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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Resource Management and Decision Engineering</JournalTitle>
      <Issn></Issn>
      <Volume>4</Volume>
      <Issue>Serial Number 12</Issue>
      <PubDate PubStatus="epublish">
        <Year>2025</Year>
        <Month>09</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Identification and Prioritization of Factors Affecting Digital Financial Innovation Using the Structural Equation Modeling Approach</ArticleTitle>
    <VernacularTitle>Identification and Prioritization of Factors Affecting Digital Financial Innovation Using the Structural Equation Modeling Approach</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>15</LastPage>
    <ELocationID EIdType="doi">10.61838/kman.jrmde.129</ELocationID>
    <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>04</Month>
        <Day>25</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;The purpose of this study was to identify and prioritize the factors influencing digital financial innovation using the structural equation modeling approach. The research employed both qualitative and quantitative methods. Data were collected through interviews. The statistical population consisted of professors and experts in the field of accounting, with no defined limit; however, 18 participants were selected until theoretical saturation was achieved. The sampling method was snowball sampling, whereby interviewees were asked to introduce knowledgeable individuals relevant to the research topic for subsequent interviews. The primary data were collected through interviews. In accordance with the methodological process, data analysis was carried out in three stages: open coding, axial coding, and selective coding. Initially, from the large volume of primary data, codes related to the research topic were identified. Subsequently, using the method of constant comparison, concepts were extracted from several codes, and in the same manner, other codes were transformed into concepts. Ultimately, 93 concepts were derived. In the next stage, several concepts were grouped into categories, resulting in 18 categories for this study.The findings revealed that three categories emerged as the core categories: acceptance capability, information and communication technology (ICT) infrastructure, and the level of digital literacy and awareness. The remaining categories were presented in the proposed model across five groups: causal conditions (3 categories), context or background (3 categories), intervening conditions (3 categories), strategies (3 categories), and consequences (3 categories). Subsequently, based on the indicators, components, concepts, and categories of the proposed model, a 93-item questionnaire was developed. Using the data collected, the relationships of the proposed model were examined, and the results ultimately indicated the significance of the relationships and components of the presented model.&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Model design</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">digital financial innovation</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">grounded theory</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://journalrmde.com/index.php/jrmde/article/download/129/119</ArchiveCopySource>
  </Article>
</ArticleSet>
