Rapid developments in the media ecosystem, the emergence of convergent technologies, and the imperative to compete for audience trust and attention have made it inevitable for the national broadcaster to transition from traditional management to intelligent, learning, and data-driven governance models. The primary objective of this research is to design a model for the application of knowledge management in media governance, with a particular focus on the Islamic Republic of Iran Broadcasting (IRIB). Methodologically, this study is applied and qualitative-exploratory, employing the systematic Grounded Theory approach (Strauss and Corbin). Data were gathered through in-depth, semi-structured interviews with 15 senior media executives, editors, and experts from the national media until theoretical saturation was attained. Data analysis was conducted across open, axial, and selective coding using MAXQDA software. Coding reliability was confirmed using Holsti’s formula (0.83) and trustworthiness was established based on Lincoln and Guba’s criteria.



Findings revealed that the core phenomenon, 'Innovative Knowledge-Driven Media Governance,' is shaped by causal conditions (environmental complexity, erosion of public trust, verification requirements, and evolving audience preferences), contextual factors (knowledge-sharing culture, learning structure, and scientific linkages), and intervening conditions (knowledge leadership, smart technologies, large language models, and incentive systems). Implementing action strategies—such as tacit knowledge codification, crisis coverage style guides, cross-functional working groups, audience data analytics, and After Action Review (AAR) learning cycles—culminates in outcomes including content innovation, enhanced message credibility, audience trust recovery, and organizational agility and resilience in narrative competition.