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طراحی مدل سامانۀ دانشیِ همزیست مبتنی بر هوش مصنوعی مولد: از مدیریت منابع انسانی مثبتگرا تا خلق هوش جمعی راهبردی | ||
| مدیریت راهبردی دانش سازمانی | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 17 تیر 1405 اصل مقاله (1.81 M) | ||
| نوع مقاله: مقاله پژوهشی با اصالت | ||
| شناسه دیجیتال (DOI): 10.47176/SMOK.2026.2029 | ||
| نویسندگان | ||
| تیمور مرجانی* 1؛ علی داوری2؛ علی سعداله3 | ||
| 1استادیار، گروه مدیریت، دانشکده علوم انسانی، دانشگاه علم و فرهنگ، تهران، ایران، tmarjani@usc.ac.ir | ||
| 2دانشیار، گروه کسب و کار جدید، دانشکده کارآفرینی، دانشکدگان مدیریت، دانشگاه تهران، تهران، ایران، ali_davari@ut.ac.ir | ||
| 3استادیار، گروه مهندسی مکانیک، دانشکده فنی و مهندسی، دانشگاه علم و فرهنگ، تهران، ایران، sadollah@usc.ac.ir | ||
| تاریخ دریافت: 07 خرداد 1405، تاریخ بازنگری: 02 تیر 1405، تاریخ پذیرش: 12 تیر 1405 | ||
| چکیده | ||
| هدف: هوش مصنوعی مولد فرصتهایی برای مدیریت دانش و منابع انسانی ایجاد کرده، اما مدلی که همافزا شکوفایی کارکنان و خلق دانش راهبردی را تلفیق کند، نادر است. این پژوهش با هدف طراحی مدل سامانۀ دانشی همزیست مبتنی بر هوش مصنوعی مولد و تبیین روابط علّی مؤلفههای آن انجام شد. روش پژوهش: پژوهش با رویکرد آمیخته اکتشافی در دو مرحله انجام شد. در مرحلۀ کیفی، دلفی فازی با ۱۵ خبره مؤلفههای اولیه را استخراج کرد. در مرحلۀ کمی، دیمتل فازی روابط علّی را تحلیل و نگاشت شناختی فازی و شبیهسازی عامل ـ محور در افق پنجساله پویاییهای سامانه را مدلسازی کرد. اعتبار محتوا با نسبت روایی محتوایی و پایایی با ضریب کندال (۷۳/۰) تأیید شد. یافتهها: پنج مؤلفه شناسایی شد؛ شفافیت الگوریتمی، حفظ تنوع شناختی، بازخورد تعامل انسان‑ماشین، یادگیری شخصیسازیشده مولد و حکمرانی اخلاقی هوش مصنوعی. «حفظ تنوع شناختی» با بیشترین علیت (۷۸/۳+) قویترین متغیر علّی بود. شبیهسازی نشان داد سامانۀ کامل خلق دانش راهبردی را ۸۷ درصد افزایش، فرسودگی شغلی را از ۳۴ به ۱۲ درصد کاهش و دقت تصمیمگیری را ۴۸ درصد بهبود میبخشد. بحث: برخلاف رویکردهای سنتی، حفظ تنوع شناختی موتور همافزایی دانش و بهزیستی است. شفافیت الگوریتمی اعتماد و بازخورد را ممکن ساخته و یادگیری شخصیشده را تحت حکمرانی اخلاقی هدایت میکند. نتیجهگیری: این پژوهش چارچوبی یکپارچه برای پیوند مدیریت منابع انسانی مثبت‑گرا، هوش مصنوعی مولد و هوش جمعی راهبردی ارائه میدهد. سازمانها باید حفظ تنوع شناختی و شفافیت الگوریتمی را در پیادهسازی اولویت دهند. تعمیمپذیری به صنایع دانشبنیان ایران محدود است. | ||
| کلیدواژهها | ||
| سامانۀ دانشی همزیست؛ شبیهسازی عامل محور؛ مدیریت منابع انسانی مثبتگرا؛ هوش جمعی راهبردی؛ هوش مصنوعی مولد | ||
| عنوان مقاله [English] | ||
| Designing a Symbiotic Knowledge System Based on Generative Artificial Intelligence: From Positive Human Resource Management to Strategic Collective Intelligence | ||
| نویسندگان [English] | ||
| Taimoor Marjani1؛ Ali Davari2؛ Ali Sadollah3 | ||
| 1Assistant Professor, Department of Management, Faculty of Humanities, University of Science and Culture, Tehran, Iran, tmarjani@usc.ac.ir | ||
| 2Associate Professor, Department of New Business, Faculty of Entrepreneurship, College of Management, University of Tehran, Tehran, Iran, ali_davari@ut.ac.ir | ||
| 3Assistant Professor, Department of Mechanical Engineering, Faculty of Engineering, University of Science and Culture, Tehran, Iran, sadollah@usc.ac.ir | ||
| چکیده [English] | ||
| Purpose: Generative artificial intelligence has created opportunities for knowledge management and human resource management, yet a model that synergistically integrates employee flourishing and strategic knowledge creation is rare. This study aims to design a symbiotic knowledge system model based on generative artificial intelligence and to explain its causal relationships. Methodology: An exploratory sequential mixed‑method approach was adopted in two phases. In the qualitative phase, fuzzy Delphi with 15 experts extracted the initial components. In the quantitative phase, fuzzy DEMATEL analyzed causal relationships, and fuzzy cognitive mapping combined with agent‑based simulation over a five‑year horizon modeled system dynamics. Content validity was confirmed using CVR, and reliability was confirmed using Kendall's coefficient (0.73). Results: Five components were identified: algorithmic transparency, cognitive diversity preservation, human‑AI interaction feedback, generative personalized learning, and ethical AI governance. "Cognitive diversity preservation" with the highest causality (+3.78) was the strongest causal driver. Simulation showed the full system increases strategic knowledge creation by 87%, reduces burnout from 34% to 12%, and improves decision‑making accuracy by 48%. Discussion: Contrary to traditional approaches, cognitive diversity preservation drives knowledge‑wellbeing synergy. Algorithmic transparency enables trust and feedback, while personalized learning operates under ethical governance. Conclusion: This study provides an integrated framework linking positive HRM, generative AI, and strategic collective intelligence. Organizations should prioritize cognitive diversity preservation and algorithmic transparency in implementation. Generalizability is limited to Iranian knowledge‑based industries. | ||
| کلیدواژهها [English] | ||
| Agent Based Modeling, Cognitive Diversity Preservation, Generative Artificial Intelligence, Positive Human Resource Management, Strategic Collective Intelligence | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 36 تعداد دریافت فایل اصل مقاله: 19 |
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