https://stim.qom.ac.ir/ Sciences and Techniques of Information Management en daily 1 Sat, 21 Mar 2026 00:00:00 +0330 Sat, 21 Mar 2026 00:00:00 +0330 https://stim.qom.ac.ir/article_4592.html Digital transformations, the rapid expansion of communication networks, increasing human reliance on information, and the complexity of decision-making environments have placed contemporary humans under unprecedented information pressure. The theory of "Extra-informationalism" seeks to explain this phenomenon—a state wherein information needs are not only escalating but becoming increasingly complex, ambiguous, and infinite. This article analyzes the core components of Extra-informationalism across three dimensions: informological, sociological, and neuropsychological. It argues that crises such as warfare, natural disasters, pandemics, and rapid social shifts trap individuals in a cycle of ceaseless information-seeking. Furthermore, the relationship between Extra-informationalism and conditions such as ADHD, OCD, and Internet addiction is examined. The study posits that the fundamental challenge of the digital age is no longer the scarcity of information, but the human inability to achieve cognitive adequacy and disengage from information-seeking behaviors. It is imperative to develop strategies for the prevention and mitigation of this disorder. https://stim.qom.ac.ir/article_2184.html Objective: The purpose of this study is to analyze the research in the field of knowledge management in banks and financial institutions in order to understand the literature in this field, the axes of studies and different dimensions of knowledge management in banks and financial institutions and identify research gaps in this field at home and abroad. Is. Method: The present study was conducted using the systematic review method. In order to conduct a systematic review, the 12 steps of Pettigrew and Robert have been followed. Results: As a result of the analysis, the researches in this field were classified into 10 general axes. In addition, a summary of the important points and dimensions of knowledge management in banks and financial institutions based on original research selected from reputable scientific databases and sources was also provided. Conclusion: The results of this study pave the way for researchers and while providing new contexts for research and identification of gaps, it also prevents duplicate studies in this area. https://stim.qom.ac.ir/article_3156.html Objective: 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.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.Findings: 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.Conclusion: 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 https://stim.qom.ac.ir/article_3157.html Purpose: 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.Methodology: 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.Findings: 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.Conclusion: 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 https://stim.qom.ac.ir/article_2355.html The research, with the aim of evaluating the maturity of business intelligence in academic libraries, used the research method mixed(qualitative-quantitative) with the design science approach. In the qualitative step, in order to collect data, the Meta-synthesis of previous studies has been used. In order to make it practical, according to the capability maturity model, the research model was designed in five levels, initiated, repeatable, defined, managed and optimized. In the quantitative part, the data was collected through questionnaire and using fuzzy Delphi technique. The opinion of the experts about the dimensions and levels of the model was received and the necessary changes were applied according to their opinion. Then, with using a questionnaire, the presented model was evaluated in terms of accuracy, quality, usefulness and applicability. The presented model, which includes 4 categories, 16 dimensions and 72 indicators, was studied in University of Tehran libraries, and according to the results, it was in the second level of maturity in the categories of technology, organization and process, and in the third level of maturity in the category of human resources. https://stim.qom.ac.ir/article_3337.html Objective: 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.Method: 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.Findings: 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).Conclusion: 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.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_2433.html Abstract Methodology: The current research is mixed (library studies, qualitative, quantitative) and is practical in terms of its purpose. With previous studies, a list of content production risks was obtained, in which 9 main items and 39 sub-items using the Delphi method were confirmed and finalized based on the opinion of experts. In the quantitative part, a questionnaire was distributed among the members of the research community (100 people, managers, and experts of the government digital libraries of Tehran) based on the three criteria of the effect intensity, the probability of occurrence, and the ability of the organization to react to the risk. The statistical population of the qualitative section included experts in knowledge and information science, and experts in various fields who had expertise and experience in the field of risk management. Therefore, the sampling method was purposeful. The statistical population in the quantitative part of the research was managers and experts of digital library departments affiliated with government organizations in Tehran. Since there is no clear reference, a list was prepared by searching the internet, reviewing some sources, and referring to the online section of the National Library at the time of the research (2018). A number of 28 active digital libraries affiliated with government organizations located in Tehran were identified. In the ranking stage, using the Shannon entropy method, the data were weighted and ranked using TOPSIS software. Finally, the content production risk management model was implemented in Tehran government digital libraries with PLS software and based on confirmatory factor analysis. Findings: Based on the ranking of risks using the fuzzy TOPSIS technique in the content production of government digital libraries in Tehran, indicators of protection and maintenance risks, human source risks, technical risks, information security risks, and infrastructure risks, evaluating the content of resources and authors, authors' rights, integration, environment have been ranked 1 to 9 in terms of the importance of risks. Conclusion: With the correlation coefficient between the research variables and the structural equation modeling test, the research hypotheses were confirmed and the stated values for GOF indicate a very strong overall fit of the research model. Keywords: risk management model; Content production; Fuzzy topsis, Tehran State Digital Libraries, Risk rating. چکیده: هدف: ارائه الگوی پیشنهادی برای مدیریت ریسک تولیدمحتوا در کتابخانه های دیجیتالی دولتی شهر تهران است. روش شناسی: پژوهش حاضر آمیخته (مطالعات کتابخانه ای، کیفی، کمی) و از حیث هدف کاربردی است، با مطالعات پیشین سیاهه ای از ریسکهای تولیدمحتوا بدست آمد که بر اساس نظر خبرگان و با روش دلفی 9 گویه اصلی و 39 گویه فرعی تایید و نهایی شد، در بخش کمی بر اساس سه معیار شدت اثر، احتمال وقوع، و توانایی سازمان در واکنش به ریسک، در قالب پرسشنامه‌ای میان اعضای جامعه پژوهش (100 نفر، مدیران و کارشناسان کتابخانه‌های دیجیتالی دولتی شهر تهران) توزیع شد، جامعه آماری بخش کیفی شامل خبرگان علوم اطلاعات و دانش‌شناسی، و متخصصین حوزه های مختلفی که در زمینه مدیریت ریسک تخصص و تجربه داشتند بود، لذا روش نمونه‌گیری آنها هدفمند بوده است. جامعه آماری در بخش کمی تحقیق، مدیران و کارشناسان بخش‌های کتابخانه‌های دیجیتالی وابسته به سازمانهای دولتی مستقر در شهر تهران بود. البته مرجع درست و واضحی در این خصوص یافت نشد لذا با جستجو در اینترنت، مرور برخی منابع و نیز مراجعه به بخش https://stim.qom.ac.ir/article_3645.html Objective: 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. Methodology: 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. Findings: 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. Conclusion: 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 https://stim.qom.ac.ir/article_3618.html Objective: 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.Methodology: 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.Findings: 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."Conclusion: 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_3464.html Objective: 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. Methodology: 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. Findings: 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%. Conclusion: 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_3673.html Objective: 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. Methodology: 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. Findings: 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. Conclusion: 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_3018.html This research is conducted with the aim of designing a technological business model based on cloud computing using a hybrid approach. The current research approach is qualitative, and the seven-step metacombination method of Sandelowski and Barroso was used. The research community includes all resources related to technological business including 436 resources published from the scientific database during the years 2010-2022. Then, taking into account the degree of relevance to the research topic, primary sources were screened 55 sources were selected and coding was done on the concepts extracted from them. In total, 39 concepts were identified, which were categorized into 39 subcategories and 6 main categories. Finally, based on Strauss and Corbin's three-stage coding approach (open, central, and selective) in the foundational data theory, codes have been identified that include: key factors, important features of cloud computing, advantages of cloud computing, and basic indicators of computing. Cloud is the critical challenge of cloud computing and the risks of cloud computing. In the current research, many factors led to the formation of technological businesses; therefore, it should not be considered a simple phenomenon, and due to the intensity of electronic commerce in the world, the multi-dimensional concept of this phenomenon should be looked at more carefully.This research is conducted with the aim of designing a technological business model based on cloud computing using a hybrid approach. The current research approach is qualitative, and the seven-step metacombination method of Sandelowski and Barroso was used. The research community includes all resources related to technological business including 436 resources published from the scientific database during the years 2010-2022. Then, taking into account the degree of relevance to the research topic, primary sources were screened 55 sources were selected and coding was done on the concepts extracted from them. In total, 39 concepts were identified, which were categorized into 39 subcategories and 6 main categories. Finally, based on Strauss and Corbin's three-stage coding approach (open, central, and selective) in the foundational data theory, codes have been identified that include: key factors, important features of cloud computing, advantages of cloud computing, and basic indicators of computing. Cloud is the critical challenge of cloud computing and the risks of cloud computing. In the current research, many factors led to the formation of technological businesses; therefore, it should not be considered a simple phenomenon, and due to the intensity of electronic commerce in the world, the multi-dimensional concept of this phenomenon should be looked at more carefully.This research is conducted with the aim of designing a technological business model based on cloud computing using a hybrid approach. The current research approach is qualitative, and the seven-step metacombination method of Sandelowski and Barroso was used. The research community includes all resources related to technological business including 436 resources published from the scientific database during the years 2010-2022. Then, taking into account the degree of relevance to the research topic, primary sources were screened 55 sources were selected and coding was done on the concepts extracted from them. In total, 39 concepts were identified, which were categorized into 39 subcategories and 6 main categories. Finally, based on Strauss and Corbin's three-stage coding approach (open, central, and selective) in the foundational data theory, codes have been identified that include: key factors, important features of cloud computing, advantages of cloud computing, and basic indicators of computing. Cloud is the critical challenge of cloud computing and the risks of cloud computing. In the current research, many factors led to the formation of technological businesses; therefore, it should not be considered a simple phenomenon, and due to the intensity of electronic commerce in the world, the multi-dimensional concept of this phenomenon should be looked at more carefully. https://stim.qom.ac.ir/article_3072.html Purpose: The shadow economy is one of the substantial economic phenomena that leads to the inefficient use of society's resources, reduces tax revenues, increases interest rates, and raises inflation and unemployment in the economy through the increase in government debt. Therefore, examining the factors affecting the shadow economy is very important. Along with the development and expansion of information and communication technology (ICT), during the past decade, the digital economy has been considered by policymakers in the field of ICT as an efficient mechanism and tool to control and reduce the volume of transactions in the informal sector of the economy. The literature review indicates that the digital economy plays a considerable role in increasing the efficiency of innovation and industrial transformation, causes an increase in production efficiency, and promotes economic development. Therefore, the digital economy has an important impact on the size of the shadow economy. Some studies emphasize that the components of the digital economy, including ICT, lead to a decrease in the size of the shadow economy due to the reduction of cash transactions and the increase in the production factors productivity; on the contrary, some studies show that ICT increases the size of the shadow economy by increasing tax evasion and raising the demand for goods and services of the informal sector. Therefore, there is no consensus on the positive or negative impact of the digital economy on the shadow economy. Accordingly, the present research aims to answer the question of what effect the digital economy has on the shadow economy in Iran. Method: The paper aims to investigate the nonlinear impact of the digital economy on the shadow economy in Iran from 1990 to 2021. For this purpose, first, the digital economy index was extracted based on the principal components analysis method and using the variables of mobile phone subscription, fixed phone subscription, and the quantity of internet usage. Then, the threshold regression approach was used to measure the estimated effect of the digital economy index and other explanatory variables, i.e., direct tax burden, unemployment, inflation, degree of trade openness, and human capital on the size of the shadow economy. Findings: The findings indicated that the size of the digital economy threshold is 6.7 percent; before reaching the threshold level, the digital economy leads to a rise in the size of the shadow economy, but after surpassing the threshold level, the digital economy conducts a decrease in the volume of the shadow economy. Also, findings showed that human capital has a negative and significant impact on the shadow economy. On the contrary, variables of direct taxes, unemployment, inflation, and trade openness have a positive and significant effect on the shadow economy. Conclusion The nonlinear behavior of the digital economy shows that in the low regime of the digital economy, the digital economy by taking advantage of technological development directs to reduce the cost of transactions and facilitate the supply of goods and services, which leads to an increase in the production capacity and volume of transactions in the shadow economy sector. However, in the high regime of the digital economy, the rapid development of the digital economy by providing new technologies, and improving the method of producing goods and services leads to a rise in the expertness to use technology, and enhancing people's access to information that accompanied to an increase in the production of goods and services. Because the ability of companies active in the formal sector to use technology in the production process is more than the active companies in the informal sector of the economy, the digital economy increases the volume of activity in the formal sector and reduces the size of the shadow economy through increasing the productivity of production in the formal sector and improving the government's supervision of the exchanges of the informal sector. Considering the negative impact of the shadow economy on the underground sector of Iran, it is suggested that the government, by developing ICT and improving the efficiency in providing public services reduces the access cost of economic firms to digital technologies, prevents tax evasion, increases the production efficiency of the formal sector of the economy and reduces the size of the shadow economy. https://stim.qom.ac.ir/article_3169.html Objective: Considering that maintaining and improving the organization's knowledge capital is undoubtedly the most important component in the effective role of organizations in today's turbulent environment, the implementation of knowledge management can become a powerful and decisive tactic at the international level. As a result, this study was conducted with the aim of identifying the main elements and components of knowledge management in knowledge-based project-oriented organizations. Today, more than ever, there is a need for scholarly attention to the process of production, organization, transfer, transformation, application, maintenance of knowledge and its evaluation. Knowledge management is the intelligent design of processes, tools, structures, etc., with the intention of increasing, renewing, sharing, or improving the use of knowledge, which appears in each of the three elements of intellectual capital, i.e., structural, human, and social. Method: The approach of the current research is qualitative, and the seven-step hybrid method of Sandelowski and Barroso was used. The research community includes all sources related to the identification of elements and components of knowledge management including 163 articles, books and theses from reliable domestic and foreign sources published from the scientific database in the period of 1397-1402 and 2018-2023. Then, taking into account the degree of connection with the research topic, primary sources were screened and 63 sources were selected and coding was done on the concepts extracted from them. Results: In total, based on the meta-combination method with the Delphi approach, the number of 6 main dimensions (process, technology, human resources, organizational structure, strategy and goals, and leadership), 25 components and 76 indicators for the 25 components were extracted as elements and components. Knowledge management were identified. The results of reliability measurement using Kendall's coefficient method above 0.5 probability value, less than 0.05 indicate appropriate agreement between experts. https://stim.qom.ac.ir/article_3229.html The goal of this study is to prioritize the elements that have the most impact on the trend toward popular sports and championships, with a focus on mass media and social networks. The current study falls under the category of descriptive-comparative research, it is likewise a component of applied research and in terms of data gathering, it is also a component of field research. The 12,400 overall students, employees, and faculty members of the Islamic Azad University in the province of Qom make up the statistical population of the study. In this study, 375 students, 136 workers, and 118 professors were chosen as samples using stratified random selection using Morgan's table. The questionnaire created by Seyyed Ameri and Jamei to identify the contributing elements to the trend toward public competitions was the research instrument employed in this study (2013), which has 62 questions and six components (group media, social demand, non-sports organizations, sports organizations, globalization and communication, and social networks) to identify the influential factors in the trend towards popular sports and championships, is one of the tools used in this study Experts in sports science validated the questionnaire's face validity, and Cronbach's alpha yielded a reliability score of 0.980. The data were analyzed using both descriptive statistics and inferential statistics. Frequency and frequency percentage were employed in the descriptive statistics portion and the reliability of the survey was Cronbach's alpha , the Kolmogorov-Smirnov test, the Friedman ranking test, and the one-sample t test were used in the inferential statistics section to determine the normality of the data distribution. Data analysis was carried out simultaneously using SPSS,23 software statistics. The study's findings revealed there is a significant difference in the way students at Islamic Azad University in the province of Qom rate the factors that influence their inclination towards popular sports and championships. From the students' perspective, social media, mass media, globalization, and communication, social networks, sports organizations, and non-sports organizations were ranked from first to sixth. There is a considerable difference between the average indicators of group media and social networks from the perspective of students and the average of society, with the average of these two indicators being greater than the latter. However, the rankings of these influential variables in the trend toward public sports and competitions differ significantly from the viewpoint of the staff at Islamic Azad University in the province of Qom. According to the staff, the order of social demand indicators is as follows: mass media, globalization, communication, social networks, sports organizations, and non-sports organizations. Other research findings revealed a significant discrepancy between the professors at the Islamic Azad University in the province of Qom's rankings of the influential factors in the trend toward popular sports and competitions and their rankings of the indicators of mass media, social demand, and globalization, respectively, And from first to sixth on the list were communications, social networks, sports organizations, and non-sports organizations. The average of these two indicators is lower than the average of society, and there is a substantial gap between the average indicators of mass media and social networks from the perspectives of workers and academics and the average of society. The findings also demonstrate that social networks serve as an additional function of virtual space, enhancing communication by establishing a network among internet users. It is important to note that the development of virtual centers and communication networks fosters a sense of community, cohesion, accurate information, information sharing, motivation, and shared interests in sports. Consequently, virtual space can be a valuable tool in the growth of university sports and the development of new sports structures. https://stim.qom.ac.ir/article_3238.html Abstract Objective: This study aimed to study the impact of digital economy and Objective: This study aims to investigate the impact of the digital economy and the role of information management on labor productivity in the provinces of Iran during 2012-2013. The digital economy, as a key driver of economic development, has gained increasing importance in recent decades. Information management, as a part of this economy, plays an effective role in improving efficiency, reducing costs, and increasing productivity. The present study seeks to provide a comprehensive analysis of the role of digital and information technologies in improving labor productivity. Method: In this study, data related to labor productivity and digital economy indicators, including the number of landline users, the number of mobile users, the number of Internet subscribers, bandwidth, financial and information transaction management, electronic payment transactions, added value of knowledge-intensive industries, and sanctions-related indicators, were collected. The research method was a combination of descriptive analysis and advanced econometrics. The data were analyzed using dynamic modeling and time series methods to examine the impact of selected indicators of the digital economy and information management on labor productivity. These methods allowed for the identification of causal relationships and the determination of the relative importance of each variable. Findings: The research findings showed that the indicators of the digital economy and information management have a positive and significant impact on labor productivity. Among the most important results, we can mention the direct and strong impact of the increase in the number of Internet users and bandwidth on labor productivity. Greater bandwidth allows for faster access to information and reduces the time spent on work-related activities, thus increasing efficiency. In addition, electronic payment transactions have had a significant impact on improving economic performance by reducing the need for face-to-face interactions and speeding up financial processes. Information management, as a key variable, has also led to the optimization of decision-making and the improvement of service quality. Discussion and Analysis: The results of this study indicate that investing in digital infrastructure and strengthening information technologies can not only increase labor productivity, but also indirectly contribute to economic and social development in Iranian provinces. At the same time, the impact of sanctions as an intervening variable has limited the performance of the digital economy; however, the findings show that effective information management and optimal use of available resources can reduce the negative effects of these limitations to some extent. Conclusion: According to the research findings, it can be concluded that the development and improvement of digital infrastructure and information technologies are essential to improve labor productivity in Iran. Policymakers and decision-makers should develop comprehensive and targeted programs to expand bandwidth, increase Internet access, and facilitate electronic transactions. Also, training the workforce in digital skills can significantly increase productivity. In future studies, it is suggested that variables such as education level, political stability, and technological changes be examined more closely to provide a more complete picture of the impact of the digital economy on labor productivity. Keywords: Labor productivity, sanctions, value added, digital economy and information management, e-banking Conclusion: According to the research findings, it can be concluded that the development and improvement of digital infrastructure and information technologies are essential to improve labor productivity in Iran. Policymakers and decision-makers should develop comprehensive and targeted programs to expand bandwidth, increase Internet access, and facilitate electronic transactions. Also, training the workforce in digital skills can significantly increase productivity. In future studies, it is suggested that variables such as education level, political stability, and technological changes be examined more closely to provide a more complete picture of the impact of the digital economy on labor productivity. Keywords: Labor productivity, sanctions, value added, digital economy and information management, e-banking https://stim.qom.ac.ir/article_3266.html Purpose: Indigenous knowledge today represents a comprehensive collection of knowledge and skills produced and maintained by individuals in rural environments. These individuals develop indigenous knowledge through history and interaction with their natural surroundings. Indigenous knowledge is the result of thousands of years of human experience and is one of the most valuable assets of any nation. However, due to its oral nature, much of this knowledge is at risk of extinction and forgetting. This study aims to identify the components of indigenous knowledge in the Turkmen Sahra region. Methods: Due to the lack of research or theories regarding the identification of components of indigenous knowledge in the Turkmen Sahra region, a method was needed to explore this phenomenon. Data and components were extracted qualitatively, and the objective of this research was exploratory. The statistical population included all experts and scholars familiar with the indigenous knowledge of the Turkmen Sahra region who have worked in this area and possess the necessary information. The criteria for participant inclusion in the interviews were: 1. A minimum of three years of residence in the Turkmen Sahra region and being of Turkmen descent, 2. A bachelor’s degree or higher, and 3. Familiarity with the field of indigenous knowledge. Semi-structured interviews were used to identify the components and sub-components. After obtaining the necessary permissions, coordination was made with the selected interviewees, and a copy of the protocol and interview questions was provided in advance to prepare them for the questions. The researcher then visited the interviewees’ workplaces at the scheduled time and conducted interviews lasting between 30 to 60 minutes. During the interviews, opinions regarding the components of indigenous knowledge in the Turkmen Sahra region were collected, and the main and sub-factors were examined and finalized. Various methods, such as note-taking and audio or video recording of the interview process, were used for documentation. After completing the interviews, detailed notes about the interview process were taken. To assess the validity of the interview texts, thematic analysis was conducted. In this phase, initial concepts were examined inductively through coding and analysis of the concepts derived from the semi-structured interviews with experts. Subsequently, the concepts were screened and analyzed to present the dimensions, components, and indicators of the proposed model. To measure the reliability of the findings, two methods were employed: test-retest and inter-coder reliability. The test-retest reliability was calculated at 0.89, and the inter-coder reliability was found to be 0.91. MAXQDA software was utilized for analysis. Thus, after the initial coding of the interviews, the initial codes were identified, which were then refined to 54 foundational concepts after eliminating repetitive and synonymous concepts. The themes were categorized into 93 foundational themes, 12 organizing themes, and 2 overarching themes, followed by the screening and analysis of concepts to present the dimensions, components, and indicators of the proposed model. Findings: In this study, to establish the initial framework of indicators that influence the implementation of indigenous knowledge policies, data were extracted from interviews with experts and researchers familiar with the indigenous knowledge of the Turkmen Sahra region. The obtained data were coded line by line to extract initial concepts. A large number of codes were generated, and among the initial codes, common and semantically similar concepts were combined through iterative study of the data. Subsequently, efforts were made to classify these factors. After the initial conceptualization was completed and coding operations were performed, the indicators were categorized into dimensions. Once all data were coded, themes emerged, and after reviewing and defining them, the results were categorized into foundational, organizing, and overarching themes. Thus, after the initial coding of the interviews, the initial codes were identified, which were then refined to 54 foundational concepts after eliminating repetitive and synonymous concepts. Conclusion: Today, the importance of indigenous knowledge and the necessity of its protection and utilization by national and international organizations are increasingly recognized. Many researchers agree on the manner of disseminating indigenous knowledge. The results indicate that the indigenous knowledge of the region consists of several primary components, among which six components were ultimately identified. Therefore, the components of indigenous knowledge in the Turkmen Sahra region include agriculture, animal husbandry, music, construction, traditional medicine, and other relevant areas. https://stim.qom.ac.ir/article_3352.html New information technologies and their wide applications have brought great changes and transformations for organizations, but many organizations and especially universities face challenges and problems in the implementation and optimal use of these technologies. are facing Despite the benefits of using new information technologies in some educational organizations, failure in using new information technologies is more common than success in them, and at the organizational level, including universities, the efficiency and effectiveness of the The expectation of investment in this field has not been achieved. Since information technology is considered as the axis of the development of societies and educational organizations, especially universities, therefore, designing the structure and removing the obstacles to its development requires deep thinking and reflection along with providing the appropriate model and examining the existing models in government organizations, especially educational organizations. It is like college. According to the things mentioned in this research, the reason for choosing the topic is due to the role that the mechanization of affairs using information technology plays on the performance of human resources. Therefore, according to the increasing use of information technology in organizations and especially in educational centers such as universities, the effects that this technology can have on the behavior and performance of employees and the excellence and growth of the organization are investigated and analyzed. So that the results of this research can be used for better use of information technology in, order to improve the performance of the university. Shahid Chamran University of Ahvaz, due to the abundance of information in it and the transformation of this information into the knowledge needed by the educational organization, and on the other hand, the need to use capable employees with high knowledge and skills, the need to use information technology correctly can be understood. But by examining the process of establishing the knowledge management system using modern information technologies in this university, it can be seen that so far a lot of expenses have been spent in various departments of this university in the form of various projects such as study projects, deployment, software and the like. It has been spent that many of these projects have been stagnant and removed from the organizational process. Therefore, the problem that the present study is looking for is to examine the obstacles and reasons for the inefficient use of modern information technologies in Shahid Chamran University of Ahvaz. Because it seems that modern information, technologies are not used, as it should be to facilitate affairs in different departments and fields in Shahid Chamran University of Ahvaz. Therefore, the investigation and analysis of the obstacles to the use of modern information technologies and the factors that cause the complete failure of the use of these technologies in the university, or cause them to be abandoned or stopped after use, are very important, in this regard, the aim of the present research The structural explanation of the obstacles to the implementation of new information technologies in the organization at Shahid Chamran University of Ahvaz is based on practical goal setting and descriptive-analytical in terms of methodology. In the process of data preparation and production, the obstacles explaining modern information technology have been identified using the opinions of 30 professors, experts and experts through the Delphi method. In order to analyze the information of 15 obstacles in 5 categories as strong influencing obstacles on the implementation of new information technologies in Shahid Chamran University of Ahvaz, ISM interpretive-structural modeling and then with Mic-Mac software have been used. The results of the research showed that managerial barriers (absence of a specific program for the development of technology in the organization) and individual barriers (attitude-motivation) are among the most influential barriers to the implementation of modern information technology, and economic barriers (financial resources and cost of skill training) are among the most influential factors. They come also, the results obtained from MIK show that out of 15 obstacles to the implementation of modern information technology in Shahid Chamran University of Ahvaz, 11 obstacles are linked variables that have high influence and dependence and require special attention from the officials of Shahid Chamran University of Ahvaz. https://stim.qom.ac.ir/article_3747.html Purpose:This study aims to examine the status of institutional repositories of Iranian universities registered in the OpenDOAR directory.Methodology:This applied research employs a survey-analytical method. Data was collected using a researcher-made checklist based on the registration criteria of the OpenDOAR system. The research population consisted of all institutional repositories from Iran listed in OpenDOAR, totaling 18 repositories. The data were analyzed using Microsoft Excel, and the results were presented and interpreted through descriptive statistics, utilizing various charts and tables.Findings:The findings indicate that medical universities in Iran were among the first institutions to establish and develop repositories. Of the 18 repositories registered from Iran, 11 were active and 7 were inactive. Nine repositories belonged to medical universities, and only one repository was affiliated with a university under the Ministry of Science, Research, and Technology. The earliest active repository was registered in 2009, and the latest in 2019, with the highest frequency of registrations occurring in 2015 and 2019. The EPrints software was the most used platform among active repositories. Mashhad University of Medical Sciences had the highest number of records, followed by Mohaghegh Ardabili University, Iran University of Medical Sciences, Ardabil University of Medical Sciences, Bushehr, Shahrekord, Ilam, Golestan, Sabzevar, Babol, and Qazvin Universities of Medical Sciences, respectively.The study revealed a slow growth trend in the development of institutional repositories in Iran. Based on the repository classification of the University of Nottingham, the registered records included 12 main types of documents: journal articles, master’s and doctoral theses, conference and workshop papers, research proposals, monographs, educational slides, administrative reports, books, book chapters and sections, datasets, and multimedia and audio materials.One of the defining features of institutional repositories is the accumulation of scientific content over time; in the Iranian case, most records were journal articles and theses. However, growth in many centers was very limited. Most repositories belonged to public institutions, and in terms of subject coverage, the majority were focused on medical and health sciences.Conclusion:The findings suggest that Iranian universities have largely neglected the implementation and potential of institutional repositories, particularly in enhancing the visibility and ranking of universities in global systems. It can be concluded that both research and practical engagement with institutional repositories in Iran are still in their infancy. The limited number of repositories listed in OpenDOAR reflects a lack of general awareness about the concept of open access repositories as a tool to support research and development. Moreover, the growth and development of existing repositories have been sluggish and are far from satisfactory.The findings suggest that Iranian universities have largely neglected the implementation and potential of institutional repositories, particularly in enhancing the visibility and ranking of universities in global systems. It can be concluded that both research and practical engagement with institutional repositories in Iran are still in their infancy. The limited number of repositories listed in OpenDOAR reflects a lack of general awareness about the concept of open access repositories as a tool to support research and development. Moreover, the growth and development of existing repositories have been sluggish and are far from satisfactory.The findings suggest that Iranian universities have largely neglected the implementation and potential of institutional repositories, particularly in enhancing the visibility and ranking of universities in global systems. It can be concluded that both research and practical engagement with institutional repositories in Iran are still in their infancy. The limited number of repositories listed in OpenDOAR reflects a lack of general awareness about the concept of open access repositories as a tool to support research and development. Moreover, the growth and development of existing repositories have been sluggish and are far from satisfactory. https://stim.qom.ac.ir/article_3540.html Abstract: Background: We live in an era where turbulence and complexity are increasing rapidly. More than ever before, the world is changing continuously. In other words, the characteristic of today's world is continuous change. In order to face such changes, organizations must be aware of the vital role of knowledge and development in their survival and growth. In fact, organizations must pay more attention to the development of knowledge, skills, and abilities of their employees than ever before. This issue has led to serious attention to human resource development by researchers and executives in recent years. Human resource development is the continuous and continuous development and improvement of various and comprehensive dimensions of the individual (Pakdel, Gholipour, and Hosseini, 2019: 51). Until recently, few people considered human resources to be the source of competitive advantage of organizations. However, today it is claimed that human resources are the greatest asset of organizations and countries, and the development and growth of these human complexes cannot be achieved without the development of humans. Numerous studies have shown that human resource development has led to the development of organizations and, consequently, the development of societies. Therefore, human resource development has become one of the most important and greatest concerns of managers and organizations of this era (Sinhe, 2019: 29). Human resource development is considered one of the most important components that distinguish effective and successful organizations from other social institutions and institutions, so that numerous studies have considered the success and sustainability of organizations in the fields of international competition and leadership in the fields of services, economy, and technology to be due to addressing human resource development as one of the main policies of the organization. In other words, the discussion of human resource development is the development of the individual in all directions. The development of the individual in work life, social life, private life, and cultural and spiritual issues (Mossikhani, 2013: 25). Human resource development refers to the production of thoughts and ideas by the organization's employees, and its new concept requires that employees be equipped with qualities and skills that, with compassion and full commitment, place their capabilities, energy, expertise, and thoughts in line with the fulfillment of the organization's missions and permanently create new intellectual and qualitative values ​​for the organization. (Roozbeh, Timornejad, and Rabiei Mandejin, 2019: 18) The purpose of the research: To examine the human resource development model in the knowledge and research structures of Faraj. Method: The present study is an applied development study in terms of its purpose and a mixed research method in which two qualitative and quantitative methods were used. The data collection tool in the qualitative part is an interview and in the quantitative part is a questionnaire. The statistical population in the qualitative part includes 20 scientific, knowledge, and law enforcement elites at the Faraj level who were selected using the snowball sampling method. The statistical population in the quantitative part also includes 2600 people, and the sample size was calculated as 335 people using the Cochran formula. We used the coding technique in the qualitative part to analyze the data, and the Como, Bartlett, and Varimax tests in the quantitative part. In order to measure the validity of the tool, the trinity method was used in the qualitative part and content validity was used in the quantitative part. Findings: The human resources development model in the Faraj knowledge and research structure consists of three main dimensions: organizational, individual, and support. Support, which can be part of the human resources development model or an organizational dimension, is considered to create and maintain the context and conditions that facilitate the development and advancement of human resources. This dimension includes human resources management processes and systems, policies and procedures, infrastructure and technologies, organizational culture, and cooperation with other organizational departments. Therefore, support is still part of the human resources development model in the knowledge and research structure of Farajah and plays an important role in improving and developing human resources in the organization. Conclusion: In order to achieve the set goals, including the scientific map of Daja, Farajah's knowledge and research structures are in dire need of human resources development by considering a model that includes three organizational, individual, and support dimensions. The development and application of the aforementioned model can lead to better synergy between knowledge and research structures including Amin Comprehensive University of Law Enforcement Sciences, the Deputy of the Directorate of Education and Training, and the Center for Strategic Studies in Human Resources Development. Keywords: Research structure - Knowledge structure - Resource development - Human resources https://stim.qom.ac.ir/article_3671.html Purpose: This research explores the transformative potential of blockchain technology in revolutionizing the life insurance industry, with a focus on enhancing transparency, reducing operational costs, and optimizing insurance processes. The study seeks to evaluate blockchain’s capacity to strengthen trust between policyholders and insurers, streamline policy issuance, claims management, and reduce reliance on traditional intermediaries through smart contracts. Additionally, it aims to identify operational, legal, and infrastructural barriers to blockchain adoption in Iran’s insurance sector. By proposing practical solutions, the research endeavors to facilitate the development of strategic policies, regulatory reforms, and alignment of existing infrastructures with blockchain requirements. The study also promotes innovation in the insurance industry by leveraging emerging technologies to enhance competitiveness and sustainability in domestic and international markets. Furthermore, it emphasizes the creation of decentralized platforms for life insurance sales and the improvement of customer experience through advanced digital services. Method: Conducted as applied research; this study employs an analytical-comparative approach. Data were collected through field methods, including surveys and interviews with 24 insurance industry experts selected via purposive, non-random sampling. The surveys were designed to assess the necessary infrastructure, implementation prerequisites, and the perceived need for blockchain technology in insurance companies. Data analysis involved examining respondents’ demographic characteristics, such as gender, education level, professional experience, and organizational role, alongside their responses to specialized questions. To enrich the analysis, prior studies, research reports, and global experiences with blockchain applications in the insurance sector were consulted, enabling comparison with international trends and validation of findings. Findings: The research reveals that blockchain technology, with its hallmark features of transparency, robust security, and decentralized architecture, holds substantial potential to fundamentally transform the life insurance industry. Smart contracts enable the automation of processes such as policy issuance, risk assessment, claims management, secure data storage, and expedited claims payments, significantly reducing operational costs. Survey responses indicate that 92% of Iranian insurance companies have yet to undertake meaningful steps toward blockchain implementation, yet the vast majority of respondents emphasize a critical need for its adoption in life insurance development, particularly for unit-linked and annuity products. Key applications include fraud prevention and detection, more precise risk assessment, fairer pricing, secure data management, and faster claims processing. However, adoption faces challenges such as inadequate technical infrastructure, legal gaps, organizational resistance, a shortage of specialized personnel, and insufficient awareness of implementation risks. Collaboration with entities like the Ministry of Health, judiciary, Ministry of Communications, healthcare institutions, and customer credit agencies for data exchange was identified as a vital requirement. Respondents also highlighted smartphones, machine learning, artificial intelligence, big data, and blockchain as pivotal technologies driving insurance industry transformation, reflecting a broader shift toward digitalization and adoption of innovative technologies including blockchainو to enhance services and customer satisfaction. Conclusion: The study underscores that blockchain technology, is a powerful catalyst for advancing digital transformation in the life insurance sector. Its benefits, including heightened transparency, enhanced data security, cost reduction, and accelerated insurance processes, position it as a strategic asset for insurance companies. Nevertheless, realizing this potential requires overcoming challenges such as regulatory reform, digital infrastructure development, workforce training, and shifting managerial perspectives. It is recommended that insurance companies pursue pilot projects, strengthen collaboration with regulatory authorities, leverage global best practices, and partner with technology startups to facilitate blockchain adoption. Establishing clear legal frameworks, fostering inter-organizational collaboration networks, promoting a culture of innovation, and investing in IT infrastructure are essential steps to fully harness blockchain’s capabilities. Ultimately, this research emphasizes the necessity of strategic planning and coordination among industry stakeholders to enhance the sustainability, competitiveness, and responsiveness to evolving customer needs in Iran’s life insurance sector through blockchain technology. https://stim.qom.ac.ir/article_3672.html Objective : Knowledge management in the health sector as an effective management system can help this system to use its knowledge resources optimally. Understanding the clinical workflow , the information needs of different users ( system designers , doctors , nurses , researchers , managers ) and the ability to transform data into practical knowledge to improve decision-making and patient care are essential requirements. This study was conducted with the aim of identifying the knowledge requirements of medical data management. Methodology : This study is an applied study in terms of purpose and is a qualitative - documentary research in terms of method that follows the Cochrane systematic review and 53 articles were entered into the final systematic analysis by using the PRISMA approach amoung various stages. To ensure the quality of the articles in terms of alignment with the research objective, research up – to – dateness , research design , sampling method , data collection method , and generalizability , the CASP standard checklist was used. In this study , data analysis including open coding to identify primary concepts , axial coding using MAXQDA software to group similar themes and finally selective coding was used. Findings : The results of the study showed that the knowledge requirements of medical data management include knowledge-based data acquisition ( identification , collection and integration ) ; Data processing and analysis are aimed at knowledge generation ( valuation, labeling and visualization of knowledge , classification and analysis of knowledge - based data , ranking and prioritization of knowledge information, data analysis ) ; storage of knowledge-based data ( formatting and separation of data format types and creation of thematic and semantic relationships ) ; selection of knowledge -based data ( relationship with knowledge goals, knowledge production potential, quality and reliability for knowledge production, context and meaning and needs of knowledge users ); sharing and dissemination of knowledge extracted from data (creation of knowledge sharing platforms, documentation and dissemination of knowledge of communities of practice and knowledge exchange); evaluation and governance of knowledge-based data (definition of key performance evaluation indicators, monitoring and evaluation, knowledge-based data governance); security of knowledge-based data (role-based access control (RBAC) and knowledge, strong authentication and authorization, data encryption, infrastructure protection, physical security and secure data deletion). Conclusion: Based on the research findings, the knowledge requirements of medical data management are interconnected and intertwined with each other. Meanwhile, knowledge-based data acquisition in the health sector is interconnected with other elements such as data processing and analysis, and other elements. Optimal management of medical data requires standardization, integration, and accurate data analysis to show its true value in saving human lives. Integrating heterogeneous medical data and converting them into operational knowledge requires the use of knowledge management principles in medical sciences, and the results of this study indicate the existence of high heterogeneity in medical data. Data processing in medical sciences with a knowledge management approach is an essential process for converting raw data into valuable information and usable knowledge. This approach, by utilizing organizational expertise and knowledge, enables more effective organization, understanding, and use of complex health data. Knowledge requirements in health data management are a vital process for organizing, sharing, and effectively using existing information and experiences in health organizations, which aims to improve the quality of patient care, promote clinical and management decisions, facilitate learning and innovation, and increase operational efficiency. It requires creating appropriate infrastructure, promoting a culture of learning and collaboration, and utilizing information technologies to transform data into valuable and accessible knowledge for all stakeholders, including system designers, physicians, nurses, and management and executive staff in the health sector. https://stim.qom.ac.ir/article_3735.html Objective: Given that pricing is based on the future expectations of managers from the company status and changes in the future will change the price of the stock, the exploration of expectations based on managers' beliefs improves the recognition of their behaviors and reduces the ability to mislead and impose costs on the investor because performance instability can provide a visual viability. In other words, from the recognition of managers' behavior and performance, it is possible to reduce the risk of the investor. Changing expectations is a factor in exacerbating anomalies, resulting in an improper risk for these investors and can lead to fluctuations in capital market trading. This is rational in the form of diversion of market participants' beliefs. Change in the quality of disclosure cannot be interpreted from simple events, but it can be considered as a sign of expectations. Therefore, changes in expectations can lead to the formation of fluctuations and abnormalities in the market. Sticky expectations remain stable despite receiving new information relevant to the organization's situation. This effect seems to exist only in situations that benefit the manager. In other words, the manager is slow to adjust his decisions as new information is received. The purpose of this study is to examine the sticky expectations based on disclosure quality.Method: The present research is descriptive in terms of purpose and correlational in terms of nature and method. Given that this research can be used in the decision-making process of investors, it is considered an applied research type. In implementing a descriptive research design, the researcher does not manipulate variables or create conditions for events to occur. Based on this classification, since none of the research variables are manipulated and it is sufficient to describe the collected information, the research method is descriptive. Correlational research includes research in which the relationship between different variables is tried to be discovered or determined using the correlation coefficient. In correlational research, the main goal is to determine the relationship between two or more variables, the size and amount of that relationship. In this research, the library method was first used to collect data and information. In the library method, the theoretical foundations of the research are collected from specialized Persian and Latin books and journals. Then, to collect data for the present study, the compressed discs of the Tehran Stock Exchange Organization's visual and statistical archives, the official website of the Tehran Stock Exchange Company and other related websites, accounting information of listed companies, and other information sources were used. In order to study the subject based on the panel regression model, in the period 2016 to 2023, data of 120 companies listed on the Tehran Stock Exchange were collected and used to test research hypotheses.Results: The results of the research indicate that in the studied companies, we could see the managers' expectations stickiness. Also, the results of the second hypothesis of the study indicate that disclosure quality has a significant effect on the stickiness of managers’ information expectations. Finally, the sensitivity analysis conducted indicates that there is a significant difference between the high and low classes of information environment quality in terms of the sticky expectations, and it can be stated that changes in the information environment quality lead to changes in the sticky expectations.In this study, the explanation of informational expectation stickiness based on disclosure quality has been studied. The first hypothesis of the study, that there is expectation stickiness among the companies under study, has been confirmed. Expectation stickiness is a phenomenon based on self-promotion bias. This argument explains the effect of sticky expectations as a consequence of people’s greater tendency to act based on information that is in their own interest and less tendency to act based on information that is to their detriment. The stickiness of managers’ expectations leads to dispersion and deviation in future earnings, which can lead to an increase in the risk of incorrect selection by capital market participants. Therefore, it is suggested that considering the stickiness of managers’ expectations in decision-making models can be a basis for predicting future earnings deviation. In this regard, this information can provide a basis for increasing the accuracy of capital market analysts’ forecasts. In this study, there are limitations including the variable measurement approach of managers’ expectations stickiness. Given that managers' behavior and reactions are based on individuals' internal and personality characteristics and cannot be directly observed and measured, it is necessary to be careful in generalizing the results. https://stim.qom.ac.ir/article_3736.html Objectives: Organizational citizenship behavior is behavior that is consciously and voluntarily performed and is not directly reinforced by the formal organizational reward system and generally improves the effectiveness of the organization. Librarians in libraries showed organizational citizenship behavior, and this behavior affects their attitudes, behaviors, and interactions in order to provide more and more quality services. Librarians, as members of society, also have a social intelligence, and this intelligence can affect library services and functions and user satisfaction. Therefore, the present study investigated the situation and relationship between social intelligence and organizational citizenship behavior among the librarians of public university libraries in Isfahan city. Methodology: The current research is based on the objective, applied; and in terms of methodology, descriptive and correlational research. The statistical population of the research consisted of 101 librarians of public university libraries in Isfahan in 2023, and 76 librarians were examined as a statistical sample using the Cochran formula and using the stratified random sampling method proportional to volume. To collect data, the Amini Abchoye organizational citizenship behavior questionnaire (2015) based on the dimensions of the Podsakov et al. (2000) model and the Silvera et al. (2001) social intelligence questionnaire were used. The reliability of the two questionnaires was calculated by calculating the Cronbach's alpha coefficient for social intelligence and organizational citizenship behavior as 0.84 and 0.9, respectively. Also, descriptive and inferential statistical tests and analyses were used to analyze the collected data, including mean, standard deviation, Pearson correlation tests, t-tests. Independent samples and one-way analysis of variance in SPSS version 26 were used to examine the research hypotheses. Findings: The findings of the research showed that the status of social intelligence and organizational citizenship behavior of librarians in public university libraries in Isfahan city is at a higher than average level (x=3) and there is a positive and significant relationship between librarians' social intelligence and their organizational citizenship behavior. The correlation coefficient between these two variables was 0.51, which indicates a moderate intensity of the relationship. Also, all dimensions of social intelligence (social information processing, social skills, and social awareness) also have a positive and significant relationship with seven dimensions of organizational citizenship behavior (sportsmanship, organizational compliance, helping behaviors, organizational loyalty, civic virtue, self development, and individual initiative). The Pearson correlation coefficient for the relationships of each of the three dimensions of social intelligence with organizational citizenship behavior was obtained for the dimensions of social information processing, social skills, and social awareness as 0.441, 0.285, and 0.545, respectively. Among the three dimensions of social intelligence, the dimensions of social awareness and social skills have the highest and lowest correlation with organizational citizenship behavior, respectively. The findings also showed that there is no significant difference between social intelligence and organizational citizenship behavior of librarians based on their demographic variables including age, service experience, gender, field of study, place of service, and level of education from the perspective of librarians of Isfahan University libraries, and they expressed the same opinions. Conclusion: The present study showed that librarians' social intelligence has a positive relationship and correlation with their organizational citizenship behavior; therefore, social intelligence can act as an influential factor on organizational citizenship behavior. Strengthening librarians' social intelligence increases the occurrence of organizational citizenship behaviors in them. Those librarians who engage in organizational citizenship behaviors are actually taking steps towards achieving the library's goals and will overall increase the productivity and effectiveness of the library. It is suggested that managers and officials of university libraries in Isfahan implement programs and strategies such as holding courses, workshops, encouraging employees to do group activities and learn social intelligence skills, and creating an environment to increase employee participation and communication in order to increase the social intelligence of their librarians in order to improve their organizational citizenship behaviors. https://stim.qom.ac.ir/article_3772.html Objective: The role of science and technology parks in the national innovation system has been significantly established, making these parks a key component of this system. They contribute to the transfer and dissemination of technology, the commercialization of research findings, and the establishment of connections between higher education institutions, industry, and society. Additionally, science and technology parks serve as foundational elements of the "knowledge-based economy," providing a space for collaboration among higher education institutions, governments, and private companies. Therefore, these entities need to be more in the spotlight of their customers and stakeholders. One way to enhance the visibility of science and technology parks is to create a mechanism that allows them to compete in terms of visibility. Evaluation, comparison, and ranking can guide parks toward implementing tools and strategies for greater visibility. The aim of this research is to identify the indicators and metrics for measuring visibility within a comprehensive framework and to construct a ranking system in this area. Methodology: Following a systematic review of the literature on visibility, two brainstorming sessions were held with key stakeholders to identify the dimensions and metrics of the visibility measurement framework. In this study, 1665 relevant works on the visibility of science and technology parks were retrieved from global databases. After importing these records into EndNote and removing duplicates based on titles, 1612 works were selected for further analysis. Based on the research question, 156 records were chosen after reviewing the titles. These records were prepared for abstract study, and ultimately, 82 works were selected for full-text analysis. The identified metrics were organized into a comprehensive framework and weighted by 17 professionals, policymakers, and experts. The weighting of visibility dimensions was conducted using the AHP method, while the metrics were weighted using a simple weighting method. Findings: After the systematic review, 23 key metrics for evaluating the visibility of science and technology parks in Iran were identified. To validate these findings, brainstorming sessions were conducted, resulting in a new framework comprising 36 metrics, which were grouped into four main categories. The results indicate that the "output" dimension holds the highest importance with a weight of nearly 40%. Following this, the "networking" dimension ranks second with 28%, the "digital identity" dimension comes third with 20%, and the "web presence" dimension ranks fourth with 12%. Digital identity refers to the online information and activities of parks, including metrics such as the number of followers on various platforms. Web presence pertains to the availability of information about parks on their websites, including metrics like the number of supported languages and the global ranking of the website. Networking refers to the extent of collaborative networks around the parks, including metrics such as the number of joint projects and memoranda of understanding. Finally, the output dimension addresses the quantity and quality of the parks' achievements, including metrics such as the number of publications and annual revenue. Conclusion: Today, institutions worldwide are seeking to improve efficiency and increase their impact in the fields of science, industry, and society due to various pressures, and science and technology parks are no exception. Undoubtedly, visibility as a fundamental concept and action can help these parks strengthen their impact in society. Visibility and its evaluation metrics are relatively new topics in the literature and scientific writings, and a comprehensive framework for measuring them has not yet been proposed. This research represents the first effort in this area, focusing on constructing such a framework to enhance the visibility of science and technology parks in Iran. Although measuring visibility is a challenging and complex process, the proposed framework in this study opens a window to this intricate concept. The results of the evaluation of the parks are available in the V.Rank system (https://vrank.irandoc.ac.ir/Home/Visibility). https://stim.qom.ac.ir/article_3773.html Purpose: The aim of this study is to present an analytical model to examine the relationship between language barriers (translation barriers, linguistic complexity, and cultural adaptation barriers) and knowledge processing in organizations, with a focus on the mediating role of artificial intelligence (AI) in the export storage units of Shazand Mahshahr Petrochemical Company. The study addresses the growing importance of knowledge management and the application of emerging AI technologies in multicultural and multilingual environments. Method: This applied research employs a descriptive–survey design and is causal (ex post facto) in nature. The statistical population included all employees of the export storage units of Shazand Mahshahr Petrochemical Company (N=70), selected through a census method. Data were collected using three standardized questionnaires measuring language barriers including three dimensions, 21 items, knowledge processing including two dimensions, 7 items, and AI including five dimensions and 22 items. Validity was confirmed by expert review, and reliability was supported by Cronbach’s alpha coefficients above 0.7. Data analysis was conducted through confirmatory factor analysis and structural equation modeling using the PLS approach Findings: The results indicated that all dimensions of language barriers (translation barriers, linguistic complexity, and cultural adaptation barriers) have a positive and significant effect on knowledge processing in the organization. Moreover, AI plays a mediating role in these relationships, with the strongest mediation observed between translation barriers and knowledge processing (path coefficient = 0.991). This suggests that part of the impact of language barriers on knowledge processing is transmitted through AI, which can mitigate the negative effects of these barriers. Conclusion: The findings highlight the importance of accurate translation, the use of plain language, culturally adaptive content, and AI adoption in enhancing knowledge processing in organizations. AI can facilitate the knowledge transfer process, especially in specialized and multilingual settings, by reducing the adverse effects of language barriers. Therefore, managers should focus not only on overcoming language barriers but also on developing the necessary infrastructure for AI adoption and providing staff training to effectively utilize this technology. Purpose: The aim of this study is to present an analytical model to examine the relationship between language barriers (translation barriers, linguistic complexity, and cultural adaptation barriers) and knowledge processing in organizations, with a focus on the mediating role of artificial intelligence (AI) in the export storage units of Shazand Mahshahr Petrochemical Company. The study addresses the growing importance of knowledge management and the application of emerging AI technologies in multicultural and multilingual environments. Method: This applied research employs a descriptive–survey design and is causal (ex post facto) in nature. The statistical population included all employees of the export storage units of Shazand Mahshahr Petrochemical Company (N=70), selected through a census method. Data were collected using three standardized questionnaires measuring language barriers including three dimensions, 21 items, knowledge processing including two dimensions, 7 items, and AI including five dimensions and 22 items. Validity was confirmed by expert review, and reliability was supported by Cronbach’s alpha coefficients above 0.7. Data analysis was conducted through confirmatory factor analysis and structural equation modeling using the PLS approach Findings: The results indicated that all dimensions of language barriers (translation barriers, linguistic complexity, and cultural adaptation barriers) have a positive and significant effect on knowledge processing in the organization. Moreover, AI plays a mediating role in these relationships, with the strongest mediation observed between translation barriers and knowledge processing (path coefficient = 0.991). This suggests that part of the impact of language barriers on knowledge processing is transmitted through AI, which can mitigate the negative effects of these barriers. Conclusion: The findings highlight the importance of accurate translation, the use of plain language, culturally adaptive content, and AI adoption in enhancing knowledge processing in organizations. AI can facilitate the knowledge transfer process, especially in specialized and multilingual settings, by reducing the adverse effects of language barriers. Therefore, managers should focus not only on overcoming language barriers but also on developing the necessary infrastructure for AI adoption and providing staff training to effectively utilize this technology. https://stim.qom.ac.ir/article_3801.html Today, universities serve as critical pillars of education, research, and technology, playing a significant role in the development of knowledge-based economies. As such, they contribute substantially to both economic and social value creation. Accordingly, evaluating the performance and positioning of universities and other higher education institutions has become a central concern for science and technology policymakers. The primary aim of this study is to provide a comprehensive and balanced account of trends in the global rankings of Iranian universities over recent years, with particular emphasis on the past five years. The importance of this issue arises from the fact that various global ranking systems employ differing perspectives, indicators, and methodologies. Without careful analysis, temporary—or even random—fluctuations in the rankings of a few institutions may attract disproportionate attention, thereby obscuring more stable and meaningful trends. Such misinterpretations can hinder the effective use of ranking outcomes in the design and implementation of appropriate policies and strategies. This study was conducted in two main phases. In the first phase, the status of Iranian universities within international ranking systems was examined. In the second phase, to explore the conditions of higher education institutions in the country, a combination of desk research and expert interviews was employed. Given that the study aims to provide a qualitative, non-experimental analysis of the positioning of top Iranian universities in global ranking systems, thematic analysis was utilized. Data obtained from in-depth interviews with subject-matter experts, along with relevant academic literature, were analyzed using MAXQDA software. Thematic analysis reveals that part of the variation in rankings is attributable to the inherent structure and criteria of the ranking systems themselves, while another part results from the performance of higher education institutions and their surrounding environments. Internal factors contributing to the decline in the rankings of Iranian universities include low student-to-faculty ratios, limited international faculty and student representation, and weak academic reputation scores. However, a key strength of Iranian universities lies in their output of scientific publications and citation rates, which remain strong relative to international benchmarks. Barriers to international collaboration among Iranian university faculty have been identified as falling into three categories: internal institutional challenges, external systemic constraints, and individual-level obstacles. This study integrates technological, organizational, and cultural dimensions into a comprehensive strategic framework. By aligning its recommendations with Iran’s political, cultural, and infrastructural context, the research seeks to bridge the gap between local conditions and the expectations of global ranking systems, offering a practical roadmap for institutional enhancement. In this regard, the study proposes new pathways to enhance the global standing of Iranian universities while simultaneously strengthening their national impact. Key distinguishing features of this study include emphasis on the development of a localized ranking system, structural reforms to align universities with mission-oriented objectives, targeted internationalization initiatives, the expansion of collaborative networks between universities and industry, the creation of a national data platform for higher education, the standardization of reporting mechanisms, and the promotion of scientific diplomacy among regional countries. These strategies contribute tangible value to the body of literature on university rankings and higher education reform. Based on the synthesized themes, several key leverage points for improving the rankings of Iranian universities have been identified, including: • Enhancing universities’ technical capacity to report accurate and comprehensive data, while implementing effective monitoring and evaluation systems. • Promoting purposeful international engagement aligned with clearly defined operational goals, particularly through collaboration with distinguished faculty members. • Developing a strong international brand identity for selected universities. Recommended policies and actions for improving university rankings include: • Integrating national knowledge-based initiatives with internal university mechanisms, such as faculty promotion criteria. • Expanding international academic networks and participating in global scientific and industrial consortia to enhance institutional reputation. • Prioritizing the quality of publications and increasing the visibility of highly cited articles and researchers. • Linking financial support directly to academic performance and removing funding caps for top-performing international scholars. • Upholding meritocracy in academic appointments and promotions to sustain faculty motivation. • Strategically allocating foreign currency resources to support international collaborations. • Defining clear expectations for global rankings that align with localized standards, while guiding the ISC ranking system toward the gradual establishment of an independent, credible, and internationally accepted identity. • Establishing monitoring and evaluation units within universities to design and implement leverage-based initiatives for ranking enhancement. https://stim.qom.ac.ir/article_3806.html Abstract Objective:This research aims to design a comprehensive knowledge-based data-driven marketing model for cultural institutions. In the current digital age, cultural institutions face numerous challenges, including changing audience behavior patterns, increasing expectations for personalized services, and the need for an effective presence in the digital space. The main goal of this research is to provide a framework that, by using data analysis capabilities and new technologies such as artificial intelligence, machine learning, and digital platforms, helps cultural institutions deepen and make their interactions with audiences more effective, improve information services, and ultimately achieve sustainable development. This research seeks to answer several key questions: How can audience data be used to improve cultural interactions? What solutions are there for personalizing information services in cultural institutions? And how can the challenges in the digital transformation of these institutions be managed? The proposed model of this study not only improves the audience experience, but also helps cultural institutions maintain and strengthen their competitive position in a rapidly changing environment. Method:This study was conducted by adopting a qualitative approach and using the grounded theory method. The research population consisted of 17 experts and specialists in the field of cultural institutions who were selected using the snowball sampling method. These individuals included senior managers of cultural organizations, university professors, and researchers in the field of digital marketing who had practical experience and theoretical expertise in the field of the research topic. Data were collected through in-depth semi-structured interviews, and the data collection process continued until reaching theoretical saturation. To ensure the validity and reliability of the data, the interview protocol was reviewed and approved by several university professors. Also, the interview questions were revised and finalized after consulting with experts and conducting two pilot interviews. Data analysis was conducted in two main stages: In the first stage (open coding), the researchers identified 149 initial codes by carefully examining the interviews. In the second stage (axial coding), these codes were organized into 30 main categories, which included six general groups: causal conditions, axial phenomena, contextual conditions, intervening conditions, strategies, and consequences. These categories ultimately led to the design of a comprehensive conceptual model that shows the complex relationships between the various factors affecting data-driven marketing in cultural institutions. Findings:The findings of this study show that data-driven marketing can create a fundamental transformation in the way cultural institutions interact with their audiences. The most important findings are presented in the form of a paradigm model: Factors such as increasing technology-driven competition, the emergence of new digital platforms, changing audience behavior towards online experiences, and the need for advanced analytical tools are driving cultural institutions to adopt a data-driven approach. The core of the model is “Data-driven marketing to enhance engagement and information services,” which includes two main dimensions: a) predictive digital engagement using audience behavior analysis and b) dynamic personalization of services based on collected data. The success of this model depends on factors such as the existence of a culture of innovation in the organization, political and financial support, the availability of expert human resources, the existence of strong digital infrastructure, and the organization’s readiness for digital transformation. Barriers such as resistance to change, concerns about data privacy, lack of digital skills in employees, and technical challenges in integrating data systems can affect the implementation of the model. Suggested solutions include developing integrated data management systems, using advanced analytics technologies such as artificial intelligence, investing in employee training and empowerment, designing personalized user experiences, and creating cross-departmental collaborations. Successful implementation of this model can lead to results such as strengthening interactions with audiences, optimizing organizational resources, increasing the competitiveness of cultural institutions, creating new revenue models, and developing international collaborations. Conclusion:This study showed that data-driven marketing can play a transformative role in cultural institutions. The presented model provides a comprehensive framework for the effective use of data to improve interactions and information services. This study has taken an important step towards integrating digital marketing knowledge with the specific needs of cultural institutions. The presented model can serve as a guide for managers and decision-makers of these institutions. However, it is suggested that future studies should empirically examine the effectiveness of this model in different operational environments and analyze the factors affecting its success or failure in different conditions. Also, future research can be devoted to the development of performance measurement tools and indicators for evaluating the effectiveness of this model. Keywords: Data-driven marketing, cultural interactions, informational services, artificial intelligence, digital platform, cultural institutions. https://stim.qom.ac.ir/article_3817.html Abstract: Purpose: In recent decades, the societal impact of research has emerged as a key dimension in evaluating the quality and effectiveness of scientific work. Accordingly, the present study aims to investigate the perceptions and self-assessments of faculty members at Qom University regarding the societal impact of their academic research. In addition, the study seeks to examine how contextual factors—such as age, gender, work experience, and academic discipline—as well as institutional policies (including promotion criteria, academic incentives, and research funding mechanisms) influence faculty members’ perceptions of the societal relevance and impact of their research. Methodology: This study employed a quantitative approach using a survey method. The statistical population included all faculty members of Qom University (319 members at the time of the study). Considering practical limitations and based on power analysis, a purposive sample of 57 faculty members was selected to participate. Data were collected through a structured questionnaire whose content validity was confirmed by four experts in information science and sociology. The reliability of the instrument was assessed using a test-retest method with a one-week interval on a subset of 20 participants, yielding a correlation coefficient of 0.93. Data analysis included both descriptive statistics (mean, frequency, standard deviation) and inferential tests such as independent t-test, one-way ANOVA, and F-test. Findings: The result of the study indicated that the average self-assessed societal impact of research among faculty members was 9.36 out of 15, reflecting a moderate level of confidence in the social influence of their academic work. Approximately half of the participants believed their research lacked tangible impact, with reported societal effects at the national and international levels at 33.3% and 12.3%, respectively. Notably, none of the respondents reported any impact at the local level. Only 22.8% of the research projects were perceived to have advanced to the stage of implementation or product development, while the majority remained at preliminary phases. Moreover, 55.9% of faculty members stated that their research had influenced only a specific group in society, and just 5.9% believed it had directly affected particular individuals. Although the practical application of research appeared limited, a significant majority (86%) of respondents believed their work contributed to enhancing public awareness or the application of knowledge in society. These findings suggest that while broad operational impact may be lacking, academic research has partially achieved its purpose in disseminating knowledge and raising public understanding. The statistical analyses further revealed that there were no significant differences in the self-assessed societal impact of research based on academic departments or gender. However, a significant relationship was found between years of professional experience and the level of self-assessed impact; faculty members with more years of experience reported higher scores for the societal relevance of their research. Although differences in average scores across age groups were also observed, the results of the ANOVA test indicated that these differences were not statistically significant at the 95% confidence level (p = 0.076), though the result was close to the threshold of significance. The findings also highlighted a low level of engagement with public dissemination methods such as newspapers, radio, or television. In contrast, 47.4% of the researchers reported using professional social networks like LinkedIn or ResearchGate to share their findings. This suggests that one of the key barriers to the societal impact of academic research lies in the limited effectiveness of knowledge transfer mechanisms aimed at reaching the general public. Finally, the analysis of participants’ views on official university policies such as grant allocations, and publication incentives, revealed that the majority of faculty members did not consider these policies effective in enhancing the societal impact of academic research. However, self-assessment scores were significantly higher among those who perceived these policies as effective. This finding suggests that a positive attitude toward institutional support mechanisms is associated with a stronger sense of research impact. Consequently, revising and aligning university policies to better support societal engagement may play a crucial role in improving the social effectiveness of academic research. Conclusion: The present study revealed that although faculty members exhibit a moderate level of belief in the societal impact of their research, this confidence remains limited and highly variable. The findings also pointed to the absence of effective mechanisms for disseminating research outcomes to the broader public and the ineffectiveness of existing institutional incentive policies in fostering stronger connections between academic research and societal needs. Based on these results, it is recommended that university policymakers reconsider current promotion criteria and research funding frameworks, placing greater emphasis on research quality and practical application. However, since the study was conducted on a limited sample from a single university, future research is encouraged to include larger and more diverse samples across multiple universities in Iran, in order to improve the generalizability of the findings and enable inter-university comparisons. https://stim.qom.ac.ir/article_3848.html Purpose: Metadata serves as an intermediary in the discovery, access, and sharing of resources between users and information. The distinct ways children think compared to adults make the use of existing metadata models challenging for this user group. As users of information systems, children have different needs and developmental characteristics than adults; factors such as cognitive development, memory, information recall ability, socio-emotional development, and physical development influence their interaction with information retrieval systems. Designing a child-centered metadata model for organizing information resources is therefore essential for improving children's access to information. This research aimed to design a metadata model for organizing information resources from children's perspectives. Methodology: This study employed a qualitative approach using the seven-step meta-synthesis method by Sandelowski and Barroso. This method was chosen based on the need to systematically synthesize scattered findings and develop a comprehensive framework for understanding children's cognitive-perceptual dimensions. The research population comprised relevant articles in three main areas: children's information behavior, selection of information resources, and child-centered metadata. Valid specialized thesauri were used to determine keywords. The search focused on sources addressing the perspectives of children, parents, educators, and child experts on the selection and organization of children's information resources; sources referring to existing metadata standards were excluded. After a systematic search of Persian (Magiran, Normags, IranDoc, SID, Civilica) and English (Web of Science, Scopus, Emerald, ScienceDirect, ProQuest, Google Scholar) databases and applying critical appraisal criteria, 18 articles were selected and coded. Research validity was ensured using critical appraisal skills program guidelines, and reliability was achieved through consultation with three experts and auditing by a metadata specialist. A descriptive coding process, wherein key concepts were extracted and categorized while preserving the original context of the text, was conducted in three stages under the supervision of two researchers. The calculation of Cohen's kappa coefficient (κ = 0.89) confirmed the reliability and validity of the coding analysis. Findings: Data analysis led to the extraction of 128 initial codes. After merging similar codes, these were categorized into 16 main concepts and three categories: 1) Resource-related attributes (including: bibliographic information concepts, book cover, storybooks, image or images, character or characters, item or entity, physical specifications, genre, series/sequel, and non-fiction), 2) User-related attributes (including: emotions, familiarity or prior knowledge, and comprehensibility), and 3) User-Resource shared attributes (including: novelty, appeal, and complexity). In response to the second research question, since most extracted metadata was descriptive, the child-centered metadata model was developed relying on the Dublin Core Metadata Initiative (DCMI) and the DCMI framework. This model emphasizes the development of descriptive elements with a child-centered approach without prescribing predetermined values. The model's structure follows an attribute-value pair framework, where the attribute refers to a descriptive aspect of the resource (e.g., title) and the value refers to the associated data (e.g., book name). The model's core elements include Name, Definition, Comment, Refinement, and Value Type, some of which are mandatory and others optional. The grouping of these elements was based on children's cognitive criteria. In this model, redundant elements were consolidated under broader related categories, and non-operationalizable components were eliminated. Analytically, the present model does not cover the value space of data, such as documentation files, subject headings, and specific children's thesauri. Attention to children's cultural, social, and cognitive metadata in various information retrieval contexts is necessary to complete the model and improve children's information access. Conclusion: The final model presented can serve as a scientific framework for organizing children's resources in libraries and information centers, representing an effective step towards improving information services for this age group. This flexible model allows for the utilization of existing standards, such as subject headings, and while maintaining standardization principles, enables adaptability to diverse needs in describing children's resources. Keywords: metadata, information organization, information retrieval, information behavior, information resources, child https://stim.qom.ac.ir/article_3850.html Abstract This study analyses the effects of information technology on the role of agents in football player transfers. The use of emerging technologies in football player transfers has attracted attention due to its potential to increase transparency and efficiency. The aim of this study is to investigate the feasibility and implications of adopting the role of technology in football transfers and to address the key question of how information technology can reduce issues such as lack of transparency, fraud and delays in player transactions. Methodology: This research is of an applied nature and uses a mixed method (qualitative-quantitative) approach. In the qualitative part, content analysis and the Glazer approach were used to extract concepts and patterns, and the data were analyzed using open and axial coding, including targeted interviews with stakeholders and experts in the blockchain field. Data collection techniques included semi-structured interviews and document analysis, followed by thematic analysis to identify key insights. The findings show that blockchain technology offers promising solutions to the challenges of player transfers. In particular, it can improve transparency by providing a secure and immutable ledger, thereby reducing the risk of fraudulent activities. Furthermore, the decentralized nature of blockchain has the potential to streamline administrative processes and shorten transaction times, benefiting both clubs and players. These results suggest that integrating blockchain into football transfers could transform the operational framework of the sports industry, paving the way for more efficient and reliable transactions. By increasing transparency and reducing transaction costs, blockchain can promote fairer practices and improve overall market liquidity in player transfers. This research contributes to the existing body of knowledge by demonstrating practical applications of blockchain in a specific sector of the sports industry. Future research could examine the scalability of different types of blockchain in different sports and assess the long-term economic and regulatory implications. Blockchain, with its features such as transparency, security and innovation, can bring about fundamental and sustainable changes in player transfers. This research shows that the convergence of blockchain and sports, especially in the areas of finance, management and fan interactions, will lead to the formation of a new era full of development and innovation opportunities in this industry. The present study analyzed the effects of information technology on the role of agents in football player transfers. The use of emerging technologies in football player transfers has attracted attention due to its potential to increase transparency and efficiency. The aim of this study is to examine the feasibility and implications of adopting the role of technology in football transfers and to address the key question of how information technology can reduce issues such as lack of transparency, fraud and delays in player transactions. Methodology: This research is of an applied nature and uses a mixed method (qualitative-quantitative). In the qualitative part, thematic analysis and Glazer’s approach were used to extract concepts and patterns, and the data were analyzed using open and axial coding, including targeted interviews with stakeholders and experts in the blockchain field. Data collection techniques included semi-structured interviews and document analysis, followed by thematic analysis to identify key insights. The findings show that blockchain technology offers promising solutions to the challenges of player transfers. In particular, it can improve transparency by providing a secure and immutable ledger, thereby reducing the risk of fraudulent activities. In addition, the decentralized nature of blockchain has the potential to simplify administrative processes and shorten transaction times, benefiting both clubs and players. These results demonstrate that integrating blockchain into football transfers can transform the operational framework of the sports industry, paving the way for more efficient and reliable transactions. By increasing transparency and reducing transaction costs, blockchain can promote fairer practices and improve overall market liquidity in player transfers. This research contributes to the existing body of knowledge by demonstrating the practical applications of blockchain in a specific sector of the sports industry. Future research can examine the scalability of different types of blockchain in different sports and assess the long-term economic and regulatory implications. Blockchain, with its features such as transparency, security and innovation, can bring about fundamental and sustainable changes in player transfers. This research shows that the convergence of blockchain and sports, especially in the areas of finance, management and fan interactions, will create a new era full of development and innovation opportunities in this industry. https://stim.qom.ac.ir/article_3858.html This research aims to present a comprehensive conceptual model for the effective implementation of knowledge management in Iranian governmental research centers, by examining the challenges, requirements, and key success factors in this field. The proposed model has been developed considering the local, cultural, and structural conditions of these centers to help improve knowledge processes and enhance their performance at the national and international levels.The main objective of this research is to develop a practical conceptual model for implementing knowledge management that is compatible with the specific needs of Iranian governmental research centers. This goal is pursued by identifying existing challenges, analyzing key success factors, and offering recommendations for policymaking and future research. The study seeks to answer the question of how the knowledge cycle (from creation to application) can be optimized and a culture of continuous learning can be institutionalized in these organizations by integrating human, cultural, structural, and technological dimensions.Methodology: This study utilized an exploratory mixed-methods research approach. In the qualitative phase, through a systematic review of 53 scientific articles (published between 2000 and 2022) and their content analysis using MAXQDA software, 106 initial indicators were extracted. These indicators were then refined in a three-round Fuzzy Delphi process with the participation of 30 knowledge management experts. Ultimately, 27 key indicators with high consensus were selected for the quantitative phase.In the quantitative phase, a questionnaire was designed based on the final 27 indicators and distributed among 100 knowledge management experts from 18 governmental research centers. The collected data were analyzed using the Structural Equation Modeling (SEM) method and SmartPLS 4 software. The validity and reliability of the model were confirmed through indicators such as Cronbach's alpha, Composite Reliability (CR), and Average Variance Extracted (AVE), and the causal relationships between variables were tested.Findings: The analysis revealed that the most significant challenges in implementing knowledge management in Iranian governmental research centers are traditional organizational culture, resistance to change, weak IT infrastructure, and the lack of a coherent national policy. In the Delphi process, the indicator "creating motivation for knowledge application" was identified as the most important factor, with the highest average score (4.18), emphasizing the importance of human and cultural dimensions.Structural Equation Modeling indicated that the three components of "knowledge collection and acquisition" (factor loading 0.339), "knowledge creation and generation" (factor loading 0.326), and "knowledge application" (factor loading 0.193) have the greatest impact on the knowledge management development strategy. In contrast, the variable "knowledge organization and codification" did not show a significant impact in the model, suggesting the complexity or inefficiency of current documentation processes. Finally, a four-layered conceptual model was presented, consisting of strategic (leadership and policy), cultural-human (motivation and trust), structural-process (knowledge cycle), and technological (infrastructure) layers, which allows for continuous improvement through a feedback and evaluation loop.Conclusion: This research concludes that the success of knowledge management in Iranian governmental research centers depends more on committed leadership, creating a learning culture, and designing effective motivational systems than on technology. The proposed conceptual model, by prioritizing the creation, acquisition, and application of knowledge and integrating it into a four-layered architecture, provides a realistic framework for these centers. The findings suggest that cumbersome "knowledge organization" processes should be simplified with smart tools to avoid becoming an obstacle to knowledge flow.From a policymaking perspective, developing a "National Knowledge Management Document" that clearly defines roles, sustainable resources, and Key Performance Indicators (KPIs) is an urgent necessity. Government investment in integrated and secure technological infrastructure and encouraging centers to share knowledge through financial and non-financial incentives are the next essential steps. Implementing this model can significantly contribute to establishing a learning culture, increasing innovation, and ultimately enhancing Iran's scientific standing. https://stim.qom.ac.ir/article_3923.html Purpose: This research's primary objective was to assess the status of eight key literacies—informational, media, research, environmental, health, financial, cultural, and emotional—among female high school students in Tehran. In addition to evaluating the level of these literacies, the study sought to uncover the complex and reciprocal relationships among them. Given the Iranian educational system's failure to foster 21st-century skills and society's need for a generation equipped with multiple literacies, this research aimed to provide a foundation for educational planning and macro-level policy-making by identifying existing strengths and weaknesses. The selection of female students was of particular importance due to their pivotal role in the country's cultural, social, and economic future, as well as their key function in transmitting values and knowledge to future generations. Methodology: This study was conducted in two phases. In the first phase, the components of the eight literacies were extracted through a content analysis of Persian and English academic texts and resources (including 8 Persian articles, 12 English articles, and 4 English books). Based on these components, a 37-item questionnaire with a 10-point Likert scale (from 1 to 10) was developed. In the second phase, the survey was distributed to 169 female high school students selected from 67 schools in Tehran. These students were chosen by their teachers based on criteria such as high grades and better performance in school activities. For data analysis, non-parametric tests and Structural Equation Modeling using the Generalized Structured Component Analysis (GSCA) method were employed. The validity and reliability of the research instrument were also confirmed using methods such as split-half reliability (Spearman–Brown coefficient), composite reliability, content validity (CVI), convergent validity, and discriminant validity. Findings: Descriptive findings revealed that the mean and median scores for all eight literacies among the elite students in Tehran were below the average threshold of 5. This indicates a significant weakness in informational, media, research, health, financial, cultural, emotional, and environmental literacies. The results of the binomial test confirmed that the frequency of responses "less than or equal to 5" (below average) was significantly higher than the frequency of responses "greater than 5" (above average). For instance, 96% of students rated their informational literacy as below average. The analysis of relationships between the literacies and demographic variables revealed several important findings. Firstly, informational literacy was the only literacy that showed a significant and positive relationship with household income. Secondly, the levels of emotional, cultural, and financial literacies decreased significantly as students' age increased, indicating a "developmental regression" due to the excessive focus on the university entrance exam (konkur). Thirdly, the highest correlations were observed between financial and cultural literacy, emotional and cultural literacy, and informational and media literacy. Environmental literacy only correlated with health literacy, suggesting its "isolation" from other life skills. Structural analysis findings showed that media literacy influences health literacy, which in turn affects emotional literacy. Research literacy also impacts financial literacy, which then has a strong effect on both emotional and cultural literacies. This path indicates that financial literacy acts as a powerful "conduit" that transfers the influence of research literacy to softer skills. No significant path was found for environmental literacy, further emphasizing its "isolation" within the students' skill sets. Conclusion: This research demonstrates that the Iranian educational system, even among its elite students, has failed to equip them with the vital literacies necessary for success in today's complex society. The overall weakness in all eight literacies, the concerning decline in emotional, cultural, and financial literacies with increasing age, and the powerful mediating role of financial literacy all confirm the deep gap between what the educational system provides and what society demands. The results suggest that effective training in financial and research literacies can act as catalysts for developing crucial soft skills like cultural and emotional literacy. The findings also underscore the need to rethink how environmental education is taught, so that this literacy is integrated and becomes an inseparable part of students' life skills rather than a detached concept. It is recommended that policymakers and educational officials reduce the focus on the university entrance exam and integrate 21st-century skills into curricula at all levels to prepare a capable generation for future challenges. https://stim.qom.ac.ir/article_3937.html Objective: In recent years, advanced technologies have undergone many transformations in the financial industry, especially insurance. However, the relationship between insurance companies and InsurTechs has its own risks. Insurtechs are new technologies that are used to improve the performance of the insurance industry and enhance the customer experience. Using tools such as artificial intelligence, big data, blockchain and the Internet of Things, these technologies enable more accurate risk analysis, smart pricing and the provision of services tailored to each customer’s needs. The key role of insurtechs in digitizing the insurance industry, increasing the speed and transparency of services, reducing costs and attracting new audiences is very significant. These technologies can also increase customer trust and improve the competitive position of insurance companies by simplifying processes and eliminating intermediaries. The current research seeks to identify and prioritize the risks of cooperation between insurance companies and InsurTechs. Method: The current research is applied in terms of orientation and in terms of methodology it has a mixed nature due to the use of quantitative and qualitative methods. The theoretical population of the research are experts in the insurance industry and insurance technologies. Sampling was done by judgment and its volume was equal to 10 people. In this research, the interview tool was used to extract research risks. Next, two questionnaires of expert assessment and prioritization were used to screen and prioritize risks. The reliability of extracted codes was measured by the agreement coefficient of two coders and its value was equal to 0.87, which is a favorable value. Findings: The present research was conducted in three stages. In the first step, the main and secondary risks of the research were extracted through interviews with experts and the method of theme analysis. 28 risks were classified into seven main risks. The main risks were: legal risks, infrastructural risks, structural risks, security risks, user trust risks, economic risks and risks related to limited technology expansion. Sub-risks were evaluated in the next step with the fuzzy Delphi method. To obtain expert opinions on the importance of risks, expert opinions were obtained on a five-point scale. 11 risks were selected for the final prioritization due to having a favorable defuzzified number. In the next stage, the selected risks were evaluated with the CoCoSo method. To obtain experts' opinions on the ultimate risks, priority assessment questionnaires were distributed among the experts. The range used to obtain experts' opinions was a range of 10. The priority risks were: the weak performance of RagTechs, the dominance of traditional business models in the financial industry, the lack of trust of large sections of users and customers in new technologies, and the weakness of systems and databases of insurance companies. Results: Practical research proposals were developed based on the most important risks. The research proposals had the nature of responding to risk. RegTechs, by utilizing regulating and data analysis technologies, help to reduce the risks arising from cooperation with FinTechs and provide the ability to predict their behavior. In Iran, FinTechs are not very diverse and are often active in the field of payments. Effective participation of large financial institutions such as banks and insurance companies, along with the creation of supportive regulations, can lead to the expansion of diverse fintechs. Despite the potential of new technologies in the field of insurance, the traditional structure, scattered information, and unintelligent decision-making prevent the full exploitation of these tools. Also, users' distrust of new technologies due to security concerns and low levels of financial literacy are other challenges that must be addressed by improving security and educating users. For InsurTechs to succeed, it is essential to focus on security, embracing technology, and strengthening integrated databases in insurance companies. The most important limitations of the research were: the inability to generalize some risks to other industries and sectors, such as banking, and the judgmental nature of the expert opinion tools in this research. In relation to research suggestions, we can also mention things such as futures study on the insurance industry with a focus on insurtechs and identifying and discovering cooperation patterns between insurance companies and insurtechs. https://stim.qom.ac.ir/article_4046.html Objective: This research, with a focus on the perspective of experts, has examined the potential application of artificial intelligence in addressing technological barriers to knowledge sharing and provides proposed solutions. The significance of the study lies in the direct impact of modern technologies on the quality of knowledge sharing in complex organizations such as the municipality, where over 218 independent information systems have created numerous information silos, disrupting the flow of knowledge. Key challenges include lack of system integration, weaknesses in technological infrastructure, insufficient technical support, inadequate employee training, and inefficient legacy systems, which result in contradictory decision-making, reduced productivity, and resource wastage. AI-based solutions not only overcome these barriers but also strengthen organizational memory, enhance intra-organizational collaboration, and improve urban services. Therefore, the following questions are raised: What are the technological barriers to knowledge sharing? What are the capabilities of artificial intelligence in eliminating technological barriers to knowledge sharing in Tehran Municipality? And how can artificial intelligence tools be applied to address technological barriers to knowledge sharing in Tehran Municipality? This research is grounded in the human-machine interaction conceptual model and theoretical frameworks such as Vuori et al. (2018) on technological barriers and Jarrahi et al. (2023) on AI capabilities, offering practical strategies for knowledge management in governmental settings.Method: The research employs an integrated and applied approach, combining meta-synthesis for collecting and synthesizing library-based findings with fuzzy Delphi to achieve expert consensus and focus thereon. In the meta-synthesis phase, over 100 domestic and international sources—including Persian and English articles from databases such as SID, MagIran, Scopus, and Google Scholar—were reviewed. The search was conducted using keywords such as "knowledge sharing," "artificial intelligence," "technological barriers," and "urban knowledge management." After screening (removing duplicates, irrelevant articles, and unreliable sources), 85 key studies were selected. This method enabled the extraction of 38 initial barrier components. In the fuzzy Delphi phase, a structured questionnaire with a fuzzy scale was distributed among 42 experts. The expert panel consisted of AI engineers from knowledge-based companies, IT specialists from Tehran Municipality, and professionals with at least 10 years of experience (61.9% with 10–20 years of experience). Purposive sampling was used, and analysis was performed using SPSS and Expert Choice software. The sample adequacy test (KMO=0.826) and Bartlett’s test (χ²=4769.959, p<0.001) confirmed data validity. Exploratory factor analysis identified 5 main components (explaining 70.22% of variance), and fuzzy Delphi—through calculations of fuzzy mean, coefficient of variation (CV<0.2 for high consensus), and Kendall’s tau—prioritized barriers and tools. This multi-stage approach managed uncertainties and enhanced the validity of results.Findings: Factor analysis results revealed that lack of integration in information technology systems (15.21% variance, mean 4.6, rank 1) is the primary barrier, leading to data silos and uncoordinated decision-making. Weaknesses in technological infrastructure (18.20% variance, mean 4.3), due to network instability and multiple repositories, ranked second. Insufficient technical support (mean 4.1), inadequate employee training (mean 3.9), and inefficient legacy systems (13.30% variance, mean 3.5) were the other barriers. From the experts’ perspective, effective AI tools include natural language processing (NLP, impact mean 4.5, rank 1) for facilitating communication and extracting tacit knowledge; collaborative intelligence (4.3) for enhancing human-machine interaction and organizational memory; intelligent data analytics (4.1) for integration and information prioritization; and predictive systems (3.9) for forecasting knowledge needs. These tools improve knowledge sharing by 40–60% through resource coordination, contradiction reduction, and increased accessibility (based on expert consensus). The findings align with prior studies such as Riege (2005) on barrier classification and Tavalayi (2023) on human-AI interaction, highlighting AI’s potential to transform the municipality into a knowledge-driven organization.Conclusion: The application of artificial intelligence in Tehran Municipality’s information systems transforms knowledge sharing from a siloed state into an integrated flow, enhancing urban service quality, strategic decision-making, and organizational learning. This study, by identifying 5 key barriers and 4 primary tools, provides a practical framework for governmental organizations. Positive impacts include enhanced knowledge security, 24/7 responsiveness, and rapid retrieval, while challenges such as employee resistance, implementation costs, and privacy concerns require careful management. Practical recommendations include declaring a knowledge management crisis, developing a change strategy, assessing infrastructure, selecting AI platforms such as NLP-based chatbots, conducting pilot implementations with training, and revising security standards. Future research can examine the empirical effects of these tools in broader samples (such as other municipalities and large government organizations) using combined methods (e.g., simulation) to elevate the proposed doctoral model into a national strategy. https://stim.qom.ac.ir/article_4074.html Objective: To present a model for an integrated content management system on the websites of the Ministry of Energy. Methodology: This applied research is of a mixed type and was conducted using an analytical survey method to gather the components of content management, a documentary method was used, Delphi method was applied for the qualitative part in designing the components, and a questionnaire was used for data collection. The statistical population of the study includes all specialists 121 people in the informatics sector and managers of subsidiary companies of the Ministry of Energy. Findings: After determining the items affecting content management on the Ministry of Energy websites in the form of 34 questions by experts, the content management factors after determining the appropriate period according to their correlation matrix in the form of 7 factors in order of special value are: The content management factors on the websites of the Ministry of Energy were identified in the form of 7 factors, ranked by their significance as follows: "Structural and technical requirements" 10/103, "Content requirements" 7/96, "Content evaluation and review requirements" 7/63, "Content accessibility requirements" 4/61, "Quality requirements and usability principles" 4/007, "General requirements" 3/12, and "Requirements for alignment with artificial intelligence" 2/.601 Conclusion: To determine the importance of each content management component from the perspective of specialists in the informatics sector and managers of subsidiary companies of the Ministry of Energy, a model was presented for an integrated content management system on the Ministry's websites after analyzing the components and transforming them into new factors through exploratory and confirmatory factor analysis. Objective: To present a model for an integrated content management system on the websites of the Ministry of Energy. Methodology: This applied research is of a mixed type and was conducted using an analytical survey method to gather the components of content management, a documentary method was used, Delphi method was applied for the qualitative part in designing the components, and a questionnaire was used for data collection. The statistical population of the study includes all specialists 121 people in the informatics sector and managers of subsidiary companies of the Ministry of Energy. Findings: After determining the items affecting content management on the Ministry of Energy websites in the form of 34 questions by experts, the content management factors after determining the appropriate period according to their correlation matrix in the form of 7 factors in order of special value are: The content management factors on the websites of the Ministry of Energy were identified in the form of 7 factors, ranked by their significance as follows: "Structural and technical requirements" 10/103, "Content requirements" 7/96, "Content evaluation and review requirements" 7/63, "Content accessibility requirements" 4/61, "Quality requirements and usability principles" 4/007, "General requirements" 3/12, and "Requirements for alignment with artificial intelligence" 2/.601 Conclusion: To determine the importance of each content management component from the perspective of specialists in the informatics sector and managers of subsidiary companies of the Ministry of Energy, a model was presented for an integrated content management system on the Ministry's websites after analyzing the components and transforming them into new factors through exploratory and confirmatory factor analysis Objective: To present a model for an integrated content management system on the websites of the Ministry of Energy. Methodology: This applied research is of a mixed type and was conducted using an analytical survey method to gather the components of content management, a documentary method was used, Delphi method was applied for the qualitative part in designing the components, and a questionnaire was used for data collection. The statistical population of the study includes all specialists 121 people in the informatics sector and managers of subsidiary companies of the Ministry of Energy. Findings: After determining the items affecting content management on the Ministry of Energy websites in the form of 34 questions by experts, the content management factors after determining the appropriate period according to their correlation matrix in the form of 7 factors in order of special value are: The content management factors on the websites of the Ministry of Energy were identified in the form of 7 factors, ranked by their significance as follows: "Structural and technical requirements" 10/103, "Content requirements" 7/96, "Content evaluation and review requirements" 7/63, "Content accessibility requirements" 4/61, "Quality requirements and usability principles" 4/007, "General requirements" 3/12, and "Requirements for alignment with artificial intelligence" 2/.601 Conclusion: To determine the importance of each content management component from the perspective of specialists in the informatics sector and managers of subsidiary companies of the Ministry of Energy, a model was presented for an integrated content management system on the Ministry's websites after analyzing the components and transforming them into new factors through exploratory and confirmatory factor analysis https://stim.qom.ac.ir/article_4094.html Abstract:Background and Purpose:Over the past few decades, digital transformation has fundamentally reshaped the ways information is created, distributed, and consumed, leading to profound implications for cultural industries worldwide. Among these industries, the publishing sector stands as a cornerstone of a nation’s cultural and informational ecosystem, deeply intertwined with education, cultural identity, and knowledge dissemination. The rise of digital technologies has disrupted traditional publishing models, giving rise to new opportunities for innovation while simultaneously posing complex structural, technological, legal, economic, and behavioral challenges.In Iran, the information ecosystem of publishing reflects these global shifts but is also shaped by unique local conditions—such as centralized cultural governance, infrastructural constraints, and evolving public behaviors toward digital media. Despite recent efforts to digitize publishing processes and expand digital literacy, the ecosystem remains fragmented and underdeveloped in many respects. This study, therefore, aims to provide a comprehensive and integrative analysis of the challenges and opportunities facing Iran’s publishing information ecosystem in the context of digital transformation. It seeks to construct a holistic picture of the current situation, identify the driving forces of change, and explore potential pathways toward a more resilient and future-oriented digital publishing environment.Methodology:Adopting a qualitative research approach, this study employed Grounded Theory as outlined by Strauss and Corbin (2015), complemented by systematic document analysis. The empirical data were collected through 20 in-depth semi-structured interviews with key stakeholders, including publishers, cultural policymakers, and technology experts. The interviews explored perceptions, strategies, and experiences related to digital transformation within the publishing sector. Data were analyzed through a three-step coding process—open coding, axial coding, and selective coding—to identify major categories, subcategories, and the relationships among them. To ensure the reliability, validity, and comprehensiveness of the findings, a systematic review of scholarly publications and official reports from reputable international databases was conducted. The integration of qualitative and documentary data ultimately led to the construction of a conceptual model illustrating the dynamics of Iran’s publishing information ecosystem under digital transformation.Findings:The findings indicate that Iran’s publishing information ecosystem is confronted with multidimensional and interrelated challenges that can be grouped into five major domains.Institutional–structural challenges: Lack of coordination among regulatory and executive bodies, overlapping responsibilities, and weak cultural governance mechanisms have created inefficiencies and fragmentation across the publishing landscape.Technological challenges: Inadequate digital infrastructure, the absence of clear technical standards, insufficient integration of digital tools, and poor user experience design hinder the effective use of available technologies.Legal challenges: Weak copyright enforcement, the absence of comprehensive intellectual property legislation, and the widespread circulation of pirated or unauthorized materials undermine the economic and moral rights of content creators.Economic challenges: The lack of sustainable business models, limited access to investment capital, weak digital marketing capacities, and the low profitability of e-books collectively reduce the economic attractiveness of digital publishing ventures.Cultural and behavioral challenges: A strong cultural preference for free content, limited trust in digital materials, and gaps in digital and media literacy have restricted the adoption and acceptance of digital reading practices among the public.Despite these obstacles, the research identifies significant opportunities that could accelerate digital transformation if properly leveraged. These include the expansion of interactive and multimedia publishing formats, the potential of data mining and artificial intelligence for personalized content delivery, lower production and distribution costs, growing interest in digital reading among younger audiences, enhanced accessibility in remote or underserved regions, and new possibilities for cross-border publishing facilitated by machine translation technologies. Harnessing these opportunities through effective policy and innovation strategies could revitalize Iran’s publishing sector and enhance its global competitiveness.Conclusion:The study concludes that Iran’s publishing information ecosystem is in a transitional phase, moving from a traditional, print-centered model toward a digital, participatory, and data-driven environment. Achieving this transformation, however, requires a multi-dimensional strategy encompassing institutional reform, the development of technological infrastructure, legislative support for intellectual property rights, and the enhancement of media literacy among both producers and consumers. Furthermore, scenario planning and foresight analysis are recommended to help policymakers and industry stakeholders anticipate future developments and make informed strategic decisions.By presenting a multilayered conceptual model of challenges and opportunities, this study contributes to a deeper understanding of the structural and behavioral dynamics shaping digital publishing in Iran. Its findings can inform evidence-based cultural policymaking, support the design of growth-oriented digital publishing strategies, and foster greater public engagement in the processes of information creation and consumption. Ultimately, this research underscores the importance of adopting a systemic, future-oriented, and interdisciplinary perspective in guiding Iran’s publishing industry through the digital era—one that not only addresses existing constraints but also uncovers hidden potentials and innovative pathways for sustainable growth. https://stim.qom.ac.ir/article_4095.html Introduction: Artificial intelligence is a branch of computer science that aims to design systems that are capable of performing human-like behaviors such as learning, reasoning, problem solving, language understanding, and decision-making. Accordingly, artificial intelligence tools, relying on these capabilities, can play an effective role in providing intelligent library services. Despite these capabilities, in many libraries there is a large gap between the availability of technology and the acceptance and effective use of these tools. The aim of the present study is to evaluate the status of acceptance and use of artificial intelligence tools among librarians in Iranian academic libraries based on UTAUT Model.Methodology: The present study is quantitative and applied; it was conducted using a survey method. The collection tool was the standard questionnaire of Venkatesh et al., which examines the level of users' acceptance and use of technology in the form of 21 items and 6 components (performance expectation, effort expectation, social influence, facilitation of conditions, purposeful behavior, and end use). The version of this tool used was developed with respect to the context of Iranian academic libraries and the subject of artificial intelligence under the supervision of experts and professors in the field of information science and knowledge, and its validity and reliability were evaluated. The statistical population was all librarians working in academic libraries affiliated with the Ministry of Science, Research and Technology, and the Ministry of Health (450 person); based on the Morgan table, the research tool in the form of an electronic questionnaire was randomly provided to 210 librarians, and finally 136 people participated in this study.Findings: The results obtained from the one-sample t-test showed that the average of all components is higher than the criterion number (3) (Sig<0.001). However, comparing the average of librarians' opinions in the 4 main components shows that the performance expectation component with an average of (3.67) is at the highest level and the facilitation component with an average of (3.42) is at the lowest level. These findings indicate that despite the fact that librarians are aware of improving the productivity and efficiency of library services by using artificial intelligence tools; however, the infrastructure and support resources for using these tools require more attention from upstream managers and policymakers. On the other hand, independent t-tests and one-way analysis of variance (ANOVA) showed that none of the demographic variables created a significant difference in the level of acceptance and use of artificial intelligence tools among librarians in academic libraries.Conclusion: Iranian academic librarians have a positive attitude towards artificial intelligence tools, and the path to acceptance of this technology in Iranian academic libraries has been opened, and there is a potential capacity for its expansion and development. The purposeful behavior and actual use of Iranian academic librarians are at a high level, which indicates that not only do librarians have the intention and motivation to use artificial intelligence tools; but they also use these tools in practice to improve and advance their careers. However, the technical infrastructure and facilities required for the optimal use of these tools are not at an appropriate level. It is suggested that in order for librarians to benefit more from these tools, the parent organization's policymakers should formulate documented strategies, hold systematic training courses for librarians, and invest in creating the necessary infrastructure for using artificial intelligence tools in libraries. The results also show that librarians in Iranian academic libraries believe in improving the quality of library services by using artificial intelligence tools and consider it a factor in transformation and innovation in providing services and changing their role from traditional librarian to modern librarian. Based on this, it can be concluded that the future path for Iranian academic libraries is clear and librarians in these libraries will be receptive to new artificial intelligence tools. https://stim.qom.ac.ir/article_4096.html Purpose: In recent years, open data has become a central pillar of data governance, recognized as a powerful instrument for promoting transparency, fostering innovation, and driving economic growth. Despite the substantial quantitative expansion of open data initiatives, a notable gap remains between data dissemination and the full realization of its intended benefits. Addressing this gap requires a rigorous assessment of the current landscape and the formulation of a strategic roadmap for progress. In this context, maturity models serve as essential tools for evaluating existing capabilities and guiding improvement efforts. However, the literature in this domain is fragmented, and the absence of a comprehensive theoretical framework that systematically integrates technological, organizational, and environmental dimensions has hindered effective maturity assessment. This research aims to systematically map the existing literature to address this theoretical gap by organizing current knowledge, identifying key maturity dimensions within the Technology–Organization–Environment (TOE) framework, analyzing research gaps, and laying the foundation for the development of more holistic future frameworks.Methodology: This research employed a Systematic Mapping Study (SMS) approach, following the five-phase procedure outlined by Petersen et al. (2008): defining research questions, conducting a systematic search, study selection, data extraction, and final mapping. The research questions were structured around three main axes: (a) development trends and model applications, (b) methodological approaches to model development, and (c) conceptual and content structures. A systematic search was conducted across 13 reputable databases, including Scopus, Web of Science, IEEE, Springer, and Google Scholar, using Boolean combinations of keywords from “open data” and “maturity model” clusters. To ensure maximum comprehensiveness, no temporal restrictions were applied. Following inclusion/exclusion criteria (English language, focus on maturity models), snowballing, and full-text review, 25 studies were selected for final analysis. A classification scheme was developed to structure data extraction and address the research questions. Finally, qualitative content analysis was used to extract and categorize the data, and the identified maturity dimensions were mapped onto the TOE framework.Findings: The literature on open data maturity models is predominantly journal- and conference-based, though non-academic technical reports are notably prevalent. Model development exhibits an irregular temporal pattern, with recent surges indicating an unsaturated field. Geographically, knowledge production is concentrated in Europe and North America, revealing a significant gap in context-specific models for other regions. Most models operate at the national level, focusing on open government data, while private-sector and domain-specific applications remain limited. Mixed-methods and qualitative approaches dominate model development. In-depth content analysis identified 11 key maturity dimensions, categorized under the TOE framework: Technological (technology and infrastructure, data management and quality, publication and accessibility); Organizational (governance and strategy, organizational structure and management, capacity building and knowledge, financial and economic); and Environmental (legal and regulatory, participation and engagement, public value and impact, domain diversity). Comparative analysis revealed that although 52% of models address all three TOE dimensions, none comprehensively cover all 11 identified dimensions. Prior models heavily emphasize technical aspects, such as “data management and quality” (72% coverage), while strategic dimensions like “financial and economic” (<20% coverage) are significantly underrepresented.Conclusion: Open data maturity models have thus far failed to comprehensively encompass the technological, organizational, and environmental dimensions, remaining predominantly focused on technical aspects. Moreover, the geographic concentration of knowledge production in Western countries and the absence of locally adapted models underscore the need to develop frameworks that are responsive to contextual specificities. By presenting a systematic classification and identifying eleven key dimensions, this study provides a theoretical foundation for the design of future models. It recommends that subsequent research adopt mixed-method approaches, apply standardized validation protocols, and integrate theoretical insights with field-based experiences to enhance the relevance and effectiveness of open data maturity assessments. https://stim.qom.ac.ir/article_4097.html Objective: The present research was designed and executed as a mixed-methods study with the aim of recognizing and understanding the phenomenon of digital storytelling, analyzing its influencing factors, and its consequences.Methodology: This research is applied in terms of objective, and a survey-exploratory study in terms of data collection method, based on a deductive-inductive research philosophy. The study follows a mixed-methods approach, encompassing qualitative and quantitative components.The statistical population included experts, comprising senior marketing managers and university professors. Twenty individuals were selected using purposive sampling based on the principle of theoretical saturation. The selection was based on the need for deep theoretical and practical knowledge of digital storytelling. Data collection tools included interviews in the qualitative phase and a paired comparison questionnaire in the quantitative phase. The validity and reliability of the tools were confirmed using content validity and internal consistency/inter-coder reliability for the interviews, and content validity and test-retest reliability for the questionnaires.Due to the exploratory mixed-methods approach, the qualitative phase was executed first. Data from the 20 expert interviews were analyzed using Maxqda software, content analysis, and coding methods. In the subsequent quantitative section, the same sample was measured via the paired comparison questionnaire. The resulting data were analyzed using the six-step method of the Fuzzy Cognitive Map (FCM). FCM is an analytical tool used to identify the most important constituent dimensions of a concept by evaluating centrality indices and examining the causal relationships between variables.Research Findings: Data analysis was performed using content analysis and coding in the qualitative section and the Fuzzy Cognitive Map (FCM) method in the quantitative section. The qualitative findings identified the influencing factors and consequences, while the quantitative section focused on prioritizing them.Conclusion: The research results include both quantitative and qualitative components, identifying various factors influencing and consequences of the digital storytelling phenomenon.In the qualitative section, the identified influencing factors on digital storytelling are:A strong central idea or message,Precise knowledge of the target audience,Basic technical skills,Visual and media literacy,Clear goal setting,Access to a suitable platform,Resource allocation,A content strategy and plan,Availability of multimedia content,Interactivity or the possibility of audience participation,The identified consequences of digital storytelling are:Increased audience interaction and engagement,Increased brand or topic awareness,Strengthening audience loyalty,Emotional impact and greater message retention,Easy shareability and organic growth,SEO improvement and website traffic,Community building around the story,Increased conversion rate,Data collection and better audience understanding through interaction analysis,Creation of a digital asset with lasting value,Based on the calculations among the 20 identified factors, content strategy and plan was identified as the most important influencing factor on digital storytelling, and strengthening audience loyalty as the most important consequence.Digital storytelling has become a strategic necessity for individuals, brands, and organizations in the current era. It merges the archetype of human connection with the unique capabilities of technology to ensure messages are not just transmitted, but resonate deeply with the audience. Success is the result of a complete and dynamic cycle that connects ‘antecedents’ to ‘consequents’ within a complex, intertwined ecosystem. This journey begins with the human foundations: a strong idea and deep audience understanding. This core must then be reinforced with technical and media literacy and a defined strategy, where platform selection, resource allocation, and content planning bridge the idea to effective execution. The interactive and multimedia nature of digital space transforms the story into a two-way dialogue.In fact, successful digital storytelling is an art based on science. The art is the ability to connect emotionally and humanly, and the science involves data analysis, understanding algorithms, and strategic planning. The culmination of this process is when stories not only engage audiences, but also turn them into members of a loyal community. This community is not just a consumer of content, but also a brand advocate and evangelist. Therefore, digital storytelling is not just a marketing tactic; it is a long-term investment in building social capital, credibility, and a lasting digital asset. In today’s fast-paced world, those who can tell authentic, compelling, and human stories will ultimately win the competition. https://stim.qom.ac.ir/article_4098.html This study designs and evaluates an innovative conceptual model for data governance in data-driven organizations by integrating international standards with artificial intelligence (AI) technologies. It aims to address the complex challenges organizations face in managing large-scale, dispersed datasets while simultaneously enhancing the quality, security, and efficiency of data-driven decision-making processes. The proposed advanced conceptual model combines three key international standards with AI technologies:ISO/IEC 38505 – Data Governance at the Board Level; COBIT 2019 – Framework for IT Control Objectives and ISO/IEC 42001 – Management Systems for Artificial Intelligence.The research follows a mixed-methods approach (qualitative–quantitative) conducted in three main stages. In the qualitative phase, a systematic content analysis was performed on 40 high-quality scientific articles published between 2010 and 2024. In the second phase, the initial conceptual model was developed and validated through feedback from 15 experts in data governance and AI. Finally, in the quantitative phase, the finalized model was tested using structured questionnaires and a case study in five large Iranian organizations. Data collection tools included 5-point Likert scale questionnaires (Cronbach’s alpha = 0.89) and semi-structured interviews. Data analysis employed advanced statistical techniques, including Structural Equation Modeling (SEM) using SmartPLS and machine learning analyses in Python.Key findings indicate that integrating AI into data governance can lead to substantial improvements in critical indicators. These include a 47% improvement in the Data Quality Index (DQI), a 50% reduction in time needed for regulatory compliance (e.g., GDPR), and a 38% increase in data security levels. Statistical analyses confirmed a significant relationship (p < 0.01) between AI adoption and improved data governance. In SEM, the path coefficients for AI’s impact were 0.68 on data quality, 0.72 on data security, and 0.59 on data transparency—all significant at the 99% confidence level.A key innovation of this research is the introduction of the DQAI (AI-Based Data Quality Index), enabling more precise assessment of improvements derived from intelligent technologies. The proposed model is tailored to the Iranian organizational context, addressing its specific challenges. Case study results revealed that implementing the model could reduce operational costs by up to 37.5% and lower human error rates by as much as 65%.Despite its contributions, the study has certain limitations, including the relatively high initial investment required for AI infrastructure and cultural resistance to changing traditional processes. Furthermore, the absence of long-term historical data in some Iranian organizations limited longitudinal trend analysis.The practical implications for managers are significant. Organizations are advised to adopt the model gradually, starting with low-risk modules such as metadata management, while investing in workforce training and fostering a data-driven culture across all organizational levels. From a policy-making perspective, the findings could inform the development of a national strategic roadmap for intelligent data governance.Compared with previous studies, this research is noteworthy for three reasons: (1) it systematically integrates AI capabilities into traditional data governance standards; (2) it provides robust empirical evidence from Iranian organizations, supporting further localization of data governance concepts; and (3) it employs advanced quantitative methods to test the model rather than relying solely on descriptive approaches.Future research could expand this work by (1) designing AI algorithms tailored to specific industries such as banking and healthcare; (2) conducting more precise cost–benefit analyses to calculate long-term return on investment (ROI) over 3–5 years; and (3) addressing potential algorithmic biases with ethics-driven solutions.In conclusion, the proposed model offers a practical and effective framework for organizations seeking to leverage their data as a sustainable competitive advantage. The findings clearly demonstrate that intelligently combining international standards with advanced AI technologies can revolutionize data governance and prepare organizations for the challenges of the digital era. This study represents an important step toward localizing data governance knowledge and developing intelligent solutions for Iranian organizations. https://stim.qom.ac.ir/article_4100.html Purpose. This study examines the effect of organizational trust and the mediating role of individual motivation on the tendency to transfer tacit knowledge of public library staff.Methodology. The present research is descriptive based on the applied purpose and in terms of data collection method. The statistical population of this research includes all employees of public libraries in Fars province in 1400 (350 people) that with the help of Cochran's formula, 142 people have been selected as the statistical sample of the research. Findings. The effect of organizational trust on the desire to transfer tacit knowledge is 0.390. The effect of organizational trust on the personal motivation of employees is 0.798 and the effect of individual motivation of employees on the desire to transfer tacit knowledge is 0.437. Therefore, organizational trust and individual motivation have a direct positive and significant effect on the desire to transfer tacit knowledge of employees. Conclusion. Although organizational trust and individual motivation each have a direct positive and significant effect on the desire to transfer tacit knowledge of public library staff, the mediating role of individual motivation has not increased the effect of organizational trust on the desire to transfer tacit knowledge. https://stim.qom.ac.ir/article_4122.html Introduction: The ever-increasing expansion and excessive use of the Internet and the huge increase in the audience of social networks, as well as the harm of online self-disclosure in the cyberspace, are growing, and the technologies of the Internet, cyberspace and (social networks) are increasingly affecting more people. have involved themselves in such a way that it has sometimes had adverse effects on families, young people and couples. Purpose: The purpose of this study is to investigate the role of media literacy in online self-disclosure and virtual addiction to social networks among users of public libraries in Zabul city. Methodology: In terms of practical purpose, this research is descriptive-correlational in terms of method. The statistical population of the research consists of all users of public libraries in Zabol city over the age of thirteen, including 1237 people who have membership cards. Based on Cochran's formula, 293 people were selected as the research sample by multi-stage sampling method. Data collection was carried out using three Philosophical Media Literacy questionnaire of 2013, Internet Addiction questionnaire of Young, 1996, and Online Self-Disclosure questionnaire of Valkenberg and Peter, 2007. In the present study, Cronbach's alpha method was used to determine the reliability of the questionnaire. The correlation coefficient of the Philosophical Media Literacy questionnaire was 0.70, the correlation coefficient of the Yang 1996 Internet Addiction questionnaire was 0.74, and the correlation coefficient of the Valkenberg and Peter Online Self-Disclosure questionnaire was 0.74. Was obtained, In order to check the validity, the questionnaire were given to a number of experts in this field and its validity was measured. Findings: The results of the study indicate that the level of media literacy among public library users in Zabul city is lower than average. Also, the results showed that the amount of online self-disclosure and the amount of virtual addiction to social networks among the users of public libraries is higher than the average. The results showed that there is a positive correlation and a significant relationship between the amount of online self-disclosure and the amount of virtual addiction to social networks. The results indicate a significant relationship between media literacy and online self-disclosure; and also between media literacy and virtual addiction of public library users in Zabul city. Originality: Based on the researcher's search, it was found that few researches have been conducted on the role of media literacy in online self-disclosure and virtual addiction to social networks, but so far there has been no research at the national and international level regarding the role of media literacy in Online self-disclosure and virtual addiction to social networks have not occurred. During this research, it was found that media literacy has a close relationship with online self-disclosure and virtual addiction to social networks. Introduction: The ever-increasing expansion and excessive use of the Internet and the huge increase in the audience of social networks, as well as the harm of online self-disclosure in the cyberspace, are growing, and the technologies of the Internet, cyberspace and (social networks) are increasingly affecting more people. have involved themselves in such a way that it has sometimes had adverse effects on families, young people and couples. Purpose: The purpose of this study is to investigate the role of media literacy in online self-disclosure and virtual addiction to social networks among users of public libraries in Zabul city. Methodology: In terms of practical purpose, this research is descriptive-correlational in terms of method. The statistical population of the research consists of all users of public libraries in Zabol city over the age of thirteen, including 1237 people who have membership cards. Based on Cochran's formula, 293 people were selected as the research sample by multi-stage sampling method. Data collection was carried out using three Philosophical Media Literacy questionnaire of 2013, Internet Addiction questionnaire of Young, 1996, and Online Self-Disclosure questionnaire of Valkenberg and Peter, 2007. In the present study, Cronbach's alpha method was used to determine the reliability of the questionnaire. The correlation coefficient of the Philosophical Media Literacy questionnaire was 0.70, the correlation coefficient of the Yang 1996 Internet Addiction questionnaire was 0.74, and the correlation coefficient of the Valkenberg and Peter Online Self-Disclosure questionnaire was 0.74. Was obtained, In order to check the validity, the questionnaire were given to a number of experts in this field and its validity was measured. Findings: The results of the study indicate that the level of media literacy among public library users in Zabul city is lower than average. Also, the results showed that the amount of online self-disclosure and the amount of virtual addiction to social networks among the users of public libraries is higher than the average. The results showed that there is a positive correlation and the amount of virtual addiction to social networks. https://stim.qom.ac.ir/article_4126.html AbstractAim: This study aims to compare the curriculum of M. S of Knowledge and Information Science in Iranian universities with that in Iraq According to extended developments in information technology, it is necessary to update & revise the educational outline in this field as much as before. Methodology: In this study, the units, structure, and contents of courses in the Iraq and Iran Universities were examined by an analytic descriptive Survey. Finding: Findings show that there are some similarities among basic and public courses, but the Iranian university system has more diversity, expantion and innovation. In Iran, there are different tendencies such as academic libraries, public universities, information management, scientometrics, and digital libraries include new courses such as Artificial Intelligence, data mining, future studies, user experience, digitalization & information retrieval, which Iraqi universities lack. These differences show that Iran goes towards novel needs in knowledge and information science and is in sync with global development; meanwhile, Iraqi universities focus mostly on basic and traditional aspects of this field. The study shows that the courses presented couyses in Iraqi universities are not the same as the courses which presented in Iranian universities, directly or indirectly. These courses focus mostly on technical aspects of knowledge and information sciences and show the tendency of the educational system of Iraq to benefit from new technologies in information sciences. These courses include Computerised information organization, Artificial or expert systems, and open text software in Almostansarieh University, Programming systems and analysing digital content in Mosel University, and information networks, digital media, databases software in Basre University. These courses have no equivalent in Iranian universities. Results show that the similarities between Iraqi and Iranian universities are different. In scienometrics, there is a low overlap (from 15% in Basra University to 36% in Almontansarieh University). In information management, there is the highest similarity, from 30% in Basra University to 55% in Almonstansarieh University, in public and academic university libraries. There are also high percentages of similarity, in public libraries about 31 – 38%, and in academic libraries about 38-46%. The most similarity was in information management and academic libraries. And the low one was in scientometrics. The research method was the only course that was common between the universities of Iran and Iraq. Acoordin to extended developments in information technology, it is needed to update & revise of educational outline in this field as much of before. Methodoloy: In this study the unites, structure and contents of courses in the Iraq and Iran University were edmined by analytic descriptive Survey. Finding: Finding show that there are some similarities among basic and public courses, but Iranian university system has morfe diversity, expantion and innovation. In Iran there are different tendencies such as academic libraries, public universities, information management, scientometrics, and digital libraries include new courses such as Artificial Intellience, data mining, future studies, user experience, digitalization & information retrieval, that Iraq universities lacke. These differentces show that Iran go towards novel needs in knowledge and information science and is synchron with global development, meanwhile, Iraq universities fouce mostly on basic and traditional aspects of this field. The study shows that presented couyses in Iraq universities are not the same as courses which presented in Iranian university, directly or indirectly. These courses focuce mostly on technical aspects of knowledge and information sciences and show the tendency of educational system of Iraq to benefit from new technologies in information sciences. These caurses include Computerised information organization, Artificial or exprt systems and open text software in Almostansarieh Universityu, Programmaing systems and analysing digital content in Mosel University, and information networks, digital media, databases software in Basre University. These courses have not any equivalent in Iranian universities. Result show that symilarities between Iraq and Iranian universities are different. In scienometrics there is a low overlape (from 15% in Basre university to 36% in Almontansarieh university). In information management, there is the highest similarity, from 30% in Basre university to 55% in Almonstansarieh university in public and academic university libraries, there are also high percent of similarity, in public libraries about 31 – 38% and in academic libraries about 38-46%. The most similarity was in information management and academic libraries. And the low one was in scientometrics. Reserch method was the only course that was common between the universities of Iran and Iraq.Conclusion:Based on the research findings, Iran’s educational system is highly organized, diverse, and advanced; however, it is largely theory-based. In contrast, Iraq’s educational system has less diversity and is more practice-oriented, focusing primarily on practical issues and implementation challenges. Nevertheless, since the educational trajectory in Iran has continually evolved, there has been a sustained effort to move in line with global developments. https://stim.qom.ac.ir/article_4434.html Abstract:In the knowledge-based economy, universities play a role beyond the production of science and are expected to be the driving force behind technological development in industry. Despite the quantitative growth of articles in Iran, the actual impact of this research on the development of products of knowledge-based companies is shrouded in ambiguity. While official statistics always emphasize the quantitative growth of articles and the increase in the number of knowledge-based companies, the lack of empirical evidence regarding the "social impact of research" in the industrial sector has created challenges on both the supply side (university) and the demand side (companies). By adopting a pathological and field-based approach, this article examines whether the two islands (university and industry) are actually connected to each other or not? Specifically, the main goal of this research is to measure the field-based impact of academic research on various dimensions of product development (ideation, problem solving, cost reduction, etc.) in Iranian knowledge-based companies.Questions:This research seeks to answer the following:• Does academic research play a role in the initial ideation of products?• Have theses been able to unravel the technical knots of production lines?• Has collaboration with professors led to the real transfer of technical knowledge?• Is the use of research results in companies done systematically or randomly?Methodology:This research is of an applied type and was conducted using a descriptive-survey method. The statistical population includes knowledge-based companies approved by the Vice President for Science, which, using a purposive sampling method, collected data from 100 companies through a researcher-made questionnaire. After collecting the questionnaires, the raw data was coded and entered into statistical software. The data analysis process was carried out in two parts: descriptive and inferential. The reliability of the instrument was confirmed with a Cronbach's alpha of 0.87, and the data were analyzed with a one-sample t-test. This method of analysis allows the researcher to measure not only the existence of a relationship, but also the direction and intensity of each dimension of research impact (such as the role of theses or the level of access) relative to the equilibrium point and, based on that, identify the main bottlenecks in university-industry interaction.Findings: The results showed that although companies have a positive attitude towards the general use of articles (average 3.38), they have an unfavorable situation in operational dimensions such as "solving technical problems through theses" (2.70), "reducing costs" (2.76), and "systematizing the use of research" (2.59). Also, academic research does not play a meaningful role in companies' strategic decisions.Conclusion: The final result of this study is that universities and knowledge-based companies in Iran are neighbors who have not yet learned each other's language well. The scientific potential of the country's universities for the development of knowledge-based products is undeniable, but this potential remains trapped behind the barriers of "lack of systematization", "lack of translation mechanisms" and "limited access". The transition from a resource-based economy to a knowledge-based economy cannot be achieved by increasing the number of articles or the number of companies; rather, it requires the construction of solid, process-oriented and two-way bridges between these two institutions. There is a "desire-performance paradox" in Iran's innovation ecosystem; the desire to use knowledge is high, but the mechanisms for absorbing and translating knowledge (such as demand-oriented theses and technology transfer offices) are not efficient. Based on the pathological findings, implementation suggestions are presented at the three levels of the enterprise, the university and the government. At the enterprise level (for CEOs and R&D managers), a transition from random interaction to a systematic process, the creation of a "technology watchdog" unit and the definition of small-scale joint projects are suggested. It is also suggested that companies make the knowledge search process mandatory in the product life cycle. At the university level (for research vice-chancellors and professors), the paradigm shift from “article production” to “problem solving” is the most important issue, and for this, measures such as the establishment of professional liaison offices, the release of research data, and the definition of problem-oriented theses are important. At the government level, incentives such as the “knowledge translator” subsidy and “smart tax allocation” are expected to have a significant impact. https://stim.qom.ac.ir/article_4129.html AbstractObjective: This research aimed to investigate the effectiveness of Media Literacy Training (MLT) on the self-control and creativity of sixth-grade female elementary school students in District 1 of Qom.Method: The current research is applied in terms of its objective and utilizes a quasi-experimental design with a pretest-posttest control group design. The statistical population included all sixth-grade female students in Qom during the 2024-2025 academic year. The research sample consisted of 52 female students, selected via convenience sampling from Shahid Mohammad Ali Qomi Elementary School. These individuals were randomly assigned to two groups: the experimental group ($n=26$) and the control group ($n=26$). A pretest was administered to both the experimental and control groups before the MLT. The MLT (twelve 45-minute sessions, two sessions per week), based on the Barán Media Training Center's Media Literacy Elementary curriculum, was administered to the experimental group, while the control group received no training. A posttest was administered to both groups upon completion of the sessions. Inferential statistics, specifically Multivariate Analysis of Covariance (MANCOVA) and Univariate Analysis of Covariance (ANCOVA), were used for data analysis via SPSS-27 statistical software. Measurement Instruments: Tangney et al.'s Self-Control Scale (2004): This scale, designed by Tangney, Baumeister, and Boone in 2004, consists of 36 items. The items utilize a five-point Likert scale, ranging from 1 (Not at all true of me) to 5 (Very true of me). The reliability and validity of Tangney's Self-Control Questionnaire were confirmed in the study by Mousavi-Moghaddam et al. (2015), with a Cronbach's alpha coefficient of $0.84$. In the Tangney et al. (2004) study, the validity of the scale was confirmed by assessing its correlation with measures of academic achievement, adjustment, positive relationships, and interpersonal skills. Its reliability was also reported using Cronbach's alpha on two samples ($0.83$ and $0.85$).Abedi Creativity Questionnaire (1984/1993): This scale, constructed by Abedi (1993) in Tehran based on Torrance's theory of creativity, was initially administered to a sample of 650 third-grade guidance school students in Tehran. In 1986, Abedi and Schumacher reconstructed the test items in the USA due to a lack of access to the original version. The new version underwent several revisions by colleagues and was first described by Anil et al. (1994). This instrument has 60 three-option questions, comprised of four subscales: Fluency, Elaboration, Originality, and Flexibility. A score of 1 indicates low creativity, 2 indicates average creativity, and 3 indicates high creativity. The sum of scores obtained in each subscale represents the subject's score in that component, and the total score across the four subscales represents the subject's overall creativity score. The range for the total creativity score is between 60 and 180. The reliability of the Abedi Creativity Test, determined through test-retest in 1984 on middle school students in Tehran, yielded the following reliability coefficients for the four sections: Fluency $0.85$, Originality $0.82$, Flexibility $0.84$, and Elaboration $0.80$. The internal consistency using Cronbach's alpha for the subscales of Fluency, Flexibility, Originality, and Elaboration was reported as $0.75$, $0.66$, $0.61$, and $0.61$, respectively, on a sample of 2270 Spanish students. Furthermore, the test's reliability was reported by Abedi ($0.85$) and by Shokrkon and Kefayat (1994) and Mobini (2000) using the split-half method and Cronbach's alpha for the entire test, ranging from $0.81$ to $0.87$.Findings: The results indicated that MLT had a positive effect on the self-control of the elementary sixth-grade students in the experimental group ($F_{(1, 49)} = 6.92, P = 0.011$). The results also demonstrated that MLT had a positive effect on the creativity of the elementary sixth-grade students in the experimental group ($F_{(1, 49)} = 118.23, P < 0.001$). In terms of the creativity components, the mean scores for Fluency, Originality, and Flexibility in the experimental group were higher than in the control group, and a significant difference was observed between the two groups ($P < 0.05$). However, no significant difference was found between the two groups for the Elaboration component ($P = 0.056$). Accordingly, fundamental steps can be taken to increase students' self-control and creativity by designing interventions based on media literacy training.Keywords: Media Literacy, Self-Control, Creativity, Fluency, Elaboration, Originality, Flexibility, Elementary Students https://stim.qom.ac.ir/article_4138.html Mental models and knowledge gaps are two key concepts in the field of information-seeking behavior. A deep understanding of these concepts can contribute to improving learning processes, decision-making, and information management across diverse contexts. The interaction between mental models and knowledge gaps is a dynamic and continuous process that is reinforced by environmental feedback and new experiences. Ultimately, this process leads to lifelong learning and the enhancement of an individual's cognitive capabilities. This study was conducted with the aim of systematically reviewing the role of mental models and knowledge gaps in information seeking behavior.Methodology: This study was conducted using a systematic review approach. The stages of the literature review were carried out based on the PRISMA framework. The inclusion criteria comprised research papers, dissertations, and scientific reports published in Persian and English between 2000 and 2025, which examined mental models, knowledge gaps, and information-seeking behavior. The exclusion criteria included sources without full text, non-scientific studies, duplicate publications, and studies that addressed only one of the intended concepts without exploring the relationships among them. In this study, searches were conducted in databases including Web of Science, Scopus, Google Scholar, SID, and Magiran using combinations of the keywords “mental model,” “knowledge gap,” and “information-seeking behavior.” Additionally, backward and forward citation tracking was employed to identify relevant sources. Initially, 93 articles were identified. After excluding 15 duplicate and 16 irrelevant articles, 62 articles were selected for analysis. In the second phase of the research, after collecting textual data from various sources, text analysis was performed using specialized text mining software. In order to identify hidden patterns and relationships among mental models, knowledge gaps, and information-seeking behavior, a clustering algorithm was used. In addition to clustering, network analysis was also used to examine the relationships among key concepts.The findings indicated that most studies focused on the influence of mental models on search behavior, with 28 articles (45% of the total studies) dedicated to this topic. This finding indicates the fundamental importance of the mental model in shaping information-seeking behavior. A precise, complete, and structured mental model can enhance an individual's ability to identify reliable sources, formulate purposeful questions, evaluate retrieved information, and integrate it with prior knowledge. Ultimately, this leads to an improvement in the quality and effectiveness of the search behavior. In the second theme, the role of knowledge gaps in information-seeking behavior was highlighted in 22 articles, accounting for 35% of the studies. A knowledge gap can be a factor in the discovery of new information and the use of multiple sources. However, its influence is less than that of the mental model, as achieving effective search behavior requires an individual's ability to manage information deficiencies, recognize appropriate sources, and integrate them with existing knowledge. In the third theme, the interaction between mental models and knowledge gaps was examined in 12 articles, which accounted for 20% of the total research. The analysis of the interaction between mental models and knowledge gaps also indicated a positive, moderate-strength relationship, suggesting that mental models can mitigate knowledge gaps and, conversely, that the presence of knowledge gaps may reflect limitations or weaknesses in an individual’s mental model. The results of the qualitative relationship analysis also indicated a strong and positive relationship between mental models and search behavior, a positive but moderate relationship between knowledge gaps and search behavior, and an interaction between mental models and knowledge gaps.Conclusion: The mental model is the most influential factor affecting information-seeking behavior. The knowledge gap acts as a driver for the search, and the interaction of these two variables can explain the behavioral complexities of users. These findings highlight the necessity of considering cognitive factors in the design of information systems and tools, as well as the importance of educating and strengthening users' mental models. https://stim.qom.ac.ir/article_4157.html Purpose: This study aims to identify the gaps in the provision of health information services in public libraries. It seeks to examine the existing shortcomings and limitations in infrastructure, human resources, policies, supportive frameworks, and cultural considerations, in order to provide guidance for improving the quality and effectiveness of health information services in public libraries. Additionally, the study emphasizes understanding how these gaps affect diverse user groups, including vulnerable populations, and aims to offer evidence-based recommendations for designing more inclusive, accessible, and sustainable health information services that meet the evolving needs of communities, ultimately supporting public libraries in playing a more proactive and strategic role in public health promotion.Method: The study was conducted using a systematic literature review based on the Kitchenham and Charters framework. This framework emphasizes transparency, rigor, and replicability, guiding the systematic identification, selection, and analysis of sources. After defining domestic and international databases and developing search queries, systematic searches were carried out and relevant sources were retrieved. During the screening stage, duplicate, irrelevant, or inaccessible sources were removed, and inclusion and exclusion criteria, including topic, language, and study type, were applied. Ultimately, 63 sources—54 in English and 9 in Persian—were included in the analysis. Data were extracted using a checklist tool and, after review and summarization, were organized and analyzed in tables according to the study’s main themes, ensuring clarity and coherence. This method enabled the precise identification of gaps and missing elements in the delivery of health information services in public libraries, while also highlighting recurring patterns, contextual factors, and potential areas for policy and practice improvement.Findings: The results indicate that public libraries face significant weaknesses and gaps across several key dimensions in providing health information services. In terms of infrastructure and human resources, insufficient equipment and IT facilities, outdated and inadequate information resources, and limited training and specialized skills among librarians restrict patrons’ access to reliable information and reduce service quality. Furthermore, limited communication skills and health literacy among staff decrease their ability to meet users’ informational needs, particularly for vulnerable groups. Regarding supportive frameworks and policies, the lack of integrated policies, unclear missions and strategies in national and regional documents, insufficient funding and sustainable financial resources, and limited organizational collaboration with health institutions have led to service fragmentation, inefficiency, and reduced program effectiveness. The dimension of social equity and cultural considerations also exhibits gaps, including digital divides, restricted access for vulnerable populations, low health literacy and language-specific resources, and insufficient attention to cultural differences and local beliefs, limiting equitable and appropriate access to health information. These limitations particularly affect groups in need, such as the elderly, children, people with disabilities, and residents in underserved areas. The findings suggest that improving infrastructure, updating information resources, continuous librarian training and empowerment, developing integrated policies and educational programs, and seriously addressing equity and cultural considerations are essential for enhancing the quality, access, and effectiveness of health information services in public libraries, while fostering community trust, engagement, and overall public health outcomes.Conclusion: The study concludes that despite their significant potential in promoting health literacy, public libraries face serious limitations and challenges in delivering health information services. Weak infrastructure and technology, lack of up-to-date and reliable resources, insufficient training and capacity-building for librarians, absence of integrated policies and financial support, and limited access for different population groups were identified as the main barriers. These limitations reduce both the quality and effectiveness of services and hinder equitable access to health information. To enhance the performance of public libraries and strengthen their role in promoting community health literacy, it is crucial to improve infrastructure and resources, provide continuous training and empowerment of staff, establish integrated supportive policies and programs, and incorporate social equity and cultural considerations in service design and delivery. Implementing these measures can improve equitable and effective access to health information, enhance service quality, foster greater community engagement, and reinforce the role of public libraries as key institutions in the health information system. https://stim.qom.ac.ir/article_4353.html Introduction and Problem Statement: Transitioning past the first decades of the 21st century, the literature on technology management and higher education has witnessed a fundamental paradigm shift from a "technology-centric" approach (Industry 4.0) to a "value-centric" approach (Industry 5.0). While the Fourth Industrial Revolution focused on automation, artificial intelligence, and efficiency, Industry 5.0, by introducing the three core pillars of "Human-centricity," "Sustainability," and "Resilience," seeks to restore human well-being and planetary health to the center of industrial systems. In this novel ecosystem, the role of researchers, as the primary custodians of knowledge creation, has undergone profound transformations. They are no longer merely technicians operating advanced tools but must act as "architects of human-machine symbiosis." Despite the critical importance of this role shift, a review of existing literature reveals a distinct theoretical and practical gap; most studies either focus on general workforce skills (ignoring the specific nature of research) or concentrate solely on technical aspects, neglecting cognitive and value-based dimensions. The lack of an integrated model that can explain both "theoretical" (the why) and "practical" (the how) competencies simultaneously serves as the primary motivation for this research. Methodology: This study aims to identify and classify researcher competencies using a "Systematic Review" approach based on the standard PRISMA 2020 protocol. The statistical population included all relevant scientific documents published from 2017 (the inception of Industry 5.0 concepts) to the end of 2025. The literature search was conducted at both international and national levels; internationally via Scopus, Web of Science, IEEE Xplore, and Emerald Insight, and nationally via SID, Noormags, and IranDoc databases, utilizing advanced search strategies and Boolean operators (AND/OR). In the initial search phase, 1,197 records were identified. Following the removal of duplicates and initial screening based on titles and abstracts, a significant number of articles that were purely technical-engineering in nature and lacked a managerial or human-centric approach were excluded. Ultimately, the full texts of 162 articles were rigorously evaluated, and after applying inclusion criteria (explicit focus on Industry 5.0, knowledge creation, and human competencies), 54 original studies were selected for final analysis. To ensure selection validity, the screening process was conducted independently by two researchers, yielding a Cohen's Kappa coefficient of 0.84, indicating excellent reliability. The extracted data were categorized using "qualitative synthesis" and thematic analysis methods. Findings: Deep analysis of the selected texts revealed that the competency profile of researchers in Industry 5.0 requires a complex combination of two main dimensions and one comparative dimension: Theoretical Competencies (Insight-based): acting as a "strategic compass." Findings showed that researchers must possess "sustainability literacy" (understanding the circular economy and planetary boundaries), "systems thinking" (viewing research within the global value chain), and "human-centric insight" to perceive technology not as a replacement but as a complement to humans. Practical Competencies (Executive): extending beyond standard digital skills to include "intelligent interaction management." Key extracted components include the ability to work with "Collaborative Robots" (Cobots), utilizing "Explainable AI" (XAI) for data validation, employing "Digital Twins" for research simulation, and big data analytics. Furthermore, high-level soft skills such as critical thinking, creativity, and complex interdisciplinary problem-solving fall within this category. Comparative Analysis (National vs. Global): A significant finding is the substantive difference in study focus. Domestic studies (Iran) predominantly emphasize "value, identity, and ethical" dimensions (such as commitment, justice-orientation, and indigenous/Islamic models), while international research focuses more on "digital adaptability" and cognitive skills. Conclusion and Implications: The present research demonstrates that the Industry 5.0 paradigm has altered the definition of an "elite researcher." A successful researcher in this era is not an isolated technician but a "Value-Creating Leader" capable of integrating theoretical wisdom (to discern ethical right from wrong) with operational power (to utilize intelligent tools). The model derived from this research suggests that universities and higher education institutions must move beyond traditional, one-dimensional models and redesign curricula to simultaneously teach "human-machine interaction" skills and "digital ethics." This integrated approach guarantees the creation of sustainable and responsible knowledge in the future. https://stim.qom.ac.ir/article_4360.html With the growing centrality of data-driven practices in organizations and governments, data governance has emerged as a core pillar of digital transformation, public policymaking, and socio-economic value creation. However, empirical experience suggests that many of the persistent challenges and failures associated with data governance are not primarily caused by the absence of formal frameworks or technical mechanisms, but rather stem from hidden and insufficiently problematized conflicts of interest operating beneath the surface of organizational and institutional arrangements. While the existing literature has extensively addressed visible dimensions of data governance—such as data quality, architecture, ownership, security, and data sharing—it has paid considerably less attention to a more fundamental question: why conflicts of interest in data governance often remain unseen, unrecognized, or systematically reduced to technical issues. This gap is particularly pronounced in developing-country contexts and in the Iranian institutional setting, where governance structures, organizational roles, and regulatory boundaries are frequently overlapping and ambiguous. The primary objective of this study is to identify and explain the hidden causes of conflicts of interest in data governance and to uncover the mechanisms through which such conflicts are structurally reproduced while remaining largely invisible in formal policies and decision-making processes. Rather than focusing on explicit or isolated instances of conflict of interest, the study adopts a deeper analytical lens, examining the latent cognitive, institutional, and conceptual foundations that normalize and conceal these conflicts within everyday organizational practices. This research employs a qualitative methodology based on the grounded theory approach, following the Strauss and Corbin paradigm. Data were collected through semi-structured interviews with 30 managers, experts, and practitioners specializing in data governance, data management, information technology, information security, and policymaking in large Iranian organizations. Sampling was conducted using a purposive and snowball strategy and continued until theoretical saturation was reached at the twentieth interview. Interviews, each lasting approximately 30 to 45 minutes, were conducted between March 2023 and November 2024. Data analysis proceeded through open, axial, and selective coding. To ensure the rigor and trustworthiness of the findings, several validation strategies were employed, including test–retest coding reliability, triangulation, and expert review. The test–retest reliability, calculated through repeated coding of selected interviews with a ten-day interval, yielded an average agreement rate of 71.05%, exceeding the commonly accepted threshold for qualitative reliability. The findings reveal that conflicts of interest in data governance are largely shaped by a set of hidden and non-obvious factors that are rarely recognized as governance problems in their own right. These factors operate across three interrelated levels. At the conceptual level, ambiguity in the understanding of data governance—often conflated with data integration, technical control, or operational data management—leads to the marginalization of governance-related values such as accountability, transparency, and ethical oversight. At the institutional level, the overlap of regulatory, executive, and commercial roles within the data value chain, combined with insufficient role separation and weak regulatory mechanisms, generates structural conflicts that are routinely normalized as legitimate organizational practices. At the contextual level, growth pressure, acceleration-oriented organizational cultures, legal ambiguity, and rapid datafication processes reinforce these conflicts and contribute to their concealment behind dominant discourses of innovation, efficiency, and development. Axial analysis indicates that these hidden causes interact and mutually reinforce one another, giving rise to consequences that extend far beyond technical inefficiencies. These consequences include increased ethical, legal, and socio-political risks; erosion of public trust; constrained data sharing; stagnation of data-driven innovation; algorithmic bias; and weakened institutional accountability. In the selective coding phase, “managing conflicts of interest in data governance” emerged as the core category, around which an integrated conceptual model was developed. This model systematically explains the relationships among causal conditions, contextual factors, intervening conditions, action–interaction strategies, and resulting outcomes. Overall, this study demonstrates that conflicts of interest in data governance are neither incidental nor exceptional phenomena, but rather structurally embedded and perpetuated through hidden mechanisms within organizational and institutional systems. Without explicitly identifying and addressing these latent causes, technical and policy-oriented interventions are unlikely to achieve meaningful or sustainable outcomes. By foregrounding the hidden dimensions of conflict of interest, this research contributes a context-sensitive and theoretically grounded framework to the data governance literature and underscores the necessity of moving beyond purely technical approaches toward reflexive, governance-oriented interventions. https://stim.qom.ac.ir/article_4437.html Purpose: Digital transformation in the public sector has predominantly been pursued through a focus on the development of technological infrastructures. Although this approach has led to the expansion of electronic services, in many cases it has failed to result in sustainable process improvement and enhanced service quality due to the neglect of social and knowledge-based dimensions. Evidence from Iranian public organizations, particularly large service-oriented institutions, indicates that the lack of meaningful stakeholder participation and the absence of systematic mechanisms for the accumulation and utilization of experiential knowledge have created a disconnect between technological development and public value creation. Accordingly, the primary objective of this study is to develop a model for improving electronic service processes with an emphasis on the interactive roles of social participation and knowledge accumulation. The study seeks to answer the question of through which pathways and by what mechanisms social participation and knowledge accumulation can contribute to enhancing the efficiency and quality of electronic service processes.Methodology: This study is applied in nature and adopts an exploratory mixed-methods (qualitative–quantitative) approach. In the qualitative phase, using snowball sampling, 11 experts from the Social Security Organization with at least five years of professional experience related to the design, implementation, or evaluation of electronic services were selected. Data were collected through semi-structured interviews structured around three dimensions: process improvement, social participation, and knowledge accumulation. The data were analyzed using inductive content analysis with the support of MAXQDA software. This process resulted in the extraction of 165 open codes, 16 subcategories, and 8 main categories organized within the three core dimensions of the study. In the quantitative phase, the fuzzy DEMATEL technique was employed to analyze causal relationships and the degree of influence and dependence among the extracted criteria. A fuzzy pairwise comparison questionnaire was developed for the 16 final criteria and completed by the experts. After converting the data into triangular fuzzy numbers, normalizing the matrices, and calculating the total relation matrix, the values of D, R, D+R, and D−R were obtained, and a cause–effect diagram was constructed. To ensure research reliability, inter-coder reliability and test–retest reliability techniques were applied. Research validity was established through five forms of validity: descriptive validity, interpretive validity, theoretical validity, validity derived from controlling researcher bias, and validity related to participants’ reactions to the researcher’s presence.Findings: The qualitative findings indicate that improvement in electronic service processes results from a combination of strategic and developmental actions. In the process improvement dimension, targeted financial resource allocation and upgrading information technology infrastructure were identified as fundamental driving components. In the social participation dimension, categories such as culture-building, facilitating participation, motivating participation, and attracting stakeholder participation were identified, reflecting the multidimensional and non-linear nature of participation. In the knowledge accumulation dimension, two levels—knowledge storage and knowledge processing and utilization—were identified, indicating that knowledge becomes value-creating only when it moves beyond documentation to analysis and operational decision-making.The results of the fuzzy DEMATEL analysis showed that upgrading information technology infrastructure and allocating financial resources for process improvement exhibit the highest levels of influence and interaction and are recognized as core causal factors in the model. In contrast, most components related to social participation and knowledge accumulation were positioned as mediating and influence-receiving factors. The findings indicate that the relationship among technology, participation, and knowledge is cyclical and dependent on the level of organizational process maturity; at lower levels of maturity, participation and knowledge lack direct effectiveness in improving processes in the absence of technical and institutional prerequisites.Conclusion: The study demonstrates that improving electronic service processes is neither the result of technology deployment alone nor the outcome of independent interventions based on social participation or knowledge accumulation. Rather, it is the consequence of a stage-based logic grounded in process maturity. Social participation and knowledge accumulation become drivers of innovation and continuous improvement only when technological infrastructures, financial resources, and process standards are firmly established. Otherwise, premature adoption of participatory or knowledge-oriented approaches may lead to increased complexity, resource waste, and reduced efficiency. The final model provides a stage-oriented framework for digital transformation policymaking in public institutions, enabling a gradual transition from technological improvement to knowledge-based and participatory improvement. While aligning with the electronic government maturity literature, the model redefines the relationship among technology, participation, and knowledge within a local context and demonstrates that process maturity plays a key moderating role in the effectiveness of social and knowledge-based mechanisms. Accordingly, the findings of this study can inform managers and policymakers in designing realistic and effective programs for improving electronic services in public organizations. https://stim.qom.ac.ir/article_4517.html Aim: The present study was conducted with the aim of systematic review and quantitative and qualitative analysis of studies in the field of application of semantic technologies in digital libraries.Methodology: This study is a descriptive-analytical one. It has been carried out through a Aviard ’s systematic review method to quantitatively and qualitatively analyze research conducted in the area of application of semantic technologies in digital libraries. For this purpose, after searching for related keywords in selected Iranian and international databases.Findings: As a result of the analysis, research in this area was categorized into three general categories, including: “The use of semantic technologies in digital libraries (including 5 thematic subsets)”, “The use of traditional tools and techniques of librarianship in the implementation of semantic technologies in digital libraries” and “Studies focused on the current situation and the important factors in the application of semantic technologies in digital libraries”. Conclusion: In this study, a comprehensive view of the status of studies in the area of application of semantic technologies in digital libraries using reliable sources extracted from selected databases was provided to researchers and stakeholders in this field https://stim.qom.ac.ir/article_4518.html Purpose: The main objective of this study is to identify the key components of sustainable management of organizational data in social networks. Organizational members use social networks to receive information and news about the organization, as well as to exchange information among themselves, and organizational audiences can communicate with the organization, receiving answers to their questions and the services they need. Managers also more easily monitor the outcomes of organizational activities and improve their performance by receiving feedback. . By generating organizational data and sharing them on social networks, organizations create a type of formal communication, and this data becomes part of organizational memory. On the other hand, it seems that social networks do not support data retention, and organizational data shared on social networks is often unstable and may not be available for the long term. Therefore, it can be acknowledged that sustainable management of social networks data poses a challenge for organizations, and identifying and implementing the components of sustainable data management, as well as providing a coherent conceptual framework of these components, can be considered as a solution in this regard.Methods: To conduct the present study, a systematic review method with a meta-synthesis approach was employed. For meta-synthesis, the present study used the Sandelowski and Barroso (2006) method. In this method, the steps included specifying the research question, systematically reviewing previous research, evaluating and selecting appropriate articles, extracting information, analyzing and synthesizing qualitative findings, quality control, and presenting the findings. All relevant research sources were identified and analyzed using keywords in scientific databases. In total, after screening the sources in the meta-synthesis stages, 48 sources were identified and extracted published between 2006 and 2024 and 101 codes were identified and extracted. Then, based on the processes introduced by Zahid et al. (2023) for the secure management of government organization data presented in the long term, the main and secondary components were identified and categorized. Findings: Based on the findings of the present study, 16 main components and 76 sub-components were identified, including collection tools and methods (data extraction, data discovery, data creation, data collection tools, collaborative collection approach), collection criteria (preservation, data evaluation, data selection, completeness of data collection, data transparency, data source information, collection objectives, optimizing the selective collection process, determining the population (users), specifying the scope of collection, data collection frequency, requirements), preparation (data preprocessing, data cleansing, quality assessment, data mapping, data indexing), data analysis (machine learning, data interpretation, data classification, data integrity, data aggregation, data cross-linking), security and privacy (data anonymization, data traceability, protection, bit-level protection, risk management), storage infrastructure (database, long-term data preservation platform, data repository mission determination, storage space, recording and maintenance, infrastructure security), organization (data update, persistent identifier assignment, metadata, file format, data appearance, process automation), access and exploitation (retrieval, searchability, accessibility, interactivity, data literacy), user interface and interaction (user interface, search interface, message interface, use of programming language, use of personas), ethical and legal issues (fairness, terms of use, publication of a code of conduct, user agreement, intellectual property, copyright, restrictions), trust and security (trust building, security standards, use of blockchain, increasing immutability), sharing ethics (ethical and legal sharing, balance between privacy and public information rights, censorship, rules and obligations), standards (use of international standards), structure and process (creation of a common terminology, policy, development, assignment of responsibilities), ethical issues (licenses) and guidelines (deletion guidelines).Conclusions: This study is a pioneering effort in the field of comprehensively examining the components of sustainable organizational data management in social networks. These components can serve as a basis for decision-making by organizational managers in the field of social network data management. Furthermore, it seems that the proposed components can be used to develop a roadmap and guide for sustainable organizational data management in social networks. https://stim.qom.ac.ir/article_4519.html Purpose: The objective of this study is to critically investigate the fundamental processes underpinning knowledge creation within knowledge-based companies, particularly those operating in dynamic and knowledge-intensive environments. Design/methodology/approach: This study is classified as an applied and qualitative research project that employs the systematic and structured grounded theory methodology as proposed by Strauss and Corbin (1997) for in-depth data analysis. The statistical population was all knowledge-based companies located in the Tabriz Science and Technology Park, which, according to available reports, comprises a total of 165 companies. Purposive sampling method was selected. Thus, 16 semi-structured interviews were conducted with 7 Chief Executive Officers (CEOs) and 9 employees working in various departments across different companies. The data collection tool was semi-structured interviews. The process of data analysis was carried out in three well-defined stages: open coding (extracting initial concepts and categorizing raw data), axial coding (identifying relationships among the emergent categories and concepts), and selective coding (determining the core category and building a comprehensive conceptual model). This step-by-step and systematic approach results in the development of a paradigmatic model that clearly illustrates the dynamic relationships between major and minor concepts identified throughout the research. Findings: The findings indicate that knowledge creation in knowledge based companies emerges from the multilayered interaction of causal, contextual, strategic, and intervening conditions. At the causal level, a set of internal motivations—such as the desire for higher income, interest in company development, and the production of high quality products—along with external market driven motivations such as competition and entry into new markets, act as the driving forces of knowledge creation. This process is grounded in individual contextual factors, including education and personal motivation, as well as organizational contextual factors such as initial capital and specialized human resources. Furthermore, companies advance knowledge creation through strategies of innovation and organizational learning—from transforming ideas into products to competitor monitoring and learning from failures—combined with synergy building through collaboration with universities, experts, and interdisciplinary networks. The process is also shaped by intervening factors, including technological capabilities, governmental policies related to challenges such as brain drain and administrative complexities, and the prevailing culture of society and the market. Ultimately, knowledge creation leads to outcomes at three levels: organizational achievements such as product improvement and enhanced competitiveness; national achievements such as cost reduction and the production of rare or imported products; and individual achievements including personal growth and a sense of success. We conclude that knowledge creation in knowledge based companies in Tabriz Science and Technology Park is a multidimensional and dynamic process shaped by the constructive interaction among individual and market driven motivations, organizational capacities, innovative strategies, and technological and institutional intervening factors. Knowledge creation not only enhances organizational performance and competitiveness but also strengthens national benefits and individual development, thereby reinforcing the role of knowledge based companies as key drivers of knowledge oriented development.Research limitations/implications: This study offers valuable insights into the mechanisms of knowledge creation in knowledge-based companies; however, several limitations should be acknowledged, which also present opportunities for future research.First, the exclusive use of qualitative methods, while enabling in-depth exploration, limits the generalizability of findings. Future research could adopt mixed-method approaches by combining grounded theory with quantitative techniques such as surveys or statistical analysis. This integration would enhance both the validity and applicability of results. Second, the study’s geographic focus on companies in the Tabriz Science and Technology Park may constrain the applicability of findings to other regions. Conducting comparative studies across different cities or countries could help identify cultural, economic, or structural variations in knowledge creation practices, supporting the development of both localized and broader models.Third, this study focused on internal organizational actors—managers and employees—while neglecting the potential roles of other stakeholders such as customers, partners, suppliers, and policymakers. Future research should explore how these external agents influence and interact with knowledge creation processes in the broader ecosystem.Originality/value: This study, using grounded theory methodology, investigates knowledge creation processes within real-world, operational knowledge-based firms, and thus offers methodological originality and empirical relevance. Unlike many conceptual or theoretical studies, this research draws directly from practical experiences and field-based data. It takes into account a range of interacting dimensions, including causal, contextual, and intervening factors, to construct a holistic understanding of knowledge generation within organizational environments. The use of primary data collected from key individuals working in active companies adds to the depth, reliability, and contextual richness of the findings. Ultimately, the results of this study lead to the formulation of a paradigmatic model that not only clarifies fundamental opportunities and challenges but also serves as a guiding framework for strategic decision-making in the field of knowledge management. Furthermore, it provides a foundational reference for policies related to the sustainable development of a knowledge-based economy in Iran, making it both academically valuable and practically applicable. https://stim.qom.ac.ir/article_4520.html Introduction: Equal and fair access for all individuals to information, knowledge, digital resources, and other scientific and research matters, regardless of their geographical, economic, or social location, is one of the main goals of governments and information societies, and for this purpose, they must strive to establish and sustain "information justice" in societies. When information justice is not prevalent in society, the knowledge gap not only among ordinary people but also among researchers and intellectuals widens and deepens. Therefore, bridging the knowledge gap itself is a basis for establishing information justice that should be given more attention, and its concept and various dimensions should be examined, explained, and described. The set of elements, indicators, and factors that not only affect researchers' information behavior, but also, over time and as a result of their interaction with information; either cause delays in the distribution of knowledge and information among members of society or delay their access to and attainment of information, have consequently created a phenomenon known as the "knowledge gap." The perspective and theory related to it are called "knowledge gap theory", which is itself a communication theory and was first proposed by Philip Titchener, George Donohue and Clarice Evelyn in 1970 in an article titled "The Flow of Mass Media and the Expansion of the Knowledge Divide".Objective: The aim of this research is to identify and explain the concept of the methodological knowledge gap, its dimensions and to present major solutions to overcome this type of knowledge gap.Method: The present research is of an applied type and was conducted using a qualitative method (content analysis and the classical Delphi technique). In the first part, by studying and examining domestic and foreign texts, 9 dimensions of the main dimensions of the knowledge gap were identified, then by using the classical Delphi technique and using the opinions of experts and researchers, 3 other dimensions of the knowledge gap were also identified, extracted and expanded. Following that, various components and items of the methodological knowledge gap were also compiled and extracted. After conducting its validity and reliability, for the second part of the work, namely strategies to overcome this type of knowledge gap, in consultation with statistical experts and their recommendations, using the semi-structured in-depth interview tool, and analyzing them, the main practical strategies to overcome this type of knowledge gap were formulated, prioritized, and proposed.Findings and Results: In this research, regarding the first research question, what is the concept, components, and items of the methodological knowledge gap?To explain and conceptualize the methodological knowledge gap, components have been formulated and obtained, which include: strengthening knowledge and awareness in methodology, choosing an approach appropriate to the type of research, innovation and use of new technologies, diversity and combination in research methods, critical evaluation and review of sources, and institutionalization of methodological standards. Regarding the second research question, which are the main strategies to reduce and overcome the methodological knowledge gap? Also, based on semi-structured in-depth interviews with experts, important strategies for overcoming this type of knowledge gap were presented by the experts in order of the score and importance of repetition of each component, which include: artificial intelligence and innovation in research design, diversification of research methods, strengthening methodological knowledge, tailoring research to its own approach, utilizing mixed approaches, critical evaluation, consulting with statistical experts, systematic review of studies conducted, and creating frameworks and standards. Although implementing these strategies does not guarantee the complete elimination of the methodological knowledge gap, observing them can significantly reduce the negative effects of this type of knowledge gap and enhance the scientific credibility of research. Finally, practical and executive suggestions are provided, taken from the text of the research. https://stim.qom.ac.ir/article_4521.html AbstractPurpose: In today's world, people are immersed in a sea of information. Information, along with human resources, plays the role of capital for an organization; therefore, the success of organizations in the information age depends on the protection and utilization of information resources. Access to the Internet and working with new digital technologies have created risks that have challenged individuals and organizations. There are various factors in information security threats that can be identified, and steps can be taken to confront and repel them. The present study was conducted to identify the components of digital information security and design a conceptual framework for them, using the qualitative method of content analysis.Methodology: This study used an inductive approach, that is, reaching from the part to the whole. The method used is also a qualitative content analysis approach. Also, the statistical population of this study consisted of expert university professors in the fields of management and information technology.Findings: This research led to the identification of 7 main themes and 35 sub-themes that comprise the country's macro-policies, including: allocating a course unit in the field of cyber, establishing a research and development department for security measures, producing and broadcasting educational programs, a relief committee for cyber-attacks, and allocating budgets in the field of information security; human factors including: personality traits, motivation, individual experiences, human error, level of literacy and individual skills, recognition and understanding of individuals, awareness, bias, individual tendencies, and overwork; technological factors including: malware, localization of technologies, software threats, security software, software weaknesses; structural factors including: structural reform, device upgrades, internal organization policies, monitoring and control; cultural factors including: human resource training, human resource awareness, manager actions, managers' role models for employees, management support, and finally communication-related factors including effective communication and communication security.Conclusion: Organizations and societies are made up of individuals; therefore, human aspects play the most important role in implementing and understanding information security, as well as in creating uncertainty, insecurity, and related threats, which refers to the human factor in information security. Technology-related factors are another important component in maintaining digital information security in organizations. Since the advent of ATMs until today, technologies have saved costs, but they also bring potential threats, which are dangerous for the survival and success of the organization. Problems such as viruses, worms, and intrusions can cause severe damage to the information systems of organizations. The identified management factors are related to all the actions of the manager in the field of digital information security. Every organization is required to maintain the confidentiality of information, data, and personal statements of human resources, etc., so the management of organizations plays an important role in ensuring information security. Humans are multifaceted beings who are influenced by various factors, therefore, awareness, education, and promotion of knowledge and literacy of employees of organizations and users who use information; support and backing of the manager and creating a collaborative and supportive atmosphere among colleagues; creating information security policies and modeling them by experienced managers and employees; creating intimacy, honesty, and a positive atmosphere, cooperation, and effort in the workplace; motivational and leadership actions are considered a subset of cultural factors. Structural factors are also important in maintaining digital information security, and as the findings of the present study show, the most frequent theme in terms of assigned codes belongs to this factor and such things as structural modification and updating of the devices used. Today's world is a world of boundless communications, and an organization that cannot use this important skill and opportunity is doomed to failure. Therefore, the factors related to communications identified in this study are related to maintaining effective communications between individuals to access accurate, reliable, and timely information that must be exchanged and made available to individuals in a secure environment; and the last identified item includes the country's macro policies, which are based on other identified environmental factors. Since awareness and education of individuals an important factor in maintaining information security, it is incumbent on officials to be diligent in making macro decisions and policies and to provide the ground for education and awareness of individuals.Limitations: A major limitation of the present study was insufficient time for in-depth interviews with experts, which occurred due to the high workload of the professors and the difficulty of coordination.Originality: Applying an inductive approach and using a qualitative method of content analysis to identify and classify digital security components and present a conceptual framework derived from them. https://stim.qom.ac.ir/article_4522.html AbstractPurpose: In today’s competitive and complex environment, digital transformation has emerged as a strategic imperative for organizations. This transformation not only enhances organizational agility, innovation, and resilience, but also paves the way toward achieving sustainable competitive advantage. However, success in implementing digital transformation depends on multiple factors, the identification, analysis, and modeling of which remain a major challenge in the fields of management, information technology, and data science. Despite investing in advanced technologies, many organizations fail to achieve desired outcomes—highlighting the multifaceted and intricate nature of the digital transformation process. In response to this scientific and practical gap, the present study aims to develop a predictive model based on machine learning to examine the moderating role of organizational agility in digital transformation success. The proposed model serves as an analytical tool for assessing organizational readiness and forecasting transformation outcomes, thereby supporting strategic decision-making at both macro and operational levels.Method: This study introduces a custom-designed Organizational Agility Index (OAI ), which incorporates a set of key variables including organizational size, timing of digital technology adoption, level of digital skill training among employees, type and scope of digital tools in use, and changes in customer interaction patterns. The OAI was integrated into the model as a moderating variable to evaluate its influence on the relationship between digital transformation factors and overall success. Standardized digital transformation data were collected from reliable sources and merged with the OAI. These combined datasets were then fed into a suite of machine learning algorithms, including ensemble methods such as XGBoost, Random Forest, Voting, Bagging, and AdaBoost. The objective was to assess the predictive accuracy of these models and compare their performance across different organizational scenarios. Evaluation metrics such as Accuracy, F1 Score, and Confusion Matrix were employed to measure model effectiveness. Additionally, cross-validation techniques were applied to ensure the generalizability and robustness of the trained models against real-world data.Findings: The analysis revealed that ensemble learning models—particularly XGBoost—demonstrated superior performance in predicting digital transformation success. XGBoost achieved the highest accuracy (0.9703) and F1 score (0.9703), indicating its strong capability in correctly classifying successful and unsuccessful transformation cases. While other algorithms also performed reasonably well, XGBoost emerged as the most precise and effective model in this study. Furthermore, the moderating role of the Organizational Agility Index was validated, showing that agility significantly enhances model performance. Supplementary analyses indicated that organizations with higher agility levels are more likely to succeed in digital transformation compared to less agile counterparts. These findings underscore the importance of organizational factors alongside technological investments. Sensitivity analysis further revealed that digital skill training and changes in customer engagement were the most influential variables in boosting agility and transformation success.Conclusion: The findings of this study highlight that integrating organizational indicators with advanced machine learning algorithms can provide a powerful framework for strategic decision-making in digital transformation initiatives. Organizational agility, in particular, plays a critical role in improving the accuracy of predictive models and can help managers better understand internal capabilities to navigate transformation paths more effectively. The proposed model not only offers predictive insights into transformation success but also serves as a practical framework for assessing organizational readiness in the face of technological change. Moreover, it lays the groundwork for developing analytical tools in future research and can inform policy design, resource allocation, and risk management strategies related to digital transformation. Ultimately, this study emphasizes the value of combining data-driven approaches with organizational insight, offering a novel pathway for analysis and decision-making in the dynamic and complex landscape of digital transformation.Keywords: Digital Transformation, Digital transformation success, Organizational Agility Index, Machine Learning, Digital Technologies, Moderating Variable https://stim.qom.ac.ir/article_4523.html Optical Character Recognition (OCR), as an advanced technology in the field of document processing, plays a pivotal role in the digitization process. By leveraging sophisticated image processing algorithms and deep learning models, this system is capable of detecting and extracting textual characters from document images. The output of this process consists of structured textual data that can be processed and analyzed by computer systems. The applications of this technology in intelligent document management are extensive, encompassing advanced archiving and indexing systems, automatic information extraction from documents, and the development of efficient information retrieval systems. Furthermore, OCR capabilities provide the necessary foundation for developing specialized language models and advanced text processing systems in scientific and industrial domains. By converting paper documents into digital data, this technology not only enables efficient storage and retrieval of information but also paves the way for advanced AI-driven analyses of documents.In recent years, vision-language models based on the Transformer architecture, leveraging massive pre-trained models and extensive datasets, have achieved remarkable progress in this field, particularly for the English language. However, the performance of these models on languages with more complex structures, such as Persian, still faces significant challenges, and only a limited number of studies with narrow applicability have been presented in this area. This research investigates the performance and challenges of vision-language models in extracting Persian text from images. The Persian language, due to its unique characteristics, such as connected letter forms, right-to-left writing direction, and the presence of overlapping characters, poses considerably greater challenges compared to Latin-based languages.In this study, first, a high-quality dataset comprising 174,361 Persian sentences in both text and image formats, sourced from digital books, was generated to train models for the Persian language. Additionally, a separate, diverse, and challenging evaluation dataset was designed to cover real-world scenarios. Subsequently, a vision-language model is proposed, designed based on a two-stage architecture: first, a pre-trained visual encoder extracts complex visual features, and then a specialized language decoder, specifically pre-trained on a large corpus of Persian texts, generates the corresponding text while adhering to Persian grammatical and orthographic structures. This decoupling enables independent optimization of both components, granting the model stronger visual understanding of images alongside deeper linguistic comprehension of Persian. The final model was then fine-tuned using the aforementioned training dataset.The results were compared against Tesseract, a convolutional neural network-based model developed by Google, as well as Qwen2.5-VL-7B, a vision-language model introduced by Alibaba. Comprehensive evaluations demonstrate that the proposed model achieves 98% word-level accuracy (WER = 2%) on the Persian sentence test dataset, attesting to its strong capability in processing Persian text. Nevertheless, error analysis reveals that the model performs weakly in recognizing Persian numerals, Latin numerals, and English words within mixed-language texts. This weakness in Persian numeral recognition is observed across all evaluated models and is primarily attributed to the lack of structural and linguistic diversity in the training dataset. Accordingly, enriching the training dataset with more diverse samples, followed by model retraining, is proposed as an essential step toward realizing a comprehensive, Persian-centric OCR system effective in real-world conditions.A noteworthy observation is that Tesseract, despite being based on convolutional and recurrent neural networks and featuring a simpler architecture with fewer parameters compared to vision-language models, demonstrates competitive or even superior overall performance (including in processing English texts) relative to some large-scale hybrid models. This outcome is likely because its training data, aligned with the model's architecture, is comprehensive and well-suited for optical character recognition tasks. Access to sufficient amounts of clean Persian data remains limited; however, the results clearly indicate that creating diverse datasets enhances the capability of Transformer-based OCR approaches relative to classical convolutional neural network-based methods. https://stim.qom.ac.ir/article_4524.html Objective: The rapid progress of digital technologies in high-tech industries has fundamentally transformed the logic of competition and patterns of value creation, while simultaneously exposing the inherent limitations of traditional coordination and monitoring mechanisms. Within this context, digital governance has emerged as a novel framework—grounded in algorithms and intelligent protocols—for the management of data and the regulation of digital actions. Nevertheless, the effective implementation of digital governance is contingent upon the integration of knowledge processes; that is, the systematic coordination among the activities of knowledge creation, storage, sharing, and application. In the absence of such integration, even the most sophisticated governance mechanisms are unlikely to yield sustainable improvements in knowledge management. Moreover, high-tech industries operate within environments characterized by escalating complexity, wherein multiple stakeholders, technological diversity, and rapidly evolving regulatory perspectives pose considerable challenges for the management of knowledge flows. This environmental complexity has the potential to moderate the intensity of the relationships among digital governance, knowledge process integration, and knowledge management. Although numerous studies have examined each of these constructs individually, a systematic review of the research literature reveals a clear theoretical gap regarding the simultaneous and empirical investigation of their interrelationships within the specific context of high-tech industries. Accordingly, this research aims to examine the mediating role of knowledge process integration and the moderating role of environmental complexity in the relationship between digital governance and knowledge management.Methodology: To empirically investigate these complex relationships, the research employed a quantitative design focused on companies active in high-tech industries. Data collection was carried out using structured questionnaires distributed among managers, information technology specialists, and knowledge workers within science and technology parks, in order to obtain a comprehensive perspective encompassing different organizational roles and functions. A random sampling technique was utilized, and the minimum required sample size was determined based on Daniel Soper's (2025) guidelines for structural equation modeling. Following the application of rigorous screening criteria to eliminate incomplete, inconsistent, or invalid responses, a total of 210 valid questionnaires remained for final analysis. The measurement instruments were adapted from previously validated scales to ensure their reliability and content validity.Results: The analyses confirmed that all factor loading coefficients for the questionnaire items exceeded 0.40, and the t-statistic values were greater than 1.96, indicating that the indicators for the research variables are statistically acceptable. The reliability coefficients for the research variables were above 0.70, demonstrating the adequacy of the instrument's reliability; therefore, the questionnaire items and variables possess desirable reliability. The correlation of each construct with other constructs was assessed through Average Variance Extracted (AVE), wherein all values exceeded 0.50, indicating acceptable convergent validity for the model. Furthermore, an evaluation of the correlation of each construct with other constructs in the model demonstrated that the discriminant validity of the constructs is at an acceptable level. The obtained values for R², Q², and GOF indicated a strong overall fit for the model under investigation.Conclusion: The findings demonstrate that digital governance exerts a positive and significant influence on knowledge management, as it functions as a control mechanism that enhances data quality and facilitates knowledge sharing. Additionally, by establishing coherent policies and standards, digital governance facilitates the integration of knowledge processes and prevents the fragmentation of organizational activities. The results also confirm that knowledge process integration directly and positively affects knowledge management, improving efficiency and the quality of outputs by creating a continuous chain of knowledge-related activities. Furthermore, knowledge process integration plays a significant mediating role in the relationship between digital governance and knowledge management; this implies that digital governance primarily enhances the effectiveness of knowledge management by strengthening the coherence and coordination of knowledge. Finally, environmental complexity acts as a significant moderator, influencing the strength of the relationship between digital governance and knowledge management. Specifically, in highly complex environments characterized by uncertainty and rapid changes, the role of digital governance in enabling effective knowledge management becomes more critical and prominent. All findings are consistent with prior research. https://stim.qom.ac.ir/article_4525.html Purpose: The advent of Artificial Intelligence (AI) has fundamentally transformed Information Retrieval (IR) systems. Scientific publication databases, the vital arteries of the global research ecosystem, lie at the core of this technological revolution. Today, AI-driven capabilities—such as semantic search, user personalization, and intelligent analytics—have elevated researcher expectations, transitioning from digital novelties to indispensable standards for leading platforms. However, the global velocity of adopting these technologies remains asymmetrical. Evidence indicates a profound technological divide between international scientific databases and national counterparts. This disparity is not merely a technical deficiency; it represents a strategic challenge. Left unaddressed, this gap threatens to undermine domestic scientific authority, increase reliance on foreign platforms, and precipitate a massive outflow of national research data. Recognizing this, the primary objective of this study is to systematically identify, statistically analyze, and elucidate the dimensions of this AI adoption gap between domestic and international scientific journal databases, relying on a rigorous expert-driven assessment.Method: Methodologically, this applied research employs an analytical-survey design framed within a gap analysis approach. The primary data collection instrument was a structured questionnaire encompassing 33 previously validated AI components relevant to IR systems. Empirical evaluation targeted prominent databases. The domestic group comprised major Iranian platforms: Irandoc, ISC, and Magiran. The international reference group included globally renowned platforms: ScienceDirect, Elsevier, and Emerald. Operational evaluation was conducted through hands-on assessments by a purposively selected panel of 19 experts in information science, AI, and scientometrics. Inferential statistical analyses were executed using the non-parametric Mann-Whitney U test to compare the performance of the independent database groups, supplemented by the effect size index (r) to determine the significance of the observed technological discrepancies.Findings: Data analysis definitely confirmed a pervasive and statistically significant technological gap between the two categories. Out of the 33 theoretically effective AI components initially identified, evaluation revealed only 16 components had been operationalized into the assessed user interfaces. Consequently, comparative analysis strictly focused on these 16 active features. Findings demonstrated that in 11 components (approximately 69%), international databases exhibited statistically significant superiority over domestic counterparts. This technological chasm was particularly acute in functional areas impacting research quality. Supremacy of foreign platforms was unequivocally evident in critical components like "spell checking and error correction" (p = 0.000), "automated summarization and information extraction" (p = 0.000), "individual recommender systems" (p = 0.006), and "intelligent suggestion engines" (p = 0.007). A deep divide also existed in components vital for safeguarding academic integrity, including "plagiarism and duplication detection" (p = 0.001), "article quality assessment" (p = 0.009), and "citation network analysis" (p = 0.003). Conversely, statistical analysis revealed no significant difference in 5 specific AI components. These comprised "advanced intelligent search" (p = 0.443), "automated keyword extraction" (p = 0.068), and "analysis of authors and organizations" (p = 0.688). Parity in these domains suggests domestic databases have successfully implemented foundational search capabilities, reaching an acceptable baseline standard.Conclusion: In conclusion, the documented disparity transcends technological specifications; it represents a qualitative shift in service provision philosophy. International databases have evolved from passive "information repositories" into dynamic "intelligent research assistants," autonomously providing services like predictive trend analysis and automated validation. In stark contrast, domestic databases remain predominantly tethered to conventional repository functions. This stagnation poses a severe strategic threat. To bridge this divide, this study proposes a national roadmap in three phases. Phase One (Fundamental) concentrates on implementing baseline AI components, such as word disambiguation and error correction. Phase Two (Developmental) focuses on integrating value-creating mechanisms, particularly intelligent recommender systems and citation analytics. Phase Three (Strategic) aggressively targets long-term investments in forward-looking capabilities, like predicting emerging scientific trends. Ultimately, integrating AI into domestic scientific databases is an existential necessity to safeguard national scientific sovereignty and solidify sustainable development foundations. https://stim.qom.ac.ir/article_4526.html Objective: With the rapid expansion of e-commerce and the growing reliance of users on online shopping, the threat of fraudulent websites has significantly increased. These websites imitate reputable brands, replicate visual designs, use domain names similar to legitimate ones, and create deceptive user experiences to trick users into entering sensitive information such as usernames, passwords, credit card details, verification codes, and personal identification data. The consequences of such attacks are not limited to financial losses; they also damage public trust, harm brand reputation, increase complaint-handling costs, and may even disrupt digital service supply chains. Although numerous studies have examined the application of machine learning and deep learning algorithms in fraud detection, a major gap in the literature remains the absence of a practical, prioritized, and decision-support framework. Such a framework should guide organizations in selecting which capabilities and indicators to implement under real-world constraints, including limited budgets and human resources. Most previous studies primarily focus on improving model accuracy while overlooking practical feasibility, economic benefits, and implementation and maintenance costs in an integrated manner. The objective of this research is to fill this gap by developing a systematic framework for identifying, evaluating, and prioritizing machine learning capabilities for detecting fraudulent e-commerce websites. This framework enables decision-makers to adopt a balanced perspective that considers both technical and managerial criteria when planning implementation strategies.Methodology: This study is applied and quantitative in nature. In the first stage, a systematic review of scientific and industry sources (over 150 articles and reports) was conducted to extract relevant capabilities for detecting fraudulent websites. As a result, 25 capabilities were identified, covering various dimensions: domain and URL features (e.g., domain length, lexical patterns, structural similarity), website content (text, images, keywords, and page structure), user behavioral patterns (navigation behavior, click patterns, dwell time), financial transaction data (payment anomalies and suspicious patterns), and web page changes over time (abnormal and sudden updates). Next, the fuzzy Delphi method was employed to assess the importance and usability of each capability from experts’ perspectives, allowing for the management of uncertainty and differences in judgment. In this phase, nine capabilities with a defuzzified score above 0.7 advanced to the final stage. Subsequently, the multi-criteria decision-making method (MARCOS) was applied to prioritize the selected capabilities based on three criteria: feasibility, financial benefits, and implementation cost. Expert data were aggregated, normalized, and weighted to determine the final ranking of each capability.Findings: The results indicate that five capabilities have the highest priority and impact in detecting fraudulent e-commerce websites: (1) Classifying websites as fraudulent or legitimate using machine learning and deep learning algorithms; (2) Analyzing suspicious financial transactions and identifying abnormal payment flow patterns; (3) Detecting rapid and unusual changes in web pages and website content; (4) Identifying suspicious trends and patterns in historical data and user behavior using predictive and pattern-based learning techniques; and (5) Detecting fake domains and domains similar to legitimate ones through URL feature analysis and structural similarity assessment. Other selected capabilities also contribute to improving detection accuracy and reducing risk; however, they rank lower in terms of implementation priority and overall impact.Conclusion: By providing a prioritized decision-support framework, this study bridges the gap between algorithm-focused research and the practical needs of organizations. The findings demonstrate that a combined approach integrating domain analysis, content analysis, user behavior monitoring, and financial transaction evaluation achieves higher effectiveness in the timely detection of fraudulent websites. The proposed framework can serve as a foundation for designing, investing in, and deploying digital security systems within e-commerce platforms. The study is limited by its reliance on secondary data and a relatively small number of experts. Future research is recommended to utilize real-world datasets, larger expert samples, field validation, and real-time analytical approaches to enhance accuracy and generalizability. https://stim.qom.ac.ir/article_4545.html Abstract: Regarding R&D’s key role for nations to achieve industrial development and noble technologies, this paper aims at knowing functions plus providing institutional mapping for Iran’s R&D value chain. The 4 following fundamental queries need to be catered: "How many layers and what functions are there in INS’s vale chain?", "What are the effective organizations over knowledge-based collaborations within R&D value chain?", "What are the effective layers’ functions in each of the INS value chain layers?" and “How is R&D value chain institutional mapping constituted?”Methodology: The study is descriptive-dynamic in coherence but functional in objectives. The employed statistical society comprises 15 elites chosen judgmentally. It’s a qualitative study utilizing Delphi and Fuzzy methods employing SPSS software. Findings: twin-leveled Delphi Fuzzy results depict INS bears 7 functions in the realm of R&D. elites’ consensus claims that there exist 75 effective organizations. Results: INS consists of 75 organizations having 7 functions throughout the R&D value chain from which 15 determine R&D policies, 20 organizations facilitate R&D and open innovation, 7 ones focus on creative HR, 5 create capabilities, 11 ones promote entrepreneurship and 5 others create products. Institutional mapping reveals players’ over crowdedness plus their irrelevancy with their roles. https://stim.qom.ac.ir/article_4549.html Objectives: The present study aims to identify and analyze the opportunities and challenges associated with freelance employment in the context of LLM emergence, to examine freelancers' perceptions, levels of adaptation, and future outlook toward these tools, and to propose practical strategies for more effective adaptation to such technological transformations.Methods: This research adopts a mixed-methods approach. In the quantitative phase, data were collected through questionnaires completed by 55 freelancers active on various Iranian and international freelancing platforms and analyzed descriptively and inferentially. Then, additional insights were gathered through open-ended questions and examined using content analysis. Due to the undefined population, convenience sampling was used.Results: The findings indicate that large language models, when used as professional assistants, create opportunities such as increased speed, productivity, creativity, and idea generation, while simultaneously introducing challenges including dependency, superficial work practices, unreliable or inaccurate outputs, and intensified competition in certain professional skill areas. Despite acknowledging the substantial impact of these changes on their professional activities, most freelancers are actively developing new skills to better adapt to these technologies and generally maintain a positive outlook toward the future of freelancing in the era of large language models.Conclusion: Based on the findings, practical strategies for effective adaptation include prompt engineering skills, human verification of outputs, and supportive access policies.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . https://stim.qom.ac.ir/article_4563.html Internal financial fraud committed by bank branch employees—due to their direct access to operational processes, information systems, and sensitive customer data—is considered one of the most significant threats to financial security and the integrity of the banking system. Such fraudulent activities not only cause direct financial losses for banks but can also undermine public trust in the banking sector. In recent years, significant progress has been made in detecting financial fraud such as money laundering, credit fraud, and cybercrime; however, the detailed analysis of branch employees’ behavior as a key factor in internal fraud has received comparatively less attention. Many existing monitoring systems focus primarily on analyzing customer transactions and pay limited attention to the behavioral patterns of employees interacting with banking systems. Consequently, the development of intelligent analytical models capable of integrating and analyzing both behavioral and transactional data of employees has emerged as a key requirement in the field of banking supervision and inspection.The aim of this research is to design and present an intelligent model for detecting internal fraud committed by bank branch employees, with a focus on analyzing their transactional and operational behavior. This study seeks to develop an analytical framework for identifying fraudulent patterns in banking activities by combining statistical behavioral analysis techniques with machine learning algorithms. In this approach, employee behavior during banking operations is considered an important source of information, and abnormal behavioral patterns that may indicate fraudulent activity are examined. The use of statistical analyses to extract hidden relationships among behavioral and transactional variables, along with machine learning algorithms to detect complex and nonlinear patterns, enables the development of efficient and intelligent monitoring systems.Implementation and validation of the proposed model are based on the use of data extracted from core banking systems. These data include transactional information such as types of banking operations, transaction volume and amount, timestamps, and sequences of activities recorded in operational systems. In addition, behavioral data include login patterns, frequency and type of user activities, interactions with operational systems, and other behavioral indicators related to job activities. Combining these two categories of data makes it possible to extract effective behavioral and statistical indicators for identifying suspicious activities and provides a suitable basis for training and evaluating intelligent fraud detection models.The proposed framework is designed as a layered hybrid model consisting of three main layers. The first layer is the behavioral layer, where variables related to employee behavioral patterns and system interactions are extracted and analyzed. This layer aims to identify indicators that reveal abnormal changes in employees’ operational behavior. The second layer is the statistical analysis layer, in which relationships among behavioral and transactional variables are examined using statistical modeling methods and fuzzy logic. The use of fuzzy logic in this layer enables handling uncertainty and ambiguity in behavioral data and facilitates the identification of hidden patterns in employee behavior. The third layer is the machine learning layer, where algorithms such as artificial neural networks and support vector machines are used to analyze integrated data and detect complex fraud patterns. Leveraging the capability of machine learning algorithms to process large and multidimensional datasets significantly enhances the detection of fraudulent and abnormal behaviors.It is expected that combining behavioral analysis, statistical modeling, and machine learning algorithms within an integrated framework will increase the accuracy of internal fraud detection and reduce false alarms. Integrating employee behavioral data with transactional data enables the identification of complex and multidimensional fraud patterns—patterns that are often undetectable using traditional monitoring methods. Additionally, the use of fuzzy logic alongside machine learning algorithms can help manage uncertainty in behavioral data and improve the model’s generalizability across different organizational conditions. These features allow the proposed model to perform effectively in dynamic and complex banking environments.The main innovation of this research lies in the development of a localized hybrid framework for detecting internal fraud in the banking system, in which behavioral variables of employees and organizational structural characteristics are incorporated alongside technical and transactional data. By focusing on analyzing the operational behavior of branch employees, this model introduces a new approach to intelligent banking supervision and can serve as a supporting tool for internal audit and inspection units within banks. Implementing such a model can enhance the efficiency of supervisory processes, enable faster identification of internal fraud patterns, and ultimately help reduce financial fraud and improve the overall health of the banking system. https://stim.qom.ac.ir/article_4564.html Objective: Technological advancements and the growing adoption of artificial intelligence (AI) in service organizations have fundamentally transformed traditional service delivery models. The utilization of AI technologies can enhance service efficiency, optimize work processes, and improve decision-making quality in service-oriented organizations. However, the success of AI implementation largely depends on employees’ level of digital literacy, which enables them to effectively use intelligent tools. Accordingly, this study aims to investigate the effect of AI technology adoption on service efficiency in service organizations, emphasizing the mediating role of employees’ digital literacy. Public libraries are examined as a case of service organizations.Methodology: The present research is applied and conducted using a survey method with a descriptive-analytical approach. The statistical population of this research consists of 130 librarians working in 72 public libraries in Markazi Province, all of whom were studied. Ultimately, 120 individuals responded to the questionnaire. Data were collected using questionnaires on artificial intelligence adoption (Carlos et al., 2023), librarians’ digital literacy (Amin et al., 2021), and library service performance (Panahi, 2023). Construct validity, both convergent and discriminant, was confirmed through structural equation modeling, and reliability was assessed using Cronbach’s alpha coefficients. Data analysis was conducted using SPSS 25 and SmartPLS 3.3.Findings: The results obtained from the structural equation modeling (SEM) indicated that all research hypotheses were confirmed. First, the adoption of artificial intelligence technology had a direct, positive, and significant effect on library service efficiency (β = 0.500, p < 0.001). This means that the more librarians use intelligent tools in their daily workflows, the greater the increase in the speed and accuracy of service delivery. Second, AI adoption exerted a very strong and significant impact on librarians’ digital literacy (β = 0.638, p < 0.001), suggesting that practical exposure to emerging technologies itself acts as a powerful driver for learning and enhancing digital skills. Third, digital literacy also had a direct and significant influence on service efficiency (β = 0.256, p < 0.001); in other words, librarians with higher levels of digital literacy are better able to leverage AI capabilities and provide more efficient services. Most importantly, the mediating role of digital literacy in the relationship between AI adoption and service efficiency was confirmed (β = 0.163, p < 0.001). This finding clearly demonstrates that the indirect effect of AI adoption on service efficiency through the enhancement of digital literacy is distinguishable from, and complementary to, its direct effect. In other words, in organizations where employees lack sufficient digital literacy, even with the technical adoption of AI, no significant improvement in service efficiency will occur.Conclusion: The present study empirically proved that the adoption of artificial intelligence technology in service organizations such as public libraries achieves its maximum impact on service efficiency only when accompanied by the simultaneous enhancement of employees’ digital literacy. Indeed, digital literacy serves as a critical mediating variable, acting as a bridge between technological infrastructure and the ultimate performance of the organization. Without these skills, investment in AI not only fails to lead to productivity gains but may also generate hidden costs due to improper use or employee resistance. Based on the findings, the following recommendations are offered to the public library institution of Iran and other service organizations: (1) designing and implementing continuous digital literacy training courses with an emphasis on the practical application of AI in the workplace; (2) developing ethical and practical guidelines for the responsible use of intelligent tools; (3) fostering organizational motivation and a culture based on lifelong technological learning; (4) periodically assessing employees’ digital literacy levels prior to the implementation of new intelligent systems; and (5) utilizing librarians with high digital literacy as peer-training facilitators. Finally, future research could examine the role of other moderating variables such as age, work experience, or type of organization in this relationship, and test the model in different service contexts (e.g., education, healthcare, and banking).