Evaluation and comparison of European countries: public opinion on services
AbstractWe propose the setting-up of an indicator for classifying European countries with relation to consumers' satisfaction for services of general interest. Services considered are fixed telephone service, electricity supply, railways and postal service. The reference data set is the 2002 Standard Eurobarometer database (EB58) and it refers to EU members before 2004 enlargement. The proposed indicator is based on nonlinear principal component analysis and supplies a measure for the citizen's satisfaction. Its robustness is verified by a Monte Carlo approach. The level of satisfaction is eventually used for country comparison and for detecting possible hotspots, in order to support decision making policies.
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Bibliographic InfoArticle provided by Springer in its journal Quality & Quantity.
Volume (Year): 44 (2010)
Issue (Month): 6 (October)
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Web page: http://www.springer.com/economics/journal/11135
Other versions of this item:
- Pier Alda Ferrari & Paola Annoni & Giancarlo Manzi, 2007. "Evaluation and comparison of European countries: public opinion on services," UNIMI - Research Papers in Economics, Business, and Statistics unimi-1058, Universitá degli Studi di Milano.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Pier Ferrari & Laura Pagani & Carlo Fiorio, 2011. "A Two-Step Approach to Analyze Satisfaction Data," Social Indicators Research, Springer, vol. 104(3), pages 545-554, December.
- Pier Ferrari & Silvia Salini, 2011. "Complementary Use of Rasch Models and Nonlinear Principal Components Analysis in the Assessment of the Opinion of Europeans About Utilities," Journal of Classification, Springer, vol. 28(1), pages 53-69, April.
- Ferrari, Pier Alda & Annoni, Paola & Barbiero, Alessandro & Manzi, Giancarlo, 2011. "An imputation method for categorical variables with application to nonlinear principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2410-2420, July.
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