Customer E-loyalty: From an Estimate in Electronic Commerce with an Artificial Neural Fuzzy Interface System (ANFIS)

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)


Companies lose their online customers due to the competitive business environment. Customer loyalty is one of the important topics in the Electronic Commerce (E-commerce) domain. Gaining new loyal customers requires extensive expenditure of time and money. In addition, loyal customers are an important asset for a company, which brings long-term benefits. In this research, a comprehensive conceptual framework is presented that shows E-loyalty based on E-trust and E-satisfaction. The critical factors which influence E-trust and E-satisfaction are classified in organizational, customer and technological groups. Statistical analysis is applied for validity and reliability of the model. Another important method for estimation of uncertain measures is Artificial Neural Fuzzy Network System (ANFNS). E-trust and E-satisfaction data were used as inputs of the ANFIS and the output utilized E-loyalty. The result demonstrated, there is no difference between the aforementioned and the ANFIS model can be used for estimation of E-loyalty in E-commerce.


E-commerce E-loyalty E-trust E-satisfaction Artificial neural network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jacoby, J. and R.W. Chestnut, Brand Loyalty. 1978.Google Scholar
  2. 2.
    Oliver, Whence consumer loyalty? Journal of Marketing, 1999. 63(4): p. 12.Google Scholar
  3. 3.
    Reichheld, F.F., R.G. Markey, and C. Hopton, The loyalty effect - the relationship between loyalty and profits. European Business Journal, 2000a. 12(3): p. 7.Google Scholar
  4. 4.
    Deng, W.J. and W. Pei, Fuzzy neural based importance-performance analysis for determining critical service attributes. Expert Systems with Applications, 2006. 36: p. 3774-3784.Google Scholar
  5. 5.
    Fang, Y.-H., C.-M. Chiu, and E.T.G. Wang, Understanding Customers’ Satisfaction and Repurchase Intentions: An Integration of IS Success Model, Trust, and Justice. Emerald Group Publishing Limited, 2011. 21 (4): p. 479-503.Google Scholar
  6. 6.
    Chang, H.H. and S.W. Chen, The impact of customer interface quality, satisfaction and switching costs on e-loyalty: Internet experience as a moderator. Computers in Human Behavior, 2008. 24(6): p. 2927-2944.Google Scholar
  7. 7.
    Palvia, P., The role of trust in e-commerce relational exchange: A unified model. Information & Management, 2009. 46(4): p. 213-220.Google Scholar
  8. 8.
    Lai, J.Y., Assessment of employees’ perceptions of service quality and satisfaction with e-business. International Journal of Human-Computer Studies, 2006. 64(9): p. 926-938.Google Scholar
  9. 9.
    Habibpor, K. and R. Safari, SPSS Comprehensive Guide for Research. 2008.Google Scholar
  10. 10.
    Nunally, J.C., Psychometric Theory. McGraw-Hill, 1978.Google Scholar
  11. 11.
    Brown, F.G., Principles of Educational and Psychological Testing. 1983.Google Scholar
  12. 12.
    McCulloch, W.S., A Heterarchy of Values Determined by the Topology of Nervous Nets. Bulletin of Mathematical Biophysics, 1945. 89(93).Google Scholar
  13. 13.
    Chen, C.-C., C.-S. Wu, and R.C.-F. Wu, e-Service enhancement priority matrix: The case of an IC foundry company. Information & Management, 2006. 43(5): p. 572-586.Google Scholar
  14. 14.
    Hsiao, S.-W. and H.-C. Tsai, Applying a hybrid approach based on fuzzy neural network and genetic algorithm to product form design. International Journal of Industrial Ergonomics, 2005. 35(5): p. 411-428.Google Scholar
  15. 15.
    Althuwaynee, O.F., B. Pradhan, and S. Lee, Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences, 2012. 44(0): p. 120-135.Google Scholar
  16. 16.
    Chang, H.H. and I.C. Wang, An investigation of user communication behavior in computer mediated environments. Computers in Human Behavior, 2008. 24(5): p. 2336-2356.Google Scholar
  17. 17.
    Keskin, M.E., D. Taylan, and O. Terzi, Adaptive neural-based fuzzy inference system approach for modelling hydrological time series.. Hydrological Sciences Journal, 2006. 51(4): p. 588-598.Google Scholar
  18. 18.
    You, H., et al., Development of customer satisfaction models for automotive interior materials. International Journal of Industrial Ergonomics, 2006. 36(4): p. 323-330.Google Scholar
  19. 19.
    Kwong, C.K., T.C. Wong, and K.Y. Chan, A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Systems with Applications, 2009. 36(8): p. 11262-11270.Google Scholar
  20. 20.
    Jang, S., ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern, 1993. 23(3): p. 665-685.Google Scholar
  21. 21.
    Hush, D.R. and B.G. Horne, Progress in supervised neural networks. IEEE Signal Processing Magazine Vol. 10. 1993. 32.Google Scholar
  22. 22.
    A. El-Shafie, O.J.a.S.A.A., Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. International Journal of the Physical Sciences, 2011. 6(12): p. 2875-2888.Google Scholar
  23. 23.
    Armstrong, J.S. and F. Collopy, Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting, 1992. 8(1): p. 69-80.Google Scholar
  24. 24.
    Lehmann, E.L. and G. Casella, Theory of Point Estimation (2nd ed.). New York: Springer, 1998.Google Scholar
  25. 25.
    Armstrong, S. and F. Collopy, Error Measures For Generalizing About Forecasting Methods: Empirical Comparisons. International Journal of Forecasting, 1992. 8: p. 69-81.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Department of Information Science, Faculty of Computer Science & Information TechnologyUniversity of MalayaKuala LumpurMalaysia

Personalised recommendations