<?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>3092-6238</Issn>
      <Volume></Volume>
      <Issue>In Press</Issue>
      <PubDate PubStatus="epublish">
        <Year>2027</Year>
        <Month>05</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Developing a Model for Big Data–Based Tax Collection and Management in an Integrated Tax System and Its Impact on Corporate Investment Inefficiency</ArticleTitle>
    <VernacularTitle>Developing a Model for Big Data–Based Tax Collection and Management in an Integrated Tax System and Its Impact on Corporate Investment Inefficiency</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>16</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>03</Month>
        <Day>02</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;A big data–based tax collection and management system relies on large-scale datasets shared among tax authorities, companies, and government agencies. By drawing on new-generation information technologies, such as big data analytics and cloud computing, this system improves data analysis methods through the comparison, analysis, cross-checking, and examination of tax-related information, thereby enabling the effective prediction of tax risks and the provision of multidimensional, in-depth insights. The present study aimed to develop a model for big data–based tax collection and management within an integrated tax system and to examine its impact on corporate investment inefficiency. The study adopted a qualitative approach and used semi-structured interviews with experts in taxation and big data. Data were analyzed using MAXQDA software based on the grounded theory approach proposed by Strauss and Corbin. Following open, axial, and selective coding, the findings were organized into principal categories and themes. The results indicated that data quality and standardization are among the most important prerequisites for establishing a data-driven taxation system. The absence of uniform standards and poor data quality reduce analytical accuracy and disrupt financial decision-making. Institutional integration and the development of effective mechanisms for interorganizational information exchange were also identified as key factors in reducing tax evasion and enhancing transparency. Information transparency and data security were recognized as essential components for increasing economic actors’ trust and improving the efficiency of the taxation system. Furthermore, strengthening corporate governance, improving internal control systems, and reforming legal frameworks were identified as supporting requirements for establishing a big data–based taxation system.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Big data taxation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">integrated taxation system</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">investment inefficiency</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://bmfopen.com/index.php/bmfopen/article/download/533/420</ArchiveCopySource>
  </Article>
</ArticleSet>
