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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Nowadays, with the increased utilization of information technology in documenting patient records at healthcare centers, hospital information systems play a vital role in these institutions. Usability is a critical criterion in designing the user interface of hospital information systems, and its continuous evaluation effectively identifies system issues and leads to their optimal improvement. In this quantitative study, the usability of hospital information systems was assessed from the users’ perspective using the Purdue Usability Model. The research employed a survey methodology, and data were collected through the Purdue Usability Testing Questionnaire. The study population comprised users of the Hospital Information System at Bandar Abbas Children’s Hospital, selected via convenience sampling. A total of 169 participants, comprising administrative staff and nurses, were selected to take part in this study. The Purdue Usability Testing Questionnaires were distributed to them for data collection purposes. After collecting the questionnaires, data analysis was conducted using SPSS software, version 26. The findings revealed a significant difference between each of the eight dimensions of the Purdue model across various departments when considered separately. However, the average usability score in the flexibility dimension did not show a significant difference. The average score for user guidance was below the average threshold, while the mean scores for other dimensions and overall usability were above average. In this study, the average usability score across all dimensions was reported to be above average. Consequently, it can be concluded that the system under study possesses appropriate usability and has successfully gained user satisfaction during use. Nevertheless, in certain areas such as user guidance and flexibility, the system received lower scores, indicating the need for further development, especially considering these dimensions.
Short-term training courses as one of the key tools for empowering human capital play a significant role in enhancing employees’ knowledge, skills, and attitudes. The effectiveness of these courses is crucial in improving organizational performance. The present study was conducted with the aim of identifying the factors influencing the effectiveness of short-term training courses at Iranian Research Institute for Information Science and Technology (IRANDOC). The research employed a survey method for describing participants and answering the research questions, and an exploratory factor analysis using the principal components method for predicting variables. The statistical population included all participants in IRANDOC’s training courses over a 10-year period, totaling 1,475 individuals, from which 305 were selected through simple random sampling. The research tool was a researcher-made questionnaire consisting of three sections: demographic and academic information, course objectives (assessing knowledge and skills before and after the course), and factors influencing training effectiveness. The overall validity of the questionnaire was established through a pilot study and expert judgment for content validity. The reliability was confirmed using Cronbach’s alpha coefficient, which was calculated as 0.94. To test the research hypotheses, exploratory factor analysis and regression analysis were used. The findings indicated that, based on the impact coefficients, the factors influencing the effectiveness of training courses at IRANDOC, in order of highest to lowest impact, were: 1) course objectives, 2) administrative and logistical support for course implementation, 3) alignment of facilities, materials, and equipment with course objectives, 4) quality of course content and its relevance to objectives, 5) the instructor’s knowledge, attitude, and classroom management skills, and 6) evaluation and feedback delivery.
The remarkable growth of social media platforms has led to the expansion of novel marketing tools such as electronic word-of-mouth (eWOM), which plays a significant role in shaping consumer decision-making and purchase behavior. Increasing competition, along with rapid and extensive access to information and recommendations has elevated the importance of information quality and credibility. This study investigates the qualitative components and credibility of eWOM information as well as user characteristics in forming online purchase intention. The theoretical framework is developed based on variables including quality, credibility, and attractiveness of information (as message features), and users’ need and attitude toward information (as individual traits). The target population comprises Iranian users interested in online shopping with the main objective being to elucidate the process of eWOM information acceptance and its effect on purchase intention, while identifying key influencing factors. The present study is a descriptive survey in nature and employs a quantitative approach. The statistical population consisted of 390 active online shoppers, selected through nonprobability sampling. Data was collected via a standardized Likert-scale questionnaire, and instrument validity was assessed using reliability tests (Cronbach’s alpha, composite reliability, AVE). Structural Equation Modeling (SEM) and Smart PLS software were used to analyze the data. Model fit indices including GOF, Q2, R2, and path coefficients were calculated to test the hypotheses. In addition to confirmatory factor analysis, mediating variables such as perceived usefulness and information adoption in the relationships between independent variables and purchase intention were examined. All relationships and effects among variables were statistically interpreted. Results reveal that the quality and credibility of eWOM information, coupled with a positive user attitude towards information, significantly enhance purchase intention. While message attractiveness does not directly influence perceived usefulness, qualitative factors and credibility of information lead to greater perceived usefulness and information adoption. Furthermore, the more users perceive the information as useful and acceptable, the greater its impact on purchase intention. The findings underscore the prominent mediating role of specific variables in the process of forming purchase intention. Final analysis demonstrates that users rely more on valid experiences and recommendations from other users for purchasing decisions, and that positive, credible reviews reinforce brand image and purchase trust. The study provides evidence that the quality and credibility of eWOM information play a fundamental role in enhancing the impact of promotional messages on social networks, thereby influencing consumer online purchase intention. SEM analysis indicates that perceived usefulness and information adoption act as mediators, facilitating the transmission of eWOM message features to purchase intention, while the direct effect of message attractiveness on perceived usefulness is not significant. This suggests that cognitive aspects outweigh purely visual or linguistic appeal in guiding consumer behavior. The study emphasizes the necessity for brands to invest in generating high-quality, credible, and useful content on social platforms and demonstrates that valid user reviews and recommendations can foster greater trust, loyalty, and ultimately improve purchase rates. Consequently, marketers should design strategies to enhance the credibility and value of delivered information for more impactful consumer decision-making. By presenting a context-driven and updated model in online consumer behavior, this study also sets the groundwork for future research on emotional and cultural dimensions of information acceptance.
This article investigates the implications of the field of data ethics for data policy-making. To achieve this, we employed a structural conceptual analysis method with an implication-finding approach. First, the field is introduced, and the distinction between data ethics and data science ethics is discussed. It is argued that their relationship is one of specific commonality (umum wa khusûs min wajh), as data science ethics generally focuses on the issue of ethical data processing by software professionals, whereas data ethics concentrates on the production/collection, storage, optimization, distribution, sharing, and use of data by corporations. Next, the difference between this field and big data ethics is addressed. Following a review of relevant research in data ethics, the field’s implications for data policy are presented across six dimensions: Data Economy, Data Collection and Creation, Data Ownership, Open Data, Data Quality, and Data Protection.The findings show that data ethics, extending beyond common discussions in data science ethics or big data ethics, possesses a set of fundamental implications for data policy. In the absence of these implications, the groundwork is laid for structural biases, data injustice, and corporate exploitation of users. Firstly, the data economy transforms users into unpaid knowledge workers, turning their data into a source of commercial value, a tool for power consolidation, and a mechanism for directing consumer behavior. The conceptual analysis of the texts indicates that data collection and creation are never neutral; they are always influenced by ontological, epistemological, methodological assumptions, and power structures. Therefore, raw, unbiased data or "fact without interpretation" does not exist. In the domain of data ownership, it becomes clear that the distinction between use, control, and access has different consequences for individual rights, and many common ownership models practically favor data producers rather than their potential owners. Reviews show that open data only leads to transparency and public measurability if informational justice, analytical capacity, and equal access are guaranteed. Otherwise, it becomes a tool for strengthening the monopoly of technological elites. In the dimension of data quality, it was revealed that completeness, consistency, timeliness, and especially "relevance" play a crucial role in preventing confused decisions and data-driven policy errors. Furthermore, the analysis of the findings indicates that data protection - including preventing unauthorized access, safeguarding data against unforeseen secondary uses, and controlling the transfer of risk from organizations to users - is a fundamental prerequisite for public trust and reducing misuse by corporations and governments. Finally, the research shows that some organizational ethical documents and charters are mostly performative in function and can serve as a cover for the continuation of unethical behaviors.
Industry 4.0, using advanced technologies such as the internet of things, artificial intelligence, data analytics, robotics, sensors, and smart networks has created new possibilities for businesses to improve production and operational processes automatically and without the need for human intervention. Smart supply chain of Iranian manufacturing and service companies can be very transformative and challenging at the same time. Iranian companies have always sought to improve their supply chain processes with the increasing advancement of technology, but they face many problems due to some obstacles. The present study aims to find the obstacles, capabilities, and consequences of implementing smart supply chains in Iranian industries. The research has a developmental and applied orientation and is qualitative in terms of methodology, and was conducted using the Grounded Theory strategy. The data collection tool is semi-structured interviews, which were conducted by theoretical sampling of experts, and a total of 11 managers of leading industries in smart manufacturing were interviewed to achieve the criterion of "theoretical adequacy". For validation, two methods of participant review and non-participating experts review were used in the research, and after receiving corrective comments, the final model was presented. MAXQDA software was used to implement the Grounded Theory. 372 initial open codes formed 81 concepts that were categorized into 20 categories. Software, hardware, environmental, and organizational requirements are causal conditions for the smart factory (central category). Transparency, optimization, cost efficiency, supply chain agility, and integration are strategies that result from the central category. Uncertainty, relationship breadth, and data explosion, and specific influencing conditions and infrastructure, technology and technical barriers, security issues, financial and economic barriers, organizational and managerial barriers, cultural barriers and environmental barriers are general context conditions that affect strategies. Intra-chain consequences, environmental consequences and financial consequences are the outputs of applying strategies. To digitize the supply chain, organizations need strong and scalable IT infrastructures. These infrastructures include servers, networks, data storage systems and cloud systems that can process large volumes of data. High security is essential to protect sensitive information and prevent cyber threats. Expert human resources must work in an organizational culture that is receptive to innovation and digital change. Human resources must be continuously trained in different fields of new technologies and update their capabilities.
The growing integration of artificial intelligence (AI) in scientific activities has led various stakeholders, particularly publishers and universities to establish regulations and standards to mitigate the potential negative impacts of this technology. Numerous publishers, associations, and academic institutions have developed and implemented policies to address these concerns. This study examines the approaches and positions of leading publishers, publication ethics associations, and prestigious universities worldwide to formulate a comprehensive set of guidelines delineating the permissible and impermissible uses of AI in scientific writing and evaluation. The methodology for developing these guidelines involved four key steps: data collection until saturation was reached, classification of existing policies, development and justification of the guidelines, and review and refinement of the guidelines through a focus group of relevant experts. The primary outcome of this research will be a set of guidelines tailored for the three main stakeholders in scientific writing and evaluation: authors, reviewers, and editors.
Improving productivity is one of the most important goals for higher education systems and universities. University ranking systems play a significant role in guiding university performance. Currently, the Islamic World Science Citation Center (ISC) ranking system is the only one for universities in the country. This research aims to examine the alignment of this ranking system with university productivity criteria. To achieve this, the study uses Data Envelopment Analysis (DEA) to measure the productivity of 69 public comprehensive universities in the country. It then ranks them by productivity and compares the results with the ISC ranking. The results from grouping the universities show that in the first group, large, populous universities like Payame Noor and Technical and Vocational University, despite their high position in the ISC ranking, are not highly ranked in terms of productivity. In the second and third groups, smaller universities with fewer students achieved better rankings.Overall, among the 69 public comprehensive universities, Shiraz University, Ferdowsi University of Mashhad, Tarbiat Modares University, the University of Tehran, and the University of Tabriz ranked 1st to 5th, respectively. Ayatollah Boroujerdi University, Bozorgmehr University of Qaenat, Khorramshahr University of Marine Science and Technology, Saravan Higher Education Complex, and Hazrat Masoumeh University of Humanities and Arts ranked 65th to 69th. The Wilcoxon test showed that the two ranking types do not align. In other words, the criteria used by the ISC ranking are not in line with the productivity indicators of public comprehensive universities. This ranking, therefore, cannot provide the necessary incentive for universities to improve their productivity. Increasing the number and weight of productivity indicators in the ISC ranking could help bridge the gap between these two ranking systems and create a greater incentive for universities to improve their productivity.
With the world's shift toward a knowledge-based economy, knowledge and its management have become vital factors in maintaining the competitive strength of organizations and industries. In this regard, examining the process of knowledge translation in universities and industries is of particular importance. Accordingly, the present study was conducted to assess the state of knowledge translation among researchers at Iranian universities (Shahid Chamran of Ahvaz, Lorestan, Hormozgan, and Persian Gulf University of Bushehr). This study is categorized as a survey research type and is applied in nature. The research population consisted of 285 faculty members from the engineering faculties of four universities: Shahid Chamran University of Ahvaz, Persian Gulf University of Bushehr, Lorestan University, and Hormozgan University. Data were collected using the “Self-Assessment Questionnaire on Knowledge Translation Activities of University Researchers,” which consists of 30 items organized into four domains: “Research Question,” “Knowledge Creation,” “Knowledge Transfer,” and “Promotion of Using Evidence.” In addition to descriptive statistics, the data analysis for this study utilized ANOVA tests and regression analysis. The results showed that the mean total score of the knowledge translation process at Persian Gulf Universities—Bushehr (99.97 ± 19.93) and Lorestan (99.80 ± 15.30)—was higher than at Hormozgan University (93.94 ± 21.49) and Shahid Chamran University of Ahvaz (93.55 ± 21.76). Among the examined components, the highest mean belonged to the element "Knowledge Transfer," and the lowest mean related to “Promotion of Using Evidence". Additionally, it was found that all components of the knowledge translation process significantly contributed to explaining and predicting researchers’ performance (p < 0.001), and the standardized coefficients (Beta) indicate that the knowledge transfer component had the greatest effect on knowledge translation performance with a coefficient of 0.95, followed by knowledge creation (0.90), promotion of using evidence (0.86), and research question (0.85). The results of this study indicate that establishing systematic links between universities and stakeholders can enhance the effectiveness of knowledge translation and help improve evidence-based decision-making at the national level. It is recommended that future researches focus on knowledge translation within executive organizations and industries, using tools localized for the organizational dimension.
Research excellence is widely recognized as one of the key pillars of scientific advancement and sustainable development in modern societies. The present study aims to develop a national framework for measuring research excellence in Iran. This research is applied in purpose and descriptive survey in nature. By examining national models of research evaluation and drawing on the experiences of leading countries, a comprehensive framework was developed that is tailored to Iran’s specific socio-cultural, scientific, and policy context. The framework incorporates expert opinions and is grounded in national upstream documents and policies in science and technology. The Analytic Hierarchy Process (AHP) technique was employed to prioritize the identified and validated components and indicators. The framework consists of five main dimensions, each assigned a relative weight based on expert judgment. The most significant dimension is impact beyond the university, with a weight of 43%, highlighting the importance of research that extends its influence beyond academic circles and contributes meaningfully to society. This is followed by ethics and national-Islamic identity at 22%, underscoring the value placed on moral integrity and cultural authenticity in research. Networked research and collaborations account for 17%, emphasizing the role of domestic and international partnerships in advancing knowledge. Research quality and innovation constitute 11%, reflecting the emphasis on originality, rigor, and scholarly contribution, while research environment and capacity, with a weight of 7%, addresses the institutional and infrastructural foundations necessary for sustained research activities. Within the dimension of research quality and innovation, key components include originality and innovation, scientific significance, methodological rigor, and qualitative evaluation. The dimension of impact beyond the university encompasses social, political, and managerial impacts, cultural influence, contributions to the development of modern Islamic civilization, economic outcomes, justice and inclusiveness, as well as qualitative assessment. The research environment and capacity dimension include research infrastructure, research dynamism, sustainability of research efforts, and qualitative evaluation. In the ethics and national-Islamic identity dimension, the components are research ethics, preservation and promotion of national identity, and qualitative evaluation. Finally, the networked research and interactions dimension incorporates domestic collaboration, international cooperation, the promotion of science and technology, and qualitative evaluation. Among the most prioritized indicators are those that align with the fundamental mission of research in the Iranian context: identifying and solving real societal problems, generating original knowledge, achieving inventions and receiving awards, being cited by researchers in Islamic countries, and establishing dynamic scientific networks. These indicators collectively signal a paradigm shift—from evaluating research solely by publication volume to assessing it based on its depth, ethical grounding, and civilizational relevance.
The ethics of artificial intelligence is a relatively emerging field within the scope of applied ethics, which is influenced by the transformations driven by the development and deployment of AI systems and examines the ethical and social implications and issues associated with them. Although some of these consequences and ethical issues have been somewhat predictable and have enabled ethical analyses and investigations, it appears that as AI systems increasingly penetrate all dimensions and aspects of individual and social life, certain ambiguous and complex ethical challenges are also arising. Therefore, in the field of AI ethics studies, there does not actually exist a set of issues that are well-established or enjoy an acceptable level of comprehensiveness. Nonetheless, numerous efforts have been made by national and international organizations to collect and draft principles and ethical guidelines for AI in the past decade. Each AI ethics document generally consists of two parts: ethical principles and ethical guidelines. The ethical principles in these documents form the basis for formulating the ethical guidelines. The goal of this article is to introduce principles and develop ethical guidelines for AI ethics through the study of seven national and international documents. On this basis, the five integrative ethical principles of Floridi and Cowls were used as the foundation for compiling and examining AI ethical guidelines. These guidelines were reviewed in three stages. In the first stage, ethical guidelines were extracted from the aforementioned documents and listed within the framework of the five ethical principles. In the second stage, the collected guidelines were harmonized and refined. In the third stage, to evaluate the validity of the findings and the research outcome, the refined ethical guidelines were critiqued and reviewed by a focus group of experts under the five ethical principles to ensure the credibility of the selected ethical guidelines by achieving a relative consensus among the experts.
