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 others¹⁰. The integration of multimedia technologies, intelligent algorithms and interactive features provides users with diverse digital experiences and increasing attention in the field of cultural dissemination¹¹. 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 intuitively¹². Through analyzing user interests and behavioral patterns, intelligent recommendation algorithms can precisely deliver ICH content, thereby expanding cultural dissemination while boosting engagement and learning motivation¹³. 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 reinterpretation¹⁴. 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 significance¹⁵.

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 efficiency²². This concept underscores the congruence between functional design and user requirements, representing a key aspect of digital platform user experience²³.

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 (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 satisfaction²⁷. 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:

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 (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 (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 (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 behavior⁴³. 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.

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 roots⁴⁸. 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 content⁵⁰. 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 elements⁵¹.

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 Fig. 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.

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-translation⁵⁸, 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 applicability⁵⁹, expert validity review, and subsequent CFA verification for reliability and validity⁶⁰.

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.

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 coverage⁶⁹. 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 base⁷⁰, 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 dataset⁷¹.

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.

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 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).

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 content⁹⁴. For instance, distinct usage behaviors observed in non-local users may stem from legacy migration routes and acculturation dynamics⁹⁵. 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 dynamics⁹⁶. 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 acceptance⁹⁷. 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.