Earticle

초록

영어

To better predict and classify failures of information system development projects (ISDPs), this study proposes ISDPs failure prediction models using the traditional statistical methods and artificial intelligence methods, namely multiple discriminant analysis (MDA), logistic regression (LR), multi-layer perceptron (MLP), classification and regression tree (CART), commercial version 5.0 (C5.0), and support vector machine (SVM). We performed the analysis on the audit report data of 446 projects that were conducted by a global information technology (IT) company, to build the IT service systems and relevant service infrastructures needed for a project with South Korea’s mobile telecommunication companies. The research variables, which were confirmed by project performance management system (PPMS) of the company, are composed of thirteen variables. Empirical results indicated that SVM outperforms other models such as MDA, LR, MLP, CART, and C5.0.

목차

Abstract
 1. Introduction
 2. Success and failure factors of ISDPs
 3. Experimental design
  3.1 The data set and description of input variables
  3.2 Experimental procedure
 4. Experimental results
 5. Conclusions and future directions
 References

저자

• Jae Kwon Bae [ Department of Management Information Systems, Keimyung University ]

참고문헌