https://jipm.irandoc.ac.ir/ Iranian Journal of Information Processing and Management en daily 1 Sat, 21 Mar 2026 00:00:00 +0330 Sat, 21 Mar 2026 00:00:00 +0330 https://jipm.irandoc.ac.ir/article_737168.html The aim of this study is to study the feasibility of a smart teleworking system implementation considering the role of technology and information systems in Iran Copper Industries National Company. The present study is applied in terms of its purpose and is survey-descriptive in nature. The statistical population includes all employees of the National Iranian Copper Industries Company, totaling 2120 people. The statistical sample was determined to be 266 people using the Cochran formula. The sampling method employed was simple random sampling. The data collection tool utilized was a researcher-made self-assessment questionnaire for remote work, consisting of 40 questions. To measure the validity of the questionnaire, the content validity method was applied. The validity of the questionnaire was found to be 0.991 based on the content validity index (CVR) and 0.800 based on the CVI. The reliability of the questionnaire was calculated using Cronbach’s alpha test, confirming a value of 0.896. Collected data was analyzed using descriptive statistical methods such as graphs and interferential statistical method and TOPSIS. The results of the research, utilizing TOPSIS model, indicated that the four factors of hardware facilities, software, communication infrastructure, and the ability to develop systematic programs have varying conditions for the implementation of a teleworking system. The research findings highlighted that the impact of each of these factors on the feasibility of implementing a teleworking system is not uniform. Therefore, it is important to measure and rank the impact of these factors accordingly. The ranking of the effective factors on the feasibility of implementing a teleworking system revealed that hardware facilities have the greatest impact, while software facilities have the least impact on the possibility of implementing a smart teleworking system. The research results revealed that Iran Copper Industries National Company lacks sufficient software and communication infrastructure capabilities to implement a teleworking system. Additionally, the company does not have adequate hardware capacities and systematic plans in place. As a result, it is not feasible to implement a smart teleworking system at Iran Copper Industries National Company. It is recommended that the company prioritize the following activities: enhancing software facilities to enable teleworking implementation; conducting training courses, including application software training, for all organizational levels and conducting regular assessments to strengthen employees; establishing and enhancing the necessary communication infrastructure for the successful deployment of teleworking technologies within the company; allocating the required budget to provide software, hardware, and infrastructure facilities for the teleworking process; and developing a documented plan for implementing the teleworking process in accordance with the approved policy. https://jipm.irandoc.ac.ir/article_741208.html In the contemporary knowledge-based economy, data has become the most vital asset of companies, and the obligation to maintain confidentiality plays a fundamental role in safeguarding it; this study, adopting an analytical-comparative approach, examines the legal systems of Iran, the United States, and the European Union in addressing modern challenges of information management—particularly the rise of generative artificial intelligence, the complexities of AI prompting, the right to be forgotten, the recognition of the creator of research data, and similar issues—and finds that the U.S. legal system has embraced a property- and contract-based approach in which legal protection is contingent upon demonstrating reasonable protective measures and risk management, with the recent restrictions on non-compete agreements in 2024 shifting the burden of protection onto non-disclosure agreements and proof of non-entry of sensitive data into public AI models, while the European Union, through Directive 2016/943 and data protection regulations, has adopted a regulatory and rights-based paradigm in which confidentiality is treated as part of fundamental rights and public order, with breaches leading to severe administrative sanctions and the introduction of sensitive data into large language models increasingly interpreted as a practical waiver of confidentiality rights; in Iranian law, confidentiality obligations are considered part of the principle of pacta sunt servanda and civil liability rules, with breach potentially giving rise to both contractual remedies and tort liability, and in the era of artificial intelligence, the submission of sensitive data to uncontrolled systems effectively nullifies confidentiality and eliminates legal protection for trade secrets, prompting this study to critique traditional doctrines and propose innovative solutions such as digital duty of care, segmented confidentiality, algorithmic restitution requiring destruction of models trained on misappropriated data, and recognition of loss of control as a new disclosure criterion, while also suggesting a redefinition of confidentiality obligations from a negative duty to an intensified duty of care, shifting the burden of proof in favor of the injured party, and recognizing a duty of result in managing sensitive data, thereby demonstrating that Iranian law is not devoid of protective capacity but suffers from a lack of technological interpretation, with concepts such as property value, rational benefit, possession, trust, and negligence being adaptable to data, algorithms, and AI outputs.Keywords:Commitment, confidentiality, non-disclosure, data, information management, artificial intelligence https://jipm.irandoc.ac.ir/article_737169.html Peer review process is a cornerstone of scholarly publishing, playing a pivotal role in ensuring the quality and credibility of academic articles. With the increasing volume of scientific publications and the growth of interdisciplinary research, identifying qualified reviewers has become a significant challenge for editors. To address this issue, scholarly reviewer recommendation systems have been developed, leveraging textual data, research records, collaboration networks, and intelligent algorithms to automate and optimize reviewer selection. This study aims to provide a scoping review of the methods and algorithms used in designing these systems and to propose a conceptual framework for their future development. A comprehensive scoping review was conducted using international databases (IEEE, PubMed, Scopus, Web of Science) and Persian databases (Magiran, SID, IranDoc, Noormags) for the period 2010–2025. After screening, 28 eligible studies were selected and analyzed according to technical approaches, system types, application domains, data sources, reviewer assignment criteria, and evaluation metrics. The review revealed that combining multiple technical approaches—such as natural language processing for semantic matching between articles and reviewer profiles, network analysis to detect conflicts of interest and hidden relationships, machine learning and author-topic matrix models for expertise classification, and fairness-oriented optimization algorithms for balanced workload distribution—yields the most effective performance in reviewer recommendation systems. Multi-source hybrid systems that integrate textual data, bibliometric indicators, and collaboration networks provide enhanced performance and broader interdisciplinary coverage. Integrating diverse technical approaches, multi-source data, and semantic and network-based analysis can significantly improve the efficiency and reliability of reviewer recommendation systems. Nevertheless, challenges such as the lack of standardized datasets, difficulties in integrating multiple information sources, and concerns regarding algorithmic transparency and fairness persist. The proposed conceptual framework aims to guide the development of next-generation systems capable of supporting interdisciplinary reviews, ensuring fairness, maintaining ethical standards, and ultimately enhancing the quality, trustworthiness, and advancement of scholarly research. https://jipm.irandoc.ac.ir/article_741207.html This study AIms to systematically review the literature on generative artificial intelligence in customer information processing and management and to develop a conceptual framework explAIning its role in marketing. Given the rapid expansion of generative AI applications and the fragmented nature of the existing literature, a structured synthesis is needed to identify major applications, clarify value-creation mechanisms, and highlight governance considerations. This research adopts a systematic literature review approach based on the PRISMA 2020 guideline. Relevant studies published between 2018 and 2026 were retrieved from major academic databases and analyzed using comparative and thematic synthesis after applying inclusion and exclusion criteria. The findings show that applications of generative AI in customer information management can be grouped into six major clusters: information generation and enrichment, customer feedback analysis, personalization and recommendation, conversational interaction, marketing decision support, and information governance. The results further indicate that the value of this technology emerges through transforming structured and unstructured customer data into actionable knowledge outputs; however, this process depends on data quality, transparency, human oversight, and bias control. Accordingly, the study proposes a conceptual framework linking customer information inputs, generative processes, knowledge outputs, and marketing outcomes, moderated by governance-related factors. The article contributes theoretically by integrating the literature on generative AI and customer information management and contributes practically by offering guidance for the responsible deployment of this technology in marketing. https://jipm.irandoc.ac.ir/article_732957.html Knowledge management is a process in which the knowledge available in the organization is identified, collected, organized, stored, and finally shared to help improve decision-making processes, increase productivity, and promote innovation. As one of the key concepts in the information age, improving the performance of organizations is of great importance. One of the key elements for the development and advancement of knowledge management is artificial intelligence, which has not received sufficient attention from knowledge management practitioners and theorists in many cases. Given the complexities of large and multi-layered organizations such as the Social Security Organization, knowledge management cannot proceed solely based on traditional approaches. The introduction of artificial intelligence technology into the field of knowledge management has had significant effects such as increasing efficiency and improving organizational processes. The present study was conducted with the aim of designing a framework for knowledge management based on artificial intelligence in complex organizations. The research method was qualitative and used a grounded theory approach. The statistical population of the study was managers and information technology experts of the country's Social Security Organization. Data were collected based on semi-structured interviews with 27 experts in the fields of knowledge management and information technology at universities and executive level, upon reaching theoretical saturation. Sampling was carried out using a purposeful snowball method. Data analysis led to the identification of 21 influential components in the form of causal and contextual conditions, intervening factors, and strategies and consequences of knowledge management based on artificial intelligence. Validity and reliability were assessed and confirmed using the Lincoln and Guba (1985) method. The research findings focus on the concept of smart knowledge management as the central phenomenon of the model. In the designed research framework, causal conditions were identified by discovering six components: the need for the organization to be up-to-date, converting raw knowledge into actionable knowledge, responding to employee demands, reducing costs and preventing losses and inefficiency of the status quo and customer orientation; background conditions with four components: availability of necessary infrastructure, strategic investment, internal system excellence and improvement of organization management; intervening conditions with two components: external factors and internal factors; strategies with five components: implementation of customer relationship management, implementation of strategic management, creation of motivation to increase employee participation and outsourcing of technological services; and outcomes with four components: sustainable growth of the organization, improvement of organizational image, client (customer) satisfaction and survival of the organization. As a result, the application of such a framework helps managers of the country's Social Security Organization overcome existing challenges in their knowledge management and ensure improvement of the organization's image and client satisfaction and achieve the goals of organizational growth and ultimately the survival of the organization. https://jipm.irandoc.ac.ir/article_741202.html Society-driven research aims to address society's real needs and is rooted in the shift from Mode 1 to Mode 2 knowledge production. Despite the emphasis of Iranian upstream policy documents on problem-oriented research, a deep gap persists between universities' research priorities and societal needs — a gap that prior studies have largely examined as a list of disconnected barriers at a single analytical level. This study aimed to explain these barriers from the perspective of academic research-system experts within a multi-level framework. A qualitative design with reflexive thematic analysis was adopted. Data were drawn from twenty analytical cases, comprising semi-structured interviews with policymakers and senior research managers, and were coded. The analysis yielded five main themes and twenty-eight sub-themes organized across three levels: at the institutional/governance level, the absence of coordinated governance and a trans-sectoral body, the inefficiency of the evaluation and promotion system, and supply-driven priority setting; at the organizational level, the misalignment of university structures and processes; and at the individual level, orientation toward low-risk outputs. The central finding is that these barriers form a causal chain from the institutional to the individual level and persist through two factors of the promotion procedures and evaluation system, and mutual distrust between universities and policymakers. One of the main findings was the willingness of faculty members to engage with society and what is missing is the opportunity and reward structure to facilitate the society-driven research. Accordingly, policy recommendations are proposed across all three levels simultaneously, including the redesign of promotion regulations, the creation of a trans-sectoral body, and the activation of organizational brokerage. https://jipm.irandoc.ac.ir/article_733816.html Conversational Question Answering (CQA) systems have evolved significantly with the advent of Large Language Models (LLMs). However, these advancements have predominantly favored high-resource languages, often overlooking low-resource ones. This paper introduces a novel LLM-enhanced framework specifically designed to bridge this linguistic gap. The proposed architecture comprises six components: "Input Processing" for language-specific handling, an "Adaptive LLM Core," "Knowledge Enhancement" for cross-lingual mapping, "Context Management" for efficient conversation navigation, "Response Generation" incorporating cultural adaptation, and "Human Feedback" for continuous improvement. Unlike existing approaches, this framework integrates cultural and linguistic considerations throughout the entire processing pipeline. To validate the framework, a qualitative evaluation was conducted using a focus group consisting of five Natural Language Processing (NLP) experts. Expert evaluation results confirmed the proposed framework's effectiveness in addressing fundamental challenges of low-resource languages, including data scarcity, morphological complexities, and cultural nuances. Experts particularly highlighted the framework's innovative approach to "integrated cultural processing," "resource efficiency" via optimized context management, and its "modular and scalable architecture" as key achievements. This research demonstrates that integrating human feedback and cultural adaptation within an efficient architecture offers a practical solution for developing Conversational Question Answering systems in low-resource languages. https://jipm.irandoc.ac.ir/article_739083.html Introduction: Data is now recognized as a key asset of organizations and societies. Data linkages in different industries have led to the integration of value chains and the elimination of boundaries between different ecosystems. Economic actors are striving to optimally manage data in order to create sustainable added value for their customers and stakeholders. The objective of this research is to introduce a reference model for agile data management that uses the agile paradigm to achieve business intelligence and to compare its components with other reference models. FromMethodology: the aim of this research is of an applied and developmental type. Reasoning, creativity and analogy have been used to draw conclusions. This research has used a combination of Design Science, Metasynthesis and Fuzzy Delphi methodologies. Thus, based on the design science method, the problem has been explained, the solution has been described, the initial solution has been designed (Metasynthesis), validation (Fuzzy Delphi Screening) and finally the findings have been published.Main Findings: includes codes organized into five groups: “design perspective, theme, category, core code, and open code,” and presented as a conceptual model (level zero, level one, and level two) as a reference model for an agile data management system for smart businesses. Then, the final artifact is compared with some management systems that are consistent with systems thinking. In conclusion, an Agile Data Management System, like other systems, the artifact in question will have four elements: input (goals), processing (enablers), output (results), and feedback (based on agile and lean thinking).Discussion: Industries have been slow to adopt new technologies. Many businesses still rely on legacy infrastructure. On the other hand, resistance to change in human resources is a factor that hinders agility on the path to intelligence. The proposed reference model brings intelligence to businesses by using modern technologies at the center of the operational model and embedding agile methods in a data management system. https://jipm.irandoc.ac.ir/article_737170.html This study aims to analyze the conceptual structure of the Semantic Web domain within Knowledge and Information Science (KIS) using data from the Web of Science (WoS) database spanning from 1995 to 2024. The study was conducted using quantitative content analysis and network analysis methods. The research population comprised 1,761 articles. Data analysis was performed using VOSviewer, UCINET, and BibExcel software. Following keyword standardization, a co-occurrence matrix was constructed, and concepts were categorized into ten thematic clusters using the K-means clustering algorithm. Subsequently, a strategic diagram was plotted based on centrality and density indices. The Semantic Web domain has evolved into a dynamic, multi-layered structure over the last three decades. The plotted strategic diagram offers a clear, evidence-based image of conceptual maturity, internal cohesion, and the epistemological status of research clusters. This conceptual map can serve as an analytical and policy tool for scientific decision-makers and researchers to identify knowledge gaps, determine research priorities, and guide future research paths more accurately and efficiently. The analysis identified ten clusters: (1) Ontology and Semantic Retrieval, (2) Semantic Web and Knowledge Management, (3) Linked Data and Information Organization, (4) Thesauri and Knowledge Organization Systems (KOS), (5) Digital Cultural Heritage and Knowledge Organization, (6) Metadata and Knowledge Representation, (7) Semantic Web Standards and Languages, (8) Semantic Information Retrieval in the Web Environment, (9) Open Science and Knowledge Engineering, and (10) Open Data Integration. The clusters of Thesauri and KOS, Digital Cultural Heritage, Metadata and Knowledge Representation, Open Science, and Open Data Integration were identified as emerging fields. The average growth rate of articles during the studied period was 57%. The concepts of Semantic Web, Ontology, and Linked Data comprised of the three main pillars and the central core of this research domain, with the highest frequencies. Notably, the significant presence of keywords from applied fields shows the penetration and influence of the Semantic Web in other areas. Analysis of the strategic diagram indicates that Linked Data, Information Organization, and Semantic Information Retrieval, positioned in the strategic quadrant, constitute the mature core of Semantic Web research. The findings reveal a shift in this domain from an exclusive focus on technical standards and languages—which have now achieved a foundational and established status—towards operational implementation and intelligent knowledge management. This structural evolution, consistent with trends observed in prior literature, signifies a paradigm shift in the Semantic Web from an infrastructure development phase to a practical application within information systems. https://jipm.irandoc.ac.ir/article_739039.html Abstract: The rapid emergence of generative artificial intelligence (AI) systems and large language models has transformed the ways users interact with digital technologies, creating new challenges and opportunities for user experience (UX) research. Despite the growing adoption of AI-powered conversational systems, the intellectual structure, thematic evolution, and emerging research directions of UX studies in this domain remain insufficiently understood. Therefore, this study aims to analyse the evolution of UX research in generative and conversational AI systems, identify its major thematic areas, and uncover emerging research trends and future directions. A set of 3,714 publications published between 2006 and 2026 was analysed using bibliometric techniques and topic modelling. The dataset was constructed through a systematic search of the Scopus database, resulting in 3,714 eligible records for analysis. The records were pre-processed using standard text-mining procedures, including normalisation, tokenisation, lemmatisation, and keyword integration, to ensure data quality and analytical consistency. Temporal evolution was examined through descriptive statistics, exponential moving averages, and change-point analysis. The findings reveal a major transformation in the field, characterised by the rapid expansion of research after 2023 and the diversification of UX-related themes across multiple application domains. To identify latent thematic structures, Non-negative Matrix Factorisation (NMF), a topic modelling technique that decomposes document–term matrices into interpretable topic representations, was employed. Non-negative Matrix Factorisation (NMF) topic modelling and keyword co-occurrence network analysis identified several dominant research themes, including intelligent software and product development, education and e-learning systems, retrieval-augmented generation (RAG)-based information systems, e-commerce and customer experience, immersive and extended reality environments, usability evaluation, and emotional user interaction. The co-occurrence network further revealed well-defined intellectual clusters centred on healthcare, educational technology, social computing, and intelligent interactive systems. Emerging trends were identified through a multi-signal approach combining discriminative terms, burst analysis, and growing topic trajectories. Key emerging concepts included retrieval-augmented generation (RAG), multimodality, agent-based systems, alignment, trust, privacy, hallucination, and explainability. Overall, the findings demonstrate a shift from conventional UX concerns toward increasingly adaptive, multimodal, trustworthy, and human-centred AI ecosystems. The study provides a comprehensive map of the field’s evolution and highlights future research priorities for designing transparent, explainable, and collaborative AI-powered user experiences. https://jipm.irandoc.ac.ir/article_732986.html Delivering services in digital libraries must align with users’ preferences, needs, and feedback. In this context, service personalization plays a pivotal role in enhancing user satisfaction and maximizing the utilization of digital library capacities. This study aims to systematically review the existing research on service personalization in digital libraries to identify key developments, research gaps, and future directions. Employing a qualitative approach, the study follows the systematic review framework proposed by Kitchenham and Charters. A total of 67 sources —including journal articles, conference papers, and master’s and doctoral theses— were identified and analyzed. Findings reveal that the research landscape can be categorized into three main dimensions and nineteen subcategories: 1) user-centric services (including user profiles, user modeling and privacy, user behavior, collaborative environments, personal information spaces, contextual interoperability, user context awareness, user services, information security, cognitive styles, access methods, and “My Library” features), 2) types of personalization (covering personalization processes, indicators and methods, personalized search and results, recommender systems, and filtering), and 3) applied techniques and technologies (such as data mining, intelligent technologies, big data, and cloud computing). Among these, recommender systems received the most attention. The evolution of research in this domain reflects a transition from foundational digital library infrastructure to advanced intelligent personalization, encompassing AI-driven behavior prediction, interest recognition, automated analysis, and customized service delivery. Despite notable progress, the study highlights the need for innovative and diverse research to address emerging challenges and technological shifts. https://jipm.irandoc.ac.ir/article_737706.html The expansion of artificial intelligence applications in digital platforms, particularly intelligent chatbots, has transformed the way customers interact with online services. This study aims to present a model of the impact of intelligent chatbot characteristics on customer purchase behavior through the mediating roles of satisfaction and loyalty on the SnappFood platform. This research is applied in terms of purpose and descriptive-survey in terms of method. The statistical population consisted of SnappFood users in Tehran who had at least one documented interaction with the platform's chatbot system during the three months leading up to October and November 2025. Convenience sampling was employed, and 150 valid questionnaires were collected. Data were analyzed using Structural Equation Modeling with Partial Least Squares (PLS-SEM) and SmartPLS software. The key findings indicate that chatbot characteristics—including user-friendliness (0.342), communicability (0.306), reliability (0.243), security and privacy (0.226), and perceived usefulness (0.172)—have a positive and significant effect on customer satisfaction (p ≤ 0.001). Furthermore, customer satisfaction strongly influences loyalty (0.597), and customer loyalty has a very strong impact on purchase behavior (0.655). The proposed model explained 62% of the variance in satisfaction, 35% of the variance in loyalty, and 43% of the variance in purchase behavior. Analysis of indirect effects confirmed the chain mediation role of satisfaction and loyalty in transmitting the effect of chatbot characteristics to purchase behavior. Practical recommendations derived from the findings include: (1) prioritizing user-friendly design and communicability as the strongest predictors of customer satisfaction; (2) investing in improving chatbot interaction experience as part of brand loyalty strategy; (3) clarifying privacy and data security policies as essential prerequisites for digital interaction; and (4) utilizing chatbots to increase customer lifetime value (CLV) through smart reminders, personalized suggestions, and loyalty discounts. Implementing these recommendations can lead to increased conversion rates, repeat purchases, and market share for online food ordering platforms. https://jipm.irandoc.ac.ir/article_737171.html The aim of this study is to design a contingency and conceptual model of content marketing for Iranian reading applications utilizing two theoretical lenses: customer value theory and institutional theory.This study employed a qualitative approach using thematic analysis. Data were collected through semi-structured interviews with 10 content marketing experts familiar with reading applications. Purposive snowball sampling continued until theoretical saturation was achieved. Data analysis was performed using MAXQDA software in six steps, and coding reliability was confirmed with a Holsti coefficient of 0.82.The final model consists of three main parts: 1) Dimensions of value creation for the user, including functional values (ease of access), emotional values (enjoyment of reading), social values (sharing), cognitive values (learning), and conditional values (using dead time); 2) Institutional constraints of Iran's environment at three levels: coercive pressures (filtering, sanctions), normative pressures (self-censorship, publisher monopoly), and mimetic pressures (copying competitors); 3) Operational elements of strategy including targeting, multi-format content production (podcasts, videos, text), targeted distribution (Instagram, the app itself), user engagement, and performance measurement using indicators such as engagement rate and dwell time.Successful content marketing in Iranian reading applications requires a balance between creating value for the user and adapting to institutional constraints. By identifying moderating variables (app characteristics and user characteristics), the proposed model goes beyond common linear models and provides a flexible framework for developing a strategy tailored to the conditions of each application. https://jipm.irandoc.ac.ir/article_737705.html This study aims to explore the relationship between the formal prestige of journals and the actual impact of articles, investigating whether a high Journal Impact Factor (JIF) necessarily implies greater article visibility, or if a distributional gap exists between journal-level prestige and article-level visibility. The present research is an applied study conducted through a descriptive-analytical method with a citation analysis approach. Data were extracted from the Web of Science database for the period 2009–2018. The research population includes all articles from 13 selected journals in the field of Information Science and Library Science, chosen based on their thematic proximity to Scientometrics and their continuity of publication. The Spearman rank correlation test was utilized to examine the statistical relationship between the impact factor and the uncitedness rate. The average uncitedness rate in the studied journals was approximately 26%, ranging from 4.8% to 72.3%. Results of the Spearman test indicate a strong inverse relationship between the impact factor and the uncitedness rate at the macro level; however, detailed analyses showed that this relationship is non-linear and significant exceptions exist in certain journals. During the ten-year period under study, the uncitedness rate decreased from 31% in 2009 to 23% in 2018, yet heterogeneity in citation distribution has persisted. The analysis of document types revealed that only review articles and biographical items have a significant negative relationship with uncitedness, while other document types do not have a significant effect. The research findings indicate that the impact factor, as an average-based index at the journal level, cannot serve as a definitive guarantee for article visibility at the micro level. Even in journals with high impact factors, the phenomenon of uncitedness exists perceptibly; a fact that points to the multifaceted nature of the concept of impact and the existence of a gap between a journal’s official prestige and the attraction of scientific attention at the article level. This study emphasizes the necessity of transitioning from purely journal-centered evaluations toward more diverse metrics based on the actual performance of articles. https://jipm.irandoc.ac.ir/article_716610.html The purpose of the research is to examine the compatibility of the Schema.org with the publishing domain based on SPAR Ontologies. This research is developmental-applied in terms of its type and employs content analysis as its method. Based on this method, the “units of record” examined are the classes and properties of Schema.org entities and the semantic units of classes and properties in SPAR ontologies. The research population consists of the classes and properties of Schema.org. The classes and properties of SPAR ontologies were matched with all the existing classes and properties in Schema.org, and corresponding elements and those without a match were identified. To examine the level of consistency between the classes and properties of SPAR ontologies and Schema.org, a checklist was used as a data collection tool. The data collection method was through structured observation. Although the scope and objectives of Schema.org and SPAR ontologies differ, there is potential for interoperability between them. Since SPAR ontologies are specifically designed to meet the specific needs of the publishing industry and provide a more comprehensive and specialized set of ontologies for describing scholarly publications and all aspects of the publishing process, and on the other hand, Schema.org is not as comprehensive as SPAR ontologies in the publishing domain, these ontologies each focusing on one of the different aspects of the publishing process can be used to enrich and increase the usability of Schema.org in the publishing domain. Creating a comparative table between Schema.org and SPAR ontologies in the perspective of semantic digital publishing can improve the alignment between data and ontologies, and consequently facilitate the integration, interoperability, and stability of related data in the publishing domain. This action improves the quality, accuracy, and reliability of publishing content and semantic data and helps individuals and publishing organizations to operate more effectively and efficiently in a competitive market. It is also very important for the seamless exchange of data and ensuring semantic interoperability in various publishing platforms and applications, and improving the searchability and discoverability of content on the web, and increases user satisfaction. Ultimately, it contributes to standardization efforts in the semantic web community and ensures that semantic publishing practices are aligned with the best practices and standards of the evolving industry. https://jipm.irandoc.ac.ir/article_736822.html Objective: This research aims to conduct a study on future of Metaverse and examine its implications for Iran on the horizon of 2031. As future of cyberspace, the metaverse has the potential to create interactions, games, work, and shopping in a digital space. This study seeks to analyze and study the possible impacts of this emerging technology on Iranian society.Methodology: Multiple qualitative methods were employed in this research. Initially, environmental scanning and review of diverse sources, including academic articles, journalistic pieces, news reports, and research from institutions, were conducted to select 52 relevant contents. Subsequently, using thematic analysis, 513 codes were extracted, and 40 themes were identified for the development of future scenarios. Finally, employing the Manova School, model four distinct scenarios for the metaverse's future were formulated.Results: Through the analysis of relevant contents, 40 themes were identified. Applying the Manova School models resulted in the creation of four scenarios: "Convergence of Two Worlds," "Struggle for Survival," "Shattered Dreams," and "Unrestrained Ascension," each suggesting different potential futures for the metaverse. Subsequently, based on the analysis of desirability and probability, expert opinions in the field were considered to more precisely determine the implications of each scenario for Iran.Conclusion: This research is based on the identification and analysis of four scenarios for the future of the metaverse, each with specific implications for Iran. The most likely scenarios for Iran were “struggle for survival” and “linking two worlds,” and the most desirable scenario was “linking two worlds.” These results can help policymakers and decision-makers better prepare to face the challenges and opportunities of the future of the metaverse. https://jipm.irandoc.ac.ir/article_732408.html In recent years, university evaluation and ranking have become primary concerns for higher education institutions and policymakers. Research criteria play a vital role in determining the academic standing of universities. However, one of the fundamental challenges in this area is determining the relative importance of research criteria and accurately modeling their relationship with university rankings. Many studies have employed classical statistical methods or multi-criteria decision-making techniques but often lack a precise integration between objective criteria weighting and analyzing their impact on ranking levels. This research utilizes a hybrid approach and analyzes real-world data on the research performance of Iranian universities to address existing gaps in the effective integration of weighting and impact analysis of criteria. In the first step, the CRITIC method, as an objective and data-driven technique, is used to determine criterion weights. This method simultaneously considers the dispersion and correlation of criteria to establish their relative importance without subjective judgment. Subsequently, the obtained weights are incorporated into an Ordinal Logistic Regression model to examine the impact of each criterion on the probability of universities being placed in different ranking levels. The main innovation of this study lies in the structured combination of these two complementary methods and the focus on analyzing the coefficients of the ordinal logistic regression model. This approach, unlike some superficial methods, provides a more accurate picture of the role of each criterion. The results indicate that, contrary to expectations, some criteria do not have a significant impact on improving university rankings, while specific, others have a considerable effect. The research findings can be utilized by higher education managers and policymakers in designing research policies, optimizing research budgets, and planning for enhancing the academic standing of universities. This framework is also generalizable to other domestic and international ranking systems. https://jipm.irandoc.ac.ir/article_736699.html Evaluation constitutes an integral component of the growth and development process. Within any ecosystem, various units necessitate assessment. Information Technology (IT) are no exception, as their diverse components – from missions and processes to human resources – require evaluation. This study aims to develop a framework for evaluating IT managers, who represent the most critical human resource within these systems, and for whom the specific dimensions and indicators for assessment have not yet been systematically defined.Initially, relevant academic literature was reviewed to identify a pool of potential evaluation indicators and metrics. Subsequently, these indicators and metrics were refined and consolidated into a coherent framework through a focus group session involving IT experts and senior IT managers. Finally, the Analytic Hierarchy Process (AHP) was employed to assign weights to each evaluation dimension.The findings indicate that an IT manager can be evaluated based on three primary dimensions: Personal Attributes (comprising 14 indicators), Technical Competencies (comprising 12 indicators), and Managerial Competencies (comprising 17 indicators). Recognizing that the work context of IT managers varies across different organizations/companies, the weighting of these dimensions was further differentiated based on two variables: governmental affiliation and organizational size.Within this framework, Managerial Competencies carry greater weight in the evaluation for large public organizations and large private corporations. Conversely, for small private companies, Personal Attributes are assigned greater significance.The proposed framework in this study represents a pioneering effort to systematize and methodically structure the evaluation of IT managers. It is designed for practical application by both organizations themselves and external assessment units. https://jipm.irandoc.ac.ir/article_737172.html Grounded theory is a prominent research method in qualitative studies. This method enables researchers to conceptualize, identify problems, theorize about the studied phenomena, generalize findings, and disseminate knowledge. Despite its widespread acceptance, many researchers misunderstand and misapply grounded theory, so that impacts the quality of their studies. Neglecting the indicators for assessing the quality of grounded theory is detrimental, particularly as it applies to practical fields. Therefore, it is crucial to focus on quality assessment indicators to ensure the correct application of grounded theory. This systematic approach's structured design offers greater clarity in quality assessment. This study adopts a qualitative approach with a meta-synthesis method conducted across seven steps as outlined by Sandelowski and Barroso. The research aims to answer the question: what indicators assess the quality of studies employing grounded theory methodology (systematic design)? The study reviews all accessible full-text English-language resources, including books, dissertations, and scientific articles. Using reputable databases such as Scopus and Google Scholar between 2019 and 2025, 21 sources were selected from an initial pool of 2,262 based on specific criteria. For validation, the research utilized the CASP framework, Cohen's Kappa coefficient, and expert judgment. 831 initial indicators were identified categorized into 34 sub-criteria and 20 main criteria. The findings include: clarity of purpose; researcher awareness (expertise in qualitative research and investigation, having metacognitive information, theoretical and practical understanding, alertness and sensitivity); feeling for the subject; enthusiasm; creativity and flexibility; researcher's willingness; having an interpretive, analytical and critical perspective as well as communication skills; self-confidence, research ethics; methodological stability (coherence, clarity and explanation, logical); method adequacy; methodological sensitivity and precision; the power to explain and justify the concepts and theory produced; as well as continuous change without the interference of assumptions and the reflectivity of the concepts and theory produced from the data; depth, richness and complexity in the concept and conceptualization, being new and up-to-date; economy and brevity in the text and being attractive and creative. The findings section of this study can be categorized into several dimensions. These dimensions include: introductory, researcher, method, categorization and reporting of findings, textual, and technical. In the introductory dimension, the criterion of clarity of purpose and its related symptoms are included, criteria and symptoms such as awareness, alertness, and research expertise of the researcher in which the role of the researcher is prominent, in the researcher dimension, the criterion of stability of the method and its related symptoms, in the method dimension, criteria such as explanatory power and justifiability, and novelty and relevance that imply the production of a concept or theory, in the dimension of generating a theory from data, and finally criteria such as brevity or attractiveness can be included under the textual and technical dimension. It is suggested that researchers pay attention to the findings of this study when using the grounded theory method to improve the quality of their studies. They should set criteria for clarity of purpose, role of the researcher, method, generation of concepts and theories from data, and criteria related to the textual and technical dimensions of the report as a basis for conducting and evaluating studies conducted using this method. This will allow us to witness an increasing improvement in the quality of these studies. https://jipm.irandoc.ac.ir/article_736698.html The National System for Registration of Theses/Dissertations of Graduates Across the Country provides one of the most critical services offered by the Iranian Research Institute for Information Science and Technology (IranDoc). The statistical reports generated through this system now serve as the primary basis for policymaking by the responsible institutions in the country, particularly across various domains of science and technology. Should the quality of these reports—across dimensions such as accuracy, correctness, and consistency—fall short of acceptable standards, the resulting policies and decisions will undoubtedly be compromised.The National Quality Assurance Framework for Statistics (NQAF)[1], developed by the United Nations, is structured across four levels: domains, principles, requirements, and indicators. Its four main domains are: managing the statistical system, managing the organizational environment, managing statistical processes, and managing statistical outputs.In this study, employing the focus group method[2], inconsistencies in the official reports derived from the registration system were examined. Based on the NQAF, a framework is proposed for evaluating the quality of these reports. The proposed framework is developed with a focus on the domains of statistical outputs and management of the statistical system within the NQAF methodology.Key challenges in the statistical reports include temporal changes in the names and structures of higher education institutions, inaccuracies in graduate statistics, and insufficient detail in the reports. The dimensions addressed in developing the quality framework to mitigate these challenges include: comprehensive metadata in statistical reports, assurance of coherence and comparability, accessibility and clarity, and ultimately accuracy and reliability. https://jipm.irandoc.ac.ir/article_735599.html AbstractObjective: As user-centered information systems, digital libraries play a decisive role in shaping users' search and browsing behavior. Browsing behavior, as an essential complement to information seeking, not only offers an alternative path to accessing information but also serves as a key mechanism for serendipitous discovery. Accordingly, this study identifies various dimensions of browsing behavior within the MedlinePlus digital library to extend and validate Chang's browsing theory in digital information environments.Methods: This applied study employed a mixed-methods approach with a sequential exploratory design. Qualitative data were first collected and analyzed to develop an initial model, which was subsequently validated using quantitative data (Delphi method). The study population consisted of 60 master's and doctoral students in medical sciences from four Iranian universities of medical sciences (Isfahan, Tehran, Shiraz, and Mashhad), selected through a two-stage sampling approach (initial cluster sampling of universities followed by random selection of 15 participants per university). Various dimensions of users' browsing behavior, based on Chang's browsing theory, were gathered and analyzed using three simultaneous methods: direct observation and screen recording, think-aloud protocols, and semi-structured interviews. An initial model of information browsing in the digital library was developed, designed as a structured questionnaire, and sent to Delphi panel members over three consecutive rounds. The process continued until sufficient consensus was achieved (mean score ≥ 4 and Kendall's coefficient ≥ 0.7) for all model components, leading to the approval of the final model of information browsing in the digital library.Findings: Five dimensions of user information browsing were identified for the MedlinePlus website of the U.S. National Library of Medicine. These dimensions integrated the four dimensions of Chang's browsing theory with a "digital" dimension derived from the empirical findings of this study. The findings indicated that the behavioral dimension of browsing in digital environments relies heavily on physical movements (vertical and horizontal scrolling). Additionally, new motivations arising from the dynamics and specific capabilities of the digital platform were identified. Approximately 87% of users entered the browsing process with relatively specific cognitive intentions. This finding, along with the high frequency of accidental browsing, introduces the concept of purposeful discovery.Conclusion: This study demonstrated that information browsing in digital libraries is a far more complex and multidimensional phenomenon than can be explained solely by traditional models. The digital platform is not merely a context for passive information-seeking behavior but also an active agent shaping browsing behavior. https://jipm.irandoc.ac.ir/article_733817.html Iranian scientific database (Ganj) has hundreds of thousands of records and is one of the largest scientific treasures in the country. Optimal use of this database can be done by analyzing information and providing a representation of the research performance of stakeholders and with organizational dashboards. Therefore, this study was conducted to identify the needs of organizational dashboard design in the Iranian scientific database (Ganj). A look at the background of research in Iran as well as similar dashboards in various subject areas showed that different types of dashboards have been designed or various researches have been done in this field. However, with the exception of one example, most dashboards are designed for databases related to scholarly articles. Also, the researches related to the key indicators and metrics of the performance of educational and research institutes were only in the framework of data related to the educational activities of students and no background was found in the field of research performance for dissertations and dissertations. Therefore, the study continued with interviews with participants about their views on stakeholders and their needs; The position of ETD in their institutions and, of course, in measuring performance was examined. In Iran, "Ganj" has three dashboards: Parsa, Parsagar and TIK (similarity-check), but these dashboards are designed in general and only based on the needs of the research institute in interaction with macro-policies and the needs of educational and research institutes are not considered in their design. In the method section, The present research was applied and surveyed in a consecutive mixed manner. The research began with a review of the research background, and after reviewing the current "treasure" dashboards, policy makers, research assistants / central library heads were interviewed. The research began with a review of the research background, and after reviewing the current "treasure" dashboards, policy makers, central library heads interviewed. Then, by examining similar samples, efficient metrics and indicators to monitor the research performance of these institutes were identified and extracted. The indicators considered by the panel members for performance monitoring were approved by the members in 8 general categories. These eight categories include research quantity indicators; Research quality; Ethical; Alteration; awards; Output taken; Commercialization; And it was the kind of research. From the panel members' point of view, all indicators and metrics can be available at the general level, except for the ethics index and the similarity percentage measure (similarity-check) at the general level. In the Ethics Index, the percentage of similarity (similarity-check) measure from the perspective of panel members, because its general aspect is not important, its reports need to be accessible at higher levels. Finally, a conceptual model of organizational dashboard design requirements for this database was drawn. https://jipm.irandoc.ac.ir/article_732989.html The main goal of this research is to identify the readiness to use brainstorming techniques for innovation in knowledge-based companies at Kerman Science and Technology Park. The research method is a survey-descriptive one. The statistical population includes employees of knowledge-based companies under the University of Postgraduate Education in Kerman, including managers and experts. In this study, 55 individuals were selected as the survey sample based on availability and willingness to participate. A researcher-made questionnaire was used as the data collection tool. The validity of the questionnaire was assessed using the face validity method, and reliability was determined through Cronbach's alpha test and split-half test. The research findings indicated favorable conditions in key components such as cognition and knowledge, finance and budget, technology, innovation, and the Practical use and human resources. The organization's readiness to implement brainstorming techniques in knowledge-based companies at Kerman Science and Technology Park was also found to be favorable. It has been discovered that innovation plays a crucial role in shaping the knowledge structure. Innovation is significant for various reasons, as it helps in identifying the best ideas and executing suitable methods, techniques, and specialized processes to turn them into profitable services or efficient products, thereby fostering economic growth. Moreover, knowledge management and knowledge sharing are instrumental in enhancing brainstorming sessions and bolstering the competitive edge of knowledge-based companies. Additionally, organizational knowledge has been shown to boost creativity and can be harnessed for the future strategies of companies. Within the framework of the technology-organization-environment model, it is evident that recognition and knowledge are key factors in organizational innovation when it comes to adapting to new technologies. Additionally, software, hardware, infrastructure, tools, information systems, networks, applications, social networks, and high-speed internet lines can all contribute significantly to enhancing knowledge bases within companies. The utilization of brainstorming techniques is crucial for the advancement of knowledge-oriented companies, as it facilitates the generation of creative ideas and the effective implementation of specialized processes and methods to deliver high-quality services or products Recommendations include involving organization members in decision-making, establishing a suggestion system, conducting training courses to foster creativity and innovation, creating a knowledge base to share professional experiences, and utilizing financial and moral incentives in companies. Given the important role of brainstorming in fostering innovation and creativity within knowledge-based companies, this research is significant as it offers new insights in this area. One of the advantages of this research is the use of the Technology-Organization-Environment framework model to assess brainstorming readiness in Iran. https://jipm.irandoc.ac.ir/article_732988.html Value engineering is a systematic method used to improve the value of a project's product. It involves analyzing a service, system, or product to find ways to manage important functions while reducing costs. This study aims to explore the relationship between value engineering and the provision of electronic resources at the Central Library of Shahid Bahonar University of Kerman. The research method used is descriptive-survey. The study population includes faculty members of the university who are Central Library members and regularly use electronic resources. Selection criteria were based on their role in providing electronic resources. A total of 240 individuals met the criteria, with 140 selected as the research sample using the Morgan table. Data was collected through a researcher-made questionnaire, validated using the AVE index, and reliability was assessed using the composite validity criterion. Cronbach's alpha was above 0.7 for all cases except for identifying user needs, which was still acceptable. The composite validity criterion confirmed the reliability of the research tool. The findings revealed that all latent variables impact the value engineering variable. Based on the coefficients, the variable "providing high-quality electronic resources" has the greatest effect, while the variable "providing more electronic resources at a lower cost" has the least effect on value engineering. The t-value test statistic was utilized to assess the significance of the relationship between the variables. By examining the significant relationship at the 0.05 error level, it was directly determined in all components, confirming the research hypotheses. The results indicated that value engineering reduces costs associated with providing electronic resources and enables the provision of more resources for users. It also facilitates quicker selection of electronic resources, boosts scientific production, fosters inventions, enhances creativity and innovation, and builds trust in the use of electronic resources. Furthermore, value engineering accelerates the delivery of necessary scientific resources from electronic sources and enhances user satisfaction. It was also observed that resource provision is expedited. While information and communication technologies have addressed time constraints in resource provision, electronic resources with updated indexes can still benefit from time-saving through value engineering. This method proves to be the most efficient and effective in providing electronic resources. Based on the research findings, it can be concluded that the quality of electronic resources is closely linked to value engineering.