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  <front>
    <article-meta>
      <title-group>
        <article-title>The Role of Strategic Financial Management in Enhancing Corporate Value and Competitiveness in the Digital Economy</article-title>
      </title-group>
      <contrib-group content-type="author">
        <contrib contrib-type="person">
          <name>
            <surname>Ahmad</surname>
            <given-names>Israr</given-names>
          </name>
          <email>chaudhryisrar@gmail.com</email>
          <xref ref-type="aff" rid="aff-1"/>
        </contrib>
      </contrib-group>
      <aff id="aff-1">
        <institution>Universiti Sains Malaysia</institution>
        <country>Malaysia</country>
      </aff>
      <history>
        <date date-type="received" iso-8601-date="2023-06-08">
          <day>08</day>
          <month>06</month>
          <year>2023</year>
        </date>
        <date data-type="published" iso-8601-date="2024-02-10">
          <day>10</day>
          <month>02</month>
          <year>2024</year>
        </date>
      </history>
    </article-meta>
  </front>
  
  
<body id="body">
    <sec id="sec-1">
      <title>Introduction </title>
      <p id="_paragraph-2">Public mobilization through digital media has emerged as a transformative force in shaping collective action, opinion formation, and civic engagement on a global scale. Prominent digital campaigns such as the #MeToo and Black Lives Matter movements in the United States have demonstrated how viral content can generate significant social impact by facilitating rapid message dissemination, fostering emotional solidarity, and prompting mass participation (Jackson et al., 2020). Social media platforms including Twitter, Instagram, and TikTok have become pivotal tools in influencing public sentiment and activating political consciousness (Tufekci, 2017). While the role of social media in mobilization is well-documented in movements such as the Arab Spring (Howard et al., 2011) and the Hong Kong pro-democracy protests (Lee &amp; Chan, 2018), the mechanisms behind digital mobilization in developing countries remain relatively underexplored.</p>
      <p id="_paragraph-3">This study addresses that gap by focusing on Indonesia, a highly connected nation where digital platforms are deeply embedded in daily life. As of 2024, over 201 million Indonesians, nearly 74% of the total population, actively use social media, with a particularly high concentration among users aged 18 to 34 (Panggabean, 2024). This digital penetration, combined with a vibrant youth demographic, makes Indonesia an ideal context to investigate the dynamics of online political engagement and grassroots mobilization.</p>
      <p id="_paragraph-4">A compelling example of this phenomenon is the viral video <italic id="_italic-1">Peringatan Darurat</italic> (Emergency Warning), narrated by journalist Najwa Shihab. The video critically addressed proposed amendments to the Regional Head Election Law (UU Pilkada), sparking widespread outrage and leading to mass protests both online and offline. The movie begs issues on whether the rushed judgments taken by the House of Representatives' (DPR) could be undemocratic and unconstitutional. Given over 352,000 users contributed 1.9 million likes and over 352,000 shares, this topic attracted a lot of public reaction. Emotional intensity and the sharing of knowledge on social media platforms seem to be favorably linked empirically. Studies show that on these platforms expressing rage or discontent considerably increases the audience for content (Bellovary et al., 2021; Knutson et al., 2024). Thus, by serving as both an educational tool and a motivator of group engagement, the "Peringatan Darurat" video best depicts the power of digital activism in Indonesia. Therefore, With over 1.9 million views in 48 hours and hundreds of thousands of shares and comments (CNBC Indonesia, 2024), the video exemplifies how viral content can serve as both an informational and emotional catalyst for civic action. Its ability to engage audiences and provoke mass response highlights the intersection of credibility, emotional resonance, and participatory culture in digital activism.</p>
      <p id="_paragraph-5">Despite the growing relevance of such cases, dominant theories of digital mobilization are still largely rooted in Western, individualistic contexts. These models often fail to capture the cultural specificities of collectivist societies like Indonesia, where values such as communal trust, moral duty, and social solidarity heavily influence public behavior. This research contributes to the literature by examining how culturally grounded variables, namely source credibility, moral emotions, and social engagement, mediate the relationship between viral video exposure and public mobilization.</p>
      <p id="_paragraph-6">This study offers a culturally contextualized model of digital mobilization by integrating multiple theoretical frameworks, Viral Content Diffusion Theory, Digital Mobilization Theory, Moral Foundations Theory, the Source Credibility Framework, and the Social Engagement Framework, within a Southeast Asian context. This theoretical synthesis is rarely seen in prior research and allows for a more holistic understanding of how viral digital content shapes civic participation in Indonesia. Therefore, this study seeks to answer the following research question: <italic id="_italic-2">How do source credibility, moral emotions, and social engagement mediate the relationship between exposure to viral video content and public mobilization in the Indonesian digital context?</italic></p>
      <fig id="fig1">
        <label>Figure 1</label>
        <caption>
          <title>Screenshot from the video ’Peringatan Darurat’ (EmergencyWarning).  Instagram: @matanajwa, (2025).</title>
          <p id="_paragraph-7"/>
        </caption>
        <graphic id="_graphic-1" mimetype="image" mime-subtype="jpeg" xlink:href="Image1.png"/>
      </fig>
    </sec>
    <sec id="sec-2">
      <title>Literature Review and Hypothesis Development</title>
      <p id="_paragraph-8">In recent years, Indonesia’s socio-digital landscape has emerged as a fertile ground for analyzing digital political behavior. Social media platforms have increasingly served as primary arenas for public expression, political resistance, and civic mobilization, particularly among younger demographics (Omotayo &amp; Folorunso, 2020; Hafel, 2023). Notable movements like <italic id="_italic-3">#ReformasiDikorupsi</italic> in 2019 have demonstrated how viral content can rapidly influence public opinion and catalyze political protest. These developments underscore the need to understand not only the reach of viral content but also the underlying psychosocial mechanisms driving mobilization in collectivist, non-Western societies.</p>
      <p id="_paragraph-9">A recent example is the <italic id="_italic-4">Peringatan Darurat</italic> (Emergency Warning) video, released in August 2024 via Instagram accounts such as @najwashihab and @narasi.tv. The video’s narrative, accompanied by symbolic imagery such as the “Blue Garuda” logo, sparked massive engagement both online and offline. Hashtags like <italic id="_italic-5">#PeringatanDarurat</italic> and <italic id="_italic-6">#KawalPutusanMK</italic> quickly trended nationwide, while thousands joined street protests against proposed amendments to Indonesia’s Regional Head Election Law (UU Pilkada) (Serikat News, 2024; CNBC Indonesia, 2024). These events exemplify the convergence of digital virality and offline mobilization, affirming the powerful influence of emotionally resonant digital content on democratic participation.</p>
      <p id="_paragraph-10">International media coverage by Bloomberg, BBC, and Al Jazeera further amplified the video’s reach, bringing global attention to Indonesia’s democratic trajectory (Tempo, 2024; Gvili &amp; Levy, 2018; Ting et al., 2023). While this coverage highlights the transformative power of digital content, existing literature often emphasizes technical aspects such as algorithms and network topology (Van Dijck et al., 2018; Hansen et al., 2011), often neglecting the emotional, cognitive, and cultural dynamics that drive civic behavior.</p>
      <p id="_paragraph-11">Some studies have examined emotional expression and content diffusion (Brady et al., 2017), while others have explored credibility assessments (Metzger et al., 2010). However, few have integrated these factors into a unified model tailored to collectivist cultures like Indonesia, where variables such as relational trust, moral duty, and community-based identity play crucial roles in shaping digital behavior (Bashir et al., 2021; Kapoor et al., 2018).</p>
      <p id="paragraph-b70de3e084144e5f6d90be20dc7b6bd0">
        <bold id="bold-d3495048832b52234b842ab357a3de95">Viral Video Exposure and Digital Mobilization</bold>
      </p>
      <p id="_paragraph-12">Viral content, particularly videos, has the potential to stimulate large-scale engagement due to its capacity to elicit emotional arousal and create narrative coherence. Berger and Milkman (2012) and Tandoc et al. (2018) emphasize that content which evokes high-arousal emotions, such as awe, anger, or anxiety, is more likely to be shared and acted upon. In the Indonesian context, the <italic id="_italic-7">Peringatan Darurat</italic> video became a digital flashpoint, igniting widespread online discourse and offline demonstrations. The video’s narrative critiqued proposed amendments to the Regional Head Election Law (UU Pilkada), presenting them as a threat to democratic values. The emotional appeal, combined with the urgency of the message, created conditions ripe for viral dissemination and collective action (CNBC Indonesia, 2024; Tempo, 2024).</p>
      <p id="_paragraph-13">Furthermore, according to Viral Content Diffusion Theory (Guadagno et al., 2013), visual and emotionally charged stimuli in videos enhance cognitive processing and retention, making them more persuasive and more likely to generate behavioral outcomes (Schreurs et al., 2017; Hollingshead et al., 2018). The potency of the <italic id="_italic-8">Peringatan Darurat</italic> video in mobilizing citizens suggests a direct pathway from digital exposure to civic action.</p>
      <p id="_paragraph-14"><bold id="_bold-1">H1: </bold>Viral video exposure has a positive and significant effect on public mobilization.</p>
      <p id="paragraph-90bbe55a4864640734ccfbc6791d956f">
        <bold id="bold-aeed5c67b6a3fbe42ca4a2b7b17f74ad">Source Credibility as a Mediator</bold>
      </p>
      <p id="_paragraph-15">The impact of a message is not solely determined by its content but also by the credibility of its source. According to the Source Credibility Framework (Ohanian, 1990), trustworthiness, expertise, and perceived authenticity of the message source significantly influence how information is received and acted upon. In collectivist societies like Indonesia, credibility extends beyond institutional authority to include relational proximity, moral character, and communal alignment (Metzger et al., 2010; Brewer &amp; Ley, 2012). The <italic id="_italic-9">Peringatan Darurat</italic> video was narrated by Najwa Shihab, a well-respected journalist and advocate for democratic values, whose public persona reinforced the legitimacy of the message.</p>
      <p id="_paragraph-16">Empirical studies have shown that individuals are more likely to engage with and act upon content disseminated by trusted figures, especially when these figures reflect the audience’s values and social norms (Flanagin &amp; Metzger, 2008). Moreover, in social media ecosystems, perceived credibility can be amplified through peer validation, comments, and shares, collectively reinforcing the reliability of both the content and its creator (Cheng &amp; Lam, 2013; Bellovary et al., 2021). Thus, source credibility functions as a critical mechanism through which the persuasive power of viral content is either enhanced or diminished.</p>
      <p id="_paragraph-17"><bold id="_bold-2">H2: </bold>Source credibility positively and significantly mediates the relationship between viral video exposure and public mobilization.</p>
      <fig id="figure-panel-ff60892dc8ac9eacf032971f2c113bd8">
        <label>Figure 2</label>
        <caption>
          <title>Research Hypotheses. Personal Data Processing, (2025).</title>
          <p id="paragraph-b0edc9c001be3f30ade211bd3462f94c"/>
        </caption>
        <graphic id="graphic-4816ab615cfeac37e622402e5a8e3dfc" mimetype="image" mime-subtype="png" xlink:href="Image2.png"/>
      </fig>
      <p id="paragraph-36405ed6fe882d44380cab6b51a1c431">
        <bold id="bold-ce5a4d6ee92f9f7fafb4c7f88d75319c">Moral Emotions as a Mediator</bold>
      </p>
      <p id="_paragraph-18">Moral emotions, such as anger, guilt, compassion, and moral outrage, play a pivotal role in motivating individuals toward collective action. According to Haidt’s (2012) Moral Foundations Theory, individuals possess innate moral sensitivities that are activated when they perceive violations of ethical or societal norms. In the context of digital mobilization, emotionally charged content often triggers these moral responses, leading to civic action, particularly when individuals feel a collective moral duty to respond (Van Zomeren et al., 2008; Brady et al., 2017).</p>
      <p id="_paragraph-19">In Indonesia, moral identity and emotional triggers are deeply embedded in cultural narratives. Feelings of <italic id="_italic-10">malu</italic> (shame), <italic id="_italic-11">rasa bersalah</italic> (guilt), and <italic id="_italic-12">peduli</italic> (empathy) are strong motivators of public behavior, particularly in matters related to injustice or governmental overreach (Vaswani et al., 2022). The <italic id="_italic-13">Peringatan Darurat</italic> video’s framing of legislative amendments as a betrayal of democratic values likely activated these moral emotions among viewers, creating a psychological imperative to act. Studies suggest that such emotional arousal not only intensifies engagement but also sustains it over time, allowing moral emotions to serve as both catalysts and reinforcers of civic behavior (Skitka et al., 2021; Aquino &amp; Reed, 2002). Therefore, this study proposes that moral emotions mediate the link between viral video exposure and public mobilization.</p>
      <p id="_paragraph-20"><bold id="_bold-3">H3: </bold>Moral emotions positively and significantly mediate the relationship between viral video exposure and public mobilization.</p>
      <p id="paragraph-ce45ccdf4edc6febcd9af8d078879e99">
        <bold id="bold-3c2c0376e40dd05186b0a7bc999dbb72">Social Engagement as a Mediator</bold>
      </p>
      <p id="_paragraph-21">While digital exposure initiates awareness, it is through social engagement that awareness transforms into coordinated action. The Social Engagement Framework (Boulianne, 2015; Castells, 2012) posits that online interactivity, including sharing, commenting, liking, and participating in hashtag campaigns, functions as a digital rehearsal space for offline mobilization. In collectivist societies, engagement is often motivated by <italic id="_italic-14">gotong royong</italic>, a cultural value emphasizing mutual assistance, solidarity, and communal responsibility (Efendy et al., 2023).</p>
      <p id="_paragraph-22">Unlike Western contexts where civic action may stem from individual expression, Indonesian digital activism often represents an extension of collective identity. Platforms like TikTok and Instagram allow users to co-construct meaning, reinforce shared values, and mobilize around common concerns. The <italic id="_italic-15">Peringatan Darurat</italic> campaign, for instance, saw users not only reposting the video but also initiating discussions, tagging influencers, and attending coordinated protests, demonstrating how online engagement laid the groundwork for real-world mobilization. Research shows that high levels of social media interactivity increase perceived group efficacy, reduce fear of isolation, and enhance readiness to participate in collective action (Valenzuela, 2013; Kogut et al., 2015; Gorodnichenko &amp; Roland, 2020; Sabghatullah, 2018). Accordingly, this study posits that social engagement plays a central role in bridging viral content and civic action.</p>
      <p id="_paragraph-23"><bold id="_bold-4">H4: </bold>Social engagement positively and significantly mediates the relationship between viral video exposure and public mobilization.</p>
      <p id="_paragraph-24">The integration of these four constructs, viral content, source credibility, moral emotions, and social engagement, within a single mediation model reflects a novel contribution to the field of digital mobilization research. This framework moves beyond linear communication models to embrace a culturally contextualized, emotionally grounded, and socially embedded view of civic engagement in the digital age. By focusing on Indonesia, a country marked by rapid digital expansion and strong collectivist values, this study provides a much-needed extension of existing theories into new cultural terrain. Furthermore, by drawing on the synergy between Western-developed theories and Indonesian sociocultural realities, the study offers a theoretical bridge that may inform similar inquiries in other collectivist societies across Southeast Asia, Latin America, and Africa.</p>
    </sec>
    <sec id="sec-3">
      <title>Methodology </title>
      <p id="_paragraph-25">This study employs a quantitative research design to investigate the relationships among viral video exposure, source credibility, moral emotions, social engagement, and public mobilization. The research is theoretically grounded in five interrelated frameworks: Viral Content Diffusion Theory (Berger &amp; Milkman, 2012; Tandoc et al., 2018), Digital Mobilization Theory (Tufekci, 2017; Bennett &amp; Segerberg, 2013), Moral Foundations Theory (Haidt, 2012; Graham et al., 2013), the Source Credibility Framework (Ohanian, 1990; Pennycook &amp; Rand, 2021), and the Social Engagement Framework (Castells, 2012; Papacharissi, 2014). Together, these frameworks provide a comprehensive lens through which to examine how digital content influences trust, moral affect, and participatory behaviors in mobilization processes.</p>
      <p id="_paragraph-26">Data were collected from Indonesian social media users who had viewed the viral <italic id="_italic-16">Peringatan Darurat</italic> (Emergency Alert) video on digital platforms including Instagram, X (formerly Twitter), YouTube, TikTok, WhatsApp, and Facebook. A non-probability purposive sampling strategy was employed to ensure that respondents met the inclusion criteria: individuals between 17 and 65 years of age who had seen the <italic id="_italic-17">Peringatan Darurat</italic> video. To ensure a diverse and representative sample, the online questionnaire was widely distributed via social networking sites using Google Forms.</p>
      <p id="_paragraph-27">The minimum required sample size was calculated using the Lemeshow formula, which is appropriate when the total population size is unknown. Based on a 95% confidence level (Z = 1.96), a population proportion of 0.5, and a margin of error of 5%, the recommended sample size was 384 respondents (Lemeshow et al., 1990; Pourhoseingholi et al., 2013). Out of 420 completed responses, 384 were deemed valid and suitable for analysis, resulting in a high response rate of 91.4%.</p>
      <p id="_paragraph-28">The study employed previously validated measurement instruments adapted from established research. Items measuring viral video impact were drawn from Berger and Milkman (2012) and Tandoc et al. (2018), while source credibility items were based on Pennycook and Rand (2021). Moral emotions were assessed using items from Brady et al. (2017), social engagement from Boulianne (2015), and public mobilization from Castells (2012). All items were adapted to reflect the Indonesian digital activism context. A six-point Likert scale was used for all constructs, ranging from 1 (strongly disagree) to 6 (strongly agree). Both content and construct validity were assessed to ensure the relevance and clarity of the adapted items.</p>
      <p id="_paragraph-29">Data analysis was conducted using the Generalized Structured Component Analysis (GSCA) technique, implemented via the GeSCA Pro software. GSCA is a variance-based structural equation modeling approach that is particularly well-suited for analyzing complex models with non-normally distributed data (Hwang &amp; Takane, 2014; Henseler et al., 2014; Sarstedt et al., 2017, 2022). One of GSCA’s major strengths lies in its ability to simultaneously estimate direct and indirect effects, making it highly effective for testing mediation mechanisms (Hayes, 2013; Preacher &amp; Hayes, 2008). The analytical procedures adopted in this study are consistent with those used in similar research exploring media influence on political behavior, such as the study by Pamungkas et al. (2025).</p>
      <p id="_paragraph-30">Through this methodological framework, the study aims to generate in-depth and nuanced insights into how source credibility, moral emotions, and social engagement function as mediating variables in the relationship between viral digital content and public mobilization within Indonesia’s networked society.</p>
    </sec>
    <sec id="sec-4">
      <title>Result</title>
      <p id="paragraph-86c527c71f1d46d55b83c5e4773026c4">
        <bold id="bold-f4ddbb383ae21284401cf5278a208888">Respondent Description</bold>
      </p>
      <p id="_paragraph-31">Table 1 shows the demographics of Indonesian social media users engaged in this research.  By means of an online questionnaire sent via Google Forms, the data were gathered effectively and responders from all backgrounds throughout Indonesia could be reached.</p>
      <table-wrap id="tbl1">
        <label>Table 1</label>
        <caption>
          <title><bold id="bold-c07ba17597422c119da6e3d1c093bd55"/>Demographic Characteristics of Social Media Users in Indonesia (n=384, Margin of Error: 5%)</title>
          <p id="_paragraph-33">
            <bold id="_bold-5"/>
          </p>
        </caption>
        <table id="_table-1">
          <tbody>
            <tr id="table-row-7dc212170dc6bb5ba5325979f8cded0b">
              <th id="3ff51d3bb4415a5e30bc141120204ddd">
                <bold id="_bold-6">Demographic</bold>
              </th>
              <th id="9b100adbf22404b7b7e4f1a29ea59e78">
                <bold id="_bold-7">Description</bold>
              </th>
              <th id="e6fca6d1db60f93962f3e3c9f117c9a2">
                <bold id="_bold-8">Percentage (%)</bold>
              </th>
            </tr>
            <tr id="table-row-06e6649b48cb89d34cd13b1ff0158b1f">
              <td id="135a5bc204b3917ef989f89a5e0235a6">Gender</td>
              <td id="b7cb71caa98567b9ee94f86107f3c215">Female</td>
              <td id="4ad472647b59d79994bd2beaf2e7d517">53.1%</td>
            </tr>
            <tr id="table-row-a6ba73562356dcafd4e3584ba73cef8a">
              <td id="b42dbab3ebbf5128c5d4cfdfbfe5194d"/>
              <td id="0fb4f0d13a6cd1ccc58e77582124bd70">Male</td>
              <td id="a3a3ce65670f041cbd7c1536bbd9ecc4">46.9%</td>
            </tr>
            <tr id="table-row-54255e29f2e247d6f42ab2dd8d6a372a">
              <td id="b8b536260906c106384e6dc3c4682127">Age Group</td>
              <td id="c4fd13dbf8920f58a8ab70c0499aae00">17-25 years</td>
              <td id="b2a0e9f183f700c8b4956dd75b0d0dcd">42.3%</td>
            </tr>
            <tr id="table-row-23cce99eadbf9fe996321d4222e3cfb7">
              <td id="3500407935849b366abcf767a2b832c4"/>
              <td id="677a863c57384212a6c6f7ecbcc2a754">26-35 years</td>
              <td id="17f52609a06236c98101f5bb4dda32aa">34.6%</td>
            </tr>
            <tr id="table-row-6aad4e66e0378c3d79ae7059686e8d00">
              <td id="7677002ae42081d9e4459627dc078a4c"/>
              <td id="d602e5217333ff1e026d50d06f606178">36-45 years</td>
              <td id="028cfb46f80a0676270994221f8fc7f2">15.1%</td>
            </tr>
            <tr id="table-row-3d2f419124959a53dc363cae96c899cf">
              <td id="c30dbc58bd02c689f098f49e4b176c44"/>
              <td id="0f6160f21cb56039c1fc9a4f81b5bd56">46-55 years</td>
              <td id="4d10465ed54fdc78e228a32ee591dde3">5.8%</td>
            </tr>
            <tr id="table-row-b9386452a5fae3cfaa55ea1311588d98">
              <td id="c9dc57513c02154c1ee5a732c626bf95"/>
              <td id="5af9a61c00fa2cb8d4fced4622e329ae">56-65 years</td>
              <td id="44d21486335d317e5e53815287c6b45a">2.2%</td>
            </tr>
            <tr id="table-row-fa6d826fc26057b5fc463b20872a27aa">
              <td id="81ebd62e2f379faf856f00a053cd930f">Education Level</td>
              <td id="3de002a278c33e021764ccc16d057c31">High School or Equivalent</td>
              <td id="afeed42743e3be8f63aa9ec50a3e1ad5">52.4%</td>
            </tr>
            <tr id="table-row-ea2ce48b2a519ee0674fbdb7ff185cc4">
              <td id="ea9442618ac289e8e83b8269a41fd8ce"/>
              <td id="af7c6046a36fd975c74fabaf752e5fe6">Diploma (D3/D4)</td>
              <td id="a0aaffb512af789e205b80e1f0b738eb">18.2%</td>
            </tr>
            <tr id="table-row-c0cbf843ee694fddd0422c7443fdc3dd">
              <td id="a5b54cf4d50f268aa40495f7ac7f687c"/>
              <td id="3216de8552de147acf4e516862dc500a">Bachelor's Degree (S1) or Higher</td>
              <td id="cdf26cf1b01824fcb88d5505f060dd45">29.4%</td>
            </tr>
            <tr id="table-row-923fcc3f0e69f68806d83386f8a3ba9c">
              <td id="3c407b2ee729fe69f683c0100fa1ca47">Occupation</td>
              <td id="e68b01e8e966be1b612124fb3f222487">Students</td>
              <td id="35409c27941062ed9aace4ae18f91ea9">36.5%</td>
            </tr>
            <tr id="table-row-f03a740d7bdd0785904201b067afa6f7">
              <td id="3e2129bbf395a1f96e7ba46001243094"/>
              <td id="2c3d622c7f9154fc9a7dcf10033fbc30">Private Employees</td>
              <td id="578c4666a6c13de7876bfd59e0d14d51">29.8%</td>
            </tr>
            <tr id="table-row-34a3a1a41cdd41038939f441e69dcdd5">
              <td id="74bdf207937c35de1bb79ac33f492a68"/>
              <td id="f65b75fdc3b33f73711a381a155e42a3">Civil Servants</td>
              <td id="fc8c4cfe9ab750d0cbe6fd545657cd34">12.0%</td>
            </tr>
            <tr id="table-row-59b50d794cf4c230c068c4fa16ac3342">
              <td id="b38a246022ced01483f2fa9de3dbbe32"/>
              <td id="53ff65bec30ef934aefbeeb14b58d523">Entrepreneurs/Freelancers</td>
              <td id="5eaad6048be34c280e4aacc7a7fa2638">14.7%</td>
            </tr>
            <tr id="table-row-fd5e3e8a7875958f4ac029385d88cacb">
              <td id="3b28af38d74ab3a7bc64192dd8ac36a5"/>
              <td id="2311fe79d11cb14fb5adeb9bf1e3db0a">Other</td>
              <td id="4f57d840fbfcde1e4d0deaec2831e00a">7.0%</td>
            </tr>
            <tr id="table-row-01c445b0896b2d88ad396fc1983f1168">
              <td id="015ae2b030f63152ad0e9d1ef2d86dba">Social Media Usage</td>
              <td id="468fc78b489fbfaa1d25c1468ae1e021">High (≥ 3 hours/day)</td>
              <td id="c638d6ee0db2689ee386f60b291f106f">74.8%</td>
            </tr>
            <tr id="table-row-1d873722102ce8408192b51951f1d4b8">
              <td id="be6d6dcff2a4ca996f0b9d9a5e22f56d"/>
              <td id="2aa5aba06f2e7b66125df6b4c5f1d18f">Low (&lt; 3 hours/day)</td>
              <td id="180defd6561fb0a938bbbb0407847d01">25.2%</td>
            </tr>
            <tr id="table-row-c2a5b1031b509fa842d8b9f7080bcec1">
              <td id="01eb925ccca3b3012c8fdc523802f54b">Social Media Platform</td>
              <td id="51da6e263c5a9dd7272a8aba5ef21580">TikTok </td>
              <td id="f3c3ca66d0720d4dd97e712f1dc36af6">36.5%</td>
            </tr>
            <tr id="table-row-7ed11e1cb46b1e74608ef79793e71003">
              <td id="cfa34c8bd7eb7c535a70c3f6f06fa946"/>
              <td id="8faa285e065c76037ea56af86a113589">Instagram</td>
              <td id="f5a1c929dda4700cd5e9b58121e52bdf">28.4%</td>
            </tr>
            <tr id="table-row-ccd70f221a3cccf27e317a2e30cfbdf4">
              <td id="fb18bdede1808a58ba5d00908606d9f8"/>
              <td id="e28e89513d6c722d3682c1bafed04821">YouTube </td>
              <td id="fec9bc9f395cc749ada2bdb3d9f9fa36">16.2%</td>
            </tr>
            <tr id="table-row-73420f659f35fea9eca65180b205ff51">
              <td id="db78ffce346d6693c2fc6be8819867dd"/>
              <td id="a157b819e95e522a5d3ebd30730b8b87">X (formerly Twitter) </td>
              <td id="b7e21dea2af24cf61afa7d09aed9147a">9.7%</td>
            </tr>
            <tr id="table-row-8f2d477636aedfbeed708070758b3ee8">
              <td id="179752b2ea6089271757cda30b411ee3"/>
              <td id="b0e978c0a17e569255f74dc7050927b2">WhatsApp </td>
              <td id="da68369d92067ef99af4717487eddd52">5.1%</td>
            </tr>
            <tr id="table-row-e50ce5ec9478f7afb6e6ea0806e30e17">
              <td id="070fcc873e04c8300ddb9dc4c0dba662"/>
              <td id="0b90999c52642e3e5b3d367d67b01959">Facebook </td>
              <td id="ca7ba67d547250adddf252a9bbae10c9">4.1%</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-34">Source: Processed Data (2025).</p>
      <p id="paragraph-e15611ca20ae74b8645b880295b53ca8">
        <bold id="bold-53f03b29bf2db1f560dc9bb3ab70ffe7">Respondent Overview</bold>
      </p>
      <p id="_paragraph-35">Indonesian social media users between the ages of 17 and 65 exhibited notable engagement with the Peringatan Darurat video. Among the respondents, 46.9% were male and 53.1% were female, reflecting a balanced gender distribution. Regarding educational attainment, 52.4% had completed high school or its equivalent, while 18.2% held a diploma (D3/D4), indicating a moderately educated sample. In terms of occupation, 36.5% of participants identified as students, followed by 29.8% as private sector employees, 12.0% as civil servants, 14.7% as entrepreneurs or freelancers, and 7.0% fell into other professional categories. This occupational diversity adds depth to the analysis by representing a broad spectrum of Indonesian society.</p>
      <p id="_paragraph-36">Regarding social media usage, 74.8% of respondents reported high engagement levels (more than 3 hours per day), while 25.2% reported lower usage levels (less than 3 hours per day). This highlights the predominance of digital connectivity among the sample population. Respondents also identified the platforms through which they viewed the Peringatan Darurat video. TikTok was the most frequently cited platform (36.5%), followed by Instagram (28.4%), YouTube (16.2%), X (formerly Twitter) (9.7%), WhatsApp (5.1%), and Facebook (4.1%). This distribution underscores the dominant role of TikTok in circulating politically charged content among Indonesian users. Notably, the high level of engagement, especially among younger demographics, suggests a strong political and social awareness within the Indonesian online community. These findings imply that viral content has substantial potential to trigger public mobilization, particularly when disseminated through trusted sources and emotionally resonant messages. The results also reflect the evolving landscape of political discourse in Indonesia, which is increasingly shaped by user-driven digital participation.</p>
      <p id="paragraph-1aa7d597a423b314b78f4acb3110c15f">
        <bold id="bold-5b7bc05039c6b53ff76f6627c2e6f371">Measurement Model Evaluation</bold>
      </p>
      <p id="_paragraph-37">The reliability and validity of the measurement model were confirmed. All item loadings exceeded 0.70, ensuring strong indicator reliability. Composite reliability values for all constructs were above 0.89, and Cronbach’s alpha values also exceeded the 0.85 threshold, confirming high internal consistency. PVE values ranged from 0.601 to 0.632, indicating strong convergent validity. HTMT ratios were below 0.90 for all construct pairs, supporting discriminant validity.</p>
      <p id="paragraph-cc861494749aab04cc6e518befd6443e">
        <bold id="bold-4d029170a4a2a487d3a013ef665256de">Loading Factor</bold>
      </p>
      <p id="_paragraph-38">Table 2 shows the loading factor values for each indicator representing its latent construct. The loading factor measures the correlation between an indicator and its latent construct. According to Hair et al. (2019), a loading factor value of  ≥0.708 is considered ideal, but values ≥0.6 are still acceptable in the context of exploratory research (Ngatno &amp; Dewi, 2019). Indicators with values below 0.6 were eliminated, ensuring that all remaining indicators are valid in representing their respective latent constructs.</p>
      <table-wrap id="tbl2">
        <label>Table 2</label>
        <caption>
          <title><bold id="_bold-9"/>Factor Loading</title>
          <p id="_paragraph-40"/>
        </caption>
        <table id="_table-2">
          <tbody>
            <tr id="table-row-ec55469310a1666a82fb44abce80056f">
              <th id="52defce20c1f4ceba39656b9b343a944">
                <bold id="_bold-10">Construct</bold>
              </th>
              <th id="23ea7d73e3ab1d39b20ba22b6d17987a">
                <bold id="_bold-11">Indicator</bold>
              </th>
              <th id="75e503965c43b08aed4de54610a6835d">
                <bold id="_bold-12">Estimate</bold>
              </th>
              <th id="550147856773d9dde2304195d0f333c1">
                <bold id="_bold-13">SE</bold>
              </th>
              <th id="729077f0bd810cbf99f1af2840f31949">
                <bold id="_bold-14">95% CI</bold>
              </th>
            </tr>
            <tr id="table-row-00423b24a94713dc403de64459a0ca4a">
              <td id="3ee34653227dc2aceff93dd25c754dd5">VV</td>
              <td id="80bd0ed4d8a5b3895bf45ac2cc83d25f">VV1.1</td>
              <td id="99de598bbe5d17132a4154a0c1bd3ead">0.748</td>
              <td id="750f8bdca3465dcd63cbfd33d3695b10">0.029</td>
              <td id="9daf1c1f3e07b6daf63abfb91b1747e7">0.685–0.789</td>
            </tr>
            <tr id="table-row-13a0e9ab413ef33bb8cf63e9ece3a2ba">
              <td id="d2bee4c629c35f3c06f7c1123eb197d1">VV</td>
              <td id="92c7acbe53793afab612e050ab3ab177">VV1.2</td>
              <td id="44317cd1425178726712f8051275276e">0.791</td>
              <td id="eb35dfffb7aff2f00f9d3418b7663d72">0.023</td>
              <td id="762eec9e046ca0b1e63057736a99872d">0.747–0.840</td>
            </tr>
            <tr id="table-row-01d846b0200af94c42d001cac60e8c56">
              <td id="5583d7daa4748d43ac041bd6b5fbb546">VV</td>
              <td id="dbe33e91140cbf632dbf5c55e47c2102">VV1.3</td>
              <td id="5e47da914fbc0e8b2cf9f7955461c27e">0.757</td>
              <td id="bc4fbfd83f0bd902ecabcb6ba2535f1e">0.032</td>
              <td id="9aa5f4b966b3f5f916ca28ae3b78e507">0.676–0.833</td>
            </tr>
            <tr id="table-row-583c566ef7fbe908f09a158ae8e2e4e3">
              <td id="137f8f86f930fcb602a3aea30e25aece">VV</td>
              <td id="a01a2440a61932da705b4af69443ac01">VV2.1</td>
              <td id="0c86dcac0293b0cbd0f083e55f49a622">0.760</td>
              <td id="74344d6c536ca17b6183c0fa33c31bdd">0.031</td>
              <td id="2f82313cd99d283eee4a1c0583a2127e">0.686–0.811</td>
            </tr>
            <tr id="table-row-f9f971d8e3c921953bf224f3b9282d25">
              <td id="a9fe204ea980f8d84473da3946a0fd7e">VV</td>
              <td id="b3519c2ed5bf4fc3dd31685dd4fc0f12">VV2.3</td>
              <td id="83368a84bb6d2a10b502ced467722420">0.785</td>
              <td id="0d536b35bf6d4f9cf991a67f4311c9b2">0.025</td>
              <td id="bef01482c17f1039271a0e831694a161">0.735–0.830</td>
            </tr>
            <tr id="table-row-ac9002fc2815a4e3636a0b3701ae4daa">
              <td id="345c7928f6b3af1f1c219e1020620634">VV</td>
              <td id="c3dc19d6dadee6e984a0c39906e1d416">VV3.1</td>
              <td id="eb359327c73f5e42f9eade6d3c034928">0.785</td>
              <td id="36cac37a1688995893d67aa0a3c9433f">0.026</td>
              <td id="7a5ee2bb0c7eea6dad112cef94b8980e">0.728–0.828</td>
            </tr>
            <tr id="table-row-9a2c0672ce412dfa1bf64119872805dc">
              <td id="c1feefd09028852565a8dc9e882dbc39">VV</td>
              <td id="87e1cd8a59f57870ec531fd2a4324e9c">VV3.2</td>
              <td id="9ba72e44dc080f3e2d56d7b92322c9c1">0.772</td>
              <td id="4d8d5b9cd4f885d7881afd59afde6803">0.032</td>
              <td id="51dbdf82713c4a0691bb920ba5c63d3b">0.702–0.822</td>
            </tr>
            <tr id="table-row-0b679fc40f018116f30d554278d91820">
              <td id="a8ce297d43dd61128555f59a1bd5d2f2">VV</td>
              <td id="982422a7cf7a09cbf61ae55268f45620">VV3.3</td>
              <td id="b41e57175e89cd4225acf9465bc5689d">0.815</td>
              <td id="43a50ed5199cbae9813f91eaa646fc6c">0.021</td>
              <td id="3cc8d1cffabe7ce06921e190e3567b7a">0.773–0.853</td>
            </tr>
            <tr id="table-row-94a52c1b13834d4cf7ff2d0bc04cf7a4">
              <td id="5835ebd3a35a3a7226a90271a5e02c62">PM</td>
              <td id="bfb35b48322e7a4bc7179024674cd98b">PM1.1</td>
              <td id="78a8ea38408e158d183b0a785deeb77c">0.836</td>
              <td id="55c7b71360f1dedbc2a7b4269d6a4087">0.021</td>
              <td id="813c5a23d396b4cdd69d7b858e23d8df">0.792–0.872</td>
            </tr>
            <tr id="table-row-44367e23e18a046b07eec44cfa37d964">
              <td id="05f8b70ba9ad6c8d99ec876867b6ba82">PM</td>
              <td id="4e857e8dcd51eb970900d16f673a0a37">PM1.2</td>
              <td id="1bcc6b549e67a3d30732568e8247d961">0.866</td>
              <td id="0eb709284e03cf4491526643bb56963a">0.014</td>
              <td id="eea0ee84063e99b372d410b98dc9f130">0.835–0.888</td>
            </tr>
            <tr id="table-row-d69f3ee61afaac2f48a15748f5634500">
              <td id="6f2ae68e1963e123984d130cca5dd57a">PM</td>
              <td id="288bfdaf568f6a45739f91755e66efb3">PM2.1</td>
              <td id="d0652df62d77b31e2df2f9767a1fe758">0.832</td>
              <td id="830367668384ec12c8f9ff6a8962f63d">0.022</td>
              <td id="dc3206c8f0cc1111110bf9c13d89b76b">0.781–0.868</td>
            </tr>
            <tr id="table-row-ead7407d1f8d8e38c2f122603684968d">
              <td id="a8fc58a8a38ad7a1649c284b94deb426">PM</td>
              <td id="f14f19dd28d40237fc8dc50c70ff9566">PM2.2</td>
              <td id="1ebfba31d97e1bc794838a529b6e93bf">0.822</td>
              <td id="5ccf2e83b8521d2687268652903f6f75">0.024</td>
              <td id="836fc78ffa111b3b3fde078ca0a39600">0.770–0.864</td>
            </tr>
            <tr id="table-row-29238404a4c0fdaf28a34c91872956f5">
              <td id="16fa4658bfe33947a22e2aa2b2d92da6">PM</td>
              <td id="8f39ed52b3d6425764d254c1b73d58eb">PM2.3</td>
              <td id="177c5eaecf926a2f904849442a742e2e">0.806</td>
              <td id="ca69ef73aa713186d39187b815cf1c97">0.025</td>
              <td id="bb23ad7013a88a1dd73357085305fdbc">0.750–0.849</td>
            </tr>
            <tr id="table-row-67cafa1a2809d6c4e8e8ced151ce0e9b">
              <td id="7d696d626d4de40831e1d37af0e3042b">PM</td>
              <td id="1762f10dffdb47f89c3ca5e8b16e9358">PM3.3</td>
              <td id="7c67a92bc34da0ad3b869f59e3a158b7">0.797</td>
              <td id="d41b1773110c2b7d8a662ee9b5a7b230">0.029</td>
              <td id="8d4a731cf16c657bf3a267fc5dff293d">0.734–0.846</td>
            </tr>
            <tr id="table-row-4683dada2d86b84b7411d9029767e3b8">
              <td id="1f03d7ff34d8229ef8271ef2a54d0c39">SC</td>
              <td id="31bae974b568bf8b989b807443541b26">SC1.3</td>
              <td id="2c10658579472ca41deddd712bbe4941">0.754</td>
              <td id="2a47682318b46af2b4fd43f90b50c827">0.026</td>
              <td id="e086bd3d2fd2555a7a86711c5ec55441">0.694–0.803</td>
            </tr>
            <tr id="table-row-69289955bf18f7bcafb8e377ca495690">
              <td id="c696378f41c21b008f3b8d66ab2a838a">SC</td>
              <td id="fdefe85538221b140628e3e121a52c71">SC2.1</td>
              <td id="6f0d51f0ecf3a670347ba443d6d9c817">0.825</td>
              <td id="35449693d3dc86b3d110d53ba1110f33">0.018</td>
              <td id="144465cfc94a249a8e3414d3edf19638">0.792–0.861</td>
            </tr>
            <tr id="table-row-2ab8422762796a60125ea2ae7200ed6e">
              <td id="3dc2f4877c1c632e0d68876b6e3df3fd">SC</td>
              <td id="b8ac829e890a168cb35fb41f342a3eed">SC2.3</td>
              <td id="ca50694505907a3d4a22f8fe031485dc">0.761</td>
              <td id="1b7d84d584c371be5d4639e2be913fee">0.035</td>
              <td id="6a0f2434869d27674ea9b8c67772ba72">0.693–0.826</td>
            </tr>
            <tr id="table-row-2d54c5b474f9a5985a5a82231003d380">
              <td id="1cd78b9838199c24843b766b76aeb06a">SC</td>
              <td id="72f0e9ce199e5927beb09461df2b4c6f">SC3.1</td>
              <td id="8885c2a8e74a4090ffec0541006d34bc">0.751</td>
              <td id="d7c0e32e4a219bcb793d6f92c5b57d65">0.028</td>
              <td id="59d0292c9c1eb74fc61ccc0ec5d56272">0.703–0.802</td>
            </tr>
            <tr id="table-row-c4cd8927686acb4857a29cc65377eeec">
              <td id="d7fe255c5026bb417ff5870b70423398">SC</td>
              <td id="c7df2a1b83c35c223dd059adca0d5855">SC3.3</td>
              <td id="0772fc715f655c8885ad049d9c92aec4">0.787</td>
              <td id="8dee0ba8c1c4deac338cb501f12435ca">0.032</td>
              <td id="6e252f8ac37a141ec383eff16c736f1f">0.719–0.845</td>
            </tr>
            <tr id="table-row-d78c476e0747e33f4a0b410b20f48079">
              <td id="601090347df9f1fbd9ef9ea2f4f5fa61">ME</td>
              <td id="467e9fed3e72a0379949812d2f3af08e">ME1.1</td>
              <td id="846c7d3de714fbb2916795c8aecc1114">0.801</td>
              <td id="4f247d2ecc82826d2fac27c5adab97ac">0.031</td>
              <td id="7cee3c9da782b0c184b4f912d54f7934">0.750–0.864</td>
            </tr>
            <tr id="table-row-3543d3ce86e579d997e4372f2b37a934">
              <td id="4926bd646eefdd02cf8d2bda478a8e66">ME</td>
              <td id="e751d0e92830558fcea9099755a7d23a">ME2.3</td>
              <td id="05f541987115e68f1900ade2fb50c991">0.853</td>
              <td id="54a6dd92a0476e3ef3b08fb31468099b">0.021</td>
              <td id="34c2d93a118ec35ded4a68cdf8b8d99b">0.806–0.892</td>
            </tr>
            <tr id="table-row-ec524c8cfb2f343d51d2f23b4baa52a3">
              <td id="1d837d2e6563afa3a8de1c4120c89876">ME</td>
              <td id="583a3db6bce44fab137472b114e47886">ME3.2</td>
              <td id="79ba04de0a6e7671b9ef8f987d1e07f0">0.884</td>
              <td id="d5c7782e8ffd85bf5a6219171343cf56">0.017</td>
              <td id="3ac131be2a6c83b6800966c86bb9b95a">0.843–0.915</td>
            </tr>
            <tr id="table-row-23b82aaf8fb995a6c0ba890930c7a6dc">
              <td id="4d827dc804bd728cf10827eca4bd500f">SE</td>
              <td id="9eb2f3ce812177db0c0bb62a0d1bc4fd">SE1.2</td>
              <td id="aee611b57d904b242f11bb7a4127daa1">0.762</td>
              <td id="4f851283dd6a44a14cddad7c5e94704d">0.034</td>
              <td id="be8abfa93a3ff0e790f5e7d8aa9c2304">0.701–0.827</td>
            </tr>
            <tr id="table-row-a01ae62b00a418e026dc4d6d5185a979">
              <td id="e41fb6dc1a3579d4617b7732f02657ed">SE</td>
              <td id="7ce6f42c65feb208994bfd20b538d719">SE2.3</td>
              <td id="d7af2554cb73406f6825813b89cf60fe">0.872</td>
              <td id="903f970da23fc93afd0bb9dd935c2a40">0.019</td>
              <td id="debe61a1fb64df88348bd1af1acaff07">0.835–0.904</td>
            </tr>
            <tr id="table-row-1ff20b75788da9040a885ec8d903da52">
              <td id="caa904cb202c9f83d81b09f1c62ec301">SE</td>
              <td id="98381e5788257a5f688b7a3dd8d21e0b">SE3.1</td>
              <td id="491eda1df4b8f0b126a7976b2133358f">0.871</td>
              <td id="ef0bd65fa7116a5bd1a31560fb506621">0.020</td>
              <td id="6137722ee90d73a265a5f670f11f29bd">0.834–0.910</td>
            </tr>
            <tr id="table-row-5b848a5d467628ca4f095e891ecaabdd">
              <td id="5c7bf989e12492129f91b9662153837b">SE</td>
              <td id="87d33d09982d94d9efde32768736196c">SE3.2</td>
              <td id="d3f529411ca9ae0d06ffb47b00d6d72f">0.882</td>
              <td id="3416c1322414fd18b349e7287c7d31d2">0.013</td>
              <td id="64ea5468419d0be28c0893527ce1af6d">0.857–0.906</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-41">Source: Processed Data (2025).</p>
      <p id="paragraph-1636a22171878a9cb12b7fe7106887f7">
        <bold id="bold-e06e338983a2830d7c769b7d6c94e75b">Reliability and Construct Validity</bold>
      </p>
      <p id="_paragraph-42">The validity and reliability analysis shows that every concept in the model satisfies the advised standards.  Good internal consistency shown by high values of Cronbach's Alpha (α) and Composite Reliability (ρ&gt;0.7) confirms that the constructions are dependable for estimating the required latent variables (Hair et al., 2019).  Furthermore, Proportion of Variance Explained (PVE) values above 0.5 show sufficient convergent validity, i.e., the indicators of each construct fairly assess the same latent variable (Fornell &amp; Larcker, 1981).  All constructions are unidimensional according to dimensionality analysis, which also shows that the indicators inside every build measure the same underlying dimension (Table 3).</p>
      <table-wrap id="tbl3">
        <label>Table 3</label>
        <caption>
          <title><bold id="_bold-15"/>Reliability and Construct Validity</title>
          <p id="_paragraph-44"/>
        </caption>
        <table id="_table-3">
          <tbody>
            <tr id="table-row-6baba41194af9d72e71664905f1b7eec">
              <th id="3e15d24a7fc9c78ea493671db0b7d401">
                <bold id="_bold-16">Construct</bold>
              </th>
              <th id="b41ec648164a90711064df59c7b2b597">
                <bold id="_bold-17">Cronbach's Alpha (α)</bold>
              </th>
              <th id="85412ead2c8f63e739ac2753e8472e8c">
                <bold id="_bold-18">Composite Reliability (ρ)</bold>
              </th>
              <th id="18811f0e3e113cdc31463a4d4f9c5f3a">
                <bold id="_bold-19">PVE</bold>
              </th>
              <th id="73ffd8d2b6188e5fa74ebc026bcfa936">
                <bold id="_bold-20">Dimensionality</bold>
              </th>
            </tr>
            <tr id="table-row-faca41e41c1d25e83ed53e59ebb894e2">
              <td id="1f652d690afb5afb060b3cf60fa9417c">VV</td>
              <td id="02740ba75d64e21dafea3d52222745b5">0.891</td>
              <td id="8dc6bad2d17ffece0f74c3fc1c9230b8">0.912</td>
              <td id="fc84400c65ccbea1e5fb63244043fb1e">0.632</td>
              <td id="cc369a6d7838095aff5d61d37762d03e">Unidimensional</td>
            </tr>
            <tr id="table-row-ecda6e560224f62259ea3de27dca3b07">
              <td id="904c8d653475e5d48b49ec1adfd75c8a">PM</td>
              <td id="25cb0b1ce7fd50b0dc4d14ece9523940">0.876</td>
              <td id="71b4935c1a90e2e8f14bad4e0be168dd">0.901</td>
              <td id="40a1108a54ad8d0b031c6d967ecdbe3d">0.618</td>
              <td id="5e5d8ae79d4184da9450fda39eb343bc">Unidimensional</td>
            </tr>
            <tr id="table-row-c71d615ae073c6b71c73e2d6946d3141">
              <td id="63ffc233f01017bb0b58e96f5840ce95">SC</td>
              <td id="a4092e61c6f86d438245bb7fa6686d84">0.854</td>
              <td id="54418b70758a93fe605669850c13b6d4">0.889</td>
              <td id="3b8994f11ca783e491dd6fab10d8e650">0.601</td>
              <td id="423c8ba5cbdedcc64d7b3d252d2fd058">Unidimensional</td>
            </tr>
            <tr id="table-row-f5d56aef8176cd6115805c385f9b8b88">
              <td id="d3e8ef1d3cc6773db4102d2f042326bf">ME</td>
              <td id="0fd2528a92beb3919740ff20677f7246">0.882</td>
              <td id="542284fd835965376e9459e79fa81279">0.908</td>
              <td id="ef61040f0d7298fd854c2527fbd0d49b">0.627</td>
              <td id="a9243075a94c14ba45d48d7a98bd0cb5">Unidimensional</td>
            </tr>
            <tr id="table-row-cb20db31a4a4f608fce5d59c999c73e6">
              <td id="f34509a0c1d86f400a712576e5e003f9">SE</td>
              <td id="e7ef940f1a857e662e799ee9f1528155">0.868</td>
              <td id="e28e9a1a156283b7ed300319202ea7b2">0.895</td>
              <td id="3e1be1468cd8752a256711fc641b5dab">0.610</td>
              <td id="9142f3bd51378c6ac4cd87661f69bc30">Unidimensional</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-45">Source: Processed Data (2025).</p>
      <p id="paragraph-daae0196e49b55b294ded918d46c8c9e">
        <bold id="bold-f2c172791e2fafcc1584e8ba5c533dce">HTMT Ratio</bold>
      </p>
      <p id="_paragraph-46">Discriminant validity was assessed using the Heterotrait-Monotrait (HTMT) ratio.  Good discriminant validity, according to values less than 0.90, Henseler et al., 2015  The HTMT study's findings (Table 4) reveal that none of the values above are 0.90, therefore verifying that the model satisfies the requirements for discriminant validity.</p>
      <table-wrap id="tbl4">
        <label>Table 4</label>
        <caption>
          <title><bold id="_bold-21"/>HTMT Ratio</title>
          <p id="_paragraph-48"/>
        </caption>
        <table id="_table-4">
          <tbody>
            <tr id="table-row-2cd9646336efde597428114516bbd94f">
              <th id="504cf422df3fff9d3eae91abe15edb9b">
                <bold id="_bold-22">Construct Pair</bold>
              </th>
              <th id="8dc28ab0ac5eda2e3a246b5c9c69b97c">
                <bold id="_bold-23">Value</bold>
              </th>
              <th id="ab995c089aed2450820f45cdb46ade1c">
                <bold id="_bold-24">SE</bold>
              </th>
              <th id="be1943dfdf6123e2f98ad1726ee535fc">
                <bold id="_bold-25">95% CI</bold>
              </th>
            </tr>
            <tr id="table-row-7a51c75b6b182adb533d64c74c1dffce">
              <td id="d25017ca15793f07dbc4eb3c17439612">VV ↔ PM</td>
              <td id="cc215819b10fca0caee29734bef6b796">0.69</td>
              <td id="893210dd403f2b3f7ae11eab6e809261">0.044</td>
              <td id="40b35edfa488d0347c780aa7f78578ff">0.541–0.768</td>
            </tr>
            <tr id="table-row-26eb74ee6ad575d71bdea23fc93a4d06">
              <td id="4be2e81a47c43b1e3b45b81849180f3c">VV ↔ SC</td>
              <td id="c687d27b3a7c76522d281c154bc30a70">0.771</td>
              <td id="65f7ff8cd858df06fb0c8bcccf02dd6e">0.040</td>
              <td id="888088d7a3ce84ea0677a0328380ee81">0.652–0.843</td>
            </tr>
            <tr id="table-row-caaa66ce81639f23f50b60ff3bca464d">
              <td id="4591583da309a6f899be3ab30549f514">VV ↔ ME</td>
              <td id="53caf14591e1ac94612268f2bd3c1601">0.618</td>
              <td id="873d97a38cfd68b7d3750debd69bee9d">0.047</td>
              <td id="64fe4947712c0cd4bf546cec3d3e8f49">0.434–0.704</td>
            </tr>
            <tr id="table-row-e28ecc0dee152ac9648101f41e71a47b">
              <td id="30b0f14aac70366ec1003484a668a656">VV ↔ SE</td>
              <td id="5b7434d8610f58dc33f94d04a35803d1">0.629</td>
              <td id="3f86494f42e98389fc669a6bbe5a0515">0.051</td>
              <td id="64e930b350c72a482bd15f89d2bb9c8b">0.431–0.719</td>
            </tr>
            <tr id="table-row-7702cc68d3dff48f1c3c2889fb00b31a">
              <td id="ef76aa92b6369aeadd5e7045408a1585">PM ↔ SC</td>
              <td id="b800ce2de232142bb8ad53e8e2b2c65d">0.717</td>
              <td id="61a2d48185b4f2978ce4a97131f3c461">0.040</td>
              <td id="25dd038496455c11fee4d01601cf0505">0.592–0.795</td>
            </tr>
            <tr id="table-row-0d0704f85a6ac8007ded53f5cadbd3c2">
              <td id="4832609d1d861d96d0c9fc76d8954db9">PM ↔ EM</td>
              <td id="f8fc02783ba736673bef826a8b1c1651">0.804</td>
              <td id="4d183ccecbad62e01f18360277123b97">0.038</td>
              <td id="d0b9175a41b2fdf5a9e9db97aa0b1f11">0.708–0.868</td>
            </tr>
            <tr id="table-row-41679df54e8ec3f946625aa24343d3ec">
              <td id="9213f36ce5fabda90adfaf0bb262ce14">PM ↔ SE</td>
              <td id="ffbfdf5e2ea103b98393c76ed94bf54f">0.875</td>
              <td id="ac38e948b751f37ad73dc67f00dc1680">0.023</td>
              <td id="cbe02c455be17b974f95a4ea77a313c0">0.808–0.914</td>
            </tr>
            <tr id="table-row-421a6c746ab54928b6275427ec2bdbd4">
              <td id="dcd66c346eed735d21d8d09f17a81454">SC ↔ ME</td>
              <td id="32bac92fedc4939645c0d8066194acec">0.756</td>
              <td id="2439d70c555d87593a5d240c14cd9d69">0.041</td>
              <td id="de01f063c6f2161d006e7c29f3646b4d">0.636–0.834</td>
            </tr>
            <tr id="table-row-641ea6f64bf20232876c1f359e77596a">
              <td id="b0b5ceea0113c1105aacf74a1f034a78">SC ↔ SE</td>
              <td id="a555dff9b012730c63eec12bda9b84d3">0.666</td>
              <td id="a35355594043613277d1d5ebeeb9e851">0.054</td>
              <td id="d25e98c8e6497a574ad752c3aa9a7cd5">0.482–0.758</td>
            </tr>
            <tr id="table-row-71f46a8d972b53f26d5fccc51bdf6816">
              <td id="772d88236646aca2648aefbbdfc662ef">ME ↔ SE</td>
              <td id="526c6833ceeb8833b8191f30b47b913f">0.842</td>
              <td id="c5eb0f5a06879b8556e31b9575fff941">0.039</td>
              <td id="3ae245a8124d1e2560da8598a625e8db">0.712–0.910</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-49">Source: Processed Data (2025).</p>
      <p id="paragraph-6ee1a06d25c6bdd3523f7b97cf61e730">
        <bold id="bold-d2520297973657d523386e938ae8d859">Goodness of Fit (GoF) Evaluation</bold>
      </p>
      <p id="_paragraph-50">The structural model demonstrated excellent fit, with a FIT value of 0.604 and an adjusted FIT (AFIT) of 0.602. The Goodness-of-Fit Index (GFI) was 0.992, very close to the ideal threshold, and the SRMR was 0.043, well below the 0.08 cutoff. These indicators collectively confirm that the model fits the observed data well and that the hypothesized relationships can be interpreted with confidence (Table 5).</p>
      <table-wrap id="tbl5">
        <label>Table 5</label>
        <caption>
          <title><bold id="_bold-35"/>Structural Model FIT Measures</title>
          <p id="_paragraph-51"/>
        </caption>
        <table id="_table-5">
          <tbody>
            <tr id="table-row-f4a76c54b0de1841540239863321006f">
              <th id="f7b69567eac77b31803d992f0b7f15a2">
                <bold id="_bold-26">FIT</bold>
              </th>
              <th id="7b56943eb937ae799c75471abc4b28e4">
                <bold id="_bold-27">AFIT</bold>
              </th>
              <th id="9105a239d56443ef8343ad8b2d4b3aac">
                <bold id="_bold-28">FITs</bold>
              </th>
              <th id="2c30239a41bd8fabb4cc938d19c2e278">
                <bold id="_bold-29">FITm</bold>
              </th>
              <th id="655bc10443c20d1887204fe620da74df">
                <bold id="_bold-30">GFI</bold>
              </th>
              <th id="f47b8c156ab16d7084823b111b32c01a">
                <bold id="_bold-31">SRMR</bold>
              </th>
              <th id="889b8e97b1d4cca376159c861967c8fa">
                <bold id="_bold-32">OPE</bold>
              </th>
              <th id="3836ebce5540be8f951f9a0b29b1c5de">
                <bold id="_bold-33">OPEs</bold>
              </th>
              <th id="2621e186f9d48c72505f2a079f28a11d">
                <bold id="_bold-34">OPEm</bold>
              </th>
            </tr>
            <tr id="table-row-c100ed297701f12fe35984b297f69cd8">
              <td id="2abc6b499f358d5be4e161dea62c50d9">0.604</td>
              <td id="6dcd13aab9b9401777184a8f1aad3cb6">0.602</td>
              <td id="3217be427aa10d4c38e1f73e2f61a117">0.352</td>
              <td id="919dee379bc30865f1da59ddcd313577">0.653</td>
              <td id="6c400b553280de17f64b301735096014">0.992</td>
              <td id="a15024f96936d883991ca78d91e75a7a">0.043</td>
              <td id="8403fa70e5fb163e09321cb33f553226">0.401</td>
              <td id="a6575726b9683ff1cdc454006eb6bb2a">0.657</td>
              <td id="cd0e0661321c652de1a99314eec276bc">0.352</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-53">Source: Processed Data (2025).</p>
      <p id="paragraph-5a14c073f324650932ed77de138bb532">
        <bold id="bold-9590ee776fd058bd6def3c02c59fd4a5">Structural Model Evaluation and Hypothesis Testing</bold>
      </p>
      <p id="_paragraph-54">The model showed that viral video exposure (VV) has a direct, positive, and statistically significant effect on public mobilization (PM), with a standardized path coefficient of 0.187 and a p-value &lt; 0.05. This confirms Hypothesis 1 (H1), indicating that viral content, in itself, plays a meaningful role in activating public response.</p>
      <p id="_paragraph-55">The mediating effect of source credibility (SC) was also statistically significant. Viral videos positively influenced perceptions of source credibility, which in turn influenced public mobilization. The indirect effect through SC was 0.076, supported by a Sobel Z = 2.29, indicating partial mediation and validating Hypothesis 2 (H2). This suggests that the perceived credibility of the communicator, here, Najwa Shihab, enhanced the mobilizing potential of the video.</p>
      <p id="_paragraph-56">Similarly, moral emotions (ME) were found to partially mediate the relationship between viral video exposure and public mobilization. The indirect effect was 0.097, with a Sobel Z = 2.12 and p &lt; 0.05, confirming Hypothesis 3 (H3). This implies that emotional responses such as anger, empathy, or a sense of injustice triggered by the video contributed to individuals' motivation to act.</p>
      <p id="_paragraph-57">Among the three mediators, social engagement (SE) demonstrated the strongest mediating effect, with an indirect effect of 0.275 and a Sobel Z = 5.01, indicating a highly significant relationship and confirming Hypothesis 4 (H4). This suggests that online actions, such as sharing, commenting, and participating in hashtag discussions, were central mechanisms through which exposure to viral content translated into real-world mobilization.</p>
      <p id="paragraph-4e4e256c0c1e37a15e31d9168ffa82fd">
        <bold id="bold-f49570d200645bd2bac819322eeef8c3">R-squared and Variance Explained</bold>
      </p>
      <p id="_paragraph-58">The R-squared values show the ratio of variance in the endogenous latent variables explained by the exogenous latent variables.  Particularly in explaining the variance in public mobilization (PM), the study findings (Table 6) reveal that the model has great predictive ability.  With an R-squared value of 0.692 for PM, moral emotions (ME), source credibility (SC), and social engagement (SE) taken together account for 69.2% of the variance in PM.  Indicating lesser but still substantial contributions, the R-squared values for SC, ME, and SE are 0. 457, 0.288, and 0.320, respectively.</p>
      <table-wrap id="tbl6">
        <label>Table 6</label>
        <caption>
          <title><bold id="_bold-36"/>R-squared Values for Dependent and Mediating Variables</title>
          <p id="_paragraph-60"/>
        </caption>
        <table id="_table-6">
          <tbody>
            <tr id="table-row-9146971ffd138f8fa88ebb8a679204e1">
              <th id="cc4442e0580466ca75c97a27eaf0c3f8">
                <bold id="_bold-37">Variable</bold>
              </th>
              <th id="e447772d6258ee7f54cd7f5fc00b4688">
                <bold id="_bold-38">R-squared</bold>
              </th>
              <th id="ccffd65bb46a1b721810937cfbc79d0c">
                <bold id="_bold-39">Interpretation</bold>
              </th>
            </tr>
            <tr id="table-row-90f8f08fd2fa56a1509af547f35577f2">
              <td id="4df4705ad813ae59305bee201304d2f2">Public Mobilization (PM)</td>
              <td id="849638a4992cfe51cf68d3c165bf0a2e">0.692</td>
              <td id="d87e5bbdd50f00ef84d807c5f8b0592b">69.2% of the variance in PM is explained by VV, SC, ME, and SE.</td>
            </tr>
            <tr id="table-row-200a625c1cc8056fe79402631f8c2e3c">
              <td id="3a6d45d2f45efebcfdfec81d51448c65">Source Credibility (SC)</td>
              <td id="5559a38e63f32026658e0617033f2b58">0.457</td>
              <td id="f1b36ba26a6e48437a16878f54a501db">45.7% of the variance in SC is explained by VV.</td>
            </tr>
            <tr id="table-row-8819f6dc3d51237b083d22f32721e203">
              <td id="891ea04ea4c2f1a1524f6127269005f4">Moral Emotions (ME)</td>
              <td id="25bb680f5074521ec3174b78729f74dc">0.288</td>
              <td id="be81513d3604446a9bd11d0c028308fe">28.8% of the variance in ME is explained by VV.</td>
            </tr>
            <tr id="table-row-21dc8ab8e9f987efd7f8f48602d4aecf">
              <td id="d1a1738d3ca5ee80dd3307872c04976e">Social Engagement (SE)</td>
              <td id="1347b7fb7c6fcf3a4b4324f901c26765">0.320</td>
              <td id="1261a3914a76b027691fdb176fdeeec1">32.0% of the variance in SE is explained by VV.</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-61">Source: Processed Data (2025)</p>
      <p id="paragraph-679abbb4c61b805328e6e72c09b908b3">
        <bold id="bold-8a7fbbbb94b1886fc9cd660bea4b23ff">Direct and Indirect Effects</bold>
      </p>
      <p id="_paragraph-62">The mediation analysis confirms that viral video exposure (VV) has a statistically significant direct effect on public mobilization (PM), with a standardized path coefficient of 0.187 (<italic id="_italic-18">p</italic> &lt; 0.05; CI: 0.098–0.294). This indicates that even without considering mediators, viral content independently contributes to citizens' willingness to participate in collective action. However, the analysis also reveals substantial indirect effects through three mediating constructs: source credibility (SC), moral emotions (ME), and social engagement (SE). The total indirect effect across all mediators is 0.448, demonstrating that a majority of the mobilizing influence occurs through these psychological and social mechanisms.</p>
      <p id="_paragraph-63">Among the mediators, social engagement (SE) shows the strongest indirect effect (0.275), followed by moral emotions (ME) (0.097) and source credibility (SC) (0.076). This ranking highlights the central role of digital participation, such as liking, sharing, commenting, and hashtag activism, in translating content exposure into real-world mobilization. To evaluate the significance of the mediation effects, Sobel tests were conducted. All three mediators produced Z-values greater than 1.96, confirming statistically significant mediation. These findings, summarized in Table 7, indicate that each mediator contributes uniquely to explaining how viral content influences public mobilization. Since the direct effect (VV → PM) remains significant even when mediators are included, the model demonstrates partial mediation across all three pathways.</p>
      <table-wrap id="tbl7">
        <label>Table 7</label>
        <caption>
          <title><bold id="_bold-40"/>Mediation Test Results</title>
          <p id="_paragraph-65"/>
        </caption>
        <table id="_table-7">
          <tbody>
            <tr id="table-row-9883b2d75ac6b0f5fe6dfff76f5da033">
              <th id="6785dc9e2e8d9599592c7590dc108a71">
                <bold id="_bold-41">Mediator</bold>
              </th>
              <th id="10e2dc560edc6d5436ea3485b5e2c897">
                <bold id="_bold-42">Direct Effect (VV → PM)</bold>
              </th>
              <th id="f486c3c495b3f2e436159e7f5d73d7a0">
                <bold id="_bold-43">Indirect Effect (VV → Mediator → PM)</bold>
              </th>
              <th id="dfc9ae2b55b22b229f5394fd2404cc03">
                <bold id="_bold-44">Sobel Test (Z)</bold>
              </th>
              <th id="8fc1cef976d77fcd184a468953f26c1c">
                <bold id="_bold-45">Mediation Conclusion</bold>
              </th>
            </tr>
            <tr id="table-row-cafca2b5b47d07383d66d204fac72385">
              <td id="8421639dac05bf8ac5d008082623b139">SC</td>
              <td id="ad0f5416bc9104b705d4f828460c836a">0.187</td>
              <td id="4819706a85c840885512cf694e813d76">0.076</td>
              <td id="c60c7f0a8e19cbae3f1eb88c178fdc9e">2.29</td>
              <td id="084e2167ed84098ec9c44b8b2f7e8ec1">Partial Mediation</td>
            </tr>
            <tr id="table-row-d35d305a354df0627ea4f6a9bd8d3521">
              <td id="d3bdd4eb18361b983dd45d5b647c73d1">ME</td>
              <td id="8be323fb11fa959d7887d6daab921db8">0.187</td>
              <td id="b448c311848c92bcad73ed7f86f1ceee">0.097</td>
              <td id="f1778484d9c2fc28f4854faf4ab7bc12">2.12</td>
              <td id="f67c5a5064e051b5785c4d4f65ff5df4">Partial Mediation</td>
            </tr>
            <tr id="table-row-6f74a745685fdb2a2d273a64da19aee6">
              <td id="92b2396404262cb50b17807fb5693f49">SE</td>
              <td id="9b2582539e279f1f25f8a5e8e4f50858">0.187</td>
              <td id="42514d5c4136940e8f104732f9adcf8c">0.275</td>
              <td id="8ae7465bc0f835a12b8754b28c10d457">5.01</td>
              <td id="651e4b9cd0accba19f3e1233991325bc">Partial Mediation</td>
            </tr>
          </tbody>
        </table>
      </table-wrap>
      <p id="_paragraph-66"><bold id="_bold-46">Source:</bold> Processed Data (2025)</p>
      <p id="_paragraph-67">These results demonstrate that the model meets both measurement and structural evaluation criteria. All indicators exhibit strong loading values, reliability, and discriminant validity. Moreover, model fit indices, FIT, AFIT, GFI, and SRMR, confirm that the theoretical model aligns well with the empirical data. These validated relationships form the foundation for the subsequent discussion of theoretical implications, practical relevance, and potential areas for future research.</p>
    </sec>
    <sec id="sec-5">
      <title>Discussion</title>
      <p id="_paragraph-68">This study offers a contextualized understanding of how viral content contributes to public mobilization in a collectivist society. By examining the <italic id="_italic-19">Peringatan Darurat</italic> video, it reveals that source credibility, moral emotions, and social engagement serve as key psychological and sociocultural mechanisms that shape collective responses to politically charged digital content. The findings extend digital mobilization theory by demonstrating that viral content alone is insufficient to mobilize public action; rather, it is the mediated pathways, particularly trust, emotion, and participatory behavior, that determine the intensity and reach of such mobilization.</p>
      <p id="_paragraph-69">One of the most significant contributions of this study is the demonstration that viral content can directly influence public mobilization, aligning with foundational ideas from the Viral Content Diffusion Theory. Prior research by Guadagno et al. (2013) and Tandoc et al. (2018) supports the notion that highly emotional and visually stimulating content increases the likelihood of sharing and response. The current findings reinforce this by showing that Indonesian audiences responded strongly to the <italic id="_italic-20">Peringatan Darurat</italic> video not only because of its content but because of how it emotionally and morally resonated with viewers.</p>
      <p id="_paragraph-70">Moreover, the mediating role of source credibility builds on previous studies that emphasize the importance of trust in digital communication. This aligns with the work of Flanagin and Metzger (2008) and O’Keefe (2002), who found that trust in a messenger significantly shapes how audiences interpret and act upon messages. However, this study adds cultural nuance by showing that in collectivist societies like Indonesia, credibility is also constructed through moral reputation and communal values. This finding complements recent Southeast Asian research that suggests trust is not only perceived in institutional terms but also through moral alignment and social proximity (Lim, 2017; Kieling et al., 2022).</p>
      <p id="_paragraph-71">The emotional component of digital mobilization has been addressed in earlier work, particularly within Moral Foundations Theory (Haidt, 2012). In line with these frameworks, this study shows that moral emotions such as outrage, empathy, and guilt are not merely psychological responses but also motivators of collective behavior. In collectivist settings, where individuals often act in accordance with shared norms and social expectations, emotional resonance becomes a powerful mobilizing force. This resonates with studies by Van Zomeren et al. (2008) and Skitka et al. (2021), and further supports the growing consensus that moral emotions are central to digital political engagement.</p>
      <p id="_paragraph-72">The study's most compelling insight concerns the role of social engagement as the strongest mediator between viral video exposure and mobilization. While many prior studies, particularly in Western contexts, have emphasized the importance of individual-level expression (Bennett &amp; Segerberg, 2013), this study suggests that in Indonesia, engagement is deeply embedded in collective digital behavior. This finding is consistent with the Social Engagement Framework (Boulianne, 2015; Valenzuela, 2013), but adds new cultural depth by showing how online participation reflects a digital form of <italic id="_italic-21">gotong royong</italic>, a collectivist ethic of mutual aid. The viral spread of hashtags such as #PeringatanDarurat and #KawalPutusanMK exemplifies how digital participation is not just reactive but symbolic of a shared political identity.</p>
      <p id="_paragraph-73">In comparison with studies conducted in other Southeast Asian countries, such as Malaysia and the Philippines, this research affirms regional similarities in how digital media are used for political expression. For example, cases of youth-led mobilization in the Philippines during elections or Malaysian environmental protests demonstrate similar patterns of emotionally driven, socially amplified activism. However, unlike those contexts where religious or ethnic affiliations often dominate mobilization discourse, this study emphasizes legal and democratic values as the unifying cause, showing that digital mobilization can transcend identity politics when framed around national democratic ideals.</p>
      <p id="_paragraph-74">Theoretically, the integration of multiple frameworks such as Viral Content Diffusion Theory, Digital Mobilization Theory, Moral Foundation Theory, Source Credibility Framework, and Social Engagement, enables a more layered understanding of how digital mobilization unfolds in non-Western contexts. The study moves beyond simplistic causal models and instead presents a culturally embedded view of activism (Kietzmann et al., 2011). It highlights that emotional resonance, social trust, and participatory affordances must be understood as mutually reinforcing components in driving public action. This challenges existing assumptions in Western-centric mobilization models, which often neglect the relational, moral, and communal aspects of communication in collectivist societies.</p>
      <p id="paragraph-6c8b504cf88297aafb652b50c64bf05d">
        <bold id="bold-f21ae3ba33ea13562449b4306a2f023b">Practical Implications</bold>
      </p>
      <p id="_paragraph-75">The findings of this study offer valuable practical insights for legislators, activists, media professionals, and policymakers aiming to foster civic engagement through digital platforms. When delivered by credible and trusted sources, viral videos can be strategically utilized to disseminate public knowledge and raise awareness about pressing social or political issues. This is particularly important in environments where institutional trust is fragile and where digital media serve as the primary channel for information and discourse. To enhance public participation, media professionals are encouraged to create content that not only captures attention but also evokes strong moral emotions such as empathy, moral outrage, or guilt. Emotionally resonant narratives that highlight injustice or social inequality have the potential to catalyze collective support and stimulate civic action. Therefore, well-crafted digital storytelling becomes a vital tool for initiating and sustaining social movements.</p>
      <p id="_paragraph-76">Moreover, government agencies and non-governmental organizations (NGOs) can amplify civic engagement by collaborating with credible public figures to deliver emotionally engaging and trustworthy messages. For example, NGOs advocating for electoral transparency could partner with respected journalists or influencers to launch viral campaigns that encourage youth participation and political awareness. Policymakers may also consider incorporating participatory communication strategies into digital democracy initiatives to improve public responsiveness and inclusivity. By understanding the interplay between credibility, emotion, and engagement, stakeholders can design more effective digital interventions that not only inform but also mobilize audiences toward constructive democratic participation.</p>
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    <sec id="sec-6">
      <title>Conclusions </title>
      <p id="_paragraph-77">This study demonstrates that the <italic id="_italic-22">Peringatan Darurat</italic> video successfully catalyzed mass mobilization through the combined influence of source credibility, emotionally charged narrative, and participatory digital engagement. The findings highlight how viral content, when aligned with credible messaging and moral appeals, can significantly shape public behavior, particularly within developing countries such as Indonesia. This reinforces earlier findings by Hapsari and Pamungkas (2024), who emphasized the role of cognitive engagement in shaping online behavior through social media.</p>
      <p id="_paragraph-78">The study confirms that viral videos exert both direct and indirect influences on public mobilization, with source credibility, moral emotions, and social engagement serving as significant mediators. These insights contribute to a deeper understanding of the mechanisms driving digital mobilization and provide a practical framework for designing effective civic-oriented communication strategies. Furthermore, the study emphasizes the need to prioritize ethical communication practices in Indonesia’s digital ecosystem to ensure that viral content supports inclusive democratic engagement rather than promoting polarization or misinformation.</p>
      <p id="_paragraph-79">Nevertheless, the study is not without limitations. The reliance on purposive sampling, focusing on active social media users, may limit the generalizability of the findings to broader populations, particularly those with limited digital access or lower levels of media literacy. Additionally, the use of self-reported data introduces the possibility of response biases, including social desirability effects. The absence of qualitative triangulation is another limitation, as the study relied solely on quantitative measures, which may not fully capture the complexity of emotional and motivational factors underlying user behavior.</p>
      <p id="_paragraph-80">Future research should consider employing mixed-method designs to provide more nuanced insights into user motivations and emotional responses. Experimental approaches may also help to identify causal relationships more precisely and enhance the robustness of communication strategies aimed at fostering democratic engagement through digital media.</p>
      <p id="_paragraph-81"><bold id="_bold-47">Acknowledgement Statement: </bold>The authors sincerely express their gratitude to all individuals and institutions that contributed to this research. Special appreciation is extended to the Communication Science Program at Universitas Semarang for their generous support and provision of essential resources, which played a crucial role in the successful completion of this study.</p>
      <p id="_paragraph-82"><bold id="_bold-48">Conflicts of interest: </bold>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</p>
      <p id="_paragraph-83"><bold id="_bold-49">Authors'</bold><bold id="_bold-50"> contribution statements:</bold> Yoma Bagus Pamungkas conceptualized the study, provided resources, managed project administration, and secured funding. Richiana Hapsari was responsible for the methodology, investigation, formal analysis, and data curation. Both Yoma Bagus Pamungkas and Richiana Hapsari jointly supervised the research process. The original draft of the manuscript was written by Yoma Bagus Pamungkas, with contributions to writing, review, and editing by Yuwanto, Muhammad Adnan, and Nurul Hasfi. Richiana Hapsari also contributed to the writing, reviewing, and editing stages of the manuscript.</p>
      <p id="_paragraph-84"><bold id="_bold-51">Funding</bold> <bold id="_bold-52">statements:</bold> As there was no external funding received for this research, the study was conducted without financial support from any funding agency or organization.</p>
      <p id="_paragraph-85"><bold id="_bold-53">Data availability statement: </bold>Data is available at request. Please contact the corresponding author for any additional information on data access or usage.</p>
      <p id="_paragraph-86"><bold id="_bold-54">Disclaimer:</bold> The views and opinions expressed in this article are those of the author(s) and contributor(s) and do not necessarily reflect JICC's or editors' official policy or position. All liability for harm done to individuals or property as a result of any ideas, methods, instructions, or products mentioned in the content is expressly disclaimed.</p>
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