میز خدمت الکترونیک
دوره ویراستاری

  اخبار و اعلانات

 آمار

تعداد نشریات127
تعداد شماره‌ها7,196
تعداد مقالات77,227
تعداد مشاهده مقاله157,217,848
تعداد دریافت فایل اصل مقاله118,403,642
Shababi, Hooman. (1404). Unveiling Key Determinants of Science and Technology Policy Effectiveness in Iran: A Machine Learning-Based Analysis Using Soft Computing Techniques. , (), -. doi: 10.22059/ijms.2025.398164.677769
Hooman Shababi. "Unveiling Key Determinants of Science and Technology Policy Effectiveness in Iran: A Machine Learning-Based Analysis Using Soft Computing Techniques". , , , 1404, -. doi: 10.22059/ijms.2025.398164.677769
Shababi, Hooman. (1404). 'Unveiling Key Determinants of Science and Technology Policy Effectiveness in Iran: A Machine Learning-Based Analysis Using Soft Computing Techniques', , (), pp. -. doi: 10.22059/ijms.2025.398164.677769
Shababi, Hooman. Unveiling Key Determinants of Science and Technology Policy Effectiveness in Iran: A Machine Learning-Based Analysis Using Soft Computing Techniques. , 1404; (): -. doi: 10.22059/ijms.2025.398164.677769

Unveiling Key Determinants of Science and Technology Policy Effectiveness in Iran: A Machine Learning-Based Analysis Using Soft Computing Techniques

Interdisciplinary Journal of Management Studies
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 19 آذر 1404
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/ijms.2025.398164.677769
نویسنده
Hooman Shababi*
Faculty Member at Rahedanesh Institute of Higher Education
چکیده
Complex national innovation systems (NIS) in developing countries need advanced analytical tools to identify and optimize key drivers of science, technology, and innovation (STI) policy outcomes. This study applies a hybrid machine learning framework—combining Random Forest, Support Vector Machines, K-means clustering, and Principal Component Analysis—to evaluate Iran's STI policy performance from 2010 to 2024. Drawing on multi-source data, including R&D expenditures, university-industry collaboration indices, human capital metrics, and innovation outputs, the proposed model uncovers nonlinear relationships among policy variables and prioritizes the most influential factors. The results reveal that R&D intensity, institutional collaboration quality, policy implementation coherence, and human capital development are the dominant predictors of policy success. By integrating soft computing methods and empirical policy data, this work offers a replicable approach for evidence-based policymaking in emerging economies. The findings align with recent advancements in AI-driven decision support systems and contribute to the growing body of research advocating for data-driven innovation governance.
کلیدواژه‌ها
Machine learning؛ Soft computing؛ Science and technology policy؛ National innovation systems؛ R&؛ D investment
آمار
تعداد مشاهده مقاله: 84





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب