DOI QR코드

DOI QR Code

An Exploratory Study of Platform Government in Korea : Topic Modeling and Network Analysis of Public Agency Reports

한국 플랫폼 정부의 방향성 모색 : 공공기관 연구보고서에 대한 토픽 모델링과 네트워크 분석

  • Nam, Hyun-Dong (Graduate School of Governance, Sungkyunkwan University) ;
  • Nam, Taewoo (Graduate School of Governance, Sungkyunkwan University)
  • 남현동 (성균관대학교 국정전문대학원) ;
  • 남태우 (성균관대학교 국정전문대학원)
  • Received : 2019.01.03
  • Accepted : 2020.02.20
  • Published : 2020.02.28

Abstract

New platform governments will play a role to pull intelligent information technology to drive new ecological government innovation and sustainable development in which the government and people work together. On this, in order to establish the platform of the platform government, we will look at recent research trends and lay the foundation for future policy directions and research bases. using Text Mining method, and went through Topic modeling for the collected text data and network analysis was conducted. According to the result, based on latent topic, the stronger the connection center, the weaker the relationship. Through this study, we hope that discussions will take place in a variety of ways to improve the understanding of the supply and demand approach of Korea's platform government and implement appropriate change management methods such as service public base and service provision in accordance with the value and potential topics of platform government.

새로운 플랫폼 정부는 지능적인 정보기술을 활용하여 정부와 국민이 서로 협력하는 새로운 생태계 기반 정부 혁신과 지속 가능한 발전을 견인하는 역할을 할 것이다. 이에 플랫폼 정부의 플랫폼 구축을 위해 최근 관련 연구 동향에 대해 살펴보고 향후 미래정책 방향 및 연구기반을 마련하기 위한 토대를 구축하고자 한다. 연구 분석을 위해 각 부처와 정부산하기관에서 발행된 연구보고서를 텍스트마이닝 기법을 활용하여 텍스트 자료를 수집하고, 수집된 텍스트 자료를 토픽 모델링과 네트워크 분석을 시행하였다. 분석결과 미래전략과 집단 내에서의 네트워크 연결이 제대로 이루워지지 않고 있으며 연결 중심성이 강할수록 관계성이 약해지는 것을 도출하였다. 이는 정부가 플랫폼을 설계하고 데이터와 서비스를 공급하는 공급 역할에서 통합적, 상호 교류적 접점이 필요하며 정부와 시민, 기업의 협치가 가능한 생태계가 조성되어야 할 것이다. 본 연구를 통해 플랫폼 정부의 공급과 수요적 접근의 이해를 높이고 잠재적 토픽에 따라 적절한 변경관리 방법을 구현하기 위한 논의가 다각적으로 이루어지길 기대한다.

Keywords

References

  1. S. W. Kim. (2015). Administration Process Extension and Public Data Convergence Management. Journal of Digital Convergence, 13(5), 41-49. DOI: 10.14400/JDC.2015.13.5.41
  2. C. S. Chung. (2017). From Electronic Government to Platform Government. JOURNAL OF PLATFORM TECHNOLOGY, 5(3), 3-10.
  3. M. J. Kim, J. T. Park, M. H. Hwang & M. J. Kang. (2015). Cloud Computing Adoption for the Geospatial Information System of Public Sector. Anyang : Korea Research Institute for Human Settlements.
  4. Y. Shin. (2017). The Realities and Conditions of Mid-term Policy Management from a Platform Perspective. The Korean Association for Local Govenrment Studies, 61-92.
  5. B. J. Seo & S. Y. Shin. (2017). A Study on the Research Trends on Domestic Platform Government using Topic Modeling. Informatization Policy, 24(3), 3-26. DOI: https://doi.org/10.22693/NIAIP.2017.24.3.003
  6. C. H. Oh. (2017). Issue: Data-based Policy Analysis Research and Application of Evaluation. The Korea Association For Policy Analysis and Evaluation, 27(2), 155-167.
  7. C. S. Jeong. (2012). The Future and Administration of E-Government. Future Public Adminstration Review, 1(1), 27-57.
  8. M. S. Ahn. (2008). Therories of the Korean e-Government. Seoul : PYBook.
  9. B. G. Seok & J. W. Moon. (2010). A Study on the Paradigm Shift and the Future of the Electronic Government. KISDI Premium Report, 22(19).
  10. Y. B. Lee. (2012). The introduction of e-government in Korea. Knowledge Sharing Program: KSP Modularization.
  11. M. J. Lee & S. J. Kim. (2019). Exploratory Study on E-Government Service in the Transitonal Era: Cases of the United States. The Journal of Convergence Society and Public Policy, 13(2), 121-151. DOI: 10.1017/pan.2016.7
  12. H. Kim. (2010). Cultural Contents, ICT Platform and Humanities Knowledge. Philosophical Studies, (90), 63-88.
  13. A. Gawer & M. A. Cusumano. (2008). How Companies Become Platform Leaders. MIT Sloan management review, 49(2), 25-38.
  14. S. H. Park & K. J. Cho. (2016). The Diversity of Policy Tools : Comparisons of Governance Structures in the Production of Public Services. korean policy sciences review, 20(1), 1-27.
  15. S. H. Myeong, C. J. Heo & S. S. Hwang. (2011). Government of the Smart Society: Focusing on the Platform-type Government Model. Korean Public Administration Review, 1-31.
  16. H. C. Moon. (2011). Understanding of Platform Strategy and Conditions of Success. Korea employers federation Monthly Magazine, (390), 36-37.
  17. H. H. Oh. (2014). 2014 KISTEP Issue Paper. Seoul : Korea Institute of Science and Technology Evaluation and Planning
  18. J. S. Hwang. (2010). A Study on the Transition of Public Information to a Platform-type Government. IT&SOCIETY, 21(0), 20-22.
  19. Tim O'Reilly. (2010). Open Government. O'Reilly Media.
  20. Y. S. Ha & S. Y. Lee (2019). A Study on the Application of Platform Model in Governance Operations Focused on the Innovation School Policy. The Korean Association for Policy Studies, 2019(1), 1-18.
  21. B. S. Kim. (2019). Online Citizen Participation and Changes in the Delegation. Center for Digital Social Science, (20).
  22. G. H. Jeong. (2011). A Study of foresight method based on textmining and complexity network analysis. Korea Institute of Science and Technology Evaluation and Planning
  23. M. Bonchek & S. P. Choudary. (2013). Three elements of a successful platform strategy. Harvard Business Review, 92(1-2).
  24. Fujitsu.. (2016). White paper Fujitsu Government as a platform.
  25. National Information Society Agency. (2018). Japan, Data Utilization Strategies to Realize Society 5.0.
  26. H. J. Kim, H. J. Jung & M. Song, (2014). A Comparison of Author Name Disambiguation Performance through Topic Modeling. Korean Society for Information Management Conference, 149-152.
  27. J. H. Park & M. Song. (2013). A Study on the Research Trends in Library & Information Science in Korea using Topic Modeling. Journal of the Korean Society for Information Management, 30(1), 7-32. DOI: https://doi.org/10.3743/KOSIM.2013.30.1.007
  28. C. H. Nam. (2016). An Illustrative Application of Topic Modeling Method to a Farmer's Diary. Institute of cross-cultural studies, 22(1), 89-135.
  29. D. M. Blei. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77-84. DOI: 10.1145/2133806.2133826
  30. X. Wang & A.. McCallum. (2006). Topics over time: a non-Markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 424-433. DOI: 10.1145/1150402.1150450
  31. T. H. Lee, Y. J. Youn & H. W. Kim. (2016). The Analysis of Information Security Awareness Using A Text Mining Approach. Informatization Policy, 23(4), 76-94. DOI: 10.22693/NIAIP.2016.23.4.076
  32. S. J. Nam & H. C. Lee. (2019). Airline Passenger Characterizations Using LDA Topic Modeling. Korea Management Science review, 36(3), 67-85. DOI: 10.3390/su11216153
  33. D. Mimno, & A. McCallum. (2008). Modeling career path trajectories.
  34. D. H. Lee, H. Y. Choi & J. H. Yoon. (2017). Patent-based trend analysis of bio-fuels: Application of topic modeling and social network analysis. The Korean Institute of Industrial Engineers, 2017(4), 5793-5797.
  35. S. M. Gerrish & D. M. Blei. (2010). A Language-based Approach to Measuring Scholarly Impact. Proceedings of the 27th International Conference on Machine Learning, (Vol. 10, pp. 375-382).
  36. T. Griffiths & M. Steyvers. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences, 101(1), 5228-5235. DOI: 10.1073/pnas.0307752101
  37. R. Popping. (2000). Computer-assisted text analysis. Sage.
  38. C. H. Park. (2014). A Study on the Intergovernmental Conflict Analysis on the Standardization of Public Service: Focused on the Traffic Card Standardization Conflict between the Seoul Metropolitan Government and the Ministry of Land, Infrastructure and Transport using Text Network Analysis. Chung-Ang Public Administration review, 28(3), 209-235.
  39. G. Ercan & I. Cicekli. (2008). Lexical cohesion based topic modeling for summarization. In International Conference on Intelligent Text Processing and Computational Linguistics (pp. 582-592). Berlin : Springer.
  40. L. Hagen, O. Uzuner, C. Kotfila, T. M. Harrison & D. Lamanna. (2015). Understanding citizens' direct policy suggestions to the federal government: a natural language processing and topic modeling approach. 2015 48th Hawaii International Conference on System Sciences (pp. 2134-2143). IEEE. DOI: 10.1109/HICSS.2015.257
  41. D. Greene & J. P. Cross. (2017). Exploring the political agenda of the European parliament using a dynamic topic modeling approach. Political Analysis, 25(1), 77-94. DOI: 10.1017/pan.2016.7
  42. S. B. Cho, S. A. Shin & D. S. Kang. (2018). A Study on the Research Trends on Open Innovation using Topic Modeling. Informatization policy, 25(3), 52-74. DOI: 10.22693/NIAIP.2018.25.3.052
  43. Y. H. Yang, Y. J. Kwon & S. C. Lee. (2019). Research Trends Analyses on Public Conflicts through Topic Modeling and Network Analysis. The Korean Association for Local Government Studies, 23(3), 309-332. DOI: 10.20484/klog.23.3.18