In recent years, numerous changes and increased competition in media organizations, especially in the news and information section, have made it difficult to advance the goals and policies of the news sector of media organizations, including IRIB. In line with that, adopting new policies, including attracting talents, can be the beginning of a correct and worthy move in this field. Due to the fact that recognizing the required properties and talents to work in the news department of the IRIB is essential, this study seeks to explain the properties of the required talents to work in the news section of the IRIB. This research has been done by using the method of content analysis and data collection through interviews with managers and editors of the IRIB’s news department and other experts. Benefiting from the target inherent characteristics, properties related to learning, providing appropriate and properties resulted from the experience are the three comprehensive themes resulted from this research.

One of the most important things that can lead to the competitive advantage of the Islamic Republic of Iran Broadcasting (IRIB) organization in the turbulent environment of media organizations is the formulation and implementation of strategy or strategic management. Identifying strategic risks and preparing actions for them can increase the probability of strategic management success in the IRIB organization and increase the competitiveness of this organization in the media sphere of the country and the world. The purpose of this study is to identify and present a model of strategic risks in the IRIB. In order to achieve the research goal, grounded theory (GT) method have been used. The data required for the research in the qualitative section were obtained through in-depth interviews with fourteen experts. These people have been purposefully selected from among the deputies, consultants, managers, and experts of the IRIB organization whose organizational position or duties are related to the category of strategy, as well as university professors who are familiar with the category of risk and strategy. Data were analyzed in three stages of open, axial, and selective coding using Atlas.ti software. Based on this, 115 concepts were identified and categorized in the form of seven risks including structural risks, knowledge risks, production risks, managerial risks, technical and technological risks, financial risks, and human resource risks.

Objective This research addresses a critical organizational challenge facing the Islamic Republic of Iran Broadcasting (IRIB): the profound misalignment between conventional human resource management systems and the unique characteristics of Expert Talents from the Digital Native Generation. The study emerges from IRIB's complex operational environment, characterized by rapid technological obsolescence, fundamental shifts in audience consumption patterns, and intense competition from global digital platforms. Digital Natives—individuals born and socialized in a digitally saturated environment—now constitute a vital segment of IRIB's specialized and expert workforce in areas such as media technology, data analytics, multiplatform producers, and digital content creation. These professionals exhibit distinctive characteristics including innate digital fluency, preference for collaborative and flexible work structures, demand for continuous learning opportunities, and expectations of real-time feedback and meaningful work. The persistence of hierarchical, standardized HR models has resulted in significant organizational consequences: a documented 35% turnover rate among technical specialists during 2023–2024, measurable decline in innovation output, and strategic vulnerabilities in IRIB's digital transformation initiatives. This misalignment is particularly problematic given the crucial role these expert talents play in maintaining IRIB's competitive advantage in the evolving media landscape. The research aims to develop and validate a comprehensive, context-sensitive framework for empowering Digital Native expert talents that addresses the unique institutional, cultural, and operational realities of IRIB's environment. The study seeks to answer two primary research questions: first, identifying the causal conditions, contextual factors, intervening conditions, strategies, and outcomes in the empowerment framework; and second, validating the qualitative framework for empowering expert talents based on the Digital Native generation. Research Methodology This applied research employed the paradigmatic variant of grounded theory methodology following Strauss and Corbin’s (1998) approach. The study utilized a qualitative, exploratory research design based on an inductive approach. Participants included 12 experts selected through purposive and snowball sampling techniques, comprising university faculty members from Tehran University, Allameh Tabatabaei University, Islamic Azad University, IRIB University, and Imam Sadiq University; senior IRIB managers; and researchers from the IRIB Research Center. Data collection was conducted through in-depth, semi-structured interviews lasting 40–60 minutes each, with all interviews audio-recorded and transcribed verbatim. The data analysis followed a rigorous three-stage coding process using MAXQDA software. Open coding generated 451 initial codes, which were refined to 221 conceptual codes through merging similar concepts. Axial coding organized these codes into categories aligned with Strauss and Corbin’s paradigm model, while selective coding integrated and refined the categories to develop the core narrative. Methodological rigor was ensured through multiple validation strategies: quantitative content validation using Content Validity Ratio (CVR) with Lawshe’s threshold (≥0.59), member checking where participants reviewed preliminary findings, and peer debriefing with independent qualitative researchers. Reliability was established through retest reliability (83%), inter-coder reliability (82%), and model reliability testing (80%), with all ethical considerations—including informed consent and confidentiality—maintained throughout the research process. Findings The analysis yielded a comprehensive, validated framework comprising 27 axial categories and 182 validated open codes (82.4% of initial codes meeting CVR threshold), organized around the paradigmatic model. The causal conditions included three fundamental elements: explicit empowerment programs (6/6 validated codes), clearly defined strategic objectives (8/8 codes), and transparent performance expectations (4/4 codes). Contextual factors encompassed four critical dimensions: organizational culture emphasizing psychological safety and innovation-friendly norms (9/13 codes), transformational leadership styles that grant autonomy (7/9 codes), organizational structure with flatter hierarchies (5/7 codes), and adaptable HR policies regarding compensation and development (6/9 codes). Intervening conditions revealed three types of barriers: organizational obstacles including bureaucracy and administrative complexity (9/16 codes), individual resistance stemming from factors like technological displacement fears (8/11 codes), and environmental challenges including regulatory constraints (7/8 codes). The core phenomenon of empowerment comprised four elements: performance indicators (6/9 codes), success factors (7/8 codes), knowledge and skills development (10/13 codes), and personality traits including adaptability and intrinsic motivation (10/11 codes). Eight strategic pillars were identified: AI-driven talent identification (5/5 codes), tailored development programs using modular micro-learning (9/13 codes), technology-enabled learning including VR simulations (4/8 codes), learning culture development (5/6 codes), employee participation mechanisms (5/5 codes), continuous evaluation systems using real-time metrics (6/12 codes), workplace redesign for agility (7/7 codes), and comprehensive recognition systems (5/5 codes). Outcomes demonstrated significant positive impacts: performance improvement (8/9 codes), talent retention (6/10 codes), motivation enhancement (6/7 codes), employer branding strengthening (8/9 codes), and digital transformation acceleration (6/6 codes). Discussion & Conclusion This study presents the first empirically validated, context-sensitive framework for empowering Expert Talents from the Digital Native generation within a state media organization. The research makes significant theoretical contributions by advancing grounded theory application in HRM, demonstrating how paradigmatic analysis can effectively model complex interactions between generational characteristics, technological disruption, and institutional realities. The framework moves beyond generic best practices to provide nuanced, context-embedded solutions that address the unique challenges of media organizations undergoing digital transformation. The practical implications for IRIB are substantial and immediately actionable. Key recommendations include establishing a dedicated Digital Talent Empowerment Unit with cross-functional representation, redesigning dual career ladders to separate technical and managerial tracks, implementing digital platforms for real-time feedback mechanisms, embedding digital fluency into leadership competency models, and comprehensively revising HR policies to support flexible work arrangements and personalized development plans. The framework emphasizes that effective talent empowerment requires a holistic approach integrating cultural, structural, technological, and human dimensions tailored to generational characteristics. Study limitations include the single-case focus on IRIB, which may affect generalizability. Future research should quantitatively validate this framework across diverse cultural contexts and organizational types, particularly in other media organizations and creative industries. Longitudinal studies examining the framework's impact on key performance indicators such as innovation rates, talent retention, and digital transformation success would provide valuable insights. This research ultimately underscores that in the digital age, strategic talent empowerment is not merely an HR function but a fundamental organizational imperative for survival, innovation, and competitive advantage in the evolving media landscape.

According to Articles 44 and 175 of the Iranian Constitution and the Parliament’, s statutory approach, the Islamic Republic of Iran Broadcasting (IRIB) holds the monopoly of broadcasting radio and television programs. This paper examines the privatization capacity of IRIB. The most important obstacles for the realization of such a capacity include the Guardian Council’, s interpretation of Articles 44 and 175 of the Iranian Constitution, articles like the Statute of the Organization and Resolutions of the Supreme Council of the Cultural Revolution. However, after having reviewed the mentioned articles and through the analysis of the Guardian Council’, s interpretations, the authors came to the conclusion that privatization or at least removing such a monopoly could be possible.

The current research set out to identify and rank motivational factors that may reinforce talented individuals to work at the Islamic Republic of Iran Broadcasting Company (IRIB) through Delphi Ranking. In the first stage of the study, a comprehensive interview was conducted with a purposive sample of 30 experts based on which 245 related factors were initially detected through qualitative coding method. Next, similar factors were eliminated leading to 64 unique factors which were subcategorized into three major categories. The second and third stages of the study involved scrutinizing the five main factors in each category. Further, the identified factors were ranked and the calculation of the Kendall coefficient indicated a strong consensus among participating experts. Ultimately, three main factors were delineated including those influencing the occupational nature and space, the main factors governing organizational structure and those impacting remunerations, extra benefits and welfare facilities. Taking these factors into account can function as added incentive for the talented to cooperate with the IRIB that might be considered as a creative industry with human capital as the strongest predictor of its success.

Since the establishment of the Islamic republic of Iran Broadcasting organization (IRIB), no specific laws have been made regarding its legal nature under any of the applicable laws. Some of the laws of the country have vaguely referred to the legal nature of this important and influential entity which compares them with the structure and function of IRIB, The nature of this entity is compatible with both the nature of state-owned corporation and state institute at some time and in some cases with neither and in a period of time it is relatively compatible with the state institute. In spite of this ambiguity, the appropriate legal nature of the organization didn’t attention to it sufficiently in the drafts of law that have so far been considered by the Islamic parliament of Iran. Although the legal nature of this organization is a state institute under existing law, But Since all executive and governing bodies must have a certain legal nature in order to be governed and supervised, it is essential that this is also the case with broadcasting organization. Therefore, it is necessary to amend the existing laws, define a new and appropriate legal nature for this system and be finished all existing ambiguities.

The aim of this study was Environment Analysis of IRIB in field of sport. Mix method was methodology of this study that Run sequentially exploratory. The Population of this study in Qualitative method included experts, producers and managers in IRIB that Member of the Sports Council of the Organization that number of them was 9 persons. At the Quantitative method included Faculty members of sport science, IRIB activities and sport activities that number of them was 60 persons that selected in a targeted an accessible sampling. For data collection in Qualitative method used of Semi-structured interview and in Quantitative method used of questionnaire that made According to finding of interview. Its validity was confirmed by 11 media and sports experts and its reliability was in the strengths (0.85), weaknesses (0.94), opportunities (89.9) and threats (0.7) was calculated. For data analysis used CFA test and Mean test. The finding of this study showed that Islamic Republic of IRAN Broadcasting in field of sport has 10 Strong point, 15 Weakness point, 10 Opportunity and 5 threat. High-speed news release in field of sport was the most important of strong points. Not alignment of IRIB university graduates with field of sport TV producer was the most important of weakness points. Younger generation interested in sports was the important of opportunity points and Multiplicity of external satellite networks and easy access to the community was the important of threat points. SPSS 22 and Smart Pls 3 was 2 software that be used in data analysis.

There are two mainstreams for explaining the media policy. First approach which is the traditional approach to media policy regards the media outlets as institutions on which policies can be applied. In this respect, it rather highlights the role of governments in how they attempt to make policies for protecting the public interests. The second approach regards the media as a realm not subject to policy in terms of technological advances in communication and emergence of new media devices. Under this theoretical framework, the models and theories in the field of media policy can be separated into two mainstreams. Relying on the first theoretical framework, this qualitative study intended to theorize the policymaking procedure at Islamic Republic of Iran Broadcasting (IRIB). The gained theory suggests that such a phenomenon revolves around a theme titled "subject-oriented or authentic event", which explains the effective media policy within the context of intervening factors. replying on several causal contributing parameters. Therefore, based on information obtained from semi-structured interviews and the model derived from the research, it can be concluded that policy-making process in the Islamic Republic of Iran Broadcasting should change its approach from counseling to object oriented in critical times such as elections to attract audiences and make the policies of organization true.

Islamic Republic of Iran Broadcasting (IRIB) as one of the biggest broadcasting organizations, produces thousands of hours of media content daily. Accordingly, the IRIBchr('39')s archive is one of the richest archives in Iran containing a huge amount of multimedia data. Monitoring this massive volume of data, and brows and retrieval of this archive is one of the key issues for this broadcasting. The aim of this research is to design a content retrieval engine for the IRIB’ s media and production using spoken term detection (STD) or keyword spotting. The goal of an STD system is to search for a set of keywords in a set of speech documents. One of the methods for STD is using a speech recognition system in which speech is recognized and converted into text and then, the text is searched for the keywords. Variety of speech documents and the limitation of speech recognition vocabulary are two challenges of this approach. Large vocabulary continuous speech recognition systems (LVCSR) usually have limited but large vocabulary and these systems canchr('39')t recognize out of vocabulary (OOV) words. Therefore, LVCSR-based STD systems suffer OOV problem and canchr('39')t spotting the OOV keywords. Methods such as the use of sub-word units (e. g., phonemes or syllables) and proxy words have been introduced to overcome the vocabulary limitation and to deal with the out of vocabulary (OOV) keywords. This paper proposes a Persian (Farsi) STD system based on speech recognition and uses the proxy words method to deal with OOV keywords. To improve the performance of this method, we have used Long Short-Term Memory-Connectionist Temporal Classification (LSTM-CTC) network. In our experiments, we have designed and implemented a large vocabulary continuous speech recognition systems for Farsi language. Large FarsDat dataset is used to train the speech recognition system. FarsDat contains 80 hours voices from 100 speakers. Kaldi toolkit is used to implement speech recognition system. Since limited dataset, Subspace Gaussian Mixture Models (SGMM) is used to train acoustic model of the speech recognition. Acoustic model is trained based context tri-phones and language model is probability tri-gram words model. Word Error Rate (WER) of Speech recognition system is 2. 71% on FARSDAT test set and also 28. 23% on the Persian news collected from IRIB data. Term detection is designed based on weighted finite-state transducers (WFST). In this method, first a speech document is converted to a lattice by the speech recognizer (the lattice contains the full probability of speech recognition system instead of the most probable one), and then the lattice is converted to WFST. This WFST contains the full probability of words that speech recognition computed. Then, text retrieval is used to index and search over the WFST output. The proxy words method is used to deal with OOV. In this method, OOV words are represented by similarly pronunciation in-vocabulary words. To improve the performance of the proxy words methods, an LSTM-CTC network is proposed. This LSTM-CTC is trained based on charterers of words separately (not a continuous sentence). This LSTM-CTC recomputed the probabilities and re-verified proxy outputs. It improves proxy words methods dues to the fact that proxy words method suffers false alarms. Since LSTM-CTC is an end-to-end network and is trained based on the characters, it doesnchr('39')t need a phonetic lexicon and can support OOV words. As the LSTM-CTC is trained based on the separate words, it reduces the weight of the language model and focuses on acoustic model weight. The proposed STD achieve 0. 9206 based Actual Term Weighted Value (ATWV) for in vocabulary keywords and for OOV keywords ATWV is 0. 2 using proxy word method. Applying the proposed LSTM-CTC improves the ATWV rate to 0. 3058. On Persian news dataset, the proposed method receives ATWV of 0. 8008.