<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName>KMAN Publication Inc. (KMANPUB)</PublisherName>
      <JournalTitle>International Journal of Innovation Management and Organizational Behavior (IJIMOB)</JournalTitle>
      <Issn>3041-8992</Issn>
      <Volume>6</Volume>
      <Issue>Serial Number 24</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>01</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Explainable AI Models of Organizational Creativity: Influences of Inclusive Leadership and Team Psychological Safety</ArticleTitle>
    <VernacularTitle>Explainable AI Models of Organizational Creativity: Influences of Inclusive Leadership and Team Psychological Safety</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>11</LastPage>
    <ELocationID EIdType="doi">10.61838/kman.ijimob.5270</ELocationID>
    <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>07</Month>
        <Day>20</Day>
      </PubDate>
    </History>
    <Abstract>&lt;table&gt;&#13;
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&lt;tr&gt;&#13;
&lt;td&gt;&#13;
&lt;p&gt;&lt;strong&gt;Objective:&lt;/strong&gt; The primary objective of this study was to utilize an explainable artificial intelligence (XAI) machine learning framework to predict organizational creativity and mathematically elucidate the complex, non-linear interactions between inclusive leadership and team psychological safety.&lt;/p&gt;&#13;
&lt;p&gt;&lt;strong&gt;Methods and Materials:&lt;/strong&gt; A quantitative, cross-sectional predictive design was employed, utilizing a multi-stage stratified random sample of employees nested within work teams across the IT, finance, and advanced manufacturing sectors in Indonesia. Data were collected via structured digital questionnaires measuring inclusive leadership (Cronbach’s ), team psychological safety (Cronbach’s ), and organizational creativity (Cronbach’s ) using -point Likert scales. Data analysis moved beyond traditional linear models by employing the Extreme Gradient Boosting (XGBoost) algorithm for prediction, coupled with SHapley Additive exPlanations (SHAP) to quantify global and local feature importance and map non-linear synergistic effects.&lt;/p&gt;&#13;
&lt;p&gt;&lt;strong&gt;Findings:&lt;/strong&gt; The XGBoost model demonstrated robust predictive accuracy ( , ), significantly outperforming traditional multiple linear regression ( ). SHAP analysis identified team psychological safety as the paramount global predictor of creativity (Mean ), followed by inclusive leadership (Mean ). Crucially, SHAP dependence plots revealed a distinct non-linear threshold: inclusive leadership only yielded a positive, synergistic impact on organizational creativity when team psychological safety scores exceeded approximately on a -point scale, with veteran employees showing the highest sensitivity to these dynamics.&lt;/p&gt;&#13;
&lt;p&gt;&lt;strong&gt;Conclusion:&lt;/strong&gt; Cultivating a baseline threshold of team psychological safety is a mandatory structural prerequisite that must be mathematically satisfied before inclusive leadership behaviors can effectively catalyze organizational creativity.&lt;/p&gt;&#13;
&lt;/td&gt;&#13;
&lt;/tr&gt;&#13;
&lt;/tbody&gt;&#13;
&lt;/table&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Organizational Creativity</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Inclusive Leadership</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Team Psychological Safety</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://journals.kmanpub.com/index.php/ijimob/article/download/5270/9578</ArchiveCopySource>
  </Article>
</ArticleSet>
