Knowledge Retrieval and Semantic Systems

Abstract Introduction
Information seeking behavior (ISB) is a complex, multidimensional process at the intersection of cognitive, affective, and technological dimensions. Traditional models describe ISB as a conscious, goal-oriented process. However, the advent of Artificial Intelligence (AI), including large language models, recommender systems, and conversational agents, has fundamentally transformed this paradigm. AI has shifted ISB from reactive, keyword-based searching to proactive, conversational, and algorithm-driven interactions. Despite these shifts, there is a lack of comprehensive, quantitative mapping of the global scientific structure at the intersection of AI and ISB. This study addresses this gap by employing scientometrics to map the intellectual structure, evolutionary trends, and emerging thematic clusters of this domain.
Research Questions
1. Which countries, institutions, authors, and journals have the highest scientific output in the domain of “AI in the transformation of ISB”?
2. What are the temporal trends, language distribution, and annual growth of publications from 1976 to 2025?
3. What is the conceptual structure, keyword co-occurrence network, and thematic clustering of this research field?
Literature Review
Foundational ISB models emphasize cognitive and affective stages, highlighting uncertainty and information anxiety. Later systemic models incorporated social and organizational barriers. In the digital era, scholars note that AI introduces new concepts such as “hidden information needs” and “conversational information interaction.” While previous studies have qualitatively explored specific AI systems, they lack a macro-level quantitative analysis. Scientometric approaches, utilizing databases like Web of Science (WoS), offer robust tools for mapping knowledge domains, identifying core contributors, and revealing hidden evolutionary patterns in scientific production, which this study leverages.
Methodology
This applied research utilized a scientometric approach. The statistical population comprised all documents indexed in the Web of Science (WoS) core collection, extracted on January 15, 2025, covering the period from 1976 to 2025. The search strategy combined AI-related terms with ISB terms. The initial search yielded 2, 147 records. After data cleaning (including removing duplicates, non-research documents, and standardizing author names) 1, 902 records were retained. Data analysis and visualization were conducted using Excel and R software with the bibliometrix package, employing co-word analysis, co-authorship networks, and citation analysis.
Results
The United States led scientific production with 392 records, followed by China (324) and the UK (162). Harvard University was the top institution (33 articles), followed by the University of Toronto (28). “IEEE” and “Lecture Notes in Computer Science” were the most prolific sources (56 articles each). Trend Analysis: English dominated the literature (98.26%). The publication trend exhibited two distinct phases: a slow growth phase (1976–2020, comprising 31% of outputs) and an exponential acceleration phase post-2020. The peak occurred in 2024 with 393 documents. Conceptual Structure: Computer Science was the dominant subject category (965 articles), while Information Science accounted for only 161. Co-word analysis revealed seven distinct thematic clusters. Cluster 1 focused on AI in digital health, trust, and bias. Cluster 2 covered deep learning. Cluster 3 centered on natural language processing. Cluster 4 (16 keywords) related to data mining applications, precision, feature extraction, machine learning, neural networks, information retrieval, and visualization. Cluster 5 highlighted generative AI and ethics. Cluster 6 (5 keywords) focused on recommender systems and personalization techniques such as collaborative filtering and case-based reasoning. Cluster 7 (3 keywords) related to algorithms and the music information retrieval process.
Discussion
The findings revealed that AI is no longer merely a retrieval tool but an active agent shaping user behavior, trust, and cognitive processes. The exponential growth post-2020 aligned with the rise of conversational AI. However, a significant disciplinary imbalance exists; the field is heavily dominated by computer science and engineering, while the human, cognitive, and ethical dimensions (traditionally the domain of information science and psychology) are marginalized. This technological determinism risks creating systems that are highly efficient but lack human-centric design. Furthermore, the geographical concentration of research in Western institutions highlighted a global scientific divide. The emergence of clusters focusing on “ethics” and “bias” indicated a growing awareness of socio-technical challenges.
Conclusion
The intersection of AI and ISB is a rapidly evolving, interdisciplinary field transitioning from traditional search to proactive, generative interactions. To ensure these technologies serve humanity effectively, future research must pivot towards three main axes: developing ethical frameworks for transparent AI systems, investigating the long-term cognitive impacts of AI on vulnerable groups like students, and fostering deep interdisciplinary collaborations. Only by integrating computer science with information science, psychology, and ethics can we design AI-driven information systems that are not only technologically robust but also socially equitable and human-centric.

Abstract Introduction
Information organization is a cornerstone of every information-retrieval system, since query effectiveness depends on how information is organized and represented. Digital archives, especially historical ones such as the Islamic Revolution Document Center (IRDC) of Iran, face a persistent challenge: traditional retrieval systems cannot provide comprehensive, meaning-based access to document content. Established in 1981 to compile the history of the Islamic Revolution and collect the documents and memoirs of Imam Khomeini's movement, the IRDC holds more than 4.5 million written-document pages, about 31,000 hours of oral-history interviews, nearly 372,000 photographs, more than 13 million news titles, and around 90,000 domestic and foreign articles. Although much of this material has been digitized through optical character recognition (OCR) and automatic speech recognition (ASR), the center's retrieval system relies mainly on descriptive metadata and full-text search, so that key entities (persons, places, events, and concepts) remain hidden, limiting comprehensive access. This calls for a redesign based on knowledge-graph and ontology approaches that can extract and organize entities semantically. This research models a domain ontology for the IRDC digital archive through a hybrid approach combining text analysis with the reuse of existing ontologies, establishing a foundation for a knowledge graph that supports semantic retrieval and AI systems for managing Iran's historical documents.
Research Questions
1. What are the entities and classes of the subject domain of the IRDC digital-archive documents, and how can they be represented within a domain ontology?
2. What should the hierarchical structure and the relationships among the ontology classes be?
3. Which ontologies and classification systems are most suitable for reuse in building the IRDC domain ontology?
Literature Review
Research on ontology- and knowledge-graph-based knowledge organization falls into several strands: automatic or hybrid extraction of structured information from text (Emami, 2022; Moradi et al., 2015; Gutierrez et al., 2016; Wimalasuriya & Dou, 2010); knowledge-graph development, including a Persian knowledge graph (Sajjadi & Minaei Bidgoli, 2019) and FarsBase (Asgari-Bidhendi et al., 2019); knowledge-graph-based question answering (Yang et al., 2023; Tong et al., 2019); human-driven ontology design in specialized domains (Torabi et al., 2022; Sadeghi Niaraki et al., 2018; Servati et al., 2017); knowledge-graph-based retrieval and search engines (Homayouni et al., 2018; Li, 2019; Wang et al., 2019); ontology-based retrieval optimization (Jafari Pavarsi et al., 2020; Asim et al., 2019); and thesaurus enrichment for ontology construction (Hosseini Beheshti & Ejei, 2015; Wu, 2018). A review of this body of work shows that, although these studies advance meaning-based organization, most rely on relatively structured or general sources, including Wikipedia, web tables, scientific databases, and technical texts, rather than real archival environments marked by heterogeneous sources, diverse formats, and semantic ambiguity, and they emphasize technical extraction methods over practical application in operational systems. The present study therefore occupies a distinct, applied, problem-driven position, designing and implementing, for the first time, a domain ontology for contemporary Iranian history within a real digital-archive context and addressing the gap in the domestic literature.
Methodology
A mixed-method design was adopted. Informational entities were identified through semi-automatic and automatic text analysis, and the ontology was designed following the best practices of Arp et al. (2015) for building domain ontologies on Basic Formal Ontology (BFO): defining the scope, identifying universals from existing ontologies and domain texts, arranging them in a general-to-specific hierarchy, ensuring logical coherence and human-readable definitions, and formalizing the result in a computer-usable language. The scope was limited to contemporary Iranian history, that is, the second Pahlavi period, the Islamic Revolution and its aftermath, and the Islamic Republic to the present, with fine-grained classes for persons, parties, governance periods, organizations, places, and related events.
The research population comprised all digitized resources of the archive, from which a purposive sample was selected in consultation with the center's managers and experts. Two tools supported entity recognition: the open-source annotation software INCEpTION for manual and semi-automatic tagging, and a large language model (Grok v3.0) for automatic named-entity recognition, whose errors were corrected under human supervision. Existing ontologies and classifications were analyzed with Protégé and SPARQL queries over RDF/OWL files, reusing DBpedia, the Persian knowledge-graph ontology (FarsBase), BIBFRAME, and the Library of Congress classification for the history of Iran. The hierarchy was built around an is_a backbone with single inheritance, the open-world assumption, and the objectivity principle, using DBpedia as the base. Finally, the classes were validated through the Nominal Group Technique with eleven managers and experts across two three-hour sessions, applying four criteria: hierarchical logic, historical and cultural accuracy, alignment and conceptual equivalence, and completeness and simplicity.
Results
The combination of text analysis with the reuse of DBpedia, BIBFRAME, and the Library of Congress Iranian-history classification produced 535 terms, organized under main classes including agent, person, organization, family, event, work, government period, subject concept, place, architectural structure, and ethnic group. After validation by the expert group and majority consensus, 457 classes were confirmed, 70 were added, 53 were relocated within the hierarchy, 19 were removed, 5 terms were renamed, and 2 classes were both relocated and renamed.
Most of the additions concerned the agent–person and place branches, and a dedicated subject-concept branch was created for intellectual, political, social, and economic currents, schools of thought, and ideologies. The architectural-structure branch was merged into the place branch as spatial subdivisions, and several unconventional or overlapping class labels were corrected or removed. Regarding the third question, no domain ontology for contemporary Iranian history was found domestically; the Library of Congress expansion was preferred over the Dewey-based one because it subsumes the Dewey classes, DBpedia was confirmed as the core general ontology (the Persian knowledge graph differing in only ten classes), and BIBFRAME served as the reference for the work-related classes.
Discussion
The study yielded a validated, extensible base ontology for contemporary Iranian history grounded in the IRDC archive. Its results agreed with earlier Persian-language extraction studies and with hybrid ontology-based extraction approaches, but its data derive from authentic, semi-structured historical documents rather than structured or general texts, with emphasis on a native conceptual structure suited to the Iranian historical domain rather than on extraction algorithms. The scale of the expert-driven revision showed that general structures such as DBpedia or the Persian knowledge graph cannot accurately cover native Iranian-history concepts without extensive localization, consistent with Sajjadi and Minaei Bidgoli (2019) and Asgari-Bidhendi et al. (2019). Unlike purely conceptual ontology-design studies, this work was validated through practical feedback from archival experts in a real institutional setting, making its output directly applicable within the digital-archive system.
Conclusion
By combining text analysis with the reuse of established ontologies and rigorous expert validation, this research produced a base domain ontology for contemporary Iranian history within the real context of the IRDC digital archive. The ontology provided a foundation for a historical knowledge graph, enhanced semantic retrieval, and served as a logical basis for artificial-intelligence systems that manage the country's historical documents, filling a gap in practical ontology design for this domain. Future research should develop interoperable domain ontologies in other subject areas, implement and evaluate an operational knowledge graph, advance more automated extraction of concepts and relations with large language models, and study relation-weighting algorithms.

Abstract Introduction The business model has evolved from a simple operational blueprint into a complex, adaptive system. Osterwalder and Pigneur (2010) define it as the logic by which an organization creates, delivers, and captures value. In recent years, environmental uncertainty has increased the emphasis on dynamism and adaptability. Business model innovation is now seen as a core capability for sustaining competitive advantage—going beyond product or process innovation to a fundamental reconfiguration of how the firm operates. Organizations today face complex, uncertain environments that intensify during economic, political, social, or health crises. Static, traditional business models have proven vulnerable, as shown by COVID-19 and global conflicts. Resilience, dynamic capabilities, digitalization, and temporary innovation are widely recognized as critical elements of crisis-ready business models. Semantic network models offer a systematic way to represent and analyze relationships among concepts in nonlinear, complex domains (Schops & Jaffrentz, 2024). Drawing on cognitive psychology, semantic networks can map how concepts are mentally associated and support clearer understanding of how managers conceptualize business models under instability. This study uses semantic network analysis to identify key dimensions of a crisis-compatible business model and to examine how those dimensions interact. Research Questions l. What are the key dimensions of a business model suited to crisis conditions? 2. What relationships and interactions exist among the dimensions and components of a crisis-compatible business model? Literature Review Research on business models in crisis contexts has grown substantially. Facchinetti and Petruzzelli (2021) pioneered semantic network analysis combined with big data to study business model innovation and revealed hidden patterns in conceptual linkages. Schops and Jaffrentz (2024) highlighted semantic network analysis as a powerful tool for understanding complex conceptual relationships in marketing and consumer research. Martínez et al. (2021), studying SMEs in developing countries during COVID-19, found that firms able to redesign their business models navigated the crisis more successfully. Behroozian (2024), in the UAE oil and gas sector, emphasized transparency and speed in strategic crisis management. Garrido-Moreno et al. (2024) showed that organizational resilience mediates the relationship between service innovation and business performance. Ou and Rakthin (2025) confirmed the mediating role of innovation quality between absorptive capacity and organizational resilience in Thai SMEs. In the Iranian context, Davari et al. (2021) reported positive effects of all corporate resilience dimensions (human resources, marketing, finance, supply chain, and services) on SME performance during COVID-19. Khabazan et al. (2023), in a review, stressed flexibility and innovation in unstable environments. Despite this body of work, three limitations remain: (1) many studies focus on narrow aspects such as digitalization or resilience rather than a multidimensional view; (2) there is little systemic understanding of synergistic interactions among business model components in crises; and (3) few studies combine quantitative network findings with qualitative validation (e.g., expert interviews) in a mixed-methods design. Methodology This is an applied, mixed-methods study using qualitative and quantitative content analysis and semantic network analysis. Data were analyzed with VOSviewer and Gephi for network construction, clustering, and visualization. Articles published from 2020 to 2025 on business model design were identified in John Wiley, Scopus, and Web of Science. Initial retrieval yielded 223 records; after title and abstract screening against inclusion criteria, 190 remained; after full-text review, duplicate removal, exclusion of non-generalizable case studies, conference papers, short notes, inaccessible texts, and low-quality studies, 150 articles entered the final analysis. Selection followed a PRISMA-based, two-stage screening process (title/abstract, then full text) by two independent reviewers, with a third reviewer resolving disagreements. Inclusion required publication between 2020 and 2025 in peer-reviewed or review journals, with clear reference in title, abstract, or keywords to business model design/formulation or business models in crisis/unstable conditions. Exclusion criteria removed duplicates, purely descriptive case studies without generalizable frameworks, and methodologically weak or off-topic papers. Articles were also scored on internal validity, external validity, structural validity, dependability, and relevance/novelty; low-scoring items were dropped. Abstracts and keywords underwent two-stage preprocessing: removal of stop words, special characters, and numbers; spelling normalization; synonym merging; and stemming (NLTK for English, HAZM for Persian). Co-occurrence networks were built in VOSviewer (minimum occurrence threshold = 5; association strength normalization; modularity-based clustering). Sensitivity analyses varied occurrence thresholds (4 and 6) and normalization methods to assess stability. Gephi was used for centrality analysis (LinLog layout; Jaccard-normalized link weights). Inter-cluster relationships were summarized with external link density. Discussion Findings aligned with Salampour et al. (2023), who proposed that business model innovation affects crisis management through entrepreneurial capability, resilience, and performance. The network structure supported business model as the integrative core, with crisis management and innovation acting as bridges among clusters. Mohammadi Hassanzadeh et al. (2024), using expert judgment and the analytic hierarchy process, ranked “adaptability to crisis conditions” as the top criterion for evaluating crisis-compatible business models, followed by innovation, compatibility, unity, comprehensiveness, and performance. High centrality of innovation and crisis management in the present network is consistent with that ranking. A crisis-compatible business model should combine: (1) flexibility and resilience; (2) continuous innovation in model components; (3) entrepreneurial and dynamic capabilities; (4) diversified revenue models; (5) customer orientation; (6) flexible organizational structures; (7) preventive and reactive strategic actions; and (8) strategic collaborations to reduce vulnerability. Practical levers include agile decision-making, supply chain and revenue diversification, strategic reserves, experimental learning, open innovation, customer intelligence systems, and scenario-based crisis planning. Conclusion Using semantic network analysis of 150 scholarly articles (2020–2025), this study identified eight interrelated dimensions of a business model suited to crisis conditions and quantified their interactions. “Business model” forms the conceptual core; crisis management, innovation, resilience, and digital transformation are critical connectors. The eight-cluster framework offered a systemic basis for designing and adapting business models under instability. Future research could combine bibliometric networks with qualitative expert validation and sector-specific case studies to strengthen external validity and managerial applicability.

Abstract Introduction
Research Data Management (RDM) has emerged as a critical component of modern scientific research, particularly in the field of clinical and medical sciences where large volumes of heterogeneous, sensitive, and complex data are generated, maintained, and used for further research and for clinical purposes. Effective management of clinical research data contributes to data integrity, security, accessibility, reproducibility, and long-term preservation of scientific outputs. The rapid expansion of digital technologies, electronic health records, clinical trials, and data-intensive research has increased the need for systematic approaches to managing research data throughout its lifecycle.
Clinical research data encompass diverse forms of information, including patient records, laboratory findings, diagnostic images, and observational data. Given the significance of these data in evidence-based medicine and healthcare decision-making, understanding the evolution of scholarly research on clinical research data management is essential. Scientometric studies provide valuable insights into the intellectual structure, development patterns, influential contributors, and collaboration networks within a scientific field. Therefore, this study aimed to analyze and visualize the global scientific publications related to clinical research data management from 1990 to 2023.
Literature Review
Previous studies have highlighted the growing importance of research data management across various disciplines. Whyte and Tedds (2011) emphasized the necessity of systematic data management practices to ensure data preservation and reuse. Cox and Pinfield (2014) examined the evolving role of libraries in supporting research data management services, while Marlina et al. (2022) identified organizational and technological factors affecting RDM readiness. Several scientometric investigations have explored the broader field of research data management. Gupta et al. (2021) conducted a bibliometric analysis of RDM literature and found that the United States and the United Kingdom were leading contributors to the field. Lee et al. (2024) similarly reported substantial international growth in RDM-related publications and identified Germany, the United States, and the United Kingdom as major scientific producers. Within the medical and clinical domains, studies have focused on data sharing, ethical considerations, and challenges associated with managing health research data. Bull et al. (2015) discussed ethical frameworks for sharing individual-level health research data, while Rosenbaum (2018) examined the growing movement toward data sharing in medical research. Despite these contributions, few studies have specifically investigated the scientific landscape of clinical research data management using an integrated scientometric approach. This research addresses that gap by combining data from both Scopus and Web of Science databases to provide a comprehensive overview of the field.
Methodology
This study employed an applied scientometric research design. The statistical population consisted of all publications related to clinical research data management indexed in Scopus and Clarivate Analytics (Web of Science) between 1990 and 2023. Data retrieval was conducted through structured search strategies applied to titles, abstracts, and keywords in both databases. After data extraction and removal of duplicate records, a total of 13,978 publications remained for analysis. Multiple bibliometric and network analysis tools were utilized, including ScientoPy, Ravar Matrix, VOSviewer, UCINET, Microsoft Excel, and Python programming. These tools facilitated the examination of publication growth trends, geographical distribution of research output, collaboration networks among countries, and co-authorship patterns among researchers. Network analysis indicators such as degree centrality, closeness centrality, and betweenness centrality were calculated to identify influential countries and researchers within the scientific collaboration networks. Visualization techniques were employed to illustrate the structure and dynamics of scientific production in the field.
Results
The findings revealed a substantial increase in scientific publications related to clinical research data management over the study period. While only a limited number of publications appeared during the early years of the 1990s, scientific output grew steadily after 2005 and accelerated significantly from 2018 onward. The highest number of publications was recorded in 2021 and 2022, reflecting the increasing importance of data-driven healthcare research and digital health technologies. Geographical analysis showed that the United States ranked first in scientific production, followed by the United Kingdom and Germany. These countries consistently maintained their leading positions throughout the study period. Other significant contributors included Italy, France, Canada, and the Netherlands. The international collaboration network demonstrated that the United Kingdom, the United States, Switzerland, Germany, and Australia occupied central positions within global research partnerships. According to degree centrality measures, the United Kingdom exhibited the strongest collaborative activity, while the United States showed the highest closeness and betweenness centrality values, indicating its strategic role in connecting different research communities. Analysis of co-authorship networks identified several influential scholars in the field. Researchers such as Axel Junger, Hans-Ulrich Prokosch, Rainer Röhrig, Martin Sedlmayr, Dagmar Waltemath, and Carole Goble were among the most productive and influential contributors. These researchers played key roles in fostering collaboration and advancing the development of clinical research data management.
Discussion
The results indicated that clinical research data management has evolved into a rapidly expanding area of scientific inquiry. The notable growth in publications after 2018 may be attributed to advances in information technology, the proliferation of electronic health systems, increasing emphasis on data sharing, and the growing adoption of data-driven approaches in healthcare and biomedical research. The dominance of the United States, the United Kingdom, and Germany reflected their strong research infrastructures, substantial investment in health research, supportive policy environments, and advanced technological capabilities. Their leadership in international collaboration networks further demonstrated the importance of global partnerships in addressing complex challenges associated with clinical data management.
The study also highlighted the significance of influential researchers who act as bridges between different scientific communities. Such collaborative networks contributed to knowledge dissemination, methodological innovation, and the establishment of best practices in data management. Nevertheless, challenges remain, including issues related to data standardization, interoperability, privacy protection, ethical governance, and management of large-scale datasets. The findings were consistent with previous bibliometric studies on research data management and reinforced the growing recognition of data as a strategic scientific asset. As healthcare systems continue to generate increasingly complex datasets, effective data management will become even more critical for ensuring research quality and facilitating evidence-based decision-making.
Conclusion
This scientometric analysis provided a comprehensive overview of global research trends in clinical research data management between 1990 and 2023. The results demonstrated significant growth in scientific output, expanding international collaboration, and increasing scholarly attention to the management of clinical research data. The United States, the United Kingdom, and Germany emerged as the leading contributors, while several prominent researchers played central roles in shaping the intellectual structure of the field. The continued expansion of clinical research data management reflected its strategic importance in supporting high-quality healthcare research and innovation. Future developments are expected to involve greater integration of artificial intelligence, machine learning, automation technologies, and advanced data analytics. Moreover, increasing attention to data standardization, interoperability, and privacy protection will be essential for maximizing the value of clinical research data. The findings of this study can assist policymakers, research institutions, and scholars in understanding the evolution of the field, identifying influential contributors, and developing strategies to strengthen research capacity and international collaboration in clinical research data management.

Abstract Introduction
With the outbreak of the COVID-19 pandemic and the closure of universities, it seemed more necessary than ever to take a deeper look at online education. In such a context, Iranian universities, like those in other countries, made use of online education. Online education, due to its nature, has a strong dependence on information and communication technologies. Furthermore, not having enough previous experience with online education has resulted in various problems and challenges. Considering these problems, the main goal of the present study was to investigate the effect of knowledge sharing on the metacognition of Teaching English as a Foreign Language (TEFL) instructors, with creativity as a mediator, in the era of COVID-19.
Regarding the relationship between knowledge sharing, creativity, and metacognition, no research has been carried out so far; however, there have been several studies related to the effect and relationship between knowledge sharing and creativity, as well as the relationship between metacognition and creativity. In a study, Rezapasand et al. (2016) addressed the significant effect of knowledge sharing on creativity among employees. The findings showed that knowledge sharing has a significant effect on employees’ creativity. Similarly, Rahoo et al. (2020) studied the effect of knowledge management on the creativity of library employees. The results indicated that the creation, implementation, storage, and sharing of knowledge have a significant effect on employees’ creativity.
Regarding the relationship between creativity and higher thinking, especially metacognition, the research literature has mostly addressed the effect of metacognition on creativity (VanTassel-Baska & MacFarlane, 2009; Jia, Li, & Cao, 2019), and the influence of creativity on higher thinking and metacognition has been considered less. Meanwhile, Ramirez and Ganaden (2008) investigated the effect of creative activity on students’ higher thinking and reported that creative activities have no effect on higher thinking. Considering the lack of research on this issue and taking into account the importance of knowledge sharing for metacognition, creativity can play the role of a mediator between the two variables. Therefore, this study investigated whether knowledge sharing affects the metacognition of TEFL instructors through the mediation of creativity in the era of COVID-19.
Research Questions
1. Does knowledge sharing have the ability to predict the variable of creativity?
2. Does knowledge sharing have the ability to predict the variable of metacognition?
3. Does creativity have the ability to predict the variable of metacognition?
4. Does creativity have the ability to mediate the effect of knowledge sharing on metacognition?
Methodology
In terms of purpose, the study was applied, and in terms of methodology, it was survey‑based. The statistical population of the research included TEFL faculty members teaching in state and private universities in Iran. Out of the 450 questionnaires sent to professors’ emails, 300 were returned, of which 288 were selected. Therefore, the sample size was considered to be 288 participants according to the available sampling method. The first questionnaire was the knowledge‑sharing scale prepared by Yi (2009) for non‑university environments. Ramayah et al. (2014) modified the questionnaire for the academic environment. This questionnaire has 28 questions and four subscales. The second questionnaire was the creativity questionnaire compiled by Khany and Boghayeri (2014). This questionnaire has 43 items and three subscales. The third questionnaire was the metacognition scale developed by Balcikanli (2011). This questionnaire has 24 items and six subscales.The results of Cronbach’s alpha showed that the reliability of the knowledge‑sharing questionnaire was 0.793, the creativity questionnaire was 0.921, and the metacognition questionnaire was 0.752, which indicates acceptable reliability of the scales. Data analysis was conducted using descriptive statistics, multiple regression, and path analysis with the help of SPSS 23 and AMOS 21 software.
Results
Demographic analysis showed that out of the total of 288 participants in this research, 150 (52.1%) were females and 138 (47.9%) were males. In terms of academic rank, 39 (14.4%) were instructors, 201 (74.4%) assistant professors, 24 (8.9%) associate professors, and 6 (2.2%) professors.
The first hypothesis examined whether knowledge sharing can explain the variance of the creativity variable. To answer this hypothesis, multiple regression was used. The results showed that the obtained R value was 0.558. The R² coefficient showed that the four variables together explained 31% of the variance of the creativity variable. Considering the standardized beta (β) coefficients, participation in writing (β = 0.266), organizational communication (β = 0.244), personal relationships (β = 0.184), and group activity (β = 0.235) could explain changes related to creativity. The correlation coefficients showed that participation in writing explains 8.8%, organizational communication 7.56%, personal relationships 4%, and group activity 9.16% of the variance of creativity.
The second hypothesis explored whether knowledge sharing can explain the variance of the metacognition variable. The results of the multiple regression test showed that the obtained R value was 0.170. The R² coefficient showed that the four variables together explained 2.9% of the variance of the metacognition variable. Considering the standardized beta (β) coefficients, the coefficient for participation in writing (β = 0.133) indicated that this variable could explain changes related to metacognition. However, the other variables (organizational communication, personal relationships, and group activity) did not contribute significantly in this research. The proportional correlation coefficient showed that participation in writing explained 69.1% of the variance of metacognition.
The third hypothesis explored whether creativity can explain the variance of the metacognition variable. The results showed that the obtained R value was 0.234. The R² coefficient indicated that creativity explains 5.5% of the variance of the metacognition variable. Considering the standardized beta coefficient (β = 0.234), creativity can explain the changes related to metacognition. The parametric correlation coefficient showed that creativity explains 47.5% of the variance of metacognition.
Regarding the question of whether there is a relationship between the knowledge sharing subscales and metacognition, and considering the mediating role of creativity, the results of the path analysis showed that the variable of participation in writing does not directly affect metacognition, although in the regression analysis this variable directly affected metacognition. Therefore, this variable was removed from the model. In general, the fit indices of the model were favorable. Although the presented model had a good fit, the question remained whether the subscales of knowledge sharing have a significant effect on metacognition through creativity as a mediating variable. The results indicated that all subscales of knowledge sharing, through creativity, were able to affect metacognition.
Conclusion
The general conclusion indicated that university instructors who have higher levels of knowledge sharing are more creative in teaching, and this may help develop metacognition. Based on the findings, it is suggested that universities develop their infrastructures and train faculty members to become involved in knowledge management processes, as this may enhance the creativity of university teachers.

Abstract Introduction In recent years, the rapid development of information and communication technologies (ICT) has significantly transformed the structure and delivery of services in higher education institutions. Universities, as knowledge-based organizations, increasingly rely on efficient information systems to manage student services and enhance service quality. Among these systems, service desks play a crucial role as the primary interface between students and institutional information services. Despite technological advancements, many public universities in Iran still face challenges in providing high-quality, efficient, and user-oriented student information services. These challenges highlight the need to identify and analyze the key factors influencing the performance of service desks. The present study aims to investigate the factors affecting the improvement of student information service systems within service desks of public universities in Iran and to propose a comprehensive model for enhancing service quality. Literature Review Previous studies emphasize the importance of ICT infrastructure, organizational culture, and human resource capabilities in improving service quality. Research has shown that technological readiness and integration of digital systems significantly enhance service efficiency and user satisfaction. Additionally, organizational factors such as service-oriented culture and flexible structures contribute to better performance of service desks. Human resource empowerment is also widely recognized as a critical factor, as skilled and knowledgeable staff can effectively manage user requests and provide accurate information. Furthermore, studies in service management highlight the importance of identifying system weaknesses (pathology) and continuously improving processes. Domestic and international research highlights growing attention to the “service desk” concept across diverse settings, including libraries, banks, government, universities, and urban management (Gooran et al., 2025; Ristanto et al., 2025). Internally, studies have identified key influencing factors such as accessibility, visibility, staff expertise, and user training (Najafgholi Nejad & Mohammadi, 2019; Najafgholi Nezhad et al., 2020). Externally, Oud and Genzinger (2016) emphasized organizational structures, while Bankston et al. (2022) stressed staff, technology, and physical space in specialized service desks. Information technology’s role is a recurring theme. Internally, Rabiei Mandejin and Mazloom (2016) confirmed the impact of internet banking service desks on customer loyalty, and Mesbahi and Davoudi (2021) highlighted e-service desks for justice and customer orientation. Externally, Smith (2018) linked e-government to agility and transparency, Kannabiran et al. (2016) identified system quality and habit as drivers of continued use, and Ristanto et al. (2025) expanded traditional technology acceptance models by incorporating psychosocial dimensions of e-service platforms. Service quality and user satisfaction remain central. Haji and Khoshghalb (2022) prioritized faculty knowledge as a key quality indicator in higher education, while Lowe et al. (2021) confirmed SERVQUAL dimensions (reliability, responsiveness, empathy, tangibles) as appropriate for public services in developing countries. Barriers to effective service desks include environmental, organizational, managerial, and individual factors (Abdoli & Mehrabi, 2016). Gooran et al. (2025) identified infrastructure, supervision, content, and interaction challenges in virtual education. Research gaps include the lack of integrated multi-dimensional models (structural, process, technological, human), limited mixed-method studies, insufficient context-specific frameworks for Iranian administrative structures, and underdeveloped perspectives on the service desk as a unified system with administrative and psychosocial functions (Ristanto et al., 2025). This study aims to develop a comprehensive, locally grounded model addressing these gaps. Methodology This study employs a mixed-methods approach consisting of qualitative and quantitative phases. In the qualitative phase, data were collected through semi-structured interviews with experts in the fields of information science, higher education management, and ICT. The data were analyzed using thematic analysis, resulting in the identification of 6 main themes, 8 sub-themes, and 51 basic concepts. In the quantitative phase, the proposed model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected through a structured questionnaire distributed among participants. Additionally, the SWARA method was used to prioritize the identified factors. Results The results showed that all proposed relationships were statistically significant. Among all constructs, human resource empowerment had the strongest effect on improving student information service systems (β = 0.869). The model demonstrated strong explanatory power with R² = 0.754, indicating that 75% of the variance in the dependent variable is explained by the model. Fit indices confirmed the adequacy of the model (GOF = 0.659, SRMR = 0.049). SWARA results indicated that increasing university flexibility, implementing training programs, and enhancing technical knowledge of human resources were the most important priorities. Discussion and Conclusion The findings highlight the multidimensional nature of improving student information service systems. Technological infrastructure, organizational structure, and human factors must be integrated to achieve effective service delivery. Human resource empowerment emerged as the most influential factor, emphasizing the importance of continuous training and skill development. Service desk pathology further highlights the need for systematic evaluation and improvement. This study developed a comprehensive model for improving student information service systems in Iranian public universities. The findings demonstrate that technological, organizational, and human factors collectively shape service quality. Policymakers and university administrators should focus on integrated strategies combining ICT development, organizational reform, and human resource empowerment.

Abstract Introduction
Data are recognized as a strategic asset for organizations. However, valuing data remains one of the central challenges in data management, due to its inherently multidimensional nature (encompassing financial, content-based, and functional aspects) and its strong context-dependence across industries.
This study focuses on information services (including data analytics, information consulting, and digital health) and aims to conduct a systematic analysis of the existing literature to identify data valuation frameworks within this domain and to explain their relationship with cost of information services.
Research Questions
1. What criteria determine the value of data in information services?
2. How can the value of data in these services be quantitatively measured?
3. How is the relationship between data value and the costs of information services described in the scientific literature, and what factors influence this relationship?
Literature Review
The existing body of literature on data valuation demonstrates that diverse approaches have been developed to assess the value of data in information services, which can be broadly categorized into three dimensions: financial, content-based, and functional. Financial models focus on production, storage, and transaction costs; content-based models emphasize data quality, freshness, rarity, and entropy; and functional models address operational utility and the role of data in decision-making and innovation processes. Several studies (e.g., Mendizabal-Arrieta et al., 2023; Lim et al., 2018) underscore the necessity of integrating these dimensions and employing hybrid models to achieve greater flexibility.
A systematic review of 24 selected studies, conducted using the PRISMA methodology, revealed that there is no single universal model for data valuation. Instead, the choice of an optimal model should be contingent on industry characteristics, data type, organizational technological maturity, and legal requirements. Market-based, income-based, and AI-driven approaches have emerged as complementary methods capable of addressing the shortcomings of traditional cost-based models.
Key challenges identified include conceptual heterogeneity (context-dependent definitions), computational limitations (complexity of measuring quality and entropy), and weaknesses in comprehensive modeling (lack of simultaneous coverage of financial, qualitative, and functional dimensions). The reviewed research further reports a positive and significant relationship between data value and the cost of information services, influenced by factors such as data update frequency, secure sharing capabilities, and the impact on customer decision-making. These findings highlight the necessity of developing hybrid, context-specific, and multidisciplinary frameworks for data valuation within information service chains.
Methodology
This study adopted a theoretical-analytical approach, utilizing library-documentary research and systematic literature review methods. Following the PRISMA methodology, we identified 138 relevant studies from reputable academic databases (Scopus, ScienceDirect, IEEE) published between 2008 and 2024. After rigorous screening, 24 articles met the inclusion criteria for systematic data extraction and analysis.
Studies were selected based on the following criteria: Relevance to data valuation in information services; inclusion of a pricing model or cost-value analysis; and publication in peer-reviewed journals.
Results
The findings of this study revealed three key dimensions of data valuation: (1) the financial dimension (production and exchange costs), (2) the content-based dimension (data quality and entropy), and (3) the functional dimension (operational utility). These dimensions have been incorporated in various ways across different valuation models, including cost-based models, market-based models, income-based models, and others.
Synthesis of the selected studies (n=24) indicated a reported positive relationship between intrinsic data value and information service costs in 75% of the studies (18 out of 24). A structured and methodological integration of findings from various studies showed that cost-based models were used in 9 studies, while market-based models were employed in 7 studies. The financial dimension was present in nearly all models, while the functional dimension was considered in 17 studies. Hybrid models demonstrated the best performance in 60% of cases, typically combining factors such as data quality and production costs.
Sectoral analysis revealed distinct patterns: in the healthcare industry, data value was primarily dependent on quality and privacy considerations, while in the financial sector, market-based models proved challenging due to price volatility. Consequently, hybrid models emerged as the most effective approach due to their superior flexibility.
Conclusion
This paper introduced a novel framework characterized by three key features: (1) a three-dimensional structure encompassing financial, content-based, and functional aspects; (2) parametric flexibility for industry-specific customization; and (3) compatibility with both structured and unstructured data types. Developed through a PRISMA-compliant systematic literature review, this framework established a comprehensive foundation for data valuation models in information services. Analysis of the 24 selected studies revealed that the healthcare sector demonstrated the highest valuation impact (85%).
The study proposed three strategic recommendations for enhancing data valuation:
• AI-driven real-time valuation for time-sensitive domains like cloud services requiring dynamic processing.
• Industry-specific standardization (e.g., GDPR-inspired frameworks in healthcare) to unify metrics and ensure regulatory compliance.
• Specialized training programs for data managers, analysts, and policymakers to improve understanding of data quality-cost interdependencies.
These integrated solutions can foster transparent, efficient, and scalable data valuation ecosystems.
Two primary limitations emerged: (1) predominant focus on structured data (≈80% of studies), resulting in insufficient attention to unstructured data, and (2) inadequate research on emerging markets.
Future studies should: (1) develop nonlinear models for multidimensional data complexity; and (2) explore blockchain applications to enhance valuation transparency. These approaches could address critical gaps in current literature.
The research established that effective information service data valuation requires multidimensional, industry-adapted approaches. Hybrid models (particularly AI-enhanced variants) showed significant potential in bridging theory-practice gaps. Findings offered actionable insights for data managers, service designers, and digital policymakers.

Abstract Introduction Knowledge has become one of the most valuable intangible assets and a critical source of competitive advantage for contemporary organizations. Effective knowledge management enables organizations to acquire, store, share, and apply knowledge to improve performance and ensure long-term sustainability. Among knowledge management processes, knowledge sharing is considered a fundamental component because knowledge that is not shared has limited organizational value. However, knowledge sharing is not an automatic process; rather, it requires individual motivation, organizational support, appropriate culture, and technological infrastructure. Knowledge sharing is particularly important in knowledge-based organizations such as public libraries, where employees possess valuable explicit and tacit knowledge gained through professional experiences. The National Library and Public Libraries Institution of Iran has accumulated extensive organizational experiences; however, the lack of systematic mechanisms for transferring knowledge, documenting lessons learned, and preserving employees’ expertise may result in knowledge loss due to retirement, employee turnover, and organizational changes. Therefore, identifying the factors influencing knowledge sharing and developing an appropriate model can contribute to improving organizational learning and knowledge sustainability. The present study aims to identify the key indicators affecting knowledge sharing implementation in the National Library and Public Libraries Institution of Iran, prioritize these indicators, and develop a comprehensive knowledge-sharing model. Research Questions 1. What are the dimensions and indicators of knowledge sharing in the National Library and Public Libraries Institution of Iran? 2. How are the effective indicators of knowledge sharing in the National Library and Public Libraries Institution of Iran ranked? 3. What is the knowledge-sharing model in the National Library and Public Libraries Institution of Iran? Literature Review Previous studies have demonstrated that knowledge sharing is influenced by multiple dimensions, including technological, organizational, cultural, and individual factors. Research has shown that organizational trust, management support, employee participation, motivation, and organizational culture play significant roles in encouraging knowledge-sharing behaviors. Trust among employees facilitates the exchange of both tacit and explicit knowledge, while supportive leadership creates an environment in which employees are more willing to share their expertise. Furthermore, information technology provides essential infrastructure for knowledge storage, communication, and dissemination. However, technology alone cannot guarantee successful knowledge sharing unless it is supported by appropriate organizational processes and a knowledge-oriented culture. Studies conducted in libraries and other knowledge-intensive organizations emphasize the importance of developing structured processes, employee training, and incentive systems to promote knowledge-sharing practices. Despite previous research, limited studies have focused on developing a comprehensive knowledge-sharing model specifically for public libraries. Therefore, this study attempts to fill this research gap by identifying and integrating the major dimensions and indicators of knowledge sharing in the context of public libraries. Methodology This study employed a mixed-methods research design to identify, prioritize, and model knowledge-sharing indicators in the National Library and Public Libraries Institution of Iran. In the qualitative phase, a meta-synthesis approach based on the seven-step model of Sandelowski and Barroso was conducted. Relevant studies published from 1990 onward were reviewed through international and national databases. From 189 identified sources, 43 articles were selected after applying quality assessment and screening criteria. Based on the extracted indicators, an expert-based questionnaire was developed. Twenty-eight experts, including managers, administrative employees, provincial directors, and specialists from the National Library and Public Libraries Institution, participated in the study. The indicators were evaluated using the Delphi method and prioritized through the Analytic Hierarchy Process (AHP). Subsequently, Interpretive Structural Modeling (ISM) was applied to develop the knowledge-sharing model, and Structural Equation Modeling (SEM) using SmartPLS was employed to validate the final model. Results The findings revealed that 80 initial indicators were identified and categorized into four main dimensions: technological, organizational, cultural, and individual factors. After expert evaluation and Delphi analysis, 12 key indicators remained. The technological dimension included information technology management; the organizational dimension consisted of knowledge transfer processes, knowledge-sharing expertise, internal processes, strategic processes, and performance processes. The cultural dimension included management support, employee participation, and organizational belonging, while the individual dimension included employee understanding and awareness, employee and managerial expertise, and human resource motivation. The AHP results indicated that the cultural dimension had the highest importance in implementing knowledge sharing, followed by individual, organizational, and technological dimensions. The ISM analysis demonstrated that organizational processes, strategic processes, internal processes, and organizational belonging were among the most influential factors. The SEM results confirmed the appropriate fit and predictive capability of the proposed model. Discussion The results highlight that successful implementation of knowledge sharing requires more than technological solutions. Although information technology provides important support for knowledge exchange, organizational culture and human factors have a stronger influence on knowledge-sharing behaviors. The dominant role of cultural factors indicates that management support, trust, employee involvement, and organizational commitment are essential for creating an environment where knowledge sharing becomes part of daily organizational practices. Moreover, individual factors such as motivation, expertise, and employees’ understanding of the value of knowledge sharing significantly contribute to knowledge exchange. Managers should therefore develop incentive mechanisms, training programs, and communication opportunities to encourage employees to share their experiences. Organizational factors, including documentation processes, knowledge repositories, and strategic planning, are also necessary to ensure the sustainability of knowledge-sharing activities. For public libraries, where employees hold valuable professional knowledge and experiences, establishing systematic knowledge-sharing mechanisms can prevent knowledge loss and improve service quality. Conclusion This study developed a comprehensive model for implementing knowledge sharing in the National Library and Public Libraries Institution of Iran. The findings demonstrate that knowledge sharing is a multidimensional process influenced by cultural, individual, organizational, and technological factors. Among these dimensions, organizational culture and management support represent the most critical foundations for successful implementation. The proposed model can assist policymakers and managers in designing effective knowledge-sharing strategies, preserving organizational knowledge, and improving organizational performance. Future research may examine the effectiveness of this model in other knowledge-based organizations and investigate the long-term outcomes of knowledge-sharing initiatives.

Abstract Introduction
The Holy Defense, as one of the most significant events in Iran’s contemporary history, has deeply influenced all political, social, and religious aspects of society. Due to the risks of distortion and forgetting this era, there is an increasing need for scientific investigation and documentation. Accordingly, using scientometric methods and creating ‘science maps’ is a crucial tool for policymakers to identify research trends, top authors, and journals, and to avoid wasting resources on redundant studies. This research aims to map the scientific landscape of articles in the field of Holy Defense, providing a scientific basis to guide future studies and improve research quality by identifying active research areas and existing gaps.
Research Questions
1. Which of the searched scientific databases (Noormags, Magiran, Civilica, SID, and the Comprehensive Portal of Humanities) have the highest frequency of scientific articles in the subject area of Holy Defense?
2. What is the distribution of articles in the field of Holy Defense based on the type of publication (scientific journals and conferences)?
3. What has been the trend of scientific article production in the field of Holy Defense over the years?
4. What is the co-authorship pattern in the published articles of the Holy Defense field?
5. Who are the most productive authors of articles in the field of Holy Defense?
6. What is the organizational affiliation of authors in the production of articles in the field of Holy Defense?
7. Which journals have the largest share in the production of scientific articles in the field of Holy Defense?
8. What is the academic rank or educational level of authors in the field of Holy Defense?
9. What is the academic discipline of the authors in the subject area of Holy Defense?
10. What is the level of collaboration among authors of articles in the field of Holy Defense?
11. What is the co-occurrence pattern of the terms used by authors in the field of Holy Defense?
12. What is the level of collaboration among institutions and universities in the publication of articles in the field of Holy Defense?
13. What is the level of collaboration among authors in the field of Holy Defense with respect to their scientific disciplines?
Literature Review
International scientometric studies have increasingly mapped the scientific landscape of global conflicts. Research by Sillanpää and Koivula (2010) identified “Democratic Peace Theory” as a dominant discourse in conflict studies. Subsequent research has addressed specialized dimensions, including military-induced soil pollution (Stadler et al., 2022), social security for veterans (Radzi, 2024), and the evolving status of women in war zones (Obeidat et al., 2025). Recent crises, such as the Russia-Ukraine war, have been analyzed for their global socio-economic and financial consequences using platforms like Web of Science (WOS) and Scopus (Ahlawat et al., 2022; Zozulinskyy, 2024). Regarding the Iran-Iraq conflict, international efforts—indexed in databases such as Scopus, MEDLINE, and ISI—have remained limited to clinical aspects, such as mustard gas exposure (Nokhodian et al., 2015).
In contrast, domestic studies on the Holy Defense have been fragmented, focusing on specific niches such as theatre dissertations (Motahari et al., 2014), thematic clustering of martyrs’ wills (Tavakolizadeh Ravari & Mahmoudi, 2016; Bahramian, 2021), or limited indexing of selected journals (Ghasemzadeh, 2022). Despite these contributions, there is a notable absence of a comprehensive, systematic mapping of the broader scientific output related to the Holy Defense. This study aims to fill this critical research gap by providing a holistic visualization of thematic clusters and knowledge networks in this field.
Methodology
This study employed a scientometric approach using co-occurrence and network analysis to identify conceptual relationships among research works published on the Holy Defense (Iran–Iraq War). The population comprised all available articles on this topic published between 1991 and 2023 in five Iranian databases: the Comprehensive Portal of Humanities, SID, Noormags, Civilica, and Magiran. The search was performed using 98 specialized keywords. After collecting 10,447 initial records from the five databases using various formats (Excel, RIS, or manual entry), duplicate and irrelevant items were removed, resulting in 4,998 unique articles. Incomplete metadata were corrected through supplementary searches. For each article, additional fields including authors’ academic degree, field of study, and affiliated organization were also collected and harmonized. Finally, a co-occurrence matrix was constructed using RaverPremp software, followed by analysis in Excel, UCINet (for centrality measures), and VOSviewer for visualization.
Conclusion
The present study aimed to map the scientific network of the Holy Defense field in Iran using a scientometric approach, incorporating co-word analysis and co-authorship network analysis. Analysis of 4,998 articles published between 1991 and 2023 showed that scientific output in this field has grown significantly since 2015, with over two thirds of all works published in recent years. Most articles have been published in peer-reviewed scientific journals, reflecting researchers’ tendency toward producing deep and theoretical knowledge. The co-authorship pattern is mostly limited to one to three authors, while broader scientific collaborations are less common.
Mohammad Reza Soroush, Mostafa Ghanei, and Batool Mousavi were identified as the most prolific authors, while Islamic Azad University, Baqiyatallah University of Medical Sciences, and University of Tehran have played a central role in knowledge production in this field. In terms of disciplines, psychology, Persian language and literature, and medical sciences have the largest share, but the absence of defense sciences among the top disciplines represents a critical gap.
Co-authorship network analysis revealed that several authors with high betweenness centrality act as bridging nodes in the scientific network. Furthermore, concepts such as ‘veterans,’ ‘martyrdom,’ ‘culture,’ ‘women,’ ‘post-traumatic stress disorder (PTSD),’ and ‘mustard gas’ are among the most frequent and central keywords, aligning with the findings of previous domestic and international studies.
In summary, Holy Defense studies in Iran have an interdisciplinary nature, with diverse scientific disciplines contributing. However, author dispersion, limited participation of full professors, and the underrepresentation of non-university institutions are among the main weaknesses. Strengthening interdisciplinary collaborations, supporting early career researchers, establishing a unified national database, and increasing the role of experienced faculty members are among the most important recommendations for the sustainable development of this field.