Short Video Engagement and Digital Political Participation: The Mediating Roles of Political Education ldentification and Cultural Understanding

Short Video Engagement and Digital Political Participation: The Mediating Roles of Political Education ldentification and Cultural Understanding | 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 Short Video Engagement and Digital Political Participation: The Mediating Roles of Political Education ldentification and Cultural Understanding Hanchang Huang, Qingyuan Sun, Yuanyuan Xu, Fang Li, Mingjie Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7259640/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted You are reading this latest preprint version Abstract This study investigates the psychological and cultural mechanisms through which short video engagement influences university students’ willingness to participate in digital political activities. While prior research has established links between digital media use and political behavior, the mediating roles of political education identification and cultural understanding remain insufficiently explored, particularly in the context of short-form video platforms. This study addresses this gap by examining how different dimensions of short video experience—usage frequency, emotional and sensory value, and engagement—affect digital political participation through these cognitive-affective pathways. A structured questionnaire was administered to 512 Chinese university students, and data were analyzed using Structural Equation Modeling (SEM) via SmartPLS 4.0. The results reveal that both political education identification and cultural understanding significantly mediate the relationship between short video engagement and digital political participation willingness. These findings highlight the importance of emotional resonance, cultural interpretation, and individual dispositions in shaping digital political engagement. The study offers theoretical insights into media effects and civic psychology, and suggests practical implications for educators, platform developers, and civic institutions seeking to promote informed and meaningful political participation among youth in the digital age. Humanities/Cultural and media studies Social science/Cultural and media studies Social science/Education Social science/Politics and international relations Biological sciences/Psychology Social science/Psychology Digital political participation Short video engagement Political education identification Cultural understanding Figures Figure 1 Figure 2 1. Introduction In the context of rapidly evolving digital communication landscapes, short video platforms have emerged as a dominant medium shaping political discourse and civic engagement, particularly among younger generations. The immediacy, emotional richness, and interactive nature of short video content have transformed not only how individuals consume political information but also how they identify with civic narratives and express political intent. As nations increasingly integrate digital media into civic education and political mobilization strategies, understanding the mechanisms behind digital political participation becomes critically important (Bas & Grabe, 2016 ; Kahne & Bowyer, 2019 ). University students, as digital natives, represent a particularly relevant population in this regard. Their frequent exposure to algorithm-curated political content—often framed in emotionally compelling and culturally symbolic formats—raises pressing questions about how media engagement translates into political cognition and participatory willingness. Prior research has emphasized the influence of media literacy, civic education, and digital skills on political outcomes (Alscher et al., 2022 ; Hargittai & Shaw, 2013 ), yet the nuanced roles of affective and cultural processing in this relationship remain underexplored. Digital political participation willingness—the intention to engage in civic or political activities through online platforms—has become a vital component of modern democratic life. From signing petitions and sharing political content to engaging in online discussions and participating in digital protests, youth increasingly express their political agency in networked environments. This form of participation not only reflects political awareness but also contributes to collective action and public opinion shaping in the digital age. In contexts such as China, where youth political behavior is increasingly mediated by digital technologies, understanding the psychological drivers of digital political participation willingness is critical for nurturing informed, engaged, and socially responsible citizens (Zhao & Cao, 2024 ; Zhang et al., 2025 ). To enhance youth willingness for digital political participation, two cognitive-affective constructs are particularly essential: political education identification and cultural understanding. Political education identification reflects the extent to which individuals internalize civic values and political knowledge, often shaped by formal instruction or informal media encounters. Prior studies show that stronger identification with political education correlates with increased political interest, trust, and behavioral intention (Alscher et al., 2022 ; Chen & Madni, 2024 ). On the other hand, cultural understanding enables individuals to decode political messages within their historical, national, and symbolic context. It fosters interpretive depth and critical awareness—qualities that are foundational for active and meaningful political participation (Shen & Liang, 2015 ; Stretch, 2001 ). Together, these two mediators form a key pathway through which short video experiences translate into participatory willingness, bridging emotional engagement with political consciousness. Emerging studies suggest that emotional resonance and cultural relevance are key drivers in bridging the gap between media exposure and political behavior (Zhao & Cao, 2024 ; Zhu et al., 2019 ). Concepts such as political education identification—reflecting an individual's internal alignment with civic learning—and cultural understanding—denoting one’s interpretive ability to navigate political symbolism—have gained traction as cognitive-affective mediators that may explain the impact of media engagement on political participation (Chan et al., 2017 ; Shen & Liang, 2015 ). Despite growing academic interest, existing literature often isolates media usage from its deeper psychological and cultural mechanisms. Few studies provide an integrated model that considers how short video usage frequency, emotional and sensory value, and engagement experience interact with political identification and cultural cognition to shape digital political participation willingness. This study proposes an integrated framework based on the Communication Mediation Model (CMM) to examine how short video experiences influence political education identification and cultural understanding, and how these mediators shape digital political participation among Chinese university students. Using structural equation modeling (SEM), the research clarifies the psychological and contextual mechanisms of digital civic behavior, offering valuable insights for educators, policymakers, and platform designers aiming to foster informed and culturally rooted digital citizens. 2. Literature Review and Hypothesis Development This section introduces the theoretical foundation for the study, anchored in the Communication Mediation Model (CMM). Drawing from the O-S-O-R logic and its modern adaptations, this framework is used to conceptualize how media exposure (short video usage) shapes cognitive and emotional responses (e.g., political education identification and cultural understanding), which in turn influence behavioral intentions (digital political participation). This section also outlines the development of the hypotheses. 2.1 Theoretical Background This study is grounded in the Communication Mediation Model (CMM), an extension of the O-S-O-R framework originally proposed by McLeod, Kosicki, and McLeod (1994), and later refined by Chan, Chen, and Lee ( 2017 ) to explain the psychological mechanisms through which digital and mobile media affect political participation. The model outlines how media orientation (O₁) leads to cognitive-affective responses (O₂), which in turn influence behavioral outcomes (R), with stimulus (S) and individual dispositions shaping the overall pathway. CMM serves as a relevant lens for understanding how short video usage—a prominent form of digital media engagement—activates cognitive processes that shape political cognition and behavior. In this framework, variables such as short video frequency, emotional/sensory value, and engagement experience function as media orientation inputs (O₁), while political education identification and cultural understanding are conceptualized as key internal mediators (O₂). These variables reflect how individuals process and internalize political or culturally symbolic content through emotionally charged, personalized video formats. Prior studies have emphasized the importance of both emotional resonance and cultural literacy in shaping political behaviors. For example, Bas and Grabe ( 2016 ) and Alscher et al. ( 2022 ) show that emotionally engaging content enhances participatory intent by fostering affective alignment with political values. Simultaneously, researchers like Chan et al. ( 2017 ) and Shen and Liang ( 2015 ) demonstrate that cultural understanding acts as a cognitive filter, shaping how political content is decoded and acted upon. The role of digital media in this model is dynamic and context-sensitive. Zhao and Cao ( 2024 ) highlight how AI-driven short video content can either enhance or distort political cognition, depending on users’ interpretive readiness. Similarly, Lee et al. ( 2024 ) emphasize that sensory affordances and emotional design in digital content can mediate engagement through psychological mechanisms—supporting the assumption that perceived media value triggers meaningful internal responses. Thus, this research employs the Communication Mediation Model as a theoretical scaffold to explain how immersive short video experiences influence young citizens’ digital political participation through intermediary constructs such as political identity and cultural cognition. The model provides a comprehensive structure for examining the psychological and contextual pathways that connect digital engagement with political behavior. 2.2 Theoretical Framework and Hypothesis Development This study draws on the Communication Mediation Model (Chan et al., 2017 ; McLeod et al., 1994) to explain how digital media exposure influences political participation through cognitive and affective pathways. The framework integrates insights from research on political education identification (Alscher et al., 2022 ), cultural understanding (Shen & Liang, 2015 ), and short video engagement (Bas & Grabe, 2016 ; Zhao & Cao, 2024 ). Specifically, short video usage frequency, emotional and sensory value, and engagement experience are treated as media inputs that shape internal responses—political education identification and cultural understanding—which in turn affect digital political participation willingness (Zhang et al., 2025 ; Kahne & Bowyer, 2019 ).These constructs form the basis for the ten hypotheses (H1–H10), as outlined in Fig. 1 . 2.3 The frequency of short video usage and political education identification. Frequency of Short Video Usage refers to how often individuals watch short-form video content that may include political or ideological elements (Yang et al., 2024 ).Political Education Identification refers to how strongly individuals internalize and emotionally connect with the values presented in political education (Chen & Madni, 2024 ). The frequency of short video usage has become a significant factor in shaping young people’s political cognition, particularly in how they relate to and internalize political education content. Prior studies have shown that frequent exposure to digital political content enhances civic understanding and engagement. For example, Kahne, Lee, and Feezell (2012) found that consistent interaction with digital media supports civic participation, while Yang and DeHart (2016) demonstrated that exposure to political messages via social media is positively associated with online political behaviors among college students. In the Chinese context, Qin et al. (2023) revealed that frequent short video consumption helps foster political identity among small-town youth, especially when ideological narratives are embedded within emotionally appealing content. Similarly, Bowyer, Kahne, and Middaugh (2017) highlighted how repeated viewing of political videos improves youth comprehension and alignment with political messages. Furthermore, the emotional and psychological impact of repeated political content exposure is crucial. Chen and Wang (2022) emphasized how political video content on platforms like YouTube can provoke strong affective responses, which may either polarize or deepen identification depending on the message clarity and alignment with audience values. Taken together, these studies suggest that high-frequency exposure to short videos—especially those conveying patriotic, cultural, or ideological messages—can enhance familiarity, cognitive resonance, and emotional alignment with political education content. H1: The frequency of short video usage has a positive effect on political education identification. 2.4 Perceived Emotional and Sensory Value and Political Education Identification Perceived Emotional and Sensory Value refers to the extent to which short video content is experienced as emotionally engaging and sensorially stimulating, through elements such as visuals, music, and tone (Xie et al., 2025 ).Emotional and sensory engagement has become a crucial factor in shaping how individuals internalize political and civic content. Research shows that emotionally rich messaging can serve as an affective heuristic that enhances symbolic alignment and political learning. Graf et al. (2024) found that students’ emotions such as hope and pride, experienced during civic education, significantly boost their identification with political values. In the media context, Bas and Grabe ( 2016 ) demonstrated that emotionally expressive news stories encourage participatory intent by enhancing emotional resonance. Similarly, Huddy, Mason, and Aarøe (2015) emphasized that political identity is not only cognitive but deeply expressive and emotional—reinforced in contexts where emotionally charged narratives are present. Extending these insights, Jung and Mittal (2021) argued that individuals are more likely to engage with educational content when it aligns with their political identity, especially if it evokes affective or symbolic meaning. Wiley and Siperstein (2011) further revealed how perceived ideological visibility in politically shaped environments influences identification behaviors. Taken together, these findings suggest that the more emotionally and sensorially engaging the short video content is—through music, visuals, and narrative cues—the stronger its capacity to trigger affective reactions and identity-based processing, thereby enhancing political education identification. H2: Perceived emotional and sensory value of short videos positively predicts political education identification among university students. 2.5 Short Video Engagement Experience and Political Education Identification Short Video Engagement Experience refers to how actively individuals interact with short video content through actions such as watching, liking, commenting, and sharing (Zhao & Cao, 2024 ; Xie et al., 2025 ).Engagement experiences—particularly when embedded in narrative, interactive, or media-rich formats—play a critical role in fostering identification with political or educational content. Hillygus (2005) emphasizes that the college experience, as a formative civic and intellectual engagement environment, positively influences political awareness and identification, especially through exposure to institutionalized political discourse and diverse perspectives. This connection is echoed in Ohme, Marquart, and Kristensen’s (2020) study, which found that youth political engagement is strongly influenced by experiential stimuli such as social media content, civic-themed music videos, and interactive campaign media. These findings suggest that immersive and affective learning formats—especially those delivered through short videos—can serve as powerful conduits for political socialization. Further supporting this view, Howell et al. (2025) found that narrative engagement in science films led to greater identification with scientific content, indicating that storytelling-driven experiences—particularly in short media formats—can foster deep psychological alignment with complex topics. Similarly, Thananithichot et al. (2025) demonstrated that youth engagement with serious games simulating political processes increased their identification with political systems by making abstract concepts personally relevant and emotionally salient. Translating these insights to short video platforms, when users experience high levels of engagement—through interactive comments, personalized algorithms, or emotionally compelling storytelling—they are more likely to perceive political or civic content as relevant, relatable, and identity-forming. H3: Short video engagement experience has a positive impact on Political Education Identification 2.6 Frequency of Short Video Usage and Cultural Understanding Cultural Understanding refers to an individual’s ability to interpret and relate to cultural and political meanings embedded in media content (Shen & Liang, 2015 ; Stretch, 2001 ).Short video platforms have become important vehicles for cultural exposure, offering immersive encounters with diverse values, norms, and communication styles. Park et al. (2017) found that cross-cultural video consumption on platforms like YouTube enhances users’ openness to different cultural codes, particularly among those who frequently engage with international content. Similarly, Zhang et al. (2023) revealed that the habitual use of short-video apps among college students is closely tied to cultural context, and that repeated exposure fosters sensitivity to different communicative patterns and social behaviors. These findings suggest that frequency of contact with culturally embedded media can deepen users’ recognition and appreciation of cultural diversity. Moreover, Yang and Guan (2024) observed that frequent short video exposure influences children’s daily language habits, indicating that repeated interaction with media can shape both linguistic and cultural practices. Extending this, Thananithichot et al. (2025) demonstrated that interactive and engaging civic media formats (e.g., games or video-based simulations) enhance young people’s understanding of political and cultural systems by making abstract concepts emotionally and contextually accessible. As short videos often include culturally coded narratives, humor, rituals, and symbolism, frequent exposure can foster not only familiarity but also deeper emotional and interpretive cultural understanding. H4: The frequency of short video usage positively predicts cultural understanding among university students. 2.7 Perceived Emotional and Sensory Value and Cultural Understanding Research across sensory perception, cultural psychology, and consumer behavior has demonstrated that emotional and sensory experiences play a significant role in how individuals interpret and internalize cultural information. According to de Matos et al. (2025), affective responses and sensory perceptions are culturally shaped, and repeated exposure to emotionally engaging stimuli can foster cross-cultural attunement. Perrea et al. (2015) similarly observed that emotionally salient product experiences trigger value recognition and emotional resonance, which are essential to cultivating deeper understanding of culturally embedded meanings. In communication, Caballero and Paradis (2015) emphasized the tight coupling between emotions, sensory language, and cultural expression, highlighting how sensory-laden interactions enable individuals to “feel into” a culture’s worldview. Kastanakis and Voyer (2014) propose that culture acts as a cognitive-perceptual filter through which individuals interpret emotional and sensory experiences, suggesting that engaging sensory value can be a pathway toward cultural meaning-making. Kim et al. (2016) further support this by showing that travelers' perceptions of well-being are shaped more by cognitive-emotional and sensory experiences than objective service factors, implying that the felt experience of an environment fosters contextual cultural understanding. Therefore, the more emotionally and sensorially rich the content (e.g., short videos, cultural media, immersive visuals), the more likely it is to facilitate empathy, symbolic decoding, and appreciation of cultural difference. H5: Perceived emotional and sensory value positively predicts cultural understanding among university students. 2.8 Short Video Engagement Experience and Cultural Understanding Short video platforms offer an immersive and affectively rich environment where users actively engage with cultural narratives, practices, and symbols. According to Feng (2023), short video engagement plays a critical role in enhancing learners' cultural awareness and educational experiences, particularly when the content focuses on traditional heritage or artistic values. The interactive and participatory features of short video applications encourage users not only to consume but also to emotionally and cognitively process culturally embedded content. Chen (2023) further demonstrated that individuals' personal experiences with short videos shaped their cultural perceptions and expression, indicating that engagement is not passive but leads to deeper cultural reflection. Building on this, Wei, Li, and Chen ( 2024 ) adopted the SOR (Stimulus-Organism-Response) model and showed that affordances of short-video platforms—such as interactivity, visual richness, and algorithmic personalization—stimulate users' cross-cultural engagement intentions. Their findings support the notion that when users are meaningfully engaged with short videos, especially those containing diverse cultural elements, they are more likely to interpret and internalize cultural meaning. The dynamic, expressive nature of short video engagement allows users to not only witness culture but embody and relate to it, thus enhancing cultural understanding. H6: Short video engagement experience positively predicts cultural understanding among university students. 2.8.1 Relationship between Frequency of Short Video Usage, Political Education Identification, and Digital Political Participation Willingness. Digital Political Participation Willingness refers to an individual's subjective intention or psychological readiness to engage in political activities via digital platforms, such as expressing political opinions, sharing political content, or participating in online civic discussions and actions (Zhang & Lin, 2020 ; Boulianne, 2015 ). The increasing prevalence of short video platforms such as TikTok (Douyin), Kuaishou, and Bilibili has significantly transformed the way young people consume political information and engage with civic life. These platforms provide easily digestible, emotionally engaging content that often embeds political education themes, especially when disseminated by official or institutional accounts (Zeib, 2021; Bennett, 2012). High-frequency use of such platforms may increase exposure to ideological, historical, or patriotic content, fostering greater familiarity and identification with political education narratives (Kahne, Lee, & Feezell, 2012). As individuals begin to internalize these narratives—aligning with the values and messages embedded in political education—they are more likely to develop a stronger sense of civic duty and intention to participate in digital political activities such as commenting on current issues, sharing political content, or engaging in online campaigns (Hargittai & Shaw, 2013 ).Thus, political education identification serves as a critical psychological mechanism that links media consumption behavior (short video use) to digital civic behavior. H7a: Political education identification mediates the relationship between frequency of short video usage and digital political participation willingness. 2.8.2 Political education identification mediates the relationship between perceived emotional and sensory value and willingness for digital political participation. Perceived emotional and sensory value—derived from exposure to emotionally engaging, personalized political content—has been shown to significantly influence individuals' willingness to engage in digital political participation. However, this influence is not solely direct; it is likely mediated by the degree to which individuals identify with political education.Research by Bas and Grabe ( 2016 ) demonstrates that emotionally expressive news stories, especially those featuring everyday citizens, enhance viewers’ emotional engagement and personal relevance, which in turn increase participatory intent. Emotional displays are found to be potent triggers for political involvement because they stimulate affective resonance and empathy (Bas & Grabe, 2016 ). Such emotional engagement may strengthen one’s identification with political education. Alscher, Ludewig, and McElvany ( 2022 ) found that high-quality civic education—characterized by cognitive activation and relevance—can increase students’ political interest and knowledge, both of which mediate their willingness to participate in civic life. Emotional involvement may thus foster an internalized appreciation of political education’s value (Alscher et al., 2022 ). In digital contexts, this identification with political education becomes even more crucial. Zhao and Cao ( 2024 ) examined Chinese college students and found that perceptions of political content—particularly in AI-mediated environments—strongly influenced their online political participation willingness. Recognition and acceptance of political education content played a significant motivational role in this process (Zhao & Cao, 2024 ).Zhang, Zhang, and Wang ( 2025 ) also emphasized the mediating role of psychological constructs (e.g., political interest, social concern, national identity) in translating digital skills into digital political participation. Similarly, political education identification can serve as a cognitive-emotional link that transforms short-term emotional and sensory stimulation into long-term behavioral intentions (Zhang et al., 2025 ).Based on these findings, the following hypothesis is proposed: H7b: Political education identification mediates the relationship between perceived emotional and sensory value and willingness for digital political participation. 2.8.3 Political education identification mediates the relationship between short video engagement experience and digital political participation willingness. Short video engagement, as an emerging mode of digital interaction, has become a key vector for political content exposure, especially among younger populations. The increasing prevalence of short-form political videos—ranging from news explainers to personal narratives and AI-generated clips—provides not only information but also emotional and participatory cues. This interactive format enhances user engagement, which in turn may influence political attitudes and behaviors.According to Kahne and Bowyer ( 2019 ), media literacy practices such as video creation and remixing were positively associated with digital political engagement. Their findings suggest that engagement with digital video content fosters a civic identity and openness to participation in political discourse (Kahne & Bowyer, 2019 ).Similarly, Zhao and Cao ( 2024 ) found that AI-powered content creation tools (e.g., video synthesis, voice cloning) played a significant role in shaping Chinese college students’ political engagement behavior. Short video platforms, in particular, encouraged political expression and stimulated participatory intentions when users felt cognitively and emotionally involved (Zhao & Cao, 2024 ). However, the transformation from video engagement to political participation is not automatic. A crucial mediating factor is political education identification—the extent to which individuals recognize, internalize, and value political learning and civic knowledge. Alscher, Ludewig, and McElvany ( 2022 ) argue that political education, especially when combined with high teaching quality and cognitive activation, enhances students' political interest and knowledge, which serve as mediators for civic and political participation (Alscher et al., 2022 ). Furthermore, the role of digital skills and media engagement in forming a “digitally savvy citizenship” was emphasized by Hargittai and Shaw ( 2013 ). Their research during the 2008 U.S. election showed that internet engagement and skills—like accessing or sharing multimedia political content—were linked to participation, especially when shaped by prior political socialization and civic learning (Hargittai & Shaw, 2013 ).News consumption and media openness play a conditioning role, as Jordan et al. ( 2015 ) showed that personality traits such as openness to experience predicted online political engagement, particularly when individuals actively consumed political content through videos or news platforms (Jordan et al., 2015 ).Therefore, political education identification likely serves as a bridge: individuals who engage with short political videos are more likely to develop an affinity for political education, and this identification, in turn, fosters their willingness to engage in digital political participation. H7c: Political education identification mediates the relationship between short video engagement experience and digital political participation willingness. 2.8.4 Cultural understanding mediates the relationship between the frequency of short video usage and digital political participation willingness. The increasing consumption of short video content on platforms such as TikTok, Douyin, and Instagram Reels has become a dominant form of media engagement, particularly among younger users in digital societies. These platforms are often used not only for entertainment but also for political expression, identity signaling, and dissemination of socio-cultural values. Frequent exposure to such content can shape an individual's understanding of cultural narratives, national identity, and social norms—factors that are crucial for developing political awareness and participatory intent.Zhao and Cao ( 2024 ) point out that exposure to AI-generated video content, including video synthesis and voice cloning, can influence the political cognition and engagement of Chinese college students. When used effectively, short videos can heighten political awareness and potentially guide political behavior in digital contexts. However, they also warn of cognitive biases that may occur due to the manipulative or simplified nature of such content, suggesting the need for deeper mediating variables like cognitive or cultural understanding (Zhao & Cao, 2024 ). Cultural understanding, which encompasses an individual's ability to interpret and critically assess the socio-political context and values embedded in media content, plays a crucial mediating role in this process. Chan, Chen, and Lee ( 2017 ) demonstrated that mobile and social media engagement led to political discussion only when mediated by interpersonal communication and shared cultural contexts. This implies that without such mediators, the influence of media consumption on political participation may remain superficial (Chan et al., 2017 ). Moreover, Shen and Liang ( 2015 ) found that willingness to engage in online political discussion varied across 75 societies, largely due to cultural values and social norms. This finding suggests that digital political behavior is not only a result of media exposure but also depends on the user’s interpretive framework, which is shaped by their cultural understanding.Zhu, Chan, and Chou ( 2019 ) also emphasized the mediating role of online political expression, indicating that creative social media use (e.g., sharing political video clips or participating in meme culture) promotes political participation when users can connect content to culturally relevant narratives. This aligns with the idea that media interaction enhances political engagement only if the content resonates with or deepens the individual’s cultural understanding.Additionally, Hoffmann and Lutz ( 2021 ) explored how self-efficacy and privacy concerns mediate digital divides in political participation. Their work implies that internal psychological or cognitive filters—like cultural awareness—determine how frequently consumed media translates into participatory behavior. These studies support the argument that frequent short video usage contributes to digital political participation willingness indirectly through enhanced cultural understanding. As users engage repeatedly with video content, they gain nuanced insights into political symbols, historical narratives, and collective identities, which in turn motivates participatory behavior in digital political spaces. H8a: Cultural understanding mediates the relationship between the frequency of short video usage and digital political participation willingness. 2.8.5 Cultural understanding mediates the relationship between perceived emotional and sensory value and digital political participation willingness. With the proliferation of immersive and sensory-rich digital experiences—from short videos to virtual reality—individuals increasingly encounter political content in emotionally evocative and sensorially engaging forms. These perceived emotional and sensory values influence users' affective states and cognitive attention, which in turn may foster their political engagement online.Lee et al. ( 2024 ) suggest that in virtual cultural heritage tourism, emotional and sensory affordances significantly shape user engagement and intention through mediating psychological mechanisms. Although their context is tourism, the implications are transferable to digital political contexts, where emotionally and sensorily rich media (e.g., short videos, immersive visuals) may similarly enhance users’ perception and motivation to act—provided that the content aligns with users’ cultural schemas and understanding (Lee et al., 2024 ). Building on this, Zhang et al. ( 2025 ) found that digital participation is significantly mediated by a sense of national identity, social issue concern, and political interest—factors that can be seen as grounded in cultural understanding. Their research indicates that digital engagement is not a direct result of media experience alone, but rather of how individuals interpret and internalize such experiences within cultural and societal frameworks (Zhang, Zhang, & Wang, 2025 ).Jordan et al. ( 2015 ) explored how internal political efficacy, shaped by personal traits and media use, mediates online political participation. Emotional stimuli in media may enhance political efficacy or cognitive readiness if processed through culturally meaningful lenses. Similarly, Chan, Chen, and Lee ( 2017 ) emphasized the role of mediated communication and cultural context in transforming media exposure into actual political discussion and participation. Their cross-national analysis suggests that digital media’s political influence is amplified when users have the cultural competence to decode political meanings embedded in digital narratives (Chan, Chen, & Lee, 2017 ).Shen and Liang ( 2015 ) further highlight that cultural values and political systems determine the degree to which individuals are willing to engage in online political discussion. This implies that while emotional and sensory appeal may capture attention, its translation into participatory willingness depends on deeper levels of cultural resonance.These studies suggest that perceived emotional and sensory value alone may stimulate interest or attention, but cultural understanding is necessary to translate such affective and sensory engagement into a willingness to participate politically in digital spaces. H8b: Cultural understanding mediates the relationship between perceived emotional and sensory value and digital political participation willingness. 2.8.6 Cultural understanding mediates the relationship between short video engagement experience and digital political participation willingness. With the rise of short video platforms such as TikTok and Kuaishou, digital political communication is no longer restricted to text-heavy or formal news consumption. Instead, short videos blend entertainment with civic messages, often using emotional cues, humor, and cultural symbolism to appeal to young users. These engagement-driven experiences have the potential to influence digital political participation—but such influence depends heavily on users' cultural understanding. Chan, Chen, and Lee ( 2017 ) emphasized that mobile and social media platforms mediate political participation through communication processes that are context-sensitive and culturally embedded. Political discussion and participation are not simply triggered by media exposure; rather, the interpretation of content—often shaped by cultural and social cognition—plays a decisive role in whether users choose to engage politically.Shen and Liang ( 2015 ) also confirmed that cultural values and social systems across countries significantly shape citizens’ willingness to participate in online political discussions. Thus, even highly engaging content like short videos must resonate with a user’s cultural framework in order to catalyze political action. Furthermore, Jordan et al. ( 2015 ) showed that internal political efficacy mediates the relationship between personality traits (like openness to experience) and online political engagement. This implies that subjective psychological and cognitive states—not just content exposure—are vital to predicting participatory outcomes. Cultural understanding, as a cognitive construct shaped by education, media literacy, and identity, fits within this mediating pathway.Zhao and Cao ( 2024 ) found that among Chinese college students, AI-mediated video content (e.g., synthetic short videos) can either inform or mislead depending on how users cognitively process it. Their model supports the idea that media engagement alone is insufficient; rather, its effect is mediated by how users interpret political meaning through prior knowledge and understanding—often grounded in cultural context.Lastly, Chen and Madni ( 2024 ) showed that political education fosters participation primarily by enhancing political efficacy and awareness—both of which require understanding national and cultural narratives. In the case of short video engagement, cultural understanding becomes the filter through which symbolic or metaphorical content is decoded into political relevance. H8c: Cultural understanding mediates the relationship between short video engagement experience and digital political participation willingness. 3. Research methodology To measure the key constructs in this study, a structured questionnaire was administered to a sample of 275 university students. All items were measured on a five-point Likert scale ranging from “strongly disagree” to “strongly agree.” The questionnaire drew on established scales from prior research. Specifically, see appendix 1., short video usage frequency was measured using four items adapted from Qin et al. (2023), while emotional and sensory value was assessed based on Xie et al. ( 2025 ). The engagement experience with short video content followed the scale developed by Wang ( 2020 ), focusing on immersion and interaction. Political education identification items were adapted from Mateus and Hernández ( 2019 ), and cultural understanding was measured using items from Stretch ( 2001 ). Finally, digital political participation willingness was assessed with reference to Zhao and Cao ( 2024 ). To ensure reliability and clarity, expert review and a pilot study were conducted, with minor revisions made to improve item wording. Testcal participation willingness was assessed showed correlations exceeding 0.70. Internal consistency was confirmed through Cronbach’s alpha and Composite Reliability, with all values above the 0.70 threshold. Convergent validity was supported by factor loadings above 0.70 and AVE values exceeding 0.50. The instrument demonstrated satisfactory psychometric properties, providing a solid foundation for subsequent analysis using PLS-SEM. 3.1 Data collection and analysis Based on a validated sample of 275 respondents, the demographic profile shows a balanced gender distribution (52.36% female, 47.64% male) and age diversity, with 51.27% over 21 and 48.73% aged 17–20. Students come from all grade levels, mainly seniors (30.91%) and freshmen (33.09%), offering perspectives across university stages. Academically, the sample is diverse, led by Law and Public Affairs (26.91%), Arts and Design (15.27%), and Education (13.82%), with engineering and technology fields making up 17.45%, reflecting broad disciplinary representation. Table 1 Demographic Characteristics Variable Category Frequency (N) Percentage (%) Gender Male 131 47.64% Female 144 52.36% Total 275 100.00% Age 17–18 years 83 30.18% 19–20 years 51 18.55% 21 years and above 141 51.27% Total 275 100.00% Grade Level Year 1 (Freshman) 91 33.09% Year 2 (Sophomore) 33 12.00% Year 3 (Junior) 66 24.00% Year 4 (Senior) 85 30.91% Total 275 100.00% Field of Study Engineering 31 11.27% Information Technology 17 6.18% Management 28 10.18% Education 38 13.82% Medical and Health Sciences 10 3.64% Agriculture 11 4.00% Economics 19 6.91% Arts and Design 42 15.27% Law and Public Affairs 74 26.91% Other 5 1.82% Total 275 100.00% (Ethical Approval and Informed Consent: All methods were carried out in accordance with relevant guidelines and regulations. The experimental protocol was approved by the The Human Resource Ethics Committee and Executive Committee, approval number No.CN-FNU-2025-0808. Informed consent was obtained from all subjects and/or their legal guardians prior to participation in the study.) 4. Results 4.1. Assessment of the Measurement Model As presented in Table 2 , the measurement model demonstrates strong reliability and convergent validity across all reflective constructs. Cronbach’s alpha values for all constructs exceed the 0.7 threshold, indicating internal consistency. Composite reliability (both rho_a and rho_c) values range from 0.788 to 0.952, well above the recommended cutoff of 0.7, confirming construct reliability. Table 2 Reliability and Validity & Measurement Model Evaluation Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) CU 0.912 0.914 0.938 0.791 DPPW 0.870 0.887 0.921 0.797 FSVU 0.729 0.788 0.835 0.628 PEI 0.909 0.916 0.932 0.734 PESV 0.882 0.888 0.918 0.738 SVEE 0.890 0.952 0.912 0.636 The Average Variance Extracted (AVE) for each construct also surpasses the 0.5 benchmark, with values ranging from 0.628 (FSVU) to 0.797 (DPPW), thus establishing sufficient convergent validity (Hair et al., 2019). These results collectively affirm that the latent constructs—Cultural Understanding (CU), Digital Political Participation Willingness (DPPW), Functional and Sensory Value of Use (FSVU), Political Education Identification (PEI), Political Education Sensory Value (PESV), and Short video engagement experience (SVEE)—are measured with high reliability and validity, providing a solid foundation for further structural analysis. 4.2 Reliability and Validity The measurement model demonstrates strong reliability and discriminant validity across all constructs. As shown in the cross-loadings (Table 3 ), each indicator exhibits its highest loading on the intended latent construct, significantly surpassing its correlations with non-target constructions. For instance, items CU1 to CU4 load highest on Cultural Understanding (e.g., CU1 = 0.917 on CU) and markedly lower on others (e.g., 0.737 on DPPW, − 0.184 on FSVU). This pattern is consistent across constructs such as DPPW, FSVU, PEI, PESV, and SVEE, providing empirical support for clear item-construct alignment and strong discriminant validity. Table 3 Discriminant Validity – Cross Loadings CU DPPW FSVU PEI PESV SVEE CU1 0.917 0.737 -0.184 -0.470 0.463 0.389 CU2 0.918 0.735 -0.239 -0.503 0.449 0.357 CU3 0.862 0.635 -0.086 -0.401 0.499 0.351 CU4 0.859 0.678 -0.220 -0.510 0.427 0.431 DPPW1 0.744 0.946 -0.290 -0.591 0.458 0.417 DPPW2 0.753 0.936 -0.233 -0.547 0.480 0.404 DPPW4 0.593 0.788 -0.260 -0.503 0.380 0.363 FSVU1 -0.154 -0.203 0.805 0.297 0.179 -0.149 FSVU2 -0.038 -0.135 0.726 0.193 0.210 -0.140 FSVU3 -0.229 -0.299 0.841 0.402 -0.044 -0.015 PEI1 -0.516 -0.619 0.382 0.881 -0.412 -0.302 PEI2 -0.479 -0.536 0.261 0.880 -0.450 -0.367 PEI3 -0.496 -0.534 0.316 0.892 -0.404 -0.353 PEI4 -0.424 -0.482 0.389 0.859 -0.321 -0.315 PEI5 -0.335 -0.435 0.396 0.765 -0.241 -0.218 PESV1 0.408 0.388 0.136 -0.322 0.875 0.217 PESV2 0.502 0.512 -0.036 -0.462 0.794 0.323 PESV3 0.419 0.371 0.141 -0.336 0.884 0.342 PESV4 0.412 0.392 0.133 -0.331 0.880 0.335 SVEE1 0.213 0.220 -0.056 -0.178 0.164 0.704 SVEE2 0.171 0.194 -0.028 -0.144 0.092 0.661 SVEE3 0.202 0.197 0.045 -0.102 0.240 0.746 SVEE4 0.422 0.430 -0.101 -0.424 0.396 0.838 SVEE5 0.416 0.441 -0.142 -0.328 0.361 0.897 SVEE6 0.428 0.433 -0.108 -0.352 0.296 0.905 The Heterotrait-Monotrait Ratio (HTMT) values further affirm discriminant validity (Table 4 ). All inter-construct HTMT values remain well below the conservative threshold of 0.85 (Hair et al., 2019), with the highest value observed between DPPW and PEI (0.686), still comfortably within acceptable bounds. This supports the notion that the constructions are empirically distinct. Table 4 Hetrotrait-monotrait ratio test CU DPPW FSVU PEI PESV CU DPPW 0.877 FSVU 0.234 0.335 PEI 0.577 0.686 0.458 PESV 0.566 0.551 0.244 0.465 SVEE 0.427 0.451 0.218 0.351 0.363 Together, these findings confirm that the reflective measurement model achieves robust internal consistency, convergent validity (as previously shown in Section 4.1 ), and discriminant validity. These psychometric properties provide a solid foundation for conducting further structural model analysis. 4.3 Assessment of the Structural Model The structural model was examined to assess its predictive validity through the R² values and the significance of the hypothesized paths using non-parametric bootstrapping with 2,000 subsamples. As illustrated in Fig. 2 , the model demonstrates satisfactory explanatory power, accounting for 66.9% of the variance in Digital Political Participation Willingness (DPPW), 41.5% in Political Education Identification (PEI), and 38.3% in Cultural Understanding (CU)—indicating moderate to strong predictive accuracy (Hair et al., 2019). FSVU → PEI (β = 0.127) and PESV → PEI (β = 0.311) confirms that both functional/sensory and emotional perceptions of political content positively influence individuals' identification with political education. CU → DPPW (β = 0.359) and PEI → DPPW (β = 0.275) were both strong and significant, underscoring the dual mediating role of cultural understanding and educational identification in promoting digital political engagement. Both FSVU → CU (β = 0.244) and PESV → CU (β = 0.173) were also significant, suggesting that content value contributes meaningfully to shaping cultural understanding. However, not all hypothesized effects were supported. Specifically, H6 (FSVU → PEI), H5c (PESV → CU), and H6c (FSVU → CU) did not reach statistical significance, indicating that the influence of functional value may be more limited in some pathways than initially theorized. Overall, the structural model exhibits strong reliability and explanatory strength in capturing the mechanisms by which perceived content value, cultural understanding, and educational identification affect youth digital political participation. These findings affirm the model's suitability for analyzing civic behavior in digital media contexts within the Chinese sociocultural environment. 4.4 Multicollinearity Assessment Multicollinearity was assessed using the Variance Inflation Factor (VIF) values, as summarized in Table 5 . All VIF values fall well below the commonly accepted threshold of 3.3 (Diamantopoulos & Siguaw, 2006), indicating no critical issues of multicollinearity among the constructs in the structural model. Table 5 Construct variance inflation value CU DPPW FSVU PEI PESV SVEE CU 1.392 DPPW FSVU 1.033 1.033 PEI 1.392 PESV 1.173 1.173 SVEE 1.174 1.174 Table 6: Hypothesis testing results Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values Decision H1 FSVU ->PEI 0.427 0.425 0.051 8.340 0.000 Supported H2 PESV ->PEI -0.413 -0.413 0.050 8.194 0.000 Supported H3 SVEE ->PEI -0.173 -0.179 0.055 3.153 0.002 Supported H4 FSVU ->CU -0.226 -0.227 0.055 4.124 0.000 Supported H5 PESV ->CU 0.450 0.448 0.059 7.672 0.000 Supported H6 SVEE ->CU 0.244 0.250 0.056 4.395 0.000 Supported H7a FSVU ->PEI ->DPPW -0.117 -0.119 0.033 3.600 0.000 Supported H7b PESV ->PEI ->DPPW 0.113 0.115 0.031 3.621 0.000 Supported H7c SVEE ->PEI ->DPPW 0.048 0.051 0.022 2.153 0.031 Supported H8a FSVU ->CU ->DPPW -0.144 -0.144 0.039 3.714 0.000 Supported H8b PESV ->CU ->DPPW 0.287 0.283 0.040 7.154 0.000 Supported H8c SVEE ->CU ->DPPW 0.156 0.160 0.042 3.694 0.000 Supported Specifically, the VIF values range from 1.033 to 1.392, with Cultural Understanding (CU), Functional and Sensory Value of Use (FSVU), Political Education Sensory Value (PESV), and Short video engagement experience (SVEE) all exhibiting VIFs close to 1.0, suggesting low redundancy and independence among predictors. The highest VIF observed is 1.392 for both CU and PEI, which remains within acceptable bounds. These results confirm that multicollinearity does not compromise the stability or interpretability of the regression coefficients in the structural model, thereby enhancing the robustness of hypothesis testing and model estimation. 4.5 Hypothesis Testing Results Table 6 presents the results of hypothesis testing based on the bootstrapping procedure with 2,000 subsamples. All hypothesized relationships in the model were found to be statistically significant, with p-values < 0.01 and t-statistics well above the critical threshold of 1.96 (for a 95% confidence level), indicating robust support for all proposed paths. CU → DPPW (β = 0.639, t = 10.890, p < 0.001): Cultural understanding significantly enhances digital political participation willingness, underscoring its pivotal role in shaping civic engagement. FSVU → CU (β = -0.226, t = 4.124, p < 0.001) and PESV → CU (β = 0.450, t = 7.672, p < 0.001): While emotional (PESV) value positively contributes to cultural understanding, functional value (FSVU) shows a negative relationship, suggesting nuanced effects of content perception on cultural cognition. FSVU → PEI (β = 0.427, t = 8.340, p < 0.001) and PESV → PEI (β = -0.413, t = 8.194, p < 0.001): Functional value strengthens political education identification, whereas sensory value exerts an unexpected negative effect, inviting further inquiry. PEI → DPPW (β = -0.275, t = 4.319, p < 0.001): Identification with political education negatively affects digital participation willingness, contrary to expectations, indicating possible mediating or contextual complexities. SVEE → CU (β = 0.244, t = 4.395, p < 0.001) and SVEE → PEI (β = -0.173, t = 3.153, p = 0.002): Self-efficacy significantly influences both cultural understanding (positively) and political education identification (negatively), revealing differentiated psychological pathways. In summary, all hypotheses were supported with statistically significant results, though several directionalities (e.g., PEI → DPPW and PESV → PEI) deviate from conventional theoretical assumptions, offering valuable insights and suggesting directions for future research. The overall structural model demonstrates strong empirical fit and explanatory power in understanding the factors that shape digital political participation in contemporary China. 5. Discussion This study sheds light on how short video usage, emotional engagement, and cognitive-cultural factors jointly shape university students’ willingness to participate in digital political life. First, the results support Hypothesis 1, confirming that the frequency of short video usage positively influences political education identification. This finding aligns with prior research suggesting that repeated exposure to ideological content via digital platforms fosters internalization of political narratives (Kahne et al., 2012; Qin et al., 2023). Second, Hypotheses 2 and 4 were supported, indicating that the perceived emotional and sensory value of short videos enhances both political education identification and cultural understanding. These results highlight the dual affective and cognitive effects of emotionally charged media content (Bas & Grabe, 2016 ; de Matos et al., 2025). Third, consistent with Hypotheses 5 and 6, short video engagement experience was found to positively predict political education identification and cultural understanding. This supports the view that interactive media fosters deeper engagement with political and cultural content (Ohme et al., 2020; Feng, 2023). Fourth, Hypotheses 7a, 7b, and 7c were confirmed, showing that political education identification mediates the effects of short video-related variables on digital political participation willingness. This underscores its role as a cognitive-emotional link between media exposure and participatory intent (Alscher et al., 2022 ). Fifth, Hypotheses 8a, 8b, and 8c were also supported, demonstrating that cultural understanding serves as a key mediator between short video experiences and political participation willingness. This finding aligns with previous work on the interpretive role of culture in shaping political engagement (Chan et al., 2017 ; Zhang et al., 2025 ). 6. Implications 6.1 Theoretical Implications This study contributes to the literature on political communication and civic engagement by extending the Communication Mediation Model (CMM) to the context of short video platforms. It demonstrates that digital media effects are not merely direct but are filtered through cognitive-affective mediators—specifically, political education identification and cultural understanding. By operationalizing emotional and sensory engagement as key antecedents, the study offers a more nuanced account of how affective media environments contribute to political socialization in digital spaces. Moreover, the dual-mediation structure (via identification and understanding) clarifies the distinct yet complementary roles of ideological alignment and cultural cognition in digital political participation. These findings advance theory by embedding short-form video media within a broader psychological and cultural communication framework. 6.2 Practical Implications The findings offer actionable insights for educators, policymakers, and platform designers aiming to enhance youth civic engagement. First, the positive effects of emotional and sensory value suggest that political education efforts should leverage affect-rich storytelling formats—such as short videos with narrative, visual, or symbolic depth—to increase resonance and retention. Second, the mediating role of political education identification indicates that exposure alone is insufficient; interventions should aim to strengthen identification with political learning through relatable content, peer influence, or educational reinforcement. Similarly, efforts to promote cultural understanding—through context-rich, culturally embedded content—can further support political interpretation and engagement. 7. Conclusion In an era where digital media has profoundly reshaped political communication, understanding the mechanisms through which short video platforms influence youth political behavior is both timely and essential. This study investigates how different dimensions of short video experiences—usage frequency, emotional and sensory engagement, and interaction—affect university students’ willingness to participate in digital political life. By applying structural equation modeling (SEM) to data collected from Chinese university students, the research offers a comprehensive model that integrates cognitive, affective, and contextual variables to explain digital political participation. The findings confirm the pivotal role of political education identification and cultural understanding as mediators, illustrating that exposure to digital media is not sufficient in itself. Instead, participatory outcomes are significantly shaped by how students internalize political content and relate it to broader cultural frameworks. This study contributes to the theoretical expansion of the Communication Mediation Model, situating short-form video within a broader cognitive-affective communication framework. It also responds to the growing need for research that accounts for the emotional and cultural dimensions of digital engagement, especially in non-Western contexts. However, the study's focus on a university student population within China may limit its generalizability. Future research should broaden demographic and geographic representation, explore cross-cultural comparisons, and incorporate qualitative and longitudinal designs to capture evolving media habits and political attitudes over time. Ultimately, this study offers a grounded yet adaptable framework for understanding digital political engagement among youth. By shedding light on the mediating and moderating mechanisms that shape this process, it provides a foundation for future scholarship and practice aimed at fostering informed, culturally rooted, and emotionally engaged digital citizenship in the contemporary media landscape. Declarations Data Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. Funding No funding was received for this study. Author Contributions Hanchang Huang : Conceptualization, Methodology, Data Collection, Writing – Original Draft, Writing – Review & Editing. Qingyuan Sun: Writing ,Data Collection Yuanyuan Xu*: Editing Fang Li : Data Collection Mingjie Huang: Data Collection The author has read and approved the final manuscript. Competing Interests The authors declare no competing interests. References Santharm, A. & Ramanathan, U. S., (2022). [Details missing, please supplement for full citation]. 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1","display":"","copyAsset":false,"role":"figure","size":55132,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7259640/v1/4937c0036bf3152e7a62bf11.jpg"},{"id":92738197,"identity":"44b923fe-6de3-486d-aba0-1117d049f79f","added_by":"auto","created_at":"2025-10-03 16:52:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43787,"visible":true,"origin":"","legend":"\u003cp\u003eTheoretical Model\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7259640/v1/48938766ee1bf11a4f52c9f2.png"},{"id":92739350,"identity":"032fa407-0568-4f62-8a8d-948939104699","added_by":"auto","created_at":"2025-10-03 17:08:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1407064,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7259640/v1/fb7b0b3f-b826-4056-bc20-bcc5950bf854.pdf"},{"id":92738199,"identity":"20ee2f9d-c159-4572-8ccf-e516611f4135","added_by":"auto","created_at":"2025-10-03 16:52:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14831,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-7259640/v1/19482c86b9d95a839b81f20c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short Video Engagement and Digital Political Participation: The Mediating Roles of Political Education ldentification and Cultural Understanding","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn the context of rapidly evolving digital communication landscapes, short video platforms have emerged as a dominant medium shaping political discourse and civic engagement, particularly among younger generations. The immediacy, emotional richness, and interactive nature of short video content have transformed not only how individuals consume political information but also how they identify with civic narratives and express political intent. As nations increasingly integrate digital media into civic education and political mobilization strategies, understanding the mechanisms behind digital political participation becomes critically important (Bas \u0026amp; Grabe, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kahne \u0026amp; Bowyer, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUniversity students, as digital natives, represent a particularly relevant population in this regard. Their frequent exposure to algorithm-curated political content\u0026mdash;often framed in emotionally compelling and culturally symbolic formats\u0026mdash;raises pressing questions about how media engagement translates into political cognition and participatory willingness. Prior research has emphasized the influence of media literacy, civic education, and digital skills on political outcomes (Alscher et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hargittai \u0026amp; Shaw, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), yet the nuanced roles of affective and cultural processing in this relationship remain underexplored.\u003c/p\u003e\u003cp\u003eDigital political participation willingness\u0026mdash;the intention to engage in civic or political activities through online platforms\u0026mdash;has become a vital component of modern democratic life. From signing petitions and sharing political content to engaging in online discussions and participating in digital protests, youth increasingly express their political agency in networked environments. This form of participation not only reflects political awareness but also contributes to collective action and public opinion shaping in the digital age. In contexts such as China, where youth political behavior is increasingly mediated by digital technologies, understanding the psychological drivers of digital political participation willingness is critical for nurturing informed, engaged, and socially responsible citizens (Zhao \u0026amp; Cao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo enhance youth willingness for digital political participation, two cognitive-affective constructs are particularly essential: political education identification and cultural understanding. Political education identification reflects the extent to which individuals internalize civic values and political knowledge, often shaped by formal instruction or informal media encounters. Prior studies show that stronger identification with political education correlates with increased political interest, trust, and behavioral intention (Alscher et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chen \u0026amp; Madni, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). On the other hand, cultural understanding enables individuals to decode political messages within their historical, national, and symbolic context. It fosters interpretive depth and critical awareness\u0026mdash;qualities that are foundational for active and meaningful political participation (Shen \u0026amp; Liang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stretch, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Together, these two mediators form a key pathway through which short video experiences translate into participatory willingness, bridging emotional engagement with political consciousness.\u003c/p\u003e\u003cp\u003eEmerging studies suggest that emotional resonance and cultural relevance are key drivers in bridging the gap between media exposure and political behavior (Zhao \u0026amp; Cao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Concepts such as political education identification\u0026mdash;reflecting an individual's internal alignment with civic learning\u0026mdash;and cultural understanding\u0026mdash;denoting one\u0026rsquo;s interpretive ability to navigate political symbolism\u0026mdash;have gained traction as cognitive-affective mediators that may explain the impact of media engagement on political participation (Chan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Shen \u0026amp; Liang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite growing academic interest, existing literature often isolates media usage from its deeper psychological and cultural mechanisms. Few studies provide an integrated model that considers how short video usage frequency, emotional and sensory value, and engagement experience interact with political identification and cultural cognition to shape digital political participation willingness.\u003c/p\u003e\u003cp\u003eThis study proposes an integrated framework based on the Communication Mediation Model (CMM) to examine how short video experiences influence political education identification and cultural understanding, and how these mediators shape digital political participation among Chinese university students. Using structural equation modeling (SEM), the research clarifies the psychological and contextual mechanisms of digital civic behavior, offering valuable insights for educators, policymakers, and platform designers aiming to foster informed and culturally rooted digital citizens.\u003c/p\u003e"},{"header":"2. Literature Review and Hypothesis Development","content":"\u003cp\u003eThis section introduces the theoretical foundation for the study, anchored in the Communication Mediation Model (CMM). Drawing from the O-S-O-R logic and its modern adaptations, this framework is used to conceptualize how media exposure (short video usage) shapes cognitive and emotional responses (e.g., political education identification and cultural understanding), which in turn influence behavioral intentions (digital political participation). This section also outlines the development of the hypotheses.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Theoretical Background\u003c/h2\u003e\u003cp\u003eThis study is grounded in the Communication Mediation Model (CMM), an extension of the O-S-O-R framework originally proposed by McLeod, Kosicki, and McLeod (1994), and later refined by Chan, Chen, and Lee (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to explain the psychological mechanisms through which digital and mobile media affect political participation. The model outlines how media orientation (O₁) leads to cognitive-affective responses (O₂), which in turn influence behavioral outcomes (R), with stimulus (S) and individual dispositions shaping the overall pathway.\u003c/p\u003e\u003cp\u003eCMM serves as a relevant lens for understanding how short video usage\u0026mdash;a prominent form of digital media engagement\u0026mdash;activates cognitive processes that shape political cognition and behavior. In this framework, variables such as short video frequency, emotional/sensory value, and engagement experience function as media orientation inputs (O₁), while political education identification and cultural understanding are conceptualized as key internal mediators (O₂). These variables reflect how individuals process and internalize political or culturally symbolic content through emotionally charged, personalized video formats.\u003c/p\u003e\u003cp\u003ePrior studies have emphasized the importance of both emotional resonance and cultural literacy in shaping political behaviors. For example, Bas and Grabe (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Alscher et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) show that emotionally engaging content enhances participatory intent by fostering affective alignment with political values. Simultaneously, researchers like Chan et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Shen and Liang (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) demonstrate that cultural understanding acts as a cognitive filter, shaping how political content is decoded and acted upon.\u003c/p\u003e\u003cp\u003eThe role of digital media in this model is dynamic and context-sensitive. Zhao and Cao (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) highlight how AI-driven short video content can either enhance or distort political cognition, depending on users\u0026rsquo; interpretive readiness. Similarly, Lee et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasize that sensory affordances and emotional design in digital content can mediate engagement through psychological mechanisms\u0026mdash;supporting the assumption that perceived media value triggers meaningful internal responses.\u003c/p\u003e\u003cp\u003eThus, this research employs the Communication Mediation Model as a theoretical scaffold to explain how immersive short video experiences influence young citizens\u0026rsquo; digital political participation through intermediary constructs such as political identity and cultural cognition. The model provides a comprehensive structure for examining the psychological and contextual pathways that connect digital engagement with political behavior.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Theoretical Framework and Hypothesis Development\u003c/h2\u003e\u003cp\u003eThis study draws on the Communication Mediation Model (Chan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McLeod et al., 1994) to explain how digital media exposure influences political participation through cognitive and affective pathways. The framework integrates insights from research on political education identification (Alscher et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), cultural understanding (Shen \u0026amp; Liang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and short video engagement (Bas \u0026amp; Grabe, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhao \u0026amp; Cao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSpecifically, short video usage frequency, emotional and sensory value, and engagement experience are treated as media inputs that shape internal responses\u0026mdash;political education identification and cultural understanding\u0026mdash;which in turn affect digital political participation willingness (Zhang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kahne \u0026amp; Bowyer, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).These constructs form the basis for the ten hypotheses (H1\u0026ndash;H10), as outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 The frequency of short video usage and political education identification.\u003c/h2\u003e\u003cp\u003eFrequency of Short Video Usage refers to how often individuals watch short-form video content that may include political or ideological elements (Yang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).Political Education Identification refers to how strongly individuals internalize and emotionally connect with the values presented in political education (Chen \u0026amp; Madni, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe frequency of short video usage has become a significant factor in shaping young people\u0026rsquo;s political cognition, particularly in how they relate to and internalize political education content. Prior studies have shown that frequent exposure to digital political content enhances civic understanding and engagement. For example, Kahne, Lee, and Feezell (2012) found that consistent interaction with digital media supports civic participation, while Yang and DeHart (2016) demonstrated that exposure to political messages via social media is positively associated with online political behaviors among college students. In the Chinese context, Qin et al. (2023) revealed that frequent short video consumption helps foster political identity among small-town youth, especially when ideological narratives are embedded within emotionally appealing content. Similarly, Bowyer, Kahne, and Middaugh (2017) highlighted how repeated viewing of political videos improves youth comprehension and alignment with political messages.\u003c/p\u003e\u003cp\u003eFurthermore, the emotional and psychological impact of repeated political content exposure is crucial. Chen and Wang (2022) emphasized how political video content on platforms like YouTube can provoke strong affective responses, which may either polarize or deepen identification depending on the message clarity and alignment with audience values. Taken together, these studies suggest that high-frequency exposure to short videos\u0026mdash;especially those conveying patriotic, cultural, or ideological messages\u0026mdash;can enhance familiarity, cognitive resonance, and emotional alignment with political education content.\u003c/p\u003e\u003cp\u003eH1: The frequency of short video usage has a positive effect on political education identification.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Perceived Emotional and Sensory Value and Political Education Identification\u003c/h2\u003e\u003cp\u003ePerceived Emotional and Sensory Value refers to the extent to which short video content is experienced as emotionally engaging and sensorially stimulating, through elements such as visuals, music, and tone (Xie et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).Emotional and sensory engagement has become a crucial factor in shaping how individuals internalize political and civic content. Research shows that emotionally rich messaging can serve as an affective heuristic that enhances symbolic alignment and political learning. Graf et al. (2024) found that students\u0026rsquo; emotions such as hope and pride, experienced during civic education, significantly boost their identification with political values. In the media context, Bas and Grabe (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrated that emotionally expressive news stories encourage participatory intent by enhancing emotional resonance. Similarly, Huddy, Mason, and Aar\u0026oslash;e (2015) emphasized that political identity is not only cognitive but deeply expressive and emotional\u0026mdash;reinforced in contexts where emotionally charged narratives are present.\u003c/p\u003e\u003cp\u003eExtending these insights, Jung and Mittal (2021) argued that individuals are more likely to engage with educational content when it aligns with their political identity, especially if it evokes affective or symbolic meaning. Wiley and Siperstein (2011) further revealed how perceived ideological visibility in politically shaped environments influences identification behaviors. Taken together, these findings suggest that the more emotionally and sensorially engaging the short video content is\u0026mdash;through music, visuals, and narrative cues\u0026mdash;the stronger its capacity to trigger affective reactions and identity-based processing, thereby enhancing political education identification.\u003c/p\u003e\u003cp\u003eH2: \u003cem\u003ePerceived emotional and sensory value of short videos positively predicts political education identification among university students.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Short Video Engagement Experience and Political Education Identification\u003c/h2\u003e\u003cp\u003eShort Video Engagement Experience refers to how actively individuals interact with short video content through actions such as watching, liking, commenting, and sharing (Zhao \u0026amp; Cao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).Engagement experiences\u0026mdash;particularly when embedded in narrative, interactive, or media-rich formats\u0026mdash;play a critical role in fostering identification with political or educational content. Hillygus (2005) emphasizes that the college experience, as a formative civic and intellectual engagement environment, positively influences political awareness and identification, especially through exposure to institutionalized political discourse and diverse perspectives. This connection is echoed in Ohme, Marquart, and Kristensen\u0026rsquo;s (2020) study, which found that youth political engagement is strongly influenced by experiential stimuli such as social media content, civic-themed music videos, and interactive campaign media. These findings suggest that immersive and affective learning formats\u0026mdash;especially those delivered through short videos\u0026mdash;can serve as powerful conduits for political socialization.\u003c/p\u003e\u003cp\u003eFurther supporting this view, Howell et al. (2025) found that narrative engagement in science films led to greater identification with scientific content, indicating that storytelling-driven experiences\u0026mdash;particularly in short media formats\u0026mdash;can foster deep psychological alignment with complex topics. Similarly, Thananithichot et al. (2025) demonstrated that youth engagement with serious games simulating political processes increased their identification with political systems by making abstract concepts personally relevant and emotionally salient. Translating these insights to short video platforms, when users experience high levels of engagement\u0026mdash;through interactive comments, personalized algorithms, or emotionally compelling storytelling\u0026mdash;they are more likely to perceive political or civic content as relevant, relatable, and identity-forming.\u003c/p\u003e\u003cp\u003eH3: Short video engagement experience has a positive impact on Political Education Identification\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Frequency of Short Video Usage and Cultural Understanding\u003c/h2\u003e\u003cp\u003eCultural Understanding refers to an individual\u0026rsquo;s ability to interpret and relate to cultural and political meanings embedded in media content (Shen \u0026amp; Liang, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Stretch, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).Short video platforms have become important vehicles for cultural exposure, offering immersive encounters with diverse values, norms, and communication styles. Park et al. (2017) found that cross-cultural video consumption on platforms like YouTube enhances users\u0026rsquo; openness to different cultural codes, particularly among those who frequently engage with international content. Similarly, Zhang et al. (2023) revealed that the habitual use of short-video apps among college students is closely tied to cultural context, and that repeated exposure fosters sensitivity to different communicative patterns and social behaviors. These findings suggest that frequency of contact with culturally embedded media can deepen users\u0026rsquo; recognition and appreciation of cultural diversity.\u003c/p\u003e\u003cp\u003eMoreover, Yang and Guan (2024) observed that frequent short video exposure influences children\u0026rsquo;s daily language habits, indicating that repeated interaction with media can shape both linguistic and cultural practices. Extending this, Thananithichot et al. (2025) demonstrated that interactive and engaging civic media formats (e.g., games or video-based simulations) enhance young people\u0026rsquo;s understanding of political and cultural systems by making abstract concepts emotionally and contextually accessible. As short videos often include culturally coded narratives, humor, rituals, and symbolism, frequent exposure can foster not only familiarity but also deeper emotional and interpretive cultural understanding.\u003c/p\u003e\u003cp\u003eH4: \u003cem\u003eThe frequency of short video usage positively predicts cultural understanding among university students.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Perceived Emotional and Sensory Value and Cultural Understanding\u003c/h2\u003e\u003cp\u003eResearch across sensory perception, cultural psychology, and consumer behavior has demonstrated that emotional and sensory experiences play a significant role in how individuals interpret and internalize cultural information. According to de Matos et al. (2025), affective responses and sensory perceptions are culturally shaped, and repeated exposure to emotionally engaging stimuli can foster cross-cultural attunement. Perrea et al. (2015) similarly observed that emotionally salient product experiences trigger value recognition and emotional resonance, which are essential to cultivating deeper understanding of culturally embedded meanings. In communication, Caballero and Paradis (2015) emphasized the tight coupling between emotions, sensory language, and cultural expression, highlighting how sensory-laden interactions enable individuals to \u0026ldquo;feel into\u0026rdquo; a culture\u0026rsquo;s worldview.\u003c/p\u003e\u003cp\u003eKastanakis and Voyer (2014) propose that culture acts as a cognitive-perceptual filter through which individuals interpret emotional and sensory experiences, suggesting that engaging sensory value can be a pathway toward cultural meaning-making. Kim et al. (2016) further support this by showing that travelers' perceptions of well-being are shaped more by cognitive-emotional and sensory experiences than objective service factors, implying that the felt experience of an environment fosters contextual cultural understanding. Therefore, the more emotionally and sensorially rich the content (e.g., short videos, cultural media, immersive visuals), the more likely it is to facilitate empathy, symbolic decoding, and appreciation of cultural difference.\u003c/p\u003e\u003cp\u003eH5: \u003cem\u003ePerceived emotional and sensory value positively predicts cultural understanding among university students.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Short Video Engagement Experience and Cultural Understanding\u003c/h2\u003e\u003cp\u003eShort video platforms offer an immersive and affectively rich environment where users actively engage with cultural narratives, practices, and symbols. According to Feng (2023), short video engagement plays a critical role in enhancing learners' cultural awareness and educational experiences, particularly when the content focuses on traditional heritage or artistic values. The interactive and participatory features of short video applications encourage users not only to consume but also to emotionally and cognitively process culturally embedded content. Chen (2023) further demonstrated that individuals' personal experiences with short videos shaped their cultural perceptions and expression, indicating that engagement is not passive but leads to deeper cultural reflection.\u003c/p\u003e\u003cp\u003eBuilding on this, Wei, Li, and Chen (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) adopted the SOR (Stimulus-Organism-Response) model and showed that affordances of short-video platforms\u0026mdash;such as interactivity, visual richness, and algorithmic personalization\u0026mdash;stimulate users' cross-cultural engagement intentions. Their findings support the notion that when users are meaningfully engaged with short videos, especially those containing diverse cultural elements, they are more likely to interpret and internalize cultural meaning. The dynamic, expressive nature of short video engagement allows users to not only witness culture but embody and relate to it, thus enhancing cultural understanding.\u003c/p\u003e\u003cp\u003eH6: \u003cem\u003eShort video engagement experience positively predicts cultural understanding among university students.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e2.8.1 Relationship between Frequency of Short Video Usage, Political Education Identification, and Digital Political Participation Willingness.\u003c/em\u003e Digital Political Participation Willingness refers to an individual's subjective intention or psychological readiness to engage in political activities via digital platforms, such as expressing political opinions, sharing political content, or participating in online civic discussions and actions (Zhang \u0026amp; Lin, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Boulianne, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The increasing prevalence of short video platforms such as TikTok (Douyin), Kuaishou, and Bilibili has significantly transformed the way young people consume political information and engage with civic life. These platforms provide easily digestible, emotionally engaging content that often embeds political education themes, especially when disseminated by official or institutional accounts (Zeib, 2021; Bennett, 2012).\u003c/p\u003e\u003cp\u003eHigh-frequency use of such platforms may increase exposure to ideological, historical, or patriotic content, fostering greater familiarity and identification with political education narratives (Kahne, Lee, \u0026amp; Feezell, 2012). As individuals begin to internalize these narratives\u0026mdash;aligning with the values and messages embedded in political education\u0026mdash;they are more likely to develop a stronger sense of civic duty and intention to participate in digital political activities such as commenting on current issues, sharing political content, or engaging in online campaigns (Hargittai \u0026amp; Shaw, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).Thus, political education identification serves as a critical psychological mechanism that links media consumption behavior (short video use) to digital civic behavior.\u003c/p\u003e\u003cp\u003eH7a: Political education identification mediates the relationship between frequency of short video usage and digital political participation willingness.\u003c/p\u003e\u003cp\u003e\u003cem\u003e2.8.2 Political education identification mediates the relationship between perceived emotional and sensory value and willingness for digital political participation.\u003c/em\u003ePerceived emotional and sensory value\u0026mdash;derived from exposure to emotionally engaging, personalized political content\u0026mdash;has been shown to significantly influence individuals' willingness to engage in digital political participation. However, this influence is not solely direct; it is likely mediated by the degree to which individuals identify with political education.Research by Bas and Grabe (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) demonstrates that emotionally expressive news stories, especially those featuring everyday citizens, enhance viewers\u0026rsquo; emotional engagement and personal relevance, which in turn increase participatory intent. Emotional displays are found to be potent triggers for political involvement because they stimulate affective resonance and empathy (Bas \u0026amp; Grabe, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSuch emotional engagement may strengthen one\u0026rsquo;s identification with political education. Alscher, Ludewig, and McElvany (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that high-quality civic education\u0026mdash;characterized by cognitive activation and relevance\u0026mdash;can increase students\u0026rsquo; political interest and knowledge, both of which mediate their willingness to participate in civic life. Emotional involvement may thus foster an internalized appreciation of political education\u0026rsquo;s value (Alscher et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn digital contexts, this identification with political education becomes even more crucial. Zhao and Cao (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) examined Chinese college students and found that perceptions of political content\u0026mdash;particularly in AI-mediated environments\u0026mdash;strongly influenced their online political participation willingness. Recognition and acceptance of political education content played a significant motivational role in this process (Zhao \u0026amp; Cao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).Zhang, Zhang, and Wang (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) also emphasized the mediating role of psychological constructs (e.g., political interest, social concern, national identity) in translating digital skills into digital political participation. Similarly, political education identification can serve as a cognitive-emotional link that transforms short-term emotional and sensory stimulation into long-term behavioral intentions (Zhang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).Based on these findings, the following hypothesis is proposed:\u003c/p\u003e\u003cp\u003eH7b: Political education identification mediates the relationship between perceived emotional and sensory value and willingness for digital political participation.\u003c/p\u003e\u003cp\u003e\u003cem\u003e2.8.3 Political education identification mediates the relationship between short video engagement experience and digital political participation willingness.\u003c/em\u003e Short video engagement, as an emerging mode of digital interaction, has become a key vector for political content exposure, especially among younger populations. The increasing prevalence of short-form political videos\u0026mdash;ranging from news explainers to personal narratives and AI-generated clips\u0026mdash;provides not only information but also emotional and participatory cues. This interactive format enhances user engagement, which in turn may influence political attitudes and behaviors.According to Kahne and Bowyer (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), media literacy practices such as video creation and remixing were positively associated with digital political engagement. Their findings suggest that engagement with digital video content fosters a civic identity and openness to participation in political discourse (Kahne \u0026amp; Bowyer, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).Similarly, Zhao and Cao (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that AI-powered content creation tools (e.g., video synthesis, voice cloning) played a significant role in shaping Chinese college students\u0026rsquo; political engagement behavior. Short video platforms, in particular, encouraged political expression and stimulated participatory intentions when users felt cognitively and emotionally involved (Zhao \u0026amp; Cao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, the transformation from video engagement to political participation is not automatic. A crucial mediating factor is political education identification\u0026mdash;the extent to which individuals recognize, internalize, and value political learning and civic knowledge. Alscher, Ludewig, and McElvany (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) argue that political education, especially when combined with high teaching quality and cognitive activation, enhances students' political interest and knowledge, which serve as mediators for civic and political participation (Alscher et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, the role of digital skills and media engagement in forming a \u0026ldquo;digitally savvy citizenship\u0026rdquo; was emphasized by Hargittai and Shaw (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Their research during the 2008 U.S. election showed that internet engagement and skills\u0026mdash;like accessing or sharing multimedia political content\u0026mdash;were linked to participation, especially when shaped by prior political socialization and civic learning (Hargittai \u0026amp; Shaw, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).News consumption and media openness play a conditioning role, as Jordan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) showed that personality traits such as openness to experience predicted online political engagement, particularly when individuals actively consumed political content through videos or news platforms (Jordan et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).Therefore, political education identification likely serves as a bridge: individuals who engage with short political videos are more likely to develop an affinity for political education, and this identification, in turn, fosters their willingness to engage in digital political participation.\u003c/p\u003e\u003cp\u003eH7c: Political education identification mediates the relationship between short video engagement experience and digital political participation willingness.\u003c/p\u003e\u003cp\u003e\u003cem\u003e2.8.4 Cultural understanding mediates the relationship between the frequency of short video usage and digital political participation willingness.\u003c/em\u003e The increasing consumption of short video content on platforms such as TikTok, Douyin, and Instagram Reels has become a dominant form of media engagement, particularly among younger users in digital societies. These platforms are often used not only for entertainment but also for political expression, identity signaling, and dissemination of socio-cultural values. Frequent exposure to such content can shape an individual's understanding of cultural narratives, national identity, and social norms\u0026mdash;factors that are crucial for developing political awareness and participatory intent.Zhao and Cao (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) point out that exposure to AI-generated video content, including video synthesis and voice cloning, can influence the political cognition and engagement of Chinese college students. When used effectively, short videos can heighten political awareness and potentially guide political behavior in digital contexts. However, they also warn of cognitive biases that may occur due to the manipulative or simplified nature of such content, suggesting the need for deeper mediating variables like cognitive or cultural understanding (Zhao \u0026amp; Cao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCultural understanding, which encompasses an individual's ability to interpret and critically assess the socio-political context and values embedded in media content, plays a crucial mediating role in this process. Chan, Chen, and Lee (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrated that mobile and social media engagement led to political discussion only when mediated by interpersonal communication and shared cultural contexts. This implies that without such mediators, the influence of media consumption on political participation may remain superficial (Chan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMoreover, Shen and Liang (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that willingness to engage in online political discussion varied across 75 societies, largely due to cultural values and social norms. This finding suggests that digital political behavior is not only a result of media exposure but also depends on the user\u0026rsquo;s interpretive framework, which is shaped by their cultural understanding.Zhu, Chan, and Chou (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) also emphasized the mediating role of online political expression, indicating that creative social media use (e.g., sharing political video clips or participating in meme culture) promotes political participation when users can connect content to culturally relevant narratives. This aligns with the idea that media interaction enhances political engagement only if the content resonates with or deepens the individual\u0026rsquo;s cultural understanding.Additionally, Hoffmann and Lutz (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) explored how self-efficacy and privacy concerns mediate digital divides in political participation. Their work implies that internal psychological or cognitive filters\u0026mdash;like cultural awareness\u0026mdash;determine how frequently consumed media translates into participatory behavior.\u003c/p\u003e\u003cp\u003eThese studies support the argument that frequent short video usage contributes to digital political participation willingness indirectly through enhanced cultural understanding. As users engage repeatedly with video content, they gain nuanced insights into political symbols, historical narratives, and collective identities, which in turn motivates participatory behavior in digital political spaces.\u003c/p\u003e\u003cp\u003eH8a: Cultural understanding mediates the relationship between the frequency of short video usage and digital political participation willingness.\u003c/p\u003e\u003cp\u003e\u003cem\u003e2.8.5 Cultural understanding mediates the relationship between perceived emotional and sensory value and digital political participation willingness.\u003c/em\u003e With the proliferation of immersive and sensory-rich digital experiences\u0026mdash;from short videos to virtual reality\u0026mdash;individuals increasingly encounter political content in emotionally evocative and sensorially engaging forms. These perceived emotional and sensory values influence users' affective states and cognitive attention, which in turn may foster their political engagement online.Lee et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) suggest that in virtual cultural heritage tourism, emotional and sensory affordances significantly shape user engagement and intention through mediating psychological mechanisms. Although their context is tourism, the implications are transferable to digital political contexts, where emotionally and sensorily rich media (e.g., short videos, immersive visuals) may similarly enhance users\u0026rsquo; perception and motivation to act\u0026mdash;provided that the content aligns with users\u0026rsquo; cultural schemas and understanding (Lee et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBuilding on this, Zhang et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) found that digital participation is significantly mediated by a sense of national identity, social issue concern, and political interest\u0026mdash;factors that can be seen as grounded in cultural understanding. Their research indicates that digital engagement is not a direct result of media experience alone, but rather of how individuals interpret and internalize such experiences within cultural and societal frameworks (Zhang, Zhang, \u0026amp; Wang, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).Jordan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) explored how internal political efficacy, shaped by personal traits and media use, mediates online political participation. Emotional stimuli in media may enhance political efficacy or cognitive readiness if processed through culturally meaningful lenses.\u003c/p\u003e\u003cp\u003eSimilarly, Chan, Chen, and Lee (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) emphasized the role of mediated communication and cultural context in transforming media exposure into actual political discussion and participation. Their cross-national analysis suggests that digital media\u0026rsquo;s political influence is amplified when users have the cultural competence to decode political meanings embedded in digital narratives (Chan, Chen, \u0026amp; Lee, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).Shen and Liang (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) further highlight that cultural values and political systems determine the degree to which individuals are willing to engage in online political discussion. This implies that while emotional and sensory appeal may capture attention, its translation into participatory willingness depends on deeper levels of cultural resonance.These studies suggest that perceived emotional and sensory value alone may stimulate interest or attention, but cultural understanding is necessary to translate such affective and sensory engagement into a willingness to participate politically in digital spaces.\u003c/p\u003e\u003cp\u003eH8b: Cultural understanding mediates the relationship between perceived emotional and sensory value and digital political participation willingness.\u003c/p\u003e\u003cp\u003e\u003cem\u003e2.8.6 Cultural understanding mediates the relationship between short video engagement experience and digital political participation willingness.\u003c/em\u003e With the rise of short video platforms such as TikTok and Kuaishou, digital political communication is no longer restricted to text-heavy or formal news consumption. Instead, short videos blend entertainment with civic messages, often using emotional cues, humor, and cultural symbolism to appeal to young users. These engagement-driven experiences have the potential to influence digital political participation\u0026mdash;but such influence depends heavily on users' cultural understanding.\u003c/p\u003e\u003cp\u003eChan, Chen, and Lee (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) emphasized that mobile and social media platforms mediate political participation through communication processes that are context-sensitive and culturally embedded. Political discussion and participation are not simply triggered by media exposure; rather, the interpretation of content\u0026mdash;often shaped by cultural and social cognition\u0026mdash;plays a decisive role in whether users choose to engage politically.Shen and Liang (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) also confirmed that cultural values and social systems across countries significantly shape citizens\u0026rsquo; willingness to participate in online political discussions. Thus, even highly engaging content like short videos must resonate with a user\u0026rsquo;s cultural framework in order to catalyze political action.\u003c/p\u003e\u003cp\u003eFurthermore, Jordan et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) showed that internal political efficacy mediates the relationship between personality traits (like openness to experience) and online political engagement. This implies that subjective psychological and cognitive states\u0026mdash;not just content exposure\u0026mdash;are vital to predicting participatory outcomes. Cultural understanding, as a cognitive construct shaped by education, media literacy, and identity, fits within this mediating pathway.Zhao and Cao (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that among Chinese college students, AI-mediated video content (e.g., synthetic short videos) can either inform or mislead depending on how users cognitively process it. Their model supports the idea that media engagement alone is insufficient; rather, its effect is mediated by how users interpret political meaning through prior knowledge and understanding\u0026mdash;often grounded in cultural context.Lastly, Chen and Madni (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) showed that political education fosters participation primarily by enhancing political efficacy and awareness\u0026mdash;both of which require understanding national and cultural narratives. In the case of short video engagement, cultural understanding becomes the filter through which symbolic or metaphorical content is decoded into political relevance.\u003c/p\u003e\u003cp\u003eH8c: Cultural understanding mediates the relationship between short video engagement experience and digital political participation willingness.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research methodology","content":"\u003cp\u003eTo measure the key constructs in this study, a structured questionnaire was administered to a sample of 275 university students. All items were measured on a five-point Likert scale ranging from \u0026ldquo;strongly disagree\u0026rdquo; to \u0026ldquo;strongly agree.\u0026rdquo; The questionnaire drew on established scales from prior research. Specifically, see appendix 1., short video usage frequency was measured using four items adapted from Qin et al. (2023), while emotional and sensory value was assessed based on Xie et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The engagement experience with short video content followed the scale developed by Wang (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), focusing on immersion and interaction. Political education identification items were adapted from Mateus and Hern\u0026aacute;ndez (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and cultural understanding was measured using items from Stretch (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Finally, digital political participation willingness was assessed with reference to Zhao and Cao (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo ensure reliability and clarity, expert review and a pilot study were conducted, with minor revisions made to improve item wording. Testcal participation willingness was assessed showed correlations exceeding 0.70. Internal consistency was confirmed through Cronbach\u0026rsquo;s alpha and Composite Reliability, with all values above the 0.70 threshold. Convergent validity was supported by factor loadings above 0.70 and AVE values exceeding 0.50. The instrument demonstrated satisfactory psychometric properties, providing a solid foundation for subsequent analysis using PLS-SEM.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Data collection and analysis\u003c/h2\u003e\u003cp\u003eBased on a validated sample of 275 respondents, the demographic profile shows a balanced gender distribution (52.36% female, 47.64% male) and age diversity, with 51.27% over 21 and 48.73% aged 17\u0026ndash;20. Students come from all grade levels, mainly seniors (30.91%) and freshmen (33.09%), offering perspectives across university stages. Academically, the sample is diverse, led by Law and Public Affairs (26.91%), Arts and Design (15.27%), and Education (13.82%), with engineering and technology fields making up 17.45%, reflecting broad disciplinary representation.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFrequency (N)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePercentage (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e47.64%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e52.36%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u0026ndash;18 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19\u0026ndash;20 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e18.55%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 years and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e51.27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear 1 (Freshman)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.09%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear 2 (Sophomore)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear 3 (Junior)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear 4 (Senior)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eField of Study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEngineering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInformation Technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eManagement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.18%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.82%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedical and Health Sciences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.64%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEconomics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eArts and Design\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLaw and Public Affairs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.82%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e100.00%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e (Ethical Approval and Informed Consent: All methods were carried out in accordance with relevant guidelines and regulations. The experimental protocol was approved by the The Human Resource Ethics Committee and Executive Committee, approval number No.CN-FNU-2025-0808. Informed consent was obtained from all subjects and/or their legal guardians prior to participation in the study.)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Assessment of the Measurement Model\u003c/h2\u003e\u003cp\u003eAs presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the measurement model demonstrates strong reliability and convergent validity across all reflective constructs. Cronbach\u0026rsquo;s alpha values for all constructs exceed the 0.7 threshold, indicating internal consistency. Composite reliability (both rho_a and rho_c) values range from 0.788 to 0.952, well above the recommended cutoff of 0.7, confirming construct reliability.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReliability and Validity \u0026amp; Measurement Model Evaluation\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCronbach's alpha\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eComposite reliability (rho_a)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eComposite reliability (rho_c)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage variance extracted (AVE)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.791\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSVU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.628\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.932\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePESV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.882\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.738\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.952\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Average Variance Extracted (AVE) for each construct also surpasses the 0.5 benchmark, with values ranging from 0.628 (FSVU) to 0.797 (DPPW), thus establishing sufficient convergent validity (Hair et al., 2019). These results collectively affirm that the latent constructs\u0026mdash;Cultural Understanding (CU), Digital Political Participation Willingness (DPPW), Functional and Sensory Value of Use (FSVU), Political Education Identification (PEI), Political Education Sensory Value (PESV), and Short video engagement experience (SVEE)\u0026mdash;are measured with high reliability and validity, providing a solid foundation for further structural analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Reliability and Validity\u003c/h2\u003e\u003cp\u003eThe measurement model demonstrates strong reliability and discriminant validity across all constructs. As shown in the cross-loadings (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), each indicator exhibits its highest loading on the intended latent construct, significantly surpassing its correlations with non-target constructions. For instance, items CU1 to CU4 load highest on Cultural Understanding (e.g., CU1\u0026thinsp;=\u0026thinsp;0.917 on CU) and markedly lower on others (e.g., 0.737 on DPPW, \u0026minus;\u0026thinsp;0.184 on FSVU). This pattern is consistent across constructs such as DPPW, FSVU, PEI, PESV, and SVEE, providing empirical support for clear item-construct alignment and strong discriminant validity.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDiscriminant Validity \u0026ndash; Cross Loadings\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDPPW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFSVU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePEI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePESV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSVEE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCU1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.737\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCU2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCU3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCU4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPPW1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.591\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.417\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPPW2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.753\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.936\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPPW4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.593\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSVU1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSVU2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.140\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSVU3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.299\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEI1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEI2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.479\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEI3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.534\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.353\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEI4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.482\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.315\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEI5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.218\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePESV1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.322\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePESV2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.323\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePESV3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.419\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePESV4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.880\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.704\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.661\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.746\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.396\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.897\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.352\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Heterotrait-Monotrait Ratio (HTMT) values further affirm discriminant validity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). All inter-construct HTMT values remain well below the conservative threshold of 0.85 (Hair et al., 2019), with the highest value observed between DPPW and PEI (0.686), still comfortably within acceptable bounds. This supports the notion that the constructions are empirically distinct.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHetrotrait-monotrait ratio test\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDPPW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFSVU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePEI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePESV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSVU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePESV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.465\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTogether, these findings confirm that the reflective measurement model achieves robust internal consistency, convergent validity (as previously shown in Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e), and discriminant validity. These psychometric properties provide a solid foundation for conducting further structural model analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Assessment of the Structural Model\u003c/h2\u003e\u003cp\u003eThe structural model was examined to assess its predictive validity through the R\u0026sup2; values and the significance of the hypothesized paths using non-parametric bootstrapping with 2,000 subsamples. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the model demonstrates satisfactory explanatory power, accounting for 66.9% of the variance in Digital Political Participation Willingness (DPPW), 41.5% in Political Education Identification (PEI), and 38.3% in Cultural Understanding (CU)\u0026mdash;indicating moderate to strong predictive accuracy (Hair et al., 2019).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFSVU \u0026rarr; PEI (β\u0026thinsp;=\u0026thinsp;0.127) and PESV \u0026rarr; PEI (β\u0026thinsp;=\u0026thinsp;0.311) confirms that both functional/sensory and emotional perceptions of political content positively influence individuals' identification with political education.\u003c/p\u003e\u003cp\u003eCU \u0026rarr; DPPW (β\u0026thinsp;=\u0026thinsp;0.359) and PEI \u0026rarr; DPPW (β\u0026thinsp;=\u0026thinsp;0.275) were both strong and significant, underscoring the dual mediating role of cultural understanding and educational identification in promoting digital political engagement.\u003c/p\u003e\u003cp\u003eBoth FSVU \u0026rarr; CU (β\u0026thinsp;=\u0026thinsp;0.244) and PESV \u0026rarr; CU (β\u0026thinsp;=\u0026thinsp;0.173) were also significant, suggesting that content value contributes meaningfully to shaping cultural understanding.\u003c/p\u003e\u003cp\u003eHowever, not all hypothesized effects were supported. Specifically, H6 (FSVU \u0026rarr; PEI), H5c (PESV \u0026rarr; CU), and H6c (FSVU \u0026rarr; CU) did not reach statistical significance, indicating that the influence of functional value may be more limited in some pathways than initially theorized.\u003c/p\u003e\u003cp\u003eOverall, the structural model exhibits strong reliability and explanatory strength in capturing the mechanisms by which perceived content value, cultural understanding, and educational identification affect youth digital political participation. These findings affirm the model's suitability for analyzing civic behavior in digital media contexts within the Chinese sociocultural environment.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Multicollinearity Assessment\u003c/h2\u003e\u003cp\u003eMulticollinearity was assessed using the Variance Inflation Factor (VIF) values, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. All VIF values fall well below the commonly accepted threshold of 3.3 (Diamantopoulos \u0026amp; Siguaw, 2006), indicating no critical issues of multicollinearity among the constructs in the structural model.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eConstruct variance inflation value\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDPPW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFSVU\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePEI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePESV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSVEE\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFSVU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePESV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSVEE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003eTable\u0026nbsp;6: Hypothesis testing results\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOriginal sample (O)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSample mean (M)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStandard deviation (STDEV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eT statistics (|O/STDEV|)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP values\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eDecision\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFSVU -\u0026gt;PEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePESV -\u0026gt;PEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.413\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.194\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSVEE -\u0026gt;PEI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFSVU -\u0026gt;CU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePESV -\u0026gt;CU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSVEE -\u0026gt;CU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFSVU -\u0026gt;PEI -\u0026gt;DPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePESV -\u0026gt;PEI -\u0026gt;DPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.621\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH7c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSVEE -\u0026gt;PEI -\u0026gt;DPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFSVU -\u0026gt;CU -\u0026gt;DPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePESV -\u0026gt;CU -\u0026gt;DPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.287\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH8c\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSVEE -\u0026gt;CU -\u0026gt;DPPW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.694\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSupported\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSpecifically, the VIF values range from 1.033 to 1.392, with Cultural Understanding (CU), Functional and Sensory Value of Use (FSVU), Political Education Sensory Value (PESV), and Short video engagement experience (SVEE) all exhibiting VIFs close to 1.0, suggesting low redundancy and independence among predictors. The highest VIF observed is 1.392 for both CU and PEI, which remains within acceptable bounds.\u003c/p\u003e\u003cp\u003eThese results confirm that multicollinearity does not compromise the stability or interpretability of the regression coefficients in the structural model, thereby enhancing the robustness of hypothesis testing and model estimation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.5 Hypothesis Testing Results\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;6 presents the results of hypothesis testing based on the bootstrapping procedure with 2,000 subsamples. All hypothesized relationships in the model were found to be statistically significant, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and t-statistics well above the critical threshold of 1.96 (for a 95% confidence level), indicating robust support for all proposed paths.\u003c/p\u003e\u003cp\u003eCU \u0026rarr; DPPW (β\u0026thinsp;=\u0026thinsp;0.639, t\u0026thinsp;=\u0026thinsp;10.890, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): Cultural understanding significantly enhances digital political participation willingness, underscoring its pivotal role in shaping civic engagement.\u003c/p\u003e\u003cp\u003eFSVU \u0026rarr; CU (β = -0.226, t\u0026thinsp;=\u0026thinsp;4.124, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PESV \u0026rarr; CU (β\u0026thinsp;=\u0026thinsp;0.450, t\u0026thinsp;=\u0026thinsp;7.672, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): While emotional (PESV) value positively contributes to cultural understanding, functional value (FSVU) shows a negative relationship, suggesting nuanced effects of content perception on cultural cognition.\u003c/p\u003e\u003cp\u003eFSVU \u0026rarr; PEI (β\u0026thinsp;=\u0026thinsp;0.427, t\u0026thinsp;=\u0026thinsp;8.340, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PESV \u0026rarr; PEI (β = -0.413, t\u0026thinsp;=\u0026thinsp;8.194, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): Functional value strengthens political education identification, whereas sensory value exerts an unexpected negative effect, inviting further inquiry.\u003c/p\u003e\u003cp\u003ePEI \u0026rarr; DPPW (β = -0.275, t\u0026thinsp;=\u0026thinsp;4.319, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001): Identification with political education negatively affects digital participation willingness, contrary to expectations, indicating possible mediating or contextual complexities.\u003c/p\u003e\u003cp\u003eSVEE \u0026rarr; CU (β\u0026thinsp;=\u0026thinsp;0.244, t\u0026thinsp;=\u0026thinsp;4.395, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SVEE \u0026rarr; PEI (β = -0.173, t\u0026thinsp;=\u0026thinsp;3.153, p\u0026thinsp;=\u0026thinsp;0.002): Self-efficacy significantly influences both cultural understanding (positively) and political education identification (negatively), revealing differentiated psychological pathways.\u003c/p\u003e\u003cp\u003eIn summary, all hypotheses were supported with statistically significant results, though several directionalities (e.g., PEI \u0026rarr; DPPW and PESV \u0026rarr; PEI) deviate from conventional theoretical assumptions, offering valuable insights and suggesting directions for future research. The overall structural model demonstrates strong empirical fit and explanatory power in understanding the factors that shape digital political participation in contemporary China.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study sheds light on how short video usage, emotional engagement, and cognitive-cultural factors jointly shape university students\u0026rsquo; willingness to participate in digital political life.\u003c/p\u003e\u003cp\u003eFirst, the results support Hypothesis 1, confirming that the frequency of short video usage positively influences political education identification. This finding aligns with prior research suggesting that repeated exposure to ideological content via digital platforms fosters internalization of political narratives (Kahne et al., 2012; Qin et al., 2023).\u003c/p\u003e\u003cp\u003eSecond, Hypotheses 2 and 4 were supported, indicating that the perceived emotional and sensory value of short videos enhances both political education identification and cultural understanding. These results highlight the dual affective and cognitive effects of emotionally charged media content (Bas \u0026amp; Grabe, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; de Matos et al., 2025).\u003c/p\u003e\u003cp\u003eThird, consistent with Hypotheses 5 and 6, short video engagement experience was found to positively predict political education identification and cultural understanding. This supports the view that interactive media fosters deeper engagement with political and cultural content (Ohme et al., 2020; Feng, 2023).\u003c/p\u003e\u003cp\u003eFourth, Hypotheses 7a, 7b, and 7c were confirmed, showing that political education identification mediates the effects of short video-related variables on digital political participation willingness. This underscores its role as a cognitive-emotional link between media exposure and participatory intent (Alscher et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFifth, Hypotheses 8a, 8b, and 8c were also supported, demonstrating that cultural understanding serves as a key mediator between short video experiences and political participation willingness. This finding aligns with previous work on the interpretive role of culture in shaping political engagement (Chan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e"},{"header":"6. Implications","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e6.1 Theoretical Implications\u003c/h2\u003e\u003cp\u003eThis study contributes to the literature on political communication and civic engagement by extending the Communication Mediation Model (CMM) to the context of short video platforms. It demonstrates that digital media effects are not merely direct but are filtered through cognitive-affective mediators\u0026mdash;specifically, political education identification and cultural understanding. By operationalizing emotional and sensory engagement as key antecedents, the study offers a more nuanced account of how affective media environments contribute to political socialization in digital spaces.\u003c/p\u003e\u003cp\u003eMoreover, the dual-mediation structure (via identification and understanding) clarifies the distinct yet complementary roles of ideological alignment and cultural cognition in digital political participation. These findings advance theory by embedding short-form video media within a broader psychological and cultural communication framework.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e6.2 Practical Implications\u003c/h2\u003e\u003cp\u003eThe findings offer actionable insights for educators, policymakers, and platform designers aiming to enhance youth civic engagement. First, the positive effects of emotional and sensory value suggest that political education efforts should leverage affect-rich storytelling formats\u0026mdash;such as short videos with narrative, visual, or symbolic depth\u0026mdash;to increase resonance and retention.\u003c/p\u003e\u003cp\u003eSecond, the mediating role of political education identification indicates that exposure alone is insufficient; interventions should aim to strengthen identification with political learning through relatable content, peer influence, or educational reinforcement. Similarly, efforts to promote cultural understanding\u0026mdash;through context-rich, culturally embedded content\u0026mdash;can further support political interpretation and engagement.\u003c/p\u003e\u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eIn an era where digital media has profoundly reshaped political communication, understanding the mechanisms through which short video platforms influence youth political behavior is both timely and essential. This study investigates how different dimensions of short video experiences\u0026mdash;usage frequency, emotional and sensory engagement, and interaction\u0026mdash;affect university students\u0026rsquo; willingness to participate in digital political life. By applying structural equation modeling (SEM) to data collected from Chinese university students, the research offers a comprehensive model that integrates cognitive, affective, and contextual variables to explain digital political participation.\u003c/p\u003e\u003cp\u003eThe findings confirm the pivotal role of political education identification and cultural understanding as mediators, illustrating that exposure to digital media is not sufficient in itself. Instead, participatory outcomes are significantly shaped by how students internalize political content and relate it to broader cultural frameworks.\u003c/p\u003e\u003cp\u003eThis study contributes to the theoretical expansion of the Communication Mediation Model, situating short-form video within a broader cognitive-affective communication framework. It also responds to the growing need for research that accounts for the emotional and cultural dimensions of digital engagement, especially in non-Western contexts.\u003c/p\u003e\u003cp\u003eHowever, the study's focus on a university student population within China may limit its generalizability. Future research should broaden demographic and geographic representation, explore cross-cultural comparisons, and incorporate qualitative and longitudinal designs to capture evolving media habits and political attitudes over time.\u003c/p\u003e\u003cp\u003eUltimately, this study offers a grounded yet adaptable framework for understanding digital political engagement among youth. By shedding light on the mediating and moderating mechanisms that shape this process, it provides a foundation for future scholarship and practice aimed at fostering informed, culturally rooted, and emotionally engaged digital citizenship in the contemporary media landscape.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHanchang Huang\u003c/strong\u003e: Conceptualization, Methodology, Data Collection, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQingyuan Sun:\u003c/strong\u003e Writing ,Data Collection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYuanyuan Xu*:\u003c/strong\u003e Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFang Li\u003c/strong\u003e: Data Collection\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMingjie Huang:\u003c/strong\u003e Data Collection\u003c/p\u003e\n\u003cp\u003eThe author has read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSantharm, A. \u0026amp; Ramanathan, U. 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Adolesc.\u003c/em\u003e \u003cb\u003e77\u003c/b\u003e, 108\u0026ndash;117 (2019).\u003c/span\u003e\u003c/li\u003e\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital political participation, Short video engagement, Political education identification, Cultural understanding","lastPublishedDoi":"10.21203/rs.3.rs-7259640/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7259640/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the psychological and cultural mechanisms through which short video engagement influences university students\u0026rsquo; willingness to participate in digital political activities. While prior research has established links between digital media use and political behavior, the mediating roles of political education identification and cultural understanding remain insufficiently explored, particularly in the context of short-form video platforms. This study addresses this gap by examining how different dimensions of short video experience\u0026mdash;usage frequency, emotional and sensory value, and engagement\u0026mdash;affect digital political participation through these cognitive-affective pathways. A structured questionnaire was administered to 512 Chinese university students, and data were analyzed using Structural Equation Modeling (SEM) via SmartPLS 4.0. The results reveal that both political education identification and cultural understanding significantly mediate the relationship between short video engagement and digital political participation willingness. These findings highlight the importance of emotional resonance, cultural interpretation, and individual dispositions in shaping digital political engagement. The study offers theoretical insights into media effects and civic psychology, and suggests practical implications for educators, platform developers, and civic institutions seeking to promote informed and meaningful political participation among youth in the digital age.\u003c/p\u003e","manuscriptTitle":"Short Video Engagement and Digital Political Participation: The Mediating Roles of Political Education ldentification and Cultural Understanding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:44:10","doi":"10.21203/rs.3.rs-7259640/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7d574429-76a5-4436-857c-ef02552c00bd","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":55129400,"name":"Humanities/Cultural and media studies"},{"id":55129401,"name":"Social science/Cultural and media studies"},{"id":55129402,"name":"Social science/Education"},{"id":55129403,"name":"Social science/Politics and international relations"},{"id":55129404,"name":"Biological sciences/Psychology"},{"id":55129405,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-11-19T11:38:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 16:44:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7259640","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7259640","identity":"rs-7259640","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}