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<p>International mobility is a recognized driver of scientific innovation, yet its dynamic relationship with academic impact remains underexplored. Here, we investigate the measurement of academic mobility and its co-evolution with <i>h</i>-index trajectories. By disambiguating and integrating heterogeneous bibliometric records (ResearcherID and ORCID), we construct high-resolution longitudinal career trajectories for computer scientists to continuously track mobility behaviors and time-series impact metrics. Applying group-based dual-trajectory modeling, we then uncover joint developmental patterns. Our analysis reveals three distinct mobility profiles (stagnant, mid-increasing, and continuously active) and three <i>h</i>-index growth pathways (slow, steady, and rapid). We find that high-frequency mobility is significantly associated with a higher likelihood of being in a high-impact trajectory, though this effect exhibits clear temporal lags. Furthermore, artificial intelligence and interdisciplinary researchers are disproportionately represented in both the most mobile and fastest-growing cohorts. Methodologically, this study provides a dynamic evaluation framework that accounts for the delayed returns of academic mobility. Practically, it offers targeted empirical evidence for policymakers to design career-stage-specific interventions and optimize talent attraction strategies, particularly for high-demand and rapidly evolving fields like artificial intelligence.</p>

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The association between international mobility and academic impact: an empirical analysis using trajectory modeling

  • 期刊论文

  • 发表日期：

    2026年7月17日

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  • Yi Lu,
  • Bo Yang,
  • Shan Huang,
  • Yanqing Shi

摘要

International mobility is a recognized driver of scientific innovation, yet its dynamic relationship with academic impact remains underexplored. Here, we investigate the measurement of academic mobility and its co-evolution with h-index trajectories. By disambiguating and integrating heterogeneous bibliometric records (ResearcherID and ORCID), we construct high-resolution longitudinal career trajectories for computer scientists to continuously track mobility behaviors and time-series impact metrics. Applying group-based dual-trajectory modeling, we then uncover joint developmental patterns. Our analysis reveals three distinct mobility profiles (stagnant, mid-increasing, and continuously active) and three h-index growth pathways (slow, steady, and rapid). We find that high-frequency mobility is significantly associated with a higher likelihood of being in a high-impact trajectory, though this effect exhibits clear temporal lags. Furthermore, artificial intelligence and interdisciplinary researchers are disproportionately represented in both the most mobile and fastest-growing cohorts. Methodologically, this study provides a dynamic evaluation framework that accounts for the delayed returns of academic mobility. Practically, it offers targeted empirical evidence for policymakers to design career-stage-specific interventions and optimize talent attraction strategies, particularly for high-demand and rapidly evolving fields like artificial intelligence.