Digital Technology and Local Policy: An Evidence-Based Collaborative Model for Sports Talent Identification

Background: Talent identification requires objective and consistent data use, yet many athlete development systems still rely on limited technological support and uneven policy implementation. Aligning digital tools with local policy is therefore essential for creating a more coherent evidence-based approach. Aims: This study explains the relationship between technology utilization and local policy support in enhancing talent identification effectiveness and formulates a collaborative conceptual model integrating both components. Methods: This study used a quantitative explanatory design involving 50 participants consisting of coaches, physical education teachers, and student athletes selected through purposive sampling. Data were obtained through TIDev outputs, validated questionnaires, and structured observations. Analysis was performed using descriptive statistics, Pearson's correlation, and multiple regression through SPSS to evaluate technology utilization, policy support, and talent mapping efficiency. A conceptual model was formulated through interpretive synthesis based on empirical patterns and relevant theories. Results: Technology utilization showed high mean scores, while policy implementation and impact were moderate. Correlation analysis indicated no significant relationship between policy support and technology use. Regression results showed that TIDev significantly improved talent-mapping efficiency, whereas policy support had no direct effect. Expert validation yielded a high I-CVI score (0.88), confirming relevance of the proposed collaborative model. Conclusion: This study shows that TIDev contributes meaningfully to improving the effectiveness of talent identification, whereas local policy support has not yet been fully integrated into operational practice. Based on the empirical patterns, a collaborative conceptual model was formulated to illustrate how technological evidence and policy structures can be aligned to strengthen talent identification.