Intelligent Modeling of E-Business Maturity

Expert Systems with Applications
Expert Systems with Applications 32 (2007) 687–702 www.elsevier.com/locate/eswa Intelligent modeling of e-business maturity
George Xirogiannis b a,*

, Michael Glykas

b

a
University of Piraeus, Department of Informatics, 80, Karaoli & Dimitriou St., 185 34 Piraeus, Athens, Greece
University of Aegean, Department of Financial and Management Engineering, 31, Fostini Street, 82 100 Chios, Greece

Abstract
E-business has a significant impact on managers and academics. Despite the rhetoric surrounding e-business strategy formulation mechanisms, which support reasoning of the effect of strategic change activities to the maturity of the e-business models, are still emerging. This paper describes an attempt to build and operate such a reasoning mechanism as a novel supplement to e-business strategy formulation exercises. This new approach proposes the utilization of the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) as the underlying methodology in order to generate a hierarchical and dynamic network of interconnected maturity indicators. By using
FCMs, this research aims at simulating complex strategic models with imprecise relationships while quantifying the impact of strategic changes to the overall e-business efficiency. This research establishes generic adaptive domains – maps in order to implement the integration of hierarchical FCMs into e-business strategy formulation activities. Finally, this paper discusses experiments with the proposed mechanism and comments on its usability.
Ó 2006 Elsevier Ltd. All rights reserved.
Keywords: Fuzzy cognitive maps; E-business modeling; Strategy planning; Decision support

1. Introduction
Today, there is an increasing demand for a strategiclevel assessment of e-business capabilities that can be assembled and analyzed rapidly at low cost and without significant intrusion into the subject enterprises. The benefits from completing such an exercise are quite straightforward, for instance,

References: Albrecht, C. C., Dean, D. D., & Hansen, J. V. (2003). Using situation calculus for e-business agents Axelrod, R. (1976). Structure of decision: The cognitive maps of political elites Bharati, P., & Chaudhury, A. (2003). An empirical investigation of decision-making satisfaction in web-based decision support systems. Decision Support Systems, 1051, 1–11. Bourne, M., Mills, J., Wilcox, M., Neely, A., & Platts, K. (2000). Burgess, T. F. (1998). Modeling the impact reengineering with system dynamics Burgess, T. F. (1996). Modelling quality-cost dynamics. International Journal of Quality and Reliability Management, 13(3), 8–26. Carlsson, C., & Turban, E. (2002). DSS: directions for the next decade. Chung, W., Yam, A. Y. K., Chan, M. F. S., & Potter, A. (2005). Craiger, J. P., Goodman, D. F., Weiss, R. J., & Butler, A. (1996). Crowe, T. J., Fong, P. M., Bauman, T. A., & Zayas-Castro, J. L. (2002). Dickerson, J. A., & Kosko, B. (1997). Virtual Worlds as Fuzzy Cognitive Maps Diffenbach, J. (1982). Influense diagrams for complex strategic issues. Disney, S. M., Naim, M. M., & Potter, A. (2005). Assessing the impact of e-business on supply chain dynamics Duffy, J. (2001). Maturity models. Blueprints for e-volution. Strategy and Leadership, 29(6), 19–26. Flach, P. A., & Kakas, A. (1998). Abduction and Induction in Al: Report of the IJCAI97 workshop Georgopoulos, V., Malandraki, G., & Stylios, C. (2002). A fuzzy cognitive map approach to differential diagnosis of specific language impairment Hagiwara, M. (1992). Extended fuzzy cognitive maps. In Presented at proceedings of the 1st IEEE international conference on fuzzy systems, Harmon, P., & King, D. (1985). Expert Systems: Artificial Intelligence in Business Hong, T., & Han, I. (2002). Knowledge based data mining of news information of the internet using cognitive maps and neural networks. Hooft, F. V., & Stegwee, R. A. (2001). E-business strategy: how to benefit from a hype Jackson, P., & Harris, L. (2003). E-business and organisational change. Johnson, R. J., & Briggs, R. O. (1994). A model of cognitive information retrieval for ill-structured managerial problems and its benefits for Jones, R. T., & Ryan, C. (2002). Matching process choice and uncertainty: Modeling quality management Kaplan, R. S., & Norton, D. P. (1996). Using the balanced scorecard as a strategic management system Kaplan, R. S., & Norton, D. P. (2001). Leading change with the balanced scorecard Kaufmann, A. (1975). Introduction to the theory of of fuzzy subsets. Klein, J. C., & Cooper, D. F. (1982). Cognitive maps of decision makers in a complex game Koh, J., & Kim, Y. G. (2005). Knowledge sharing in virtual communities: an e-business perspective Kosko, B. (1986). Fuzzy Cognitive Maps. Journal of Man-Machine Studies, 24, 65–75. Kosko, B. (1991). Neural networks and fuzzy systems. Englewood Cliffs: Prentice Hall. Kwahk, K. Y., & Kim, Y. G. (1999). Supporting business process redesign using cognitive maps Langbert, M., & Friedman, H. (2002). Continuous improvement in the history of human resource management Lee, K. C., & Kim, H. S. (1997). A fuzzy cognitive map-based bidirectional inference mechanism: An application to stock investment analysis Lee, K. C., & Kim, H. S. (1998). Fuzzy implications of fuzzy cognitive map with emphasis on fuzzy causal relationship and fuzzy partially Lee, K. C., Kim, J. S., Chung, N. H., & Kwon, S. J. (2002). Fuzzy Cognitive Map approach to web-mining inference amplification. Lee, K. C., & Kwon, O. B. (1998). A strategic planning simulation based on cognitive map knowledge and differential game Lin, F. R., Yang, M. C., & Pai, Y. H. (2002). A generic structure for business process modeling Liu, Z. Q. (2000). Fuzzy cognitive maps: Analysis and extension. Tokyo: