<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>The Research Department of Economics and Management of Tadbir Nikan</PublisherName>
      <JournalTitle>Business, Marketing, and Finance Open</JournalTitle>
      <Issn></Issn>
      <Volume></Volume>
      <Issue>In Press</Issue>
      <PubDate PubStatus="epublish">
        <Year>2025</Year>
        <Month>12</Month>
        <Day>21</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Designing a Model of the Relationship Between Advertising Content Risks in Social Networks</ArticleTitle>
    <VernacularTitle>Designing a Model of the Relationship Between Advertising Content Risks in Social Networks</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>2025</Year>
        <Month>08</Month>
        <Day>04</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;With the rapid growth of social media, purchasing risks have also increased, and these concerns influence consumer purchasing behavior. In this regard, the present study identifies and models the risks associated with advertising content in social networks using both qualitative and quantitative approaches. This study was conducted for reputable brands in Iran. The research population consisted of experts, including marketing researchers and marketing managers of brands active in social media marketing, who were selected through purposive non-probability sampling using the snowball technique, resulting in a total of 25 participants. Through a semi-structured interview process with experts, qualitative data were collected and coded using content analysis. The results indicated that the model includes “functional risk,” “content risk,” “operational risk,” “security and privacy risk,” “product risk,” “technology risk,” “environmental risk,” and “behavioral purchase intention.” Subsequently, quantitative data were collected through a pairwise comparison questionnaire and analyzed using the interpretive–structural modeling (ISM) method to develop the model. The findings of the interpretive–structural modeling showed that “environmental risk,” “technology risk,” “product risk,” and “security and privacy risk” exert the highest influence, while “functional risk” has the lowest influence on behavioral intention in social networks. Therefore, managers must manage and control the influential risks to encourage customers to make purchases.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">risks</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">advertising</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value"> social networks</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.bmfopen.com/index.php/bmfopen/article/download/353/273</ArchiveCopySource>
  </Article>
</ArticleSet>
