نوع مقاله : مقاله پژوهشی
نویسندگان
1 استادیار گروه مطالعات علم و فناوری، مؤسسه مطالعات فرهنگی و اجتماعی، تهران، ایران.
2 کارشناسی ارشد مدیریت فناوری، دانشگاه تهران، تهران، ایران
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
This study aims to identify and explain the functional, structural, and contextual bottlenecks of the artificial intelligence (AI) technological innovation system in Iran and to derive key points for policy intervention. Drawing on the Technological Innovation Systems (TIS) framework, the performance of the system is analyzed through the status of its core functions and the interactions among system functions, actor and institutional structures, and contextual conditions. The study adopts a qualitative approach using deductive–inductive thematic analysis. First, evidence concerning the performance and dysfunctions of system functions was extracted through a review of the literature, national documents, and policy reports. Second, data from 12 semi-structured interviews with academic experts (3), industry experts (4), and policymakers/regulators (5) were coded and interpreted using a “function–structure–context” matrix. The credibility of the findings was enhanced through participant validation, documentary triangulation, and peer auditing. The findings indicate that, despite the growth of knowledge production capacity, weaknesses in three key functions—resource mobilization, legitimation, and market formation—constitute the main bottlenecks of Iran’s AI technological innovation system. At the structural level, institutional fragmentation and overlap, geographical concentration, and weak university–industry linkages constrain the capacity of these functions. At the contextual level, macroeconomic uncertainty, sanctions, and gaps in data governance intensify external pressures and institutional uncertainty. The interaction of these factors generates three chains of systemic failure, leading to constrained development and commercialization capacity, increased regulatory uncertainty and weakened legitimacy, and, ultimately, a disconnect between knowledge production, innovative demand, and technological learning. Accordingly, policy interventions should focus on addressing systemic points of failure rather than merely creating new institutions. Key interventions include establishing or restructuring coordinated and sufficiently empowered AI regulatory arrangements; developing a data governance framework covering data quality, access, sharing, and protection; expanding computational infrastructure and innovative public procurement; strengthening university–industry intermediary institutions; and promoting clusters and innovation centers across different regions of the country. The contribution of this study lies in reconstructing the relationships among functional weaknesses, structural configurations, and contextual pressures and translating them into systemic failure chains and policy intervention points for the development of Iran’s AI technological innovation system.
کلیدواژهها [English]
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