<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Future of Work and Digital Management Journal</JournalTitle>
      <Issn></Issn>
      <Volume>3</Volume>
      <Issue>Serial Number 8</Issue>
      <PubDate PubStatus="epublish">
        <Year>2025</Year>
        <Month>06</Month>
        <Day>30</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>The Moderating Role of Artificial Intelligence Use in the Relationship between Environmental Attitude and Green Marketing</ArticleTitle>
    <VernacularTitle>The Moderating Role of Artificial Intelligence Use in the Relationship between Environmental Attitude and Green Marketing</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>10</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>02</Month>
        <Day>03</Day>
      </PubDate>
    </History>
    <Abstract>&lt;table&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;
&lt;p&gt;This study aims to investigate the moderating effect of artificial intelligence use on the relationship between environmental attitude and green marketing. This research adopts a quantitative correlational approach, employing structural equation modeling for analysis. A cross-sectional survey design was utilized to collect primary data from participants. Initially, 450 paper questionnaires were distributed, resulting in 400 completed responses, which reflects a participation rate of 89%. After discarding sixteen incomplete questionnaires, the final valid sample consisted of 384 responses. Data collection was conducted using standardized questionnaires, and the analysis involved Pearson correlation tests alongside structural equation modeling. The findings reveal that environmental attitudes significantly influence green marketing, evidenced by a T-value of 4.527. Furthermore, artificial intelligence use plays a crucial moderating role in the connection between environmental attitudes and green marketing, with a T-value of 6.957. The model fit analysis indicates that the research framework demonstrates a strong fit. The findings highlight the significant impact of artificial intelligence on business operations, ultimately offering actionable insights for implementing AI-enhanced green marketing strategies.&lt;/p&gt;
&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Business</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value"> green marketing</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">AI</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">attitude</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value"> strategy</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.journalfwdmj.com/index.php/fwdmj/article/download/59/56</ArchiveCopySource>
  </Article>
</ArticleSet>
