<?xml version="1.0" encoding="utf-8"?>
<journal>
<title>Journal of Tax Research</title>
<title_fa>پژوهشنامه مالیات</title_fa>
<short_title>J Tax Res</short_title>
<subject>Literature &amp; Humanities</subject>
<web_url>http://taxjournal.ir</web_url>
<journal_hbi_system_id>1</journal_hbi_system_id>
<journal_hbi_system_user>admin</journal_hbi_system_user>
<journal_id_issn>2251-6484</journal_id_issn>
<journal_id_issn_online>2717-1817</journal_id_issn_online>
<journal_id_pii>0</journal_id_pii>
<journal_id_doi>10.66224/taxjournal</journal_id_doi>
<journal_id_iranmedex></journal_id_iranmedex>
<journal_id_magiran></journal_id_magiran>
<journal_id_sid>0</journal_id_sid>
<journal_id_nlai>0</journal_id_nlai>
<journal_id_science>0</journal_id_science>
<language>fa</language>
<pubdate>
	<type>jalali</type>
	<year>1404</year>
	<month>12</month>
	<day>1</day>
</pubdate>
<pubdate>
	<type>gregorian</type>
	<year>2026</year>
	<month>3</month>
	<day>1</day>
</pubdate>
<volume>33</volume>
<number>68</number>
<publish_type>online</publish_type>
<publish_edition>1</publish_edition>
<article_type>fulltext</article_type>
<articleset>
	<article>


	<language>fa</language>
	<article_id_doi></article_id_doi>
	<title_fa>ترسیم نقشه‌ دانش مطالعات حوزه مالیات: تحلیل کتاب‌سنجی و دیداری‌سازی شبکه</title_fa>
	<title>Mapping the knowledge of tax studies: Bibliometric analysis and network visualization</title>
	<subject_fa>اقتصادی</subject_fa>
	<subject>Economic</subject>
	<content_type_fa>پژوهشي</content_type_fa>
	<content_type>Research</content_type>
	<abstract_fa>&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;line-height:90%&quot;&gt;&lt;span style=&quot;direction:rtl&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span b=&quot;&quot; lang=&quot;FA&quot; style=&quot;font-family:&quot; zar=&quot;&quot;&gt;بررسی ساختار مفهومی پژوهش&#8204;های مرتبط با مالیات در پایگاه استنادی پایش علم و فناروی جهان اسلام(&lt;/span&gt;&lt;span dir=&quot;LTR&quot; new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;ISC&lt;/span&gt;&lt;span b=&quot;&quot; lang=&quot;FA&quot; style=&quot;font-family:&quot; zar=&quot;&quot;&gt;) و شناسایی زیر حوزه&#8204;ها و ابعاد مهم مفهوم مالیات است. با تحلیل کمی مطالعات مرتبط با این حوزه، ساختار علمی مالیات از سال 1377 تا 1402 بررسی شد، خوشه&#8204;های اصلی پژوهشی، ابعاد مفهومی و موضوعات کلیدی هرخوشه شناسایی گردید. با بهره&#8204;گیری از فنون کتاب&#8204;سنجی و تحلیل هم&#8204;رخدادی واژگان، تعداد 684 مدرک از پایگاه(&lt;/span&gt;ISC&lt;span b=&quot;&quot; lang=&quot;FA&quot; style=&quot;font-family:&quot; zar=&quot;&quot;&gt;) استخراج گردید تا روند پژوهش&#8204;های مرتبط با مالیات در بازه زمانی فوق شناسایی شود. تحلیل خوشه&#8204;ای و نمودار راهبردی برای ترسیم ساختار مفهومی پژوهش&#8204;ها و شناسایی زیرحوزه&#8204;ها و روابط بین آن&#8204;ها دراین حوزه به&#8204;کار گرفته&#8204;شد. ساختار مفهومی این حوزه از ده خوشه به شرح زیر تشکیل شده است: مالیات شرکتی و چارچوب&#8204;های حسابداری(1)، مالیات&#8204;های زیست&#8204;محیطی و توسعه پایدار در ایران(2)، نظام مالیاتی اسلامی و اجرای حقوق مالیاتی(3)، مالیات&#8204;های کسب وکار و سیاست&#8204;های یارانه&#8204;ای(4)، فرار مالیاتی و حکمرانی شرکتی(5)، اصلاحات ساختاری مالیات مستقیم و غیرمستقیم(6)، عدالت مالیاتی و توزیع درآمد(7)، سیاست مالی و رشد اقتصادی کلان(8)، مدل&#8204;سازی مالیاتی و فناوری دیجیتال(9)، مالیات بر ارزش افزوده و تأثیرات کلان اقتصادی(10). علاوه&#8204;براین، با تحلیل محتوای مقالات منتشرشده درطول 26 سال، موضوعات نوظهور در حوزه مالیات شناسایی شدند. خوشه&#8204;ها، ابعاد و موضوعات کلیدی این حوزه را بازتاب می&#8204;دهند. تحلیل محتوای مقالات نشان داد که حوزه مالیات به&#8204;تدریج به&#8204;سمت موضوعات پیچیده&#8204;تر و بین&#8204;رشته&#8204;ای گرایش یافته است. یافته&#8204;های این پژوهش می&#8204;تواند به سیاست&#8204;گذاران و پژوهشگران در تدوین برنامه&#8204;های راهبردی و شناسایی شکاف&#8204;های پژوهشی کمک کرده و مسیر پژوهش&#8204;های آتی را روشن سازد&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&amp;nbsp;</abstract_fa>
	<abstract>&lt;span style=&quot;font-size:10pt&quot;&gt;&lt;span style=&quot;line-height:normal&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;&lt;span style=&quot;font-weight:bold&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;Abstract &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;line-height:normal&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;b&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;font-family:&quot;Times New Roman&quot;,serif&quot;&gt;&lt;/span&gt;&lt;/span&gt;&lt;/b&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;line-height:normal&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;b&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;Introduction&lt;/span&gt;&lt;/span&gt;&lt;/b&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;Studies related to the field of taxation have expanded in various dimensions such as tax policy, tax justice, tax evasion, economic effects of taxation, and more, over recent decades. This expansion highlights the high importance of this topic in academic research. However, the rapid growth in the volume of publications and the complexity of related topics have increasingly emphasized the need for a comprehensive map of existing knowledge in this field. Despite the growing importance of taxation studies, the dispersion of topics and the diversity of research methods in this area make it difficult for researchers to access a comprehensive and coherent perspective. Researchers and policymakers often face challenges such as determining research priorities, identifying research gaps, and understanding dominant trends in this field. In such circumstances, the absence of a comprehensive map of existing knowledge can lead to redundancy, resource wastage, and missed research opportunities. Therefore, this study, using bibliometric methods and network visualization tools, aims to map the knowledge structure of taxation studies. The main objective of this research is to identify the conceptual structure, research trends, and emerging topics of interest in this field. The results can help researchers and policymakers make more informed decisions in designing and implementing taxation studies by providing a better understanding of the current state of the field.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;b&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;Methods and Materials&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/b&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;This research was conducted using bibliometric analysis. Data were processed using VOSviewer, UCINet, and BibExcel software. The data for this study were extracted from the Islamic World Science Citation Center (ISC) using the keyword &amp;ldquo;taxation.&amp;rdquo; The extracted data over the past 26 years (from 1998 to 2023) included 684 documents, in which the authors used 2,665 keywords. After extracting the keywords, standardization and normalization of concepts were performed. Following standardization, 1,401 unique keywords remained. By selecting a threshold, a 154x154 matrix was created, with diagonal cell values set to zero. Cluster analysis using the K-means method in VOSviewer was employed to draw the map.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;b&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;Results and Discussion&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/b&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;img alt=&quot;&quot; id=&quot;Chart_x0020_293635734&quot; o:gfxdata=&quot;UEsDBBQABgAIAAAAIQB7D+HPTgEAAOYDAAATAAAAW0NvbnRlbnRfVHlwZXNdLnhtbKyTyU7DMBBA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&quot; src=&quot;data:image/png;base64,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&quot; style=&quot;width:274.8pt; height:144.6pt&quot; &gt; &lt;b&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:&quot;Times New Roman&quot;,serif&quot;&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/b&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;b&gt;&lt;span style=&quot;font-size:10.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;Figure 1.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/b&gt;&lt;span style=&quot;font-size:10.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt; Strategic diagram of the taxation field&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;According to the strategic diagram, the first quadrant includes tax evasion &amp; corporate governance(5), tax modeling &amp; digital economy(9), and VAT &amp; macroeconomic dynamics(10), which are the main topics of this period. These clusters are cohesive and central to the research field, focusing on a large part of the network. The second quadrant, in terms of importance and impact, is ranked lower than the clusters in the first quadrant. This section remains cohesive but has lost its centrality, representing a smaller, specialized part of the research field. No clusters were placed in this quadrant in this study. The clusters of corporate taxation and accounting frameworks(1), Business taxation &amp; subsidy policies(4), Direct &amp; indirect tax reforms(6), Tax equity &amp; income distribution(7), and Fiscal policy &amp; macroeconomic growth(8) represent emerging or declining sections of the network. The fourth quadrant includes clusters that have not yet matured but have the potential to become main sections. In this study, the clusters of environmental taxes and sustainable development in Iran(2), and the Islamic taxation systems &amp; legal enforcement(3), fall into this quadrant.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;b&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;Conclusion&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/b&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;The clusters in the first quadrant of the strategic diagram, including tax evasion &amp; corporate governance(5), Tax modeling &amp; digital economy(9), and VAT &amp; Macroeconomic dynamics(10), are recognized as the core and cohesive clusters of taxation research during the studied period. These clusters, with centrality and density above the average (29.7 and 0.555, respectively), indicate a high concentration of research on these topics and strong intra-cluster and inter-cluster connections. Cluster 10, with the highest centrality of 31.6 and a density of 1.511, plays a pivotal role in linking value-added tax with macroeconomic issues and serves as the hub of thematic interactions in the field. In this cluster, the highest weight is assigned to the concept of value-added tax. Cluster 9, with a density of 0.758, highlights the importance of digital technology and innovation in the tax system. This cluster focuses on emerging concepts such as electronic taxation and e-commerce, reflecting technological advancements in the tax system.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;Meanwhile, Cluster 5, with a centrality of 8.4, addresses the challenges of tax evasion and corporate governance as influential topics in the research network. The high weight of optimal tax rates reflects the search for a balance between government revenues and private sector incentives. Concepts such as earnings quality and earnings management may also reflect concerns about financial reporting transparency. The placement of these clusters in the first quadrant indicates their thematic maturity, widespread influence in the network structure, and the focus of research on fundamental and strategic issues in taxation.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;The second quadrant of the strategic diagram includes clusters with high density but lower centrality compared to the average. These clusters represent specialized and emerging topics that, despite internal cohesion, have not yet established extensive interactions with other research areas or gained a central position in the overall research structure. The absence of clusters in the second quadrant may indicate a focus in the research literature on strategic issues (first quadrant) and the dominance of emerging (third quadrant) and potential (fourth quadrant) topics, without independent, cohesive specialized areas emerging.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;The third quadrant of the strategic diagram includes clusters with lower density and centrality compared to the average, representing emerging or declining topics in the research network. The clusters in this quadrant include corporate taxation and accounting frameworks(1), Business taxation &amp; subsidy policies(4), Direct &amp; indirect tax reforms(6), Tax equity &amp; income distribution(7), and fiscal policy &amp; macroeconomic growth(8).&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:11pt&quot;&gt;&lt;span style=&quot;unicode-bidi:embed&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span style=&quot;font-family:Calibri,sans-serif&quot;&gt;&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;The fourth quadrant of the strategic diagram includes clusters with centrality above the average but density below it, identified as topics with growth potential. The clusters of environmental taxes and sustainable development in Iran(2), and the Islamic taxation systems &amp; legal enforcement(3), fall into this quadrant. These clusters have the potential to become main topics in the future, and their cohesion and influence in the network will strengthen with targeted research.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br&gt;
&lt;span style=&quot;font-size:12.0pt&quot;&gt;&lt;span style=&quot;line-height:107%&quot;&gt;&lt;span new=&quot;&quot; roman=&quot;&quot; style=&quot;font-family:&quot; times=&quot;&quot;&gt;Cluster analysis shows that value-added tax, as the core of research in this field, holds a unique position with a focus on macroeconomic impacts such as inflation and GDP. Overall, the dominant focus of research has been on strategic and technology-driven issues, while emerging clusters are still in the early stages of maturity and require more attention. These clusters show that taxation is a topic that has expanded from basic concepts to specialized applications in various fields.&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;</abstract>
	<keyword_fa>مالیات, عدالت مالیاتی, نظام مالیاتی, مالیات بر ارزش افزوده, پایگاه آی‌اس‌سی.</keyword_fa>
	<keyword>Tax, Tax justice, Tax system, Value-added tax, ISC database.</keyword>
	<start_page>7</start_page>
	<end_page>38</end_page>
	<web_url>http://taxjournal.ir/browse.php?a_code=A-10-2649-1&amp;slc_lang=fa&amp;sid=1</web_url>


<author_list>
	<author>
	<first_name>saleh</first_name>
	<middle_name></middle_name>
	<last_name>rahimi</last_name>
	<suffix></suffix>
	<first_name_fa>صالح</first_name_fa>
	<middle_name_fa></middle_name_fa>
	<last_name_fa>رحیمی</last_name_fa>
	<suffix_fa></suffix_fa>
	<email>s.rahimi@razi.ac.ir</email>
	<code>100319475328460012415</code>
	<orcid>100319475328460012415</orcid>
	<coreauthor>Yes
</coreauthor>
	<affiliation>Razi university</affiliation>
	<affiliation_fa>دانشگاه رازی، کرمانشاه، ایران.</affiliation_fa>
	 </author>


	<author>
	<first_name>Faramarz</first_name>
	<middle_name></middle_name>
	<last_name>Soheili</last_name>
	<suffix></suffix>
	<first_name_fa>فرامرز</first_name_fa>
	<middle_name_fa></middle_name_fa>
	<last_name_fa>سهیلی</last_name_fa>
	<suffix_fa></suffix_fa>
	<email>f_soheili@pnu.ac.ir</email>
	<code>100319475328460012416</code>
	<orcid>100319475328460012416</orcid>
	<coreauthor>No</coreauthor>
	<affiliation>Payame Noor University, Tehran, Iran.</affiliation>
	<affiliation_fa>دانشگاه پیام نور، تهران، ایران.</affiliation_fa>
	 </author>


</author_list>


	</article>
</articleset>
</journal>
