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  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Digital Transformation and Administration Innovation</JournalTitle>
      <Issn></Issn>
      <Volume></Volume>
      <Issue>In Press</Issue>
      <PubDate PubStatus="epublish">
        <Year>2027</Year>
        <Month>01</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Evaluating National Media Convergence Strategies on Social Networks: The Role of Technology and Legal Challenges</ArticleTitle>
    <VernacularTitle>Evaluating National Media Convergence Strategies on Social Networks: The Role of Technology and Legal Challenges</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>15</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2026</Year>
        <Month>04</Month>
        <Day>20</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;The present study aimed to design and explain a model of national media convergence strategies on social networks, with a particular focus on technological and legal variables. In terms of purpose, this study was applied research, and in terms of implementation method, it employed a mixed-methods qualitative–quantitative design. In the qualitative phase, a grounded theory approach was adopted. Accordingly, in-depth interviews were conducted through purposive sampling with 18 media experts and managers, communication scholars, and cyberspace specialists. In the quantitative phase, a structured questionnaire was distributed among 117 experts and professionals in the fields of media and cyberspace to test the model derived from the qualitative findings. The data were analyzed using structural equation modeling (SEM) and SmartPLS software. The theoretical framework of the study was based on an integration of media convergence theory, network society theory, platform governance theory, and public sphere theory. The qualitative findings indicated that media convergence strategies are influenced by causal conditions, including emerging artificial intelligence technologies and platform infrastructures; contextual conditions, including legal and governance policies; and intervening conditions, including professional ethics and copyright regulations. The resulting outcome is the formation of an interactive news ecosystem. In the quantitative phase, hypothesis testing confirmed the significant effect of technological variables and platform governance indicators on convergence strategies (β = 0.881). Nevertheless, although filtering emerged as an important intervening variable in the qualitative analysis of the interviews, it did not exert a direct and statistically significant effect on media convergence strategies in the quantitative phase. Professional ethics challenges and legal monitoring mechanisms were also identified as key components affecting the formation of an interactive newsroom. To achieve successful convergence on social networks, national media organizations need to reconstruct their newsroom structures on the basis of artificial intelligence and develop dynamic legal and ethical frameworks. Such measures would enable them to move beyond technical and platform-related constraints and re-establish their authority as a trusted media reference.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">media convergence</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">national media</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">social networks</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">artificial intelligence</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">media law</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">structural equation modeling</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.journaldtai.com/index.php/jdtai/article/download/300/272</ArchiveCopySource>
  </Article>
</ArticleSet>
