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Application of the European Customer Satisfaction Index to Postal Services. Structural Equation Models versus Partial Least Squares


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  • O'Loughlin, Christina


  • Coenders, Germà


Customer satisfaction and retention are key issues for organizations in today’s competitive market place. As such, much research and revenue has been invested in developing accurate ways of assessing consumer satisfaction at both the macro (national) and micro (organizational) level, facilitating comparisons in performance both within and between industries. Since the instigation of the national customer satisfaction indices (CSI), partial least squares (PLS) has been used to estimate the CSI models in preference to structural equation models (SEM) because they do not rely on strict assumptions about the data. However, this choice was based upon some misconceptions about the use of SEM’s and does not take into consideration more recent advances in SEM, including estimation methods that are robust to non-normality and missing data. In this paper, both SEM and PLS approaches were compared by evaluating perceptions of the Isle of Man Post Office Products and Customer service using a CSI format. The new robust SEM procedures were found to be advantageous over PLS. Product quality was found to be the only driver of customer satisfaction, while image and satisfaction were the only predictors of loyalty, thus arguing for the specificity of postal services.

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Bibliographic Info

Paper provided by Department of Economics, University of Girona in its series Working Papers of the Department of Economics, University of Girona with number 4.

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Date of creation: Sep 2002
Date of revision:
Handle: RePEc:udg:wpeudg:004

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Keywords: European Customer Satisfaction Index; ECSI; Structural Equation Models; Robust Statistics; Missing Data; Maximum Likelihood;

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  1. Eugene W. Anderson & Mary W. Sullivan, 1993. "The Antecedents and Consequences of Customer Satisfaction for Firms," Marketing Science, INFORMS, vol. 12(2), pages 125-143.
  2. Dijkstra, Theo, 1983. "Some comments on maximum likelihood and partial least squares methods," Journal of Econometrics, Elsevier, vol. 22(1-2), pages 67-90.
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Cited by:
  1. Parrini, Alessandro & Doretti, Marco & Lapini, Gabriele, 2010. "Modelli a Equazioni Strutturali per la Valutazione dell'Esperienza Universitaria nell'Ateneo Fiorentino
    [Structural Equation Models for the assessment of the University experience at the University
    ," MPRA Paper 43412, University Library of Munich, Germany.
  2. Monika Oleksiak, 2009. "Satisfaction Drivers in Retail Banking: Comparison of Partial Least Squares and Covariance Based Methods," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 1(1), pages 83-102, March.
  3. Coenders, Germà & Bisbe, Josep & Saris, Willem E. & Batista-Foguet, Joan M., 2003. "Moderating Effects of Management Control Systems and Innovation on Performance. Simple Methods for Correcting the Effects of Measurement Error for Interaction Effects in Small Samples," Working Papers of the Department of Economics, University of Girona 7, Department of Economics, University of Girona.


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