Research Paper
Knowledge Management
Mohammad Ali Siahsarani Kojuri; Mahmood Reza Cheraghali
Abstract
1.IntroductionUniversities need to manage their knowledge assets and work creatively to maximize the enablers and minimize the barriers associated with knowledge management processes. Globally, universities are considered key drivers of the economy and have significant potential to act as engines of ...
Read More
1.IntroductionUniversities need to manage their knowledge assets and work creatively to maximize the enablers and minimize the barriers associated with knowledge management processes. Globally, universities are considered key drivers of the economy and have significant potential to act as engines of economic growth and development. However, universities face a wide range of challenges, including the emergence of the knowledge society, the globalization and internationalization of universities, reduced funding and government support, increasing undergraduate enrollment, and expanding access (Ramjeawon & Rowley, 2020). Effective knowledge management practices help universities improve their efficiency and effectiveness, become more competitive, and contribute to the wealth of their countries (Fussy, 2018).The purpose of the present research is to examine the status of the infrastructural indicators of the successful implementation of knowledge management at Golestan University at three levels: managers, faculty members, and scientific assistants, and design a predictive model for it. For this purpose, the status of various infrastructure dimensions such as management, information technology, human resources, organizational culture, and organizational structure for the successful implementation of knowledge management in the university was examined, and then based on the knowledge management infrastructure indicators and using artificial neural networks, an optimal predictive model was presented.2.Literature ReviewPrevious studies have identified several enablers (factors that enhance knowledge management) and barriers (factors that have an adverse effect on knowledge management). Many of these factors can have a positive or negative impact on knowledge management processes such as knowledge creation, knowledge sharing, and knowledge transfer. The most frequently identified factors include culture, rewards and incentives, technology, leadership, organizational structures, and university-industry linkages. Few studies have also identified the importance of strategies and policies, human resources, and resources and budget (Ramjeawon & Rowley, 2020). In past research, approaches to barriers to knowledge management implementation have varied. Wolf et al. (2024) identified three root challenges that frequently appear in the path of knowledge management implementation: 1- Organizational prerequisites for implementing a knowledge sharing culture 2- Use of ICT for knowledge transfer 3- Knowledge transformation. Remus (2012) distinguishes challenges to implementing knowledge management based on the stage of the implementation process in which they appear (such as knowledge creation, sharing, transfer, or retention).3.MethodologyThis research is based on the purpose of applied research and was conducted using a survey method and an analytical approach. The statistical population of the research was managers, faculty members, and scientific assistants. Due to the limited research population, the Morgan table was used. The sample size required for the present study was 187 based on the statistical population of 365 people, and on this basis, 190 questionnaires were collected. Data collection was carried out through a questionnaire using a relative stratified sampling method. In the first part of the questionnaire, the demographic information of the respondents, including gender, education, etc., was collected, and in the second part, the research variables were measured. In the first step, the status of Golestan University in terms of infrastructure indicators for maintaining and developing human resources knowledge was examined at three levels of managers, faculty members, and scientific assistants, and the results of each class were compared. In the second step, artificial neural networks were used to predict the maintenance and development of human resource knowledge based on infrastructure indicators.4.ResultsAn examination of the status of human resource knowledge maintenance and development factors at the overall level of Golestan University shows that, in a general view, the level of management variables, organizational structure, and organizational culture is not in a desirable state and is below the average level, and the human resource and information technology variables are above the average level. In order to predict the success rate of human resource knowledge maintenance and development at Golestan University, artificial neural networks were used. The results showed that the present research model with three neurons in the hidden layer will reach the highest level of prediction accuracy, which is 0.893. The results of sensitivity analysis using artificial neural networks showed that all the infrastructural dimensions of human resource knowledge maintenance and development studied in the present study are important in the success of knowledge management and their existence is mandatory in the university complex, but the dimensions of reward, support, and knowledge sharing have been assigned the first, second, and third ranks, respectively, which indicates the greater importance of these dimensions than other dimensions based on the impact on the predictive power of the model.5.DiscussionThe results showed that in terms of the status of the human resources knowledge maintenance and development indicators at Golestan University at all three levels of managers, faculty members, and scientific assistants, the status of the two factors of human resources and information technology is above the average level, and the status of the three factors of organizational culture, organizational structure, and management is below the average level. In other words, there is consensus at each level studied at Golestan University regarding the identification of the most important obstacles to the implementation of knowledge management. On the other hand, although in terms of the status of the infrastructural indicators of knowledge management at Golestan University at all three levels of managers, faculty members, and scientific assistants, the two factors of human resources and information technology have obtained numbers above the average level, the numbers obtained are not so impressive and desirable that these two factors can be considered as strengths in the implementation of knowledge management and it can be inferred that the university is in a good position in these two factors and does not need any activity. In fact, the results of this step show that the infrastructural indicators at Golestan University, especially in the three factors of organizational culture, organizational structure, and management, require immediate attention and action.6.ConclusionThe findings showed that in the barriers to maintaining and developing human resources knowledge, three factors of organizational culture, organizational structure, and management are below average, and two factors of information technology and human resources are slightly above average. Also, the use of artificial neural networks to predict the level of maintaining and developing human resources knowledge showed that the current research model with three neurons in the hidden layer will reach the highest level of prediction accuracy, which is 0.893, and the dimensions of reward, support, and knowledge sharing, respectively, have the highest role in the predictive power of the model. The results showed that the infrastructural indicators of knowledge management at Golestan University, especially in the three factors of organizational culture, organizational structure, and management, require immediate attention and action.AcknowledgmentsWe would like to express our deepest gratitude to Golestan University for participating in this research and covering the costs in the form of a customized research project.
Research Paper
Intelligent Systems Recovery
Masoumeh Al-Sadat Abtahi; Parvaneh Fathali Beigi
Abstract
The aim of the present research is to identify and rank the factors influencing the implementation of an electronic city plan using the Analytic Hierarchy Process. The research method is descriptive, applied, and quantitative, with a sample population of 21 experts. Data collection was done through library ...
Read More
The aim of the present research is to identify and rank the factors influencing the implementation of an electronic city plan using the Analytic Hierarchy Process. The research method is descriptive, applied, and quantitative, with a sample population of 21 experts. Data collection was done through library research and questionnaires. The validity of the questionnaires was confirmed by experts, and reliability was assessed using pairwise comparisons, showing an overall inconsistency rate of 0.06, indicating acceptable reliability. Data analysis was conducted using Choice Expert software and Analytic Hierarchy Process (AHP). According to experts, organizational factors are the most important (weight of 29.0) while political factors are the least important (weight of 19.0). Within organizational factors, the strategic information systems index is the most important (relative weight of 26.0 and final weight of 76.0), while the power distribution index is the least important (relative weight of 12.0 and final weight of 37.0). Among technology factors, the security and privacy index is the most important (relative weight of 39.0 and final weight of 100.0), while the access and e-government portal index is the least important (relative weight of 13.0 and final weight of 34.0).1.IntroductionCities, as the cradle of civilizations, have always been places of thought and innovation. Since ancient times, they have been recognized as centers of trade, culture, and knowledge, playing a significant role in the progress of humanity. Over time, with the increase in population and the complexities of urban life, the need for new urban designs and structures has become more pronounced. These changes are essential to improve the quality of life for citizens and to create spaces that align with the modern needs of humanity. Consequently, urban planners and architects seek ways to design cities that are sustainable, accessible, and aesthetically pleasing—meeting the needs of today while ensuring longevity and resilience for the future. (Kiani, 2011).Modern cities, through the use of advanced technologies, enhance the quality of life and contribute to sustainable development. They function as hubs for diverse cultural and economic services while addressing citizens' needs in the digital era through electronic services (Maleki & Madanloujibari, 2016).An electronic city leverages information technology to improve urban services and foster economic and social growth (Alizadeh Asl et al., 2015).By utilizing information technologies, electronic cities enhance the quality of life and facilitate access to services. The provision of governmental and private services online reduces traffic and pollution while saving time and costs (Mosazadeh, Mirketuli, Ata, & Kiaei, 2014).Research Question(s)What are the factors and indicators influencing the implementation of the electronic city project in Parand City?How is the prioritization (weighting) of factors and indicators influencing the implementation of the electronic city project in Parand City determined?2.Literature ReviewThe electronic city represents the digital revolution and the information age, utilizing digital technologies to offer diverse services. These cities, by transforming areas such as security, healthcare, education, and employment, enhance the quality of life and enable continuous access to urban services (Tachenko & Sustiano, 2023). An electronic city, by leveraging advancements in information technology, facilitates access to urban services through the internet. These cities, providing governmental and private services online, contribute to reducing traffic and pollution while saving time and energy (Mohammadi et al., 2021). The first virtual city was established in 1994 in Amsterdam and quickly became a global phenomenon, offering solutions to urban challenges such as congestion and pollution (Raeesi, 2021).3.MethodologyGiven that the present study seeks to obtain information regarding perspectives and opinions through surveys and questionnaires, it is classified as descriptive research. This study is applied in terms of its objective and descriptive survey in nature. It is cross-sectional research focusing on identifying and prioritizing factors affecting the implementation of the electronic city project. Two populations are utilized in this research. The first population consists of 21 experts, including managers and specialists from governmental organizations in Parand City, who are familiar with information technology and the concept of an electronic city. The second statistical population comprises 120 citizens of Parand City who have interacted with governmental organizations and utilized information technology and electronic city services to some extent. For the first population, due to its limited size (21 individuals), the sample size is considered equal to the population size using purposive sampling. For the second population, the sample size is determined through random sampling using Cochran's formula. Based on the number of respondents (120 individuals) and applying the formula (Cochran Z=1.96, P=0.5, with an error margin of E=0.05), the statistical sample size was calculated as 91 individuals, which aligns with Morgan's table (91 sample size for a population of 120).4.ConclusionThe analysis of experts' opinions revealed that the main factors influencing the implementation of the electronic city project are organizational, technological, social, and political factors. Each of these main factors consists of sub-indicators identified through pairwise comparisons and the geometric mean of the experts' final opinions.Organizational factors include organizational structure, power distribution, information systems strategy, future organizational needs, and organizational culture.Technological factors cover IT standards, security and privacy concerns, system integration, access issues, and the electronic government portal.Social factors emphasize citizen-centric focus, citizens' awareness, training and education, and the digital divide.Political factors involve government support, funding, organizational leadership, public institutions, and legal and regulatory issues.Among the main factors, organizational factors held the highest weight (0.29), with the information systems strategy being the most significant sub-factor, receiving a weight of 0.076. The research findings also indicated that political factors had the lowest impact on the implementation of the electronic city project, with a weight of 0.19, making it the least influential among the four categories, as assessed by the experts.AcknowledgmentsWe express our deepest gratitude and appreciation to all the material and spiritual supporters who accompanied us in conducting this research.
Research Paper
Information and Knowledge Management
Zohre Sharei; Marzeyeh Dehghanizadeh; Mohammad Hasanzadeh Odorji
Abstract
1.IntroductionKnowledge management, as a critical organizational asset, plays a vital role in success and competitiveness (Abbas et al., 2021). Organizational success increasingly depends on employees’ behaviors, particularly knowledge sharing (Chopra & Gupta, 2019). Sustaining organizational ...
Read More
1.IntroductionKnowledge management, as a critical organizational asset, plays a vital role in success and competitiveness (Abbas et al., 2021). Organizational success increasingly depends on employees’ behaviors, particularly knowledge sharing (Chopra & Gupta, 2019). Sustaining organizational knowledge is essential for digital transformation and sustainable development (Di Vaio et al., 2021; Steiner, 2018). Consequently, knowledge management has become a strategic priority, offering tools to balance business objectives and competitive advantages (Al-Kurdi et al., 2018). The primary challenge is developing knowledge management strategies, such as intra-organizational knowledge sharing, that address the dynamics of the organizational knowledge base (Nadason et al., 2017). To achieve this, organizations must foster a culture that encourages knowledge-sharing behaviors (Halisah et al., 2021; Fattahian et al., 2022). Knowledge is recognized as a factor of production alongside land, labor, and capital (Sasmita, 2023). Each individual possesses unique knowledge shared through learning and experience (Goswami, 2023). Knowledge sharing is a powerful tool for human development and social progress (Yasir, 2023) and is defined as the exchange of experiences and insights to gain a deeper understanding (Mapionaya, 2005). This process benefits both the sharer and the recipient (Adamseged, 2018). Knowledge management has evolved into a scientific discipline, considered a natural activity in academic institutions, particularly universities (Javaid, 2020; Hasirchi et al., 1399). Knowledge sharing is essential for institutional growth and societal development, enhancing organizations’ ability to respond to business opportunities (Hussain & Nasiura, 2011). In higher education, faculty members play a pivotal role in transferring knowledge to students and society (Adamseged, 2018). Given the constant evolution of knowledge due to technology and innovation, sharing knowledge among faculty is critical for its preservation and dissemination (Javaid, 2020).Motivating knowledge sharing is a global priority in knowledge management (Salehi & Alanbari, 2023). Organizational culture and work motivation are key to encouraging this behavior (Iman et al., 2023; Bawik et al., 2018). Research Question(s)This study aims to explore the motivations for intra-organizational knowledge sharing among faculty members of Payame Noor University, focusing on two questions:What is the process of creating motivation for faculty participation in knowledge sharing?What are the influencing and influenced conditions (causal, contextual, intervening, strategies, and outcomes) of knowledge-sharing motivation?2.Literature ReviewAttitudes toward knowledge sharing reflect individuals’ positive or negative feelings, which can be instrumental (useful or harmful) or affective (enjoyable or unpleasant) (Wu et al., 2023). Sharing knowledge may reduce individual power within an organization, posing a barrier to participation. Prior studies fall into two categories:Research addressing knowledge-sharing motivation.Studies exploring the role of knowledge sharing in universities and among faculty.Domestic studies, such as Goldasteh et al. (2022), found a significant relationship between organizational climate, attitudes, and knowledge sharing. Fattahian et al. (2022) confirmed a positive relationship between influencing factors and attitudes toward knowledge sharing. Hasirchi et al. (2020) identified components for extracting expert knowledge. International studies, such as Nguyen et al. (2022) and Yu & Meng (2021), emphasize the role of intrinsic motivation in enhancing knowledge sharing. However, no qualitative study has directly explored knowledge-sharing motivation in universities.3.MethodologyThis qualitative study adopted an interpretive approach with a developmental-applied goal, conducted cross-sectionally with an exploratory strategy. Data were collected through library studies and semi-structured interviews. The population consisted of Payam-e Noor University faculty in human resource management and organizational behavior, with associate or assistant professor ranks and over 5 years of experience, from 8 provinces. Purposive sampling continued until theoretical saturation (16 participants). Validity and reliability were ensured using acceptability (participant review) and confirmability (retest with over 80% similarity).Data analysis followed the grounded theory approach with open, axial, and selective coding (Corbin & Strauss, 2008). Data were categorized and developed into a theoretical model.4.ResultsDemographic Characteristics: 87.5% male, 12.5% female; 62.5% assistant professors, 37.5% associate professors.Open Coding: 120 conceptual codes were extracted. For example, organizational injustice leads to knowledge hoarding, while recognition and rewards enhance motivation.Axial Coding: Main categories included:Causal Conditions: Strategic alignment, managerial factors, individual factors, content creation.Contextual Conditions: Sharing environment, educational aspects, evaluation, technical issues, infrastructure, information issues.Intervening Conditions: Faculty personality factors, organizational culture instability.Strategies: Transformation, growth orientation, organizational context, organizational strategies.Outcomes: Organizational dynamism, social outcomes, structural development, learning organization.Paradigm Model: Designed based on Corbin and Strauss (2008). 5.DiscussionThe findings indicate that strategic alignment, managerial factors, individual factors, and content creation influence knowledge-sharing motivation, aligning with Nguyen et al. (2019) and Gaiodenko (2021). Social networks facilitate knowledge sharing. Contextual conditions, such as the sharing environment, educational aspects, and technical infrastructure, are essential for accelerating knowledge sharing (Alrehbi, 2022; Ade, 2021). Managers should promote activities like knowledge flow, participatory decision-making, and smart systems, though Payam-e Noor University’s geographic dispersion makes this process time-consuming and costly. Intervening conditions, such as faculty personality (e.g., extroversion or introversion) and organizational culture instability, are key barriers. Workplace health, faculty involvement, and bureaucracy impact motivation. Strategies like transformation, growth orientation, and strengthening organizational context (e.g., social education, decision-making involvement, infrastructure) enhance knowledge sharing. Outcomes include organizational dynamism, improved social interactions, structural development, and a learning organization, which elevate the university’s status and student recruitment.6.ConclusionPayam-e Noor University can enhance knowledge-sharing motivation through managerial support, rewards, teamwork, and human resource empowerment. Addressing challenges like personality factors and cultural instability removes barriers. Technology use and precise planning are essential to overcome geographic dispersion. Outcomes such as enhanced sharing culture, reduced social tensions, and a learning organization can improve the university’s quality. Recommendations include leveraging technology, providing technical and administrative support, planning for knowledge retention amid retirements, fostering interpersonal relationships, and replicating the study with other disciplines and universities. Knowledge sharing is a prerequisite for successful organizational systems, and by removing barriers and boosting motivation, effective knowledge management and organizational development can be achieved.
Research Paper
Information and Knowledge Economics
Ehsan Parvin; Mohsen Nazarzadeh Zare
Abstract
1.IntroductionUniversities play a pivotal role in advancing knowledge and fostering societal progress. However, their ability to align research with real-world challenges remains a critical global concern. In Iran, this issue is particularly pressing. Despite significant achievements in quantitative ...
Read More
1.IntroductionUniversities play a pivotal role in advancing knowledge and fostering societal progress. However, their ability to align research with real-world challenges remains a critical global concern. In Iran, this issue is particularly pressing. Despite significant achievements in quantitative scientific output, ranking 15th and 16th globally in Scopus and Web of Science in 2022, Iranian research often fails to address pressing national problems. This paradox highlights systemic inefficiencies, including institutional misalignment, fragmented innovation ecosystems, and policies prioritizing publication metrics over societal impact. National frameworks, such as Iran's Fifth Development Plan (2010), emphasize the need to direct research toward addressing societal needs, yet implementation gaps persist. This study examines the institutional, organizational, environmental, and individual barriers that prevent Iranian universities from translating academic research into practical solutions, aiming to propose reforms that bridge the gap between scholarly output and societal relevance. Based on this framework, the present study, considering the aforementioned challenges and issues in academic research and its qualitative development about society and its needs, seeks to answer the following key question:2.Literature ReviewThe disconnect between academic research and societal needs has been widely debated in global scholarship. Universities worldwide are increasingly encouraged to adopt a "third mission" focused on social responsibility, integrating community engagement and problem-solving into their core activities. Scholars such as Jones et al. (2021) argue that universities must evolve from isolated knowledge producers to active contributors to societal development, emphasizing collaborative frameworks that connect academia with industry and policymakers. In emerging economies, this challenge is exacerbated by structural barriers, including rigid funding models, bureaucratic inertia, and a lack of institutional incentives for applied research. In Iran, studies critique the predominance of "scientific formalism," where procedural rigor and publication metrics overshadow practical relevance. Researchers like Shafiei et al. (2020) highlight the disconnect between academic output and industry demands, attributing it to weak collaboration mechanisms and a cultural preference for theoretical over applied studies. Similarly, Ghoreishi et al. (2021) identify systemic flaws in promotion policies that reward quantity (e.g., article counts) over quality or societal impact, discouraging faculty from undertaking time-intensive, problem-driven projects. Farastkhah (2016) further critiques the erosion of universities’ core mission—science for societal advancement—due to bureaucratic pressures and misaligned institutional priorities. Globally, similar trends exist. For instance, Shaffer et al. (2018) note that academic systems prioritizing high-impact journals often neglect local problem-solving, while Makhatini et al. (2022) highlight demand-supply mismatches in innovation ecosystems. Existing literature, however, tends to focus on isolated challenges (e.g., funding or industry collaboration) rather than systemic analyses. This study addresses this gap by taking a holistic approach to examining multidimensional barriers that hinder impactful research in Iran.3.MethodologyIn the study, a qualitative research design by content analysis was used to investigate the challenges of academic research in solving Iran's issues. Semi-structured interviews were conducted with 16 Iranian expert academics who are senior faculty members, research directors, and policymakers. The participants were selected according to purposive sampling because of their experience in applied research. Data collection stopped when theoretical saturation had been achieved, with interviews taped and analyzed through Granheim and Lundman's (2004) inductive content analysis approach. This included the identification of meaningful units, coding for emerging themes, and categorizing results under institutional, organizational, environmental, and individual problems. Rigor was ensured through triangulation, member-checking, and peer-checking, with ethical considerations being the anonymizing of participant data and seeking informed consent.4.ResultsThe analysis of study data identified four interconnected forms of challenges:Institutional barriers include promotion systems that prioritize publication volume over societal impact and the absence of national accreditation frameworks to ensure research quality and relevance.Organizational challenges involve structural inefficiencies, such as faculty-student imbalances, non-performance-based funding models, and recruitment practices misaligned with national priorities.Environmental barriers highlight fragmented innovation ecosystems characterized by weak linkages between academia, industry, and policymakers, as well as low demand from the private sector for collaborative research.Individual challenges primarily consist of motivational gaps among faculty, driven by institutional incentives favoring easily publishable topics over complex, problem-driven projects.These factors collectively divert academic efforts from addressing societal needs, perpetuating a cycle of non-impactful research.5.DiscussionThe findings underscore the systemic misalignment between Iran’s academic ecosystem and societal needs. Institutional policies, such as promotion criteria favoring publication volume, mirror global "publish or perish" cultures but lack compensatory mechanisms for applied work. Organizational challenges, including overcrowded supervision roles and rigid funding structures, reflect broader governance issues observed in other contexts, such as India and Brazil. Environmental barriers, particularly weak industry-academia linkages, highlight the need for ecosystem-level reforms to foster demand-driven research. At the individual level, faculty motivations shaped by institutional incentives perpetuate the cycle of non-impactful output. Addressing these issues requires integrated reforms, including policy revisions to reward societal impact, funding models tied to performance, and capacity-building initiatives to enhance problem-solving skills.6.ConclusionThis study highlights the urgency of realigning Iran’s academic research ecosystem with national priorities. Key recommendations include revising promotion policies to incentivize applied research, establishing national accreditation systems, and fostering industry partnerships through targeted grants. By addressing institutional, organizational, environmental, and individual barriers, universities can transition from passive knowledge hubs to dynamic contributors to societal development. Future research should explore the socioeconomic impacts of these reforms and benchmark progress against international models, such as South Korea’s integrated innovation ecosystems.
Research Paper
Information and Knowledge Management
Seifallah Andayesh; Zahra Kianrad
Abstract
1.IntroductionIn today’s knowledge-driven economy, knowledge is considered a fundamental organizational asset, yet its value is only realized through effective sharing across individuals and teams. Particularly in knowledge-based organizations such as academic libraries, knowledge sharing enhances ...
Read More
1.IntroductionIn today’s knowledge-driven economy, knowledge is considered a fundamental organizational asset, yet its value is only realized through effective sharing across individuals and teams. Particularly in knowledge-based organizations such as academic libraries, knowledge sharing enhances innovation, task performance, and employee satisfaction. As Artificial Intelligence (AI) technologies continue to evolve—especially with the rise of generative tools like ChatGPT, they are increasingly integrated into library workflows, transforming how information is managed, disseminated, and utilized. This study explores the relationship between AI capabilities and employee performance, with a focus on the mediating role of knowledge sharing among staff in medical science libraries under the Ministry of Health and Medical Education in Tehran. AI offers powerful tools for automating routine tasks, facilitating content creation, and improving decision-making processes, which in turn may foster greater knowledge exchange among employees. By examining these dynamics, the research aims to shed light on how AI-driven knowledge sharing can contribute to enhanced service quality and organizational effectiveness in academic library environments.Research Question(s)Artificial intelligence has a positive and significant relationship with the performance of librarians.Artificial intelligence has a positive and significant relationship with knowledge sharing among academic librarians.Knowledge sharing has a positive and significant relationship with the performance of librarians.Knowledge sharing plays a mediating role in the relationship between artificial intelligence and performance.2.Literature ReviewPrevious studies have highlighted the significant impact of Artificial Intelligence (AI) on organizational performance and knowledge management. Abayomi et al. (2021) found that AI enhances the job performance of academic librarians. Malik et al. (2021) showed that AI-driven systems promote knowledge sharing, improving job satisfaction and reducing turnover. Nguyen and Malik (2022) demonstrated that knowledge sharing enhances service quality, moderated by AI system quality. Hussain (2023) identified barriers to AI adoption in libraries, including budget constraints and technical skills. Barsha and Munshi (2024) emphasized AI's potential in improving library services in developing countries. Femi Olan et al. (2024) argued that AI combined with knowledge sharing boosts employee performance and creativity, while Wirawan et al. (2024) confirmed AI's role in talent management. Despite these findings, further research is needed to explore AI's impact in medical library settings, which this study addresses by examining the relationship between AI capabilities, librarian performance, and knowledge sharing in Tehran’s medical science libraries.3.MethodologyThis applied study employed a descriptive-correlational design using a quantitative approach to examine the relationship between artificial intelligence and employee performance, with knowledge sharing as a mediating variable. The statistical population included managers, administrative staff, and heads of medical science libraries affiliated with the Ministry of Health in Tehran. Due to the limited population size, a census sampling method was used, resulting in 176 participants. Standardized questionnaires were utilized to collect data: a 22-item AI questionnaire by Chen et al. (2022), a 12-item knowledge sharing scale by Damaj et al. (2016), and a 12-item employee performance scale by Stephen (2005). Responses were measured using a five-point Likert scale. Convergent and discriminant validity were applied to confirm the instrument's validity. Structural equation modeling (SEM) was conducted using Smart PLS software to test the conceptual model and hypotheses.4.ResultsThe results of the structural equation modeling (SEM) confirm the significance of the mediating role of knowledge sharing in the relationship between artificial intelligence (AI) and employee performance. Specifically, the Z-values for all tested hypotheses exceeded the critical value of 1.96, indicating statistical significance at the 95% confidence level (P ≤ 0.05). The Variance Accounted For (VAF) value was calculated to be 0.849, which exceeds the 80% threshold, suggesting full mediation. Further analysis showed that knowledge sharing had a significant and positive effect on employee performance (β = 0.766, t = 12.104, P ≤ 0.05), implying that a one-unit increase in knowledge sharing corresponds to a 0.766 standard deviation increase in employee performance. Similarly, AI had a significant direct impact on employee performance (β = 0.739, t = 9.986, P ≤ 0.05), indicating a strong positive relationship between these variables. Additionally, AI significantly influenced knowledge sharing (β = 0.642, t = 8.572, P ≤ 0.05), suggesting that the adoption and integration of AI technologies foster a more effective knowledge-sharing culture within organizations. Overall, the findings provide robust empirical evidence supporting the theoretical model and affirm that knowledge sharing fully mediates the relationship between AI and employee performance. These results highlight the importance of fostering a knowledge-sharing environment to fully leverage the potential benefits of AI in enhancing organizational effectiveness, particularly in knowledge-intensive sectors such as academic and medical libraries.5.ConclusionThe purpose of this study was to examine the relationship between artificial intelligence (AI) and employee performance, with knowledge sharing playing a mediating role, among managers, administrative staff, and library directors of medical sciences libraries affiliated with the Ministry of Health, Treatment, and Medical Education in Tehran. The study aimed to address a research gap by providing valuable insights into the impact of AI on employee performance in this important sector. The results showed that AI has a positive and significant impact on the performance of employees in medical sciences libraries. This finding is consistent with previous research, which emphasized that AI and machine learning enhance organizational performance by eliminating human productivity limitations and reducing repetitive tasks. The study also found that effective use of AI algorithms in big data analysis improves employee retention and attracts new users. Additionally, AI accelerates daily library tasks, reducing time and cost, while enhancing librarian productivity and improving job performance by reducing human errors. Furthermore, the study demonstrated that knowledge sharing has a positive and significant impact on employee performance, with a strong emphasis on how fostering a culture of knowledge sharing can promote innovation, teamwork, and organizational efficiency. The research also revealed that AI positively impacts knowledge sharing among employees by providing digital platforms and advanced tools, facilitating data analysis, and enabling quick access to valuable resources. The findings indicated that knowledge sharing mediates the relationship between AI and employee performance, confirming that a combination of AI and knowledge sharing strategies provides a more sustainable approach to improving organizational performance. The study suggests that medical sciences libraries should invest in AI technologies, provide specialized training, and create a collaborative culture to enhance employee performance and facilitate knowledge sharing, ultimately leading to improved organizational efficiency and productivity.AcknowledgmentsThe author expresses sincere gratitude to all those who contributed to this research.
Research Paper
Knowledge Management
Zeinab Mondalizadeh; Sadaf Nagsh Javahei
Abstract
1.IntroductionAbsorptive capacity is a knowledge creation and knowledge utilization dynamic asset that enhances the potential of an organization to enhance performance. Enhancing human capital in this regard can raise absorptive capacity. Considering the significance of these three variables in sport ...
Read More
1.IntroductionAbsorptive capacity is a knowledge creation and knowledge utilization dynamic asset that enhances the potential of an organization to enhance performance. Enhancing human capital in this regard can raise absorptive capacity. Considering the significance of these three variables in sport organizations, the primary objective of the study is to examine the role of purposeful organizational forgetting on the capacity for absorptive capacity with the mediating effect of human capital in Tehran province sport and youth departments.Research HypothesisPurposeful organizational forgetting affects human capital.Human capital affects the capacity for knowledge absorption.Purposeful organizational forgetting affects the capacity for knowledge absorption.Purposeful organizational forgetting, with the mediating role of human capital, affects the capacity for knowledge absorption.2.Literature ReviewThe study validates that in organizational sport literature, there is no analysis of the phenomenon of intentional organizational forgetting and absorptive capacity in the context of human capital's role. Absorptive capacity is a dynamic capacity in terms of the acquisition and utilization of knowledge that enhances the organization's ability for improved performance. By doing so, it would appear that enhancing human capital can enhance the ability to absorb knowledge, as well as the removal of outdated knowledge, old scientific concepts and attitudes, which can assist in developing incremental innovations in sporting organizations and organizational learning, and therefore to enhance the ability to absorb knowledge. Alternatively, however, it appears that relinquishing old knowledge, with consideration for new technology and technologies, can influence the ability to intake knowledge and implement knowledge via human capital. Since human capital and its function have been studied in intentional organizational forgetting research such as Yeh et al. (2020), Hosseini et al. (2019), and Mohammad Esmaeil and Seyed Vakili (2018), and the significance of human capital has been investigated. Conversely, organizational forgetting by intention and its impact on the capacity for knowledge absorption have been explained in Ghiasi et al.'s (2015) and Doostar et al.'s (2015) studies.3.MethodologyThe given study followed a descriptive-analytical applied research approach. The researchers' statistical population included all employees of the Ministry of Sports and Youth. Deciding the minimum number of samples necessary for collecting data in reference to structural equation modeling is critical; however, a minimum of 200 samples is acceptable. In the context of confirmatory factor analysis, the minimum sample size is based on factors and not on variables. For structural equation modeling, it requires about 20 samples per factor (latent variable). Based on that, taking into consideration the dropout rate, 300 questionnaires were distributed, of which 213 were ultimately usable and uploaded to the software. Questionnaire distribution was done face-to-face. The tool formulated by Manbiwa et al. (2003) was utilized to measure the knowledge absorption capacity dimension, which had 10 items. While the questionnaire established by Meshbaki et al. (2013) was used for measuring the purposeful organizational forgetting dimension, which had 5 items, and the questionnaire developed by Pike et al. (2002) was used for measuring the human capital dimension, which included 10 items.4.ResultsThe results of the study showed that purposeful organizational forgetting has a positive and significant effect on human capital and knowledge absorption capacity, and human capital has a significant effect on knowledge absorption capacity. In addition, purposeful organizational forgetting has a significant effect on knowledge absorption capacity with the mediating role of human capital. Knowledge absorption is influenced by getting rid of useless organizational knowledge and creating change and transformation by increasing the individual abilities and skills of employees. Considering the model indicators, it can be concluded that the model is in a suitable condition in terms of indicators. In fact, the level of these indicators (adaptive fit, goodness of fit index, incremental fit index, non-soft fit index and normalized fit index) should be higher than 0.9 for the model to have a good fit, which was evaluated as appropriate.5.DiscussionPurposeful organizational forgetting has a positive and significant effect on human capital. In addition, purposeful organizational forgetting has a positive and significant effect on the capacity to absorb knowledge. Human capital also has a positive and significant effect on the level of the organization's knowledge absorption capacity. Another finding of the research was the mediating role of human capital in the effect of purposeful organizational forgetting on the capacity to absorb knowledge. In other words, when organizational forgetting is purposeful and new knowledge is applied and old knowledge is discarded, the level of human capital is improved, and by improving the level of human capital, including knowledge, abilities, skills, and mindset of employees, the organizational absorption capacity is improved.6.ConclusionKnowledge absorption is influenced by getting rid of useless organizational knowledge and creating change and transformation by increasing the individual abilities and skills of employees. In fact, in order to absorb new knowledge and apply organizational knowledge, it is necessary to make changes and transformations in the organization, which can happen with the help of employee empowerment. As a result, organizations need to move towards new knowledge due to facing changing environments and, in other words, take the lead in applying new knowledge, which will be driven by the human capital of each organization. Sports organizations need knowledge dynamics due to environmental changes.
Research Paper
Information and Knowledge Management
Omid Alipour; Faramarz Soheili; Soraya Ziaei; Ali Akbar Khasseh
Abstract
1.IntroductionKnowledge organization is an old but fundamental topic in library and information science. The importance of knowledge organization is not limited to the fact that knowledge must be organized; otherwise, it cannot be retrieved and reused. Knowledge organization must be updated regularly ...
Read More
1.IntroductionKnowledge organization is an old but fundamental topic in library and information science. The importance of knowledge organization is not limited to the fact that knowledge must be organized; otherwise, it cannot be retrieved and reused. Knowledge organization must be updated regularly to reflect the progress of human knowledge; therefore, changes are inevitable. Hence, it can be said that knowledge organization has become more important, diverse, and widespread in today's world. Published research often describes only specific aspects, which only provides a partial picture of the landscape of knowledge organization research. In fact, it is difficult for researchers to gain a comprehensive view of the field by reviewing such articles. This is possible by using scientometric tools. To promote the progress of scientific research and its publication, collaborations between researchers should be examined. Co-authorship, as one of the most formal manifestations of scientific collaboration, is an activity in which two or more authors participate in the production of science together. The expansion, specialization, and complexity of science in all fields have made it impossible for researchers to master not only all sciences, but also all topics in their field of expertise. Given this on the one hand, and the growth of multidisciplinary and interdisciplinary sciences and research on the other, researchers are forced to collaborate with other people. This has led to co-authorship and its growth and expansion. By analyzing co-authorship networks, the social characteristics of the knowledge structure at different levels such as individuals, organizations, sectors, and countries can be revealed. It is expected that the results of this study will help experts in this field to understand the structure of the scientific collaboration community and identify its active authors. This will allow them to quickly decide on emerging issues, trends, and key scientists. The results of this study will also play an important role in future policy-making in knowledge organization and will provide better insight into the authors and co-authorship network in this field. In light of the above, this paper examines the co-authorship structure of Persian knowledge organization articles using centrality indices.Literature ReviewAs mentioned, scientometrics can provide a clear view of the changes in the field under study and can be of great help in this regard. Various studies have been published on various topics with the focus of examining co-authorship networks. In this section, due to the large number of published studies, a list of some of them will be presented. The first group examines national or regional co-authorship collaborations. For example, Hong and Hwang (2017) examined co-authorship networks of faculty and students in humanities and social sciences journals in South Korea. In the second category, researchers have examined the extent of scientific collaboration between universities, centers, and institutions at the national and international levels. Fujita et al. (2018) also examined co-authorship networks in physics and biology in organizational research. The third group of co-authorship studies is related to scientific collaborations in a specific subject area. In this type of research, the extent of national and international collaboration of researchers in a specific subject area or discipline at the national and international levels has been examined. Gonzalez-Valente, Santos, and Arencibia (2019) also examined the social structure of co-authorship in knowledge management in their study. Fan, Li, and Lu (2020) also examined co-authorship networks in the tourism industry. The fourth group of co-authorship studies examines the extent of scientific collaborations within a specific journal or journals. Kim et al. (2017) examined the co-authorship network of articles in the Journal of the Korean Academy of Child and Adolescent Psychiatry, and Zheng et al. (2017) examined the co-authorship network of the Annals of the American Geographical Society.MethodologyThis research is an applied type that has been done with the approach of scientometrics and analysis of social networks. The research data were selected from 106 keywords of his knowledge organization in the title field, which were selected after consultation with subject matter experts in this field; Together with all the articles published in selected journals of information science and epistemology indexed in the Islamic World Science Citation Center from 1378 to 1398. Finally, the retrieved records were limited to research papers, conference papers, and review papers and limited to the subject of the Library and Information field. Out of 1482 authors who were involved in the publication of 1410 articles, 168 with at least 4 articles were analyzed using UCINET software. After that, a square matrix of dimensions 168 by 168 was formed, and finally, the co-authorship network was drawn based on the centrality indicators. Bib Excel software was used to draw the matrix, and NetDraw software was used to draw the co-authorship network.ResultsResults indicated that the average number of authors per article is 1.05. Analysis of data related to co-authorship analysis indicates the two-author approach as the most common approach in knowledge organization (35.17%), and the three-author approach (26.80%) is in the next rank. Dr. Rahmatollah Fattahi (56 articles), Dr. Morteza Kokabi (44 articles) and Dr. Yaghoub Norouzi (39 articles) have the highest number of articles in knowledge organization, respectively. Also, the co-authorship of Dr. Rahmatollah Fattahi and Dr. Mehri Parirokh (10 articles) won the top rank. In the study of centrality indicators, Dr. Rahmatollah Fattahi gained the top scores in the Degree Centrality, Closeness Centrality, Betweenness Centrality, and Eigen vector, respectively.DiscussionResults indicated that the average number of authors per article is 1.05. Analysis of data related to co-authorship analysis indicates the two-author approach as the most common approach in knowledge organization (35.17%), and the three-author approach (26.80%) is in the next rank. Dr. Rahmatollah Fattahi (56 articles), Dr. Morteza Kokabi (44 articles), and Dr. Yaghoub Norouzi (39 articles) have the highest number of articles in knowledge organization, respectively. Also, the co-authorship of Dr. Rahmatollah Fattahi and Dr. Mehri Parirokh (10 articles) won the top rank. In the study of centrality indicators, Dr. Rahmatollah Fattahi gained the top scores in the Degree Centrality, Closeness Centrality, Betweenness Centrality, and Eigen vector, respectively.ConclusionAccording to the results of the present study, it seems that prominent researchers in the field of knowledge organization, despite being productive, have not been able to play a significant role in the formation of the co-authorship network in this field.
Research Paper
Semantic Web and Ontology
Mohammad Hassan Azimi; Samira Esmaeili
Abstract
The purpose of the current research is to draw and analyze the intellectual structure and evolution of knowledge in the field of RDF with the method of co-occurrence analysis of words and clustering of concepts and events in this field. This is an applied research that was carried out with a scientometric ...
Read More
The purpose of the current research is to draw and analyze the intellectual structure and evolution of knowledge in the field of RDF with the method of co-occurrence analysis of words and clustering of concepts and events in this field. This is an applied research that was carried out with a scientometric approach. The statistical population of this research includes all the researches conducted in the field of RDF in the Web of Science database from 1998-2021. Also, the data collection tool in this research is note-taking and the data analysis tool is co-occurrence analysis of words and network analysis using Vosviewer, Netdrow, SPSS and Bibexecl software. The findings of the research showed that the keywords RDF, Semantic web, Ontology, linked data and SPARQL are the most frequent words and the keywords RDF* semantic web, RDF* Academic Ontology and RDF* SPARQL are the most frequent word pairs. Also, the co-occurrence analysis of words network includes six clusters named "data model scalability", "RDF representation of bibliographic entities and relations", "ontology alignment", "semantic web and linked data", "data management and publishing" and "data mining". In addition, the network density is equal to 0.068, which is not in a favorable condition. The clusters of "data model scalability", "ontology alignment", "data management and publishing" and "data mining" have not yet reached sufficient maturity and require a lot of follow-up and research in these fields. The results showed that the scientific productions of the RDF field, despite its upward publication trend, have more subject dispersion and are more oriented towards the semantic web, and the analysis of the co-occurrence network of words in this field also has a greater subject dispersion, which indicates the interest of researchers to various topics in this area.1.IntroductionThe abundance of publications in the field of Resource Description Framework (RDF) presents a challenge for researchers seeking a comprehensive understanding of the domain. RDF, a graph-based data model crucial to the Semantic Web, enables machine-readable data representation and interoperability across systems. The growing volume of RDF-related literature highlights the need for a structured analysis to identify key concepts, trends, and thematic evolution in this interdisciplinary field. Therefore, creating a scientific map of articles in the RDF field using the thesaurus method and presenting a strategic diagram will enhance awareness of published research status, illustrate topic relationships, identify influential topics, mature, emerging, and underdeveloped topics, thematic gaps, and establish sound scientific policies in the field. This study aims to map the intellectual structure and track the knowledge evolution in the RDF domain using scientometric approaches.Research Question(s)What has been the trend in scientific publications within the field of RDF from 1998 to 2021 in the Web of Science database?How the frequency distribution of the most is commonly used keywords in RDF-related articles from 1900 to 2021?What does the co-word network in the RDF domain look like during the period 1900 to 2021?How are the co-word clusters in the RDF domain structured, and what are the thematic topics within each cluster from 1900 to 2021?To what extent have the co-word clusters in the RDF domain matured over the period from 1900 to 20212.Literature ReviewPrevious studies have utilized co-word analysis in various domains such as digital libraries, military trauma, COVID-19, and knowledge management to reveal thematic structures and developmental trajectories. However, there is a gap in applying this approach specifically within the RDF domain. Studies by Alipour-Hafezi et al. (2017), Rezaeizadeh & KaramAli (2018), and Jin & Li (2019) demonstrate the effectiveness of scientometric techniques in visualizing knowledge structures, identifying research gaps, and tracing emergent topics. This study builds upon these methodological foundations to comprehensively explore the RDF field.3.MethodologyThis applied research employs a scientometric methodology grounded in co-word analysis. The dataset includes 1,271 scholarly articles published between 1998 and 2021 and indexed in the Web of Science database. Tools such as VOSviewer, Netdraw, SPSS, BibExcel, and UCINET were used to conduct word co-occurrence analysis, hierarchical clustering, and strategic diagramming. The analytical process involved keyword standardization, matrix generation, network visualization, and calculation of centrality and density indices for identified clusters.4.ResultsThe research findings reveal that keywords such as RDF, Semantic Web, Ontology, Linked Data, and SPARQL are the most frequent, while word pairs like RDF* Semantic Web, RDF* Academic Ontology, and RDF* SPARQL are common. The co-occurrence analysis of the word network reveals six clusters named "data model scalability", "RDF representation of bibliographic entities and relations", "ontology alignment", "semantic web and linked data", "data management and publishing", and "data mining". The network density is 0.068, indicating a less favorable condition. Clusters like "data model scalability", "ontology alignment", "data management and publishing", and "data mining" are not yet mature and require further research.5.DiscussionThe findings suggest that while the RDF domain has seen an increase in publication volume, it still faces thematic fragmentation and limited interdisciplinary integration. High centrality in certain clusters indicates dominance, but low-density values suggest underdeveloped interrelations among concepts. This highlights the need for broader collaboration and diversification of research topics within RDF. The prevalence of semantic web topics reflects current research interests, while emerging areas like data scalability and ontology alignment require more attention.6.ConclusionThis study offers a detailed intellectual mapping of the RDF field, highlighting dominant themes and emerging areas for further exploration. The low network density and dispersed thematic structure emphasize the need for increased interdisciplinary collaboration. Policymakers and researchers are encouraged to support studies in underdeveloped RDF subdomains to promote comprehensive scientific growth. The strategic insights provided by this analysis can guide future research priorities and contribute to the development of a cohesive knowledge structure within the RDF domain.
Research Paper
Knowledge Management
Laleh Foroutan Rad; Esmat Momeni
Abstract
Knowledge management has gained a good place among various sciences. Today, successful organizations optimize their human resources by using knowledge management. Universities and libraries are considered to be the main centers of knowledge production and storage, therefore the aim of the present study ...
Read More
Knowledge management has gained a good place among various sciences. Today, successful organizations optimize their human resources by using knowledge management. Universities and libraries are considered to be the main centers of knowledge production and storage, therefore the aim of the present study was to review the concept of knowledge management in academic communities and libraries. This study was conducted by reviewing domestic and foreign sources and articles with the main topic of knowledge management in educational and library environments. This study was conducted by reviewing domestic and foreign sources and articles with the main topic of knowledge management in educational and library environments. Search engines on the Internet, scientific sites, and databases were also used. The time range for selecting articles was 1384-1404. The results of studies conducted by researchers in various dimensions of knowledge management show the positive and significant impact of applying knowledge management components with important variables in universities and libraries. In these studies, in some cases, more favorable results have been reported, and in some, poor results have been reported, but in general, all of them agree on the important and key role in using the knowledge management process. Academic libraries, as knowledge production and storage organizations, must pay attention to the knowledge management process and provide the necessary framework for its implementation.1.IntroductionKnowledge management has strengthened its position as one of the achievements of the knowledge and information age, with the entry into the area of various sciences. Nowadays, successful organizations, with the acquisition of the necessary tools with the knowledge management approach, create the right opportunities for optimizing their human and organizational resources. Universities and libraries are considered the main centers of knowledge production and storage. Therefore, the purpose of this study was to review the concept of knowledge management in academic societies and libraries.2.Literature ReviewThe results of the background and research conducted on knowledge management, which has been conducted in various dimensions by many researchers, indicate the positive and significant impact of applying knowledge management components with important variables in various organizations. Some researchers have studied the components of knowledge management and its implementation from the perspective of faculty members and in universities and higher education centers (Siahsarani Kajouri and Cheraghali, 1403). Some have also searched for this issue among organizations (Taqvi and Mohajerani, 1402; Jabbari Dastjerd et al., 1402; Ahmadvand et al., 1402; Koivisto and Typalos, 2025; Wu and Cho, 2024), and some researchers have designed a knowledge management model (Ahmadvand et al., 1401). In these studies, some aspects have been reported as more desirable and in some cases weaker, but in general, in all of them, there is a consensus on the important and key role of using the knowledge management process and the importance of addressing this issue.3.MethodologyThis study was conducted by reviewing the sources and internal and external articles with the main subject of knowledge management in educational and library settings. Also, a search was conducted on the Internet, as well as scientific sites and databases. The range of articles selected was from 2006-2025.4.ResultsThe results of studies conducted by researchers in different aspects of knowledge management show a positive and significant effect on the usage of knowledge management components with important variables in universities and libraries. In these studies, the results are more favorable and some are reported to be weak, but in general, all of them consensus on the important role of using the knowledge management process.5.DiscussionThe main goal of knowledge management is to create and organize an environment in which people develop their knowledge, exchange it with each other, combine the knowledge of others with their own knowledge, and ultimately apply it. The application of knowledge, in turn, will lead to innovation in the organization. This is why it can be said that knowledge management is known as the main source and reference of innovation and is considered one of the basic requirements of the innovation process in the organization and can be called the key to the success of organizations. Therefore, it is appropriate for universities, and especially university libraries, as the main centers and organizations for knowledge production and management, to pay more attention to this issue. Certainly, in the next few years, the category of knowledge will become an inseparable part of all organizational complexes in the public and private sectors, and organizations that provide the necessary infrastructure for its implementation and design the appropriate framework will be successful in this field. In this direction, we can take a model from the experiences of advanced countries.6.ConclusionUniversities, and especially university libraries, as the main centers and knowledge production and storage organizations, should pay more attention to the knowledge management process and provide the necessary infrastructures to implement it in this regard.
