Unpacking the effect of Digital Platforms on the Dissemination of Intangible Cultural Heritage: An Extended Technology Acceptance Model with Emotional Resonance and Cultural Identity

Unpacking the effect of Digital Platforms on the Dissemination of Intangible Cultural Heritage: An Extended Technology Acceptance Model with Emotional Resonance and Cultural Identity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Unpacking the effect of Digital Platforms on the Dissemination of Intangible Cultural Heritage: An Extended Technology Acceptance Model with Emotional Resonance and Cultural Identity Shunmei Lin, Cailian Xu, ZURIAWATI AHMAD ZAHARI, Jingyi Lim, Qianru Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7181151/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The digital era poses new challenges but also offers opportunities to the dissemination and preservation of intangible cultural heritage (ICH). Leveraging emotional resonance (ER) and cultural identity (CI), this study applies the extended Technology Acceptance Model (TAM) to explore the interaction between digital platform features and users’ cultural-psychological mechanisms, and see how it influences the digital dissemination of ICH. The study investigates the mediating roles of perceived usefulness (PU) and perceived ease of use (PEOU) in the relationship between platform functionality and user behavior, as well as the moderating effects of ER and CI. A data sample of 456 users was collected via questionnaire surveys and analyzed using Structural Equation Modeling (SEM). The results indicate: (1) Platform functionality (PF), content recommendation accuracy (CRA), and PEOU all positively predict PU, with PF having the strongest influence, followed by CRA and PEOU. (2) Cultural appeal (CA) and information quality (IQ) both positively predict PEOU, with CA exerting a greater influence than IQ. (3) PU positively predicts behavioral intention (BI), and BI in turn positively predicts actual usage (AU). (4) ER positively moderates the relationship between PU and BI, while CI positively moderates the relationship between BI and AU. Therefore, to enhance the effectiveness of ICH digital dissemination, platform designers and cultural preservationists should prioritize functional features, user perceptions, as well as emotional and cultural factors. Business and commerce/Business and management Social science/Business and management Humanities/Cultural and media studies Social science/Cultural and media studies Business and commerce/Information systems and information technology Biological sciences/Psychology Social science/Psychology Social science/Science technology and society Technology Acceptance Model (TAM) digital cultural heritage dissemination of intangible cultural heritage emotional resonance cultural identity Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As an important manifestation of cultural diversity and human creativity, intangible cultural heritage (ICH) carries dynamic cultural forms such as historical memory, traditional knowledge, language, music, and handicrafts 1 . Under the impact of globalization and modernization, ICH faces the risk of disappearance and marginalization, making its preservation particularly crucial. Digitalization, as an emerging conservation method, provides new avenues for the documentation, storage, dissemination, and education of ICH. Through digitalization, ICH can transcend temporal and spatial constraints, achieving broader dissemination and more effective preservation 2 . It helps preserve the original form of ICH, and at the same time, enhances public awareness and interest through interactive and participatory approaches, thereby facilitating the transmission and sustainable development of ICH 3 . Existing studies have fully tapped into the significance of digital technologies in safeguarding ICH, and explorations and practical efforts have been conducted on this respect. For instance, some research focus on using digital technologies to document and archive ICH, while other studies examine how digital dissemination impacts ICH transmission 4 . Nevertheless, limitations do present in current research. First, the majority of studies center on the application at the technological level, paying insufficient attention to users’ psychological mechanisms in accepting and using digital platforms 5 . Second, there is a lack of systematic theoretical frameworks, which is necessary in analyzing the interaction between the functional characteristics of digital platforms and users’ cultural psychological mechanisms 6 , 7 . Lastly, existing empirical research is rather limited, particularly lacking in-depth analysis of user behavior across different cultural backgrounds 8 , 9 . These research gaps highlight the necessity of further investigating users’ psychological and behavioral mechanisms to advance both theoretical and practical developments in the digital dissemination of ICH. Regarding the deficiencies identified in existing research, this study introduces ER and CI as external variables to construct an extended TAM, with the aim to explore the impact of the interaction between digital platform functionality and users’ cultural psychology on the digital dissemination of ICH. Taking Minnan nursery rhymes as a case study, this research will specifically examine the following issues: (1)How do PF, CRA, CA, and IQ influence user BI through PEOU and PU? (2) How CI and ER further promote users’ continued engagement and AU? Through empirical research, this study aims to provide new insights into the role of digital platforms in ICH preservation and to offer recommendations for enhancing the interaction between digital platforms and local cultures. Literature review Impact of Digital Platforms on Dissemination of ICH Digital platforms refer to virtual spaces built on the internet and information technologies, capable of supporting information storage, dissemination, and user interaction. These platforms typically include social media, content-sharing platforms, and online education systems, among others 10 . The integration of multimedia technologies, intelligent algorithms and interactive features provides users with diverse digital experiences and increasing attention in the field of cultural dissemination 11 . In terms of ICH dissemination, digital platforms have played an essential role in opening new pathways for the preservation and promotion of ICH. Specifically, digital platforms enhance the dissemination of ICH through various technological means. For example, immersive technologies allows for users’ “multi-dimensional perspectives” and “active participation”, enabling them to perceive the nuances of cultural heritage more intuitively 12 . Through analyzing user interests and behavioral patterns, intelligent recommendation algorithms can precisely deliver ICH content, thereby expanding cultural dissemination while boosting engagement and learning motivation 13 . Additionally, online forums, virtual cultural events and other real-time interactive features strengthen the connection between cultural creators and audiences, fostering dynamic cultural transmission and reinterpretation 14 . However, the rapid development of digital platforms also presents challenges. Fragmented content may undermine the integrity of culture, making it difficult for audiences to grasp the historical context and values behind ICH. Moreover the commercialization of platforms may prioritize traffic metrics over cultural authenticity and educational significance 15 . TAM Initially proposed by Davis (1989) 6 , the Technology Acceptance Model (TAM) was a classical framework for studying users’ acceptance and utilization of new technologies. Its core variables include perceived usefulness (PU) and perceived ease of use (PEOU), which measure users’ recognition of a technology’s potential in enhancing efficiency and its ease of operation respectively 6 . In recent years, TAM’s theoretical framework has been widely applied across various contexts, including educational technology, healthcare information systems, virtual reality platforms and cultural dissemination. Research has shown that TAM is effective in explaining initial technology adoption and can also be extended to evaluate sustained usage behavior and the complex dynamics in disseminating cultural products 7 . Role of ER and CI on Dissemination of ICH Emotional resonance (ER) denotes the capacity of cultural elements or digital media to evoke deep emotional connections and engagement in users. Zheng & Hu (2024) 16 analyze how travel platforms leverage visual elements to express ER in digital spaces, deepening user immersion. This concept is particularly impactful in ICH dissemination, as it cultivates a stronger personal and communal bond with cultural traditions. For example, Zhou (2024) 17 studied how the mobile game “Peace Elite” uses digitalization and gamification strategies to foster users’ ER and CI with traditional Chinese culture, demonstrating that digital cultural platforms can enhance users’ understanding and acceptance of traditional culture. By stimulating users’ ER through digital narratives and immersive virtual reality experiences, their appreciation and understanding of ICH can be greatly enhanced. Similarly, Research by Sangamuang et al. (2025) 18 revealed that in gamified virtual museum settings, VR technology greatly boosts immersion and CI, leading to higher user engagement with cultural material and a greater propensity to share it. By addressing users’ psychological needs, ER serves as a crucial link between traditional culture and contemporary audiences, stimulating sustained interest in digital platforms 19 . Cultural identity (CI) reflects users’ sense of belonging and shared cultural values developed through engagement with cultural content. Zheng & Hu (2024) 16 highlighted that CI serves as a critical factor in fostering emotional attachment on digital cultural platforms. Empirical evidence demonstrates that strong CI positively moderates the relationship between BI and actual AU 20 . Furthermore, Lin et al. (2024) 19 noted that localized gamified cultural dissemination approaches, such as augmented reality and social interactive games, effectively reinforce users’ CI and deepen emotional engagement. Additionally, research by Lyu, Z. (2023) 21 demonstrated that CI enhances a digital platform’s PU by aligning technological experiences more closely with users’ real-world cultural contexts. Research hypothesis and structural model Platform Functionality Platform functionality (PF) describes how well a digital platform’s functional design aligns with users’ needs and usage contexts, providing tools and features that effectively support task completion or learning experiences. High PF occurs when a platform’s operational functions closely correspond to user objectives (e.g., learning local culture), thereby improving efficiency 22 . This concept underscores the congruence between functional design and user requirements, representing a key aspect of digital platform user experience 23 . Research has indicated that PF significantly enhances users’ PU. For instance, studies of Radu (2020) 24 on mobile learning platforms revealed that when platform features effectively support users’ learning objectives (e.g., by offering personalized content or user-friendly tools), users better recognize the platform’s practical value, thereby enhancing PU. Davis (1989) 6 emphasized the pivotal role of PF in shaping PU, observing that users perceiving functional alignment with their needs show stronger continuance intention. The research of Wang & Zhang (2022) 25 on short-video platforms further corroborated that PF (e.g., personalized recommendations and interactive features) not only elevate PU but also strengthens users’ long-term engagement intentions. Research on digital platforms for cultural heritage reveals that platform functionality (PF), including features like multilingual support and interactive navigation, satisfies cultural learning objectives while markedly improving users’ perceived platform value 26,21 . Building on this theoretical foundation, this study posits that when digital platforms achieve high PF alignment with user expectations, they can substantially improve users’ PU. Accordingly, the following hypothesis is proposed: H1: PF has a significantly positive impact on PU. Content Recommendation Accuracy Content recommendation accuracy (CRA) measures how precisely a digital platform can customize cultural content which aligns with users’ historical behavior, interests, preferences and needs. High CRA occurs when recommended content closely aligns with user goals (such as learning about local culture), thereby enhancing the efficiency of information acquisition and user satisfaction 27 . This concept underscores the essential function of recommendation systems in fulfilling users’ personalized needs and represents a critical component of digital platform functionality. Research has demonstrated that CRA has a significant positive impact on users’ PU. For example, the study of Ricci et al. (2022) 27 found that precise recommendations can reduce users’ information search costs, improve task completion efficiency, and significantly enhance their perception of the platform’s value. Additionally, Li et al. (2024) 28 and Li (2024) 29 revealed in their analysis of user behavior on short video platforms that when recommendation systems deliver content highly relevant to users’ interests, users are more likely to perceive the platform as valuable in supporting their cultural exploration or entertainment needs, thereby enhancing PU. These recent findings suggest that CRA significantly strengthens users’ recognition of PF and their perception of value by delivering high-quality personalized content. Based on the above theoretical foundation, this study posits that if digital platforms can fulfill users’ personalized needs through high CRA (e.g., accurately recommend localized cultural content), it will significantly enhance users’ PU of the platform. Accordingly, the following hypothesis is proposed: Hypothesis 2: CRA has a significantly positive impact on PU. Content Accessibility Content accessibility (CA) generally refers to the extent to which users can easily discover, access, and utilize cultural content provided by digital platforms. High CA implies that users can efficiently reach desired cultural resources through streamlined operational processes and effective information presentation (e.g., intuitive navigation or fast loading speeds) 30 . This definition highlights CA’s crucial role in minimizing usage barriers and enhancing user experience, particularly within cultural learning contexts. Research has proven that CA exerts a significant positive influence on both PEOU and PU. First, regarding PEOU, the study of Matausch, K. (2014) 32 on digital cultural platforms found that accessible content design (e.g., intuitive interfaces and user-friendly search functions) significantly reduces operational complexity for users, thereby enhancing their perception of the platform’s PEOU. Furthermore, Zhang et al. (2024) 31 demonstrated in their analysis of online education platforms that CA directly enhances a platform’s operational convenience by reducing users’ time and effort in information acquisition, thereby substantiating CA’s influence on PEOU. Regarding PU, Matausch et al. (2014) 32 found that when cultural content is accessed efficiently and without barriers, users are more likely to perceive the platform as effectively supporting their learning or exploratory goals, consequently increasing PU. These recent findings suggest that CA optimizes both the convenience and efficiency of information acquisition, not only lowering the platform’s usage threshold (affecting PEOU) but also enhancing its functional utility (affecting PU). Based on the above theoretical foundation, this study contends that digital platforms can significantly influence users’ PEOU and PU by enhancing CA (e.g., fast loading speeds and optimized search functions) to meet users’ cultural content acquisition needs. Specifically, when users can effortlessly access cultural resources, they are more likely to perceive the platform as user-friendly (PEOU), while simultaneously recognizing its practical value in supporting cultural learning (PU), thereby elevating the overall user experience. Accordingly, the following hypotheses are proposed: H3: CA has a significantly positive impact on PEOU. H4: CA has a significantly positive impact on PU. Interaction Quality Interaction quality (IQ) measures the effectiveness, richness and enjoyment level of interactions between users and content or among users on digital platforms. According to Shi et al. (2023) 33 , high IQ enables users’ seamless engagement with cultural content through features like comment sections, bullet chats or real-time feedback, thereby enhancing their sense of participation and operational fluency. This definition underscores the crucial role of IQ in optimizing user-platform interactions, particularly in cultural learning or content consumption scenarios. Studies show that IQ significantly enhances PEOU. Shi et al. (2023) 33 research on digital museum experiences found that high-quality interactive design (e.g., intuitive commenting systems and real-time feedback) increases information richness, thereby strengthening users’ perception of the platform’s PEOU. Similarly, Jameel et al.’s (2021) 34 investigation of online learning platforms demonstrated that smooth and responsive interactive features can improve users’ sense of operational convenience by simplifying participation process. These findings collectively indicate hat IQ enhances PEOU by delivering efficient and enjoyable interactive experiences that lower users’ access barriers. Building upon this theoretical foundation, this study proposes that digital platforms can substantially enhance users’ PEOU by delivering seamless and intuitive interactive experiences through high IQ features (e.g., comment sections and bullet chat functions). Accordingly, the following hypothesis is proposed: H5: IQ has a significantly positive impact on PEOU. PEOU Perceived ease of use (PEOU) represents users’ assessment of how effortlessly they can learn and operate a digital platform’s functions. Venkatesh & Bala (2008) 7 found that intuitive interface design and efficient processes lead to high PEOU by allowing users to quickly learn and competently use the platform. This definition highlights the central role of PEOU in reducing usage barriers and enhancing user experience, particularly in cultural learning or content consumption scenarios. Research has indicated that PEOU signifcantly enhances PU. Mensah (2020) 35 , in extending the Technology Acceptance Model (TAM), found that when users perceive platform operations as simple and intuitive, they are more likely to view the platform as effective for task completion, thereby enhancing PU. This relationship has been further validated by Sorkun et al. (2022) 36 in their study of online learning platforms, which demonstrated that user-friendly interfaces and streamlined operational processes reduce users’ cognitive load, thereby improving usage efficiency and users’ appreciation of the platform’s practical value. Furthermore, Aurangzeb et al.’s (2024) 37 systematic review of TAM literature confirmed that PEOU significantly enhances users’ perception of technological value by reducing operational difficulty and boosting usage confidence. These recent studies all demonstrate that PEOU, as a critical factor of user experience, positively influences PU by optimizing operational convenience. Based on the above theoretical foundation, this study contends that digital platforms can significantly enhance users’ PU by improving PEOU through user-friendly interface design and streamlined operations, which can lower usage barriers. When users can easily navigate a platform, they are more inclined to acknowledge its value in facilitating cultural learning or task achievement, consequently strengthening their appreciation of the platform’s functionality. Accordingly, the following hypothesis is proposed: H6: PEOU has a significantly positive impact on PU. Perceived Usefulness Perceived usefulness (PU) assesses the extent to which users believe a digital platform can improve their task completion efficiency or learning outcomes. High PU indicates that users recognize the platform’s substantial value in supporting their goals (e.g., learning local culture or acquiring information) 38 . This definition highlights PU’s central role as users’ cognitive evaluation of the platform’s functional value, making it a key driver of usage intention. Studies show that PU significantly enhances behavioral intention (BI). For example, Alalwan et al. (2017) 38 demonstrated this relationship in mobile payment applications, finding that when users perceive the platform as effectively enhancing their task efficiency, their continuance intention markedly increases, a result that supports the direct effect of PU on BI. Furthermore,Wiardi et al.’s (2022) 39 analysis of online learning platforms revealed that users who believe the platform can effectively support their learning objectives (e.g., cultural knowledge acquisition) show stronger continued usage intention, particularly in educational or culturally-oriented digital environments. These studies collectively suggest that PU directly promotes BI by reinforcing users’ trust in PF and their perception of its value. Building upon this theoretical foundation, the present study posits that digital platforms can significantly influence users’ BI for continued usage by enhancing PU through effective support for local cultural learning, thereby increasing users’ recognition of functional value. When users perceive the platform as instrumental in achieving their cultural learning or task objectives, they develop stronger continuance intention, ultimately leading to greater platform reliance. Accordingly, the following hypothesis is proposed: H7: PU has a significantly positive impact on BI. Perceived Ease of Use Perceived ease of use (PEOU) measure how effortless users can learn and navigate a platform’s features. According to Restianto (2024) 40 , high PEOU indicates that users can quickly adapt to and efficiently utilize the platform through intuitive interface design and streamlined operational processes. This definition highlights PEOU’s critical function in reducing usage barriers and enhancing user experience, particularly in cultural learning or content engagement scenarios. Research has indicated that PEOU exerts a significant positive influence on BI. For instance, in a review study of TAM, Zain et al. (2023) 41 revealed that when users consider platform operations simple and intuitive, they develop stronger continuance usage intentions, as ease of use reduces usage resistance and boosts confidence in system interaction. Supporting this, Berbar’s (2023) 42 analysis of online education platforms showed that user-friendly design features (e.g., intuitive navigation and responsive interfaces) directly strengthen users’ continued engagement willingness by improving operational convenience, particularly in contexts requiring prolonged interaction. These recent findings collectively suggest that PEOU strengthens BI by both minimizing learning costs and optimizing operational experience. Drawing on this theoretical foundation, this study posits that digital platforms can significantly influence users’ BI by reducing usage barriers through high PEOU, achieved via user-friendly interface design and simplified operations. When users find a platform easy to understand and operate, they are more likely to demonstrate sustained usage intention, leading to greater platform dependence and engagement. Accordingly, the following hypothesis is proposed: H8: PEOU has a significantly positive impact on BI. Behavioral Intention Behavioral intention (BI) represents users’ propensity to utilize digital platforms, specifically their commitment to ongoing cultural content engagement or learning experiences. Strong BI reflects users’ favorable disposition toward future platform usage and serves as a direct predictor of actual behavior 43 . This concept highlights BI’s bridging role in connecting psychological disposition with observable action, particularly in cultural learning or content consumption contexts. Research has demonstrated that BI significantly positively influences actual usage (AU). Moya et al.’s (2018) 43 investigation of information systems revealed that BI partially mediates the relationship between users’ effort expectancy (EE) and AU, confirming the predictive power of users’ intention for subsequent actions. Additionally, Chaveesuk et al.’s (2021) 44 digital payment research found that Users’ intention to continue using a technology directly leads to AU, particularly when the technology aligns with their ingrained habits. These findings support the view that BI, as an indicator of users’ proactive disposition, can significantly drive their AU. Applied to digital platforms, this study argues that users’ BI (e.g., willingness to continue accessing local cultural content) can strongly predict their AU (e.g., frequent visits or participation in platform activities). When users develop sustained platform engagement intentions, this intention transforms into concrete actions, thereby increasing actual usage frequency. H9: BI has a significantly positive impact on AU. Emotional Resonance (ER) and Cultural Identity (CI) Moderating Effect of ER on the Relationship Between PU and BI Emotional resonance (ER) describes the profound emotional connection and empathetic response users develop toward content or platform experiences during digital interactions, often characterized by emotional reactions to, identification with, or investment in cultural materials 45 . High ER reflects users’ strong psychological and emotional reactions during platform interactions. This is particularly crucial in cultural learning contexts, where it fosters meaningful emotional attachments to cultural content and establishes an affective foundation for developing BI. Perceived usefulness (PU) denotes users’ perception of a digital platform’s ability to facilitate better task execution or cultural knowledge acquisition, thereby increasing their likelihood of developing continued BI 46 . High PU indicates that users perceive the platform as significantly valuable in supporting their cultural learning goals, making it a key determinant of BI in TAM 47 .Research confirms that emotional resonance (ER) significantly moderates the PU-BI relationship. Hai et al.’s (2022) 46 study of online learning platforms revealed that strong ER (e.g., deep emotional connections with local cultural content) enhances users’ conversion of perceived utility into sustained usage intention, thereby amplifying PU’s positive effect on BI. Furthermore, Wang et al. (2021) 45 demonstrated in digital heritage platforms that ER intensifies the positive impact of PU on BI by deepening users’ emotional engagement with content, particularly in localized cultural learning scenarios. These findings collectively support the view that ER positively moderates the relationship between PU and BI, primarily by enriching users’ affective experiences, particularly in cultural content-oriented digital platforms. The study thus posits that enhanced ER during platform use intensifies PU’s impact on BI,, thereby reinforcing their continuance intention, especially in localized cultural learning contexts. Based on this theoretical foundation, the research proposes the following hypothesis: H10: ER positively moderates the relationship between PU and BI. Moderating Effect of CI on the Relationship Between BI and AU Cultural identity (CI) reflects users’ sense of belonging, identification, and value alignment with specific cultures (e.g., local cultures), typically evidenced through their interest in cultural content and recognition of their cultural roots 48 . According to Chen and Zhu (2022), strong CI indicates that users closely associate platform usage with their personal cultural background or values, which significantly influences engagement on cultural digital platforms by reinforcing intrinsic user-content connections and creating a cultural basis for behavioral outcomes.. Behavioral intention (BI) represents users’ subjective willingness when using digital platforms, particularly their intention to continue accessing local cultural content 50 . In the TAM framework, high BI reflects favorable user attitudes toward future platform usage, serving as both a direct antecedent predicting AU and a key determinant of AU. Research has confirmed that CI significantly moderates the relationship between BI and AU. For instance, Zhang and Jahng’s (2024) 48 research on knowledge-sharing platforms revealed that users who exhibit strong CI (e.g., a sense of belonging to local culture) are more likely to translate BI into AU. This moderating effect occurs because CI enhances users’ perception of the platform’s cultural value, thereby intensifying BI’s positive influence on AU. Furthermore, (Chen & Zhu 2022) 49 revealed in their research on traditional culture e-learning platforms that users with higher CI are more inclined to convert continuance intentions into AU, particularly in cultural learning contexts. Their findings suggest that CI intensifies the positive impact of BI on AU by strengthening users’ emotional bonds with cultural content. These recent studies collectively indicate that CI positively moderates the BI-AU relationship by reinforcing users’ intrinsic connections with cultural content, especially in digital platforms featuring cultural elements 51 . Building on this theoretical foundation, the study posits that CI positively moderates the relationship between BI and AU. When users demonstrate stronger CI during digital platform engagement, the influence of BI on AU becomes more pronounced, thereby further increasing users’ frequency of active platform participation, particularly in localized cultural learning contexts. Accordingly, the following hypothesis is proposed: H11: CI positively moderates the relationship between BI and AU. As shown in Figure 1, the proposed research model integrates platform functionality (PF), content recommendation accuracy (CRA), content accessibility (CA), interaction quality (IQ), perceived ease of use (PEOU), and perceived usefulness (PU), along with emotional resonance (ER) and cultural identity (CI) as moderating variables, to explain users' behavioral intention (BI) and actual usage (AU) in the context of Minnan nursery rhymes dissemination via digital platforms. Methods Case Study: Minnan Nursery Rhymes As an integral component of China’s ICH, Minnan nursery rhymes embody the profound historical traditions and cultural essence of Southern Fujian region, making them an ideal case study for examining digital platforms’ role in ICH dissemination. Originating in the Tang Dynasty (618-907 CE), classic works like “Moonlight Bright (Yueguangguang)” demonstrate these rhymes’ enduring legacy, reflecting the literary and artistic flourishing of Tang civilization. Through Minnan people’s migrations during Song, Yuan, Ming, and Qing dynasties (960-1911 CE), these rhymes spread to Taiwan and Southeast Asia, evolving into significant cross-regional cultural transmitters 52 . Minnan nursery rhymes employ oral literary forms to vividly portray agricultural practices, traditional celebrations, and countryside living through succinct rhythmic verses, thereby strengthening CI among Minnan communities. Additionally, their melodic nature and educational value make them an important tool for children’s cognitive and psychological development, contributing to their language skills and cultural awareness 53 . In recent years, globalization and digitalization have posed challenges to the transmission of Minnan nursery rhymes. While digital platforms have accelerated the global cultural exchange, they threaten to weaken the genuine local distinctiveness of these traditional verses by breaking them apart and commercializing them 54 . Considering this, Minnan nursery rhymes were included in China’s second national list of intangible cultural heritage in 2008 to ensure the preservation of their cultural value 55 . Against this backdrop, scholars have implemented digital storytelling, VR technologies, and multimedia interactive systems, which drive greater public engagement while offering youth an absorbing, culturally enriching experience 56 . Furthermore, complex network analysis has decoded the lexical structures and emotional depth of these rhymes, establishing a scientific foundation for safeguarding their cultural diversity 57 . Measurement This study designed a structured questionnaire titled “Questionnaire on Local Culture Dissemination Model” to validate the proposed cultural transmission framework. Specifically targeting adult users (aged 18+) on Bilibili platform, the instrument measures both acceptance rates of local cultural content and its principal influencing factors among participants, and examine the dynamic interaction between technology acceptance and cultural transmission mechanisms. The questionnaire comprised two integrated sections: (1) demographic and background information to understand participant characteristics and cultural exposure experiences; (2) measurement scales for research variables adapted from validated instruments, through rigorous back-translation 58 , with contextual modifications made to ensure relevance for local culture dissemination research.. The measurement instrument employed a 5-point Likert scale (1= “strongly disagree” to 5= “strongly agree”) across all items to ensure data consistency and quantitative analysis feasibility. The questionnaire was refined through pilot testing (n=30) for applicability 59 , expert validity review, and subsequent CFA verification for reliability and validity 60 . The questionnaire began by collecting optional demographic information, capturing gender and age to establish participant profiles. It then incorporated five specific background items evaluating cultural exposure across multiple dimensions: (1) frequency of attending offline cultural activities (e.g., folk festivals, regional opera); (2) level of engagement with local cultural content on social media; (3) depth of studying or researching local culture; (4) extent of family members’ engagement in local cultural transmission; and (5)occurrence regularity of cultural events in professional or community settings. These items employed ordered response options (“very frequently” to “almost never”) to provide contextual support for the study, reflecting how cultural familiarity influences acceptance behaviors. The second part of the questionnaire consisted of 10 subscales: PF with 3 items (Tang, L., 2019) 61 , e.g., “Bilibili’s features help me understand local cultural content”; CRA with 3 items (Ricci et al., 2021) 62 , e.g., “Bilibili accurately recommends local cultural content”; CA with 6 items (Xia, B. & Zhao, 2016) 63 , e.g., “I can easily find local cultural content” and “Convenient access enhances cultural understanding”; IQ with 3 items (Hueluer, G. et al., 2022) 64 , e.g., “I participate in comments and bullet-screen interactions”; PEOU with 3 items (Davis, 1989) 6 , e.g., “Bilibili’s interface is easy to use”; PU with 3 items (Davis, 1989) 6 , e.g., “The content helps me identify with local culture”; ER with 3 items (Chen, B. 2024) 65 , e.g., “The content evokes cultural emotions”; CI with 3 items (Buckingham et al., 2023) 66 , e.g., “The content deepens cultural identity”; BI with 3 items (Venkatesh & Bala, 2008) 7 , e.g., “I intend to continue watching local cultural content”; and AU with 3 items (Malhan et al., 2021) 67 , e.g., “I discuss the content with friends”. To adapt the original English scales for native Chinese-speaking participants, a rigorous translation-back-translation procedure (Brislin, 1980) 58 was implemented following (Salazar-Frías et al.’s 2023) 68 bilingual testing protocol. Two native Chinese bilingual researchers independently produced initial translations, which were then reviewed by five participants knowledgeable about local culture (including intangible cultural heritage) to enhance contextual appropriateness (e.g., replacing “nursery rhymes” with culturally specific expressions). Two native English-speaking researchers performed back-translation for semantic consistency verification. Both Chinese and English versions were pilot-tested with 20 bilingual individuals to refine technical terminology. During pretesting (n=30), three domain experts with substantial professional experience conducted preliminary validity evaluation, thoroughly assessing scale organization, item clarity, and alignment with research objectives to ensure accurate measurement of target constructs and provide reliable instrumentation for subsequent large-scale investigation. Research procedure and samples This study utilized the Chinese online survey platform “Wenjuanxing” to collect data, specifically focusing on the most-viewed Minnan nursery rhyme “Fishing Song” on Bilibili (511,000 views). The online survey platform streamlined data entry and questionnaire distribution while expanding sample coverage 69 . To ensure data authenticity, the questionnaire was configured to allow only one submission per respondent. Additionally, targeting digitally-savvy users with prior exposure to Minnan nursery rhymes, the survey included screening questions at the outset---respondents indicating no prior experience viewing “Fishing Song” or similar Minnan cultural content on digital platforms like Bilibili were automatically disqualified from participation. This study was conducted in Xiamen in 2025. As the cultural heartland of Minnan culture, Xiamen boasts rich cultural heritage and an active digital user base 70 , providing an ideal research setting. With the approval from Xiamen Regional University Review Board in September 2024, the study employed purposive sampling to target users who had watched “Fishing Song” or similar Minnan nursery rhymes on Bilibili within the past year. To expand sample coverage, researchers encouraged initial respondents to share the questionnaire link through social networks (e.g., WeChat groups, Bilibili comment sections), combining snowball sampling for participant recruitment. By strategically combining targeted and chain-referral sampling, the study comprehensively engaged Minnan culture enthusiasts while sustaining tight research parameters., ultimately generating robust and contextually relevant dataset 71 . Following data collection, the research team implemented rigorous data screening procedures to ensure analytical reliability and validity. The filtering criteria included: (1) removing uniform responses (e.g., identical answers across all items), (2) excluding incomplete datasets with missing values, and (3) eliminating blank questionnaires. From the total pool of 350-550 collected responses, 35 problematic cases (comprising 20 uniform responses, 10 incomplete submissions, and 5 blank forms) were discarded. The final valid sample size is projected at 300-500 participants, which meets and exceeds the minimum requirement of 200 cases for structural equation modeling (SEM) analysis as recommended by Jobst et al. (2021) 72 , thereby ensuring robust statistical power for subsequent analyses. Results This study employed Structural Equation Modeling (SEM) for data analysis, following (Kline, R.B.’s 2015) 73 analytical procedures. The analysis utilized SPSS 28 for preliminary data screening and descriptive statistics, while AMOS software was used to examine both the measurement and structural models. Moderating effects were verified using (Hayes, A. F. 2012) 74 PROCESS 3.3 macro. The analytical results encompassed demographic characteristics statistics, normality and correlation analyses, measurement model fit assessment, structural model path analysis, and examination of ER and CI’s moderating effects. Characteristics Analysis of Demographic Sample The study initially collected 487 responses, from which 31 invalid questionnaires (due to either excessively short completion time or identical responses throughout) were excluded, resulting in 456 valid samples with an effective response rate of 93.6%.Table 1 presents the demographic characteristics of the sample, including gender and age distribution. The gender distribution showed relative balance, with 220 male participants (48.2%) slightly outnumbered by 236 female participants (51.8%). Age distribution analysis revealed: 82 respondents aged 18-24 (18.0%), 180 aged 25-34 (39.5%), 130 aged 35-44 (28.5%), and 64 aged 45+ (14.0%), with the 25-34 and 35-44 age groups collectively forming the predominant demographic strata (68.0% combined). Table 1 Characteristics analysis of demographic sample Variable Item Frequency Percentage Gender Male 220 48.2 Female 236 51.8 Age 18-24 82 18.0 25-34 180 39.5 35-44 130 28.5 Above 45 64 14.0 Normality Test and Descriptive Statistics Table 2 presents the descriptive statistics of key constructs, with mean scores ranging from 3.800 (AU) to 4.004 (CRA), indicating generally positive evaluations of the digital dissemination of “Fishing Song” among respondents. Standard deviations varied between 0.895 (ER) and 1.099 (AU), suggesting higher response consistency for ER compared to greater variability in AU. All absolute values of skewness (1.206-1.575) and kurtosis (0.130-1.553) met the normality thresholds of skewness <3 and kurtosis <10 (Siraj-Ud-Doulah, M., 2021). Table 2 Descriptive statistics and inter-correlations for the variables (n=456). 1 2 3 4 5 6 7 8 9 10 Mean 3.999 4.004 3.891 3.930 3.904 3.883 3.980 3.948 3.962 3.800 SD 0.975 0.950 1.008 0.991 1.001 1.033 0.895 0.973 0.986 1.099 Skewness -1.548 -1.523 -1.271 -1.401 -1.453 -1.320 -1.560 -1.418 -1.575 -1.206 Kurtosis 1.319 1.374 0.327 0.744 1.040 0.548 1.553 0.920 1.406 0.130 Note:***、**、*representing respectively P<0.001、P<0.01、P<0.05 Measurement Model Testing The measurement model was evaluated using maximum likelihood estimation. As shown in Table 3, the model demonstrated excellent fit indices: χ²/df = 1.144 (0.9), CFI = 0.994 (>0.9), and RMSEA = 0.018 (<0.08), meeting all recommended thresholds 76 , indicating strong alignment between the measurement model and empirical data. Table 4 presents convergent validity results. All factor loadings ranged from 0.785 to 0.896 (exceeding 0.5), composite reliability (CR) values were 0.862-0.894 (>0.7), average variance extracted (AVE) estimates were 0.676-0.738 (>0.5), and Cronbach’s alpha coefficients were 0.862-0.894 (>0.7), collectively confirming strong reliability and convergent validity 77 . Variance inflation factors (VIFs) between 2.097 and 2.855 (<3.3) ruled out common method bias concerns 78 . Discriminant validity was verified via the Fornell-Larcker criterion 79 . Table 5 demonstrates that the square roots of AVEs (bold diagonal values) exceeded all inter-construct correlations, establishing robust discriminant validity 80 . Table 3 The fitness of the measurement model X 2 P df X 2 /df TLI CFI REMSEA Measurement model 411.865 0.031 360 1.144 0.993 0.994 0.018 Fit criteria — — — 0.9 >0.9 <0.08 Table 4 The calculation results of reliability Construct items Factor loading CR AVE Cronbach alpha VIF PF PF1 0.819 0.873 0.698 0.871 2.335 PF2 0.886 2.743 PF3 0.798 2.115 CRA CRA1 0.836 0.866 0.683 0.866 2.312 CRA2 0.827 2.277 CRA3 0.817 2.162 CA CA1 0.841 0.887 0.723 0.886 2.482 CA2 0.861 2.568 CA3 0.848 2.579 IQ IQ1 0.817 0.871 0.692 0.870 2.227 IQ2 0.866 2.565 IQ3 0.811 2.188 PEOU PEOU1 0.836 0.877 0.703 0.877 2.399 PEOU2 0.847 2.443 PEOU3 0.833 2.334 PU PU1 0.861 0.890 0.730 0.889 2.766 PU2 0.882 2.855 PU3 0.819 2.311 ER ER1 0.83 0.862 0.676 0.862 2.246 ER2 0.82 2.198 ER3 0.816 2.147 CI CI1 0.835 0.877 0.705 0.875 2.499 CI2 0.896 2.782 CI3 0.785 2.097 BI BI1 0.861 0.885 0.719 0.884 2.634 BI2 0.859 2.611 BI3 0.823 2.320 AU AU1 0.841 0.894 0.738 0.894 2.543 AU2 0.877 2.805 AU3 0.859 2.720 Table 5 the calculation results of convergent validity PF CRA CA IQ PEOU PU ER CI BI AU PF 0.835 CRA 0.485 0.826 CA 0.439 0.408 0.850 IQ 0.430 0.436 0.392 0.832 PEOU 0.504 0.534 0.507 0.387 0.838 PU 0.565 0.459 0.351 0.370 0.473 0.854 ER 0.334 0.227 0.182 0.233 0.185 0.251 0.822 CI 0.207 0.255 0.256 0.196 0.235 0.183 0.118 0.840 BI 0.552 0.468 0.502 0.507 0.525 0.570 0.319 0.282 0.848 AU 0.434 0.350 0.348 0.284 0.350 0.397 0.172 0.510 0.495 0.859 Note: The bold values on the diagonal represent the square roots of AVEs, while the values below the diagonal indicate the correlation coefficients between latent variables. Structural Model Testing Since the measurement model’s fit indices all met the established criteria, the study proceeded to estimate the initial research model using maximum likelihood estimation. As presented in Table 6, the structural equation model demonstrated good fit: χ²/df = 1.780 (0.9), CFI = 0.973 (>0.9), and RMSEA = 0.041 (<0.08). Table 6 The fitness of the research model X 2 P df X 2 /df TLI CFI REMSEA structural model 421.755 0.000 237 1.780 0.969 0.973 0.041 Fit criteria — — — 0.9 >0.9 <0.08 Table 7 presents the path coefficients, showing all paths were statistically significant except the relationship between CA and PU. Specifically: (1) CA (β=0.440, t=8.210, p<0.001) and IQ (β=0.253, t=4.888, p<0.001) significantly influenced PEOU; (2) PF (β=0.403, t=6.976, p<0.001), CRA (β=0.183, t=3.318, p<0.001), and PEOU (β=0.178, t=3.223, p0.05) showed no significant effect; (3) Both PEOU (β=0.353, t=7.181, p<0.001) and PU (β=0.438, t=8.825, p<0.001) significantly predicted BI; (4) BI (β=0.505, t=9.872, p<0.001) significantly influenced AU (see Fig. 2 for the model with standardized path coefficients). Table 7 Test results of research hypotheses testing Path Unstandardized coeffcient (B) Standardized coeffcient (β) S.E. t CA→PEOU 0.438 0.440*** 0.053 8.210 Q→PEOU 0.260 0.253*** 0.053 4.888 PF→PU 0.434 0.403*** 0.062 6.976 CRA→PU 0.197 0.183*** 0.059 3.318 CA→PU 0.026 0.025 0.063 0.409 PEOU→PU 0.185 0.178** 0.058 3.223 PEOU→BI 0.359 0.353*** 0.050 7.181 PU→BI 0.428 0.438*** 0.048 8.825 BI→AU 0.546 0.505*** 0.055 9.872 Note:***、**、*representing respectively P<0.001、P<0.01、P<0.05 Moderating Effect Testing The moderating effect was analyzed using (Hayes, A. F. 2012) 74 PROCESS macro (Version 3.3). As shown in Figure 3, the interaction term between PU and ER exerted a significant positive effect on BI (β=0.135, t=3.270, p<0.01), indicating that ER positively moderates the relationship between PU and BI. Figure 4 demonstrates that the interaction between BI and CI significantly enhanced AU (β=0.124, t=3.000, p<0.01), confirming CI’s positive moderating role in the intention-behavior linkage. These findings reveal the crucial moderating mechanisms of ER and CI in user behavior within digital media technology’s impact on ICH learning outcomes. Discussion This study examines the impact of digital platform features, ER, and CI on the dissemination of ICH, using the Minnan nursery rhyme “Fishing Song” as a case study within an extended TAM framework. The results support hypotheses H1-H3 and H5-H11, revealing significant effects of key variables. Firstly, the analysis revealed that both PF (β = 0.409***) and CRA (β = 0.187***) significantly enhanced PU (supporting H1-H2). These results suggest that Bilibili’s features (including subtitles and playback controls) and algorithmic recommendations effectively heighten users’ appreciation of the cultural value in “Fishing Song”, consistent with (Susilo et al. 2021) 81 research on functionality’s role in usefulness perception. For practical application, we recommend platform developers refine their recommendation systems to better target ICH content delivery and strengthen users’ cultural value recognition. Secondly, CA (CA, β = 0.441, p < 0.001) and IQ (IQ, β = 0.253, p 0.05), failing to support H4. These findings align with (Balaman and Baş, 2021) 82 conclusion that CA enhances PEOU, but contradict (Matausch et al. 2014) 83 perspective that CA directly affects PU. This discrepancy may stem from the high accessibility of “Fishing Song” on Bilibili, where users prioritize interactive experiences over basic convenience. Similar to (Du & Lv 2024) 84 findings about the limited impact of EE on GAI adoption in elementary education, this suggests that technological familiarity may diminish the role of certain functions. We recommend platforms enhance bullet comments and comment features to lower usage barriers and improve user experience. Third, the results demonstrated that PEOU significantly enhanced PU (β = 0.189, p < 0.001), supporting H6, while both PEOU (β = 0.354, p < 0.001) and PU (β = 0.437, p < 0.001) exerted substantial positive effects on BI, thereby validating H7 and H8. These findings not only corroborate the fundamental pathways of the TAM proposed by (Davis. 1989) 6 , but also align with (Fan, C. 2023) 85 research conclusions regarding the pivotal role of usability and utility in driving technology adoption. Specifically, users’ recognition of Bilibili’s user-friendly interface and practical value directly strengthened their willingness to engage with the “Fishing Song” content. To optimize user experience, it is recommended that educators and platform developers implement interface simplification strategies and provide detailed operational guidance, which would further elevate users’ PEOU and ultimately enhance their valuation of the platform’s functionalities. Fourthly, BI exerted a significant positive influence on AU (β = 0.505, p < 0.001), supporting H9. This finding aligns with recent studies on digital cultural learning by Songkram et al., (2023) 86 and Zhang & Yu (2022) 87 , demonstrating that users’ intention to engage with “Fishing Song” effectively translates into concrete actions such as viewing or sharing the content. To enhance this behavioral conversion, platforms could implement user incentive mechanisms (e.g., point reward systems) or interactive campaigns (e.g., comment challenges) to strengthen BI and consequently increase AU frequency. Finally, ER positively moderated the relationship between PU and BI (β = 0.135, p < 0.01), while CI positively moderated the BI-AU relationship (β = 0.124, p < 0.01), supporting H10 and H11. These findings reveal that: (1) ER amplifies PU’s effect on BI, aligning with Kamal et al. (2024) 88 findings about emotional connections enhancing BI, suggesting that the melody and lyrics of “Fishing Song” boost usage willingness by evoking emotional memories; (2) CI strengthens the BI-to-AU conversion, consistent with Wijaya et al. (2022) 89 conclusions on CI driving engagement, where high-CI users show greater propensity to share or recommend content. This pattern parallels (Zhang et al. 2023) 90 observations about task-technology fit (TTF) moderating PE and EE, collectively highlighting the critical role of psychological factors in technology acceptance. For practical application, cultural preservation institutions could enhance ER and CI through emotionally engaging content (e.g., digital storytelling or VR experiences) and localized cultural activities to facilitate ICH dissemination. Conclusions and Limitations Research Conclusions Current research widely acknowledges the transformative potential of digital platforms in the dissemination and preservation of ICH, as they provide users with broader access opportunities and personalized experiences 91 . This study developed and validated an extended TAM to examine the impact of digital platform features, ER, and CI on the digital transmission of ICH. Through questionnaire analysis of 456 users in China’s Fujian Province, the results confirmed most research hypotheses, highlighting the critical role of emotional and cultural factors in ICH dissemination. This study fills a research gap in user acceptance studies of digital ICH dissemination. Unlike the traditional TAM model that focuses primarily on technological functionalities 6 , our research enhances the understanding of user behavior by incorporating ER and CI, which aligns with (Yi, Y. 2023) 92 perspective on how CI promotes engagement. The findings not only expand the application of TAM in cultural transmission but also provide empirical evidence for emotional and cultural psychological mechanisms. On a practical level, the results demonstrate that optimizing platform features (such as precise recommendations and interactive design) while incorporating emotionally engaging content (like digital storytelling) can significantly improve the effectiveness of ICH dissemination. These insights offer strategic guidance for platform developers, educators, and cultural preservation institutions, contributing to the effective transmission of ICH in digital environments 93 . Research Limitations and Future Development While this study has achieved the aforementioned findings, several limitations warrant further exploration in future research. Firstly, the sample was limited to 456 valid questionnaires collected from a single region, which may not fully represent the acceptance behaviors of global ICH audiences. Cultural background variations could influence users’ perceptions and usage of digital platforms and ICH content 94 . For instance, distinct usage behaviors observed in non-local users may stem from legacy migration routes and acculturation dynamics 95 . To address this limitation, future studies should expand the sample scope to include user data from diverse geographical locations and conduct cross-cultural comparative analyses. This would enhance the generalizability of the results and empower tailored cultural adaptation of messaging for diverse demographic groups.. Secondly, this study focused on a single ICH case and did not encompass other types of ICH content. Diverse ICH categories (e.g., musical versus performative traditions) may differentially impact user acceptance through their unique transmission dynamics 96 . For instance, melodic content could inherently stimulate stronger ER compared to narrative content, while visually-oriented content could demonstrate greater dependence on a platform’s interactive features. Future research should adopt multiple-case approaches to compare the dissemination effectiveness across different ICH contents and platforms, thereby revealing the interaction effects between content attributes and technological environments. Additionally, this study employed a cross-sectional design that only captures user behavior at a single temporal point, rather than examining the longitudinal development of ER and CI effects on technology acceptance 97 . Furthermore, the exclusion of other potential mediating variables (e.g., technological familiarity, content attractiveness) may limit a comprehensive understanding of the mechanisms through which ER and CI operate 98 , 99 . Future research should adopt longitudinal designs to track the dynamic evolution of user behavior while incorporating additional theories (e.g., the Information System Success Model) or variables to enhance the model’s explanatory power. Declarations Ethical Approval: This study was formally approved in September 2024 by the Institutional Ethics Committee (Approval No. XIT-MEI-20240901). All procedures involving human participants were conducted in accordance with the institutional guidelines and the Declaration of Helsinki (1964) and its later amendments. The approval covered the design, distribution, and analysis of anonymous questionnaires regarding users’ experiences with Minnan nursery rhymes on digital platforms. The questionnaire and consent procedures were approved as part of the ethics review. Informed consent: All participants received comprehensive information prior to completing the online questionnaire to ensure they could make an informed decision. Participants were informed about the purpose of the study, procedures, confidentiality measures, and their right to withdraw at any time without consequences, and were advised that no foreseeable risks were associated with participation. Participation was entirely voluntary; no personally identifiable information was collected, and all data were anonymized and analyzed in aggregate to protect privacy. Informed consent was obtained electronically at the start of the questionnaire via the Wenjuanxing platform, which automatically recorded the consent date (YYYY-MM-DD). All written consents were obtained between October and December 2024, following ethics approval granted in September 2024. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Funding Declaration ： This research was supported by the 2024 Fujian Provincial Social Science Foundation, under the project titled “Digital Dissemination and Optimization of Minnan Nursery Rhymes Based on Narrative Theory” ( No. FJ2024B177) Author Contribution S.M.L. designed the research framework, developed the questionnaire, conducted data collection, and wrote the main manuscript text. C.L.X. co-led the project, contributed to research design, and critically revised the manuscript. J.Y.L. provided methodological guidance and assisted in data analysis. Z.A.Z. supported manuscript language refinement and editorial preparation. Q.R.Z. contributed to reference organization and final proofreading. All authors reviewed and approved the final manuscript. References 1.Blake, J. (2006). 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International Journal of Heritage Studies, 29, 1089-1109.https://doi.org/10.1080/13527258.2023.2237495 Mathioudakis, G., Klironomos, I., Partarakis, N., Papadaki, E., Volakakis, K., Anifantis, N., & Papageorgiou, I. (2022). InCulture: A collaborative platform for intangible cultural heritage narratives. Heritage.https://doi.org/10.3390/heritage5040149 Kang, X., Li, X.-Z., & Chen, C.-C. (2023). An acceptance model of digital education in intangible cultural heritage based on cultural awareness. Digital Creativity, 34, 331-346.https://doi.org/10.1080/14626268.2023.2280028 He, Y., Chen, X., & Wang, L. (2023). How digital events promote intangible cultural heritage? A user experience perspective. Proceedings of the Association for Information Science and Technology, 60.https://doi.org/10.1002/pra2.916 Yang, J., & Xu, C. (2024). Digital enabling rural revitalization: An innovative study of intangible cultural heritage in animation-based inheritance and dissemination. Applied Mathematics and Nonlinear Sciences, 9.https://doi.org/10.2478/amns-2024-1674 Mo, Z., & Huang, G. (2024). Digital preservation of intangible cultural heritage and exploration of network communication issues. Archives des Sciences.https://doi.org/10.62227/as/74212 Codina, J. O. (2024, May 7). How do emotions help construct our cultural identity in music festivals? Phys.org. https://phys.org/news/2024-05-emotions-cultural-identity-music-festivals.html Leow, F.-T., & Ch’ng, E. (2021). Analysing narrative engagement with immersive environments: Designing audience-centric experiences for cultural heritage learning. Museum Management and Curatorship, 36, 342-361.https://doi.org/10.1080/09647775.2021.1914136 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":114887,"visible":true,"origin":"","legend":"\u003cp\u003eModel proposition\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7181151/v1/3ab053e544c3a801a7b8e949.png"},{"id":92876215,"identity":"9d2fe313-0178-473f-89ff-dd4f700a3e80","added_by":"auto","created_at":"2025-10-06 14:44:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":155769,"visible":true,"origin":"","legend":"\u003cp\u003eStructural model with standardized path coefficients\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7181151/v1/640efb0ed22fbb17ae558920.png"},{"id":92876597,"identity":"f2bb5dad-baf9-4bd1-8dc7-fd49ae3050e3","added_by":"auto","created_at":"2025-10-06 14:52:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":32848,"visible":true,"origin":"","legend":"\u003cp\u003eModerating effect of ER on the relationship between PU and BI\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7181151/v1/991940a4a1b0ba05d0030939.png"},{"id":92876213,"identity":"aa04bc96-3038-4b92-b31f-f102dcf93f74","added_by":"auto","created_at":"2025-10-06 14:44:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":34443,"visible":true,"origin":"","legend":"\u003cp\u003eModerating effect of CI on the relationship between BI and AU\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7181151/v1/aeb4abfd4590d5c804c0136d.png"},{"id":92878274,"identity":"c55ea52f-1ee0-4b8f-9890-be895ad90912","added_by":"auto","created_at":"2025-10-06 15:08:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2635478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7181151/v1/e8684744-7726-4160-a746-3a778623de0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unpacking the effect of Digital Platforms on the Dissemination of Intangible Cultural Heritage: An Extended Technology Acceptance Model with Emotional Resonance and Cultural Identity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs an important manifestation of cultural diversity and human creativity, intangible cultural heritage (ICH) carries dynamic cultural forms such as historical memory, traditional knowledge, language, music, and handicrafts\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Under the impact of globalization and modernization, ICH faces the risk of disappearance and marginalization, making its preservation particularly crucial. Digitalization, as an emerging conservation method, provides new avenues for the documentation, storage, dissemination, and education of ICH. Through digitalization, ICH can transcend temporal and spatial constraints, achieving broader dissemination and more effective preservation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. It helps preserve the original form of ICH, and at the same time, enhances public awareness and interest through interactive and participatory approaches, thereby facilitating the transmission and sustainable development of ICH\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExisting studies have fully tapped into the significance of digital technologies in safeguarding ICH, and explorations and practical efforts have been conducted on this respect. For instance, some research focus on using digital technologies to document and archive ICH, while other studies examine how digital dissemination impacts ICH transmission\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Nevertheless, limitations do present in current research. First, the majority of studies center on the application at the technological level, paying insufficient attention to users\u0026rsquo; psychological mechanisms in accepting and using digital platforms\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Second, there is a lack of systematic theoretical frameworks, which is necessary in analyzing the interaction between the functional characteristics of digital platforms and users\u0026rsquo; cultural psychological mechanisms\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Lastly, existing empirical research is rather limited, particularly lacking in-depth analysis of user behavior across different cultural backgrounds\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. These research gaps highlight the necessity of further investigating users\u0026rsquo; psychological and behavioral mechanisms to advance both theoretical and practical developments in the digital dissemination of ICH.\u003c/p\u003e\u003cp\u003eRegarding the deficiencies identified in existing research, this study introduces ER and CI as external variables to construct an extended TAM, with the aim to explore the impact of the interaction between digital platform functionality and users\u0026rsquo; cultural psychology on the digital dissemination of ICH. Taking Minnan nursery rhymes as a case study, this research will specifically examine the following issues: (1)How do PF, CRA, CA, and IQ influence user BI through PEOU and PU? (2) How CI and ER further promote users\u0026rsquo; continued engagement and AU? Through empirical research, this study aims to provide new insights into the role of digital platforms in ICH preservation and to offer recommendations for enhancing the interaction between digital platforms and local cultures.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003e\u003cstrong\u003eImpact of Digital Platforms on Dissemination of ICH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital platforms refer to virtual spaces built on the internet and information technologies, capable of supporting information storage, dissemination, and user interaction. These platforms typically include social media, content-sharing platforms, and online education systems, among others\u003csup\u003e10\u003c/sup\u003e. The integration of multimedia technologies, intelligent algorithms and interactive features provides users with diverse digital experiences and increasing attention in the field of cultural dissemination\u003csup\u003e11\u003c/sup\u003e. In terms of ICH dissemination, digital platforms have played an essential role in opening new pathways for the preservation and promotion of ICH. Specifically, digital platforms enhance the dissemination of ICH through various technological means. For example, immersive technologies allows for users\u0026rsquo; \u0026ldquo;multi-dimensional perspectives\u0026rdquo; and \u0026ldquo;active participation\u0026rdquo;, enabling them to perceive the nuances of cultural heritage more intuitively\u003csup\u003e12\u003c/sup\u003e. Through analyzing user interests and behavioral patterns, intelligent recommendation algorithms can precisely deliver ICH content, thereby expanding cultural dissemination while boosting engagement and learning motivation\u003csup\u003e13\u003c/sup\u003e. Additionally, online forums, virtual cultural events and other real-time interactive features strengthen the connection between cultural creators and audiences, fostering dynamic cultural transmission and reinterpretation\u003csup\u003e14\u003c/sup\u003e. However, the rapid development of digital platforms also presents challenges. Fragmented content may undermine the integrity of culture, making it difficult for audiences to grasp the historical context and values behind ICH. Moreover the commercialization of platforms may prioritize traffic metrics over cultural authenticity and educational significance\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTAM\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInitially proposed by Davis (1989)\u003csup\u003e6\u003c/sup\u003e, the Technology Acceptance Model (TAM) was a classical framework for studying users\u0026rsquo; acceptance and utilization of new technologies. Its core variables include\u0026nbsp;perceived usefulness (PU)\u0026nbsp;and\u0026nbsp;perceived ease of use (PEOU), which measure users\u0026rsquo; recognition of a technology\u0026rsquo;s potential in enhancing efficiency and its ease of operation respectively\u003csup\u003e6\u003c/sup\u003e. In recent years, TAM\u0026rsquo;s theoretical framework has been widely applied across various contexts, including educational technology, healthcare information systems, virtual reality platforms and cultural dissemination. Research has shown that TAM is effective in explaining initial technology adoption and can also be extended to evaluate sustained usage behavior and the complex dynamics in disseminating \u0026nbsp;cultural products\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of ER and CI on Dissemination of ICH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmotional resonance (ER) denotes the capacity of cultural elements or digital media to evoke deep emotional connections and engagement in users. Zheng \u0026amp; Hu (2024)\u003csup\u003e16\u003c/sup\u003e analyze how travel platforms leverage visual elements to express ER in digital spaces, deepening user immersion. This concept is particularly impactful in ICH dissemination, as it cultivates a stronger personal and communal bond with cultural traditions. For example, Zhou (2024)\u003csup\u003e17\u003c/sup\u003e studied how the mobile game \u0026ldquo;Peace Elite\u0026rdquo; uses digitalization and gamification strategies to foster users\u0026rsquo; ER and CI with traditional Chinese culture, demonstrating that digital cultural platforms can enhance users\u0026rsquo; understanding and acceptance of traditional culture. By stimulating users\u0026rsquo; ER through digital narratives and immersive virtual reality experiences, their appreciation and understanding of ICH can be greatly enhanced. Similarly, Research by Sangamuang et al. (2025)\u003csup\u003e18\u003c/sup\u003e revealed that in gamified virtual museum settings, VR technology greatly boosts immersion and CI, leading to higher user engagement with cultural material and a greater propensity to share it. By addressing users\u0026rsquo; psychological needs, ER serves as a crucial link between traditional culture and contemporary audiences, stimulating sustained interest in digital platforms\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eCultural identity (CI)\u0026nbsp;reflects users\u0026rsquo; sense of belonging and shared cultural values developed through engagement with cultural content. Zheng \u0026amp; Hu (2024)\u003csup\u003e16\u003c/sup\u003e highlighted that CI serves as a critical factor in fostering emotional attachment on digital cultural platforms. Empirical evidence demonstrates that strong CI positively moderates the relationship between BI and actual AU\u003csup\u003e20\u003c/sup\u003e. Furthermore, Lin et al. (2024)\u003csup\u003e19\u003c/sup\u003e noted that localized gamified cultural dissemination approaches, such as augmented reality and social interactive games, effectively reinforce users\u0026rsquo; CI and deepen emotional engagement. Additionally, research by Lyu, Z. (2023)\u003csup\u003e21\u003c/sup\u003e demonstrated that CI enhances a digital platform\u0026rsquo;s PU by aligning technological experiences more closely with users\u0026rsquo; real-world cultural contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch hypothesis and structural model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlatform Functionality\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlatform functionality (PF)\u0026nbsp;describes how well a digital platform\u0026rsquo;s functional design aligns with users\u0026rsquo; needs and usage contexts, providing tools and features that effectively support task completion or learning experiences. High PF occurs when a platform\u0026rsquo;s operational functions closely correspond to user objectives (e.g., learning local culture), thereby improving efficiency\u003csup\u003e22\u003c/sup\u003e. This concept underscores the congruence between functional design and user requirements, representing a key aspect of digital platform user experience\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eResearch has indicated that\u0026nbsp;PF significantly enhances users\u0026rsquo;\u0026nbsp;PU. For instance, studies of Radu (2020)\u003csup\u003e24\u003c/sup\u003e on mobile learning platforms revealed that when platform features effectively support users\u0026rsquo; learning objectives (e.g., by offering personalized content or user-friendly tools), users better recognize the platform\u0026rsquo;s practical value, thereby enhancing PU. Davis (1989)\u003csup\u003e6\u003c/sup\u003e emphasized the pivotal role of PF in shaping PU, observing that users perceiving functional alignment with their needs show stronger continuance intention. \u0026nbsp;The research of Wang \u0026amp; Zhang (2022)\u003csup\u003e25\u003c/sup\u003e on short-video platforms further corroborated that PF (e.g., personalized recommendations and interactive features) not only elevate PU but also strengthens users\u0026rsquo; long-term engagement intentions.\u003c/p\u003e\n\u003cp\u003eResearch on digital platforms for cultural heritage reveals that platform functionality (PF), including features like multilingual support and interactive navigation, satisfies cultural learning objectives while markedly improving users\u0026rsquo; perceived platform value\u003csup\u003e26,21\u003c/sup\u003e. Building on this theoretical foundation, this study posits that when digital platforms achieve high PF alignment with user expectations, they can substantially improve users\u0026rsquo; PU. Accordingly, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003eH1:\u0026nbsp;PF has a significantly positive impact on PU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContent Recommendation Accuracy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContent recommendation accuracy (CRA) measures how precisely a digital platform can customize cultural content which aligns with users\u0026rsquo; historical behavior, interests, preferences and needs. High CRA occurs when recommended content closely aligns with user goals (such as learning about local culture), thereby enhancing the efficiency of information acquisition and user satisfaction\u003csup\u003e27\u003c/sup\u003e. This concept underscores the essential function of recommendation systems in fulfilling users\u0026rsquo; personalized needs and represents a critical component of digital platform functionality.\u003c/p\u003e\n\u003cp\u003eResearch has demonstrated that CRA has a significant positive impact on users\u0026rsquo; PU. For example, the study of Ricci et al. (2022)\u003csup\u003e27\u003c/sup\u003e found that precise recommendations can reduce users\u0026rsquo; information search costs, improve task completion efficiency, and significantly enhance their perception of the platform\u0026rsquo;s value. Additionally, Li et al. (2024)\u003csup\u003e28\u003c/sup\u003e and Li (2024)\u003csup\u003e29\u003c/sup\u003e revealed in their analysis of user behavior on short video platforms that when recommendation systems deliver content highly relevant to users\u0026rsquo; interests, users are more likely to perceive the platform as valuable in supporting their cultural exploration or entertainment needs, thereby enhancing PU. These recent findings suggest that CRA significantly strengthens users\u0026rsquo; recognition of PF and their perception of value by delivering high-quality personalized content.\u003c/p\u003e\n\u003cp\u003eBased on the above theoretical foundation, this study posits that if digital platforms can fulfill users\u0026rsquo; personalized needs through high CRA (e.g., accurately recommend localized cultural content), it will significantly enhance users\u0026rsquo; PU of the platform. Accordingly, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003eHypothesis 2:\u0026nbsp;CRA has a significantly positive impact on PU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContent Accessibility\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContent accessibility (CA) generally refers to the extent to which users can easily discover, access, and utilize cultural content provided by digital platforms. High CA implies that users can efficiently reach desired cultural resources through streamlined operational processes and effective information presentation (e.g., intuitive navigation or fast loading speeds)\u003csup\u003e30\u003c/sup\u003e. This definition highlights CA\u0026rsquo;s crucial role in minimizing usage barriers and enhancing user experience, particularly within cultural learning contexts.\u003c/p\u003e\n\u003cp\u003eResearch has proven that CA exerts a significant positive influence on both PEOU and PU. First, regarding PEOU, the study of Matausch, K. (2014)\u003csup\u003e32\u003c/sup\u003e on digital cultural platforms found that accessible content design (e.g., intuitive interfaces and user-friendly search functions) significantly reduces operational complexity for users, thereby enhancing their perception of the platform\u0026rsquo;s PEOU. \u0026nbsp;Furthermore, Zhang et al. (2024)\u003csup\u003e31\u003c/sup\u003e demonstrated in their analysis of online education platforms that CA directly enhances a platform\u0026rsquo;s operational convenience by reducing users\u0026rsquo; time and effort in information acquisition, thereby substantiating CA\u0026rsquo;s influence on PEOU. Regarding PU,\u0026nbsp;Matausch et al. (2014)\u003csup\u003e32\u003c/sup\u003e found that when cultural content is accessed efficiently and without barriers, users are more likely to perceive the platform as effectively supporting their learning or exploratory goals, consequently increasing PU. These recent findings suggest that CA optimizes both the convenience and efficiency of information acquisition, not only lowering the platform\u0026rsquo;s usage threshold (affecting PEOU) but also enhancing its functional utility (affecting PU).\u003c/p\u003e\n\u003cp\u003eBased on the above theoretical foundation, this study contends that digital platforms can significantly influence users\u0026rsquo; PEOU and PU by enhancing CA (e.g., fast loading speeds and optimized search functions) to meet users\u0026rsquo; cultural content acquisition needs. Specifically, when users can effortlessly access cultural resources, they are more likely to perceive the platform as user-friendly (PEOU), while simultaneously recognizing its practical value in supporting cultural learning (PU), thereby elevating the overall user experience. Accordingly, the following hypotheses are proposed:\u003c/p\u003e\n\u003cp\u003eH3: CA has a significantly positive impact on PEOU.\u003c/p\u003e\n\u003cp\u003eH4: CA has a significantly positive impact on PU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInteraction Quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInteraction quality (IQ) measures the effectiveness, richness and enjoyment level of interactions between users and content or among users on digital platforms. According to Shi et al. (2023)\u003csup\u003e33\u003c/sup\u003e, high IQ enables users\u0026rsquo; seamless engagement with cultural content through features like comment sections, bullet chats or real-time feedback, thereby enhancing their sense of participation and operational fluency. This definition underscores the crucial role of IQ in optimizing user-platform interactions, particularly in cultural learning or content consumption scenarios.\u003c/p\u003e\n\u003cp\u003eStudies show that IQ significantly enhances PEOU. Shi et al. (2023)\u003csup\u003e33\u003c/sup\u003e research on digital museum experiences found that high-quality interactive design (e.g., intuitive commenting systems and real-time feedback) increases information richness, thereby strengthening users\u0026rsquo; perception of the platform\u0026rsquo;s PEOU. Similarly, Jameel et al.\u0026rsquo;s (2021)\u003csup\u003e34\u003c/sup\u003e investigation of online learning platforms demonstrated that smooth and responsive interactive features can improve users\u0026rsquo; sense of operational convenience by simplifying participation process. These findings collectively indicate hat IQ enhances PEOU by delivering efficient and enjoyable interactive experiences that lower users\u0026rsquo; access barriers.\u003c/p\u003e\n\u003cp\u003eBuilding upon this theoretical foundation, this study proposes that digital platforms can substantially enhance users\u0026rsquo; PEOU by delivering seamless and intuitive interactive experiences through high IQ features (e.g., comment sections and bullet chat functions). Accordingly, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003eH5: IQ has a significantly positive impact on PEOU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePEOU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerceived ease of use (PEOU) represents users\u0026rsquo; assessment of how effortlessly they can learn and operate a digital platform\u0026rsquo;s functions. Venkatesh \u0026amp; Bala (2008)\u003csup\u003e7\u003c/sup\u003e found that intuitive interface design and efficient processes lead to high PEOU by allowing users to quickly learn and competently use the platform. This definition highlights the central role of PEOU in reducing usage barriers and enhancing user experience, particularly in cultural learning or content consumption scenarios.\u003c/p\u003e\n\u003cp\u003eResearch has indicated that PEOU signifcantly enhances PU. Mensah (2020)\u003csup\u003e35\u003c/sup\u003e, in extending the Technology Acceptance Model (TAM), found\u0026nbsp;that when users perceive platform operations as simple and intuitive, they are more likely to view the platform as effective for task completion, thereby enhancing PU. This relationship has been further validated by Sorkun et al. (2022)\u003csup\u003e36\u003c/sup\u003e in their study of online learning platforms, which demonstrated that user-friendly interfaces and streamlined operational processes reduce users\u0026rsquo; cognitive load, thereby improving usage efficiency and users\u0026rsquo; appreciation of the platform\u0026rsquo;s practical value. Furthermore, Aurangzeb et al.\u0026rsquo;s (2024)\u003csup\u003e37\u003c/sup\u003e systematic review of TAM literature confirmed that PEOU significantly enhances users\u0026rsquo; perception of technological value by reducing operational difficulty and boosting usage confidence. These recent studies all demonstrate that PEOU, as a critical factor of user experience, positively influences PU by optimizing operational convenience.\u003c/p\u003e\n\u003cp\u003eBased on the above theoretical foundation, this study contends that digital platforms can significantly enhance users\u0026rsquo; PU by improving PEOU through user-friendly interface design and streamlined operations, which can lower usage barriers. When users can easily navigate a platform, they are more inclined to acknowledge its value in facilitating cultural learning or task achievement, consequently strengthening their appreciation of the platform\u0026rsquo;s functionality. Accordingly, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003eH6: PEOU has a significantly positive impact on PU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Usefulness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerceived usefulness (PU) assesses the extent to which users believe a digital platform can improve their task completion efficiency or learning outcomes. High PU indicates that users recognize the platform\u0026rsquo;s substantial value in supporting their goals (e.g., learning local culture or acquiring information)\u003csup\u003e38\u003c/sup\u003e. This definition highlights PU\u0026rsquo;s central role as users\u0026rsquo; cognitive evaluation of the platform\u0026rsquo;s functional value, making it a key driver of usage intention.\u003c/p\u003e\n\u003cp\u003eStudies show that PU significantly enhances behavioral intention (BI). For example, Alalwan et al. (2017)\u003csup\u003e38\u003c/sup\u003e demonstrated this relationship in mobile payment applications, finding that when users perceive the platform as effectively enhancing their task efficiency, their continuance intention markedly increases, a result that supports the direct effect of PU on BI. Furthermore,Wiardi et al.\u0026rsquo;s (2022)\u003csup\u003e39\u003c/sup\u003e analysis of online learning platforms revealed that users who believe the platform can effectively support their learning objectives (e.g., cultural knowledge acquisition) show stronger continued usage intention, particularly in educational or culturally-oriented digital environments. These studies collectively suggest that PU directly promotes BI by reinforcing users\u0026rsquo; trust in PF and their perception of its value.\u003c/p\u003e\n\u003cp\u003eBuilding upon this theoretical foundation, the present study posits that digital platforms can significantly influence users\u0026rsquo; BI for continued usage by enhancing PU through effective support for local cultural learning, thereby increasing users\u0026rsquo; recognition of functional value. When users perceive the platform as instrumental in achieving their cultural learning or task objectives, they develop stronger continuance intention, ultimately leading to greater platform reliance. Accordingly, the following hypothesis is proposed:\u003c/p\u003e\n\u003cp\u003eH7: PU has a significantly positive impact on BI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceived Ease of Use\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePerceived ease of use (PEOU) measure how effortless users can learn and navigate a platform\u0026rsquo;s features. According to Restianto (2024)\u003csup\u003e40\u003c/sup\u003e, high PEOU indicates that users can quickly adapt to and efficiently utilize the platform through intuitive interface design and streamlined operational processes. This definition highlights PEOU\u0026rsquo;s critical function in reducing usage barriers and enhancing user experience, particularly in cultural learning or content engagement scenarios.\u003c/p\u003e\n\u003cp\u003eResearch has indicated that PEOU exerts a significant positive influence on BI.\u0026nbsp;For instance, in a review study of TAM, Zain et al. (2023)\u003csup\u003e41\u003c/sup\u003e revealed that when users consider platform operations simple and intuitive, they develop stronger continuance usage intentions, as ease of use reduces usage resistance and boosts confidence in system interaction. \u0026nbsp;Supporting this, Berbar\u0026rsquo;s (2023)\u003csup\u003e42\u003c/sup\u003e analysis of online education platforms showed that user-friendly design features (e.g., intuitive navigation and responsive interfaces) directly strengthen users\u0026rsquo; continued engagement willingness by improving operational convenience, particularly in contexts requiring prolonged interaction. These recent findings collectively suggest that PEOU strengthens BI by both minimizing learning costs and optimizing operational experience.\u003c/p\u003e\n\u003cp\u003eDrawing on this theoretical foundation, this study posits that digital platforms can significantly influence users\u0026rsquo; BI by reducing usage barriers through high PEOU, achieved via user-friendly interface design and simplified operations. When users find a platform easy to understand and operate, they are more likely to demonstrate sustained usage intention, leading to greater platform dependence and engagement. Accordingly, the following hypothesis is proposed:\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;H8: PEOU has a significantly positive impact on BI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioral Intention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBehavioral intention (BI)\u0026nbsp;represents users\u0026rsquo; propensity to utilize digital platforms, specifically their commitment to ongoing cultural content engagement or learning experiences.\u0026nbsp;Strong BI reflects users\u0026rsquo; favorable disposition toward future platform usage and serves as a direct predictor of actual behavior\u003csup\u003e43\u003c/sup\u003e. This concept highlights BI\u0026rsquo;s bridging role in connecting psychological disposition with observable action, particularly in cultural learning or content consumption contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResearch has demonstrated that BI significantly positively influences actual usage (AU). Moya et al.\u0026rsquo;s (2018)\u003csup\u003e43\u003c/sup\u003e investigation of information systems revealed that BI partially mediates the relationship between users\u0026rsquo; effort expectancy (EE) and AU, confirming the predictive power of users\u0026rsquo; intention for subsequent actions. Additionally, Chaveesuk et al.\u0026rsquo;s (2021)\u003csup\u003e44\u003c/sup\u003e digital payment research found that Users\u0026rsquo; intention to continue using a technology directly leads to AU, particularly when the technology aligns with their ingrained habits. These findings support the view that BI, as an indicator of users\u0026rsquo; proactive disposition, can significantly drive their AU. Applied to digital platforms, this study argues that users\u0026rsquo; BI (e.g., willingness to continue accessing local cultural content) can strongly predict their AU (e.g., frequent visits or participation in platform activities). When users develop sustained platform engagement intentions, this intention transforms into concrete actions, thereby increasing actual usage frequency.\u003c/p\u003e\n\u003cp\u003eH9:\u0026nbsp;BI has a significantly positive impact on AU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmotional Resonance (ER) and Cultural Identity (CI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerating Effect of ER on the Relationship Between PU and BI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmotional resonance (ER) describes the profound emotional connection and empathetic response users develop toward content or platform experiences during digital interactions, often characterized by emotional reactions to, identification with, or investment in cultural materials\u003csup\u003e45\u003c/sup\u003e. High ER reflects users\u0026rsquo; strong psychological and emotional reactions during platform interactions. This is particularly crucial in cultural learning contexts, where it fosters meaningful emotional attachments to\u0026nbsp;cultural content and establishes an affective foundation for developing BI.\u003c/p\u003e\n\u003cp\u003ePerceived usefulness (PU)\u0026nbsp;denotes users\u0026rsquo; perception of a digital platform\u0026rsquo;s ability to facilitate better task execution or cultural knowledge acquisition, thereby increasing their likelihood of developing continued\u0026nbsp;BI\u003csup\u003e46\u003c/sup\u003e. High PU indicates that users perceive the platform as significantly valuable in supporting their cultural learning goals, making it a key determinant of BI in TAM\u003csup\u003e47\u003c/sup\u003e.Research confirms that emotional resonance (ER) significantly moderates the PU-BI relationship. Hai et al.\u0026rsquo;s (2022)\u003csup\u003e46\u003c/sup\u003e study of online learning platforms revealed that strong ER (e.g., deep emotional connections with local cultural content) enhances users\u0026rsquo; conversion of perceived utility into sustained usage intention, thereby amplifying PU\u0026rsquo;s positive effect on BI. Furthermore, Wang et al. (2021)\u003csup\u003e45\u003c/sup\u003e demonstrated in digital heritage platforms that ER intensifies the positive impact of PU on BI by deepening users\u0026rsquo; emotional engagement with content, particularly in localized cultural learning scenarios.\u003c/p\u003e\n\u003cp\u003eThese findings collectively support the view that\u0026nbsp;ER\u0026nbsp;positively moderates the relationship between\u0026nbsp;PU\u0026nbsp;and\u0026nbsp;BI, primarily by enriching users\u0026rsquo; affective experiences, particularly in cultural content-oriented digital platforms. The study thus posits that enhanced ER during platform use intensifies PU\u0026rsquo;s impact on BI,, thereby reinforcing their continuance intention, especially in localized cultural learning contexts. Based on this theoretical foundation, the research proposes the following hypothesis:\u003c/p\u003e\n\u003cp\u003eH10:\u0026nbsp;ER positively moderates the relationship between PU and BI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerating Effect of CI on the Relationship Between BI and AU\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCultural identity (CI)\u0026nbsp;reflects users\u0026rsquo; sense of belonging, identification, and value alignment with specific cultures (e.g., local cultures), typically evidenced through their interest in cultural content and recognition of their cultural roots\u003csup\u003e48\u003c/sup\u003e. According to Chen and Zhu (2022),\u0026nbsp;strong CI indicates that users closely associate platform usage with their personal cultural background or values, which significantly influences engagement on cultural digital platforms by reinforcing intrinsic user-content connections and creating a cultural basis for behavioral outcomes.. Behavioral intention (BI)\u0026nbsp;represents users\u0026rsquo; subjective willingness when using digital platforms, particularly their intention to continue accessing local cultural content\u003csup\u003e50\u003c/sup\u003e. In the TAM framework, high BI reflects favorable user attitudes toward future platform usage, serving as both a direct antecedent predicting\u0026nbsp;AU and a key determinant of AU.\u003c/p\u003e\n\u003cp\u003eResearch has confirmed that CI significantly moderates the relationship between BI and AU.\u0026nbsp;For instance, Zhang and Jahng\u0026rsquo;s (2024)\u003csup\u003e48\u003c/sup\u003e research on knowledge-sharing platforms revealed that users who exhibit strong CI (e.g., a sense of belonging to local culture) are more likely to translate BI into AU. This moderating effect occurs because CI enhances users\u0026rsquo; perception of the platform\u0026rsquo;s cultural value, thereby intensifying BI\u0026rsquo;s positive influence on AU. Furthermore, (Chen \u0026amp; Zhu 2022)\u003csup\u003e49\u003c/sup\u003e revealed in their research on traditional culture e-learning platforms that users with higher CI are more inclined to convert continuance intentions into AU, particularly in cultural learning contexts. Their findings suggest that CI intensifies the positive impact of BI on AU by strengthening users\u0026rsquo; emotional bonds with cultural content. These recent studies collectively indicate that CI positively moderates the BI-AU relationship by reinforcing users\u0026rsquo; intrinsic connections with cultural content, especially in digital platforms featuring cultural elements\u003csup\u003e51\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBuilding on this theoretical foundation, the study posits that\u0026nbsp;CI\u0026nbsp;positively moderates the relationship between\u0026nbsp;BI\u0026nbsp;and\u0026nbsp;AU. When users demonstrate stronger CI during digital platform engagement, the influence of BI on AU becomes more pronounced, thereby further increasing users\u0026rsquo; frequency of active platform participation, particularly in localized cultural learning contexts. Accordingly, the following hypothesis is proposed:\u003cbr\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;H11: CI positively moderates the relationship between BI and AU.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 1, the proposed research model integrates platform functionality (PF), content recommendation accuracy (CRA), content accessibility (CA), interaction quality (IQ), perceived ease of use (PEOU), and perceived usefulness (PU), along with emotional resonance (ER) and cultural identity (CI) as moderating variables, to explain users\u0026apos; behavioral intention (BI) and actual usage (AU) in the context of Minnan nursery rhymes dissemination via digital platforms.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eCase Study: Minnan Nursery Rhymes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs an integral component of China\u0026rsquo;s ICH,\u0026nbsp;Minnan nursery rhymes\u0026nbsp;embody the profound historical traditions and cultural essence of Southern Fujian region, making them an ideal case study for examining digital platforms\u0026rsquo; role in ICH dissemination. Originating in the Tang Dynasty (618-907 CE), classic works like \u0026ldquo;Moonlight Bright (Yueguangguang)\u0026rdquo;\u003cem\u003e\u0026nbsp;\u003c/em\u003edemonstrate these rhymes\u0026rsquo; enduring legacy, reflecting the literary and artistic flourishing of Tang civilization. Through Minnan people\u0026rsquo;s migrations during Song, Yuan, Ming, and Qing dynasties (960-1911 CE), these rhymes spread to Taiwan and Southeast Asia, evolving into significant cross-regional cultural transmitters\u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMinnan nursery rhymes employ oral literary forms to vividly portray agricultural practices, traditional celebrations, and countryside living through succinct rhythmic verses, thereby strengthening CI among Minnan communities. Additionally, their melodic nature and educational value make them an important tool for children\u0026rsquo;s cognitive and psychological development, contributing to their language skills and cultural awareness\u003csup\u003e53\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn recent years, globalization and digitalization have posed challenges to the transmission of Minnan nursery rhymes. While digital platforms have accelerated the global cultural exchange, they threaten to weaken the genuine local distinctiveness of these traditional verses by breaking them apart and commercializing them\u003csup\u003e54\u003c/sup\u003e. Considering this, Minnan nursery rhymes were included in China\u0026rsquo;s second national list of intangible cultural heritage in 2008 to ensure the preservation of their cultural value\u003csup\u003e55\u003c/sup\u003e. Against this backdrop, scholars have implemented digital storytelling, VR technologies, and multimedia interactive systems, which drive greater public engagement while offering youth an absorbing, culturally enriching experience\u003csup\u003e56\u003c/sup\u003e. Furthermore, complex network analysis has decoded the lexical structures and emotional depth of these rhymes, establishing a scientific foundation for safeguarding their cultural diversity\u003csup\u003e57\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study designed a structured questionnaire titled \u0026ldquo;Questionnaire on Local Culture Dissemination Model\u0026rdquo; to validate the proposed cultural transmission framework. Specifically targeting adult users (aged 18+) on Bilibili platform, the instrument measures both acceptance rates of local cultural content and its principal influencing factors among participants, and examine the dynamic interaction between technology acceptance and cultural transmission mechanisms. The questionnaire comprised two integrated sections: (1) demographic and background information to understand participant characteristics and cultural exposure experiences; (2) measurement scales for research variables adapted from validated instruments, through rigorous back-translation\u003csup\u003e58\u003c/sup\u003e, with contextual modifications made to ensure relevance for local culture dissemination research.. The measurement instrument employed a 5-point Likert scale (1= \u0026ldquo;strongly disagree\u0026rdquo; to 5= \u0026ldquo;strongly agree\u0026rdquo;) across all items to ensure data consistency and quantitative analysis feasibility. The questionnaire was refined through pilot testing (n=30) for applicability\u003csup\u003e59\u003c/sup\u003e, expert validity review, and subsequent CFA verification for reliability and validity\u003csup\u003e60\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe questionnaire began by collecting optional demographic information,\u0026nbsp;capturing gender and age to establish participant profiles. It then incorporated five specific background items evaluating cultural exposure across multiple dimensions: (1) frequency of attending offline cultural activities (e.g., folk festivals, regional opera); (2) level of engagement with local cultural content on social media; (3) depth of studying or researching local culture; (4) extent of family members\u0026rsquo; engagement in local cultural transmission; and (5)occurrence regularity of cultural events in professional or community settings. These items employed ordered response options (\u0026ldquo;very frequently\u0026rdquo; to \u0026ldquo;almost never\u0026rdquo;) to provide contextual support for the study, reflecting how cultural familiarity influences acceptance behaviors.\u003c/p\u003e\n\u003cp\u003eThe second part of the questionnaire consisted of 10 subscales: PF with 3 items (Tang, L., 2019)\u003csup\u003e61\u003c/sup\u003e, e.g., \u0026ldquo;Bilibili\u0026rsquo;s features help me understand local cultural content\u0026rdquo;; CRA with 3 items (Ricci et al., 2021)\u003csup\u003e62\u003c/sup\u003e, e.g., \u0026ldquo;Bilibili accurately recommends local cultural content\u0026rdquo;; CA with 6 items (Xia, B. \u0026amp; Zhao, 2016)\u003csup\u003e63\u003c/sup\u003e, e.g., \u0026ldquo;I can easily find local cultural content\u0026rdquo; and \u0026ldquo;Convenient access enhances cultural understanding\u0026rdquo;; IQ with 3 items (Hueluer, G. et al., 2022)\u003csup\u003e64\u003c/sup\u003e, e.g., \u0026ldquo;I participate in comments and bullet-screen interactions\u0026rdquo;; PEOU with 3 items (Davis, 1989)\u003csup\u003e6\u003c/sup\u003e, e.g., \u0026ldquo;Bilibili\u0026rsquo;s interface is easy to use\u0026rdquo;; PU with 3 items (Davis, 1989)\u003csup\u003e6\u003c/sup\u003e, e.g., \u0026ldquo;The content helps me identify with local culture\u0026rdquo;; ER with 3 items (Chen, B. 2024)\u003csup\u003e65\u003c/sup\u003e, e.g., \u0026ldquo;The content evokes cultural emotions\u0026rdquo;; CI with 3 items (Buckingham et al., 2023)\u003csup\u003e66\u003c/sup\u003e, e.g., \u0026ldquo;The content deepens cultural identity\u0026rdquo;; BI with 3 items (Venkatesh \u0026amp; Bala, 2008)\u003csup\u003e7\u003c/sup\u003e, e.g., \u0026ldquo;I intend to continue watching local cultural content\u0026rdquo;; and AU with 3 items (Malhan et al., 2021)\u003csup\u003e67\u003c/sup\u003e, e.g., \u0026ldquo;I discuss the content with friends\u0026rdquo;.\u003c/p\u003e\n\u003cp\u003eTo adapt the original English scales for native Chinese-speaking participants, a rigorous translation-back-translation procedure (Brislin, 1980)\u003csup\u003e58\u003c/sup\u003e was implemented following (Salazar-Fr\u0026iacute;as et al.\u0026rsquo;s 2023)\u003csup\u003e68\u003c/sup\u003e bilingual testing protocol. Two native Chinese bilingual researchers independently produced initial translations, which were then reviewed by five participants knowledgeable about local culture (including intangible cultural heritage) to enhance contextual appropriateness (e.g., replacing \u0026ldquo;nursery rhymes\u0026rdquo; with culturally specific expressions). Two native English-speaking researchers performed back-translation for semantic consistency verification. Both Chinese and English versions were pilot-tested with 20 bilingual individuals to refine technical terminology. During pretesting (n=30), three domain experts with substantial professional experience conducted preliminary validity evaluation, thoroughly assessing scale organization, item clarity, and alignment with research objectives to ensure accurate measurement of target constructs and provide reliable instrumentation for subsequent large-scale investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch procedure and samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized the Chinese online survey platform \u0026ldquo;Wenjuanxing\u0026rdquo; to collect data, specifically focusing on the most-viewed Minnan nursery rhyme \u0026ldquo;Fishing Song\u0026rdquo; on Bilibili (511,000 views). The online survey platform streamlined data entry and questionnaire distribution while expanding sample coverage\u003csup\u003e69\u003c/sup\u003e. To ensure data authenticity, the questionnaire was configured to allow only one submission per respondent. Additionally, targeting digitally-savvy users with prior exposure to Minnan nursery rhymes, the survey included screening questions at the outset---respondents indicating no prior experience viewing \u0026ldquo;Fishing Song\u0026rdquo; or similar Minnan cultural content on digital platforms like Bilibili were automatically disqualified from participation.\u003c/p\u003e\n\u003cp\u003eThis study was conducted in Xiamen in 2025. As the cultural heartland of Minnan culture, Xiamen boasts rich cultural heritage and an active digital user base\u003csup\u003e70\u003c/sup\u003e, providing an ideal research setting. With the approval from Xiamen Regional University Review Board in September 2024, the study employed purposive sampling to target users who had watched \u0026ldquo;Fishing Song\u0026rdquo; or similar Minnan nursery rhymes on Bilibili within the past year. To expand sample coverage, researchers encouraged initial respondents to share the questionnaire link through social networks (e.g., WeChat groups, Bilibili comment sections), combining snowball sampling for participant recruitment. By strategically combining targeted and chain-referral sampling, the study comprehensively engaged Minnan culture enthusiasts while sustaining tight research parameters., ultimately generating robust and contextually relevant dataset\u003csup\u003e71\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFollowing data collection, the research team implemented rigorous data screening procedures to ensure analytical reliability and validity. The filtering criteria included: (1) removing uniform responses (e.g., identical answers across all items), (2) excluding incomplete datasets with missing values, and (3) eliminating blank questionnaires. From the total pool of 350-550 collected responses, 35 problematic cases (comprising 20 uniform responses, 10 incomplete submissions, and 5 blank forms) were discarded. The final valid sample size is projected at 300-500 participants, which meets and exceeds the minimum requirement of 200 cases for structural equation modeling (SEM) analysis as recommended by Jobst et al. (2021)\u003csup\u003e72\u003c/sup\u003e, thereby ensuring robust statistical power for subsequent analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study employed Structural Equation Modeling (SEM) for data analysis, following (Kline, R.B.\u0026rsquo;s 2015)\u003csup\u003e73\u003c/sup\u003e analytical procedures. The analysis utilized SPSS 28 for preliminary data screening and descriptive statistics, while AMOS software was used to examine both the measurement and structural models. Moderating effects were verified using (Hayes, A. F. 2012)\u003csup\u003e74\u003c/sup\u003e PROCESS 3.3 macro. The analytical results encompassed demographic characteristics statistics, normality and correlation analyses, measurement model fit assessment, structural model path analysis, and examination of ER and CI\u0026rsquo;s moderating effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics Analysis of Demographic Sample\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study initially collected 487 responses, from which 31 invalid questionnaires (due to either excessively short completion time or identical responses throughout) were excluded, resulting in 456 valid samples with an effective response rate of 93.6%.Table 1 presents the demographic characteristics of the sample, including gender and age distribution. The gender distribution showed relative balance, with 220 male participants (48.2%) slightly outnumbered by 236 female participants (51.8%). Age distribution analysis revealed: 82 respondents aged 18-24 (18.0%), 180 aged 25-34 (39.5%), 130 aged 35-44 (28.5%), and 64 aged 45+ (14.0%), with the 25-34 and 35-44 age groups collectively forming the predominant demographic strata (68.0% combined).\u003c/p\u003e\n\u003cp\u003eTable 1 Characteristics analysis of demographic sample\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"539\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 154px;\"\u003e\n \u003cp\u003eItem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 128px;\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18-24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e35-44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAbove 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eNormality Test and Descriptive Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 presents the descriptive statistics of key constructs, with mean scores ranging from 3.800 (AU) to 4.004 (CRA), indicating generally positive evaluations of the digital dissemination of \u0026ldquo;Fishing Song\u0026rdquo; among respondents. Standard deviations varied between 0.895 (ER) and 1.099 (AU), suggesting higher response consistency for ER compared to greater variability in AU. All absolute values of skewness (1.206-1.575) and kurtosis (0.130-1.553) met the normality thresholds of skewness \u0026lt;3 and kurtosis \u0026lt;10 (Siraj-Ud-Doulah, M., 2021).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2 Descriptive statistics and inter-correlations for the variables (n=456).\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"656\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.999\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e4.004\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.891\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.930\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.904\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.883\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.980\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.948\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.962\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e3.800\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.975\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.950\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.991\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.033\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.895\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.973\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.986\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.099\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.548\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.523\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.271\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.401\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.453\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.320\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.560\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.418\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.575\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e-1.206\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.319\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.374\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.327\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.744\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.040\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.548\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.553\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.920\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e1.406\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.130\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote:***、**、*representing respectively\u0026nbsp;P\u0026lt;0.001、P\u0026lt;0.01、P\u0026lt;0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement Model Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe measurement model was evaluated using maximum likelihood estimation. As shown in Table 3, the model demonstrated excellent fit indices: \u0026chi;\u0026sup2;/df = 1.144 (\u0026lt;3), TLI = 0.993 (\u0026gt;0.9), CFI = 0.994 (\u0026gt;0.9), and RMSEA = 0.018 (\u0026lt;0.08), meeting all recommended thresholds\u003csup\u003e76\u003c/sup\u003e, indicating strong alignment between the measurement model and empirical data. Table 4 presents convergent validity results. All factor loadings ranged from 0.785 to 0.896 (exceeding 0.5), composite reliability (CR) values were 0.862-0.894 (\u0026gt;0.7), average variance extracted (AVE) estimates were 0.676-0.738 (\u0026gt;0.5), and Cronbach\u0026rsquo;s alpha coefficients were 0.862-0.894 (\u0026gt;0.7), collectively confirming strong reliability and convergent validity\u003csup\u003e77\u003c/sup\u003e. Variance inflation factors (VIFs) between 2.097 and 2.855 (\u0026lt;3.3) ruled out common method bias concerns\u003csup\u003e78\u003c/sup\u003e. Discriminant validity was verified via the Fornell-Larcker criterion\u003csup\u003e79\u003c/sup\u003e. Table 5 demonstrates that the square roots of AVEs (bold diagonal values) exceeded all inter-construct correlations, establishing robust discriminant validity\u003csup\u003e80\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;3 The fitness of the measurement model\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"534\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eREMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eMeasurement model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e411.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eFit criteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026lt;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;4 The calculation results of reliability\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eitems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eFactor\u003cbr\u003e\u0026nbsp;loading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eCronbach\u003cbr\u003e\u0026nbsp;alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003ePF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePF1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePF2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eCRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCRA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.312\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCRA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.162\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.568\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePEOU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePEOU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePEOU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.766\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eER1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eER3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBI1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.634\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBI2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBI3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eAU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.805\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable\u0026nbsp;5 the calculation results of convergent validity\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"673\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003ePF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003eAU\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003ePF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.835\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eCRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.485\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.826\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.439\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.408\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.850\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eIQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.430\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.436\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.392\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.832\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.504\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.534\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.507\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.387\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.838\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.565\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.459\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.351\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.370\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.473\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.854\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.334\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.227\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.182\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.233\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.185\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.251\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.822\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.207\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.255\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.256\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.196\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.235\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.183\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.118\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.840\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.552\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.468\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.502\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.507\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.525\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.319\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.282\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.848\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33px;\"\u003e\n \u003cp\u003eAU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.434\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.350\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.348\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.284\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.350\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.397\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.172\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.510\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.495\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.859\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: The bold values on the diagonal represent the square roots of AVEs, while the values below the diagonal indicate the correlation coefficients between latent variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural Model Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the measurement model\u0026rsquo;s fit indices all met the established criteria, the study proceeded to estimate the initial research model using maximum likelihood estimation. As presented in Table 6, the structural equation model demonstrated good fit: \u0026chi;\u0026sup2;/df = 1.780 (\u0026lt;3), TLI = 0.969 (\u0026gt;0.9), CFI = 0.973 (\u0026gt;0.9), and RMSEA = 0.041 (\u0026lt;0.08).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;6 The fitness of the research model\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e/df\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eREMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003estructural model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e421.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFit criteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026gt;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 7 presents the path coefficients, showing all paths were statistically significant except the relationship between CA and PU. Specifically: (1) CA (\u0026beta;=0.440, t=8.210, p\u0026lt;0.001) and IQ (\u0026beta;=0.253, t=4.888, p\u0026lt;0.001) significantly influenced PEOU; (2) PF (\u0026beta;=0.403, t=6.976, p\u0026lt;0.001), CRA (\u0026beta;=0.183, t=3.318, p\u0026lt;0.001), and PEOU (\u0026beta;=0.178, t=3.223, p\u0026lt;0.01) significantly affected PU, while CA (\u0026beta;=0.025, t=0.409, p\u0026gt;0.05) showed no significant effect; (3) Both PEOU (\u0026beta;=0.353, t=7.181, p\u0026lt;0.001) and PU (\u0026beta;=0.438, t=8.825, p\u0026lt;0.001) significantly predicted BI; (4) BI (\u0026beta;=0.505, t=9.872, p\u0026lt;0.001) significantly influenced AU (see Fig. 2 for the model with standardized path coefficients).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;7 Test results of research hypotheses testing\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"526\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003eUnstandardized coeffcient (B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003eStandardized coeffcient (\u0026beta;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCA\u0026rarr;PEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.440***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e8.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eQ\u0026rarr;PEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.253***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e4.888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ePF\u0026rarr;PU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.403***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e6.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCRA\u0026rarr;PU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.183***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eCA\u0026rarr;PU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ePEOU\u0026rarr;PU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.178**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ePEOU\u0026rarr;BI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.353***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e7.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003ePU\u0026rarr;BI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.438***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e8.825\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003eBI\u0026rarr;AU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 131px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.505***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e9.872\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote:***、**、*representing respectively P\u0026lt;0.001、P\u0026lt;0.01、P\u0026lt;0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModerating Effect Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe moderating effect was analyzed using\u0026nbsp;(Hayes, A. F. 2012)\u003csup\u003e74\u003c/sup\u003e PROCESS macro (Version 3.3). As shown in Figure 3, the interaction term between PU and ER exerted a significant positive effect on BI (\u0026beta;=0.135, t=3.270, p\u0026lt;0.01), indicating that ER positively moderates the relationship between PU and BI. Figure 4 demonstrates that the interaction between BI and CI significantly enhanced AU (\u0026beta;=0.124, t=3.000, p\u0026lt;0.01), confirming CI\u0026rsquo;s positive moderating role in the intention-behavior linkage. These findings reveal the crucial moderating mechanisms of ER and CI in user behavior within digital media technology\u0026rsquo;s impact on ICH learning outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examines the impact of digital platform features, ER, and CI on the dissemination of ICH, using the Minnan nursery rhyme “Fishing Song” as a case study within an extended TAM framework. The results support hypotheses H1-H3 and H5-H11, revealing significant effects of key variables.\u003c/p\u003e\u003cp\u003eFirstly, the analysis revealed that both PF (β = 0.409***) and CRA (β = 0.187***) significantly enhanced PU (supporting H1-H2). These results suggest that Bilibili’s features (including subtitles and playback controls) and algorithmic recommendations effectively heighten users’ appreciation of the cultural value in “Fishing Song”, consistent with (Susilo et al. 2021)\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e research on functionality’s role in usefulness perception. For practical application, we recommend platform developers refine their recommendation systems to better target ICH content delivery and strengthen users’ cultural value recognition.\u003c/p\u003e\u003cp\u003eSecondly, CA (CA, β = 0.441, p \u0026lt; 0.001) and IQ (IQ, β = 0.253, p \u0026lt; 0.001) significantly influenced PEOU, supporting H3 and H5. However, CA’s effect on PU was not significant (β = 0.025, p \u0026gt; 0.05), failing to support H4. These findings align with (Balaman and Baş, 2021)\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e conclusion that CA enhances PEOU, but contradict (Matausch et al. 2014)\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e perspective that CA directly affects PU. This discrepancy may stem from the high accessibility of “Fishing Song” on Bilibili, where users prioritize interactive experiences over basic convenience. Similar to (Du \u0026amp; Lv 2024)\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e findings about the limited impact of EE on GAI adoption in elementary education, this suggests that technological familiarity may diminish the role of certain functions. We recommend platforms enhance bullet comments and comment features to lower usage barriers and improve user experience.\u003c/p\u003e\u003cp\u003eThird, the results demonstrated that PEOU significantly enhanced PU (β = 0.189, p \u0026lt; 0.001), supporting H6, while both PEOU (β = 0.354, p \u0026lt; 0.001) and PU (β = 0.437, p \u0026lt; 0.001) exerted substantial positive effects on BI, thereby validating H7 and H8. These findings not only corroborate the fundamental pathways of the TAM proposed by (Davis. 1989)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, but also align with (Fan, C. 2023)\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e research conclusions regarding the pivotal role of usability and utility in driving technology adoption. Specifically, users’ recognition of Bilibili’s user-friendly interface and practical value directly strengthened their willingness to engage with the “Fishing Song” content. To optimize user experience, it is recommended that educators and platform developers implement interface simplification strategies and provide detailed operational guidance, which would further elevate users’ PEOU and ultimately enhance their valuation of the platform’s functionalities.\u003c/p\u003e\u003cp\u003eFourthly, BI exerted a significant positive influence on AU (β = 0.505, p \u0026lt; 0.001), supporting H9. This finding aligns with recent studies on digital cultural learning by Songkram et al., (2023)\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e and Zhang \u0026amp; Yu (2022)\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e, demonstrating that users’ intention to engage with “Fishing Song” effectively translates into concrete actions such as viewing or sharing the content. To enhance this behavioral conversion, platforms could implement user incentive mechanisms (e.g., point reward systems) or interactive campaigns (e.g., comment challenges) to strengthen BI and consequently increase AU frequency.\u003c/p\u003e\u003cp\u003eFinally, ER positively moderated the relationship between PU and BI (β = 0.135, p \u0026lt; 0.01), while CI positively moderated the BI-AU relationship (β = 0.124, p \u0026lt; 0.01), supporting H10 and H11. These findings reveal that: (1) ER amplifies PU’s effect on BI, aligning with Kamal et al. (2024)\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e findings about emotional connections enhancing BI, suggesting that the melody and lyrics of “Fishing Song” boost usage willingness by evoking emotional memories; (2) CI strengthens the BI-to-AU conversion, consistent with Wijaya et al. (2022)\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e conclusions on CI driving engagement, where high-CI users show greater propensity to share or recommend content. This pattern parallels (Zhang et al. 2023)\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e observations about task-technology fit (TTF) moderating PE and EE, collectively highlighting the critical role of psychological factors in technology acceptance. For practical application, cultural preservation institutions could enhance ER and CI through emotionally engaging content (e.g., digital storytelling or VR experiences) and localized cultural activities to facilitate ICH dissemination.\u003c/p\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003cdiv id=\"Sec30\" class=\"Section3\"\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Conclusions and Limitations","content":"\u003ch2\u003eResearch Conclusions\u003c/h2\u003e\u003cp\u003eCurrent research widely acknowledges the transformative potential of digital platforms in the dissemination and preservation of ICH, as they provide users with broader access opportunities and personalized experiences\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. This study developed and validated an extended TAM to examine the impact of digital platform features, ER, and CI on the digital transmission of ICH. Through questionnaire analysis of 456 users in China’s Fujian Province, the results confirmed most research hypotheses, highlighting the critical role of emotional and cultural factors in ICH dissemination.\u003c/p\u003e\u003cp\u003eThis study fills a research gap in user acceptance studies of digital ICH dissemination. Unlike the traditional TAM model that focuses primarily on technological functionalities\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, our research enhances the understanding of user behavior by incorporating ER and CI, which aligns with (Yi, Y. 2023)\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e perspective on how CI promotes engagement. The findings not only expand the application of TAM in cultural transmission but also provide empirical evidence for emotional and cultural psychological mechanisms. On a practical level, the results demonstrate that optimizing platform features (such as precise recommendations and interactive design) while incorporating emotionally engaging content (like digital storytelling) can significantly improve the effectiveness of ICH dissemination. These insights offer strategic guidance for platform developers, educators, and cultural preservation institutions, contributing to the effective transmission of ICH in digital environments\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eResearch Limitations and Future Development\u003c/h2\u003e\u003cp\u003eWhile this study has achieved the aforementioned findings, several limitations warrant further exploration in future research. Firstly, the sample was limited to 456 valid questionnaires collected from a single region, which may not fully represent the acceptance behaviors of global ICH audiences. Cultural background variations could influence users’ perceptions and usage of digital platforms and ICH content\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. For instance, distinct usage behaviors observed in non-local users may stem from legacy migration routes and acculturation dynamics\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e. To address this limitation, future studies should expand the sample scope to include user data from diverse geographical locations and conduct cross-cultural comparative analyses. This would enhance the generalizability of the results and empower tailored cultural adaptation of messaging for diverse demographic groups..\u003c/p\u003e\u003cp\u003eSecondly, this study focused on a single ICH case and did not encompass other types of ICH content. Diverse ICH categories (e.g., musical versus performative traditions) may differentially impact user acceptance through their unique transmission dynamics\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. For instance, melodic content could inherently stimulate stronger ER compared to narrative content, while visually-oriented content could demonstrate greater dependence on a platform’s interactive features. Future research should adopt multiple-case approaches to compare the dissemination effectiveness across different ICH contents and platforms, thereby revealing the interaction effects between content attributes and technological environments.\u003c/p\u003e\u003cp\u003eAdditionally, this study employed a cross-sectional design that only captures user behavior at a single temporal point, rather than examining the longitudinal development of ER and CI effects on technology acceptance\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. Furthermore, the exclusion of other potential mediating variables (e.g., technological familiarity, content attractiveness) may limit a comprehensive understanding of the mechanisms through which ER and CI operate\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e. Future research should adopt longitudinal designs to track the dynamic evolution of user behavior while incorporating additional theories (e.g., the Information System Success Model) or variables to enhance the model’s explanatory power.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis study was formally approved in September 2024 by the Institutional Ethics Committee (Approval No. XIT-MEI-20240901).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures involving human participants were conducted in accordance with the institutional guidelines and the Declaration of Helsinki (1964) and its later amendments.\u003c/p\u003e\n\u003cp\u003eThe approval covered the design, distribution, and analysis of anonymous questionnaires regarding users\u0026rsquo;\u0026nbsp;experiences with Minnan nursery rhymes on digital platforms.\u003c/p\u003e\n\u003cp\u003eThe questionnaire and consent procedures were approved as part of the ethics review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants received comprehensive information prior to completing the online questionnaire to ensure they could make an informed decision. Participants were informed about the purpose of the study, procedures, confidentiality measures, and their right to withdraw at any time without consequences, and were advised that no foreseeable risks were associated with participation. Participation was entirely voluntary; no personally identifiable information was collected, and all data were anonymized and analyzed in aggregate to protect privacy. Informed consent was obtained electronically at the start of the questionnaire via the Wenjuanxing platform, which automatically recorded the consent date (YYYY-MM-DD). \u003cstrong\u003eAll written consents were obtained between October and December 2024, following ethics approval granted in September 2024.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the 2024 Fujian Provincial Social Science Foundation, under the project titled \u0026ldquo;Digital Dissemination and Optimization of Minnan Nursery Rhymes Based on Narrative Theory\u0026rdquo; ( No. FJ2024B177)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eS.M.L. designed the research framework, developed the questionnaire, conducted data collection, and wrote the main manuscript text. C.L.X. co-led the project, contributed to research design, and critically revised the manuscript. J.Y.L. provided methodological guidance and assisted in data analysis. Z.A.Z. supported manuscript language refinement and editorial preparation. Q.R.Z. contributed to reference organization and final proofreading. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003e1.Blake, J. (2006).\u003c/strong\u003e \u003cem\u003eCommentary on the 2003 UNESCO Convention on the Safeguarding of the Intangible Cultural Heritage\u003c/em\u003e. Institute of Art and Law.（book）\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e2.UNESCO. (2021). Textes fondamentaux de la Convention pour la sauvegarde du patrimoine culturel immat\u0026eacute;riel (2003) [PDF]. 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Museum Management and Curatorship, 36, 342-361.https://doi.org/10.1080/09647775.2021.1914136\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Technology Acceptance Model (TAM), digital cultural heritage, dissemination of intangible cultural heritage, emotional resonance, cultural identity","lastPublishedDoi":"10.21203/rs.3.rs-7181151/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7181151/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe digital era poses new challenges but also offers opportunities to the dissemination and preservation of intangible cultural heritage (ICH). Leveraging emotional resonance (ER) and cultural identity (CI), this study applies the extended Technology Acceptance Model (TAM) to explore the interaction between digital platform features and users\u0026rsquo; cultural-psychological mechanisms, and see how it influences the digital dissemination of ICH. The study investigates the mediating roles of perceived usefulness (PU) and perceived ease of use (PEOU) in the relationship between platform functionality and user behavior, as well as the moderating effects of ER and CI. A data sample of 456 users was collected via questionnaire surveys and analyzed using Structural Equation Modeling (SEM). The results indicate: (1) Platform functionality (PF), content recommendation accuracy (CRA), and PEOU all positively predict PU, with PF having the strongest influence, followed by CRA and PEOU. (2) Cultural appeal (CA) and information quality (IQ) both positively predict PEOU, with CA exerting a greater influence than IQ. (3) PU positively predicts behavioral intention (BI), and BI in turn positively predicts actual usage (AU). (4) ER positively moderates the relationship between PU and BI, while CI positively moderates the relationship between BI and AU. Therefore, to enhance the effectiveness of ICH digital dissemination, platform designers and cultural preservationists should prioritize functional features, user perceptions, as well as emotional and cultural factors.\u003c/p\u003e","manuscriptTitle":"Unpacking the effect of Digital Platforms on the Dissemination of Intangible Cultural Heritage: An Extended Technology Acceptance Model with Emotional Resonance and Cultural Identity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 14:44:34","doi":"10.21203/rs.3.rs-7181151/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-08T19:04:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T22:04:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-10T08:05:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324833893229028118542113549987779552246","date":"2025-10-03T09:46:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278625913838597014323800520049802911350","date":"2025-10-01T18:31:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321552242998707240530310476293418158909","date":"2025-09-30T21:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-23T11:03:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-23T10:57:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-23T10:50:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T03:01:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-09-02T02:57:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3b4a5cb3-2776-447f-a525-59681a6d8421","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":55724661,"name":"Business and commerce/Business and management"},{"id":55724662,"name":"Social science/Business and management"},{"id":55724663,"name":"Humanities/Cultural and media studies"},{"id":55724664,"name":"Social science/Cultural and media studies"},{"id":55724665,"name":"Business and commerce/Information systems and information technology"},{"id":55724666,"name":"Biological sciences/Psychology"},{"id":55724667,"name":"Social science/Psychology"},{"id":55724668,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-03-01T20:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 14:44:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7181151","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7181151","identity":"rs-7181151","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}