فصلنامه روندها و دستاوردها در فناوری یادگیری

فصلنامه روندها و دستاوردها در فناوری یادگیری

پیشران‌های بنیادین پذیرش هوش مصنوعی در سازمان‌های آموزش‌محور: یک مدل علّی غیرخطی مبتنی بر ISM–MICMAC

نوع مقاله : پژوهشی اصیل

نویسندگان
1 گروه مدیریت فناوری اطلاعات، واحد بین‌المللی کیش، دانشگاه آزاد اسلامی،کیش، ایران.
2 گروه مدیریت فناوری اطلاعات، واحد تهران مرکزی، دانشگاه آزاد اسلامی، تهران، ایران
3 گروه مدیریت فناوری اطلاعات، واحد تهران جنوب، دانشگاه آزاد اسلامی، تهران، ایران
چکیده
گسترش فناوری‌های نوظهور به ویژه هوش مصنوعی (AI)، سازمان‌های آموزش‌محور را دچار تحولات عمیق و چالش‌های پیچیده‌ای در مسیر پذیرش نوآوری مواجه ساخته است. هدف این پژوهش طراحی و ارائه یک مدل ساختاری، سلسله‌مراتبی و غیر خطی برای شناسایی و تبیین روابط علّی میان عوامل مؤثر بر پذیرش نوآوری‌های هوش مصنوعی در این سازمان‌ها می باشد. این مدل برخلاف مدل‌های خطی و فناوری‌محور مرسوم نظیر TAM و UTAUT از طریق تبیین روابط علّی چندلایه، رویکردی نظام‌مند و جامع در تحلیل مکانیسم‌های پذیرش نوآوری هوش مصنوعی ارائه می‌دهد. پژوهش حاضر با هدف کاربردی و با اتخاذ رویکرد آمیخته در قالب مراحل کیفی و کمی انجام شد. بدین منظور، ۱۳ عامل کلیدی شناسایی شده از طریق مرور نظام‌مند ادبیات با رویکرد مدل‌سازی ساختاری-تفسیری تحلیل و در هفت سطح سلسله‌مراتبی سازمان‌دهی شد.اعتبارسنجی مدل و سنجش قدرت نفوذ و وابستگی متغیرها از طریق تحلیل میک‌مک انجام شد.یافته‌ها نشان داد بسترهای ادراک و پردازش اطلاعاتی به عنوان ریشه‌ای‌ترین و نافذترین پیشران، زیربنای سایر مؤلفه‌ها نظیر ساز و کارهای انگیزشی دیجیتال و چارچوب‌های تحول سازمانی و پویایی ساختاری را تشکیل می‌دهد. نتایج تحلیل میک‌مک نیز حاکی از ثبات ساختاری سیستم و شفافیت روابط علّی میان متغیرها بود. مدل پیشنهادی با ارائه چارچوبی چند بعدی، راهنمای عملی برای سیاست‌گذاران فراهم می‌کند تا منابع استراتژیک خود را به‌جای تمرکز بر لایه‌های ابزاری سطحی، بر توسعه بسترهای ادراکی و پردازش اطلاعاتی متمرکز کنند. این رویکرد با جلوگیری از اتلاف منابع ، پذیرش پایدار و مؤثر هوش مصنوعی در سازمان‌های آموزش‌محور را فراهم می‌سازد.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Fundamental Drivers of Artificial Intelligence Adoption in Education-Oriented Organizations: An ISM–MICMAC–Based Nonlinear Causal Model

نویسندگان English

Aref Shams 1
Seyed Abdollah Amin Mousavi 2
Mohammad Malekinia 3
1 Department of Information Technology Management, KI.C., Islamic Azad University, Kish, Iran, Email: a.shams@iau.ac.ir
2 Department of Information Technology Management, CT.C., Islamic Azad University, Tehran, Iran
3 Department of Information Technology Management, ST.C., Islamic Azad University, Tehran,
چکیده English

The rapid expansion of emerging technologies, particularly artificial intelligence (AI), has subjected education-oriented organizations to profound transformations and complex challenges in the process of innovation adoption. The purpose of this study is to design and propose a structural, hierarchical, and non-linear model to identify and explain the causal relationships among the factors influencing the adoption of AI-driven innovations in such organizations. Unlike conventional linear and technology-centered models such as the (TAM) and the (UTAUT), the proposed model adopts a systematic and holistic perspective by elucidating multi-layered causal relationships underlying AI innovation adoption.This applied research employs a mixed-methods approach, integrating qualitative and quantitative phases. Thirteen key factors were identified through a systematic literature review and subsequently analyzed using Interpretive Structural Modeling (ISM), through which they were organized into seven hierarchical levels. Model validation and the assessment of variables’ driving and dependence power were conducted using MICMAC analysis. The findings reveal that perceptual and information-processing infrastructures constitute the most fundamental and influential driving forces, forming the foundation for other components such as digital motivational mechanisms, organizational transformation frameworks, and structural dynamism. The MICMAC results further confirm the structural stability of the system and the clarity of causal relationships among variables. By offering a multidimensional analytical framework, the proposed model provides practical guidance for policymakers and organizational leaders to allocate strategic resources toward the development of perceptual and information-processing infrastructures rather than focusing on superficial instrumental layers. This approach not only prevents resource misallocation but also facilitates the sustainable and effective adoption of artificial intelligence within education-oriented organizations.

کلیدواژه‌ها English

Artificial Intelligence
Innovation Adoption
Education-Oriented Organizations
Interpretive Structural Modeling
MICMAC Analysis
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  • تاریخ دریافت 25 آذر 1404
  • تاریخ بازنگری 20 بهمن 1404
  • تاریخ پذیرش 03 اسفند 1404
  • تاریخ انتشار 01 فروردین 1405