Decision Tree and MCDA Under Fuzziness to Support E-Customer Satisfaction Survey

  • Houda ZaimEmail author
  • Mohammed Ramdani
  • Adil Haddi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)


The proposed work extends existing approaches by analyzing customer click stream data and online reviews to implicitly identify satisfaction level when customer’s rate is not available and find the website criteria score that positively influence e-customer satisfaction. Fuzzy mining customer navigation data is our task to set up inputs of the two proposed supervised evaluation approaches; a multi criteria analysis approach for the website assessment and a new decision tree algorithm to classify customers. A case study from the B2C Chinese website “TMALL” has been used for validating our proposal, and a comparison between the proposed approaches has shown promising results.


Website quality determinants Fuzzy membership degree Similarity Class prototype Confusion matrix 


  1. 1.
    Vercamer, D., Steurtewagen, B., Van den Poel, D., Vermeulen, F.: Predicting consumer load profiles using commercial and open data. IEEE Trans. Power Syst. 31(5) (2015). Scholar
  2. 2.
    Li, R., Li, F., Smith, N.D.: Multi-Resolution load profile clustering for smart metering data. In: 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, p. 1 (2016).
  3. 3.
    Wang, Q., Yu, X., Chou, P., Savage, D., Zhang, X.: Power usage spike detection using smart meter data for load profiling. In: 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE), Santa Clara, CA, pp. 732–737 (2016).
  4. 4.
    Lu, Y., Zhang, T., Zeng, Z.: Adaptive weighted fuzzy clustering algorithm for load profiling of smart grid customers. In: 2016 IEEE/CIC International Conference on Communications in China (ICCC), Chengdu, pp. 1–6 (2016).
  5. 5.
    Ghuman, M.K., Singh Mann, B.: Profiling customers based on their social risk perception: a cluster analysis approach. 2018 Indian Institute of Management. SAGE Publications, Lucknow. Scholar
  6. 6.
    Rai, R., Chettri, P., Chettri, L.: E-commerce and its software (2018).
  7. 7.
    Sriram, A., Rao, A.P.: Security Issues in E-commerce (2018).
  8. 8.
    Soto-Acosta, P., Popa, S., Palacios-Marqués, D.: E-business, organizational innovation and firm performance in manufacturing SMEs: an empirical study in Spain. Technol. Econ. Dev. Econ. 22(6), 885–904 (2015). Scholar
  9. 9.
    Chuanga, S., Lin, H.: Performance implications of information-value offering in e-service systems: examining the resource-based perspective and innovation strategy. J. Strat. Inf. Syst. 26(1), 22–38 (2017). Scholar
  10. 10.
    Sun, T., Watanabe, W.C.: The study of critical success factors of cross-border e-commerce freight forwarder from China to Thailand. In: 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, pp. 1848–1852.
  11. 11.
    Adebanjo, D.: Classifying and selecting e-CRM applications: an analysis-based proposal. Manag. Decis. 41(6), 570–577 (2015). Scholar
  12. 12.
    Kourtesopoulou, A., Kehagias, J., Papaioannou, A.: Evaluation of E-service quality in the hotel sector: a systematic literature review. In: Springer Proceedings in Business and Economics (2018). Scholar
  13. 13.
    Fanga, J., Lia, J., Prybutok, V.R.: Posting-related attributes driving differential engagement behaviors in online travel communities. Telemat. Inform. 35(5), 1263–1276 (2018). Scholar
  14. 14.
    Rouyendegh, B.D., Topuz, K., Oztekin, A.D.: An AHP-IFT integrated model for performance evaluation of E-commerce web sites. Inf. Syst. Front. (2018).
  15. 15.
    Wu, C., Tsai, S.: Using DEMATEL-Based ANP model to measure the successful factors of E-commerce. J. Glob. Inf. Manag. 26(1), 120–135 (2018). Scholar
  16. 16.
    Qin, J., Liu, X., Pedrycz, W.: A multiple attribute interval type-2 fuzzy group decision making and its application to supplier selection with extended LINMAP method. Soft. Comput. 21, 3207 (2017). Scholar
  17. 17.
    Zaim, H., Ramdani, M., Haddi, A.: Splitting method for decision tree based on similarity with mixed fuzzy categorical and numeric attributes. In: Tabii, Y., Lazaar, M., Al Achhab, M., Enneya, N. (eds.) Big Data, Cloud and Applications, BDCA 2018. Communications in Computer and Information Science, vol. 872. Springer, Cham (2018)Google Scholar
  18. 18.
    Zaim, H., Ramdani, M., Haddi, A.: Multi-criteria analysis approach based on consumer satisfaction to rank B2C E-commerce websites. In: 11th International Conference on Intelligent Systems: Theories and Applications, SITA 2016 (2016).
  19. 19.
    Zaim, H., Ramdani, M., Haddi, A.: Fuzzy-based mining framework of browsing behavior to enhance E-commerce website performance: case study from In: 12th International Conference on Intelligent Systems: Theories and Applications, SITA 2018 (2018, in press)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Informatics Department, LIM Laboratory, Faculty of Sciences and Techniques of MohammediaUniversity Hassan IICasablancaMorocco
  2. 2.Informatics Department, LAMSAD Laboratory, Superior School of Technology of BerrechidUniversity Hassan ISettatMorocco

Personalised recommendations