Advertisement

Structural Neural Networks for Modeling Customer Satisfaction

  • Cristina DavinoEmail author
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

The aim of this paper is to provide a Structural Neural Network to model Customer Satisfaction in a business-to-business framework. Neural Networks are proposed as a complementary approach to PLS path modeling, one of the most widespread approaches for modeling and measuring Customer Satisfaction. The proposed Structural Neural Network allows to overcome one of the main drawbacks of Neural Networks because they are usually considered as black boxes.

Keywords

Hide Layer Customer Satisfaction Structural Neural Network Customer Expectation Widespread Approach 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Bishop, C. M. (1995). Neural network for pattern recognition. Oxford: Clarendon.Google Scholar
  2. Davino, C., Mola, F., Siciliano, R., & Vistocco, V. (1997). A statistical approach to neural networks. In K. Fernandez-Aguirre & A. Morineau (Eds.), Analyses Multidimensionnelles des Donnees (pp. 37–51). Saint-Mande: CISIA Ceresta.Google Scholar
  3. Esposito Vinzi, V., Chin, W. W., Henseler, J., & Wang, H. (Eds.) (2008). Handbook of partial least squares: Concepts, methods and applications. Berlin: Springer.Google Scholar
  4. Lee, C., Rey, T., Mentele, J., & Garver, M. (2005). Structured neural network techniques for modeling loyalty and profitability. In Proceedings of SAS User Group International (SUGI 30).Google Scholar
  5. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representation by error propagation. In Parallel distributed processing: Explorations in macrostructure of cognition (Vol. 1, pp. 318–362). Cambridge, MA: Badford Books.Google Scholar
  6. Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 48, 159–205.zbMATHCrossRefMathSciNetGoogle Scholar
  7. XLSTAT (2008). Addinsoft. Paris, France. Retrieved from http://www.xlstat.com.
  8. White, H. (1992). Artificial neural networks. New York: Springer.Google Scholar
  9. Wold, H. (1982). Soft modeling. The basic design and some extensions. In Jöreskog Wold (Eds.), Systems under indirect observation (Vol. II). Amsterdam: North-Holland.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Dipartimento di Studi sullo sviluppo economicoUniversity of MacerataMacerataItaly

Personalised recommendations