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      이동통신 세대 전환의 구조적 지연 특성을 이용한 SD 모델링 = System Dynamics Modeling of Structural Delays in Mobile Generation Transitions

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      https://www.riss.kr/link?id=A110097768

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      This study develops a System Dynamics (SD) model to analyze the structural delay mechanisms in mobile generational transitions by integrating device-bound contract constraints into the Norton-Bass multi-generation diffusion framework. The model explicitly defines a ‘Transition Decision Pending Group’ to structurally represent the competition, coexistence, and substitution between 4G and 5G. Simulation results demonstrate high validity, with a Mean Absolute Percentage Error (MAPE) of 4.2% for 4G and 4.0% for 5G, accurately reproducing the crossover point of the generational shift. Notably, the dual-peak patterns o bserved in k ey flow variables r eveal that marketing effects a re realized with time lags due to structural lock-in. As the 6G era introduces non-human entities like AI agents and robots as new network users, this SD-based framework offers essential insights for forecasting complex structural transitions in the future telecommunications ecosystem.
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      This study develops a System Dynamics (SD) model to analyze the structural delay mechanisms in mobile generational transitions by integrating device-bound contract constraints into the Norton-Bass multi-generation diffusion framework. The model explic...

      This study develops a System Dynamics (SD) model to analyze the structural delay mechanisms in mobile generational transitions by integrating device-bound contract constraints into the Norton-Bass multi-generation diffusion framework. The model explicitly defines a ‘Transition Decision Pending Group’ to structurally represent the competition, coexistence, and substitution between 4G and 5G. Simulation results demonstrate high validity, with a Mean Absolute Percentage Error (MAPE) of 4.2% for 4G and 4.0% for 5G, accurately reproducing the crossover point of the generational shift. Notably, the dual-peak patterns o bserved in k ey flow variables r eveal that marketing effects a re realized with time lags due to structural lock-in. As the 6G era introduces non-human entities like AI agents and robots as new network users, this SD-based framework offers essential insights for forecasting complex structural transitions in the future telecommunications ecosystem.

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