Journal of Digital Convergence (디지털융복합연구)

The Society of Digital Policy and Management (한국디지털정책학회)
권호 표지
DOI QR코드

DOI QR Code

Evaluating Blockchain Research Trend using Bibliometrics-based Network Analysis

블록체인 분야의 학술연구 동향분석: 계량정보학적 네트워크분석을 중심으로

  • Zhu, Yu-Peng (Blockchain Policy Research Center, Cyber Emotions Research Institute/Interdisciplinary Program of Digital Convergence Business, Yeungnam University) ;
  • Park, Han-Woo (Cyber Emotions Research Institute / Dept of Media & Communication, Interdisciplinary Program of Digital Convergence Business, Yeungnam University)
  • 주우붕 (영남대학교 사이버감성연구소 블록체인정책연구센터/디지털융합비즈니스학과) ;
  • 박한우 (영남대학교 사이버감성연구소/언론정보학과.디지털융합비즈니스학과)
  • Received : 2019.04.24
  • Accepted : 2019.06.20
  • Published : 2019.06.28
Download PDF
⟨ Previous Next ⟩

Abstract

This study aims to examine Blockchain research trend using bibliometrics-based network analysis. The data were collected from WoS, Scopus, Korea Citation Index and National science & Technology Information Service, from 2009 to 2018. As results, the number of publications has started increasing rapidly from 2017 and it showed the initial stage of formation of coauthor network. Words often used in the title of the publications were related to application development, controversy and technology development. In addition, the majority of domestic papers are in the subject of social science, while international papers tend to focus on engineering issues. The results of the temporal analysis show that Korean researchers' block chain 3.0 started in 2017 and are rapidly increasing in 2018. The number of citations was associated with publication year in a statistically signifiant way. By examining these research trends, we hope that this paper can be a useful basis for the development of blockchain. Future research is expected to reveal more clearly the knowledge structure and characteristics of blockchain around the world.

본 연구는 계량정보학 네트워크 분석을 중심으로 블록체인 분야의 한국 연구자의 학술연구 동향을 계량학적 시각으로 살펴보고자 하였다. 데이터는 2009년부터 2018년까지 KCI와 NTIS, WoS 및 Scopus에 등재된 블록체인 논문으로 선정하였다. 계량정보학 연구방법으로 논문 발행, 연구의 분야, 공저자, 단어분석을 수행했다. 분석결과, 2017년부터 논문 수가 급증하였으며 공저자 네트워크 형성의 초기단계를 보였다. 논문제목에 많이 사용된 단어는 응용발전과 쟁점 및 기술개발과 관련된 단어들이 주로 나타났다. 또한 국내논문연구 분야는 사회과학 주제가 가장 많은 반면에 국제논문연구는 공학 주제가 가장 많았다. 시기별 분석결과는 한국학자들의 블록체인 3.0 연구는 2017년 시작되었고 2018년 빠르게 늘고 있다. 논문 피인용 수는 발행연도와 관련이 있었지만, 공저자 수와는 무관하였다. 이러한 계량정보학적 동향을 살펴봄으로써 블록체인 분야의 발전에 유용한 기초자료가 될 수 있을 것으로 기대한다. 향후연구는 블록체인 분야의 세계범위 지적 구조와 특징이 더욱 명확하게 드러날 것으로 기대한다.

Keywords

DJTJBT_2019_v17n6_219_f0001.png 이미지

Fig. 1. Data collection method of WoS and Scopus

DJTJBT_2019_v17n6_219_f0002.png 이미지

Fig. 2. Annual trends of both Korean and English publications on blockchain

DJTJBT_2019_v17n6_219_f0003.png 이미지

Fig. 3. Co-authorship networks in both Korean and English publications in terms of individual researchers

DJTJBT_2019_v17n6_219_f0004.png 이미지

Fig. 4. Co-authorship networks in both Korean and English publications in terms of individual institutions

DJTJBT_2019_v17n6_219_f0005.png 이미지

Fig. 5. Clusters using Concor analysis

DJTJBT_2019_v17n6_219_f0006.png 이미지

Fig. 6. Change in research subject across periods

Table 1. Centrality definition

DJTJBT_2019_v17n6_219_t0001.png 이미지

Table 2. Network statistics

DJTJBT_2019_v17n6_219_t0002.png 이미지

Table 3. Frequency and centrality of words in Korean publications

DJTJBT_2019_v17n6_219_t0003.png 이미지

Table 4. Frequency and centrality of words in English publications

DJTJBT_2019_v17n6_219_t0004.png 이미지

Table 5. Correlations of words centrality in Korean publications

DJTJBT_2019_v17n6_219_t0005.png 이미지

Table 7. Regression model explaining the number of citations

DJTJBT_2019_v17n6_219_t0006.png 이미지

Table 6. Correlations of words centrality in English publications

DJTJBT_2019_v17n6_219_t0007.png 이미지

References

  1. S. Nakamoto. (2008). Bitcoin: A peer-to-peer electronic cash system, Bitcoin. https://bitcoin.org.
  2. H. Cheng & Y. Z. Yang. (2016). The development trend of blockchain and the countermeasures of commercial banks, Financial Regulation Research, 6, 73-92.
  3. S. J. Park. (2017). Blockchain paradigm and FinTech security, The Journal of the Korean Institute of Communication Sciences, 34(3), 23-28.
  4. M. Hancock & E. Vaizey. (2016). Distributed ledger technology: beyond block chain. London: UK Government Office for Science.
  5. Nomura Research Institute. (2016). Survey on blockchain technologies and related services FY2015 report. Tokyo: Nomura Research Institute.
  6. J. Polis. (2017). Congress Gets Serious about Blockchains. https://polis.house.gov
  7. China State Council. (2016). "Thirteenth Five-Year" National informatization planning. Beijing: China State Council.
  8. Korea ministry of science and ICT. (2018). Block chain technology development strategy, Gwacheon: Korea ministry of science and ICT.
  9. E. G. Seo & I. Han. (1997). Bibliometric analysis on MIS research, Asia Pacific Journal of Information Systems, 7(3), 145-165.
  10. J. Li, D. Li & X. C. Yu. (2016). Characteristics and evolution of the research communities of bibliometrics, Document, Information & Knowledge, 3, 71-79.
  11. A. Pritchard. (1969). 'Statistical Bibliography or Bibliometrics?', Journal of Documentation, 25(4), 348-349.
  12. J. Y. Lee. (2006). Centrality measures for bibliometric network analysis. Journal of Korean Society for Library and Information Science, 40(3), 191-214. https://doi.org/10.4275/KSLIS.2006.40.3.191
  13. D. H. Lee, W. S. Kim, S. J. Moon & L. S. Jung. (2012). The study on characteristics of Korean academy of business historian research-based on meta and bibliometric data analysis, The Review of Business History, 63(0), 97-117.
  14. Y. J. Yoo & J. Y. Lee. (2015). A bibliometric study on the 7 different versions of the heidelberg catechism, Faith & Scholarship, 20(4), 135-159. https://doi.org/10.30806/FS.20.4.201512.135
  15. S. S. Lee. (2010). A preliminary study on the co-author network analysis of Korean library & information science research community, Korean Library and Information Science Society, 41(2), 297-315. https://doi.org/10.16981/kliss.41.2.201006.297
  16. F. M. Wang & M. J. Zhu. (2017). Bibliometric analysis on the research hotspots of blockchain in China, Journal of Intelligence, 36(12), 69-74, 28.
  17. J. Yli-Huumo, D. Ko, S. Choi, S. Park & K. Smolander. (2016). Where is current research on blockchain technology?-A systematic review, PLoS ONE, 11(10), 1-27.
  18. S. Miau & J. M. Yang. (2018). Bibliometrics-based evaluation of the Blockchain research trend: 2008 - March 2017, Technology Analysis & Strategic Management, 30(9), 1029-1045. https://doi.org/10.1080/09537325.2018.1434138
  19. H. W. Park & S. J. Park. (2018). Big data analysis of cryptocurrencies : Evidence from top cryptocurrency websites, Journal of the Korean Data Analysis Society, 20(2), 923-936. https://doi.org/10.37727/jkdas.2018.20.2.923
  20. Y. H. Song, Y. E. Kwon, C. S. Lim, K. S. Choi & M. S. Yang. (2018). An analysis of blockchain technology research trend by using a social network analysis, Proceedings of the Korean Institute of Communication Sciences Conference, 1, 542-543.
  21. H. W. Park & L. Leydesdorff. (2004). Understanding the KrKwic: A computer program for the analysis of Korean text, Journal of the Korean Data Analysis Society, 6(5), 1377-1387.
  22. H. E. Driver & A. L. Kroebe. (1932). Quantitative expression of cultural relationships, University of California Publications in American Archaeology and Ethnology, 31(4), 211-256.
  23. A. Clauset, M. E. J. Newman & C. Moore. (2004). Finding community structure in very large networks, Physical Review E, 70, 066111. https://doi.org/10.1103/PhysRevE.70.066111
  24. M. S. An & Y. G. Min. (2016). Big data analytics on motivation of staying at 5-star hotel: Focusing on the Push-Pull factor from motivation theory, Korean Journal of Tourism Research, 31(5), 87-104.
  25. G. M. Lee, C. H. Kim, H. W. Park. (2017). Network analysis of SNS-mediated learning community: A facebook group 'magic of flipped-learning', Journal of the Korean Data Analysis Society, 19(2), 873-884. https://doi.org/10.37727/jkdas.2017.19.2.873
  26. S. P. Borgatti, M. G. Everett & L. C. Freeman. (2002). Ucinet 6 for Windows: software for social network analysis. Harvard, MA: Analytic Technologies.
  27. NodeXL Korea. (2015). Catching up with NodeXL, Seoul: Paradigmbook.
  28. L. C. Freeman. (1979). Centrality in social networks: I. conceptual classification, Social Networks, 1, 215-239. https://doi.org/10.1016/0378-8733(78)90021-7
  29. M. Beauchamp. (1965). An improved index of centrality. Behavioral Science, 10, 161-163. https://doi.org/10.1002/bs.3830100205
  30. P. Bonacich. 2007. some unique properties of eigenvector centrality, Social Networks, 29, 555-564. https://doi.org/10.1016/j.socnet.2007.04.002