<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Management and Business Solutions</JournalTitle>
      <Issn>3092-7226</Issn>
      <Volume>4</Volume>
      <Issue>Serial Number 20</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>07</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Modeling the Factors Affecting the Position of Artificial Intelligence in Tax Administration</ArticleTitle>
    <VernacularTitle>Modeling the Factors Affecting the Position of Artificial Intelligence in Tax Administration</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>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2025</Year>
        <Month>12</Month>
        <Day>28</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;The present study was conducted with the aim of modeling the factors affecting the position of artificial intelligence in tax administration. This research is an applied, survey-based, interpretive-exploratory, and quantitative study. The research participants consisted of &lt;/p&gt;
&lt;p&gt;166experts in the fields of artificial intelligence and taxation, who were selected through cluster random sampling using G*Power software. The data collection instrument was a researcher-developed questionnaire derived from qualitative findings. For data analysis, in addition to descriptive statistics, Structural Equation Modeling (SEM) was employed using SmartPLS software (version 3.1.1) at a significance level of 0.05. The results indicated that the research model demonstrates an appropriate fit, and three variables—political factors (β=-0.190), inhibiting factors (β=-0.112), and infrastructural challenges (β=-0.196)—have a negative and significant relationship, while five variables—economic factors (β=0.149), socio-cultural factors (β=0.238), educational factors (β=0.171), legal factors (β=0.122), and managerial factors (β=0.749)—have a positive and significant relationship with the position of artificial intelligence in the future of Iran’s tax administration. Therefore, to maximize the utilization of artificial intelligence capabilities in taxation, it is necessary to seriously consider and manage the identified barriers and challenges while simultaneously strengthening the positive factors.&lt;/p&gt;
&lt;p&gt; &lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Artificial Intelligence</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Taxation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Drivers</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Barriers</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.journalmbs.com/index.php/jmbs/article/download/254/231</ArchiveCopySource>
  </Article>
</ArticleSet>
