طراحی مدل حکمرانی تحقیق و توسعه در هوش مصنوعی با رویکرد همگرایی سیاست‌گذاری، سرمایه‌گذاری و نوآوری: مطالعه‌ای کیفی مبتنی بر تحلیل مضمون

نجمه عنبری; رضا رادفر; محمدکاظم  صیادی

Authors

Najmeh Anbari Department of Technology Management, SR.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0009-0000-7919-0166
Reza Radfar Department of Technology Management, SR.C., Islamic Azad University, Tehran, Iran Author https://orcid.org/0000-0002-3951-9905
Mohammad Kazem Sayadi ICT Research Institute, Tehran, Iran Author https://orcid.org/0000-0002-1401-7319

Keywords:

Artificial Intelligence Governance, Research and Development (R&D), Policy, making, Investment, Innovation, Thematic Analysis, Convergence

Abstract

This study aimed to design and explain a governance model for artificial intelligence research and development (AI R&D), emphasizing the mechanisms through which policy-making, investment, and innovation converge within the Information and Communication Technology Research Institute. This applied, exploratory qualitative study employed thematic analysis. The study population comprised managers, senior experts, and researchers working at the Information and Communication Technology Research Institute. Fourteen experts were recruited through purposive and snowball sampling, and data were collected through in-depth semi-structured interviews until theoretical saturation was achieved. Data were analyzed following Braun and Clarke’s six-phase thematic analysis procedure using MAXQDA. Credibility and trustworthiness were strengthened through participant review, coding review, comparison of expert perspectives, and repeated reference to the original data. Inter-coder reliability was assessed using Cohen’s kappa coefficient. Analysis of the 14 interviews generated 4,708 initial coding instances, which were progressively refined and organized into 130 basic themes, 45 organizing themes, and 11 global themes. The final model comprised three principal governance domains: AI R&D policy governance, R&D investment governance, and AI innovation governance. The “convergence capability of policy-making, investment, and innovation” emerged as the central mechanism of the model and consisted of four dimensions: alignment of goals and priorities, joint decision-making and resource allocation, integration of information and processes, and feedback and adaptability. Institutional structure and organizational capacity, data governance and technical infrastructure, responsible AI, stakeholder participation, and risk and lifecycle governance were also identified as key enabling components. A kappa coefficient of 0.882 demonstrated a high level of coding agreement. Effective AI R&D governance requires a systematic linkage among policy priorities, resource allocation, innovation capabilities, and institutional, data, and technical mechanisms. Accordingly, establishing joint decision-making arrangements, aligning budgets with research priorities, and developing integrated monitoring, feedback, and adaptive mechanisms can strengthen coordination, accountability, risk management, and the creation of public value in AI research and development.

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