Advertisement

Social Indicators Research

, Volume 146, Issue 1–2, pp 383–394 | Cite as

Analyzing Customer Requirements to Select a Suitable Service Configuration Both for Users and for Company Provider

  • Antonello D’Ambra
  • Pietro AmentaEmail author
  • Antonio Lucadamo
Article
  • 85 Downloads

Abstract

The analysis of Customer Satisfaction is an important tool in planning business activities. It allows firms to identify which features and attributes are important for their services or products. In this paper we define nine possible scenarios for a public train transport, by means of design of experiments. Each scenario is identified by some quality factors with 3 possible levels. Our aim is to select the scenario that maximizes the satisfaction of potential users. To define the levels composing the best feasible scenario we propose to use Cumulative Correspondence Analysis (by Taguchi method) and the Likelihood Ratio in the logistic regression model. It is also suggested a suitable scenario both for users and company provider.

Keywords

Expected customer satisfaction Public transport Cumulative correspondence analysis Likelihood ratio Logistic regression 

References

  1. Agresti, A. (2013). Categorical data analysis (3rd ed.). New York: Wiley.Google Scholar
  2. Beh, E. J., D’Ambra, L., & Simonetti, B. (2011). Correspondence analysis of cumulative frequencies using a decomposition of Taguchi‘s statistic. Communications in Statistics: Theory and Methods, 40, 1620–1632.CrossRefGoogle Scholar
  3. Bolton, R. N., & Drew, J. H. (1991). A longitudinal analisys of the impact of service changes on customer attitudes. Journal of Marketing, 55(1), 1–10.CrossRefGoogle Scholar
  4. Ciavolino, E., & Carpita, M. (2015). The GME estimator for the regression model with a composite indicator as explanatory variable. Quality and Quantity, 49(3), 955–965.CrossRefGoogle Scholar
  5. Ciavolino, E., & Dahlgaard, J. J. (2007). ECSI—Customer satisfaction modelling and analysis: A case study. Total Quality Management and Business Excellence, 18, 545–555.CrossRefGoogle Scholar
  6. Ciavolino E., D’Ambra A., Venuleo C., & Vernai M. (2017). Non-symmetrical correspondence analysis to evaluate how age influences the addiction discourses. Statistica e Applicazioni, XV(1), 3–18Google Scholar
  7. Cronin, J. J., & Taylor, S. A. (1992). Measuring service quality: A reexamination and extension. Journal of Marketing, 56(3), 55–68.CrossRefGoogle Scholar
  8. D’Ambra, L., Amenta, P., & D’Ambra, A. (2017). Decomposition of cumulative chi-squared statistics, with some new tools for their interpretation. Statistical Methods and Applications, https://doi.org/10.1007/s10260-017-0401-3.CrossRefGoogle Scholar
  9. D’Ambra, L., Köksoy, O., & Simonetti, B. (2009). Cumulative correspondence analysis of ordered categorical data from industrial experiments. Journal of Applied Statistics, 36(12), 1315–1328.CrossRefGoogle Scholar
  10. Dowie, U., Helferich, A., Herzwurm, G., & Schockert, S. (2005). QFD for services: The service matrix of matrices. In: 11th International symposium on QFD. Izmir, TurkeyGoogle Scholar
  11. Hauser, J. R., & Clausing, D. (1988). The house of quality. The Harvard Business Review, 3, 63–73.Google Scholar
  12. Kamalja, K., & Khangar, N. (2017). Multiple correspondence analysis and its applications. Electronic Journal of Applied Statistical Analysis, 10(2), 432–462.Google Scholar
  13. Krivobokova, O. V. (2009). Evaluating customer satisfaction as an aspect of quality management. World Academy of Science, Engineering and Technology, Vol. 53Google Scholar
  14. Lombardo, R., & Ringrose, T. (2012). Bootstrap confidence regions in non-symmetrical correspondence analysis. Electronic Journal of Applied Statistical Analysis, 5(3), 413–417.Google Scholar
  15. Montgomery, D. C. (1996). Introduction to statistical quality control. Hoboken: Wiley.Google Scholar
  16. Nair, V. N. (1986). Testing in industrial experiments with ordered categorical data. Technometrics, 28(4), 283–291.CrossRefGoogle Scholar
  17. Nair, V. N. (1987). Chi-squared type tests for ordered alternatives in contingency tables. Journal of American Statistical Association, 82, 283–291.CrossRefGoogle Scholar
  18. Nitti, M., & Ciavolino, E. (2014). A deflated indicators approach for estimating second-order reflective models through PLS-PM: An empirical illustration. Journal of Applied Statistics, 41(10), 2222–2239.CrossRefGoogle Scholar
  19. O’Connel, A. A. (2006). Logistic regression models for ordinal response variables (Vol. 146). Thousand Oaks: Sage Publications.CrossRefGoogle Scholar
  20. Oliver, R. L., & John, E. S. (1989). Consumer perceptions of interpersonal equity and satisfaction in transactions: A field survey approach. Journal of Marketing, 53, 21–35.CrossRefGoogle Scholar
  21. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(Fall), 41–50.CrossRefGoogle Scholar
  22. Reilly, N. B. (1999). The team based product development guidebook. Milwaukee, WI: ASQ Quality Press.Google Scholar
  23. Rosenthal, S. R. (1992). Effective product design and development (p. 60430). Business One Irwin, Homewood, Illinois: How to cut lead time and increase customer satisfaction.Google Scholar
  24. Sarnacchiaro, P., & D’Ambra, A. (2011). Cumulative correspondence analysis to improve the public train transport. Electronic Journal of Applied Statistical Analysis: Decision Support System and Service Evaluation, 2(1), 15–24.Google Scholar
  25. Satterthwaite, F. (1946). An approximate distribution of estimates of variance components. Biometrical Bullettin, 2, 110–114.CrossRefGoogle Scholar
  26. Taguchi, G. (1966). Statistical analysis. Tokyo: Maruzen.Google Scholar
  27. Taguchi, G. (1974). A new statistical analysis for clinical data, the accumulating analysis, in contrast with the chi-square test. Saishin Igaku, 29, 806–813.Google Scholar
  28. Taguchi, G. (1991a). Taguchi methods case studies from the U.S. and Europe. Michigan: American Supplier Institute.Google Scholar
  29. Taguchi, G. (1991b). Taguchi methods: Research and development. Michigan: American Supplier Institute.Google Scholar
  30. Taguchi, G. (1991c). Taguchi methods: Signal-to-noise ratio for quality evaluation. Michigan: American Supplier Institute.Google Scholar
  31. Takeuchi K., & Hirotsu, C. (1982). The cumulative chi square method against ordered alternative in two-way contingency tables. Technical Report 29, Reports of Statistical Application Research. Japanese Union of Scientists and Engineers.Google Scholar
  32. Tapke, J. (1997). House of quality. Steps in understanding the house of quality. http://www.public.iastate.edu/~vardeman/IE361/f01mini/johnson.pdf.

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of Campania “Luigi Vanvitelli”CapuaItaly
  2. 2.Department of Law, Economics, Management and Quantitative MethodsUniversity of SannioBeneventoItaly

Personalised recommendations