University of QomSciences and Techniques of Information Management2476-665812120260321Design and evaluation of e-learning knowledge management model for Iran's higher education systemDesign and evaluation of e-learning knowledge management model for Iran's higher education system741315610.22091/stim.2024.11006.2125FAElahehGholipour HajmahmoodPh.D. Student, Department of Knowledge and Information Science, Science and Research Branch,
Islamic Azad University, Tehran, Iran0000-0000-0000-0000MohammadHassanzadehProfessor, Tarbiat Modares University & President, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran.0000-0002-6175-0855NadjlaHaririProfessor, Department of Knowledge and Information Science, Science and Research Branch, Islamic Azad University, Tehran, Iran0000-0003-2320-7023DariushMatlabiAssociate Professor, Department of Educational Sciences, Yadgar Imam Khomeini Shahrari Branch, Islamic Azad University, Tehran, Iran0000-0002-2503-6558Journal Article19700101<strong>Objective: </strong>This study aims to identify the processes and mechanisms of e-learning knowledge management within the Iranian higher education system and to evaluate and validate a corresponding conceptual model.<br />Methodology: This research is applied in nature and employs a descriptive-survey design. The primary dimensions of the model were initially derived through a qualitative approach using Grounded Theory and semi-structured expert interviews. In the quantitative phase, the statistical population comprised 380 experts and managers in the fields of knowledge management and e-learning, selected via simple random sampling. Data were collected using a questionnaire, with validity and reliability confirmed via factor loadings, Average Variance Extracted (AVE), Cronbach's alpha, and Composite Reliability (CR) using SmartPLS software. Structural Equation Modeling (SEM), the one-sample t-test, and the Friedman test were employed to analyze variable relationships and validate the model.<br /><strong>Findings: </strong>Structural Equation Modeling revealed that all relationships between variables and components were statistically significant at a 95% confidence level. The final validated model comprises seven core processes—knowledge selection, acquisition, creation, organization, sharing, assessment/audit, and application—supported by 21 underlying mechanisms. Friedman test results indicate that "Knowledge Creation" (mean rank: 4.58) is the most influential factor, followed by "Knowledge Organization" (mean rank: 4.54) and "Knowledge Acquisition" (mean rank: 4.53). Furthermore, among the 21 mechanisms, knowledge exchange, fusion, and visualization were ranked as the most significant.<br /><strong>Conclusion: </strong>Implementing knowledge management within e-learning platforms serves as a strategic tool for higher education institutions—inherently knowledge-based organizations—to sustain a competitive advantage. This study demonstrates that the knowledge lifecycle in virtual universities depends heavily on collaborative creation, systematic digital content organization, and dynamic exchange. The validated model provides policy-makers and administrators in higher education with a practical framework to enhance the quality, productivity, and overall effectiveness of virtual learning environments<strong>Objective: </strong>This study aims to identify the processes and mechanisms of e-learning knowledge management within the Iranian higher education system and to evaluate and validate a corresponding conceptual model.<br />Methodology: This research is applied in nature and employs a descriptive-survey design. The primary dimensions of the model were initially derived through a qualitative approach using Grounded Theory and semi-structured expert interviews. In the quantitative phase, the statistical population comprised 380 experts and managers in the fields of knowledge management and e-learning, selected via simple random sampling. Data were collected using a questionnaire, with validity and reliability confirmed via factor loadings, Average Variance Extracted (AVE), Cronbach's alpha, and Composite Reliability (CR) using SmartPLS software. Structural Equation Modeling (SEM), the one-sample t-test, and the Friedman test were employed to analyze variable relationships and validate the model.<br /><strong>Findings: </strong>Structural Equation Modeling revealed that all relationships between variables and components were statistically significant at a 95% confidence level. The final validated model comprises seven core processes—knowledge selection, acquisition, creation, organization, sharing, assessment/audit, and application—supported by 21 underlying mechanisms. Friedman test results indicate that "Knowledge Creation" (mean rank: 4.58) is the most influential factor, followed by "Knowledge Organization" (mean rank: 4.54) and "Knowledge Acquisition" (mean rank: 4.53). Furthermore, among the 21 mechanisms, knowledge exchange, fusion, and visualization were ranked as the most significant.<br /><strong>Conclusion: </strong>Implementing knowledge management within e-learning platforms serves as a strategic tool for higher education institutions—inherently knowledge-based organizations—to sustain a competitive advantage. This study demonstrates that the knowledge lifecycle in virtual universities depends heavily on collaborative creation, systematic digital content organization, and dynamic exchange. The validated model provides policy-makers and administrators in higher education with a practical framework to enhance the quality, productivity, and overall effectiveness of virtual learning environmentshttps://stim.qom.ac.ir/article_3156_e958d7b7d5de2d6bea9956322f6c7220.pdfUniversity of QomSciences and Techniques of Information Management2476-665812120260321Performance evaluation of Knowledge enterprises on the three-pronged model of knowledge management and organizational innovationPerformance evaluation of Knowledge enterprises on the three-pronged model of knowledge management and organizational innovation4260315710.22091/stim.2024.11242.2153FAGhasemAzadi AhmadabadiInformation Science and Knowledge Studies, Policy Evaluation and Science, Technology and Innovation Monitoring ,Institute for Science and Technology Policy Research.Tehran, Iran0000-0002-3610-2573MaryamBehifarPHD student of Technology Management, Department of Technology Management, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran0000-0003-0180-4329MitraAmiriGraduated in Educational Management, Department of Higher Education Management, Faculty of Management and Economics, Science and Research Unit, Islamic Azad University, Tehran, Iran.0009-0006-8767-6485Journal Article20240827<strong>Purpose: </strong>The rapid pace of organizational transformation is increasing the level of ambiguity, uncertainty, and complexity within modern enterprises, fundamentally altering the core competencies required for effectiveness. Therefore, this research aims to analyze the roles of knowledge management and organizational innovation in the performance of knowledge-based enterprises.<br /><strong>Methodology: </strong>This study is applied in nature and employs a descriptive, survey-based methodology. Data were collected using a questionnaire distributed in 2023 among a statistical population comprising senior managers, middle managers, and employees of knowledge-based enterprises in Tehran province (n=280). To ensure validity, the survey underwent face validity testing via a pilot study of 30 participants, and content validity was confirmed through the expert judgment of ten specialists. Reliability was established using Cronbach's alpha. A structural equation modeling (SEM) approach was employed to analyze the relationships between independent and dependent variables and to validate the conceptual research model.<br /><strong>Findings: </strong>The results indicate that knowledge management has a significant, direct, and positive effect on both organizational innovation and organizational performance. Furthermore, organizational innovation exerts a significant, direct, and positive influence on organizational performance.<br /><strong>Conclusion: </strong>It is imperative for knowledge-based enterprises to implement robust knowledge management strategies to address existing knowledge gaps. Such strategies facilitate greater productivity from human capital, foster efficient employee learning, and enhance satisfaction among internal and external stakeholders. Moreover, effective knowledge management helps prevent the repetition of past errors, promotes creativity and innovation, and strengthens the enterprise's overall competitive positio<strong>Purpose: </strong>The rapid pace of organizational transformation is increasing the level of ambiguity, uncertainty, and complexity within modern enterprises, fundamentally altering the core competencies required for effectiveness. Therefore, this research aims to analyze the roles of knowledge management and organizational innovation in the performance of knowledge-based enterprises.<br /><strong>Methodology: </strong>This study is applied in nature and employs a descriptive, survey-based methodology. Data were collected using a questionnaire distributed in 2023 among a statistical population comprising senior managers, middle managers, and employees of knowledge-based enterprises in Tehran province (n=280). To ensure validity, the survey underwent face validity testing via a pilot study of 30 participants, and content validity was confirmed through the expert judgment of ten specialists. Reliability was established using Cronbach's alpha. A structural equation modeling (SEM) approach was employed to analyze the relationships between independent and dependent variables and to validate the conceptual research model.<br /><strong>Findings: </strong>The results indicate that knowledge management has a significant, direct, and positive effect on both organizational innovation and organizational performance. Furthermore, organizational innovation exerts a significant, direct, and positive influence on organizational performance.<br /><strong>Conclusion: </strong>It is imperative for knowledge-based enterprises to implement robust knowledge management strategies to address existing knowledge gaps. Such strategies facilitate greater productivity from human capital, foster efficient employee learning, and enhance satisfaction among internal and external stakeholders. Moreover, effective knowledge management helps prevent the repetition of past errors, promotes creativity and innovation, and strengthens the enterprise's overall competitive positiohttps://stim.qom.ac.ir/article_3157_d7280c6feca0e1188e2ce64140681734.pdfUniversity of QomSciences and Techniques of Information Management2476-665812120260321Digital transformation in the Central Bank of the Islamic Republic of Iran (with a focus on identifying knowledge areas and indicators)Digital transformation in the Central Bank of the Islamic Republic of Iran (with a focus on identifying knowledge areas and indicators)6183333710.22091/stim.2025.11483.2171FASamiraTahmasebi AshtianiPhD student in Information and Knowledge Management, Faculty of Management and Economics, Tarbiat Modares University,Tehran, Iran0009-0007-5156-5203MohammadHassanzadehDepartment of Management & Economics, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran0000-0002-6175-0855AtefeSharifAssistant Professor, Department of Information Science and Knowledge, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran0000-0003-4761-6761MostafaAminiPostdoctoral Research in Data-Driven Digital Transformation, Faculty of Management and Economics, Tarbiat Modares University,Tehran, Iranhttps://orcid.org/00Journal Article20241015<strong>Objective:</strong> Rapid advancements in emerging technologies have triggered profound societal changes. Consequently, banking and credit institutions, alongside the Central Bank, have undergone significant technological transformations. Leveraging these technologies offers a solution to existing challenges and creates opportunities to improve services within the Central Bank. This article aims to identify the knowledge areas and indicators of digital transformation within the Central Bank of the Islamic Republic of Iran to establish a systematic framework for digital transformation in the Central Bank and other financial institutions.<br /><strong>Method:</strong> This study employs a two-fold approach: a systematic literature review to identify indicators of digital transformation, and the Delphi method to refine these findings through expert consultation. The identified components and indicators were presented in a questionnaire to a panel of 24 experts—comprising managers, researchers, and faculty members—who possessed both relevant academic backgrounds (at least a bachelor’s degree) and significant professional experience (minimum five years). Ultimately, 15 subject matter experts, selected via purposive sampling, refined and validated the initial indicators. During this process, the relative importance of each element was determined using a 5-point Likert scale. A consensus was reached among the Delphi panel, and the Kendall correlation coefficient was calculated to measure the level of inter-rater agreement and determine the termination point of the survey rounds.<br /><strong>Findings:</strong> Initial qualitative analysis identified 16 components and 101 indicators. Following the Delphi process, these were refined into 14 main components and 51 indicators. The results indicate that the most significant factors for the Central Bank are: intelligentizing data collection and statistical analysis (mean: 4.93), management of analytical tools for opportunity identification (mean: 4.66), strategic IT architecture management (mean: 4.66), and intelligentizing business processes to prevent fraud and illicit transactions (mean: 4.60).<br /><strong>Conclusion:</strong> Through the synthesis of the study's findings, a conceptual model for the digital transformation of the Central Bank was developed. This model can serve as a foundation for future research in designing banking transformation frameworks. Furthermore, identifying these core knowledge areas acts as an enabler for the Central Bank, fostering greater dynamism, agility, flexibility, and innovation.<br />Contribution to Knowledge: This research underscores the importance of the Central Bank’s commitment to adopting transformative technologies, which is critical for enhancing the overall financial performance of banking and credit institutions.<strong>Objective:</strong> Rapid advancements in emerging technologies have triggered profound societal changes. Consequently, banking and credit institutions, alongside the Central Bank, have undergone significant technological transformations. Leveraging these technologies offers a solution to existing challenges and creates opportunities to improve services within the Central Bank. This article aims to identify the knowledge areas and indicators of digital transformation within the Central Bank of the Islamic Republic of Iran to establish a systematic framework for digital transformation in the Central Bank and other financial institutions.<br /><strong>Method:</strong> This study employs a two-fold approach: a systematic literature review to identify indicators of digital transformation, and the Delphi method to refine these findings through expert consultation. The identified components and indicators were presented in a questionnaire to a panel of 24 experts—comprising managers, researchers, and faculty members—who possessed both relevant academic backgrounds (at least a bachelor’s degree) and significant professional experience (minimum five years). Ultimately, 15 subject matter experts, selected via purposive sampling, refined and validated the initial indicators. During this process, the relative importance of each element was determined using a 5-point Likert scale. A consensus was reached among the Delphi panel, and the Kendall correlation coefficient was calculated to measure the level of inter-rater agreement and determine the termination point of the survey rounds.<br /><strong>Findings:</strong> Initial qualitative analysis identified 16 components and 101 indicators. Following the Delphi process, these were refined into 14 main components and 51 indicators. The results indicate that the most significant factors for the Central Bank are: intelligentizing data collection and statistical analysis (mean: 4.93), management of analytical tools for opportunity identification (mean: 4.66), strategic IT architecture management (mean: 4.66), and intelligentizing business processes to prevent fraud and illicit transactions (mean: 4.60).<br /><strong>Conclusion:</strong> Through the synthesis of the study's findings, a conceptual model for the digital transformation of the Central Bank was developed. This model can serve as a foundation for future research in designing banking transformation frameworks. Furthermore, identifying these core knowledge areas acts as an enabler for the Central Bank, fostering greater dynamism, agility, flexibility, and innovation.<br />Contribution to Knowledge: This research underscores the importance of the Central Bank’s commitment to adopting transformative technologies, which is critical for enhancing the overall financial performance of banking and credit institutions.https://stim.qom.ac.ir/article_3337_2365328a1130e1df5a9d7caade866c3d.pdfUniversity of QomSciences and Techniques of Information Management2476-665812120260321Evaluating the quality of data in articles by faculty members of the Faculty of Pharmacy, Kerman University of Medical Sciences, based on the DQA modelEvaluating the quality of data in articles by faculty members of the Faculty of Pharmacy, Kerman University of Medical Sciences, based on the DQA model8493364510.22091/stim.2025.11962.2199FAAdelSoleimani NezhadDepartment of Knowledge and
Information Science, Shahid Bahonar University of Kerman, Kerman, Iran.0000-0002-9757-6836FariborzDourodiScientometrics and Information Analysis Research Institute, Information Science and Technology Research Institute, Iran0000-0003-0386-5301MarziyehHossian ZadehDepartment of Information Science and Knowledge, Shahid Bahonar University of Kerman. Kerman. Iran0000-0002-9757-6836Journal Article20241223<strong>Objective:</strong> The main purpose of this study is to evaluate the data quality of research articles authored by faculty members of the Faculty of Pharmacy at Kerman University of Medical Sciences, based on the Data Quality Assessment (DQA) model.
<strong>Methodology:</strong> This applied research employs a descriptive-evaluative survey design. The statistical population consists of 340 articles published by faculty members of the Faculty of Pharmacy at Kerman University of Medical Sciences between 2018 and 2022. A sample of 181 articles was randomly selected from the PubMed database using the Morgan table. Data extraction was conducted by first identifying relevant keywords, then applying key filters to extract data based on the research components of the DQA model: validity, reliability, timeliness/up-to-dateness, accuracy, integrity, and consistency. Data quality was evaluated using the Bazargan table scale and a one-sample t-test.
<strong>Findings:</strong> The findings indicate that, among the dimensions of data quality, "data validity" most frequently (13) received a "completely undesirable" rating, while "data integrity" least frequently (7) received a "completely desirable" rating. Conversely, "data up-to-dateness" (42) and "data consistency" (40) were identified as having the highest level of quality, categorized as "completely desirable." The results of the primary hypothesis test, with a mean data quality score of $\mu_0 = 3.83$, indicate that the articles are generally in a desirable state.
<strong>Conclusion:</strong> The study demonstrates that the data quality of the examined articles is generally desirable according to the DQA model. To further enhance the quality of scientific papers, it is recommended that researchers prioritize the collection of data from valid, reliable, and up-to-date sources, employ advanced statistical methods, and ensure the rigorous documentation of all data collection and processing stages. Additionally, efforts such as removing invalid data, correcting missing values, mitigating human errors, and utilizing machine learning algorithms for predictive analysis are essential for continuous improvement in research quality<strong>Objective:</strong> The main purpose of this study is to evaluate the data quality of research articles authored by faculty members of the Faculty of Pharmacy at Kerman University of Medical Sciences, based on the Data Quality Assessment (DQA) model.
<strong>Methodology:</strong> This applied research employs a descriptive-evaluative survey design. The statistical population consists of 340 articles published by faculty members of the Faculty of Pharmacy at Kerman University of Medical Sciences between 2018 and 2022. A sample of 181 articles was randomly selected from the PubMed database using the Morgan table. Data extraction was conducted by first identifying relevant keywords, then applying key filters to extract data based on the research components of the DQA model: validity, reliability, timeliness/up-to-dateness, accuracy, integrity, and consistency. Data quality was evaluated using the Bazargan table scale and a one-sample t-test.
<strong>Findings:</strong> The findings indicate that, among the dimensions of data quality, "data validity" most frequently (13) received a "completely undesirable" rating, while "data integrity" least frequently (7) received a "completely desirable" rating. Conversely, "data up-to-dateness" (42) and "data consistency" (40) were identified as having the highest level of quality, categorized as "completely desirable." The results of the primary hypothesis test, with a mean data quality score of $\mu_0 = 3.83$, indicate that the articles are generally in a desirable state.
<strong>Conclusion:</strong> The study demonstrates that the data quality of the examined articles is generally desirable according to the DQA model. To further enhance the quality of scientific papers, it is recommended that researchers prioritize the collection of data from valid, reliable, and up-to-date sources, employ advanced statistical methods, and ensure the rigorous documentation of all data collection and processing stages. Additionally, efforts such as removing invalid data, correcting missing values, mitigating human errors, and utilizing machine learning algorithms for predictive analysis are essential for continuous improvement in research qualityhttps://stim.qom.ac.ir/article_3645_60deaa347425604d97463fecac8548d9.pdfUniversity of QomSciences and Techniques of Information Management2476-665812120260321Analysis the effect of Self-Citation on the fluctuations of Impact Factor and Journal Quartiles in the PJCR.ISC DatabaseAnalysis the effect of Self-Citation on the fluctuations of Impact Factor and Journal Quartiles in the PJCR.ISC Database94110361810.22091/stim.2025.12394.2216FANarjesVaraResource Development and Evaluation Department, Citation and Monitoring Institute of Science and technology of Islamic World (ISC), Shiraz, Iran0000-0001-9324-7480TahereJowkarInformation Science and Knowledge Department, Shiraz University, Shiraz, Iran0000-0001-9644-5554Journal Article20250219<strong>Objective: </strong>Citation analysis is a critical indicator for evaluating the performance of journals and researchers, as it reflects the dynamic nature of scientific communication and influences the trajectory of scientific advancement. Among the various challenges in evaluating scientific outputs, "unconventional self-citation"—including author, journal, linguistic, organizational, and national self-citation—remains a major concern. This study specifically examines the impact of unconventional journal self-citation (defined as citations of articles within the same journal) on the ranking of journals indexed in JCR.ISC.<br /><strong>Methodology: </strong>This descriptive-analytical study employed a survey-based approach. The research population consisted of 1,713 journals across six broad subject areas (Social Sciences, Basic and Engineering Sciences, Medical and Health Sciences, Life Sciences, Arts and Humanities, and Multidisciplinary) indexed in the JCR.ISC system based on the 2023 assessment. Data were extracted from the ISC Scientific Journals System. Subsequently, the impact of self-citation on journal rankings, as well as its relationship with impact factors, quartiles, and article volume, was analyzed using descriptive statistics and SPSS.<br /><strong>Findings: </strong>In 2023, 1,713 journals achieved an impact factor. The distribution across quartiles was as follows: Q1 (21%), Q2 (25%), Q3 (24%), and Q4 (30%). The highest impact factors were observed in "Arts and Humanities" and "Social Sciences," while the lowest were in "Multidisciplinary" fields. Compared to the previous year, 896 journals improved their ranking, 588 declined, and the remainder remained unchanged. The mean impact factor of all journals decreased from 0.229 (including self-citations) to 0.153 (excluding self-citations). A significant negative correlation ($p < 0.01$) was found between journal quartiles and the percentage of self-citations. Furthermore, the number of published articles showed a significant positive correlation ($p < 0.01$) with both the volume and percentage of self-citations. A comparative analysis revealed that the disparity between impact factors (with and without self-citations) was highest in the "Social Sciences" and "Multidisciplinary" groups and lowest in "Medical and Health Sciences" and "Arts and Humanities."<br /><strong>Conclusion:</strong> Given the high average rate of self-citation observed, it is essential for editorial boards to formulate clear policies regarding self-citation thresholds and to implement strategies for attracting external citations. Raising awareness among researchers and stakeholders, combined with rigorous monitoring of citation performance, is crucial to preventing potential sanctions or suspension from reputable databases. Strategies for enhancing legitimate citation attraction include promoting high-quality research, conducting workshops on visibility and ethical citation practices, encouraging active participation in scientific social networks, and prioritizing the publication of original research. Furthermore, the JCR.ISC system should integrate advanced data-monitoring technologies to detect unconventional self-citation, establish stronger ethical guidelines, and incorporate metrics such as the EigenFactor and SCImago (SCI) alongside the traditional impact factor. Adhering to strict self-citation thresholds within ministerial journal commissions will likely improve both research quality and the standing of national scientific journals.<strong>Objective: </strong>Citation analysis is a critical indicator for evaluating the performance of journals and researchers, as it reflects the dynamic nature of scientific communication and influences the trajectory of scientific advancement. Among the various challenges in evaluating scientific outputs, "unconventional self-citation"—including author, journal, linguistic, organizational, and national self-citation—remains a major concern. This study specifically examines the impact of unconventional journal self-citation (defined as citations of articles within the same journal) on the ranking of journals indexed in JCR.ISC.<br /><strong>Methodology: </strong>This descriptive-analytical study employed a survey-based approach. The research population consisted of 1,713 journals across six broad subject areas (Social Sciences, Basic and Engineering Sciences, Medical and Health Sciences, Life Sciences, Arts and Humanities, and Multidisciplinary) indexed in the JCR.ISC system based on the 2023 assessment. Data were extracted from the ISC Scientific Journals System. Subsequently, the impact of self-citation on journal rankings, as well as its relationship with impact factors, quartiles, and article volume, was analyzed using descriptive statistics and SPSS.<br /><strong>Findings: </strong>In 2023, 1,713 journals achieved an impact factor. The distribution across quartiles was as follows: Q1 (21%), Q2 (25%), Q3 (24%), and Q4 (30%). The highest impact factors were observed in "Arts and Humanities" and "Social Sciences," while the lowest were in "Multidisciplinary" fields. Compared to the previous year, 896 journals improved their ranking, 588 declined, and the remainder remained unchanged. The mean impact factor of all journals decreased from 0.229 (including self-citations) to 0.153 (excluding self-citations). A significant negative correlation ($p < 0.01$) was found between journal quartiles and the percentage of self-citations. Furthermore, the number of published articles showed a significant positive correlation ($p < 0.01$) with both the volume and percentage of self-citations. A comparative analysis revealed that the disparity between impact factors (with and without self-citations) was highest in the "Social Sciences" and "Multidisciplinary" groups and lowest in "Medical and Health Sciences" and "Arts and Humanities."<br /><strong>Conclusion:</strong> Given the high average rate of self-citation observed, it is essential for editorial boards to formulate clear policies regarding self-citation thresholds and to implement strategies for attracting external citations. Raising awareness among researchers and stakeholders, combined with rigorous monitoring of citation performance, is crucial to preventing potential sanctions or suspension from reputable databases. Strategies for enhancing legitimate citation attraction include promoting high-quality research, conducting workshops on visibility and ethical citation practices, encouraging active participation in scientific social networks, and prioritizing the publication of original research. Furthermore, the JCR.ISC system should integrate advanced data-monitoring technologies to detect unconventional self-citation, establish stronger ethical guidelines, and incorporate metrics such as the EigenFactor and SCImago (SCI) alongside the traditional impact factor. Adhering to strict self-citation thresholds within ministerial journal commissions will likely improve both research quality and the standing of national scientific journals.https://stim.qom.ac.ir/article_3618_917d5093525e25ba35fbd0eedb0f42da.pdfUniversity of QomSciences and Techniques of Information Management2476-665812120260321Proposed Model for Data Governance Implementation with an Emphasis on PrivacyProposed Model for Data Governance Implementation with an Emphasis on Privacy111123346410.22091/stim.2025.12492.2221FAMORTEZAMAHMODI PARCHINIazad kish uniDepartment of Information Technology Management, Kish International Branch, Islamic Azad University, Kish , Iran0009-0007-7781-2583LADANRiaziDepartment of Information Technology Management, School of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran0009-0000-6813-0854ALIREZAPorebrahimiDepartment of Industrial Management, Faculty of Management, Islamic Azad University, Karaj Branch, Karaj, Iran0000-0001-5741-0260Seyed Abdollah AminMousaviDepartment of Information Technology, Central Tehran Branch, Islamic Azad University, Tehran, Iran0009-0005-3052-5910Journal Article20250310<strong>Objective: </strong>In the contemporary digital landscape, data governance stands as a critical challenge in both IT policy and technology law. Given the increasing volume of personal data processing and exchange, the necessity for a localized data governance framework, tailored to a country’s specific legal and technological infrastructure, is more pressing than ever. This study proposes a data governance model with a strong emphasis on privacy, addressing current challenges and providing practical solutions for improving personal data management. The primary objective is to develop a localized data governance framework for Iran, aligned with international standards such as the GDPR and the CCPA. By analyzing legal gaps, implementation challenges, and potential solutions, this study aims to establish a practical framework that not only enhances security and privacy protection but also fosters public trust in digital services.
<strong>Methodology</strong>: This applied-developmental study utilizes a mixed-methods approach. In the qualitative phase, legal documents, regulatory policies, and existing data governance models from Iran and other countries were analyzed. Furthermore, the Delphi method was employed to collect and examine the insights of 58 experts in technology, digital law, and information security. In the quantitative phase, Confirmatory Factor Analysis (CFA) was applied to validate the proposed model. Data were gathered through semi-structured expert interviews, a review of domestic laws and policies, and comparative studies with international frameworks. Key model indicators were extracted using qualitative content analysis, and statistical tests were employed to assess the model’s validity and reliability.
<strong>Findings: </strong>Results indicate that the primary challenges to data governance in Iran include the lack of an integrated legal framework, the absence of an independent regulatory body, and deficiencies in the enforcement of data protection policies. A comparative analysis with the European GDPR and the U.S. CCPA revealed that Iran lacks clear requirements for data processing transparency and independent oversight—two pillars of international standards. Furthermore, findings indicate high levels of privacy concern among Iranian users; 78% expressed anxiety regarding how their data is managed on domestic platforms. The proposed model comprises three dimensions (legal-policy, technical-technological, and organizational-regulatory), six components, and 24 operational indicators. Data analysis demonstrated that implementing this model could reduce privacy violations by 30.8%, increase user trust in digital services by 44.6%, and improve regulatory efficiency by 38.2%.
<strong>Conclusion: </strong>Data governance in Iran requires a transparent and binding framework that enhances security and user data protection while improving database interoperability and the efficiency of regulatory institutions. This study underscores that drafting comprehensive data governance legislation and establishing an independent regulatory body are critical steps. Additionally, implementing robust security policies, advanced encryption, and revising laws related to data collection and processing can mitigate cybercrimes and bolster public trust. A comparative analysis shows that the proposed model possesses a high degree of adaptability to global standards, suggesting that its proper implementation could enhance data protection standards by up to 79.4%. Ultimately, this research highlights the urgent need to reform data governance policies, enact new regulations, and increase transparency in personal data management. Future studies should focus on evaluating the practical implementation of this model within both public and private sectors.<strong>Objective: </strong>In the contemporary digital landscape, data governance stands as a critical challenge in both IT policy and technology law. Given the increasing volume of personal data processing and exchange, the necessity for a localized data governance framework, tailored to a country’s specific legal and technological infrastructure, is more pressing than ever. This study proposes a data governance model with a strong emphasis on privacy, addressing current challenges and providing practical solutions for improving personal data management. The primary objective is to develop a localized data governance framework for Iran, aligned with international standards such as the GDPR and the CCPA. By analyzing legal gaps, implementation challenges, and potential solutions, this study aims to establish a practical framework that not only enhances security and privacy protection but also fosters public trust in digital services.
<strong>Methodology</strong>: This applied-developmental study utilizes a mixed-methods approach. In the qualitative phase, legal documents, regulatory policies, and existing data governance models from Iran and other countries were analyzed. Furthermore, the Delphi method was employed to collect and examine the insights of 58 experts in technology, digital law, and information security. In the quantitative phase, Confirmatory Factor Analysis (CFA) was applied to validate the proposed model. Data were gathered through semi-structured expert interviews, a review of domestic laws and policies, and comparative studies with international frameworks. Key model indicators were extracted using qualitative content analysis, and statistical tests were employed to assess the model’s validity and reliability.
<strong>Findings: </strong>Results indicate that the primary challenges to data governance in Iran include the lack of an integrated legal framework, the absence of an independent regulatory body, and deficiencies in the enforcement of data protection policies. A comparative analysis with the European GDPR and the U.S. CCPA revealed that Iran lacks clear requirements for data processing transparency and independent oversight—two pillars of international standards. Furthermore, findings indicate high levels of privacy concern among Iranian users; 78% expressed anxiety regarding how their data is managed on domestic platforms. The proposed model comprises three dimensions (legal-policy, technical-technological, and organizational-regulatory), six components, and 24 operational indicators. Data analysis demonstrated that implementing this model could reduce privacy violations by 30.8%, increase user trust in digital services by 44.6%, and improve regulatory efficiency by 38.2%.
<strong>Conclusion: </strong>Data governance in Iran requires a transparent and binding framework that enhances security and user data protection while improving database interoperability and the efficiency of regulatory institutions. This study underscores that drafting comprehensive data governance legislation and establishing an independent regulatory body are critical steps. Additionally, implementing robust security policies, advanced encryption, and revising laws related to data collection and processing can mitigate cybercrimes and bolster public trust. A comparative analysis shows that the proposed model possesses a high degree of adaptability to global standards, suggesting that its proper implementation could enhance data protection standards by up to 79.4%. Ultimately, this research highlights the urgent need to reform data governance policies, enact new regulations, and increase transparency in personal data management. Future studies should focus on evaluating the practical implementation of this model within both public and private sectors.https://stim.qom.ac.ir/article_3464_12851f1783e557a604e50fffdf9fde70.pdfUniversity of QomSciences and Techniques of Information Management2476-665812120260321Exploring open data system promotion strategies in stakeholders' narratives: A qualitative studyExploring open data system promotion strategies in stakeholders' narratives: A qualitative study124138367310.22091/stim.2025.12883.2255FAHamidHeydarimember of the science and technology studies department- Institute for Cultcher, Social and Civilization Studies Tehran,Tehran. Iran0000-0002-3064-8823SirusMansooridepartment of education, Arak university, Arak ,Iran0000-0001-9405-9860MahmooadNaseri JezehDoctoral candidate of Allameh Tabataba'i University0009-0001-1295-769XMehdiPazhouhanphd in Knowledge and Information Science .Arak University, Arak, Iran0000-0003-4086-2986Journal Article20250530<strong>Objective:</strong> This study aimed to explore the lived experiences of policymakers, experts, and stakeholders in technology-based businesses regarding strategies for improving open data systems. The research primarily focused on identifying the requirements, challenges, and strategies necessary for the effective development of open data at both the governance and organizational levels.
<strong>Methodology:</strong> This study employed a qualitative phenomenological approach. Participants included legislators, academic experts, and owners of high-technology enterprises. Sampling continued until theoretical saturation was achieved, which occurred with 21 participants. Data were collected through both individual and group narrative interviews and analyzed using thematic analysis. To ensure the credibility and trustworthiness of the findings, audit trails, thick descriptions, and peer debriefing (code acceptability review by two external observers) were employed.
<strong>Findings:</strong> Thematic analysis revealed that strategies for improving open data systems can be categorized into six organizing themes: strengthening inter-agency interaction, developing necessary infrastructure for data publication, refining policymaking and legal frameworks, enhancing data quality, balancing data privacy with flexible government confidentiality protocols, and shifting individual and organizational attitudes toward data transparency. In total, 98 basic themes were identified under these six organizing themes.
<strong>Conclusion:</strong> The findings suggest that improving open data systems requires simultaneous reforms across infrastructure, legislation, organizational procedures, data quality, and the mindsets of institutional actors. Accordingly, policymakers and executive bodies can utilize the framework developed in this study to revise existing procedures and accelerate the advancement of open data. We recommend that legislative institutions formulate clear mandates for public data sharing, define specific roles and responsibilities for regulatory and executive bodies, enforce strict standards for organizational data archiving, and design educational and cultural programs to facilitate data sharing among employees.<strong>Objective:</strong> This study aimed to explore the lived experiences of policymakers, experts, and stakeholders in technology-based businesses regarding strategies for improving open data systems. The research primarily focused on identifying the requirements, challenges, and strategies necessary for the effective development of open data at both the governance and organizational levels.
<strong>Methodology:</strong> This study employed a qualitative phenomenological approach. Participants included legislators, academic experts, and owners of high-technology enterprises. Sampling continued until theoretical saturation was achieved, which occurred with 21 participants. Data were collected through both individual and group narrative interviews and analyzed using thematic analysis. To ensure the credibility and trustworthiness of the findings, audit trails, thick descriptions, and peer debriefing (code acceptability review by two external observers) were employed.
<strong>Findings:</strong> Thematic analysis revealed that strategies for improving open data systems can be categorized into six organizing themes: strengthening inter-agency interaction, developing necessary infrastructure for data publication, refining policymaking and legal frameworks, enhancing data quality, balancing data privacy with flexible government confidentiality protocols, and shifting individual and organizational attitudes toward data transparency. In total, 98 basic themes were identified under these six organizing themes.
<strong>Conclusion:</strong> The findings suggest that improving open data systems requires simultaneous reforms across infrastructure, legislation, organizational procedures, data quality, and the mindsets of institutional actors. Accordingly, policymakers and executive bodies can utilize the framework developed in this study to revise existing procedures and accelerate the advancement of open data. We recommend that legislative institutions formulate clear mandates for public data sharing, define specific roles and responsibilities for regulatory and executive bodies, enforce strict standards for organizational data archiving, and design educational and cultural programs to facilitate data sharing among employees.https://stim.qom.ac.ir/article_3673_9d13030a2dbd91961c808b7d3be9f275.pdf