Journal of KIISE (정보과학회 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Project Failure Main Factors Analysis using Text Mining in Audit Evaluation

감리결과에 텍스트마이닝 기법을 적용한 프로젝트 실패 주요요인 분석

  • 장경애 (서울과학기술대학교 IT정책전문대학원 산업정보시스템) ;
  • 장성용 (서울과학기술대학교 글로벌융합산업공학과) ;
  • 김우제 (서울과학기술대학교 글로벌융합산업공학과)
  • Received : 2014.10.14
  • Accepted : 2015.02.10
  • Published : 2015.04.15
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Abstract

Corporations should make efforts to recognize the importance of projects, identify their failure factors, prevent risks in advance, and raise the success rates, because the corporations need to make quick responses to rapid external changes. There are some previous studies on success and failure factors of projects, however, most of them have limitations in terms of objectivity and quantitative analysis based on data gathering through surveys, statistical sampling and analysis. This study analyzes the failure factors of projects based on data mining to find problems with projects in an audit report, which is an objective project evaluation report. To do this, we identified the texts in the paragraph of suggestions about improvement. We made use of the superior classification algorithms in this study, which were NaiveBayes, SMO and J48. They were evaluated in terms of data of Recall and Precision after performing 10-fold-cross validation. In the identified texts, the failure factors of projects were analyzed so that they could be utilized in project implementation.

기업은 프로젝트의 중요성을 인지하고 프로젝트의 실패요인을 찾아 위험을 미연에 방지하여 프로젝트의 성공율을 높이기 위해 노력해야 한다. 이것은 급변하는 외부의 변화에 신속히 대응하기 위해 필요하다. 선행연구에서도 이러한 프로젝트의 성공요인 및 실패요인에 대한 연구가 다양하게 수행되었으나, 대부분 설문조사와 샘플링 통계분석으로 연구가 수행되어 데이터의 객관성과 정량적 분석에 한계를 갖고 있었다. 따라서 본 연구에서는 프로젝트의 실패요인 분석을 객관적인 프로젝트의 평가보고서인 감리결과보고서에서 프로젝트의 문제를 발견하고 개선권고사항을 제시하는 부분의 텍스트를 도출하여 텍스트 마이닝을 수행하였다. 텍스트 마이닝에 적용한 알고리즘은 분류 성능이 우수한 NaiveBayes, SMO, J48 알고리즘이다. 실험은 10배 교차검증을 수행하였고 정확률과 재현율로 평가하였다. 도출된 텍스트에서 프로젝트의 실패요인을 분석하여 프로젝트 수행에 활용될 수 있도록 하였다.

Keywords

References

  1. Keil, M., Tiwana, A. & Bush, A., "Reconciling user and project manager perceptions of IT project risk:a Delphi study," Info Systems J, pp. 103-119, 2002.
  2. National IT Industry Promotion Agency, SW Engineering Research, National IT Industry Promotion Agency, Seoul, 2010.
  3. Sukjoo Lee, Sungchul Yoon, et al., "Study on the risk factors and the type fail on information system projects," Entrue Journal of Information Technology, pp. 53-62, 2002.
  4. Turban, E., Leidner, D., McLean, E., &Wetherbe, J. (2008), INFORMATION TECHNOLOGY FOR MANAGEMENT, (With CD), John Wiley &Sons.
  5. Park. Jong-Ki, "A Study on Effects of Individual Personality for Project Success : Focused on ERP Project Team," PhD thesis, Myeonggji University, Department of Industrial Engineering, 2007.
  6. Hyun-Soo Lee, Moon-Goo Lee, and Nam-yong Lee, "An Study about The Priority Risk Factors for Enhancing the success Rate of The Public Sector Information Projects," The Institute of Electronics And information Engineers, Vol. 48, No. 4, 2011.
  7. Song-Mee Park, Myungsin Chae, "A Study on Perception of Project Risk Factor," The Journal of Information Systems, Vol. 16, No. 4, pp. 243-268, 2007.
  8. Sukjin Cho, Seogjun Lee, Yukeun Hanm, "An Empirical Study on Risk Factors in Information System Project Management," The Journal of MIS Research, Vol. 16, No. 3, pp. 143-158, 2006.
  9. Chiyong Kim, "Research Project Business Success and Failure Factors Analysis," Science and Technology Policy Institute, pp. 8-11, 1992.
  10. PMI, A Guide to the Project Management Body of Knowledge, The 4th Edition, LA, PMI, 2007.
  11. Choong C. Lee, Joo Yeon Park and Jeong Hoon Choi, "The Effect of the Project Managers' IT Competency and Managerial Competency on the Success of SI Projects: Focusing on the Moderating Effect of Project Size," Korea Association of Information Systems, Vol. 21, No. 4, pp. 31-54, 2012.
  12. Sung-bong Jang, "A Field on the Loss Factors of IS Development Project in the Public Sector : Perspectives of Project Management, Korea Society IT Services, Vol. 3, No. 2, 2004.
  13. Jae-Kwon Bae, Jin-Hwa Kim, Dong-Wook Kim, "A Study on the Establishment of IS Project Failure Early Detection System using Rule Induction Techniques," The e-Business Studies, Vol. 13, No. 1, pp. 25-43, 2012. https://doi.org/10.15719/geba.13.1.201203.25
  14. Fabrizio Sebstiai, "Machine Learning in Automated Text Categorization," ACM Computing Surverys, Vol. 34, No. 1, 2002.
  15. Koller D. Sahami M, "Hierarchically classifying documments using very few words," Proc. of the Fourteenth International Conference on Machine Learing (ICML 97), 1997.
  16. AnTaeSeong, SeoHyeongGu and, LeeGyeongIl, "Highprecision search system based on text mining," Korea Information Processing Society Review, Vol. 11, No. 2, 2004.
  17. Deephouse, C. T. Mukopadhyay, Goldenson, D.R and Kellner, "Software Failure : Process Project Performance," Journal of Management Information Systems, Vol. 12, No. 3, pp. 187-205, 1995. https://doi.org/10.1080/07421222.1995.11518097
  18. Boehm, B.W, "Software Risk Management: Principles and Practices," IEEE Software, Vol. 8, No. 1, pp. 32-41, 1991. https://doi.org/10.1109/52.62930
  19. Terry, C.D., "The real success factors on project," International Systems, Vol. 17, No. 4, pp. 5-36, 2001.
  20. Glaser, J., Management's role in IT Project Failures, Florida : Healthcare Financial Management, 2004.
  21. National Information Society Agency, Information Systems Audit Criteria Commentary, National Information Society Agency, Seoul, 2006.
  22. Schmidt, R., Lyytinen, K., Kail, M. & Cule, P., "Identifying software project risks: An international Delphi study," Journal of Management Information Systems, Vol. 17, No. 4, pp. 5-36, 2001.

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