Journal of the Korean Society for information Management (정보관리학회지)

Korean Society for Information Management (한국정보관리학회)
권호 표지
DOI QR코드

DOI QR Code

The Impact of Network Closure and Structural Holes on Research Performance in Collaboration Networks

공동연구 네트워크의 폐쇄와 구조적 공백이 연구성과에 미치는 영향

  • Nari Lee ;
  • Ji-Hong Park
  • 이나리 (연세대학교 문헌정보학과, 서울성심병원 의학도서실) ;
  • 박지홍 (연세대학교 문헌정보학과)
  • Received : 2024.08.16
  • Accepted : 2024.09.03
  • Published : 2024.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study investigates the collaboration networks in the field of AI-driven diagnostic medical imaging, focusing on the influence of two social capital concepts-network closure and structural holes-on research performance. The analysis reveals a highly fragmented network structure with one dominant component, while individual clusters exhibit strong internal cohesion. Both network closure, measured by density, and structural holes, assessed through efficiency, positively impact research performance, as demonstrated by QAP regression analysis. The findings highlight that, in the integration of AI into diagnostic medical imaging, robust connections among researchers are vital, and the presence of structural holes, which enable the assimilation of diverse knowledge, also significantly enhances research outcomes. This underscores the importance of fostering a well-balanced network to optimize collaboration and knowledge production in this emerging interdisciplinary field.

본 연구에서는 AI 의료영상 진단 분야를 중심으로 공동연구 네트워크의 특성을 살펴보고, 사회자본의 2가지 개념인 네트워크 폐쇄와 구조적 공백이 연구성과에 미치는 영향을 분석하였다. 분석 결과, 네트워크의 구조는 하나의 큰 컴포넌트를 가지고 있으며, 이를 제외하고는 클러스터 간의 분절이 심하고 각 클러스터 내의 응집성은 매우 높은 것으로 나타났다. 또한 네트워크 폐쇄는 밀도로, 구조적 공백은 효율성으로 측정하여 연구성과와의 관계를 QAP 회귀분석을 통해 확인한 결과, 네트워크 폐쇄와 구조적 공백은 모두 연구성과에 긍정적인 영향을 미치는 것으로 나타났다. 이는 영상의학의 한 분야인 의료영상 진단에 AI 라는 새로운 분야가 접목될 때, 연구자들 간의 강한 연결뿐만 아니라 다양한 지식을 수용할 수 있는 구조적 공백 또한 연구 성과에 영향을 미친다는 것을 의미한다. 이러한 연구 결과는 공동 지식 생산을 위한 연구 협업에서 적절하게 조화를 이루는 네트워크의 필요성을 시사한다.

Keywords

Acknowledgement

이 논문은 2024년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2022S1A5C2A03093597).

References

  1. Ahn, Jae-Gwang & Kim, Jin-Han (2018). The effect of network closure and structural hole in technological knowledge exchange on radical innovation. Journal of Digital Convergence, 16(4), 95-105. https://doi.org/10.14400/JDC.2018.16.4.095 
  2. Chung, Taewon, Chung, Dongsub, & Kim, JeongHeum (2014). A study on the relationship between cooperation network and publication performance of Korean government-funded research institutes through collaborative paper status. Journal of Korea Technology Innovation Society, 17(1), 242-263. 
  3. Hong, Jun-Yong, Park, Sang-Hyun, & Jung, Young-Jin (2020). Artificial intelligence based medical imaging: An Overview. Journal of Radiological Science and Technology, 43(3), 195-208. 
  4. Kim, Somin, Lee, Hyekyung, & Kim, Jeongheum (2020). The effects of network closure and structural holes on the efficiency of a cooperation network: The case of Korean government-funded research institutes. Journal of Korea Technology Innovation Society, 23(6), 1222-1239. https://doi.org/10.35978/jktis.2020.12.23.6.1222 
  5. Kwahk, Kee-Young (2017). Social Network Analysis. Seoul: Chungram. 
  6. Lee, Jae Yun (2023). A study on scholarly communication trends in Korean library and information science studies through author group analysis. Journal of the Korean Society for Library and Information Science, 57(2), 409-434. http://doi.org/10.4275/KSLIS.2023.57.2.409 
  7. Abbasi, A., Altmann, J., & Hossain, L. (2011). Identifying the effects of co-authorship networks on the performance of scholars: A correlation and regression analysis of performance measures and social network analysis measures. Journal of Informetrics, 5(4), 594-607. https://doi.org/10.1016/j.joi.2011.05.007 
  8. Aldieri, L., Kotsemir, M., & Vinci, C. P. (2018). The impact of research collaboration on academic performance: An empirical analysis for some European countries. Socio-Economic Planning Sciences, 62, 13-30. https://doi.org/10.1016/j.seps.2017.05.003 
  9. Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Network analysis in the social sciences. Science, 323(5916), 892-895. http://doi.org/10.1126/science.1165821. 
  10. Burt, R. S. (1992). Structural holes. Social Stratification. Routledge, 659-663. 
  11. Choi, H. & Zo, H. (2022). Network closure versus structural hole: The role of knowledge spillover networks in national innovation performance. IEEE Transactions on Engineering Management, 69(4), 1011-1021. https://doi.org/10.1109/TEM.2020.2972347 
  12. Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, S95-S120. https://doi.org/10.1086/228943 
  13. Defazio, D., Lockett, A., & Wright, M. (2009). Funding incentives, collaborative dynamics and scientific productivity: Evidence from the EU framework program. Research Policy, 38(2), 293-305. https://doi.org/10.1016/j.respol.2008.11.008 
  14. Duffett, M., Brouwers, M., Meade, M. O., Xu, G. M., & Cook, D. J. (2020). Research collaboration in pediatric critical care randomized controlled trials: A social network analysis of coauthorship. Pediatric Critical Care Medicine, 21(1), 12-20. http://doi.org/10.1097/PCC.0000000000002120 
  15. Gonzalez-Alcaide, G., Park, J., Huamani, C., Belinchon, I., & Ramos, J. M. (2015). Evolution of cooperation patterns in psoriasis research: Co-authorship network analysis of papers in Medline (1942-2013). PloS ONE, 10(12), e0144837. http://doi.org/10.1371/journal.pone.0144837 
  16. Gore, J. C. (2020). Artificial intelligence in medical imaging. Magnetic Resonance Imaging, 68, A1-A4. https://doi.org/10.1016/j.mri.2019.12.006 
  17. Guan, J., Yan, Y., & Zhang, J. J. (2017). The impact of collaboration and knowledge networks on citations. Journal of Informetrics, 11(2), 407-422. https://doi.org/10.1016/j.joi.2017.02.007 
  18. Hall, K. L., Vogel, A. L., Huang, G. C., Serrano, K. J., Rice, E. L., Tsakraklides, S. P., & Fiore, S. M. (2018). The science of team science: A review of the empirical evidence and research gaps on collaboration in science. American Psychologist, 73(4), 532-548. https://psycnet.apa.org/doi/10.1037/amp0000319 
  19. He, Z. -L., Geng, X. -S., & Campbell-Hunt, C. (2009). Research collaboration and research output: A longitudinal study of 65 biomedical scientists in a New Zealand university. Research Policy, 38(2), 306-317. https://doi.org/10.1016/j.respol.2008.11.011 
  20. Huang, J., Shlobin, N. A., Lam, S. K., & DeCuypere, M. (2022). Artificial intelligence applications in pediatric brain tumor imaging: A systematic review. World Neurosurgery, 157, 99-105. https://doi.org/10.1016/j.wneu.2021.10.068 
  21. Jones, B. F., Wuchty, S., & Uzzi, B. (2008). Multi-university research teams: Shifting impact, geography, and stratification in science. Science, 322(5905), 1259-1262. https://doi.org/10.1126/science.1158357 
  22. Katz, J. S. & Hicks, D. (1997). How much is a collaboration worth? A calibrated bibliometric model. Scientometrics, 40, 541-554. https://doi.org/10.1007/BF02459299 
  23. Katz, J. S. & Martin, B. R. (1997). What is research collaboration?. Research Policy, 26(1), 1-18. https://doi.org/10.1016/S0048-7333(96)00917-1 
  24. Khalifa, M. & Albadawy, M. (2024). AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update, 100146. https://doi.org/10.1016/j.cmpbup.2024.100146 
  25. Kulkarni, S., Seneviratne, N., Baig, M. S., & Khan, A. H. A. (2020). Artificial intelligence in medicine: Where are we now?. Academic Radiology, 27(1), 62-70. https://doi.org/10.1016/j.acra.2019.10.001 
  26. Kumar, S. (2015). Co-authorship networks: a review of the literature. Aslib Journal of Information Management, 67(1), 55-73. https://doi.org/10.1108/AJIM-09-2014-0116 
  27. Kuo, R. Y. L., Harrison, C., Curran, T. A., Jones, B., Freethy, A., Cussons, D., Stewart, M., Collins, G. S., & Furniss, D. (2022). Artificial Intelligence in Fracture Detection: A Systematic Review and Meta-Analysis. Radiology, 304(1), 50-62. https://doi.org/10.1148/radiol.211785 
  28. Lariviere, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323-1332. https://doi.org/10.1002/asi.23266 
  29. Lee, D. H., Seo, I. W., Choe, H. C., & Kim, H. D. (2012). Collaboration network patterns and research performance: The case of Korean public research institutions. Scientometrics, 91, 925-942. https://doi.org/10.1007/s11192-011-0602-8. 
  30. Lee, S. & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673-702. https://doi.org/10.1177/0306312705052359 
  31. Li, E. Y., Liao, C. H., & Yen, H. R. (2013). Co-authorship networks and research impact: A social capital perspective. Research Policy, 42(9), 1515-1530. https://doi.org/10.1016/j.respol.2013.06.012 
  32. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., Ledsam, J. R., Melnick, D., Mostofi, H., Peng, L., Reicher, J. J., Romera-Paredes, B., Sidebottom, R., Suleyman, M., Tse, D., Young, K.C., De Fauw J., & Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94. https://doi.org/10.1038/s41586-019-1799-6 
  33. Molontay, R. & Nagy, M. (2019). Two decades of network science: As seen through the co-authorship network of network scientists. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 578-583. https://doi.org/10.1145/3341161.3343685 
  34. Morgan, K. (2004). The exaggerated death of geography: Learning, proximity and territorial innovation systems. Journal of Economic Geography, 4(1), 3-21. https://doi.org/10.1093/jeg/4.1.3 
  35. Nahapiet, J. & Ghoshal, S. (1998). Social capital, intellectual capital, and the organizational advantage. Academy of Management Review, 23(2), 242-266. https://doi.org/10.5465/amr.1998.533225 
  36. Newman, M. E. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), 404-409. https://doi.org/10.1073/pnas.98.2.404 
  37. Padula, G. (2008). Enhancing the innovation performance of firms by balancing cohesiveness and bridging ties. Long Range Planning, 41(4), 395-419. https://doi.org/10.1016/j.lrp.2008.01.004 
  38. Porac, J. F., Wade, J. B., Fischer, H. M., Brown, J., Kanfer, A., & Bowker, G. (2004). Human capital heterogeneity, collaborative relationships, and publication patterns in a multidisciplinary scientific alliance: A comparative case study of two scientific teams. Research Policy, 33(4), 661-678. https://doi.org/10.1016/j.respol.2004.01.007
  39. Portes, A. (1998). Social capital: Its origins and applications in modern sociology. Annual Review of Sociology, 24(1), 1-24. 
  40. Rigby, J. & Edler, J. (2005). Peering inside research networks: Some observations on the effect of the intensity of collaboration on the variability of research quality. Research Policy, 34(6), 784-794. https://doi.org/10.1016/j.respol.2005.02.004 
  41. Sooryamoorthy, R. (2009). Do types of collaboration change citation? Collaboration and citation patterns of South African science publications. Scientometrics, 81(1), 177-193. https://doi.org/10.1007/s11192-009-2126-z 
  42. Tan, J., Zhang, H., & Wang, L. (2015). Network closure or structural hole? The conditioning effects of network-level social capital on innovation performance. Entrepreneurship Theory and Practice, 39(5), 1189-1212. https://doi.org/10.1111/etap.12102 
  43. Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical combinations and scientific impact. Science, 342(6157), 468-472. https://doi.org/10.1126/science.1240474 
  44. Van Duijn, M. A. & Huisman, M. (2011). Statistical Models for Ties and Actors. The Sage Handbook of Social Network Analysis. London: Sage, 459-483. 
  45. Vinayak, Raghuvanshi, A., & Kshitij, A. (2023). Signatures of capacity development through research collaborations in artificial intelligence and machine learning. Journal of Informetrics, 17(1), 101358. https://doi.org/10.1016/j.joi.2022.101358 
  46. Wang, C., Rodan, S., Fruin, M., & Xu, X. (2014). Knowledge networks, collaboration networks, and exploratory innovation. Academy of Management Journal, 57(2), 484-514. https://doi.org/10.5465/amj.2011.0917