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Interdisciplinary research enhances scientists’ career resilience and long-term benefits: evidence from a large-scale bibliographic analysis

Author

Listed:
  • Chen Yang

    (Shenzhen University)

  • Hailong Wang

    (Shenzhen University)

  • Yiduo Liu

    (Shenzhen University)

  • Xusheng Wu

    (Shenzhen Health Development Research and Data Management Center)

  • Fei Liu

    (Shenzhen Health Development Research and Data Management Center)

  • Ben Niu

    (Shenzhen University)

Abstract

Evaluating the long-term impact of interdisciplinary research (IDR) on scientific careers remains challenging. This study addresses this gap by exploring the relationship between IDR, long-term benefits, and career resilience through large-scale bibliographic analysis. We propose InTraFlow, a dynamic metric designed to quantify IDR by capturing perturbations in knowledge flow. This framework integrates multi-dimensional indicators and incorporates scientist-level trajectory analysis. Our findings demonstrate a significant positive association between IDR and intrinsic research value, namely increased novelty and disruptiveness. While this often incurs longer recognition delays, our results suggest IDR contributes to career resilience. Scientists with higher IDR engagement exhibit more stable long-term patterns in citation impact and disruptive output (lower variability), indicating a capacity to sustain novel contributions despite potential uncertainties. This work provides empirical evidence supporting a nuanced view of IDR—highlighting its role in fostering everlasting scientific impact and scientists’ career resilience. The findings further underscore the need for evaluation frameworks that recognize IDR’s complex temporal dynamics and multi-faceted value for career development.

Suggested Citation

  • Chen Yang & Hailong Wang & Yiduo Liu & Xusheng Wu & Fei Liu & Ben Niu, 2025. "Interdisciplinary research enhances scientists’ career resilience and long-term benefits: evidence from a large-scale bibliographic analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(7), pages 3929-3959, July.
  • Handle: RePEc:spr:scient:v:130:y:2025:i:7:d:10.1007_s11192-025-05349-4
    DOI: 10.1007/s11192-025-05349-4
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