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

Author

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  • O'Loughlin, Christina
  • Coenders, Germà

Abstract

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.

Suggested Citation

  • O'Loughlin, Christina & Coenders, Germà, 2002. "Application of the European Customer Satisfaction Index to Postal Services. Structural Equation Models versus Partial Least Squares," Working Papers of the Department of Economics, University of Girona 4, Department of Economics, University of Girona.
    • Handle: RePEc:udg:wpeudg:004
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    File URL: http://www3.udg.edu/fcee/economia/n4.pdf
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    References listed on IDEAS

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    1. Dijkstra, Theo, 1983. "Some comments on maximum likelihood and partial least squares methods," Journal of Econometrics, Elsevier, vol. 22(1-2), pages 67-90.
    2. 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.
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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. Dongping Liu & Hai Zhang, 2021. "Developing a New Model for Understanding Teacher Satisfaction With Online Learning," SAGE Open, , vol. 11(3), pages 21582440211, July.
    3. 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, Central European Journal of Economic Modelling and Econometrics, vol. 1(1), pages 83-102, March.
    4. Pradeep Kumar Mohanty & N. Senthil Kumar, 2017. "Measuring farmer’s satisfaction and brand loyalty toward Indian fertilizer brands using DEA," Journal of Brand Management, Palgrave Macmillan, vol. 24(5), pages 467-488, October.
    5. Hananiel M. Gunawan & Oliandes Sondakh,, 2020. "How to Enhance Word of Mouth in the Era of E-commerce: Case study of Tokopedia," International Journal of Science and Business, IJSAB International, vol. 4(9), pages 47-59.
    6. 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.
    7. Tsao, Wen-Yu, 2013. "The fitness of product information: Evidence from online recommendations," International Journal of Information Management, Elsevier, vol. 33(1), pages 1-9.

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    More about this item

    Keywords

    European Customer Satisfaction Index; ECSI; Structural Equation Models; Robust Statistics; Missing Data; Maximum Likelihood;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • H42 - Public Economics - - Publicly Provided Goods - - - Publicly Provided Private Goods
    • L89 - Industrial Organization - - Industry Studies: Services - - - Other
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

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