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توکلی, محمدحسن, مومیوند, حجت الله. (1404). نسل‌های مدیریت دانش با تمرکز بر نسل چهارم: بازنگری مدل SECI. پدافند الکترونیکی و سایبری, (), 114-146. doi: 10.47176/smok.2025.1938
محمدحسن توکلی; حجت الله مومیوند. "نسل‌های مدیریت دانش با تمرکز بر نسل چهارم: بازنگری مدل SECI". پدافند الکترونیکی و سایبری, , , 1404, 114-146. doi: 10.47176/smok.2025.1938
توکلی, محمدحسن, مومیوند, حجت الله. (1404). 'نسل‌های مدیریت دانش با تمرکز بر نسل چهارم: بازنگری مدل SECI', پدافند الکترونیکی و سایبری, (), pp. 114-146. doi: 10.47176/smok.2025.1938
توکلی, محمدحسن, مومیوند, حجت الله. نسل‌های مدیریت دانش با تمرکز بر نسل چهارم: بازنگری مدل SECI. پدافند الکترونیکی و سایبری, 1404; (): 114-146. doi: 10.47176/smok.2025.1938

نسل‌های مدیریت دانش با تمرکز بر نسل چهارم: بازنگری مدل SECI

مدیریت راهبردی دانش سازمانی
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 08 مهر 1404    اصل مقاله (1.78 M)
نوع مقاله: مقاله مروری
شناسه دیجیتال (DOI): 10.47176/smok.2025.1938
نویسندگان
محمدحسن توکلی* 1؛ حجت الله مومیوند2
1دانشجوی کارشناسی‌ارشد، رشته مدیریت دانش، دانشکده دانش و هوش شناختی، دانشگاه جامع امام حسین (ع)، تهران، ایران
2پژوهشگر مرکز دانش و هوش شناختی دانشگاه جامع امام حسین (ع)، تهران، ایران
چکیده
هدف: بشر از دیرباز به دنبال راه‌هایی برای حفظ، انتقال و بهره‌برداری از دانش بوده است. ظهور فناوری‌های نوین مانند هوش مصنوعی، یادگیری ماشین، اینترنت اشیا و متاورس، نحوه‌ی مدیریت، تبادل و خلق دانش را به طور بنیادین دگرگون کرده است. نسل چهارم مدیریت دانش، پاسخ مستقیمی به تحولات سریع و گسترده در دنیای دیجیتال است. با وجود این پیشرفت‌ها ادبیات علمی مدیریت دانش همچنان فاقد چارچوبی جامع برای تبیین نسل چهارم مدیریت دانش و ادغام آن با فناوری‌های نوظهور است. این پژوهش با تعریف مرز روشنی میان نسل‌های مدیریت دانش به ارائه مدلی نوین برای مدیریت دانش از طریق بازنگری مدل نوناکا و تاکوچی در عصر هوش مصنوعی عمومی می‌پردازد.
روش پژوهش: این پژوهش کیفی با رویکرد مرور سیستماتیک ادبیات و بر اساس استانداردهای گزارش‌دهی PRISMA انجام شد. جامعه آماری پژوهش شامل مقالات منتشرشده بین سال‌های ۲۰۱۹ تا ۲۰۲۴ در پایگاه‌های علمی Scopus، Web of Science، SID، Noormags و Civilica بود. از میان ۱۵۵۰ منبع شناسایی‌شده، پس از نمونه‌گیری هدفمند و غربالگری سه‌مرحله‌ای (حذف ۱۰۰ مقاله تکراری، ۱۰۰ مقاله غیرمرتبط در مرحله اولیه، و ۱۱۰۰ مقاله در مرحله ثانویه با استفاده از ابزارهای ارزیابی کیفیت CASP و AMSTAR)، ۶۴ مقاله انتخاب شد. داده‌ها با استفاده از فرم استاندارد استخراج و از طریق تحلیل مضمونی با نرم‌افزار NVivo و تحلیل کتاب‌سنجی با نرم‌افزار VOSviewer بررسی شدند. روایی پژوهش از طریق روش triangulation و پایایی آن با کدگذاری مستقل توسط دو پژوهشگر تأمین شد.
یافته‌ها: نخست، هوش مصنوعی عمومی از طریق پردازش داده‌های غیرساختاریافته و کشف الگوهای پیچیده، خلق دانش سازمانی را تسهیل می‌کند؛ دوم، فناوری‌های نوظهور فرصت‌هایی نظیر ارتقای چابکی سازمانی و شخصی‌سازی یادگیری را فراهم می‌سازند، اما با چالش‌هایی از قبیل سوگیری‌های الگوریتمی، نگرانی‌های مرتبط با حریم خصوصی، و محدودیت‌های زیرساختی مواجه‌اند؛ سوم، بازنگری مدل نوناکا و تاکوچی نشان داد که هوش مصنوعی عمومی با شبیه‌سازی تعاملات انسانی و تحلیل پیشرفته داده‌ها، مراحل اجتماعی‌سازی، بیرونی‌سازی، ترکیب، و درونی‌سازی را به‌طور قابل‌توجهی بهبود می‌بخشد.
نتیجه‌گیری: نسل چهارم مدیریت دانش با ادغام هوش مصنوعی عمومی و فناوری‌های مکمل، پارادایمی هوشمند و پویا ایجاد کرده که کارایی فرآیندهای دانشی را ارتقا داده و مزیت رقابتی پایدار فراهم می‌کند.
اصالت/ارزش: این پژوهش نخستین مطالعه در سطح ملی است که با تمرکز بر هوش مصنوعی عمومی و فناوری‌های نوظهور، ضمن مرزبندی دقیق نسل‌های مدیریت دانش، به بازتعریف مدل نوناکاو تاکوچی در بستر نسل چهارم پرداخته و چارچوبی نوآورانه برای مدیریت دانش در عصر دیجیتال ارائه می‌دهد؛ امری که در ادبیات پژوهشی پیشین مغفول مانده است.
کلیدواژه‌ها
هوش مصنوعی؛ هوش مصنوعی عمومی؛ مدل نوناکا و تاکوچی؛ نسل چهارم مدیریت دانش
عنوان مقاله [English]
The Evolution of Knowledge Management Generations with a Focus on the Fourth Generation: Revisiting the SECI Model
نویسندگان [English]
Mohammad Hasan Tavakoli1؛ Hojatollah Momivand2
1Masters Student, Knowledge Management, Faculty of Knowledge and Cognitive Intelligence, Imam Hussein University, Tehran, Iran
2Researcher, Center for Knowledge and Cognitive Intelligence, Imam Hossein University, Tehran, Iran
چکیده [English]
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.
کلیدواژه‌ها [English]
Artificial Intelligence, General Artificial Intelligence, Nunaka Model and Takucci, Fourth Generation of Knowledge Management
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