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

Author

Listed:
  • Antonello D’Ambra

    (University of Campania “Luigi Vanvitelli”)

  • Pietro Amenta

    (University of Sannio)

  • Antonio Lucadamo

    (University of Sannio)

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.

Suggested Citation

  • Antonello D’Ambra & Pietro Amenta & Antonio Lucadamo, 2019. "Analyzing Customer Requirements to Select a Suitable Service Configuration Both for Users and for Company Provider," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(1), pages 383-394, November.
    • Handle: RePEc:spr:soinre:v:146:y:2019:i:1:d:10.1007_s11205-018-1935-y
    DOI: 10.1007/s11205-018-1935-y
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    References listed on IDEAS

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    1. M. Nitti & E. Ciavolino, 2014. "A deflated indicators approach for estimating second-order reflective models through PLS-PM: an empirical illustration," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(10), pages 2222-2239, October.
    2. Enrico Ciavolino & Maurizio Carpita, 2015. "The GME estimator for the regression model with a composite indicator as explanatory variable," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 955-965, May.
    3. Luigi D'Ambra & Onur Koksoy & Biagio Simonetti, 2009. "Cumulative correspondence analysis of ordered categorical data from industrial experiments," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(12), pages 1315-1328.
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    Cited by:

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