Customer Satisfaction Measurement Models: Generalised Maximum Entropy Approach
AbstractThis paper presents the methodology of the Generalised Maximum Entropy (GME) approach for estimating linear models that contain latent variables such as customer satisfaction measurement models. The GME approach is a distribution free method and it provides better alternatives to the conventional method; Namely, Partial Least Squares (PLS), which used in the context of costumer satisfaction measurement. A simplified model that is used for the Swedish customer satis faction index (CSI) have been used to generate simulated data in order to study the performance of the GME and PLS. The results showed that the GME outperforms PLS in terms of mean square errors (MSE). A simulated data also used to compute the CSI using the GME approach.
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Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0503013.
Length: 14 pages
Date of creation: 10 Mar 2005
Date of revision:
Note: Type of Document - pdf; pages: 14
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Generalised Maximum Entropy; Partial Least Squares; Costumer Satisfaction Models.;
Find related papers by JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
- C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
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- Rosa Bernardini Papalia & Enrico Ciavolino, 2011. "GME Estimation of Spatial Structural Equations Models," Journal of Classification, Springer, vol. 28(1), pages 126-141, April.
- Bustos-Reyes, César Augusto & González-Benito, Óscar, 2008. "Store and store format loyalty measures based on budget allocation," Journal of Business Research, Elsevier, vol. 61(9), pages 1015-1025, September.
- Laura Trinchera & Giorgio Russolillo, 2010. "On the use of Structural Equation Models and PLS Path Modeling to build composite indicators," Working Papers 30-2010, Macerata University, Department of Studies on Economic Development (DiSSE), revised Oct 2010.
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