The Evolution of Knowledge Management Generations with a Focus on the Fourth Generation: Revisiting the SECI Model

Tavakoli, Mohammad Hasan; Momivand, Hojatollah

doi:10.47176/smok.2025.1938

The Evolution of Knowledge Management Generations with a Focus on the Fourth Generation: Revisiting the SECI Model

Document Type : Review paper

Authors

¹ Masters Student, Knowledge Management, Faculty of Knowledge and Cognitive Intelligence, Imam Hussein University, Tehran, Iran

² Researcher, Center for Knowledge and Cognitive Intelligence, Imam Hossein University, Tehran, Iran

10.47176/smok.2025.1938

Abstract

Purpose: 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.
Methodology: 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.
Findings: 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.
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.
Practical implications: 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.
Originality/value: 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.

Keywords

Main Subjects

Knowledge Management

License

Published by Imam Hossein University. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

References

Ahmadi, S., & Rezaei, M. (2019). The application of artificial intelligence in knowledge management. Journal of Information Technology Management, 12(3), 45–63. (In Persian)

Amsari, D., Wahyuni, E. S., & Fadhilaturrahmi, F. (2024). The social learning theory Albert Bandura for elementary school students. Jurnal Basicedu, 8(2), 1654–1662. https://doi.org/10.31004/basicedu.v8i2.7247

Andriani, M., Christiandy, A., Wiratmadja, I. I., & Sunaryo, I. (2022). Knowledge management effectiveness model in facilitating generation differences. Knowledge Management Research & Practice, 20(6), 947–959. https://doi.org/10.1080/14778238.2022.2129494

Anshari, M., Syafrudin, M. A., Tan, A., Fitriyani, N., & Alas, Y. (2023). Optimisation of knowledge management (KM) with machine learning (ML) enabled. Information, 14(1), 35. https://doi.org/10.3390/info14010035

Arshi, O., & Chaudhary, A. (2024). Overview of artificial general intelligence (AGI). In Advanced technologies and societal change (pp. 1–15). Springer. https://doi.org/10.1007/978-981-97-3222-7_1

Azizian, A. (2023). An exploration of science and knowledge in ancient Iran. Proceedings of the Fourth National Conference on History Education. (In Persian)

Azmi, Z., Erlina, E., Muda, I., & Erwin, K. (2024). Exploring the use of knowledge-based view in accounting and the business environment. Ekombis Review: Jurnal Ilmiah Ekonomi Dan Bisnis, 12(3). https://doi.org/10.37676/ekombis.v12i3.5883

Bailey, A. (2017). Can CoP theory be applied? Exploring praxis in a community of practice on gender. Knowledge Management for Development Journal, 13(3), 80–92.

Barbera, J., Naibert, N., Komperda, R., Pentecost, T. C., & Barbera, J. (2023). Augmented reality in medical education: A systematic review. Medical Education Online, 28(1), 1–15. https://doi.org/10.1080/10872981.2022.2136873

Barbera, J., Naibert, N., Komperda, R., Pentecost, T. C., & Barbera, J. (2023). Augmented reality in medical education: A systematic review. Medical Education Online, 28(1), 1–15.

Barbosa, R. S., Baeta, O. V., Prado Júnior, P. P. do, & Abrantes, L. A. (2024). The theory of organizational action in the integrated worker health care subsystem (SIASS): Elucidating behaviors and integrating occupational health actions. International Conference on Integrated Care Management Systems. https://doi.org/10.56238/i-cims-028

Bergh, D. D., D’Oria, L., Crook, T. R., & Roccapriore, A. Y. (2024). Is knowledge really the most important strategic resource? A meta-analytic review. Strategic Management Journal. https://doi.org/10.1002/smj.3645

Boateng, S. L. (2025). Embracing knowledge gaps: A pathway to growth for marketers. Development and Learning in Organizations. https://doi.org/10.1108/dlo-08-2024-0238

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.

Capestro, M., & Kinkel, S. (2020). Industry 4.0 and knowledge management: A review of empirical studies. Journal of Manufacturing Technology Management, 31(5), 1019–1038.

Cerchione, R., Centobelli, P., Borin, E., Usai, A., & Oropallo, E. (2024). The WISED knowledge-creating company: Rethinking SECI model in light of the digital transition. Journal of Knowledge Management. https://doi.org/10.1108/jkm-02-2024-0133

Christin, L., Masnita, Y., Yusran, H. L., & Tanuwijaya, J. (2024). The influence of strategic human resource practices and ethical leadership on organizational performance of star hotels: Mediated by intellectual capital and sustainable competitive advantage. Dinasti International Journal of Education Management and Social Science, 6(1), 64–81. https://doi.org/10.38035/dijemss.v6i1.3060

Chuah, K. B., & Law, K. M. Y. (2020). What is organizational learning? In Organizational learning in Asia (pp. 3–14). Springer. https://doi.org/10.1007/978-3-030-23997-8_1

Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans—and where they can’t (yet). McKinsey Quarterly.

Cohendet, P., Dupouët, O., Llerena, P., Naggar, R., & Rampa, R. (2024). Knowledge-based approaches to the firm: An idea-driven perspective. Industrial and Corporate Change. https://doi.org/10.1093/icc/dtae032

Dalkir, K. (2011). Knowledge management in theory and practice (2nd ed.). MIT Press.

Dalkir, K. (2023). Key milestones in the evolution of knowledge management: What is next? In K. Dalkir (Ed.) , Knowledge management in theory and practice (pp. 47–65). Springer. https://doi.org/10.1007/978-3-031-38696-1_3

Dalkir, K. (2023).Knowledge Management in Theory and Practice (4th ed.). MIT Pres

Davenport, T. H., & Prusak, L. (1998). Working knowledge: How organizations manage what they know. Harvard Business Review Press.

Davenport, T. H., & Prusak, L. (2022). Working knowledge: How organizations manage what they know (3rd ed.). Harvard Business Review Press.

Dei, D.-G. J., Kankam, P., Anane-Donkor, L., Puttick, C. P., & Peasah, T. (2024). Knowledge repositories for managing knowledge in learning organizations. Electronic Journal of Knowledge Management, 22(1). https://doi.org/10.34190/ejkm.22.1.3018

Deng, X., & Lu, Y. (2022).Knowledge sharing in organization: A systematic review.Cogent Business & Management, 9(1), Article 2195027. https://doi.org/10.1080/23311975.2023.2195027

Drucker, P. F. (1993). Post-capitalist society. HarperBusiness.

Dutra de Andrade, R., & Benfica, V. C. (2023). The evolution of knowledge sharing and innovation: Exploring epistemological reflections. Contribuciones a Las Ciencias Sociales. https://doi.org/10.55905/revconv.16n.7-228

Edgar, W. B., & Albright, K. S. (2022). Knowledge management activities: Conceptual foundations and research issues. Journal of Information Science, 016555152110695. https://doi.org/10.1177/01655515211069538

Engel, C., Ebel, P., & Leimeister, J. M. (2022). Cognitive automation. Electronic Markets, 32(1), 339–350. https://doi.org/10.1007/s12525-021-00519-7

Franco, M. (2010). Meeting organizational performance with shared knowledge management processes. In Knowledge management. InTech. https://doi.org/10.5772/8454

Ghafarian, A., Rahmati, H., & Khajeh, A. (2025). Describing the organizational learning network and explaining the influencing factors: A case study of Khorasan Regional Electricity Company. Strategic Management of Organizational Knowledge, 8(1), 11–32. https://doi.org/10.47176/smok.2025.1821 (In Persian)

Gómez Villalpando, A. (2025). Aprendizaje organizacional de la gestión educativa de la innovación. Emerging Trends in Education, 7(14), 26–49. https://doi.org/10.19136/etie.v7n14.6307

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.

Gupta, B., & Achhnani, B. (2024). Structuring organizations for KM. Development and Learning in Organizations, 38(3), 19–22. https://doi.org/10.1108/DLO-08-2023-0169

Hadjimichael, D., Pyrko, I., & Tsoukas, H. (2023). Beyond tacit knowledge: How Michael Polanyi’s theory of knowledge illuminates theory development in organizational research. Academy of Management Review. https://doi.org/10.5465/amr.2022.0289

Halford, F., & Ables, D. M. (2023). The National Cultural Resources Information Management System (NCRIMS). Advances in Archaeological Practice, 11(1), 52–62. https://doi.org/10.1017/aap.2022.39

Hassabis, D., Silver, D., & Kavukcuoglu, K. (2020). Artificial general intelligence: Toward a new frontier. Nature Communications, 11(1), 24–31. https://doi.org/10.1038/s41467-020-16554-9

Hassani Ahangar, M. R., Tavallaei, R., & Shadmanfar, M. H. (2023). Presenting the model of a wise core - a capable network for the role-playing of universities in the knowledge management of modern Islamic civilization issues based on imam khamenei's thought. Strategic Management of Organizational Knowledge, 6(4), 21-48. https://doi.org/10.47176/smok.2023.1659

Hosseini, S. J., Rezaei-Noor, J., Masoumi-Kashani, M. H., & Akbari, A. (2021). KM evaluation in public sector using EFQM. Industrial Management & Data Systems, 121(5), 1093–1115. https://doi.org/10.1108/IMDS-03-2020-0138 (In Persian)

Huang, L., & Zhang, M. (2023). AI-driven learning paths: Enhancing personalized education through artificial intelligence. International Journal of Educational Technology, 58(2), 45–58.

Idrees, H., Xu, J., Haider, S. A., & Tehseen, S. (2023).A systematic review of knowledge management and new product development projects.Journal of Innovation & Knowledge, 8(2), 100350. https://doi.org/10.1016/j.jik.2023.100350

Järvelä, S., Zhao, G., Nguyen, A., & Chen, H. (2025). Hybrid intelligence: Human–AI coevolution and learning. British Journal of Educational Technology. https://doi.org/10.1111/bjet.13560

Jiang, Y., Zhang, H., & Chen, Y. (2024). Artificial intelligence and human interaction: A new perspective on knowledge management. Journal of AI and Knowledge Management, 12(2), 115–128.

Jorge, H., Betancur, A., Gastón, D., Rodríguez, S., Elkin, D., Rave, G., & Moncada, E. A. (2023). Gestión del conocimiento en 10 empresas colombianas siguiendo el modelo Nonaka y Takeuchi: Estudio de casos múltiples entrelazados [Knowledge management in 10 Colombian companies following the Nonaka and Takeuchi model: A multiple case study]. Cuadernos de Administración, 38(74). https://doi.org/10.25100/cdea.v38i74.11740

Joshi, K. (2024). Artificial general intelligence (AGI): A comprehensive review. Journal of the Epidemiology Foundation of India, 2(3). https://doi.org/10.56450/jefi.2024.v2i03.004

Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37–50. https://doi.org/10.1016/j.bushor.2019.09.003

Karmakar, P., Sinha, S., & Pal, D. (2024). Artificial intelligence. International Journal of Advanced Research in Science, Communication and Technology. https://doi.org/10.48175/ijarsct-19613

Kerr, J., Smith, B., & Roberts, P. (2023). Artificial general intelligence and its implications for knowledge management: A review of recent trends. Journal of Knowledge Management, 27(4), 850–865. https://doi.org/10.1108/JKM-04-2022-0289

Khalil, C., Khalil, C., & Khalil, S. (2020). Exploring knowledge management in agile software development organizations. International Entrepreneurship and Management Journal, 16(2), 555–569. https://doi.org/10.1007/s11365-019-00582-9

Khamenei, A. (2018). Remarks during a meeting with cognitive science development headquarters officials. (In Persian)

Khamenei, A. (2021). Remarks during a meeting with a group of scientific elites and outstanding talents. (In Persian)

Khamenei, A. (2024). Remarks during the first meeting with the fourteenth government cabinet. (In Persian)

Kilimova, L., Cherkashin, M. D., & Spichina, A. O. (2024). Knowledge management as a strategic resource for organizational development. Izvestiâ Ûgo-Zapadnogo Gosudarstvennogo Universiteta, 14(5), 213–226. https://doi.org/10.21869/2223-1552-2024-14-5-213-226

Kühbacher, D. (2023). A dynamic and empowering organizational learning system. In Organizational learning in dynamic environments (pp. 201–231). Springer. https://doi.org/10.1007/978-981-19-4078-1_9

Li, X., Qiang, Q., Huang, L., & Huang, C. (2022). How knowledge sharing affects business model innovation: An empirical study from the perspective of ambidextrous organizational learning. Sustainability, 14(10), 6157. https://doi.org/10.3390/su14106157

Liu, X., Zhang, T., & Wang, L. (2023). Advancements in artificial general intelligence: Challenges and potential solutions. Journal of Artificial Intelligence Research, 78(2), 145–160. https://doi.org/10.1613/jair.1.14258

Luo, Y., Yang, K., & Wu, Y. (2023). Empowering AI drug discovery with explicit and implicit knowledge. arXiv. https://doi.org/10.48550/arXiv.2305.01523

Macucule, A. J., & Valentim, M. L. P. (2024). Informação, conhecimento e inteligência organizacional: Das bases conceptuais ao comportamento epistemológico na Ciência da Informação. Transinformação, 36. https://doi.org/10.1590/2318-0889202436e248501

Madsen, D. Ø., & Andersen, P. H. (2020). Organizational knowledge management and its role in digital transformation. Knowledge and Process Management, 27(3), 239–248. https://doi.org/10.1002/kpm.1642

Magnani, L., & Biglari, A. (2024). Externalization of the mind in the meaning-making process: An integrated semiotic and cognitive science perspective. Cognitive Semiotics. https://doi.org/10.1515/cogsem-2024-2014

Mele, G., Capaldo, G., Secundo, G., & Corvello, V. (2023). Revisiting knowledge-based dynamic capabilities. Journal of Knowledge Management, 28(2), 532–563. https://doi.org/10.1108/JKM-02-2023-0121

Mihajlović, M., Marković, S., Vujanić, I., Marijanović, R. P., & Ramadhani, I. H. (2024). Knowledge and information management in the company as a strategic business resource. Oditor, 10(3), 53–67. https://doi.org/10.59864/oditor32403mm

Mihi Ramírez, A., García Morales, V. J., & Martín Rojas, R. (2011). Knowledge creation, organizational learning and their effects on organizational performance. Engineering Economics, 22(3), 309–314. https://doi.org/10.5755/j01.ee.22.3.521

Mohammadi, F. (2021). Artificial intelligence and the future of knowledge management. Tehran: University Publications. (In Persian)

Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097

Morelock, J., & Williamson, J. B. (2018). Socialization. In The Blackwell encyclopedia of sociology. Wiley. https://doi.org/10.1002/9781394260331.ch79

Motwani, J., & Kataria, A. (2024). Organizational agility: A literature review and research agenda. International Journal of Productivity and Performance Management. https://doi.org/10.1108/ijppm-07-2023-0383

Najafzadeh, M., & Soleimani, A. (2023). Deep learning in artificial intelligence. Proceedings of the Second Conference of Computer Engineering and Information Technology Students. https://civilica.com/doc/1796775 (In Persian)

Nguyen, T. Z., Le, C., Nguyen, M. T., Nguyen, G., Lien, T. T. H., & Nguyen, O. (2024). The organisational impact of agility: A systematic literature review. Management Review Quarterly. https://doi.org/10.1007/s11301-024-00446-9

Ning, X., Yang, Y., Han, Y., & Lv, Y. (2024). Incentivizing Tacit Knowledge Sharing in Competitive and Heterogeneous Environments. IEEE Transactions on Engineering Management, 1–20. https://doi.org/10.1109/tem.2024.3406707

Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press.

Nonaka, I., & Takeuchi, H. (2021). The evolving role of knowledge management in the age of digital transformation. Journal of Knowledge Management, 25(6), 1413–1435. https://doi.org/10.1108/JKM-06-2020-0456

Olan, F., Ogiemwonyi Arakpogun, E., Suklan, J., Nakpodia, F., Damij, N., & Jayawickrama, U. (2022). Artificial intelligence and knowledge sharing: Contributing factors to organizational performance. Journal of Business Research, 145, 605–615. https://doi.org/10.1016/j.jbusres.2022.03.008

Paliwal, J. P., & Girard, J. (2015). Defining knowledge management: Toward an applied compendium. Online Journal of Applied Knowledge Management, 3(1), 1–20.

Paul, M. P., & José, V. (2024). The generative AI solutions for enhancing knowledge management: Literature review and roadmap. Proceedings of the European Conference on Knowledge Management, 25(1). https://doi.org/10.34190/eckm.25.1.2770

Piwowarczyk, Z. (2024). Knowledge sharing in distributed teams—The impact of virtual collaboration tools. International Multidisciplinary Scientific GeoConference SGEM, 24, 51–58. https://doi.org/10.5593/sgem2024/2.1/s07.07

Polanyi, M. (1966). The tacit dimension. Routledge & Kegan Paul.

Prihandoko, D., Arief, M., Elidjen, E., Alamsjah, F., & Rizky, Z. S. (2024). Leveraging artificial intelligence for knowledge management: A systematic literature analysis. Proceedings of the International Conference on Computer and Information Technology, 1, 1–6. https://doi.org/10.1109/iccit62134.2024.10701138

Qin, R., Yuan, Y., & Wang, F.-Y. (2020). Blockchain-based knowledge automation for CPSS-oriented parallel management. IEEE Transactions on Computational Social Systems, 7(5), 1180–1188. https://doi.org/10.1109/TCSS.2020.3023046

Rathore, H., & Singh, A. (2022). Knowledge management and its impact on organizational performance: A systematic review. Journal of Knowledge Management, 26(2), 367–390. https://doi.org/10.1108/JKM-02-2021-0227

Raysa, G. R., Kragulj, F., & Pinheiro, P. (2022). The wise leader: Where did the roads pave by Nonaka and Takeuchi lead to? Proceedings of the European Conference on Knowledge Management, 23(2). https://doi.org/10.34190/eckm.23.2.350

Robson, E. (2008). Mathematics in ancient Iraq: A social history. Princeton University Press.

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Sabeti, S., MalekMohammadi, S., & Kasraei, A. R. (2024). The role of artificial intelligence in shaping the future of modern knowledge management. Proceedings of the Eighth National Conference on Interdisciplinary Research in Engineering and Management Sciences. (InPersian)

Salas, F. (2020). Gestión del conocimiento a través del modelo de socialización, exteriorización, combinación e interiorización (SECI): En las bibliotecas de la Universidad Nacional Abierta Centro Local Falcón. Revista de Investigación, 3(1), 41–60. https://doi.org/10.33264/RPA.202001-04

Salem, M., Salloum, S. A., & Shaalan, K. (2024). Exploiting AI’s potential in knowledge management. In Artificial intelligence for knowledge management (pp. 283–299). Springer. https://doi.org/10.1007/978-3-031-52280-2_18

Schiuma, G., Carlucci, D., & Lerro, A. (2020). Big data and knowledge management: Implications for theory and practice. Journal of Business Research, 109, 222–234. https://doi.org/10.1016/j.jbusres.2019.11.067

Sen, A. (2024). Organizational knowledge and knowledge management—A new framework. American Journal of Management Science and Engineering. https://doi.org/10.11648/j.ajmse.20240901.11

Shrestha, Y. R., & Zhao, K. (2019). Impact of AI on knowledge management: The future of business intelligence. International Journal of Information Management, 45, 127–135. https://doi.org/10.1016/j.ijinfomgt.2018.10.006

Sinnema, C., Hannah, D., Finnerty, A., & Daly, A. J. (2021). A theory of action account of an across-school collaboration policy in practice. Journal of Educational Change, 1, 1–28. https://doi.org/10.1007/s10833-020-09408-w

Smith, A., & Liu, X. (2023). AI-driven knowledge management systems: Trends and challenges. Journal of Knowledge Management, 34(3), 200–212. https://doi.org/10.1108/JKM-03-2022-0198

Smith, A., Johnson, M., & Lee, P. (2024). Development of a multi-domain adaptive artificial general intelligence system using machine learning and expert systems. Journal of AI and Cognitive Science, 12(3), 72–84.

Sutrisno, E. R., Kindangen, P., Sendow, G. M., & Jan, A. B. H. (2024). Impact of human capital and structural capital on competitive advantage: Evidence from Indonesian provincial government institutions. RGSA: Revista de Gestão Social e Ambiental, 18(12), e010228. https://doi.org/10.24857/rgsa.v18n12-037

Terzieva, M., & Morabito, V. (2016). Learning from experience: The project team is the key. Business Systems Research, 7(1), 1–15. https://doi.org/10.1515/bsrj-2016-0001

Thomas, A. (2024). Digitally transforming the organization through knowledge management: A socio-technical system (STS) perspective. European Journal of Innovation Management, 27(9), 437–460. https://doi.org/10.1108/EJIM-02-2024-0114

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375

Tvlaei, R. (2023). Human-AI interaction in knowledge management. Strategic Management of Organizational Knowledge, 6(1), 11–21. (In Persian)

Vargas-Hernández, J. G., Almanza Jiménez, R., Calderón Campos, P., Casas Cárdenaz, R., & Palomares Salceda, F. (2016). Performance y aprendizaje organizacional bajo el enfoque de las teorías organizacionales. Revista Internacional de Investigación y Docencia, 1(3), 5–16. https://doi.org/10.21892/24627593.170

Wang, F., & Siau, K. (2019). Artificial intelligence and big data analytics for smart healthcare. Journal of Computer Information Systems, 59(3), 278–285. https://doi.org/10.1080/08874417.2018.1552582

Wang, L., et al. (2023). IoT-driven knowledge management in supply chain optimization. IEEE Internet of Things Journal, 10(12), 10567–10578. https://doi.org/10.1109/JIOT.2023.3245678

Wang, P., & Lee, S. (2024). Personalized learning through AI: A transformative approach in education and knowledge management. Journal of Educational Technology and AI, 21(4), 45–60.

Wang, Y., Li, X., & Zhao, J. (2024). Personalizing learning through AI: A new frontier for educational technology. Journal of AI in Education, 33(1), 120–137.

Wiig, K. M. (1997). Knowledge management: An introduction and perspective. Journal of Knowledge Management, 1(1), 6–14. https://doi.org/10.1108/EUM0000000004578

Xie, G., Su, X., & Huang, M. (2024). Social media, knowledge management, and learning in farmer innovation. HighTech and Innovation Journal, 5(2), 295–311. https://doi.org/10.28991/hij-2024-05-02-06

Yousefi, K., & Hosseini, M. (2020). Neural networks and their application in analyzing knowledge management data. Scientific Journal of Organizational Management, 10(1), 75–88. (In Persian)

Zakeri Dana, M. (2023). Three knowledge management trends in 2024. https://danakm.com (In Persian)

Zamani, M., & Khamseh, A. (2025). Designing a conceptual framework for transferring advanced technologies in the Fifth Industrial Revolution: A thematic analysis approach. StrategicManagement of Organizational Knowledge, 8(1), 121–145. https://doi.org/10.47176/smok.2025.1854 (InPersian)

Zaoui Seghroucheni, O., Al Achhab, M., & Lazaar, M. (2023). Systematic review on the conversion of tacit knowledge. Proceedings of the International Conference on Computer and Information Sciences, 1, 1–6. https://doi.org/10.1109/cist56084.2023.10409884

Zarei, N., Mohammadi, F., & Hosseini, R. (2020). Generations of knowledge management and its evolution. Knowledge Management Research Journal, 8(2), 15–32. (In Persian)

Zgud, A., & Kulesza, K. (2024). Artificial intelligence: Concepts and interpretation. Fxyz, 22(24), 1–10. https://doi.org/10.52652/fxyz.22.24.1

Zhang, Y., & Zhan, X. (2021). A comprehensive framework of knowledge management: From theory to practice. Journal of Knowledge Management, 25(10), 2185–2204. https://doi.org/10.1108/JKM-01-2020-0102

Zhuravlev, D. V., & Smolin, V. S. (2024). Neural network structure design for AGI. Future, 2(1), 1–10. https://doi.org/10.20948/future-2024-2-3