تدوین چارچوب کیفی توانمندسازی استعدادهای متخصص نسل بومیان دیجیتال در سازمان صداوسیما: رویکرد نظریۀ داده‌‌بنیاد

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.