, learning about history (29.35%), taking photos and checking in (25.94%), parent–child education (21.16%), and interest-driven exploration (20.82%) were the primary drivers. Overall, the study sample was characterized by a young and highly educated demographic, demonstrating strong enthusiasm for tourism experiences and new technology adoption. The detailed results of the descriptive statistical analysis of the sample are shown in

Table 3. Demographic Descriptive Analysis.

Indicator	Content	Frequency	Percentage	Indicator	Content	Frequency	Percentage
Gender	Male	143	49%	Education Level	Junior high school or below	27	9%
	Female	150	51%		High school/Vocational school	69	24%
Age	Under 18 years old	23	8%		Associate degree	47	16%
	19~30	122	42%		Bachelor’s degree	92	31%
	31~40	57	19%		Master’s degree or above	58	20%
	41~50	45	15%	Occupation	Student	80	27%
	51~60	31	11%		Education/Researcher	39	13%
	60 years old and above	15	5%		Corporate Employee	57	19%
Income	0–1500 RMB	60	20%		Technician	44	15%
	1501–3000 RMB	51	17%		Freelancer	39	13%
	3001–5000 RMB	74	25%	Have you participated in industrial heritage tourism?	Yes	147	50.17%
	5001–7000 RMB	68	23%		No	146	49.83%
	7000 RMB and above	40	14%	Are you willing to try new technologies?	Yes	235	80.20%
	other	34	12%		No	58	19.80%
Main Motivation for Industrial Heritage Tourism Participation	Learning History	86	29.35%	Main Motivation for Industrial Heritage Tourism Participation	Taking Photos and “Check-ins”	76	25.94%
	Parent–Child Education	62	21.16%		Other	8	2.73%
	Interest Exploration	61	20.82%

.

3.4. Reliability and Validity Tests

The reliability and validity of the overall scale and each dimension were tested. The results show that the Kaiser-Meyer-Olkin (KMO) value is 0.948, which is greater than 0.8, indicating that the sample size is sufficient. The significance level is less than 0.05, suggesting significant differences with statistical meaning. The Cronbach’s alpha coefficients of all dimensions are greater than 0.8, indicating internal consistency. The detailed results are presented in

Table 4. KMO and Bartlett’s Test.

KMO Measure of Sampling Adequacy		0.948
Bartlett’s Test of Sphericity	Approx. Chi-Square	7052.318
	df	276
	p-value	0.000

and

Table 5. Variable Reliability Analysis and Convergent Validity.

Construct	Number of Items	Cronbach’ α	AVE	CR
PE	3	0.934	0.828	0.935
EE	3	0.928	0.812	0.928
SI	3	0.892	0.739	0.894
FC	4	0.912	0.723	0.912
HM	4	0.936	0.788	0.937
TA	4	0.899	0.694	0.901
BI	3	0.910	0.776	0.912

.

4. Research Results

4.1. Correlation Analysis

The correlations among the variables were analyzed using the Pearson correlation coefficient. As shown in

Table 6. Correlation among Variables.

Variable	PE	EE	SI	FC	HM	TA	BI
PE
EE	0.750 **
SI	0.743 **	0.667 **
FC	0.705 **	0.739 **	0.663 **
HM	0.760 **	0.703 **	0.728 **	0.725 **
TA	0.439 **	0.458 **	0.250 **	0.281 **	0.348 **
BI	0.753 **	0.744 **	0.750 **	0.730 **	0.790 **	0.425 **

** Correlation is significant at the 0.01 level (2-tailed).

, significant correlations exist among all variables. The correlation coefficients are all greater than 0, indicating that the variables in this analysis are significantly and positively correlated.

4.2. Discriminant Validity Among Variables

Convergent validity and discriminant validity were tested using AMOS 24.0. The standardized loadings of all latent variables are greater than the threshold of 0.5, and the composite reliability (C.R.) values are all above 0.8. The average variance extracted (AVE) values also exceed the threshold of 0.5, indicating high internal consistency of the latent variables, i.e., strong convergence, as shown in Table 5. The results in

Table 7. Discriminant Validity Between Variables.

Variable	1PE	2EE	3SI	4FC	5HM	6TA	7BI
1PE	0.910
2EE	0.807	0.901
3SI	0.799	0.730	0.859
4FC	0.764	0.799	0.730	0.850
5HM	0.813	0.752	0.79	0.785	0.887
6TA	0.468	0.493	0.27	0.300	0.377	0.833
7BI	0.812	0.813	0.825	0.798	0.860	0.473	0.881

Note: numbers in bold = square root of AVE.

further show that the diagonal values are greater than the corresponding off-diagonal values, demonstrating good discriminant validity.

4.3. Structural Equation Model

The study employed AMOS 24.0 to conduct SEM analysis in order to verify the applicability of the extended UTAUT2 model in the design of the digital intelligence industrial heritage tourism platform. The model fit indices all meet the evaluation criteria, as shown in

Table 8. Structural Equation Model Fit Indices.

Fitness Index	Criteria for Judgment	Metric	Fitting Situation
CMIN/DF	<3	2.694	Ideal
RMSEA	<0.05 (Ideal)/<0.08 (Acceptable)	0.076	Acceptable
NFI	>0.9	0.914	Ideal
IFI	>0.9	0.944	Ideal
TLI	>0.9	0.933	Ideal
CFI	>0.9	0.944	Ideal

[61], indicating that the model can effectively explain the data.

The path analysis results of the structural equation model are shown in

Table 9. Path coefficients and hypotheses testing.

Hypotheses	Estimate	S.E.	C.R.	β	p Value	Result
H1. PE→BI	−0.013	0.059	−0.216	−0.015	0.829	Not supported
H2. EE→BI	0.154	0.095	1.624	0.182	0.104	Not supported
H3. FC→BI	0.15	0.068	2.215	0.16	0.027	Supported
H4. SI→BI	0.224	0.059	3.792	0.252	***	Supported
H5. HM→BI	0.333	0.064	5.211	0.365	***	Supported
H6. TA→BI	0.131	0.035	3.759	0.156	***	Supported
H7. TA→PE	0.047	0.045	1.054	0.048	0.292	Not supported
H8. EE→PE	0.813	0.053	15.444	0.828	***	Supported

Note: *** p < 0.001.

and Figure 3.

Social influence, facilitating conditions, hedonic motivation, and technology anxiety had significant positive effects on the behavioral intention to use the digital intelligence industrial heritage tourism platform. Specifically, social influence had a significant impact on behavioral intention, indicating that the social environment positively affects users’ intention to use the tourism platform. Additionally, facilitating conditions positively affected behavioral intention, suggesting that the platform and its supporting infrastructure promoted users’ adoption intention. The relationship between hedonic motivation and behavioral intention was also significant, showing that the entertainment and pleasure derived from the digital intelligence industrial heritage tourism platform strongly drive users’ intention to use it. Meanwhile, technology anxiety significantly affects behavioral intention, indicating that users’ concerns about technology influenced their platform acceptance to some extent. These results demonstrated that, in the context of industrial heritage tourism, users’ behavioral intention was more strongly drove by social environment, platform support, entertainment and immersive experience, and technology anxiety—rather than by functional efficiency or ease of use emphasized in the traditional UTAUT2 model. This highlighted the crucial role of non-functional and emotional factors in users’ technology adoption behavior in cultural tourism contexts.

However, the path between performance expectancy and behavioral intention was not significant, indicating that performance expectancy had a limited effect on users’ intention to use the platform. In the context of the digital intelligence industrial heritage tourism platform, users do not regard functional performance or efficiency improvements as the primary determinant of their usage intention. Industrial heritage tourism emphasizes culture, education, and experience, with users focusing more on immersion, interactivity, and the authenticity of cultural narratives rather than on information retrieval or functional efficiency. Similarly, the path between technology anxiety and performance expectancy was not significant, suggesting that users’ technology anxiety has a weak impact on their perception of platform effectiveness. On one hand, the user group may possess certain digital skills or familiarity with intelligent platform operations, mitigating the negative effect of technology anxiety on performance expectancy. On the other hand, the platform’s interface design and operational flow are intuitive, effectively reducing uncertainty during use.

Moreover, effort expectancy did not significantly affect BI. This may be related to the sample characteristics and research context. The study primarily involved young users aged 20–30, who exhibit high acceptance of digital technologies and strong operational skills, resulting in low sensitivity to platform ease of use. Additionally, compared with other digital platforms, the industrial heritage tourism platform had fewer and more straightforward functions, so users perceive minimal learning costs and operational difficulty, reducing the influence of effort expectancy on usage intention.

4.4. Mediation Effect

To gain a deeper understanding of the transmission mechanisms among the model variables and to explore the key role of PE, the study further conducted a mediation analysis. Using SPSS 27.0, the Bootstrap method was employed to test the mediating effect of PE on the impact of EE and TA on BI. The results of the mediation analysis are presented in

Table 10. Mediation Effect Test Results.

Path Relationships		Effect Size	SE	95% Confidence Interval		p-Value
				LLCL	ULCL
TA→PE→BI	Total Effect	0.2873	0.0359	0.2166	0.3579	0.000
	Direct Effect	0.0787	0.0287	0.0065	0.0222	0.007
	Indirect Effect	0.2086	0.0303	0.1495	0.2677	0.000
EE→PE→BI	Total Effect	0.7024	0.037	0.6295	0.7752	0.000
	Direct Effect	0.3862	0.0503	0.2871	0.4853	0.000
	Indirect Effect	0.3162	0.0481	0.1495	0.2677	0.000

.

As shown in Table 10 and Figure 4, technology anxiety had a significant total effect on behavioral intention (β = 0.2873, p < 0.001). Its indirect effect via performance expectancy was also significant (β = 0.2086, p < 0.001), and the direct effect reached significance as well (β = 0.0787, p < 0.01). This indicates that performance expectancy partially mediates the relationship between technology anxiety and behavioral intention, meaning that users’ technology anxiety not only directly influences their usage intention but also affects it indirectly by altering their performance expectancy.

As illustrated in Table 10 and Figure 5, Effort expectancy exhibited a significant total effect on behavioral intention (β = 0.7024, p < 0.001). Its indirect effect through PE was significant (β = 0.3162, p < 0.001), and the direct effect was also significant (β = 0.3862, p < 0.001). This suggests that performance expectancy also partially mediates the relationship between effort expectancy and behavioral intention, indicating that when users perceive higher effort expectancy, it not only directly affects their usage intention but also indirectly strengthens it by enhancing performance expectancy.

4.5. Design Strategies for the WISCO Industrial Heritage Tourism Platform Based on the UTAUT2 Model

The structural equation modeling results based on the extended UTAUT2 model indicate that HM, FC, SI, and TA are the main factors affecting users’ intention to use the digital intelligence industrial heritage tourism platform. Meanwhile, PE as a mediating variable, influences the relationship between EE, TA, and BI. This study proposes that the design of the digital intelligence industrial heritage tourism platform should follow a three-dimensional principle: “technological adaptation—emotional resonance—behavioral guidance”.

4.5.1. Technological Adaptation: Enhancing Platform Usability and Functionality

The results indicate that effort EE not only has a significant direct effect on BI but also plays a key mediating role through PE. This suggests that users’ perception of ease of use during platform interaction further translates into an evaluation of usefulness, thereby enhancing their intention to use the platform. Therefore, platform design should emphasize technological adaptation, aiming to reduce operational complexity while strengthening the effectiveness of functions and services [62,63].

4.5.2. Emotional Resonance: Enhancing Users’ Emotional Identification and Cultural Experience

HM, FC, and SI have significant positive effects on BI. This indicates that the design of the digital intelligence industrial heritage tourism platform should pay close attention to users’ emotional drives and social recognition mechanisms. At the practical strategy level, the platform should focus on enhancing users’ sense of immersion and situational engagement by creating interface elements and immersive interactive scenarios that evoke emotional resonance [64], This approach can increase the platform’s enjoyment and interactivity, effectively stimulating users’ pleasure and cultural identification during use [65].

4.5.3. Behavioral Guidance: Promoting Continuous Engagement and Behavioral Conversion

TA significantly affects users’ behavioral intention through PE, indicating that the rational application of technology can effectively promote behavioral conversion. In the design of digital intelligence industrial heritage tourism platforms, clear user behavior goals should be established. Optimizing information architecture and providing a simple, efficient navigation interface can guide users to quickly access content of interest, thereby enhancing platform usability and task completion efficiency. Additionally, Tan et al. [28] note significant differences across demographic groups in how digital technologies influence tourism intentions. Considering this, complex underlying technical logic should be translated into intuitive and user-friendly interactive interfaces. By reducing the cognitive burden, the inhibitory effect of technology anxiety on usage behavior can be minimized, while users’ willingness and confidence to explore advanced features are enhanced

4.6. Design Object

WISCO as the first large-scale steel conglomerate in New China, is an important symbol of China’s industrialization. Since its commissioning in 1955, it has made significant contributions to China’s economic development. With industrial upgrading and urban renewal, some plant areas have ceased production and transformed into industrial heritage with preservation and reuse value. The industrial cultural heritage of WISCO encompasses material culture, behavioral culture, and industrial spirit culture, including steelmaking and ironmaking equipment, industrial products, related buildings, significant events and processes, and the “Red Steel Spirit,” as shown in

Table 11. Specific Content of WISCO Industrial Cultural Heritage.

Cultural Level	Type	Content	Representative Image
Industrial Material Culture	Production Equipment	WISCO’s 1.7 m steel rolling furnace, hot rolling mill, cold rolling mill, dust removal equipment, power generation equipment, etc.	Hot Rolling Mill
	Industrial Products	Iron ore, steel structure products, pipe products, industrial wire rods, construction structural steel, hot-rolled coils, cold-rolled coils, power equipment materials, etc.	Industrial Wire Material
	Workers’ Tools and Supplies	WISCO workers’ safety helmets, shovels, commuter trams, soda coupons, meal tickets, various badges and medals, etc.	WISCO “Salty Mate” Soda
	Architectural Relics	No. 1 Blast Furnace, hot rolling plant, cold rolling plant, office buildings, workers’ dormitories, raw material warehouses, WISCO Qingshan Red Houses, etc.	WISCO No. 1 Blast Furnace Site
Industrial Behavioral Culture	Industrial Events	1958: Mao Zedong observed the pouring of the first batch of molten iron. 1972: Introduction of the 1.7 m rolling mill. 1997: WISCO was awarded the title of “National Quality and Efficiency-Oriented Advanced Enterprise.”	WISCO 1.7-Meter Rolling Mill Workshop
	Technological Processes	1.7-m steel rolling technology, ore mining, blast furnace smelting, molten iron treatment, etc.	Molten Iron Tapping
	Worker Activities	Workers’ spirit and demeanor, workplace systems, cultural and artistic activities, commendation ceremonies, celebratory events, transportation and commuting, etc.	China’s First Female Blast Furnace Welder
Industrial Spirit and Culture	Industrial Spirit	“Hardworking and Enduring Spirit”, “Three-Competitions Spirit”, “Learning-Application-Innovation Spirit”, “Red Steel Spirit”	WISCO Worker Statue

. WISCO’s industrial heritage features distinctive spatial structures, architectural styles, and technological systems. The overall layout preserves the Soviet industrial planning model, and elements such as blast furnaces, cooling towers, pipe galleries, and workshops reflect unique industrial aesthetics and the development trajectory of metallurgical technology, holding significant exhibition and educational value.

In recent years, WISCO has actively explored new paths for cultural-tourism integration through projects such as the “WISCO Cultural Park,” “WISCO Memory·189,” and the “Red Steel City Cultural and Creative District” (as illustrated in Figure 6) These initiatives create cultural spaces that integrate exhibitions, guided tours, performances, and cultural-creative experiences, establishing a tourism system centered on industrial heritage to enhance regional cultural value and public engagement. Through tourism-oriented transformation and digital renovation, WISCO’s heritage is becoming an important force in promoting urban renewal, stimulating cultural consumption, and fostering social innovation.

4.7. Platform Functional Architecture Design

The overall architecture of the digital intelligence industrial heritage tourism platform is constructed across four layers, as shown in Figure 7. The foundational support layer provides the basis for the platform’s sustainable operation. The technical support layer leverages emerging technologies to ensure interactivity, convenience, and immersion. The functional architecture layer focuses on dual objectives of visitor experience and heritage preservation, integrating core modules such as immersive navigation, interactive exhibitions, intelligent services, and cultural dissemination. Finally, various stakeholders form a diversified and collaborative governance and operational framework, enhancing the platform’s attractiveness and user engagement, and promoting the digital intelligence transformation of industrial heritage tourism and the dissemination of industrial culture.

4.7.1. Functional Modules

The platform’s functional design uses WISCO industrial heritage as the carrier and leverages digital technologies to realize the exhibition, preservation, and dissemination of industrial heritage, thereby enhancing visitors’ cultural experience and engagement. The construction of the platform’s functional modules is shown in Figure 8. First, the platform establishes a comprehensive industrial heritage information database, fully presenting WISCO’s historical background, steel production processes, architectural features, and its historical significance in China’s modernization. At the same time, multimedia tools are integrated to enhance information delivery, providing a comprehensive presentation of the heritage’s value and helping visitors intuitively understand and perceive WISCO’s historical and cultural significance.

Next, an intelligent navigation system is designed using 3R technologies (VR, AR, MR) to provide visitors with an immersive interactive experience. Through DITs, WISCO’s historical scenes and production processes are recreated, allowing visitors to experience the factory’s working environment and operations firsthand, thereby deepening their emotional identification with and understanding of the cultural value of the industrial heritage. In addition, the platform includes interactive experience modules to enhance participation and enjoyment, further stimulating visitors’ interest in and curiosity about the industrial heritage.

Personalized services constitute another key function of the platform. On one hand, based on user behavior data and preference analysis, the platform can provide tailored tour routes, attraction recommendations, and customized services, better meeting visitors’ specific cultural needs and tourism expectations. On the other hand, the platform can integrate social interaction features to create a UGC community, offering channels for visitor communication and enhancing the platform’s personalized social attributes.

Finally, to promote academic research and knowledge dissemination of WISCO’s industrial heritage, the platform will involve heritage conservation experts, scholars, and historical witnesses from WISCO to conduct online lectures, knowledge sharing, and interactive discussions, thereby advancing the education and preservation of industrial heritage.

4.7.2. Technology Integration

Based on the practical application needs of the digital intelligence industrial heritage tourism platform, a targeted technical architecture system is proposed (see Figure 9). The platform’s technical architecture consists of five components: the perception layer, network transmission layer, data management layer, intelligent application support layer, and security and operation-maintenance layer.

First, the perception layer deploys RFID, IoT sensors, real-time positioning systems, and drone-based image collection to comprehensively capture heritage spaces, visitor behaviors, and environmental dynamics, providing the data foundation for subsequent applications. Second, the network transmission layer, supported by 5G communication, edge computing, and IoT protocols, ensures high-speed and stable transmission of large-scale data, reduces latency during platform usage, and enhances the fluency of interactions. Third, the data management layer relies on cloud storage, big data processing, and data integration technologies to achieve the consolidation and standardized processing of heritage information, user behavior data, and environmental data, thereby supporting multi-terminal applications [66]. Fourth, the intelligent application support layer integrates AI engines, natural language processing, augmented/virtual reality technologies, and intelligent recommendation algorithms to develop functions such as personalized navigation, immersive experiences, and intelligent Q&A, thereby enhancing visitors’ emotional identification and motivation for participation. Finally, the security and operation-maintenance layer employes multi-level protection mechanisms, privacy protection strategies, and intelligent monitoring systems to ensure high standards of data security, system stability, and user privacy.

This technical architecture is closely aligned with the characteristics of industrial heritage tourism scenarios. It highlights the role of DITs in enhancing visitor experience and promoting the revitalization of heritage. By strengthening user technology acceptance and ensuring platform sustainability, it provides a solid foundation for improving visitor access efficiency [67], The integrated application of DITs will effectively enhance the platform’s interactivity, intelligence, and immersion, providing strong technological support for the sustainable development of industrial heritage tourism.

To systematically present the overall service process and support structure of the WISCO digital intelligence industrial heritage tourism platform, this study develops a service blueprint of the platform (as shown in Figure 10). Centered on the user journey, it is divided into six service stages: information inquiry, online booking, on-site visit, immersive experience, feedback and evaluation, and sharing and dissemination. The blueprint illustrates the mapping relationships among user behaviors, front-end touchpoints, back-end support, and technical systems.

4.8. Platform Interface Design Based on the UTAUT2 Model

Building on the previous discussion of the functions and technical architecture of the digital intelligence industrial heritage tourism platform, the platform’s specific performance is embodied in its interface design. Interface design serves as the core of functional implementation and user interaction, directly influencing both user experience and effectiveness. The design should adhere to the principles of human–computer interaction, user experience, and information presentation, while integrating immersive experiences, personalized recommendations, and social interaction to ensure accurate communication of heritage information and convenient interactive operations.

The interface design focuses on the dual-end architecture of the digital intelligence industrial heritage tourism platform, including the B-end management system and the C-end mobile app. From the perspectives of functionality and user experience, a design framework centered on “information presentation, interactive experience, personalized recommendation, social connection, and knowledge dissemination” is established.

In the B-end interface (see Figure 11, Figure 12 and Figure 13), emphasis is placed on data visualization management, content maintenance convenience, and intelligent analytics to enhance platform operational efficiency. In the app interface (see Figure 14), multimedia presentation, immersive navigation, intelligent recommendations, and social interaction functions are integrated to increase user engagement and cultural identification. The interface design closely aligns with the user technology acceptance model and emotional factors, optimizing usage pathways, enhancing ease of use and trust, and providing unified, efficient, and intelligent interactive support for the platform.

4.9. Design Validation

As the core of user experience, the interface design of the digital intelligence industrial heritage tourism platform directly influences visitors’ intention to use the platform and their satisfaction. To scientifically evaluate interface design quality, this study introduces the fuzzy comprehensive evaluation method, establishing a comprehensive evaluation model from multiple dimensions that integrates both subjective and objective factors to systematically assess usability, aesthetics, and functionality.

Based on the extended dimensions of the UTAUT2 model and user experience theory, five primary evaluation indicators are derived: functional adaptability (U₁), interface usability (U₂), visual aesthetics (U₃), emotional resonance (U₄), and intelligent support (U₅). Each primary indicator is further divided into three specific secondary indicators, forming a complete evaluation system, as shown in

Table 12. Fuzzy Comprehensive Evaluation Indicator System.

First-Level Indicator	Functional Adaptability	Interface Usability	Visual Aesthetics	Emotional Resonance	Intelligent Support
Second-level Indicator	Comprehensive Information Display	Ease of Operation	Interface Harmony	Immersive Experience	Intelligent Interaction
	Functional Matching	Clear Navigation	Aesthetic Appeal	Cultural Identification	Real-Time Responsiveness
	Personalized Recommendation	Learning Cost	Design Innovation	Emotional Memory Activation	Technical Reliability

. The evaluation set adopts a five-level scoring standard: excellent, good, moderate, poor, and very poor, corresponding to fuzzy weight values of 1.0, 0.8, 0.6, 0.4, and 0.2, respectively.

For weight assignment, this study combines expert review with literature analysis to determine the weight vector of the primary indicators as W = [0.25, 0.20, 0.15, 0.20, 0.20], corresponding to the relative importance of the five dimensions. Eight experts from relevant fields—including industrial heritage preservation, digital technology applications, and interface design—were invited to score the secondary indicators. These experts, comprising university faculty, industry practitioners, and researchers, possess extensive practical experience and professional judgment, ensuring the scientific validity and reliability of the evaluation results. The experts’ scores were used to construct fuzzy membership matrices for each secondary indicator, which were then aggregated into fuzzy evaluation matrices for the five primary indicators. Using the weighted average method, the fuzzy comprehensive evaluation vector was calculated as B = [0.435, 0.37, 0.18, 0, 0]. By performing the product operation with the evaluation grade weights, the overall comprehensive score of the platform interface design was obtained as 0.839.

The results of the fuzzy hierarchical evaluation indicate that the overall platform interface is close to “excellent,” with functional adaptability and emotional resonance performing particularly well. This reflects strong user recognition in information presentation, navigation logic, and cultural immersion. In contrast, visual expression and personalized recommendation functions still have room for improvement. Subsequent design iterations should further optimize service logic to more effectively guide user behavior and enhance the overall tourism experience.

5. Discussion and Conclusions

5.1. Research Discussion

First, this study, based on the extended UTAUT2 model and using SEM, systematically analyzed the mechanisms through which PE, EE, SI, FC, HM, and TA affect users’ BI. SI was found to have a significant positive effect on BI, indicating that visitors to the digital intelligence industrial heritage tourism platform are more likely to be guided by recommendations from friends, social media, and public opinion, with social recognition serving as a key driver of usage behavior. FC also demonstrated a positive effect on BI, consistent with the findings of Omar et al. [68], This indicates that when the platform provides well-developed infrastructure, sufficient technical support, and user-friendly operational guidance, it can effectively lower usage barriers and enhance users’ intention for continued use. Third, HM has a particularly significant effect on BI, suggesting that visitors place high expectations on the entertainment, immersion, and emotional satisfaction provided by the platform, making emotional experience a crucial driver for platform acceptance. Finally, unlike traditional UTAUT2 studies, in this research context, TA did not inhibit users’ adoption intentions; rather, it stimulated their motivation to actively engage with the platform. The possible reason is that the platform integrates emerging DIT, which stimulates users’ curiosity and exploration. Additionally, the sample population is predominantly young, with relatively high technological adaptability, and tends to overcome uncertainty through active usage despite feelings of anxiety. Moreover, the unique experiential demands of industrial heritage tourism encourage users to accept certain technological challenges, thereby fostering stronger usage intentions. This finding provides new insights and directions for the design of digital intelligence cultural tourism platforms. During the use of the digital intelligence industrial heritage tourism platform, pleasurable experiences are a key factor driving visitors’ continued engagement. This indicates that cultural heritage tourism should prioritize user experience in its digital transformation. Enhancing platform appeal through interactivity, immersion, and engaging design is essential. Additionally, multimedia, virtual reality, and gamification can present industrial heritage culture in vivid and interesting ways, strengthening user participation and retention. Social sharing further fosters positive word-of-mouth, promoting the sustainable development of cultural heritage tourism.

In the traditional UTAUT2 model, PE and EE did not show significant effects on users’ BI, particularly in the specific context of the digital intelligence industrial heritage tourism platform, which is consistent with the findings of Fernando et al. [69]. This result indicates that, compared with functional efficiency and operational convenience, users’ acceptance of the platform is more significantly influenced by non-functional factors such as emotional motivation and social recognition. This aligns closely with current trends in the digital cultural tourism sector. With the widespread adoption of DITs such as VR, AR, and big data recommendation systems, users increasingly focus on the immersive experiences, personalized recommendation services, and social interaction features offered by cultural heritage platforms, rather than solely on technical performance or ease of use. The digital development of cultural heritage tourism should not rely solely on functional optimization. Instead, it should center on user experience, guiding smart tourism toward a more sustainable and participatory direction.

The mediation analysis results indicate that PE plays a significant mediating role in the effects of TA and EE on BI. Specifically, when users experience TA, perceiving the platform as having clear practical value can transform that anxiety into a positive driver for usage intention, highlighting the important regulatory role of PE in alleviating technological unease and enhancing platform attractiveness. Similarly, EE indirectly influences behavioral intention through PE, suggesting that designing the platform for ease of operation and user-friendliness helps users develop a stronger sense of efficacy, thereby increasing their intention to use the platform. This finding further confirms the central role of PE in the adoption mechanism of digital intelligence platforms and underscores its mediating value in enhancing users’ adoption intentions.

5.2. Theoretical Contributions and Practical Implications

5.2.1. Theoretical Contributions

Based on the UTAUT2 model and incorporating technology anxiety, this study constructs a more systematic research framework and integrated model, helping to identify key factors influencing users’ adoption of digital intelligence industrial heritage tourism platforms. The model not only reveals the main determinants of user acceptance but also provides theoretical support for platform optimization.

First, this study applies the UTAUT2 model to industrial heritage tourism, a domain with unique cultural attributes, enriching its applicability and explanatory power in interdisciplinary contexts. Second, it emphasizes the key roles of emotional experience and social recognition in the use of digital intelligence platforms, extending the understanding of technology acceptance in cultural tourism from a user experience perspective. This shows that technological intervention not only improves information access but also stimulates users’ cultural identification and engagement on an emotional level. Third, the study reveals the dual role of technology anxiety in specific contexts. Unlike previous research treating technology anxiety purely as a negative factor, moderate anxiety can stimulate users’ exploratory behavior, highlighting the need to consider emotional dynamics in adoption decisions. These findings enrich existing theory and provide a new research pathway for exploring technology acceptance mechanisms in various cultural heritage contexts.

5.2.2. Practical Implications

At the practical level, this study provides important guidance for the development and optimization of digital intelligence industrial heritage tourism platforms. First, such platforms should serve as a key medium connecting industrial heritage and the public. Through technological means, the platform can recreate historical scenes and production processes, offer immersive and interactive experiences, lower the barrier of specialized knowledge, and stimulate public interest and understanding of industrial culture, thereby promoting the dynamic preservation and dissemination of industrial heritage.

Second, to address differences in users’ technological adaptability, the platform should incorporate intelligent guidance systems and simplified operations, reducing barriers for beginners and enhancing experience inclusivity across age groups. This strategy facilitates broad public participation in industrial heritage tourism, further supporting heritage preservation and innovative dissemination, and effectively enhancing the comprehensive competitiveness and sustainable development potential of the cultural tourism industry.

Finally, the findings provide empirical evidence for policymakers and heritage management institutions, positioning DITs as a key tool for the sustainable preservation and revitalization of industrial heritage. This approach aligns with the global strategy for green cultural tourism transformation and offers a replicable and scalable path for sustainable heritage activation. By leveraging digital technologies to empower heritage management, public education, and tourism promotion, it is possible to achieve both preservation and development, enabling the intelligent and digital transmission of industrial cultural heritage in the contemporary era.

5.3. Research Limitations and Future Directions

Although this study was based on the extended UTAUT2 model, combining SEM with the fuzzy comprehensive evaluation method to systematically explore user acceptance behavior and interface design of the digital intelligence industrial heritage tourism platform, and conducted an empirical analysis using WISCO industrial heritage as a case study, several limitations remain.

First, the UTAUT2 model was originally developed in the fields of information systems and consumer technologies. Directly applying it to the specific context of digital intelligence industrial heritage tourism platforms may overlook more context-dependent and affective factors such as cultural tourism experience, emotional resonance, and heritage value perception. Although this study incorporated variables such as hedonic motivation and technology anxiety to enhance applicability, there is still a tendency toward simplification in variable coverage and path assumptions, making it difficult to fully capture the complex cognitive and emotional mechanisms of users in industrial heritage tourism settings. Second, the data in this study were mainly collected through questionnaires, which may be influenced by self-reporting bias and social desirability effects, potentially leading to partial inaccuracies in variable measurement. Moreover, part of the sample was concentrated on specific tourist groups within certain tourism settings, which may affect the generalizability and external validity of the results. Finally, WISCO, as a case study, possesses distinctive heavy-industry characteristics and a unique regional and cultural background, which may also limit the universality of the findings.

Future research could be expanded in three directions. First, future studies may integrate the Stimulus–Organism–Response (SOR) model, experience economy theory, or cultural heritage protection frameworks, and further introduce mediating variables such as perceived risk and trust to test the robustness of the proposed paths under different levels of technological familiarity and cultural backgrounds, thereby enhancing explanatory power and contextual relevance. Second, the sample can be broadened to include participants of different ages, backgrounds, and levels of technological familiarity, and longitudinal tracking of user behavior can be conducted to improve the generalizability of the findings. Third, efforts can be made to implement platform prototypes and conduct user testing, deepen industry–academia–research collaboration, and explore multi-scenario adaptation and dynamic optimization strategies to further enhance the platform’s practicality and scalability.

5.4. Conclusions

This study, based on the extended UTAUT2 model, employed SEM and mediation effect analysis to explore the formation mechanism of user behavioral intention in the context of industrial heritage tourism. The results showed that emotional factors are the core driving force for user adoption, while social influence and facilitating conditions serve as key supports for behavioral transformation. Performance expectation alleviated, to some extent, users’ uncertainty toward digital intelligence platforms. At the practical level, the study proposed intelligent digital interface design strategies and preliminarily developed user- and management-end schemes, whose feasibility was validated through fuzzy comprehensive evaluation. Overall, this study responds to the practical needs of deep integration between culture and tourism and the intelligent upgrading of tourism scenarios, offering new perspectives for the coordinated development of industrial heritage revitalization and sustainable conservation. Intelligent platform design oriented toward sustainability and digital intelligence not only reduces the interference of tourism activities with industrial heritage sites and their surrounding ecological environments but also contributes to the long-term preservation and intergenerational transmission of industrial heritage resources, providing solid theoretical support and practical technological pathways for sustainable development in the industrial heritage sector.