IEA/AIE 2010: Trends in Applied Intelligent Systems pp 691-700 | Cite as
A Decision Support Tool for Evaluating Loyalty and Word-of-Mouth Using Model-Based Knowledge Discovery
Abstract
It is crucial for any manager to keep a close watch on customer satisfaction, customer loyalty and the customer’s intention to recommend the company. In this article, a new decision support tool is developed to support a manager with this task. This tool has been developed with companies in mind that posses limited customer satisfaction data. It uses model-based knowledge discovery to extract the customer’s expectation and the expectation-performance compatibility from the data. Two hypotheses are formulated which posit that compatibility between product performance and customer expectation have a positive influence on the customer’s intentions. Both hypotheses are supported by the data. Finally, a decision support tool is developed which visualizes the impact of customer satisfaction, product performance and expectation-performance compatibility on the customer’s intentions. The decision support tool contains two views which offer the manager important information at a glance.
Keywords
Customer Satisfaction Product Performance Decision Support Tool Customer Loyalty Dark Grey AreaPreview
Unable to display preview. Download preview PDF.
References
- 1.Anderson, R.E.: Consumer dissatisfaction: The effect of disconfirmed expectancy on perceived product performance. Journal of Marketing Research 10, 38–44 (1973)CrossRefGoogle Scholar
- 2.Beliakov, G., Pradera, A., Calvo, T.: Aggregation Functions: A Guide for Practitioners, vol. 221. Springer, Heidelberg (2007)Google Scholar
- 3.Curtin, F., Schulz, P.: Multiple correlations and bonferroni’s correction. Biological Psychiatry 44, 775–777 (1998)CrossRefGoogle Scholar
- 4.Depaire, B., Vanhoof, K., Wets, G.: Managerial opportunities of uninorm based importance–performance analysis. WSEAS Transactions on Business and Economics 3(3), 101–108 (2006)Google Scholar
- 5.Dombi, J.: Basic concepts for the theory of evaluation: The aggregative operator. European Journal of Operational Research 10(3), 282–293 (1982)MATHCrossRefMathSciNetGoogle Scholar
- 6.Menard, S.: Applied Logistic Regression Analysis. In: Quantitative Applications in the Social Sciences. Sage Publications, Inc., Thousand Oaks (2002)Google Scholar
- 7.Oliver, R.L.: A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research 17(4), 460–469 (1980)CrossRefGoogle Scholar
- 8.Oliver, R.L.: Satisfaction: A Behavioral Perspective on the Consumer. McGraw-Hill, New York (1996)Google Scholar
- 9.Power, D.: Categorizing decision support systems: A multidimensional approach. In: Mora, M., Forgionne, G., Gupta, J.N. (eds.) Decision Making Support Systems: Achievements, Trends and Challenges for the New Decade, ch. 2, pp. 20–27. Idea Group Publishing, USA (2003)Google Scholar
- 10.R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2007), http://www.R-project.org ISBN 3-900051-07-0
- 11.Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures. CRC Press, Boca Raton (1997)MATHGoogle Scholar
- 12.Szymanski, D.M., Henard, D.H.: Customer satisfaction: A meta-analysis of the empirical evidence. Journal of the Academy of Marketing Science 29(1), 16–35 (2001)Google Scholar
- 13.Vanhoof, K., Brijs, T., Wets, G.: An indirect measurement for customer expectation. In: Baets, B.D., Fodor, J. (eds.) Principles of Fuzzy Preference Modelling and Decision Making, pp. 109–122. s.l. Academia Press, New York (2003)Google Scholar
- 14.Vanhoof, K., Pauwels, P., Dombi, J., Brijs, T., Wets, G.: Penalty–reward analysis with uninorms: A study of customer (dis)satisfaction. In: Ruan, D., Chen, G., Kerre, E.E., Wets, G. (eds.) Intelligent Data Mining. Techniques and Applications, pp. 237–252. Springer, Heidelberg (2005)Google Scholar
- 15.Yager, R.R.: A model of participatory learning. IEEE Transactions on Systems, Man and Cybernetics 20, 1229–1234 (1990)CrossRefMathSciNetGoogle Scholar