Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 8 3 2025 09 23 The role of Artificial Intelligence in Promoting The Knowledge Of Positive Human Resource Management: A Causal Model The role of Artificial Intelligence in Promoting The Knowledge Of Positive Human Resource Management: A Causal Model 11 35 210274 10.47176/smok.2025.1891 FA Minasadat Mousavi Doctoral student, Faculty of Economics, Management and Administrative Sciences, Semnan University, Iran 0000-0001-5079-2286 Abbas Ali Rastgar Professor, Faculty of Economics, Management and Administrative Sciences, Semnan University, Iran 0000-0001-6834-7682 Mohsen Shafiei Nikabadi Professor Faculty of Economics management and administrative sciences, Semnan University, Semnan, Iran 0000-0002-9744-960X Journal Article <strong>Purpose:</strong> The primary aim of this study is to explore the role of artificial intelligence (AI) in enhancing human resource management practices and strengthening knowledge management in this domain to improve organizational outcomes and employee well-being. In recent years, AI has attracted significant attention due to its clear potential to revolutionize business operations in organizations. In human resource management, the integration of AI can enhance decision-making processes, increase efficiency, and promote fairness in talent management. This study also examines how AI interacts with positive human resource management practices, particularly how this technology can guide human resource processes in a personalized, effective, and supportive manner to help employees thrive and optimize knowledge management in organizations. Moreover, this research aims to provide perspectives and a conceptual framework for integrating AI into human resource practices that benefit both organizations and employees, while facilitating knowledge management in organizations. While the impact of AI on operational efficiency and productivity is well-documented, this study also emphasizes the importance of employee well-being in achieving sustainable success and how it can be improved through positive human resource knowledge management practices. In fact, this study investigates how AI can not only increase productivity but also enhance employee experience and organizational culture. The findings aim to help develop human resource systems that are not only efficient but also human-centered, creating an environment where employees can flourish both personally and professionally. Focusing on the dual goals of organizational success and employee flourishing, this study provides practical recommendations for human resource professionals and organizational leaders interested in leveraging AI to create positive and productive work environments.<br /><strong>Methodology:</strong> This study employs a mixed-methods approach, incorporating both qualitative and quantitative research methods to comprehensively analyze the role of AI in human resource management. The first phase of the research involved an extensive literature review on AI in human resources, organizational psychology, and employee well-being using text mining techniques and web-based tools like Viante and RapidMiner software. The literature review helped identify key factors influencing the successful implementation of AI in human resource practices, such as transparency, fairness, and personalized employee experiences. The second phase included the use of fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) and fuzzy cognitive mapping methods, which are widely used in research analyzing complex relationships between factors. The fuzzy DEMATEL method was used to identify key factors and barriers to the successful adoption of AI in positive human resource management, while fuzzy cognitive mapping was employed to model and visualize the causal relationships between various factors affecting the adoption of AI in positive human resource knowledge management. These methods are specifically designed to analyze and assess the interdependencies and complex relationships among factors. This research involved a case study from a leading organization in human resources to deeply examine how AI is integrated into human resource practices and its impact on organizational outcomes, employee flourishing, and well-being. This combination of methods allowed the researchers to comprehensively examine fuzzy relationships and averages, providing a practical model for AI application in human resources.<br /><strong>Findings: </strong>This research used fuzzy cognitive mapping and fuzzy DEMATEL methods to analyze the application of AI in improving human resource processes and its impact on employee well-being. Initially, 188 final articles were reviewed to extract key indicators. After converting the article content into text format and removing irrelevant words, Viante was used to extract the most frequently mentioned terms. These terms were manually filtered and clustered, leading to the selection of 22 key indicators for the conceptual model. Next, RapidMiner software was used to extract key concepts, and with expert opinions and data refinement, these concepts were transformed into the final conceptual model. Then, using the fuzzy DEMATEL method, causal relationships between the indicators were identified, and the relationships between the factors were analyzed based on their influence and impact. Finally, fuzzy cognitive mapping was used to create a visual network of the indicators and their relationships. The findings showed that among the key indicators, strategic human resource modeling plays a crucial role in strengthening human resource processes and enhancing employee flourishing. Furthermore, the use of AI in performance evaluation, workforce needs prediction, and career development processes significantly improved organizational performance and employee job satisfaction.<br />Research limitations/implications: Despite its valuable insights, this study has several limitations. The research is based on a single case study, which may limit the generalizability of the findings to other industries or organizations. Additionally, while the study highlights the potential benefits of AI in HRM, it does not fully explore the long-term implications of AI adoption on employee well-being and organizational performance. Further research is needed to examine the long-term effects of AI on employee satisfaction, retention, and overall organizational culture. Moreover, the study primarily focuses on the technical and operational aspects of AI adoption, while the ethical implications of AI in HRM are only briefly touched upon. As AI continues to evolve, it is crucial to explore the ethical considerations of using AI in human resource practices, such as data privacy, bias, and accountability.<br /><strong>Practical implications:</strong> The findings of this study have several practical implications for HR professionals and organizational leaders. First, organizations should prioritize transparency and fairness when implementing AI-powered HR systems. Ensuring that AI systems are transparent and explainable can help build trust among employees and mitigate concerns about bias or discrimination. Second, organizations should invest in training programs to ensure that HR professionals and employees understand how AI works and how it can be used to augment HR practices. Furthermore, the study emphasizes the importance of a holistic approach to AI adoption in HRM. Organizations should not only focus on the technological aspects of AI but also consider the organizational culture and employee experience. By integrating AI into PHR practices in a way that supports employee well-being and promotes a positive work environment, organizations can create a culture of growth, engagement, and innovation.<br /><strong>Originality/value:</strong> This research makes a significant contribution to the field of HRM by providing a novel framework for integrating AI into PHR practices with a focus on employee well-being. While much of the existing research on AI in HRM focuses on operational efficiency and productivity, this study highlights the importance of AI in creating a supportive and positive work environment. The findings contribute to the growing body of knowledge on the intersection of technology and human resource management, offering a new perspective on how AI can be used to promote both organizational success and employee flourishing. <strong>Purpose:</strong> The primary aim of this study is to explore the role of artificial intelligence (AI) in enhancing human resource management practices and strengthening knowledge management in this domain to improve organizational outcomes and employee well-being. In recent years, AI has attracted significant attention due to its clear potential to revolutionize business operations in organizations. In human resource management, the integration of AI can enhance decision-making processes, increase efficiency, and promote fairness in talent management. This study also examines how AI interacts with positive human resource management practices, particularly how this technology can guide human resource processes in a personalized, effective, and supportive manner to help employees thrive and optimize knowledge management in organizations. Moreover, this research aims to provide perspectives and a conceptual framework for integrating AI into human resource practices that benefit both organizations and employees, while facilitating knowledge management in organizations. While the impact of AI on operational efficiency and productivity is well-documented, this study also emphasizes the importance of employee well-being in achieving sustainable success and how it can be improved through positive human resource knowledge management practices. In fact, this study investigates how AI can not only increase productivity but also enhance employee experience and organizational culture. The findings aim to help develop human resource systems that are not only efficient but also human-centered, creating an environment where employees can flourish both personally and professionally. Focusing on the dual goals of organizational success and employee flourishing, this study provides practical recommendations for human resource professionals and organizational leaders interested in leveraging AI to create positive and productive work environments.<br /><strong>Methodology:</strong> This study employs a mixed-methods approach, incorporating both qualitative and quantitative research methods to comprehensively analyze the role of AI in human resource management. The first phase of the research involved an extensive literature review on AI in human resources, organizational psychology, and employee well-being using text mining techniques and web-based tools like Viante and RapidMiner software. The literature review helped identify key factors influencing the successful implementation of AI in human resource practices, such as transparency, fairness, and personalized employee experiences. The second phase included the use of fuzzy DEMATEL (Decision-Making Trial and Evaluation Laboratory) and fuzzy cognitive mapping methods, which are widely used in research analyzing complex relationships between factors. The fuzzy DEMATEL method was used to identify key factors and barriers to the successful adoption of AI in positive human resource management, while fuzzy cognitive mapping was employed to model and visualize the causal relationships between various factors affecting the adoption of AI in positive human resource knowledge management. These methods are specifically designed to analyze and assess the interdependencies and complex relationships among factors. This research involved a case study from a leading organization in human resources to deeply examine how AI is integrated into human resource practices and its impact on organizational outcomes, employee flourishing, and well-being. This combination of methods allowed the researchers to comprehensively examine fuzzy relationships and averages, providing a practical model for AI application in human resources.<br /><strong>Findings: </strong>This research used fuzzy cognitive mapping and fuzzy DEMATEL methods to analyze the application of AI in improving human resource processes and its impact on employee well-being. Initially, 188 final articles were reviewed to extract key indicators. After converting the article content into text format and removing irrelevant words, Viante was used to extract the most frequently mentioned terms. These terms were manually filtered and clustered, leading to the selection of 22 key indicators for the conceptual model. Next, RapidMiner software was used to extract key concepts, and with expert opinions and data refinement, these concepts were transformed into the final conceptual model. Then, using the fuzzy DEMATEL method, causal relationships between the indicators were identified, and the relationships between the factors were analyzed based on their influence and impact. Finally, fuzzy cognitive mapping was used to create a visual network of the indicators and their relationships. The findings showed that among the key indicators, strategic human resource modeling plays a crucial role in strengthening human resource processes and enhancing employee flourishing. Furthermore, the use of AI in performance evaluation, workforce needs prediction, and career development processes significantly improved organizational performance and employee job satisfaction.<br />Research limitations/implications: Despite its valuable insights, this study has several limitations. The research is based on a single case study, which may limit the generalizability of the findings to other industries or organizations. Additionally, while the study highlights the potential benefits of AI in HRM, it does not fully explore the long-term implications of AI adoption on employee well-being and organizational performance. Further research is needed to examine the long-term effects of AI on employee satisfaction, retention, and overall organizational culture. Moreover, the study primarily focuses on the technical and operational aspects of AI adoption, while the ethical implications of AI in HRM are only briefly touched upon. As AI continues to evolve, it is crucial to explore the ethical considerations of using AI in human resource practices, such as data privacy, bias, and accountability.<br /><strong>Practical implications:</strong> The findings of this study have several practical implications for HR professionals and organizational leaders. First, organizations should prioritize transparency and fairness when implementing AI-powered HR systems. Ensuring that AI systems are transparent and explainable can help build trust among employees and mitigate concerns about bias or discrimination. Second, organizations should invest in training programs to ensure that HR professionals and employees understand how AI works and how it can be used to augment HR practices. Furthermore, the study emphasizes the importance of a holistic approach to AI adoption in HRM. Organizations should not only focus on the technological aspects of AI but also consider the organizational culture and employee experience. By integrating AI into PHR practices in a way that supports employee well-being and promotes a positive work environment, organizations can create a culture of growth, engagement, and innovation.<br /><strong>Originality/value:</strong> This research makes a significant contribution to the field of HRM by providing a novel framework for integrating AI into PHR practices with a focus on employee well-being. While much of the existing research on AI in HRM focuses on operational efficiency and productivity, this study highlights the importance of AI in creating a supportive and positive work environment. The findings contribute to the growing body of knowledge on the intersection of technology and human resource management, offering a new perspective on how AI can be used to promote both organizational success and employee flourishing. https://jkm.ihu.ac.ir/article_210274_680266b553ce2f65284f1d85564bdd60.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 8 3 2025 09 23 Integrating Knowledge Management And Artificial Intelligence To Improve Human Resource Performance (Case Study: Government Offices Of Markazi Province) Integrating Knowledge Management And Artificial Intelligence To Improve Human Resource Performance (Case Study: Government Offices Of Markazi Province) 36 61 210275 10.47176/smok.2025.1913 FA Amir Ehsan Zahedi Assistant Professor, Management department, Administration sciences and economy faculty, Arak university, Arak, iran 0000-0001-9491-9023 Journal Article 2025 01 05 <strong>Purpose:</strong> The integration of AI and knowledge management refers to the strategic use of technology to increase the creation, sharing, and use of knowledge in an organization. Implementing artificial intelligence in knowledge management poses challenges and limitations for organizations, especially government departments that are tasked with providing services to the public. To understand the relationship between knowledge management and AI, it is necessary to carefully examine how it affects important human resource variables of organizations, including "performance". The aim of this study is to investigate the simultaneous impact of knowledge management and AI on the performance of human resources in the public sector.<br /><strong>Methodology:</strong> The present study is applied in terms of orientation and quantitative in terms of methodology, which was conducted using a descriptive-correlation strategy. The study period is winter 2024 and spring 2025. The research population is the employees of the public sector in Markazi Province, from which 150 managers and experts were selected using snowball sampling. A questionnaire was used to collect data, and its reliability was confirmed based on Cronbach's alpha and combined reliability criteria, and validity were confirmed based on AVE and Cross-factor loading indices. Structural Equation Modeling was used to analyze the data, and Smart PLS 3.0 software was used to perform its calculations.<br /><strong>Findings:</strong> Data analysis showed that various applications of artificial intelligence have a direct relationship with knowledge management processes. Documenting tacit knowledge has a positive and significant effect on knowledge conversion, transfer, and application; personalizing access to knowledge has a positive and significant effect on knowledge retention and maintenance; and intelligent prediction and decision-making has a positive and significant effect on knowledge creation and application. Also, the integration of artificial intelligence and knowledge management has a direct relationship with the performance of human resources in the public sector. Knowledge creation has a positive and significant effect on participation; knowledge retention and maintenance has a positive and significant effect on satisfaction; knowledge conversion and transfer has a positive and significant effect on participation; and knowledge application has a positive and significant effect on retention and maintenance, satisfaction, and training costs. The highest path coefficient (0.641) is related to the effect of knowledge application on satisfaction, and the lowest path coefficient (0.174) is related to the effect of prediction and intelligent decision-making on knowledge application.<br /><br /><strong>Research limitations/implications:</strong> The following are some of the limitations of the present study:<br />1. Limited use of artificial intelligence tools in the country's public sector.<br />2. Limited access to service sector employees who use artificial intelligence tools, resulting in a limited sample size.<br />3. Limited data analysis methods and tools.<br />The following suggestions are made for future research:<br />1. The challenges and obstacles to implementing artificial intelligence and knowledge management in executive agencies should be studied.<br />2. The impact of using new technologies on various organizational performance measures should be examined.<br />3. The scope of the study should be expanded at the national level and the comparative study should be expanded at the international level.<br />4. Qualitative methodology should be used to find the implementation pattern of artificial intelligence and knowledge management in the public sector.<br /><strong>Practical implications:</strong> Comprehensive training programs should be launched to strengthen the skills and awareness of employees in the field of artificial intelligence and knowledge management. The formation of multidisciplinary teams that are a combination of employees from different departments of the organization can help in creating a culture of artificial intelligence and knowledge management in the organization. There should be standard processes for collecting, organizing and converting data into a usable format. Security measures should be considered, including the use of strong encryption algorithms to protect data, applying access policies and restrictions, and reviewing and identifying security threats. Organizations should design appropriate monitoring mechanisms to ensure the quality of shared data.<br />One of the application areas where artificial intelligence has many capabilities is knowledge management. Using artificial intelligence tools and techniques such as machine learning, artificial neural networks, natural language processing and recommender systems, knowledge can be collected, organized, extracted and shared in a structured way. This improves access to knowledge, increases the efficiency and speed of knowledge management processes, and makes better organizational decisions. However, to successfully implement artificial intelligence in knowledge management, we need to face related challenges. One of the benefits of artificial intelligence for knowledge management is the power of prediction and analysis. Using machine learning algorithms and artificial neural networks, it is possible to recognize patterns and trends in data and provide more accurate predictions about future behaviors and changes. This capability is of great importance for organizations, because they can make better decisions for the future and achieve success and sustainable growth. Artificial intelligence can also be used to aggregate knowledge. By using hybrid algorithms and intelligent decision-making systems, information and knowledge in an organization can be collected from various sources and a comprehensive picture of organizational knowledge can be obtained. This helps the organization to improve its strategies and decisions based on existing knowledge and achieve better results. In addition, artificial intelligence can play a role in increasing cooperation and interaction between people in different departments of the organization. By using recommender systems and data analysis, more effective communication and collaboration can be established between members of the organization. These systems can give individuals suggestions that increase interaction and cooperation between team members and improve the performance and creativity of groups. Another benefit of artificial intelligence for knowledge management is improving access to knowledge. Using machine learning algorithms, systems can be developed that are capable of searching and extracting knowledge from various sources, which allows for quick and easy access to the required knowledge at any time and place. Artificial intelligence can also play an important role in knowledge sharing. AI-based recommender systems can help employees in an organization share their knowledge and experiences with colleagues. These systems can recommend appropriate materials and resources to employees based on past experiences and individual profiles, and accelerate the knowledge sharing process. Using natural language processing, artificial intelligence can be used to analyze texts and information available in an organization. This technology can help identify topics, extract useful information, and summarize texts. Natural language processing systems can also be used in knowledge management automation processes. For example, automation systems can automatically categorize and assign relevant tags to related content. This speeds up the process of categorizing and organizing knowledge, improving its accessibility and usability.<br />Originality/value: Knowledge management provides the conditions for knowledge understanding to occur, while AI provides the capabilities to expand, use, and create knowledge in new and efficient ways. By effectively integrating technology with knowledge management practices, organizations can improve decision-making, innovation, and overall organizational performance. Training and development of human resources specialized in AI and knowledge management is critical to the successful implementation of this technology. <strong>Purpose:</strong> The integration of AI and knowledge management refers to the strategic use of technology to increase the creation, sharing, and use of knowledge in an organization. Implementing artificial intelligence in knowledge management poses challenges and limitations for organizations, especially government departments that are tasked with providing services to the public. To understand the relationship between knowledge management and AI, it is necessary to carefully examine how it affects important human resource variables of organizations, including "performance". The aim of this study is to investigate the simultaneous impact of knowledge management and AI on the performance of human resources in the public sector.<br /><strong>Methodology:</strong> The present study is applied in terms of orientation and quantitative in terms of methodology, which was conducted using a descriptive-correlation strategy. The study period is winter 2024 and spring 2025. The research population is the employees of the public sector in Markazi Province, from which 150 managers and experts were selected using snowball sampling. A questionnaire was used to collect data, and its reliability was confirmed based on Cronbach's alpha and combined reliability criteria, and validity were confirmed based on AVE and Cross-factor loading indices. Structural Equation Modeling was used to analyze the data, and Smart PLS 3.0 software was used to perform its calculations.<br /><strong>Findings:</strong> Data analysis showed that various applications of artificial intelligence have a direct relationship with knowledge management processes. Documenting tacit knowledge has a positive and significant effect on knowledge conversion, transfer, and application; personalizing access to knowledge has a positive and significant effect on knowledge retention and maintenance; and intelligent prediction and decision-making has a positive and significant effect on knowledge creation and application. Also, the integration of artificial intelligence and knowledge management has a direct relationship with the performance of human resources in the public sector. Knowledge creation has a positive and significant effect on participation; knowledge retention and maintenance has a positive and significant effect on satisfaction; knowledge conversion and transfer has a positive and significant effect on participation; and knowledge application has a positive and significant effect on retention and maintenance, satisfaction, and training costs. The highest path coefficient (0.641) is related to the effect of knowledge application on satisfaction, and the lowest path coefficient (0.174) is related to the effect of prediction and intelligent decision-making on knowledge application.<br /><br /><strong>Research limitations/implications:</strong> The following are some of the limitations of the present study:<br />1. Limited use of artificial intelligence tools in the country's public sector.<br />2. Limited access to service sector employees who use artificial intelligence tools, resulting in a limited sample size.<br />3. Limited data analysis methods and tools.<br />The following suggestions are made for future research:<br />1. The challenges and obstacles to implementing artificial intelligence and knowledge management in executive agencies should be studied.<br />2. The impact of using new technologies on various organizational performance measures should be examined.<br />3. The scope of the study should be expanded at the national level and the comparative study should be expanded at the international level.<br />4. Qualitative methodology should be used to find the implementation pattern of artificial intelligence and knowledge management in the public sector.<br /><strong>Practical implications:</strong> Comprehensive training programs should be launched to strengthen the skills and awareness of employees in the field of artificial intelligence and knowledge management. The formation of multidisciplinary teams that are a combination of employees from different departments of the organization can help in creating a culture of artificial intelligence and knowledge management in the organization. There should be standard processes for collecting, organizing and converting data into a usable format. Security measures should be considered, including the use of strong encryption algorithms to protect data, applying access policies and restrictions, and reviewing and identifying security threats. Organizations should design appropriate monitoring mechanisms to ensure the quality of shared data.<br />One of the application areas where artificial intelligence has many capabilities is knowledge management. Using artificial intelligence tools and techniques such as machine learning, artificial neural networks, natural language processing and recommender systems, knowledge can be collected, organized, extracted and shared in a structured way. This improves access to knowledge, increases the efficiency and speed of knowledge management processes, and makes better organizational decisions. However, to successfully implement artificial intelligence in knowledge management, we need to face related challenges. One of the benefits of artificial intelligence for knowledge management is the power of prediction and analysis. Using machine learning algorithms and artificial neural networks, it is possible to recognize patterns and trends in data and provide more accurate predictions about future behaviors and changes. This capability is of great importance for organizations, because they can make better decisions for the future and achieve success and sustainable growth. Artificial intelligence can also be used to aggregate knowledge. By using hybrid algorithms and intelligent decision-making systems, information and knowledge in an organization can be collected from various sources and a comprehensive picture of organizational knowledge can be obtained. This helps the organization to improve its strategies and decisions based on existing knowledge and achieve better results. In addition, artificial intelligence can play a role in increasing cooperation and interaction between people in different departments of the organization. By using recommender systems and data analysis, more effective communication and collaboration can be established between members of the organization. These systems can give individuals suggestions that increase interaction and cooperation between team members and improve the performance and creativity of groups. Another benefit of artificial intelligence for knowledge management is improving access to knowledge. Using machine learning algorithms, systems can be developed that are capable of searching and extracting knowledge from various sources, which allows for quick and easy access to the required knowledge at any time and place. Artificial intelligence can also play an important role in knowledge sharing. AI-based recommender systems can help employees in an organization share their knowledge and experiences with colleagues. These systems can recommend appropriate materials and resources to employees based on past experiences and individual profiles, and accelerate the knowledge sharing process. Using natural language processing, artificial intelligence can be used to analyze texts and information available in an organization. This technology can help identify topics, extract useful information, and summarize texts. Natural language processing systems can also be used in knowledge management automation processes. For example, automation systems can automatically categorize and assign relevant tags to related content. This speeds up the process of categorizing and organizing knowledge, improving its accessibility and usability.<br />Originality/value: Knowledge management provides the conditions for knowledge understanding to occur, while AI provides the capabilities to expand, use, and create knowledge in new and efficient ways. By effectively integrating technology with knowledge management practices, organizations can improve decision-making, innovation, and overall organizational performance. Training and development of human resources specialized in AI and knowledge management is critical to the successful implementation of this technology. https://jkm.ihu.ac.ir/article_210275_dcbbcb2527f500288c26a828ad681229.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 8 3 2025 09 23 Lean Higher Education: A Model Based On Knowledge Management Experts From The Ministry Of Science, Research And Technology Headquarters Lean Higher Education: A Model Based On Knowledge Management Experts From The Ministry Of Science, Research And Technology Headquarters 62 88 210276 10.47176/smok.2025.1927 FA Shahin Homayoun Arya Assistant Professor, Department of Higher Education Development Planning, Ministry of Science, Research and Technology, Tehran, Iran 0000-0002-4048-4308 Akbar Goldasteh Assistant Professor, Department of Higher Education Development Planning, Faculty of Humanities, University of Science and Culture, Tehran, Iran 0000-0002-0969-2889 Hanieh Zaferani Master's Degree Graduate, Department of Higher Education Development Planning, Faculty of Humanities, University of Science and Culture, Tehran, Iran Journal Article <strong>Background/Objectives:</strong> Over the past few decades, lean thinking has emerged as a method for optimizing and continuously improving organizations, ensuring their sustainable development when successfully implemented. While rooted in Taiichi Ohno’s lean philosophy within Toyota’s automotive industry, lean principles are now gaining traction in educational institutions and universities as a strategy for continuous improvement. Universities and higher education centers, facing shifts from traditional paradigms, grapple with numerous challenges, including: inconsistent or delayed implementation of appropriate academic actions, evolving stakeholder needs and expectations (and a corresponding lack of responsiveness), industry demands and declining output quality, flawed understanding of university processes, technological advancements, changing university processes over time, rising costs of university outputs, insufficient government funding, and persistent pressure from society, employers, students, and other stakeholders (Cudney et al., 2020; Cox et al., 2020). Consequently, universities require staff familiar with lean approaches (Bumjaid et al., 2019) who can transform challenges into opportunities through continuous process improvement and the pursuit of excellence (Sfakianaki et al., 2019).Given the advantages of lean thinking in higher education, its adoption in universities is crucial. A key initial step is developing a lean higher education model and assessing its alignment with the core dimensions of the university as a learning and knowledge-driven organization (Balzer, 2020). While characteristics and components of a lean model have been identified in some universities, this model has not been designed and developed from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology. This ministry is responsible for policymaking, planning, oversight, and evaluation of university affairs and should provide the necessary platform for universities to become “lean.” Understanding the perspectives of managers and experts at the Ministry headquarters is therefore very important and influential. By accessing a model from the perspective of these headquarters experts, it can be compared with past studies conducted “in the field” (at universities), and similarities and differences can be identified from the viewpoint of headquarters and field personnel. It’s also important to recognize that becoming lean in organizations, especially in universities as educational and research institutions, requires organizational changes, including changes in structures and work processes, perspectives, culture, the style of leadership support, and, ultimately, the development of information and communication technology. Of course, these changes should be focused on a continuous improvement approach (NaranjiSani et al., 2017). Lean thinking, as an operational and improvement-oriented approach, utilizes critical thinking to provide the basis for eliminating waste and enhancing value in educational systems through in-depth analysis, identifying the root causes of problems, and continuously evaluating processes. A literature review revealed limited research on developing lean higher education models in Iran. Therefore, given the importance of the lean approach and the numerous benefits of implementing this method in higher education, this research aims to answer the question: What is the lean higher education model, and what is the prioritization of lean higher education dimensions from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology?<br /><strong>Research Methods:</strong> This research aims to present a model for lean higher education from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology. To achieve this goal, detailed qualitative data is required. Therefore, a qualitative approach was chosen for the study. This study also falls under the category of exploratory-analytical methods. The statistical population of this research includes all experts in the headquarters of the Ministry of Science, Research, and Technology who have educational backgrounds related to education, especially higher education, and also have executive and managerial experience in the headquarters of the Ministry. Purposive sampling was used in this research. Experts were selected for interviews who have experience in teaching and training in the field of lean management, as well as executive and managerial experience in higher education and human resource management at the university and ministry levels. The number of samples in the qualitative phase was determined based on the principles of theoretical saturation. Based on this principle, theoretical data saturation was achieved by conducting interviews with 12 experts in the central and executive areas. In this study, semi-structured interviews consisting of five questions were used to identify the components of lean higher education and develop a suitable model for lean higher education. The analysis of the data obtained from the semi-structured interviews focused on the Strauss-Corbin method, which was performed by establishing a connection based on questioning and continuous comparison. In axial coding, the variety of extracted codes indicates the type of relationships. For example, to compare one category with another, the question might be asked: Is category “A” a consequence of the strategies for category “B”? When the data confirmed the question, the relationship between the two categories was determined and could be converted into a proposition, thus the coding process was formed. After ensuring that no new code could be extracted from the experts’ responses and the coding process led to the repetition of results, the stage of theoretical saturation and coding cessation occurred.<br />Research Findings: Through the coding process, components related to lean higher education in public universities were identified, and its conceptual model was developed. According to the results of thematic analysis of interviews with key stakeholders, experts, and key experts, the proposed model for the implementation of lean higher education in the Ministry of Science, Research, and Technology has been schematically drawn. The findings obtained in the target community showed that five components – structural, managerial, human resources, financial-economic, and infrastructural-technological – are the most important factors of the lean higher education model from the perspective of experts and managers of the Ministry of Science, Research, and Technology. The managerial dimension includes five components: quality monitoring, university-community interaction, independent decision-making, needs-based services, and university management and leadership. The structural dimension includes four components: structural reform of the university, higher education planning and reform of faculty promotion regulations, governance of lean attitude and thinking, and reduced dependence on the government. The human resources dimension includes three components: recruitment, development and empowerment, specialized capabilities, and individual and personality characteristics. The financial-economic dimension includes two components: securing financial resources and strengthening links with industry. Finally, the infrastructural-technological dimension includes two components: infrastructure development and the use of innovative and transformative technologies. To analyze the data obtained from semi-structured interviews, the focus was on the Strauss-Corbin method and the theme analysis technique was used, which was done by creating a connection based on the question design and making continuous comparisons, and NVivo software was used.<br /><strong>Conclusion:</strong> The human resources component, with 49 codes, the structural component, with 35 codes, the managerial component, with 24 codes, the infrastructural-technological component, with 19 codes, and finally the financial-economic component, with 12 extracted codes, were the most important and prioritized, respectively. The findings of the structural dimension are consistent with the results of the research of (Ijtihad & et al., 2007) and QureshiKhorasgani, 2016), and clearly show that the inappropriate structure in the higher education system can cause tension and conflict in the organization and prevent the initiative and creativity that is necessary for an academic organization. The findings of the managerial dimension are in line with the results of studies (Emiliani, 2015), which show that components such as the spirit of cooperation with employees and problem-solving skills are key factors in the lean university. Also, the results of research (Hajkhazimeh & et al., 2019; Jafari & et al., 2007; Akbari & et al., 2020) emphasize that the use of participatory management, empowerment and meritocracy is directly consistent with the components of the role model and participatory management. The findings of the human resources dimension are consistent with the results of the research of (Kucheryavenko & et al., 2019; Cano & et al., 2020; Moore & et al., 2007) and show the importance of the budget and financial and economic resources components in the readiness to implement the components of the lean higher education system. Finally, in the infrastructural-technological dimension, the analysis of the findings shows that the use of advanced technologies such as artificial intelligence can have significant effects on the lean transformation of the higher education system. In other words, considering the influence of technology in society and its impact on the structure of infrastructure, the higher education system and its related processes have faced structural changes, alignment with these changes seems necessary in the current situation.<br /><strong>Originality/Value: </strong>By presenting the lean higher education model from the perspective of experts from the Ministry of Science, Research and Technology, this research provides the necessary platform for localizing international theoretical models in order to implement the principles and approaches of lean higher education in the country's universities, and helps bridge the gap between macro policymaking and operational implementation. <strong>Background/Objectives:</strong> Over the past few decades, lean thinking has emerged as a method for optimizing and continuously improving organizations, ensuring their sustainable development when successfully implemented. While rooted in Taiichi Ohno’s lean philosophy within Toyota’s automotive industry, lean principles are now gaining traction in educational institutions and universities as a strategy for continuous improvement. Universities and higher education centers, facing shifts from traditional paradigms, grapple with numerous challenges, including: inconsistent or delayed implementation of appropriate academic actions, evolving stakeholder needs and expectations (and a corresponding lack of responsiveness), industry demands and declining output quality, flawed understanding of university processes, technological advancements, changing university processes over time, rising costs of university outputs, insufficient government funding, and persistent pressure from society, employers, students, and other stakeholders (Cudney et al., 2020; Cox et al., 2020). Consequently, universities require staff familiar with lean approaches (Bumjaid et al., 2019) who can transform challenges into opportunities through continuous process improvement and the pursuit of excellence (Sfakianaki et al., 2019).Given the advantages of lean thinking in higher education, its adoption in universities is crucial. A key initial step is developing a lean higher education model and assessing its alignment with the core dimensions of the university as a learning and knowledge-driven organization (Balzer, 2020). While characteristics and components of a lean model have been identified in some universities, this model has not been designed and developed from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology. This ministry is responsible for policymaking, planning, oversight, and evaluation of university affairs and should provide the necessary platform for universities to become “lean.” Understanding the perspectives of managers and experts at the Ministry headquarters is therefore very important and influential. By accessing a model from the perspective of these headquarters experts, it can be compared with past studies conducted “in the field” (at universities), and similarities and differences can be identified from the viewpoint of headquarters and field personnel. It’s also important to recognize that becoming lean in organizations, especially in universities as educational and research institutions, requires organizational changes, including changes in structures and work processes, perspectives, culture, the style of leadership support, and, ultimately, the development of information and communication technology. Of course, these changes should be focused on a continuous improvement approach (NaranjiSani et al., 2017). Lean thinking, as an operational and improvement-oriented approach, utilizes critical thinking to provide the basis for eliminating waste and enhancing value in educational systems through in-depth analysis, identifying the root causes of problems, and continuously evaluating processes. A literature review revealed limited research on developing lean higher education models in Iran. Therefore, given the importance of the lean approach and the numerous benefits of implementing this method in higher education, this research aims to answer the question: What is the lean higher education model, and what is the prioritization of lean higher education dimensions from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology?<br /><strong>Research Methods:</strong> This research aims to present a model for lean higher education from the perspective of experts in the central headquarters of the Ministry of Science, Research, and Technology. To achieve this goal, detailed qualitative data is required. Therefore, a qualitative approach was chosen for the study. This study also falls under the category of exploratory-analytical methods. The statistical population of this research includes all experts in the headquarters of the Ministry of Science, Research, and Technology who have educational backgrounds related to education, especially higher education, and also have executive and managerial experience in the headquarters of the Ministry. Purposive sampling was used in this research. Experts were selected for interviews who have experience in teaching and training in the field of lean management, as well as executive and managerial experience in higher education and human resource management at the university and ministry levels. The number of samples in the qualitative phase was determined based on the principles of theoretical saturation. Based on this principle, theoretical data saturation was achieved by conducting interviews with 12 experts in the central and executive areas. In this study, semi-structured interviews consisting of five questions were used to identify the components of lean higher education and develop a suitable model for lean higher education. The analysis of the data obtained from the semi-structured interviews focused on the Strauss-Corbin method, which was performed by establishing a connection based on questioning and continuous comparison. In axial coding, the variety of extracted codes indicates the type of relationships. For example, to compare one category with another, the question might be asked: Is category “A” a consequence of the strategies for category “B”? When the data confirmed the question, the relationship between the two categories was determined and could be converted into a proposition, thus the coding process was formed. After ensuring that no new code could be extracted from the experts’ responses and the coding process led to the repetition of results, the stage of theoretical saturation and coding cessation occurred.<br />Research Findings: Through the coding process, components related to lean higher education in public universities were identified, and its conceptual model was developed. According to the results of thematic analysis of interviews with key stakeholders, experts, and key experts, the proposed model for the implementation of lean higher education in the Ministry of Science, Research, and Technology has been schematically drawn. The findings obtained in the target community showed that five components – structural, managerial, human resources, financial-economic, and infrastructural-technological – are the most important factors of the lean higher education model from the perspective of experts and managers of the Ministry of Science, Research, and Technology. The managerial dimension includes five components: quality monitoring, university-community interaction, independent decision-making, needs-based services, and university management and leadership. The structural dimension includes four components: structural reform of the university, higher education planning and reform of faculty promotion regulations, governance of lean attitude and thinking, and reduced dependence on the government. The human resources dimension includes three components: recruitment, development and empowerment, specialized capabilities, and individual and personality characteristics. The financial-economic dimension includes two components: securing financial resources and strengthening links with industry. Finally, the infrastructural-technological dimension includes two components: infrastructure development and the use of innovative and transformative technologies. To analyze the data obtained from semi-structured interviews, the focus was on the Strauss-Corbin method and the theme analysis technique was used, which was done by creating a connection based on the question design and making continuous comparisons, and NVivo software was used.<br /><strong>Conclusion:</strong> The human resources component, with 49 codes, the structural component, with 35 codes, the managerial component, with 24 codes, the infrastructural-technological component, with 19 codes, and finally the financial-economic component, with 12 extracted codes, were the most important and prioritized, respectively. The findings of the structural dimension are consistent with the results of the research of (Ijtihad & et al., 2007) and QureshiKhorasgani, 2016), and clearly show that the inappropriate structure in the higher education system can cause tension and conflict in the organization and prevent the initiative and creativity that is necessary for an academic organization. The findings of the managerial dimension are in line with the results of studies (Emiliani, 2015), which show that components such as the spirit of cooperation with employees and problem-solving skills are key factors in the lean university. Also, the results of research (Hajkhazimeh & et al., 2019; Jafari & et al., 2007; Akbari & et al., 2020) emphasize that the use of participatory management, empowerment and meritocracy is directly consistent with the components of the role model and participatory management. The findings of the human resources dimension are consistent with the results of the research of (Kucheryavenko & et al., 2019; Cano & et al., 2020; Moore & et al., 2007) and show the importance of the budget and financial and economic resources components in the readiness to implement the components of the lean higher education system. Finally, in the infrastructural-technological dimension, the analysis of the findings shows that the use of advanced technologies such as artificial intelligence can have significant effects on the lean transformation of the higher education system. In other words, considering the influence of technology in society and its impact on the structure of infrastructure, the higher education system and its related processes have faced structural changes, alignment with these changes seems necessary in the current situation.<br /><strong>Originality/Value: </strong>By presenting the lean higher education model from the perspective of experts from the Ministry of Science, Research and Technology, this research provides the necessary platform for localizing international theoretical models in order to implement the principles and approaches of lean higher education in the country's universities, and helps bridge the gap between macro policymaking and operational implementation. https://jkm.ihu.ac.ir/article_210276_e2596b6b1286379c3520ff4dd9110e43.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 8 3 2025 09 23 Nonlinear Modeling Of The Interaction Of Ethical, Cultural, And Structural Factors On The Intention To Disclose Sensitive Knowledge Using Response Surface Methodology: A Human Versus AI Comparison Nonlinear Modeling Of The Interaction Of Ethical, Cultural, And Structural Factors On The Intention To Disclose Sensitive Knowledge Using Response Surface Methodology: A Human Versus AI Comparison 89 113 210277 10.47176/smok.2025.1929 FA Ali Asghar Abdeshahi Ph.D. Candidate in Public Administration, Department of Management, Faculty of Management and Economics, Lorestan University, Khorramabad, Iran 0009-0007-8243-760X Hojat Rezaei Arjmand Department of Business Administration, Faculty of Management, Islamic Azad University, Arak, Iran 0009-0002-7193-6618 Shervin Vahidian Qazvini Master's Student in Applied Physics, Department of Applied Physics, Faculty of Physics and Astronomy, University of Bologna, Bologna, Italy 0009-0003-8756-6495 Journal Article 2025 05 20 <strong>Purpose:</strong> Public sector organizations today operate under intensifying demands to uphold ethical standards, mitigate corruption, and ensure public trust. Whistleblowing—the deliberate, principled disclosure of sensitive internal information such as financial irregularities, misconduct, or policy violations—serves as a vital corrective tool. However, the decision to blow the whistle is not driven by a single stimulus; instead, it emerges from a complex nexus of factors. An individual’s moral inclination, shaped by personal values and integrity, provides the foundational readiness to report wrongdoing. Simultaneously, the prevailing ethical knowledge sharing culture within an organization either encourages or stifles such disclosures by signaling whether employees will be supported or ostracized. Finally, structural barriers—bureaucratic hurdles, opaque reporting procedures, fear of retaliation, and lack of legal safeguards—directly impede the act of disclosure. While prior research has examined these dimensions in isolation and largely through linear models, the present study addresses a critical gap by modeling their joint, potentially non linear influences on whistleblowing intention. Moreover, the advent of advanced language models, such as Grok 3, affords a unique opportunity to compare algorithmic predictions against actual human behavior. Accordingly, this research aims to (1) develop a comprehensive, non linear quantitative model capturing the combined effects of moral inclination, ethical culture, and structural barriers on whistleblowing intention among public service employees in Lorestan Province, Iran, and (2) evaluate the comparative predictive accuracy and limitations of human survey data versus Grok 3’s outputs under identical vignette scenarios.<br /><strong>Methodology:</strong> An applied, descriptive experimental field study was undertaken, employing Central Composite Design (CCD) in conjunction with Response Surface Methodology (RSM) to explore main, interaction, and quadratic effects. From a population of 700 service sector employees, a stratified random sample of 385 was determined via Cochran’s formula, ensuring sufficient statistical power. Data collection used a scenario based questionnaire featuring fifteen unique vignettes, each systematically varying three independent variables—individual moral inclination (A), ethical knowledge sharing culture (C), and structural barriers (B)—across low, medium, and high levels. Participants rated their likelihood to disclose sensitive knowledge on a seven point Likert scale (1 = “definitely would not” to 7 = “definitely would”). Content validity was confirmed through expert review by five scholars in knowledge management and organizational behavior, and internal consistency reliability was established with Cronbach’s alpha = 0.88 in a pilot test of 30 employees. In parallel, the same vignette scenarios were input into Grok 3 to generate AI based intention scores. Four regression frameworks—purely linear, two way interaction, quadratic (second degree), and cubic (third degree)—were fitted for both human and AI datasets using Design Expert 13. Model selection criteria included coefficient of determination (R²), adjusted R², ANOVA F tests, lack-of-fit tests, and sum of squared errors (SSE). The quadratic model demonstrated superior explanatory power (R² = 0.95 human; R² = 0.97 AI) with non significant lack-of-fit and was selected for detailed analysis. A composite utility function was then applied to pinpoint the optimal factor combination that maximizes whistleblowing intention.<br /><strong>Findings:</strong> The human‐based quadratic model explained 95% of variance in whistleblowing intention, with highly significant coefficients. Moral inclination (A) exhibited the most pronounced positive linear effect (coefficient = 0.96, p < 0.01), highlighting its central motivational role. Its accompanying negative quadratic term (–0.76, p < 0.05) revealed a saturation threshold beyond which additional moral motivation produced diminishing increases in disclosure intent—an effect seldom captured in linear analyses. Ethical knowledge sharing culture (C) registered a robust positive linear coefficient of 0.95 (p < 0.01) across all levels, with no evidence of saturation, signifying its consistent enabling function. Structural barriers (B) exerted a significant negative linear effect (–0.59, p < 0.01), indicating that each incremental barrier unit steadily suppresses willingness to report. Under the optimal conditions (A = 7, C = 7, B = 1), predicted human intention reached 5.55 on the seven point scale with a composite utility of 0.87. Grok 3’s quadratic model paralleled these trends but with distinct magnitudes: A’s linear coefficient was 1.64 (p < 0.01), B = –0.60 (p < 0.01), and C = 0.80 (p < 0.01). Notably, AI identified significant interactions: A×B (–0.375, p < 0.05) suggested that high moral drive combined with strong barriers markedly dampens intention, while A×C (0.225, p < 0.05) signified synergistic gains when moral drive aligns with a supportive culture. The AI quadratic A² term (–0.43, p < 0.05) reaffirmed saturation in moral motivation. Grok 3’s optimal predicted intention soared to 7.00, approximately 26% higher than human respondents, reflecting AI’s lack of psychosocial risk aversion and highlighting the complexity of real‐world disclosure decisions.<br />Research limitations/implications: The cross‐sectional vignette approach, though experimentally robust, cannot fully emulate the emotional stakes, group dynamics, and organizational politics of authentic whistleblowing. The geographic focus on a single province reduces the generalizability to distinct cultural, regulatory, or institutional contexts. Self‐report measures risk social desirability bias and cognitive fatigue, especially over multiple scenario evaluations. Grok 3’s opaque proprietary architecture precludes in‐depth understanding of how it weights and processes scenario information. Future research should incorporate longitudinal designs tracking actual disclosure behaviors, broaden samples across diverse settings, integrate objective whistleblowing records, and explore moderating influences such as employees’ perceived organizational support and individual risk tolerance.<br /><strong>Practical implications:</strong> This study furnishes a multi‐lever, evidence‐based roadmap for public sector management. First, cultivate intrinsic moral motivation via scenario‐based ethics training, peer support networks, and visible ethical leadership—but calibrate intensity to avoid motivational saturation. Second, strengthen an ethical knowledge‐sharing culture by embedding transparency in organizational mission statements, celebrating exemplary whistleblowers, and maintaining open communication channels for ethical concerns. Third, dismantle structural barriers through simplified, confidential reporting procedures, robust anti‐retaliation policies, and clear legal safeguards. While AI models like Grok 3 can support scenario planning and policy simulations, they must complement—rather than replace—human judgment, particularly where psychosocial and cultural nuances dictate employee risk calculations.<br /><strong>Originality/value:</strong> This research represents the first Iranian application of an integrated CCD RSM experimental design combined with a cutting‐edge large language model to analyze whistleblowing intentions. Departing from conventional linear regression, it uncovers novel saturation dynamics in moral motivation and maps intricate factor interactions. The introduction of a utility optimization metric offers actionable guidelines for calibrating moral, cultural, and structural interventions. By juxtaposing human survey data with AI predictions, the study elucidates both the promise and the limitations of AI in ethically sensitive organizational contexts, thereby advancing methodological frontiers in the study of organizational ethics and public governance. <strong>Purpose:</strong> Public sector organizations today operate under intensifying demands to uphold ethical standards, mitigate corruption, and ensure public trust. Whistleblowing—the deliberate, principled disclosure of sensitive internal information such as financial irregularities, misconduct, or policy violations—serves as a vital corrective tool. However, the decision to blow the whistle is not driven by a single stimulus; instead, it emerges from a complex nexus of factors. An individual’s moral inclination, shaped by personal values and integrity, provides the foundational readiness to report wrongdoing. Simultaneously, the prevailing ethical knowledge sharing culture within an organization either encourages or stifles such disclosures by signaling whether employees will be supported or ostracized. Finally, structural barriers—bureaucratic hurdles, opaque reporting procedures, fear of retaliation, and lack of legal safeguards—directly impede the act of disclosure. While prior research has examined these dimensions in isolation and largely through linear models, the present study addresses a critical gap by modeling their joint, potentially non linear influences on whistleblowing intention. Moreover, the advent of advanced language models, such as Grok 3, affords a unique opportunity to compare algorithmic predictions against actual human behavior. Accordingly, this research aims to (1) develop a comprehensive, non linear quantitative model capturing the combined effects of moral inclination, ethical culture, and structural barriers on whistleblowing intention among public service employees in Lorestan Province, Iran, and (2) evaluate the comparative predictive accuracy and limitations of human survey data versus Grok 3’s outputs under identical vignette scenarios.<br /><strong>Methodology:</strong> An applied, descriptive experimental field study was undertaken, employing Central Composite Design (CCD) in conjunction with Response Surface Methodology (RSM) to explore main, interaction, and quadratic effects. From a population of 700 service sector employees, a stratified random sample of 385 was determined via Cochran’s formula, ensuring sufficient statistical power. Data collection used a scenario based questionnaire featuring fifteen unique vignettes, each systematically varying three independent variables—individual moral inclination (A), ethical knowledge sharing culture (C), and structural barriers (B)—across low, medium, and high levels. Participants rated their likelihood to disclose sensitive knowledge on a seven point Likert scale (1 = “definitely would not” to 7 = “definitely would”). Content validity was confirmed through expert review by five scholars in knowledge management and organizational behavior, and internal consistency reliability was established with Cronbach’s alpha = 0.88 in a pilot test of 30 employees. In parallel, the same vignette scenarios were input into Grok 3 to generate AI based intention scores. Four regression frameworks—purely linear, two way interaction, quadratic (second degree), and cubic (third degree)—were fitted for both human and AI datasets using Design Expert 13. Model selection criteria included coefficient of determination (R²), adjusted R², ANOVA F tests, lack-of-fit tests, and sum of squared errors (SSE). The quadratic model demonstrated superior explanatory power (R² = 0.95 human; R² = 0.97 AI) with non significant lack-of-fit and was selected for detailed analysis. A composite utility function was then applied to pinpoint the optimal factor combination that maximizes whistleblowing intention.<br /><strong>Findings:</strong> The human‐based quadratic model explained 95% of variance in whistleblowing intention, with highly significant coefficients. Moral inclination (A) exhibited the most pronounced positive linear effect (coefficient = 0.96, p < 0.01), highlighting its central motivational role. Its accompanying negative quadratic term (–0.76, p < 0.05) revealed a saturation threshold beyond which additional moral motivation produced diminishing increases in disclosure intent—an effect seldom captured in linear analyses. Ethical knowledge sharing culture (C) registered a robust positive linear coefficient of 0.95 (p < 0.01) across all levels, with no evidence of saturation, signifying its consistent enabling function. Structural barriers (B) exerted a significant negative linear effect (–0.59, p < 0.01), indicating that each incremental barrier unit steadily suppresses willingness to report. Under the optimal conditions (A = 7, C = 7, B = 1), predicted human intention reached 5.55 on the seven point scale with a composite utility of 0.87. Grok 3’s quadratic model paralleled these trends but with distinct magnitudes: A’s linear coefficient was 1.64 (p < 0.01), B = –0.60 (p < 0.01), and C = 0.80 (p < 0.01). Notably, AI identified significant interactions: A×B (–0.375, p < 0.05) suggested that high moral drive combined with strong barriers markedly dampens intention, while A×C (0.225, p < 0.05) signified synergistic gains when moral drive aligns with a supportive culture. The AI quadratic A² term (–0.43, p < 0.05) reaffirmed saturation in moral motivation. Grok 3’s optimal predicted intention soared to 7.00, approximately 26% higher than human respondents, reflecting AI’s lack of psychosocial risk aversion and highlighting the complexity of real‐world disclosure decisions.<br />Research limitations/implications: The cross‐sectional vignette approach, though experimentally robust, cannot fully emulate the emotional stakes, group dynamics, and organizational politics of authentic whistleblowing. The geographic focus on a single province reduces the generalizability to distinct cultural, regulatory, or institutional contexts. Self‐report measures risk social desirability bias and cognitive fatigue, especially over multiple scenario evaluations. Grok 3’s opaque proprietary architecture precludes in‐depth understanding of how it weights and processes scenario information. Future research should incorporate longitudinal designs tracking actual disclosure behaviors, broaden samples across diverse settings, integrate objective whistleblowing records, and explore moderating influences such as employees’ perceived organizational support and individual risk tolerance.<br /><strong>Practical implications:</strong> This study furnishes a multi‐lever, evidence‐based roadmap for public sector management. First, cultivate intrinsic moral motivation via scenario‐based ethics training, peer support networks, and visible ethical leadership—but calibrate intensity to avoid motivational saturation. Second, strengthen an ethical knowledge‐sharing culture by embedding transparency in organizational mission statements, celebrating exemplary whistleblowers, and maintaining open communication channels for ethical concerns. Third, dismantle structural barriers through simplified, confidential reporting procedures, robust anti‐retaliation policies, and clear legal safeguards. While AI models like Grok 3 can support scenario planning and policy simulations, they must complement—rather than replace—human judgment, particularly where psychosocial and cultural nuances dictate employee risk calculations.<br /><strong>Originality/value:</strong> This research represents the first Iranian application of an integrated CCD RSM experimental design combined with a cutting‐edge large language model to analyze whistleblowing intentions. Departing from conventional linear regression, it uncovers novel saturation dynamics in moral motivation and maps intricate factor interactions. The introduction of a utility optimization metric offers actionable guidelines for calibrating moral, cultural, and structural interventions. By juxtaposing human survey data with AI predictions, the study elucidates both the promise and the limitations of AI in ethically sensitive organizational contexts, thereby advancing methodological frontiers in the study of organizational ethics and public governance. https://jkm.ihu.ac.ir/article_210277_8a27c5d1b8172f71f77aca36fc873e03.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 8 3 2025 09 23 The Evolution of Knowledge Management Generations with a Focus on the Fourth Generation: Revisiting the SECI Model The Evolution of Knowledge Management Generations with a Focus on the Fourth Generation: Revisiting the SECI Model 114 146 210278 10.47176/smok.2025.1938 FA Mohammad Hasan Tavakoli Masters Student, Knowledge Management, Faculty of Knowledge and Cognitive Intelligence, Imam Hussein University, Tehran, Iran 0009-0005-5571-9877 Hojatollah Momivand Researcher, Center for Knowledge and Cognitive Intelligence, Imam Hossein University, Tehran, Iran 0000-0001-9308-0617 Journal Article <strong>Purpose:</strong> Knowledge management (KM) has been a cornerstone of human progress, evolving from oral traditions to structured systems for preserving, transferring, and leveraging knowledge. Over time, humanity has sought innovative methods to harness knowledge as a strategic asset, particularly in organizational contexts. The advent of modern technologies, such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and the metaverse, has fundamentally transformed how knowledge is managed, shared, and created. These advancements have ushered in what is termed the "fourth generation" of knowledge management, a paradigm that responds directly to the rapid and pervasive developments in the digital era. Unlike previous generations, which focused on codifying explicit knowledge, fostering human interactions, or aligning knowledge with strategic objectives, the fourth generation leverages cutting-edge technologies to create intelligent, dynamic, and scalable knowledge ecosystems. However, despite these technological strides, the scientific literature on knowledge management lacks a comprehensive framework to fully articulate the characteristics of this fourth generation and its integration with emerging technologies, particularly Artificial General Intelligence (AGI). AGI, with its ability to mimic human cognitive capabilities, promises to revolutionize knowledge processes by automating complex tasks, personalizing learning, and uncovering hidden patterns in data. This study addresses this gap by delineating clear distinctions between the generations of knowledge management and proposing a novel model for the fourth generation. Specifically, it revisits and redefines the Nonaka and Takeuchi SECI model (Socialization, Externalization, Combination, Internalization) in the context of AGI, offering a framework that aligns with the demands of the digital age. By doing so, this research provides both theoretical insights and practical guidance for organizations seeking to harness advanced technologies for sustainable competitive advantage.<br /><strong>Methodology:</strong> This qualitative research was conducted using a systematic literature review (SLR) approach, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards to ensure rigor and transparency. The study targeted peer-reviewed articles published between 2019 and 2024, sourced from reputable scientific databases, including Scopus, Web of Science, SID, Noormags, and Civilica. These databases were selected for their comprehensive coverage of both international and regional (Persian-language) scholarship, ensuring a broad and inclusive perspective. The research process began with the identification of 1,550 relevant sources through a combination of keyword searches and logical operators tailored to knowledge management, AGI, and emerging technologies. A three-stage screening process was employed: first, 100 duplicate articles were removed; second, 100 unrelated articles were excluded based on title and abstract screening; and third, 1,100 articles were further eliminated using the Critical Appraisal Skills Programme (CASP) and AMSTAR quality assessment tools to ensure methodological rigor. This process resulted in the selection of 64 high-quality articles for in-depth analysis. Data extraction was performed using a standardized form to capture key details such as study objectives, methodologies, findings, and relevance to the fourth generation of KM. The extracted data were analyzed through two complementary methods: thematic analysis using NVivo software to identify recurring themes and patterns, and bibliometric analysis using VOSviewer software to map research clusters, author networks, and citation trends. To enhance the study’s validity, the triangulation method was employed, cross-referencing findings from multiple sources. Reliability was ensured through independent coding by two researchers, with discrepancies resolved through consensus. This rigorous methodology allowed for a robust synthesis of the current state of knowledge management and its evolution into the fourth generation.<br /><strong>Findings:</strong> The study’s findings highlight three key insights into the fourth generation of knowledge management and its integration with emerging technologies. First, AGI significantly enhances organizational knowledge creation by processing unstructured data and uncovering complex patterns that were previously inaccessible. Unlike traditional AI, which is limited to specific tasks, AGI’s ability to emulate human-like reasoning enables it to analyze vast datasets, identify trends, and generate actionable insights, thereby fostering innovation and agility. For instance, AGI can extract meaningful knowledge from diverse sources, such as social media, IoT-generated data, and organizational repositories, enabling organizations to respond swiftly to market changes. Second, emerging technologies, including AGI, IoT, and augmented/virtual reality (AR/VR), present both opportunities and challenges for knowledge management. Opportunities include enhanced organizational agility, personalized learning experiences tailored to individual needs, and improved decision-making through real-time data analysis. For example, IoT facilitates the collection of real-time data from interconnected devices, while AR/VR creates immersive environments for knowledge transfer, such as virtual training simulations. However, these technologies also introduce challenges, such as algorithmic biases, which may skew decision-making, privacy concerns related to data collection, and infrastructure limitations that hinder scalability. Organizations must address these challenges through robust ethical frameworks and investments in digital infrastructure. Third, the study’s revision of the Nonaka and Takeuchi SECI model demonstrates how AGI transforms its four stages. In the socialization phase, AGI simulates human interactions in virtual environments, enabling knowledge sharing without physical presence. In externalization, AGI’s advanced natural language processing capabilities convert tacit knowledge into explicit forms, such as reports or models, with greater accuracy and cultural sensitivity. In the combination phase, AGI integrates diverse knowledge sources to create novel insights, uncovering interdisciplinary connections that enhance innovation. Finally, in internalization, AGI supports experiential learning through adaptive simulations, allowing individuals to internalize explicit knowledge as tacit expertise. These advancements mark a significant departure from the third generation of KM, positioning the fourth generation as a dynamic, intelligent, and technology-driven paradigm.<br />Research limitations/implications: Despite its comprehensive approach, this study faced several methodological limitations. The focus on the 2019–2024 period, chosen to capture recent trends in AGI and the metaverse, may have excluded foundational studies from earlier periods. Additionally, the inclusion of only Persian and English-language articles, driven by publication volume and researcher accessibility, omitted potentially valuable contributions in other languages, such as Chinese or German. The reliance on literature review methodology, without incorporating empirical data from interviews or surveys, limited the depth of analysis. Furthermore, the use of specific databases (e.g., Scopus, SID) may have overlooked sources in less prominent repositories. To address these limitations, future research should adopt hybrid methodologies, combining literature reviews with empirical data, and incorporate multilingual sources to enhance inclusivity. Despite these constraints, the study’s findings have significant implications for both theory and practice, offering a foundation for further exploration of the fourth generation of KM.<br /><strong>Practical implications:</strong> The research underscores the importance of leveraging AGI, IoT, and AR/VR to build intelligent knowledge management systems. Organizations should invest in digital infrastructure to support these technologies, fostering a culture of human-machine collaboration to maximize their potential. For instance, AGI can personalize employee learning by analyzing individual preferences and performance, while IoT can optimize knowledge flows in supply chains. To mitigate challenges like algorithmic biases and privacy concerns, organizations should adopt technologies like blockchain for secure and transparent knowledge sharing. By rethinking the SECI model through the lens of AGI, organizations can enhance knowledge processes, achieve greater agility, and drive innovation, ultimately securing a sustainable competitive advantage in dynamic markets.<br /><strong>Originality/value:</strong> This research offers significant originality and scientific value by providing a novel framework for the fourth generation of knowledge management. Focusing on General Artificial Intelligence (AGI) and emerging technologies, it establishes a clear distinction between the third and fourth generations of knowledge management and redefines the Nonaka and Takeuchi SECI model within the context of AGI. This approach addresses a critical research gap in the knowledge management literature and introduces an innovative framework for managing knowledge in the digital era, which has not been previously explored. The study's value lies in offering practical guidance for organizations to leverage advanced technologies to enhance knowledge processes and achieve a sustainable competitive advantage. <strong>Purpose:</strong> Knowledge management (KM) has been a cornerstone of human progress, evolving from oral traditions to structured systems for preserving, transferring, and leveraging knowledge. Over time, humanity has sought innovative methods to harness knowledge as a strategic asset, particularly in organizational contexts. The advent of modern technologies, such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and the metaverse, has fundamentally transformed how knowledge is managed, shared, and created. These advancements have ushered in what is termed the "fourth generation" of knowledge management, a paradigm that responds directly to the rapid and pervasive developments in the digital era. Unlike previous generations, which focused on codifying explicit knowledge, fostering human interactions, or aligning knowledge with strategic objectives, the fourth generation leverages cutting-edge technologies to create intelligent, dynamic, and scalable knowledge ecosystems. However, despite these technological strides, the scientific literature on knowledge management lacks a comprehensive framework to fully articulate the characteristics of this fourth generation and its integration with emerging technologies, particularly Artificial General Intelligence (AGI). AGI, with its ability to mimic human cognitive capabilities, promises to revolutionize knowledge processes by automating complex tasks, personalizing learning, and uncovering hidden patterns in data. This study addresses this gap by delineating clear distinctions between the generations of knowledge management and proposing a novel model for the fourth generation. Specifically, it revisits and redefines the Nonaka and Takeuchi SECI model (Socialization, Externalization, Combination, Internalization) in the context of AGI, offering a framework that aligns with the demands of the digital age. By doing so, this research provides both theoretical insights and practical guidance for organizations seeking to harness advanced technologies for sustainable competitive advantage.<br /><strong>Methodology:</strong> This qualitative research was conducted using a systematic literature review (SLR) approach, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards to ensure rigor and transparency. The study targeted peer-reviewed articles published between 2019 and 2024, sourced from reputable scientific databases, including Scopus, Web of Science, SID, Noormags, and Civilica. These databases were selected for their comprehensive coverage of both international and regional (Persian-language) scholarship, ensuring a broad and inclusive perspective. The research process began with the identification of 1,550 relevant sources through a combination of keyword searches and logical operators tailored to knowledge management, AGI, and emerging technologies. A three-stage screening process was employed: first, 100 duplicate articles were removed; second, 100 unrelated articles were excluded based on title and abstract screening; and third, 1,100 articles were further eliminated using the Critical Appraisal Skills Programme (CASP) and AMSTAR quality assessment tools to ensure methodological rigor. This process resulted in the selection of 64 high-quality articles for in-depth analysis. Data extraction was performed using a standardized form to capture key details such as study objectives, methodologies, findings, and relevance to the fourth generation of KM. The extracted data were analyzed through two complementary methods: thematic analysis using NVivo software to identify recurring themes and patterns, and bibliometric analysis using VOSviewer software to map research clusters, author networks, and citation trends. To enhance the study’s validity, the triangulation method was employed, cross-referencing findings from multiple sources. Reliability was ensured through independent coding by two researchers, with discrepancies resolved through consensus. This rigorous methodology allowed for a robust synthesis of the current state of knowledge management and its evolution into the fourth generation.<br /><strong>Findings:</strong> The study’s findings highlight three key insights into the fourth generation of knowledge management and its integration with emerging technologies. First, AGI significantly enhances organizational knowledge creation by processing unstructured data and uncovering complex patterns that were previously inaccessible. Unlike traditional AI, which is limited to specific tasks, AGI’s ability to emulate human-like reasoning enables it to analyze vast datasets, identify trends, and generate actionable insights, thereby fostering innovation and agility. For instance, AGI can extract meaningful knowledge from diverse sources, such as social media, IoT-generated data, and organizational repositories, enabling organizations to respond swiftly to market changes. Second, emerging technologies, including AGI, IoT, and augmented/virtual reality (AR/VR), present both opportunities and challenges for knowledge management. Opportunities include enhanced organizational agility, personalized learning experiences tailored to individual needs, and improved decision-making through real-time data analysis. For example, IoT facilitates the collection of real-time data from interconnected devices, while AR/VR creates immersive environments for knowledge transfer, such as virtual training simulations. However, these technologies also introduce challenges, such as algorithmic biases, which may skew decision-making, privacy concerns related to data collection, and infrastructure limitations that hinder scalability. Organizations must address these challenges through robust ethical frameworks and investments in digital infrastructure. Third, the study’s revision of the Nonaka and Takeuchi SECI model demonstrates how AGI transforms its four stages. In the socialization phase, AGI simulates human interactions in virtual environments, enabling knowledge sharing without physical presence. In externalization, AGI’s advanced natural language processing capabilities convert tacit knowledge into explicit forms, such as reports or models, with greater accuracy and cultural sensitivity. In the combination phase, AGI integrates diverse knowledge sources to create novel insights, uncovering interdisciplinary connections that enhance innovation. Finally, in internalization, AGI supports experiential learning through adaptive simulations, allowing individuals to internalize explicit knowledge as tacit expertise. These advancements mark a significant departure from the third generation of KM, positioning the fourth generation as a dynamic, intelligent, and technology-driven paradigm.<br />Research limitations/implications: Despite its comprehensive approach, this study faced several methodological limitations. The focus on the 2019–2024 period, chosen to capture recent trends in AGI and the metaverse, may have excluded foundational studies from earlier periods. Additionally, the inclusion of only Persian and English-language articles, driven by publication volume and researcher accessibility, omitted potentially valuable contributions in other languages, such as Chinese or German. The reliance on literature review methodology, without incorporating empirical data from interviews or surveys, limited the depth of analysis. Furthermore, the use of specific databases (e.g., Scopus, SID) may have overlooked sources in less prominent repositories. To address these limitations, future research should adopt hybrid methodologies, combining literature reviews with empirical data, and incorporate multilingual sources to enhance inclusivity. Despite these constraints, the study’s findings have significant implications for both theory and practice, offering a foundation for further exploration of the fourth generation of KM.<br /><strong>Practical implications:</strong> The research underscores the importance of leveraging AGI, IoT, and AR/VR to build intelligent knowledge management systems. Organizations should invest in digital infrastructure to support these technologies, fostering a culture of human-machine collaboration to maximize their potential. For instance, AGI can personalize employee learning by analyzing individual preferences and performance, while IoT can optimize knowledge flows in supply chains. To mitigate challenges like algorithmic biases and privacy concerns, organizations should adopt technologies like blockchain for secure and transparent knowledge sharing. By rethinking the SECI model through the lens of AGI, organizations can enhance knowledge processes, achieve greater agility, and drive innovation, ultimately securing a sustainable competitive advantage in dynamic markets.<br /><strong>Originality/value:</strong> This research offers significant originality and scientific value by providing a novel framework for the fourth generation of knowledge management. Focusing on General Artificial Intelligence (AGI) and emerging technologies, it establishes a clear distinction between the third and fourth generations of knowledge management and redefines the Nonaka and Takeuchi SECI model within the context of AGI. This approach addresses a critical research gap in the knowledge management literature and introduces an innovative framework for managing knowledge in the digital era, which has not been previously explored. The study's value lies in offering practical guidance for organizations to leverage advanced technologies to enhance knowledge processes and achieve a sustainable competitive advantage. https://jkm.ihu.ac.ir/article_210278_cb29463cb1ea7f21dd732a6f75f2b1a9.pdf

Imam Hossein University Strategic Management of Organizational Knowledge 2645-4262 8 3 2025 09 23 Analysis Of Factors Influencing Knowledge Flow Using Multi-Criteria Decision-Making Techniques (The Case Of Study: Knowledge-Based Companies In Yazd Science And Technology Park) Analysis Of Factors Influencing Knowledge Flow Using Multi-Criteria Decision-Making Techniques (The Case Of Study: Knowledge-Based Companies In Yazd Science And Technology Park) 147 175 210279 10.47176/smok.2025.1940 FA Seyyed Habibollah Mirghafoori Associate Professor of Industrial Management, Faculty of Economics, Management, and Accounting, Yazd University, Yazd, Iran 0000-0001-5362-6590 Mohamad Mohsen Rayatpoor M.Sc. in Industrial Management, Faculty of Management and Accounting, University of Science and Arts, Yazd, Iran 0009-0009-5530-868X Ali Saffari Darberazi Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Bam Higher Education Complex, Bam, Iran 0000-0002-0933-1629 Journal Article <strong>Purpose: </strong>In the knowledge-based era, knowledge as an intangible asset plays a vital role in the success of organizations. Knowledge flow, which relies on information exchange and collaboration among individuals, facilitates the updating of knowledge and the advancement of new research. Knowledge-based companies, as key players in the knowledge-driven economy, require effective management of knowledge flow to maintain competitiveness and foster innovation. This study aims to identify and prioritize the factors influencing knowledge flow in knowledge-based companies located in the Yazd Science and Technology Park.<br /><strong>Methodology:</strong> This research employs multi-criteria decision-making (MCDM) techniques, including SWARA, ARAS, and COCOSO. Initially, factors affecting knowledge flow were extracted through a comprehensive review of literature and prior research. Subsequently, questionnaires were designed and distributed to experts, including managers, employees of knowledge-based companies, and academic professionals. The statistical population of this study consisted of managers and employees of knowledge-based companies in the Yazd Science and Technology Park, as well as academic experts. The collected data were analyzed and ranked using the aforementioned techniques. Finally, the results were integrated using the rank averaging method.<br /><strong>Findings:</strong> The results revealed that the most significant factors influencing knowledge flow in knowledge-based companies are management support and commitment (C8), continuous training (C4), IT infrastructure (E1), teamwork (A2), work ethic (A1), and support for teamwork (B5). These factors, in order of priority, play a key role in facilitating knowledge flow. Management support and commitment emerged as the most critical factor, highlighting the significant impact of leadership in fostering an organizational culture conducive to knowledge exchange. Continuous training and IT infrastructure were also identified as vital factors, enabling access to and updating of knowledge. Teamwork and work ethic, as human factors, enhance interaction and collaboration among employees.`<br /><strong>Research limitations/implications:</strong> While this study employs an innovative combination of multi-criteria methods to comprehensively analyze knowledge flow factors, it has certain limitations, including its focus on companies in Yazd Science and Technology Park (which necessitates additional studies to generalize findings to other regions) and partial reliance on expert opinions (which may be subject to cognitive biases). Nevertheless, the findings can serve as a foundation for designing policy models in technology parks, developing training programs to enhance human factors affecting knowledge flow, and improving technological infrastructure in knowledge-based companies. Furthermore, the proposed hybrid methodology can provide a framework for future research in other knowledge management domains.<br /><strong>Practical implications:</strong> The findings of this study can significantly assist managers of knowledge-based companies in developing operational strategies to enhance knowledge flow. Specifically, highlighting the crucial role of "management support and commitment" underscores the need for senior executives to foster an organizational culture conducive to knowledge sharing. Additionally, identifying key factors such as "continuous training" and "IT infrastructure" provides clear directions for future investments. The study recommends that policymakers in science and technology parks design specialized support programs to strengthen teamwork and develop knowledge infrastructure. On a broader scale, the proposed model can serve as a framework for evaluating the effectiveness of national-level initiatives aimed at developing knowledge-based ecosystems.<br /><strong>Originality/value: </strong>This study offers unique scientific originality and value from multiple perspectives. Methodologically, the innovative integration of three multi-criteria decision-making techniques (SWARA, ARAS, and COCOSO) for analyzing knowledge flow factors presents a pioneering approach in knowledge management literature, enhancing result accuracy and reliability while enabling comprehensive findings comparison. The research's focus on knowledge-based companies in Yazd Science and Technology Park as a distinctive sample of Iran's innovation ecosystems addresses existing gaps in regional studies. The practical findings, particularly identifying "management support and commitment" as a key factor, not only emphasize leadership's vital role in shaping knowledge-oriented culture but also provide an operational framework for policymaking in other science and technology parks nationwide. Furthermore, the study bridges theory and practice through empirical evidence of simultaneous impacts from human factors (e.g., work ethics) and technological factors (e.g., IT infrastructure) on knowledge flow, transcending traditional boundaries in knowledge management research. Notably, this represents the first study simultaneously applying SWARA, ARAS, and COCOSO methods to analyze knowledge flow in Iranian knowledge-based companies, significantly enhancing its scientific value and innovation. <strong>Purpose: </strong>In the knowledge-based era, knowledge as an intangible asset plays a vital role in the success of organizations. Knowledge flow, which relies on information exchange and collaboration among individuals, facilitates the updating of knowledge and the advancement of new research. Knowledge-based companies, as key players in the knowledge-driven economy, require effective management of knowledge flow to maintain competitiveness and foster innovation. This study aims to identify and prioritize the factors influencing knowledge flow in knowledge-based companies located in the Yazd Science and Technology Park.<br /><strong>Methodology:</strong> This research employs multi-criteria decision-making (MCDM) techniques, including SWARA, ARAS, and COCOSO. Initially, factors affecting knowledge flow were extracted through a comprehensive review of literature and prior research. Subsequently, questionnaires were designed and distributed to experts, including managers, employees of knowledge-based companies, and academic professionals. The statistical population of this study consisted of managers and employees of knowledge-based companies in the Yazd Science and Technology Park, as well as academic experts. The collected data were analyzed and ranked using the aforementioned techniques. Finally, the results were integrated using the rank averaging method.<br /><strong>Findings:</strong> The results revealed that the most significant factors influencing knowledge flow in knowledge-based companies are management support and commitment (C8), continuous training (C4), IT infrastructure (E1), teamwork (A2), work ethic (A1), and support for teamwork (B5). These factors, in order of priority, play a key role in facilitating knowledge flow. Management support and commitment emerged as the most critical factor, highlighting the significant impact of leadership in fostering an organizational culture conducive to knowledge exchange. Continuous training and IT infrastructure were also identified as vital factors, enabling access to and updating of knowledge. Teamwork and work ethic, as human factors, enhance interaction and collaboration among employees.`<br /><strong>Research limitations/implications:</strong> While this study employs an innovative combination of multi-criteria methods to comprehensively analyze knowledge flow factors, it has certain limitations, including its focus on companies in Yazd Science and Technology Park (which necessitates additional studies to generalize findings to other regions) and partial reliance on expert opinions (which may be subject to cognitive biases). Nevertheless, the findings can serve as a foundation for designing policy models in technology parks, developing training programs to enhance human factors affecting knowledge flow, and improving technological infrastructure in knowledge-based companies. Furthermore, the proposed hybrid methodology can provide a framework for future research in other knowledge management domains.<br /><strong>Practical implications:</strong> The findings of this study can significantly assist managers of knowledge-based companies in developing operational strategies to enhance knowledge flow. Specifically, highlighting the crucial role of "management support and commitment" underscores the need for senior executives to foster an organizational culture conducive to knowledge sharing. Additionally, identifying key factors such as "continuous training" and "IT infrastructure" provides clear directions for future investments. The study recommends that policymakers in science and technology parks design specialized support programs to strengthen teamwork and develop knowledge infrastructure. On a broader scale, the proposed model can serve as a framework for evaluating the effectiveness of national-level initiatives aimed at developing knowledge-based ecosystems.<br /><strong>Originality/value: </strong>This study offers unique scientific originality and value from multiple perspectives. Methodologically, the innovative integration of three multi-criteria decision-making techniques (SWARA, ARAS, and COCOSO) for analyzing knowledge flow factors presents a pioneering approach in knowledge management literature, enhancing result accuracy and reliability while enabling comprehensive findings comparison. The research's focus on knowledge-based companies in Yazd Science and Technology Park as a distinctive sample of Iran's innovation ecosystems addresses existing gaps in regional studies. The practical findings, particularly identifying "management support and commitment" as a key factor, not only emphasize leadership's vital role in shaping knowledge-oriented culture but also provide an operational framework for policymaking in other science and technology parks nationwide. Furthermore, the study bridges theory and practice through empirical evidence of simultaneous impacts from human factors (e.g., work ethics) and technological factors (e.g., IT infrastructure) on knowledge flow, transcending traditional boundaries in knowledge management research. Notably, this represents the first study simultaneously applying SWARA, ARAS, and COCOSO methods to analyze knowledge flow in Iranian knowledge-based companies, significantly enhancing its scientific value and innovation. https://jkm.ihu.ac.ir/article_210279_5ae300cff97e89e5204fc3dd7b59982a.pdf