Predicting ERP User Satisfaction―an Adaptive Neuro Fuzzy Inference System (ANFIS) Approach

Chengaleth Venugopal, Siva Prasanna Devi, Kavuri Suryaprakasa Rao

.

DOI: 10.4236/iim.2010.27052                

Abstract

ERP projects’ failing to meet user expectations is a serious problem. This research develops an Adaptive Neuro Fuzzy Inference System (ANFIS) model, to predict the key ERP outcome “User Satisfaction” using causal factors present during an implementation as predictors. Data for training and testing the models was from a cross section of firms that had implemented ERPs. ANFIS is compared with other prediction techniques, ANN and MLRA. The results establish that ANFIS is able to predict outcome well with an error (RMSE) of 0.277 and outperforms ANN and MLRA with errors of 0.85 and 0.86 respectively. This study is expected to provide guidelines to managers and academia to predict ERP outcomes ex ante, and thereby enable corrective actions to redirect ailing projects.

Keywords

ANFIS, ERP Implementation Outcome, Prediction, Failure Detection, CSFs, Causal Factors

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	New Standish Research Report, “Roadmap to the Megaples,” 2009. http://www.standishgroup.com
[2]	D. Robey, J. Ross and M. Boudreau, “Learning to Implement Enterprise Systems: An Exploratory Study of the Dialectics of Change,” Journal of Management Information Systems, Vol. 19, No. 1, 2002, pp. 17-46.
[3]	J. Brockner, “The Escalation of Commitment to a Failing Course of Action: Towards Theoretical Progress,” Aca- demy of Management Review, Vol. 17, No. 1, 1992, pp. 39-61.
[4]	M. Keil, “Pulling the Plug: Software Project Management and the Problem of Project Escalation,” MIS Quarterly, Vol. 19, No. 4, 1995, pp. 421-447.
[5]	M. Keil, “Why Software Projects Escalate―an Empirical Study and Analysis of Four Theoretical Models,” MIS Quarterly, Vol. 24, No. 4, 2000, pp. 631-664.
[6]	A. J. Al-Shehab, R. T. Hughes and G. Winstanley, “Modelling Risks in IS/IT Projects through Causal and Cognitive Mapping,” The Electronic Journal of Information Systems Evaluation, Vol. 8, No. 1, 2005, pp. 1-10.
[7]	C. Venugopal and S. Rao, “Detecting Project Risks in ERP Projects Measurement Models for Critical Success Factors and Success of ERP Implementations,” Proccedings of International Conference on Advances in Industrial Engineering Applications, Chennai, India, 2010.
[8]	T. M. Somers and K. Nelson, “The Impact of Critical Success Factors across the Stages of Enterprise Resource Planning Implementations,” Proceedings of the 34th Annual Hawaii International Conference on System Sciences, Hawaii, 2001, pp. 8016-8025.
[9]	W. H. DeLone and E. R. McLean, “Information Systems Success: The Quest for the Dependent Variable,” Information Systems Research, Vol. 3, No. 1, 1992, pp. 60-95.
[10]	W. H. DeLone and E. R. McLean, “The Delone and Mclean Model of Information Systems Success―a Ten Year Update,” Journal of Information Systems, Vol. 19, No. 4, 2003, pp. 9-30.
[11]	T. H. Davenport, “Putting the Enterprise into the Enterprise System,” Harvard Business Review, Vol. 76, No. 4, 1998, pp. 121-131.
[12]	A. Parr and G. Shanks, “A Model of ERP Project Implementation,” Journal of Information Technology, Vol. 15, No. 4, 2000, pp. 289-303.
[13]	K. K. Hong and Y. G. Kim, “The Critical Success Factors for ERP Implementations: An Organizational Fit Perspective,” Information and Management, Vol. 40, No. 1, 2002, pp. 25-40.
[14]	J. Hedman, “Enterprise Resource Planning Systems: Critical Factors in Theory and Practice,” Lund University, 2004.
[15]	R. B. Cooper and R. W. Zmud, “Implementation Technology Implementation Research: A Technological Diffusion Approach,” Management Science, Vol. 36, No. 2, 1990, pp. 123-139.
[16]	B. H. Wixom and H. J. Watson, “An Empirical Investigation of the Factors Affecting Data Warehousing Success,” MIS Quarterly, Vol. 25, No. 1, 2001, pp. 16-41.
[17]	P. Holland, B. Light and N. Gibson, “A Critical Success Factors Model for Enterprise Resource Planning Implementation,” Proceedings of the 7th European Conference on Information Systems, Vol. 1, 1999, pp. 273-297.
[18]	A. T. Marbach, “Detecting Risk in Information Technology Projects,” Doctoral Thesis, University of Texas, Arlington, 2003.
[19]	F. D. Davis, “User Acceptance of Information Technology: System Characteristics, User Perceptions and Behavioral Impacts,” International Journal of Man Machine Studies, Vol. 38, No. 3, 1993, pp. 475-487.
[20]	M. L. Markus and C. Tanis, “The Enterprise System Experience-from Adoption to Success,” In: R. W. Zmud, Ed., Framing the Domains of IT Management: Projecting the Future through the Past, Chapter 10, Pinnaflex Educational Resources Inc., Cincinnati, 2000, pp. 173-207.
[21]	S. Shang and P. B. Seddon, “Assessing and Managing the Benefits of Enterprise Systems: The Business Manager’s Perspective,” Information Systems Journal, Vol. 12, No. 4, 2002, pp. 271-299.
[22]	B. L. Myers, L. A. Kappelman and V. R. Prybutok, “A Comprehensive Model for Assessing the Quality and Productivity of the Information Systems Function,” Information Resources Management Journal, Vol. 10, No. 1, 1997, pp. 6-25.
[23]	J. F. Hair, W. Black, R. E. Anderson and R. L. Tatham, “Multivariate Data Analysis (6/E),” Pearson Education, 2008.
[24]	B. Eftekhar, K. Mohammad, H. E. Ardebili, G. Mohammad and E. Ketabchi, “Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Mortality in Head Trauma Based on Initial Clinical Data,” BMC Medical Informatics and Decision Making, Vol. 5, No. 3, 2005, pp. 1-8.
[25]	A. R. Gray and S. G. MacDonell, “A Comparison of Techniques for Developing Predictive Models of Software Metrics,” Information and Software Technology, Vol. 39, No. 6, 1997, pp. 425-437.
[26]	D. W. Patterson, “Artificial Neural Networks: Theory and Applications,” Prentice Hall, Englewood Cliffs, 1996.
[27]	T. Takagi and M. Sugeno, “Fuzzy Identification of Systems and its Applications to Modeling and Control,” IEEE Transactions Systems, Man, Cybernetics, Vol. 15, No. 1, 1985, pp. 116-132.
[28]	J. S. R. Jang, “Adaptive-Network-Based Fuzzy Inference system,” IEEE Transactions on Systems, Man, and Cybernetics, Vol. 23, No. 3, 1993, pp. 665-685.