Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 The Place of New Technologies and Information Systems in Teleworking Process The Place of New Technologies and Information Systems in Teleworking Process 633 655 737168 10.22034/jipm.2025.2071886.2095 FA Fariborz Doroudi Assistant Professor; Iranian Research Institute for Information Science &amp; Technology (IranDoc); Tehran, Iran 0000-0003-0386-5301 Adel Soleimani Nezhad Department of Knowledge and Information Science, Shahid Bahonar University of Kerman, Kerman, Iran. 0000-0002-9757-6836 Bentolhoda Baniasadi Master of Science in Executive Management; Islamic Azad University, Kerman Branch; Kerman Journal Article 2025 09 16 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. 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_737168_99c18a57579ea439cb6d257c7490018a.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 Scholarly Reviewer Recommendation Systems: A Scoping Review and Conceptual Framework Scholarly Reviewer Recommendation Systems: A Scoping Review and Conceptual Framework 657 684 737169 10.22034/jipm.2025.2073841.2109 FA Ghazale Mansouri medical library and information sciences; school of management; Isfahan university of medical sciences; Isfahan; iran Abolfazl Taheri medical library and information sciences; school of management; isfahan university of medical sciences; isfahan; iran Narjes Mottaghi computer sciences; school of management; isfahan university of medical sciences; isfahan; iran Rasool Noori medical library and information sciences; school of management; isfahan university of medical sciences; isfahan; iran Journal Article 2025 10 06 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. 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_737169_2bad76cf1f35abbb564aeb5d66698236.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 Designing an Artificial Intelligence-Based Knowledge Management Framework in Complex Organizations(Case Study: Social Security Organization) Designing an Artificial Intelligence-Based Knowledge Management Framework in Complex Organizations(Case Study: Social Security Organization) 685 714 732957 10.22034/jipm.2025.2073417.2106 FA Aliakbar Tabibi PhD Candidate in Information Technology Management, Smart Business Orientation, Department of Management, Na.C., Islamic Azad University, Najafabad, Isfahan, 0009-0000-3826-8609 Naser Khani PhD in Strategic Management, Information Systems Orientation, Department of Management, Na.C., Islamic Azad University, Najafabad, Isfahan 0000-0002-0027-7048 Akbar Nabiollahi PhD in Computer Science, Information Technology, Department of Computer Engineering, Na.C., Islamic Azad University, Najafabad, Isfahan 0000-0002-0785-4338 Bita Yazdani PhD in Human Resources Management, Department of Management, Na.C., Islamic Azad University, Najafabad, Isfahan Journal Article 2025 10 03 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. 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_732957_f75ddb43823a3e40b593f544f8462f64.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 Conversational Question Answering for Low-Resource Languages: A Novel Architecture Enhanced by Large Language Models Conversational Question Answering for Low-Resource Languages: A Novel Architecture Enhanced by Large Language Models 715 747 733816 10.22034/jipm.2025.2072373.2101 FA Azadeh Mohebi Assistant Professor in Iranian Research Institute for Information Science and Technology (IranDoc),Tehran 0000-0002-6443-7450 Safoura Aghadavoud Jolfaei , PhD Candidate in Iranian Research Institute for Information Science and Technology (IranDoc), Tehran Journal Article 2025 09 22 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. 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_733816_7125179c510400f2879b658e81249f49.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 Cluster Analysis of Semantic Web Research in Knowledge and Information Science Cluster Analysis of Semantic Web Research in Knowledge and Information Science 749 774 737170 10.22034/jipm.2026.2080004.2156 FA Mahsa Dokanei Department of Knowledge and Information Science, Razi University, Kermanshah, Iran. 0009-0003-4297-4751 Saleh Rahimi Department of Knowledge and Information Science, Razi University, Kermanshah, Iran. 0000-0001-6540-9825 Faramarz Soheili Department of Knowledge and Information Science, Payame Noor University, Tehran, Iran. 0000-0003-2581-7052 Journal Article 2025 12 05 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. 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_737170_8fe6128036b9238487917e23aa07e710.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 Service Personalization in Digital Libraries: a Systematic Review of Research Trends, Techniques, and Future Directions Service Personalization in Digital Libraries: a Systematic Review of Research Trends, Techniques, and Future Directions 775 814 732986 10.22034/jipm.2025.2072583.2102 FA Hossein Adabi Firozjah IHCS/Tehran/Iran 0000-0002-7707-3959 Mitra Pashotanizade Department of Knowledge and Information Science, Faculty of Educational Sciences and Psychology, University of Isfahan 0000-0003-1973-3856 Mehrdad Cheshmehsohrabi Department of Knowledge and Information Science, Faculty of Educational Sciences and Psychology, University of Isfahan 0000-0003-1856-4210 Mehdi Alipourhafezi Department of Knowledge and Information Science, Faculty of Educational Sciences and Psychology, Allameh Tabatabai university 0000-0002-3113-9887 Journal Article 2025 09 23 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. 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_732986_f68069679bad38443287cea9d2c455cb.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 Providing a Contingency and Conceptual Model of Content Marketing for Iranian Reading Applications Providing a Contingency and Conceptual Model of Content Marketing for Iranian Reading Applications 815 840 737171 10.22034/jipm.2026.2075746.2122 FA Mahshid Eltemasi PhD of Library & Information Science; Assistant Professor; Information Science & Knowledge Management; University of Tehran 0009-0009-2459-7757 Kimia Asadi Khaneghah M.A. of Information & Knowledge Science; Information Management; University of Tehran 0009-0009-2459-7757 Journal Article 2025 10 25 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. 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_737171_58cd00e529065dd9c407dee820891a9d.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 A Study of the Alignment of Schema.org for Describing the Publishing Domain Based on SPAR Ontologies A Study of the Alignment of Schema.org for Describing the Publishing Domain Based on SPAR Ontologies 841 869 716610 10.22034/jipm.2024.716610 FA Faeze Sadat Tabatabai Amiri university shahid chamran ahvaz 0000-0003-1079-6383 Abdolhossein Farajpahlou Professor Emeritus, Department of Knowledge and Information Science, School of Education & Psychology, Shahid Chamran University of Ahvaz, Ahvaz, Iran. 0000-0002-3184-4244 Shahnaz Khademizadeh Associate Professor, Knowledge and Information Science, Department of Knowledge and Information Science, Faculty of Psychology and Educational Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Iran 0000-0003-4494-7709 Seyed Mehdi Taheri Department of Information Science knowledge. University of Allameh Tabatabai, Tehran, Iran 0000-0002-3305-5986 Journal Article 2024 07 14 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. 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_716610_d158a1dde2d5be73d0a9e2de62a661eb.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 Prioritizing University Research Evaluation Criteria Using a Hybrid CRITIC and Ordinal Logistic Regression Approach: a Study Based on Real-World Data Prioritizing University Research Evaluation Criteria Using a Hybrid CRITIC and Ordinal Logistic Regression Approach: a Study Based on Real-World Data 871 895 732408 10.22034/jipm.2025.2068415.2070 FA Samira Abootalebi Ph.D. in Applied Mathematics, Assistant Professor, Department of Mathematics, Isf.C., Islamic Azad University, Isfahan 0000-0002-5424-9886 Journal Article 2025 08 09 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. 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_732408_2febbe9077109411c13edf6cfa11de54.pdf

Iranian Research Institute for Information Science and Technology (IranDoc) Iranian Journal of Information Processing and Management 2251-8223 41 3 2026 03 21 Metasynthesis of Quality Assessment Indicators of Studies Using the Grounded Theory Method Metasynthesis of Quality Assessment Indicators of Studies Using the Grounded Theory Method 897 929 737172 10.22034/jipm.2025.2058465.2000 FA Khatoon Alipour PhD student in Curriculum Studies, Department of Methods, Educational Planning and Curriculum, Faculty of Psychology and Education, University of Tehran, Tehran, Iran. Keyvan Salehi Associate Professor of Educational Assessment, Division of Research and Assessment, Faculty of Psychology and Education, University of Tehran, Tehran, Iran. 0000-0001-8673-4248 Sayyedeh Fatemeh Zare Sheikhkalai PhD student in Curriculum Studies, Department of Methods, Educational Planning and Curriculum, Faculty of Psychology and Education, University of Tehran, Tehran, Iran. Journal Article 2025 04 27 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. 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_737172_23392a0d65c60fab782c87a446609a94.pdf