Predicting information technology project escalation: A neural network approach

European Journal of Operational Research

Volumn 146, Issue 1, 2003, Pages 115-129

  Zhang, G Peter ^a   Keil, Mark ^a   Rai, Arun ^a   Mann, Joan ^b  

^a GEORGIA STATE UNIVERSITY   (United States)

^b OLD DOMINION UNIVERSITY   (United States)

Author keywords

IT project escalation; Logistic regression; Neural networks; Project management; Variable selection

Indexed keywords

INFORMATION TECHNOLOGY; NEURAL NETWORKS; PROJECT MANAGEMENT; REGRESSION ANALYSIS; THEOREM PROVING;

PROJECT ESCALATION;

OPERATIONS RESEARCH;

EID: 0037375261     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0377-2217(02)00294-1     Document Type: Article

References (46)

1. Abdel-Hamid T., Madnick S.E. Software Project Dynamics: An Integrated Approach. 1991;Prentice Hall, Englewood Cliffs, NJ.
2. Alter S., Ginzberg M. Managing uncertainty in MIS implementation. Sloan Management Review. 20(1):1978;23-31.
3. Arkes H.R., Blumer C. The psychology of sunk cost. Organizational Behavior and Human Decision Processes. 35(1):1985;124-140.
4. Betts, M., 1992. Feds debate handling of failing IS projects. Computerworld, November 2, p. 103.
5. Brockner J. The escalation of commitment to a failing course of action: Toward theoretical progress. Academy of Management Review. 17(1):1992;39-61.
6. Brockner J., Shaw M.C., Rubin J.Z. Factors affecting withdrawal from an escalating conflict: Quitting before it's too late. Journal of Experimental Social Psychology. 15:1979;492-503.
7. Doumas A., Mavroudakis K., Gritzalis D., Katsikas S. Design of a neural network for recognition and classification of computer virus. Computers and Security. 14:1995;435-448.
8. Ellis, V., 1994. Audit says DMV ignored warning. Los Angeles Times, August 18, pp. A3-A24.
9. Flowers S. Software Failure: Management Failure. 1996;John Wiley & Sons, Chichester.
10. Gibbs W.W. Software's chronic crisis. Scientific American. 271(3):1994;86-95.
11. Gladden G.R. Stop the life-cycle, I want to get off. ACM SIGSOFT Software Engineering Notes. 7(2):1982;35-39.
12. Glass R.L. Software runaways. - Some surprising findings The DATA BASE for Advances in Information Systems. 28(3):1997;16-19.
13. Glass R.L. Software Runaways. 1998;Prentice-Hall Inc. Upper Saddle River, NJ.
14. Harrison P.D., Harrell A. Impact of adverse selection on managers' project evaluation decisions. Academy of Management Journal. 36(3):1993;635-643.
15. Hornik K. Approximation capabilities of multiplayer feedforward networks. Neural Networks. 4:1991;251-257.
16. Howe K.M., McCabe G.M. On optimal asset abandonment and replacement. Journal of Financial and Quantitative Analysis. 18(3):1983;295-305.
17. Hu M.Y., Hung M.S., Shanker M.S., Chen H. Using neural networks to predict performance of Sino-foreign joint ventures. International Journal of Computational Intelligence and Organizations. 1(3):1996;134-143.
18. Huberty C.J. Issues in the use and interpretation of discriminant analysis. Psychological Bulletin. 95:1984;156-171.
19. Jiang J.J., Klein G., Chen H.-G. The relative influence of IS project implementation policies and project leadership on eventual outcomes. Project Management Journal. 32(3):2001;49-55.
20. Johnson J. Chaos: The dollar drain of IT project failures. Application Development Trends. 2(1):1995;41-47.
21. Jones M.M., McLean E.R. Management problems in large-scale software development projects. Industrial Management Review. 11(3):1970;1-15.
22. Keil M. Pulling the plug: Software project management and the problem of project escalation. MIS Quarterly. 19(4):1995;421-447.
23. Keil M., Mann J., Rai A. Why software projects escalate: An empirical analysis and test of four theoretical models. MIS Quarterly. 24(4):2000;631-664.
24. Keil, M., Rai, A., Zhang, G.P., Mann, J., 2001. Why software projects escalate: The relative importance of project management and escalation constructs. Working paper.
25. Khoshgoftaar T.M., Lanning D.L. A neural network approach for early detection of program modules having high risk in the maintenance phase. Journal of Systems Software. 29:1995;85-91.
26. Lanubile F., Visaggio G. Evaluating predictive quality models derived from software measures: Lessons learned. Journal of Systems Software. 38:1997;225-234.
27. Leib, J., 1995. DIA now in United's hands: Greewald defends fare increases. Denver Post, February 28. p. C-1.
28. Lyytinen K., Hirschheim R. Information systems failures. - A survey and classification of the empirical literature Zorkoczy P.I. Oxford Surveys in Information Technology (4). 1987;257-309 Oxford University Press, Oxford.
29. Mann, J., 1996. The Role of Project Escalation in Explaining Runaway Information Systems Development Projects: A Field Study. Doctoral Dissertation, Georgia State University, Atlanta, GA, unpublished.
30. Moghrabi C., Eid M.S. Modeling users through an expert system and a neural network. Computers and Industrial Engineering. 35:1995;583-596.
31. Montealegre R., Keil M. De-escalating information technology projects: Lessons from the Denver International Airport. MIS Quarterly. 24(3):2000;417-447.
32. Newcombe, T., 1998. Big project woes halt child support system. Government Technology, February. p. 34-35.
33. Obaidat M.S., Macchairolo D.T. A multiplayer neural network system for computer access security. IEEE Transactions on System, Man, and Cybernetics. 24:1994;806-813.
34. Press S.J., Wilson S. Choosing between logistic regression and discriminant analysis. Journal of American Statistical Association. 73:1978;699-705.
35. Richard M.D., Lippmann R. Neural network classifiers estimate Bayesian a posteriori probabilities. Neural Computation. 3:1991;461-483.
36. Rubin J.Z., Brockner J. Factors affecting entrapment in waiting situations: The Rosencrantz and Guildenstern effect. Journal of Personality and Social Psychology. 31:1975;1054-1063.
37. Schumacher M., Robner R., Vach W. Neural networks and logistic regression: Part I. Computational Statistics and Data Analysis. 21:1996;661-682.
38. Setiono R.J., Thong Y.L., Yap C.S. Symbolic rule extraction from neural networks: An application to identifying organizations adopting IT. Information and Management. 34:1998;91-101.
39. Sherer S.A. Software fault prediction. Journal of System Software. 29:1995;97-105.
40. Slevin D.P., Pinto J.K. The project implementation profile: New tool for project managers. Project Management Journal. 17(4):1986;57-70.
41. Spangler W., May J.H., Vargas L.G. Choosing data-mining method for multiple classification: Representational and performance measurement implications for decision support. Journal of Management Information Systems. 16(1):1999;37-62.
42. Staw B.M. Knee-deep in the big muddy: A study of escalating commitment to a chosen course of action. Organizational Behavior and Human Performance. 16:1976;27-44.
43. Subramanian V., Hung M.S. A GRG2-based system for training neural networks: Design and computational experience. ORSA Journal on Computing. 5:1993;386-394.
44. Tomsho R. Real dog: How Greyhound lines reengineered itself right into a deep hole. The Wall Street Journal. 20:1994;A1-A6.
45. Whyte G. Escalating commitment to a course of action: A reinterpretation. Academy of Management Review. 11(2):1986;311-321.
46. Zhang G.P. Neural networks for classification: A survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C. 30(4):2000;451-462.