Knowledge Retrieval and Semantic Systems

Abstract Introduction Quantum information is a general form of information that can be considered the fundamental material of the universe (Penchev, 2020). Its basic unit is the qubit, which, unlike a classical bit's binary 0 or 1 state, can exist in multiple states simultaneously. This phenomenon, known as superposition, allows a qubit to be a linear combination of states (Nielsen & Chuang, 2010), enabling quantum parallelism and an exponential increase in computational power. Quantum Information Theory (QIT) explores these principles, along with other key concepts such as entanglement, where qubits become interdependent regardless of distance; the no-cloning theorem, which makes it impossible to create an identical copy of an unknown quantum state; and practical applications like quantum error correction and quantum cryptography. While traditional Information Retrieval (IR) is built on core concepts like Boolean, vector space, and probabilistic models (Melucci, 2015; Tunkelang & Thelwall, 2009; Büttcher et al., 2010; Gudivada et al., 2018), QIT provides a new framework to enhance it. This theory offers a way to unify the representation, ranking, and cognitive aspects of the user by applying the formalism and mathematics of quantum mechanics. This connection is often inspired by the theory and applied to classical computers, drawing on the broad use of quantum principles in cognitive science, psychology, and decision-making (Ashoori & Rudolph, 2018; Lebedev & Khrennikov, 2020). This paper aimed to review the fundamental principles of QIT, analyze their relevance to IR, and present the opportunities and challenges at the intersection of these two fields. Literature Review The intersection of QIT and IR has evolved from foundational theoretical work to practical and evaluative models. Early research by Piwowarski et al. (2010) proposed a quantum framework to address key IR challenges like interaction, novelty, and diversity, a view expanded by van Rijsbergen (2011), who argued that quantum formalism is the appropriate language for modeling the uncertainty inherent in information. Subsequent studies demonstrated the application of quantum mathematics, such as the "Quantum Probability Ranking Principle" for ranking interdependent documents (Zuccon & Azzopardi, 2010) and the use of quantum theory for "quantum-like" modeling (Khrennikov, 2019) and meaning-focused retrieval (Aerts et al., 2013). More recently, the field has moved toward integrating quantum principles with deep learning, as seen in quantum-inspired neural matching models (Jiang et al., 2020), and addressing security through protocols like quantum private information retrieval (Yang et al., 2023). This progression indicates a shift from purely theoretical explorations (Ashoori & Rudolph, 2018) to the development of practical evaluation frameworks (Ferrari Dacrema et al., 2024) and new contextual search methods (Alodjants et al., 2024). Methodology This study was conducted as fundamental research, employing an analytical-review method appropriate for the theoretical and emerging nature of the topic. Data collection started with documentary research, using defined keywords to systematically gather sources from databases such as Web of Science and Google Scholar. Following the screening and selection of relevant literature, the research utilized an approach of conceptual analysis and literature synthesis. Foundational concepts from Quantum Information Theory were first extracted and broken down. Then, in the synthesis phase, these concepts were systematically linked to the challenges, needs, and processes of Information Retrieval to build a logical and applied bridge between the two fields. Results The results of this analysis are presented in two main parts. First, the foundational concepts of Quantum Information Theory (QIT) are detailed, organized into three categories: fundamental concepts, including qubits, quantum states, superposition, entanglement, and quantum entropy; principles and theories, such as quantum algorithms and the no-cloning theorem; and applications, like quantum error correction, cryptography, and quantum gates. The second part of the results systematically connects these principles to the domain of Information Retrieval (IR). The results demonstrated how concepts like superposition can offer a probabilistic framework for relevance, how quantum algorithms, such as Grover's algorithm, can exponentially speed up search, and how quantum entanglement and error correction can be used to model complex term dependencies and ensure the security and integrity of the retrieval process, respectively. Discussion The theoretical alignment between Quantum Information Theory (QIT) and Information Retrieval (IR) suggests that quantum principles are exceptionally well-suited to address the core limitations of classical IR systems, particularly the complex issues of context, ambiguity, and human cognition. The inherent probabilistic nature of quantum states, governed by superposition, offers a formal mathematical framework for modeling the fuzzy and subjective concept of relevance more dynamically than traditional vector-space models. Furthermore, the concept of entanglement provides a powerful mechanism to model the nonlocal dependencies between index terms, documents, and user intent, moving beyond simple co-occurrence counts to capture holistic conceptual relationships. The analysis highlighted that while the full power of quantum computing is not always necessary for these benefits, as evidenced by successful quantum-like (QL) models running on classical hardware, the ultimate, exponential performance gains promised by algorithms like Grover's algorithm for database searching remain the strongest long-term motivation for future IR systems to leverage true quantum hardware. Conclusion By analytically comparing the core principles of Quantum Information Theory (QIT) and identifying their points of convergence with classical information retrieval systems, this research confirmed that Quantum Information Theory offered a potent, mathematically grounded framework with the potential to fundamentally transform the field of Information Retrieval. By leveraging quantum principles (such as superposition to model the inherent uncertainty of relevance and entanglement to capture complex, nonlocal dependencies between terms) future IR systems could overcome the cognitive and structural limitations of classical models. While the full deployment of quantum algorithms like Grover's algorithm on dedicated quantum hardware promised exponential speed and efficiency gains in the long term, even the adoption of QL models on existing classical infrastructure could yield significant, immediate improvements in accuracy, security, and contextual awareness. Despite existing technical and theoretical obstacles, it was anticipated that continued research in this area would lead to innovative technologies based on quantum principles, resulting in enhanced precision and efficiency in future information retrieval systems.

Abstract Introduction
Semantic search behavior refers to a set of cognitive and practical processes and actions undertaken by users during information searching, in which the individual goes beyond mere word matching to understand and retrieve information based on meaning, concept, and conceptual relationships among terms. The purpose of this study was to analyze the semantic information search behavior of faculty members at Shahid Modarres Hospital in PubMed regarding COVID-19, based on Ingwersen's model.
Research Questions
1. How does the semantic search behavior for COVID-19 information in PubMed by faculty members of Shahid Modarres Hospital occur based on the 'information retrieval systems' component in Ingwersen's cognitive model?
2. How does the semantic search behavior for COVID-19 information in PubMed by faculty members of Shahid Modarres Hospital occur based on the 'identification of information content' component in Ingwersen's cognitive model?
3. How does the semantic search behavior for COVID-19 information in PubMed by faculty members of Shahid Modarres Hospital occur based on the 'user interface' component in Ingwersen's cognitive model?
4. How does the semantic search behavior for COVID-19 information in PubMed by faculty members of Shahid Modarres Hospital occur based on the 'cognitive space' component in Ingwersen's cognitive model?
5. How does the semantic search behavior for COVID-19 information in PubMed by faculty members of Shahid Modarres Hospital occur based on the 'social/organizational environment' component in Ingwersen's cognitive model?
Literature Review
Previous studies indicate that information search behavior and semantic search are influenced by individual, systemic, and contextual factors. Shohrati (2022) reported that physicians and nurses made the most use of the PubMed database for searching COVID-19-related information, and their primary objectives included understanding the virus, fulfilling information needs, and sharing information. Khoshian et al. (2024) showed that although semantic search has moderate performance in conceptual connectivity and retrieval of multi-word concepts, it contributes to improving information retrieval. Khosravi et al. (2015) found no significant difference between PubMed's suggested keywords and MeSH terms in terms of result relevance, although both contribute to the development of search vocabulary. Lazarus et al. (2020) demonstrated that combined strategies based on free text and MeSH yield the highest sensitivity in retrieving COVID-19 articles. Furthermore, studies by Das and Saha (2019), Nemeshaev et al. (2021), and Shiri & Revie (2006) have emphasized the role of semantic search, ontology, and user experience in enhancing retrieval accuracy and improving query expansion.


Methodology
The present study is applied in terms of purpose and was conducted with a quantitative approach using a descriptive-analytical survey method. The research population consisted of 64 faculty members of Shahid Modarres Hospital. Due to the limited size of the population, no sampling was performed, and 54 questionnaires were collected (response rate of 84%). The data collection instrument was a researcher-developed questionnaire designed based on Ingwersen's cognitive model and the semantic search capabilities of PubMed. The content validity of the instrument was reviewed and confirmed by experts in information retrieval, semantic search, and clinical librarianship, and its reliability was obtained with a Cronbach's alpha coefficient of 0.80. Data were collected both in-person and online and were analyzed using SPSS software version 25.
Results
Faculty members actively use PubMed's semantic search features, including simple search, auto-suggestions, term refinement, combined strategies, MeSH (especially Major Topics), synonyms, and suggested keywords. While they are generally satisfied with PubMed's layout, use of frameworks like PICO and advanced indexing tools (e.g., Show Index List) is limited. Abstracts are the primary tool for assessing relevance, while personalized features like My NCBI are underutilized. Users rely on full texts for conceptual understanding, with less use of citation tools. Boolean operators and user-friendliness are rated favorably. Search behavior is influenced by scientific, research, and clinical needs, prior experience, database awareness, and the need for authoritative information. PubMed helps reduce uncertainty and supports decision-making. Organizational factors such as infrastructure, library services, and professional priorities also shape search behavior. Significant differences in semantic search behavior were found based on academic rank, years of service, and expertise, but not by gender.
Discussion
The research findings indicate that faculty members largely utilize PubMed's semantic capacities for search development. This result is consistent with the study by Khoshian et al. (2024), which confirmed the role of semantic search in accessing more relevant scientific information. Additionally, the use of MeSH and PubMed's suggested keywords aligns with the findings of Khosravi et al. (2015), who considered these tools effective in expanding users' vocabulary range.
Despite the appropriate use of some semantic capabilities, the results showed that more advanced tools such as MeSH Subheadings, Show Index List, and the PICO framework have not yet been fully institutionalized in users' search behavior. This finding is consistent with the results of studies by Eriksen & Frandsen (2018) and Fineout-Overholt et al. (2024) regarding the limited use of structured search frameworks by users. The limited use of personalized features such as My NCBI indicates that a portion of this database's individual knowledge management capacities has not yet been fully utilized.
According to Ingwersen's cognitive model, the results confirm that semantic search behavior is not merely a function of system features, but rather the interaction among users' cognitive characteristics, professional goals, and organizational environment also plays a role in its formation. Search experience, information needs, and users' scientific and clinical objectives determine the path of selecting search strategies.


Conclusion
The results of this study showed that the semantic search behavior of faculty members in PubMed is the result of the interaction among system capabilities, users' cognitive factors, and socio-organizational conditions. Although users make good use of basic features and some of PubMed's semantic tools, there is a need for further training in utilizing advanced tools such as MeSH Subheadings, PICO, indexing features, and personalized capabilities. Accordingly, designing targeted educational programs to enhance semantic search skills, developing visual guides for the use of search frameworks, and strengthening the role of clinical librarians can lead to improved information retrieval quality and more effective support for scientific and clinical decision-making.

Abstract Introduction The rapid convergence of data expansion and artificial intelligence (AI) is transforming scientific discovery and daily decision-making. However, focusing solely on technical specialization risks dividing society into a minority of tech creators and a passive majority of consumers. To ensure active, critical, and responsible participation, it is essential to distinguish between data science (a specialized technical field) and data literacy (a fundamental competency for all citizens). With the rise of generative AI, data literacy serves as a prerequisite for evaluating AI tools, while AI itself acts as a medium to deepen data literacy (e.g., through interactive tools like GenAI Teachable Machine). Although schools are the primary institutional setting for fostering data literacy from an early age, a significant gap exists between strategic intent and classroom execution. This gap is driven by structural barriers, pedagogical challenges, practical issues, and emerging ethical concerns. Addressing these theoretical and practical challenges requires moving beyond fragmented approaches toward an integrated framework that balances learning with AI as a tool and learning about AI as content. Research Questions 1. What are the antecedents of developing AI-driven data science literacy in students? 2. What are the processes involved in developing AI-driven data science literacy in students? 3. What are the outcomes associated with primary school students' data science literacy? 4. What is the relationship among the antecedents, processes, and outcomes of developing AI-driven data science literacy in students? Literature Review The body of literature regarding data literacy education spans four major streams, each highlighting key dimensions while leaving structural gaps in the overall framework. Research on student-centered approaches and pedagogy emphasizes early intervention using active learning frameworks, such as project-based learning and authentic datasets (Friedrich et al., 2024; Huang et al., 2023; Schreiter et al., 2024; Witte et al., 2024); however, these studies primarily address the "what" and "how" of instruction while neglecting the specific competencies required of educators. Regarding teacher competencies and professional development, studies grounded in frameworks like TPACK focus on teacher self-efficacy, pedagogical skills, and demographic influences (Cui & Zhang, 2022; Demirci et al., 2025; Gregorio et al., 2024; Hazzan & Mike, 2023; Msweli et al., 2023), yet a key limitation is the view of teachers as isolated actors that often ignores organizational culture, administrative support, and infrastructure. In terms of technological integration and AI tools, research explores GenAI tools and interactive dashboards for personalized learning (Maksimenkova et al., 2020; Nasharuddin et al., 2024), but a techno-centric bias often frames technology as a standalone solution without explicitly connecting tool utilization to meaningful pedagogical practices (Memarian & Doleck, 2024). While existing literature provides valuable insights into individual components, it offers a fragmented perspective without a unified conceptual model to explain the dynamic interactions between these components, a gap this study addresses by synthesizing these elements into an integrated framework (Memarian & Doleck, 2024). Finally, existing evaluations of outcomes focus mainly on short-term cognitive and technical skills within higher education or STEM fields (Avila-Garzon & Bacca-Acosta, 2025; Qiao et al., 2024; Velaj et al., 2023), leaving broader, long-term outcomes such as data-informed citizenship, ethical agency, and systemic school impacts largely unexplored and highlighting the critical need for an integrated conceptual model. Methodology This qualitative study employed Finfgeld-Connett's (2018) seven-step meta-synthesis approach to develop an integrated model for student AI-driven data science literacy. Research objectives were first established to define the framework's antecedents, processes, and outcomes. A systematic literature search across major international and national databases (e.g., Scopus, Web of Science, ScienceDirect, Magiran, SID) yielded 167 initial records. Domain experts in curriculum studies and synthesis methodology validated the screening protocols. Applying strict criteria (peer-reviewed English studies published between 2015 and 2025 focusing on AI in data literacy instruction), 79 relevant articles were selected after screening titles, abstracts, and full texts. Data were extracted through iterative analysis, categorizing primary concepts into open, axial, and selective codes. Rigor was established using descriptive, interpretive, theoretical, and pragmatic validity criteria. Finally, qualitative content analysis synthesized the findings into a coherent conceptual framework. Results The meta-synthesis yielded a structured model mapping the antecedents, processes, and outcomes of AI-driven data science literacy in students. First, antecedents (59 open, 18 axial, and 3 selective codes) encompass curricular frameworks prioritizing early education (K–5), teacher tripartite competencies (data, technological, and pedagogical knowledge), and student prerequisites (foundational computational literacy, prompting skills, and critical curiosity). Second, instructional processes rely on specialized AI tools that scaffold learning and democratize data science: intelligent tutors (DaTu, ChatGPT) for self-regulated guidance, explainable AI (SHAP, LIME) for algorithmic transparency, automated machine learning (AutoML, TPOT) to lower technical syntax barriers, and infrastructure tools (MLflow, Docker) for lifecycle management. Third, outcomes (15 open codes) span academic gains (data cleaning, visualization, computational thinking), metacognitive/affective growth (knowledge transfer, data self-efficacy) , and social/civic dimensions (data-informed citizenship, privacy awareness, and ethical decision-making). Discussion This study synthesized multi-level empirical evidence to establish an integrated model for developing AI-driven data science literacy in school ecosystems. Navigating away from purely technical skill acquisition, the proposed architecture establishes that data literacy in the AI era requires a fundamental paradigm shift from passive tool usage to critical digital wisdom (Schüller, 2022; UNESCO, 2024). At the macro-structural level, the findings align with Chiu (2025) and Gaertig-Daugs and Schmid (2025) by asserting that data literacy must be strategically prioritized starting from early primary education (K–5). Rather than overloading existing curricula, the model integrates data concepts cross-curricularly into STEM subjects using plug-and-play gamification and unplugged computational activities. Furthermore, the framework extends traditional TPACK models into five core dimensions (content, pedagogy, technology, data, and attitude), positioning teachers as clinical data leaders capable of navigating algorithmic modeling, interpreting learning analytics, and personalizing instruction (Mandinach & Jimerson, 2021; Msweli et al., 2023). At the student level, prerequisites transition from standard digital literacy to critical data agency, leveraging algorithmic questioning to mitigate uncritical technological consumption (Schönbrodt & Podworny, 2025). Process-wise, generative AI and automated machine learning platforms (e.g., AutoML, ChatGPT, DaTu) act as cognitive scaffolds. By automating repetitive scripting and data cleaning tasks, these tools democratize data science, allowing students to shift focus toward higher-order problem formulation, pattern recognition, and evidence-based decision-making (Doleck et al., 2024; Wang et al., 2019). Simultaneously, integrating explainable AI tools (SHAP, LIME) ensures that algorithmic ethics, transparency, and bias awareness remain central to student inquiry (Wu, 2025). Process-wise, generative AI and automated machine learning platforms (e.g., AutoML, ChatGPT, DaTu) act as cognitive scaffolds. By automating repetitive scripting and data cleaning tasks, these tools democratize data science, allowing students to shift focus toward high-order problem formulation, pattern recognition, and evidence-based decision-making (Doleck et al., 2024; Wang et al., 2019). Simultaneously, integrating explainable AI tools (SHAP, LIME) ensures that algorithmic ethics, transparency, and bias awareness remain central to student inquiry (Wu, 2025). Conclusion Ultimately, the interplay between antecedents and AI-driven processes yields holistic outcomes spanning academic competence, metacognitive self-efficacy, and data-informed citizenship. Practically, policy implementations should adopt age-tailored tools such as Google Teachable Machine in primary grades, local offline LLMs in lower secondary, and automated platforms like Julius AI in upper secondary, supported by revised teacher preparation curricula in pedagogical universities.

Abstract Introduction The rapid digital transformation of the banking industry has increased the need for flexible, responsive, and data-driven approaches to data management. Agile Data Management (ADM) has emerged as an approach that emphasizes flexibility, rapid response, continuous improvement, and more effective use of data in strategic decision-making. This approach is particularly relevant to banking because banks are highly data-intensive organizations and increasingly depend on data analytics, digital platforms, intelligent banking services, and emerging technologies. Despite the growing importance of ADM, the existing literature has largely examined data management, organizational agility, and smart banking as separate areas. Consequently, there is a lack of a comprehensive scientific map showing how knowledge has developed at the intersection of these domains within the banking industry. The present study addressed this gap by examining the knowledge structure, major research clusters, influential authors and countries, emerging concepts, and international collaboration patterns in research on agile data management in banking. Literature Review Previous studies indicated that agile approaches to data management had expanded across different sectors, including healthcare, education, information systems, and banking. Earlier research emphasized the importance of data infrastructure, agile approaches to big data projects, data laboratories, incremental implementation, and agile methods such as Scrum, DSDM, and Kanban. Subsequent studies highlighted limitations of traditional data governance systems in dynamic environments and examined emerging approaches such as blockchain and Data Mesh for managing analytical data. More recent research has focused on the relationship between agility, stakeholder satisfaction, team efficiency, information management, and digital transformation. In the Iranian context, previous studies have addressed organizational agility in banks, data mining and text mining, smart banking, Internet of Things, and behavioral agility. However, the reviewed literature indicated that these streams had generally developed independently. Therefore, the present study sought to provide an integrated bibliometric perspective on the development of knowledge at the intersection of agile data management, organizational agility, and digital banking. Methodology This study employed a bibliometric research design to examine the structure and development of knowledge in agile data management within the banking industry. Bibliographic data were retrieved from the Scopus database using a search strategy combining terms related to agile banking, smart banking, digital banking, data management, knowledge management, and reference models. The search was conducted on March 20, 2025, and covered publications from 1980 onward. After the initial retrieval and screening process, duplicate and irrelevant records were removed. Accordingly, 552 documents were retained for the final bibliometric analysis. The dataset included English-language research articles, review articles, and conference publications that were available in full text. VOSviewer and the Bibliometrix package in R were used for data analysis, visualization, clustering, and network mapping. Excel was also used to support data integration and duplicate removal. The analyses included publication trends, author and country productivity, co-authorship analysis, keyword co-occurrence analysis, bibliographic coupling, co-citation analysis, and direct citation analysis. The final dataset contained 1,665 authors, with an average of 3.3 authors per publication, an annual growth rate of 12.36%, and an international collaboration rate of 28.08%. Results The findings demonstrated a substantial expansion of research related to agile data management and digital banking, particularly from 2019 onward. India emerged as the most productive country, followed by Indonesia, Jordan, and Malaysia. The analysis also identified Amity University, Bina Nusantara University, and Applied Science Private University among the important institutional contributors. Publication output reached its reported peak in 2024, with approximately 200 publications, while the decline observed in 2025 was attributed to the incompleteness of the year and the resulting dataset limitations. Sustainability (Switzerland) was identified as the leading publication source, followed by International Journal of Bank Marketing, Journal of Financial Services Marketing, and Banks and Bank Systems. In terms of author influence, Yelena Popova had the highest local h-index in the reported analysis. The co-authorship analysis identified Bardia Khorsand, John Soldatos, and Ernesto Troiano as particularly prominent researchers, each having four publications and a total link strength of 35. Other researchers, including Marina Kogura, Ignacio Elicegui, Pavlos Kranas, Dimosthenis Kyriazis, George Makridis, Alessandro Mameli, Jürgen Niess, Kostis Prakakis, and Gisela Sanchez, also occupied important positions in the collaboration network. Keyword co-occurrence analysis revealed five principal conceptual clusters. The first cluster focuses on digital transformation in banking and includes banking, digital transformation, fintech, financial technology, and innovation. The second concerns emerging technologies and data management, incorporating artificial intelligence, blockchain, machine learning, big data, data management, and cybersecurity. The third cluster addresses customer behavior and technology acceptance through concepts such as customer satisfaction, trust, perceived risk, service quality, and mobile banking. The fourth cluster concerns knowledge management and organizational innovation, including knowledge management, financial innovation, structural equation modeling, and marketing. The fifth cluster focuses on sustainability and inclusive financial development, including sustainability, financial inclusion, sustainable development, and systematic literature review. The temporal analysis further indicated increasing attention to concepts such as blockchain, digital transformation, banking services, service quality, and cloud computing. The thematic analysis identified banking and sales as central and developed themes, while blockchain and digital transformation were identified as important basic themes. Data ownership and digital platforms were presented as emerging or changing areas of interest. The country collaboration analysis showed that India occupied a central position in the international research network and maintained substantial links with Malaysia, Indonesia, Vietnam, Nigeria, Bangladesh, Latvia, and other countries. Western European countries, including the United Kingdom, Germany, France, Spain, and Portugal, were described as contributing particularly to theoretical and policy-oriented dimensions, while Southeast Asian and Middle Eastern countries demonstrated strong engagement with emerging technologies and digital transformation. Discussion The findings indicated that agile data management in banking had developed as a multidisciplinary research field situated at the intersection of data management, organizational agility, digital transformation, technology adoption, customer behavior, knowledge management, and sustainability. The five conceptual clusters demonstrated that the field could not be understood exclusively from a technical perspective. Although emerging technologies such as artificial intelligence, blockchain, machine learning, big data, and cybersecurity constituted an important research stream, the literature simultaneously addressed customer trust, satisfaction, perceived risk, organizational knowledge, innovation, sustainability, and financial inclusion. The strong position of India and the increasing participation of other Asian countries suggested that developing economies were playing an increasingly important role in producing knowledge concerning digital and agile banking. At the same time, collaboration networks involving European, Asian, Middle Eastern, and other countries demonstrated the international and interdisciplinary nature of the field. The co-authorship, bibliographic coupling, and co-citation analyses also indicated the existence of interconnected research communities and shared theoretical foundations. In particular, the co-citation analysis revealed the influence of technology acceptance models such as TAM and UTAUT, methodological approaches such as PLS-SEM, and strategic perspectives such as the resource-based view. Conclusion This study provided a bibliometric overview of the development and intellectual structure of research on agile data management in the banking industry based on 552 Scopus-indexed documents. The results demonstrated rapid growth in the field and revealed five major knowledge clusters involving digital transformation, emerging technologies and data management, customer behavior and technology acceptance, knowledge management and organizational innovation, and sustainability and financial inclusion. India emerged as the leading contributor in terms of publication output and collaboration, while researchers such as Khorsand, Soldatos, and Troiano occupied prominent positions within the co-authorship network. Overall, the findings confirmed that agile data management in banking was an increasingly important interdisciplinary field. The study provided a knowledge map that could support researchers in identifying influential authors, major conceptual streams, emerging topics, and international collaboration opportunities. For banking organizations and policymakers, the findings emphasized the importance of agile data infrastructures, data-oriented human capabilities, flexible data governance, and data-as-a-product approaches. Future research may further examine the development and empirical validation of context-specific ADM frameworks, compare developed and developing banking systems, investigate the effects of ADM on financial-service innovation and bank performance, and empirically explore Data Mesh implementation in banking environments.

Abstract Introduction Indigenous agricultural knowledge is fundamental to rural livelihoods, cultural identity, and sustainable farming systems. Developed through generations of observation and adaptation, saffron-related knowledge in South Khorasan encompasses soil preparation, irrigation timing, corm selection, harvesting, drying, storage, and quality preservation (Mohammadi et al., 2023; Vafaee et al., 2024). However, much of this knowledge remains tacit, orally transmitted, and insufficiently documented, leaving it vulnerable to gradual loss. Public librarians can contribute by collecting, organizing, digitizing, preserving, and facilitating access to this knowledge, while agricultural experts can assist in its interpretation, validation, and transmission. Despite their complementary expertise, little research has examined their joint role in managing indigenous agricultural knowledge, particularly knowledge concerning saffron cultivation in Iran. Therefore, this qualitative study investigated the roles of public librarians and agricultural experts in documenting and retrieving indigenous saffron knowledge in South Khorasan, while identifying the challenges and strategies associated with its long-term preservation and accessibility. Research Questions 1. What methods can public librarians and agricultural experts use to document and retrieve indigenous knowledge related to saffron production? 2. What challenges do public librarians and agricultural experts face in documenting and retrieving indigenous knowledge related to saffron production, and what strategies can be employed to address them? Literature Review Literature emphasizes stakeholder participation and collaborative knowledge ecosystems. Chigwada and Ngulube (2025) argue that indigenous knowledge initiatives require the involvement of local communities, librarians, cultural institutions, policymakers, and technical experts. Similarly, Dei (2024) found that indigenous knowledge is transmitted through storytelling, mentoring, festivals, rituals, and apprenticeships, although these practices often remain informal and undocumented. Indigenous knowledge management should therefore be understood as a social and cultural process grounded in trust, cultural legitimacy, and community participation, rather than as a technological activity. Although studies propose artificial intelligence-based systems, including Retrieval-Augmented Generation, for agricultural knowledge management (Han et al., 2026), such systems depend on structured, documented, and retrievable knowledge bases. Traditional agricultural practices, including saffron cultivation, are not yet systematically represented in digital infrastructures (Adefila et al., 2026). Existing research has primarily addressed preservation, dissemination, or digitization, with limited attention to agricultural knowledge retrieval. In Iran, no study has examined the combined roles of public librarians and agricultural experts in documenting, retrieving, and facilitating access to indigenous saffron knowledge in South Khorasan. Methodology This qualitative study employed a case study design to examine how indigenous saffron knowledge was documented and retrieved by public librarians and agricultural experts in South Khorasan. Using purposive judgmental sampling, 30 participants were recruited: 20 public librarians and 10 agricultural experts from Birjand, Qaen, Darmian, Sarbisheh, and Ferdows. Data were collected through semi-structured interviews lasting 40–70 minutes. Data saturation was reached after interviews with 17 librarians and eight agricultural experts; additional interviews were conducted to confirm saturation. The interview protocol was developed from the literature, and two pilot interviews were undertaken to refine the process. With informed consent, interviews were audio-recorded, transcribed, and analyzed using MAXQDA version 21. Coding proceeded through open, axial, and selective stages, involving the identification, comparison, consolidation, and categorization of codes into themes aligned with the questions. Credibility was supported through participant checking of interview interpretations. A second researcher coded the data, and intercoder reliability was assessed using Cohen's kappa, producing a coefficient of 0.98. Results The findings revealed that documenting and retrieving indigenous saffron knowledge required coordinated interaction among libraries, agricultural institutions, technologies, and local communities. Participants identified multiple documentation methods. Oral documentation was considered essential for capturing farmers' experiences and tacit knowledge. Multimedia approaches, such as audio, video, and photographic records, were viewed as suitable for preserving cultivation practices and locally embedded techniques. Participatory documentation involving farmers, librarians, and agricultural experts was also emphasized as a means of improving accuracy, contextual relevance, and community trust. For retrieval and access, participants recommended structured digital repositories, searchable databases, standardized indexing and metadata, mobile applications, and library-based information services. However, implementation was constrained by human, technological, managerial, cultural, and organizational challenges. The principal barriers included inadequate digital infrastructure, limited inter-organizational collaboration, difficulties accessing indigenous knowledge holders, insufficient managerial and financial support, limited technological competencies, and concerns regarding trust and ownership. Suggested strategies included strengthening collaboration among librarians, agricultural experts, farmers, and local institutions; expanding digital infrastructure; providing targeted education and professional training; developing supportive policies; and establishing ethical protocols for consent, ownership, and access. Collectively, these measures were considered necessary for ensuring the sustainable preservation, retrieval, dissemination, and intergenerational transmission of indigenous saffron knowledge. Discussion The findings demonstrated that the management of indigenous saffron knowledge required an integrated approach combining oral, participatory, multimedia, and digital practices. The importance of interviews, storytelling, and recording farmers' experiences confirmed that saffron knowledge remained tacit and intergenerational, consistent with Dei (2024) and Chisita (2011). Multimedia documentation can preserve the practical and contextual details of cultivation more effectively than written records alone, supporting earlier findings by Adeyemo and Adebayo (2018) and Chigwada and Ngulube (2024). However, documentation without organization and retrieval mechanisms cannot ensure meaningful access. Digital repositories, metadata, indexing, and searchable databases are therefore essential, as emphasized by Balogun (2023) and Goswami (2023). Their implementation in South Khorasan remains constrained by inadequate infrastructure, limited digital competencies, weak institutional coordination, and insufficient managerial support. Participants' concerns about trust, ownership, and reluctance to share knowledge further indicate that indigenous knowledge management is not merely a technical process but a culturally sensitive social practice. Consequently, preservation requires community consent, recognition of knowledge holders, capacity building, and collaboration among libraries, agricultural agencies, cultural institutions, and farmers. Librarians and agricultural experts should function jointly as documenters, validators, information intermediaries, and facilitators of intergenerational knowledge transmission. Conclusion This study concluded that managing indigenous saffron knowledge in South Khorasan required a comprehensive, participatory, and multidimensional approach. Much of this agricultural and cultural knowledge remained tacit, experiential, and orally transmitted among farmers and local experts, making it vulnerable to gradual disappearance. Public librarians and agricultural experts can identify, record, organize, preserve, and facilitate access to this knowledge through oral, written, multimedia, participatory, and digital documentation methods. Nevertheless, preservation is hindered by inadequate infrastructure, limited institutional cooperation, insufficient financial and managerial support, difficulties accessing knowledge holders, low appreciation of indigenous knowledge, and concerns about trust and ownership. Establishing an indigenous knowledge management system is essential. Such a system should combine documentation, organization, retrieval, access, dissemination, and intergenerational transmission while respecting cultural and ethical considerations. Institutional partnerships, community participation, digital infrastructure, information-literacy training, supportive policies, and trust can enable libraries and agricultural organizations to safeguard indigenous saffron knowledge for future generations.

Abstract Introduction The spread of digital technologies and social networks has transformed how people interact, communicate, learn, share information, and engage in social activism. Through social platforms, users can build virtual relationships, take part in civic activities, and share their information and experiences with others. At the same time, widespread use of technology has brought not only new opportunities but also outcomes such as digital fatigue, stress, burnout, cyberbullying, cybercrime, the spread of misinformation, and threats related to privacy. In such conditions, digital resilience refers to individuals' capacity to adjust their behaviors and psychological states in response to risks and pressures arising from digital technologies (Sun et al., 2022). Digital resilience is not simply a return to a prior state after a crisis; it is an ongoing process of awareness, confrontation, adaptation, learning, and rebuilding one's online presence (Lestari et al., 2025). A resilient person in the digital environment does not only resist threats; they use crisis experience to rethink their behavior, identity, and relationship with technology. Social networks, especially for civic activists and media practitioners, are both tools for informing and mobilizing society and contexts that can enable targeted cyber threats, content removal, account hacking, phishing, organized harassment, and algorithmic pressures. The central research problem is why users, despite being aware of the need for self-protection and the importance of digital resilience, cannot always put safe and adaptive behaviors into practice. In other words, there is a gap between what users know and what they do. Focusing on this gap, the present study examined the factors that produced the knowledge and performance gaps and the active coping strategies users employed when facing digital crises. Literature Review Previous studies show that digital technology can both increase social connection and social capital and be associated with stress, dependency, fatigue, and psychological harm. Some research has examined digital resilience in relation to healthy technology use, education, mental health, and the ability to adapt to technological change (Sharma et al., 2021). Recent work also emphasizes the role of algorithm design, personalized feeds, endless scrolling, social rewards, and continual changes in platform policies. Within this framework, coping is a set of cognitive, emotional, and behavioral strategies that people use to manage stressful situations (Folkman & Moskowitz, 2004; Abbas et al., 2020). Problem-focused coping, emotion-focused coping, and avoidance are among the main coping styles. In the digital context, active coping strategies include actions such as learning from threat experiences, using security tools, rebuilding online identity, managing emotions, and drawing on supportive networks (Wenting et al., 2024; Park et al., 2024). Moving beyond a purely preventive or passive view, the present study stressed user agency and the ability to learn, rebuild, and act effectively. Methodology This study used a qualitative approach based on the Straussian grounded theory paradigm (Strauss & Corbin, 1990). This method was chosen because of the interpretive nature of the topic and the need to understand people's lived experience when facing digital crises. Participants were selected through purposive sampling. The sample consisted of 25 civic activists and media practitioners on the social network Instagram, people who interact actively with the digital environment and have personal experience of digital threats or challenges. Data were collected through in-depth, semi-structured interviews. Interviews were first read line by line and given initial codes. Similar codes were then organized into categories and subcategories, and in the final stage, main categories and relationships among them were extracted. Data analysis followed three stages: open, axial, and selective coding. In axial coding, relationships among causal conditions, context, the central phenomenon, action/interaction strategies, intervening conditions, and consequences were examined. Data collection continued until theoretical saturation. For validation, findings were reviewed by five experts in media studies and library and information science. Results Findings showed that the central phenomenon of the study was active digital resilience in the face of threats. This resilience was a cyclical process that began with recognizing a threat and continued through confrontation, adaptation, learning, and rebuilding online presence. Thus, digital resilience was neither a fixed state nor merely the outcome of formal education; it was a continuous process of learning and re-creation. The knowledge gap arose mainly from three factors: first, a superficial understanding of technology-related risks; second, cognitive and practical dependence on platforms; and third, limited algorithmic literacy (Mackey & Jacobson, 2011). Many users reduce digital risks to hacking or data theft and lack adequate awareness of how algorithmic mechanisms work, how platform policies change, and pressures linked to visibility. Dependence of economic, social, or professional activity on a single platform also reduces the possibility of critical distance from it. The performance gap became visible when people knew about security measures but did not carry them out in time. For example, some participants knew they should use two-factor authentication, backups, and security settings, but continuous pressure to produce content, anxiety about declining views, and psychological fatigue prevented them from doing so. As a result, knowing does not necessarily lead to action. Weak digital emotion regulation, fear of content removal, worry about losing followers, and inability to rebuild adaptive behaviors were main drivers of this performance gap. Three main dimensions of active coping strategies were identified: 1. Reflective learning and self-regulation: Users draw on experiences such as content removal, hacking, or account restrictions to better understand platform mechanisms (Hassoun et al., 2025). Changing publishing practices, revising problematic content, pausing before posting, and documenting threats were examples of this strategy. 2. Rebuilding digital identity in the face of a data crisis: A crisis could push users to redefine their goals, roles, and modes of presence on social networks. After content removal or loss of an account, some rebuilt their online identity on other platforms or drew new boundaries among professional, civic, and personal activity (Lupton, 2023). 3. Use of supportive networks and technological mutual aid: Participants said they gained practical knowledge more from friends, colleagues, and other users than from formal training. Exchanging experience in comments, forming mutual-aid groups, sharing solutions, and helping recover accounts were examples of digital social capital (Eri et al., 2021; Zayed, 2024). Alongside these strategies, technical measures such as two-factor authentication, backups, security checklists, and threat documentation were also used. Conclusion The results indicated that digital resilience literacy combines knowledge, skills, attitudes, emotional self-regulation, technological agency, and social support. The distance between knowledge and performance related not only to lack of information but also to platform dependence, content-production pressure, algorithmic anxiety, psychological fatigue, and weak support networks. In this model, digital resilience is a cognitive, social, and identity-related process in which the individual moves from perceiving a threat to learning, rebuilding, and teaching others. On this basis, digital literacy education should go beyond transmitting general information and include adaptive skills, emotion management, algorithmic literacy, practical security, and reflection on digital identity. Workshops designed by educational institutions, innovation hubs, and digital entrepreneurship associations can better prepare users to engage actively with cyber threats. Ultimately, digital resilience is not a simple return to the pre-crisis state but a form of reconstructive coexistence with technology and the ability to turn threat experience into learning and effective action.

Abstract Introduction Today, artificial intelligence is one of the new concepts in scientific and academic communities, and its application is evident in most scientific and applied fields. The benefits of artificial intelligence for acquiring, searching, and retrieving information and academic knowledge of students are undeniable, as evidence shows; however, its possible challenges in the process of acquiring knowledge cannot be ignored. The use of artificial intelligence by students to retrieve and acquire knowledge and information can bring both advantages and challenges, but what is important is that the advantages outweigh the challenges, and this will be possible when these challenges are identified and managed. Among the diverse students who use AI to search for information and knowledge, the importance of identifying and managing challenges for Farhangian University students is heightened. These students are responsible for educating the next generation, and the challenges of AI for their knowledge acquisition and retrieval may affect students who will be educated by them in the future. In this case, identifying the challenges of using AI to acquire knowledge among Farhangian University students is essential. Research Questions 1. Considering the lived experiences of students at Farhangian University of Kurdistan Province, what challenges does the use of artificial intelligence pose for their knowledge acquisition and retrieval of scientific information? 2. Considering the lived experiences of students at Farhangian University of Kurdistan Province, what solutions are there to overcome the challenges of using artificial intelligence in students' knowledge acquisition and retrieval of scientific information? Literature Review Several studies have addressed the challenges posed by artificial intelligence for searching for information and acquiring knowledge in scientific and educational environments, as well as strategies for overcoming these challenges. These include Ghaeimi et al. (2025), who examined changes in subcultures and organizational climates of schools and universities and the formulation and adaptation of laws and regulations; Rajabiyan Dehzireh (2024), who addressed educational, interactive, technological, cultural, and economic challenges and proposed using artificial intelligence as an educational assistant, formulating policies and laws, teaching how to use artificial intelligence, and building culture; Motaghi Destenaei et al. (2024), who examined awareness of artificial intelligence and its role in education in order to reduce challenges; Mokhtari and Rezvani (2023), who addressed the inability of artificial intelligence to replace teachers; Mustopa et al. (2024), who pointed out the challenge of infrastructure and internet access in Indonesia, the challenge of the shortage of human resources and skilled instructors in the field of AI in India, and the challenge of balancing the use of advanced AI technologies, privacy protection, and capacity development to adapt to rapid advances in technology-based education in China; Jie and Kamrozzaman (2024), who pointed out the over-reliance on AI and the lack of awareness and understanding of students in using AI applications; and Saputra et al. (2023), who pointed out educational opportunities such as the provision of educational materials, assessment, management systems, and educational policy-making, and challenges such as educational ethics, limitations in preparing teachers for AI competencies, and slow changes in the structure of the curriculum and teaching. Methodology To achieve the research goal, the critical paradigm of the qualitative approach and the Colaizzi phenomenological strategy were used. The research field included students of Farhangian University of Kurdistan Province in 2025. Data collection was carried out through semi-structured interviews. A total of 32 students were interviewed through purposeful and criterion-based selection until data saturation was achieved. The findings were analyzed through inductive analysis of written text. Results The research findings were presented in two sections: challenges of artificial intelligence for students' knowledge acquisition and solutions to overcome the challenges. The section on challenges of artificial intelligence for students' knowledge acquisition was presented in two areas: challenges to the position of knowledge acquisition for students and challenges to the quality and quantity of knowledge acquisition by students. Also, solutions to overcome these challenges were discussed in two areas: individual solutions and solutions based on the scientific and educational environment of Farhangian University. Conclusion Regarding the challenge to the status of acquiring knowledge, issues such as reduced motivation to acquire knowledge, the habit of procrastinating in acquiring knowledge, reduced scientific responsibility, reduced creativity, and the loss of the value of the process of acquiring knowledge and interacting with scientific information were mentioned. Ultimately, these issues led to a decrease in the quality and quantity of students' knowledge, such as a decrease in their teaching knowledge and level of study and research, and also challenged their production of scientific texts. Such issues showed that the application of artificial intelligence by Farhangian University students did not simply lead to a decrease in their scientific level; rather, it undermined the status of acquiring knowledge and direct interaction with scientific information in their minds and made it appear less valuable, which ultimately led Farhangian University students to avoid acquiring knowledge and produced a generation of teachers who avoided science and were indifferent to the value and status of knowledge. Of course, these challenges are not limited to simply reducing the scientific level of future teachers; they also affect their students and will lead to a decrease in the level of knowledge and knowledge transfer in schools and a decrease in the status of teachers. In order to overcome the challenges, students pointed out various solutions that fell into two areas: solutions based on students and solutions based on the educational and scientific environment of Farhangian University. According to the students, what needs to be considered regarding the application of artificial intelligence tools among Farhangian University students is preparing the ground and teaching them how to use these tools correctly, before the spread of excessive and unprincipled use at Farhangian University and the cultural lag of students in using them. This can ultimately prevent some of the deep challenges in acquiring knowledge and interacting with scientific information by students, as well as challenges based on the type of interaction they have with information in their future work. Therefore, the basic solution to overcome the challenges of artificial intelligence for acquiring knowledge and interacting with information among students at Farhangian University was to create an appropriate context and background at this university and to develop a principled procedure for using artificial intelligence in acquiring knowledge and interacting with information before learning it, which was possible without the use of coercion or arbitrary rules and by reforming the culture of using artificial intelligence.

Abstract Introduction Public libraries serve as vital service-oriented cultural institutions designed to promote reading culture and enhance citizens' quality of life. The effectiveness of these institutions relies heavily on specialized human resources who possess advanced competencies and adaptability to modern technological demands. In an era characterized by an exponential growth of information, librarians must be equipped with information literacy to continuously realign library services with evolving community needs. Information literacy encompasses the skills required to recognize information needs, locate, evaluate, and effectively utilize information. From an organizational perspective, information literacy serves as a foundational element that fuels organizational creativity, defined as the generation of novel ideas and solutions to optimize workflow quality and reduce costs. Consequently, enhanced creativity and information competence directly influence job performance, which reflects the expected organizational value of an employee's behaviors over time. Despite the acknowledged importance of these variables individually, public libraries frequently encounter operational challenges stemming from deficits in information literacy, creative problem-solving, and overall performance. Therefore, this study aimed to examine the structural relationship between information literacy, organizational creativity, and job performance among public library staff in Kermanshah Province. The central research questions explored whether information literacy directly drove organizational creativity and job performance, and whether organizational creativity played a mediating role in this dynamic. Three main hypotheses were tested: 1. Information literacy had a direct positive impact on organizational creativity. 2. Information literacy had a direct positive impact on job performance. 3. Organizational creativity mediated the relationship between information literacy and job performance. Literature Review The intersection of information literacy, organizational creativity, and job performance has been explored across various organizational and educational domains. Early domestic inquiries, such as those by Bieranvand and Soheili (2015) and Rajabzadeh and Esmailpour (2016), confirmed that favorable institutional conditions and high information literacy fostered organizational creativity. Similarly, Hakimzadeh et al. (2015) and Arjmand Kermani and Tirgar (2017) demonstrated that information literacy positively influenced academic performance, computer self-efficacy, and creative decision-making. In library context studies, Khalili et al. (2022) revealed that elevating librarians' information literacy enhanced both their professional engagement and creative output. On the international front, empirical evidence consistently highlighted the pivotal role of information skills in workforce outcomes. Chang and Hsu (2015) and Hsu and Chen (2017) confirmed positive correlations between information literacy and workplace creativity. Wu (2018) established that information self-efficacy boosted creativity, which subsequently enhanced job performance. Recent scholarship further expanded this horizon: Saadia and Naveed (2024) demonstrated that information literacy directly drove lifelong learning, creativity, and job performance among media professionals. Mughari et al. (2024) and Naveed et al. (2026) integrated modern facets of information literacy, such as artificial intelligence literacy, showing its strong positive influence on occupational performance mediated by job involvement. Furthermore, Kolyvas et al. (2025) and Shahzad and Khan (2025) emphasized that fulfilling information needs and introducing challenging workplace environments were vital for nurturing imagination, handling information overload, and optimizing library performance. Building upon these empirical foundations, this study addressed a distinct research gap by comprehensively analyzing these three constructs within a unified structural model specifically tailored to public librarians. Methodology This research was an applied study that utilized a descriptive-correlational design using structural equation modeling (SEM) and path analysis. The statistical population comprised all public librarians employed in Kermanshah Province, totaling 170 individuals across 87 library branches. Using Cochran's formula, a sample size of 118 librarians was determined. The sampling technique employed was proportional stratified random sampling, wherein each county in Kermanshah Province was treated as a distinct stratum, followed by simple random sampling within each stratum. Data collection was executed using three standardized questionnaires: 1. Information Literacy Questionnaire (Ghasemi et al., 2007): Consisting of 30 items measuring five dimensions—recognizing information needs, effective access, evaluation criteria, optimal usage, and adherence to legal/ethical standards. Its Cronbach's alpha reliability was established at 0.812. 2. Organizational Creativity Questionnaire (Dorabjee et al., 1998): Containing 12 items, yielding a Cronbach's alpha reliability of 0.928. 3. Job Performance Questionnaire (Paterson, 1970): Comprising 15 items, with an established Cronbach's alpha reliability of 0.976. Content and face validity were verified by academic experts at Razi University. Data analysis was conducted using SPSS and AMOS software packages. Descriptive statistics were utilized to evaluate baseline variable scores. Structural Equation Modeling (SEM) was performed to test path relationships and evaluate structural model fit. To evaluate the indirect mediating effects, the bootstrap procedure with a 95% confidence interval was utilized. Results Descriptive analysis revealed that the mean scores for all main variables exceeded the theoretical baseline average of 3.0 on a 5-point Likert scale. Information literacy achieved an overall mean of 3.96 (standard deviation = 0.86). Among its sub-dimensions, "recognizing information needs" scored the highest mean (4.01), whereas "adherence to legal/ethical standards" recorded the lowest mean (3.93). Job performance and organizational creativity recorded mean values of 4.04 (standard deviation = 1.03) and 3.98 (standard deviation = 0.84), respectively, indicating favorable baseline conditions among Kermanshah librarians. Model fit evaluation demonstrated that the proposed structural model fit the empirical data robustly. The chi-square to degrees of freedom ratio was 2.97. Comparative fit indices were satisfactory: Normed Fit Index (NFI) = 0.887, Relative Fit Index (RFI) = 0.861, Incremental Fit Index (IFI) = 0.949, Tucker-Lewis Index (TLI) = 0.936, and Comparative Fit Index (CFI) = 0.948. The Root Mean Square Error of Approximation (RMSEA) was 0.069, confirming an acceptable model fit. Hypothesis testing yielded the following structural outcomes: • Hypothesis 1: Information literacy exerted a statistically significant direct positive effect on organizational creativity, with a standard path coefficient of 0.57 (p = 0.023). Thus, Hypothesis 1 was supported. • Hypothesis 2: Information literacy exhibited a strong, direct positive influence on job performance, with a standard path coefficient of 0.71 (p = 0.004). Thus, Hypothesis 2 was supported. • Hypothesis 3: The bootstrap analysis for indirect effects confirmed that information literacy significantly influenced job performance indirectly through organizational creativity. The indirect path coefficient was 0.17 (p = 0.014), with a 95% bootstrap confidence interval ranging from 0.050 (lower bound) to 0.372 (upper bound). Since zero fell outside this confidence interval, organizational creativity's mediating role was statistically supported. Discussion The empirical findings confirmed that information literacy significantly enhanced both organizational creativity and job performance, while organizational creativity acted as a vital intermediate mechanism. The positive link between information literacy and organizational creativity aligned with previous empirical studies (Chang & Hsu, 2015; Saadia & Naveed, 2024; Kolyvas et al., 2025). Librarians possessing advanced information competencies were adept at identifying information gaps, navigating digital retrieval tools, and critically evaluating resources. This proficiency reduced operational anxiety, stimulated divergent thinking, and allowed librarians to synthesize ideas in novel ways to solve complex workplace challenges. Furthermore, the direct impact of information literacy on job performance reinforced conclusions reached by Al-Azri et al. (2023), Mughari et al. (2024), and Naveed et al. (2026). When librarians quickly accessed reliable data, adhered to legal/ethical standards, and applied information effectively, service accuracy and administrative efficiency increased, leading to improved job performance metrics. Importantly, the confirmed mediating role of organizational creativity highlighted that information literacy alone was most effective when translated into innovative practices. Creative librarians transformed raw information skills into optimized library workflows, user-centric services, and modernized information management systems, which in turn elevated performance across public library branches. Conclusion This study concluded that information literacy was a primary determinant of both organizational creativity and overall job performance among public library personnel in Kermanshah Province. Information-literate librarians demonstrated higher creative problem-solving capacities, which directly translated into enhanced service effectiveness and superior administrative achievements. Based on these conclusions, several practical recommendations were offered to public library administrators: 1. Public library management should design and implement continuous, hands-on, in-service training workshops focused on advancing information literacy skills, emerging digital search platforms, AI-driven information tools, and critical resource evaluation. 2. Human resource departments within public library networks should incorporate standardized information literacy assessments as key criteria during recruitment, hiring, and staff promotion processes to ensure high organizational creativity and operational excellence.

Abstract Introduction Information anxiety is one of the emerging phenomena that was first introduced by Francis Bacon in 1605 and later popularized by Richard Wurman in his best-selling book. Unlike common psychological anxieties, information anxiety has an epistemological nature and arises from cognitive disorder and confusion caused by encountering vast amounts of information. With the expansion of the internet since the 1970s, the emergence of the World Wide Web in the 1990s, and especially the proliferation of social networks, this phenomenon has intensified. The five main causes of information anxiety include: not understanding information, bias regarding the amount of information to be understood, not knowing that specific needed information exists, not knowing where to find it, and knowing where to find it but lacking the access key. Academic procrastination, as a complex phenomenon with behavioral and cognitive dimensions, involves the deliberate postponement of tasks despite awareness of negative consequences. According to Freud’s psychoanalytic theory, procrastination originates from anxiety and can manifest in various forms, including academic, decisional, neurotic, and obsessive procrastination. Personal Information Management (PIM) has been proposed as a fundamental solution to these challenges. PIM encompasses seven main activities: finding, storing, organizing, maintaining, securing and managing information flow, evaluating and appraising, and sense-making. The flipped classroom approach, as a modern educational method, involves providing instructional content to learners before class sessions, with class time dedicated to interactive, collaborative, and problem-solving activities. This method can serve as an effective platform for teaching personal information management skills. Literature Review Previous research has shown that academic procrastination is on the rise in educational systems and is influenced by factors such as excessive use of social media, anxiety, and lack of managerial skills. Information anxiety has also been identified as a fundamental challenge in academic environments, and researchers have pointed to a significant relationship between information usage patterns, information anxiety, and academic procrastination. However, a research gap exists regarding the application of modern teaching methods such as flipped classrooms for teaching personal information management skills and their simultaneous effect on reducing both information anxiety and academic procrastination. Methodology This study was applied in purpose and quasi-experimental in design, utilizing a pretest–posttest control group design. The statistical population consisted of female students at Fatemeh Al-Zahra Campus of Farhangian University in Tabriz. The sample included 30 students randomly assigned to experimental and control groups (15 each). Research instruments included the Information Anxiety Questionnaire (Imanzadeh & Marandi Heidarloo, 2024) with 30 items across five dimensions, and the Academic Procrastination Scale (Solomon & Rothblum, 1984) with 27 items across three factors. A researcher-developed PIM training package was designed over ten 60-minute sessions (one introductory, eight instructional, and one concluding session). Content validity was confirmed by experts (CVR: 0.8–1; CVI: 0.9–1). Data were analyzed using descriptive statistics (mean, standard deviation) and multivariate analysis of covariance (MANCOVA) via SPSS version 22. Results and Conclusion Descriptive results showed that the mean information anxiety score in the experimental group decreased from 73.81 in the pretest to 62.53 in the posttest, while the control group increased from 70.47 to 72.93. Similarly, the mean academic procrastination score in the experimental group decreased from 71.32 to 58.58, whereas the control group increased from 66.98 to 69.81. Assumptions of MANCOVA (normality, linearity, homogeneity of variances, and homogeneity of regression slopes) were confirmed. Wilks' lambda test indicated a significant difference between the groups in at least one dependent variable. MANCOVA results revealed significant differences between the experimental and control groups for both information anxiety (F = 14.055, p = 0.003) and academic procrastination (F = 12.029, p = 0.003), after controlling for pretest effects. Effect sizes were 37% for information anxiety and 31% for academic procrastination, indicating a substantial impact of the educational intervention. Discussion and Conclusion The findings demonstrated that PIM training delivered through the flipped classroom method significantly reduced both information anxiety and academic procrastination among students. These results align with previous studies (Imanzadeh & Marandi Heidarloo, 2024; De Oliveira et al., 2023; Navid & Anvar, 2019). The explanation for these findings lies in the fact that PIM empowers students in organizing, storing, retrieving, and evaluating information, thereby reducing confusion and helplessness caused by information overload. Furthermore, these skills reduce smartphone addiction and promote purposeful information use, thereby preventing academic procrastination. The flipped classroom method, by fostering an interactive and participatory environment, provides a suitable context for deep learning of these skills. Therefore, this combined approach is not only an effective educational program but also a fundamental investment in training future teachers, enhancing their metacognitive and psychological competencies, and improving both current academic performance and future professional capabilities. According to the research findings, it is suggested that personal information management training be considered as an effective intervention for reducing information anxiety and academic procrastination among students.