<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Future of Work and Digital Management Journal</JournalTitle>
      <Issn>3092-720X</Issn>
      <Volume></Volume>
      <Issue>In Press</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>01</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>The Effect of Artificial Intelligence Components on Improving Customer Experience: The Mediating Role of Social Media Marketing and the Moderating Role of Response Strategy</ArticleTitle>
    <VernacularTitle>The Effect of Artificial Intelligence Components on Improving Customer Experience: The Mediating Role of Social Media Marketing and the Moderating Role of Response Strategy</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>23</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <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>09</Month>
        <Day>04</Day>
      </PubDate>
    </History>
    <Abstract>&lt;table&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;
&lt;p&gt;This study aimed to investigate the effect of artificial intelligence components on customer experience improvement at Iran Khodro Company, considering social media marketing as a mediating variable and response strategy as a moderating variable. This applied, quantitative study employed a descriptive, cross-sectional, correlational design based on structural equation modeling. The statistical population comprised managers, experts, and specialists employed at Iran Khodro Company in Tehran who were directly involved in product design, production, marketing, sales, and related organizational processes. Using Cochran’s formula for an unlimited population, 384 participants were selected through stratified random sampling. Data were collected using a nine-item Artificial Intelligence Components Questionnaire, the 33-item Customer Experience Questionnaire, the nine-item Social Media Marketing Questionnaire, and the 11-item organizational strategy instrument adapted to measure response strategy. Face and content validity were evaluated by the academic supervisor and 13 organizational experts. Cronbach’s alpha coefficients for the constructs ranged from 0.76 to 0.85. Data were analyzed using SPSS and SmartPLS 3 through partial least squares structural equation modeling, bootstrapping, measurement-model assessment, and direct, indirect, and moderation analyses. Artificial intelligence components had a significant direct effect on customer experience improvement (t = 8.155, p = 0.001) and social media marketing (t = 468.240, p = 0.001). Social media marketing also significantly affected customer experience improvement (t = 7.857, p = 0.026). The indirect effect of artificial intelligence components on customer experience improvement through social media marketing was significant (t = 8.160, p = 0.049), confirming the mediating role of social media marketing. However, response strategy did not significantly moderate the relationship between artificial intelligence components and customer experience improvement because its interaction t-value was below the critical threshold (t = 1.292). Artificial intelligence can improve customer experience both directly and indirectly by strengthening social media marketing; however, response strategy did not significantly alter this relationship, suggesting that effective integration of intelligent technologies with customer-oriented social media activities is more influential than general strategic orientation.&lt;/p&gt;
&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Artificial intelligence</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">customer experience</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">social media marketing</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">response strategy</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">structural equation modeling</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">automotive industry</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://journalfwdmj.com/index.php/fwdmj/article/download/330/339</ArchiveCopySource>
  </Article>
</ArticleSet>
