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Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares

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Handbook of Partial Least Squares

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Partial least squares (PLS) estimates of structural equation model path coefficients are believed to produce more accurate estimates than those obtained with covariance structure analysis (CVA) using maximum likelihood estimation (MLE) when one or more of the MLE assumptions are not met. However, there exists no empirical support for this belief or for the specific conditions under which it will occur. MLE-based CVA will also break down or produce improper solutions whereas PLS will not. This study uses simulated data to estimate parameters for a model with five independent latent variables and one dependent latent variable under various assumption conditions. Data from customer satisfaction studies were used to identify the form of typical field-based survey distributions. Our results show that PLS produces more accurate path coefficients estimates when sample sizes are less than 500, independent latent variables are correlated, and measures per latent variable are less than 4. Method accuracy does not vary when the MLE multinormal distribution assumption is violated or when the data do not fit the theoretical structure very well. Both procedures are more accurate when the independent variables are uncorrelated, but MLE estimations break down more frequently under this condition, especially when combined with sample sizes of less than 100 and only two measures per latent variable.

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Acknowledgements

John Hulland is Associate Professor of Marketing at the Katz Graduate School of Business, University of Pittsburgh. Michael J. Ryan is Professor Emeritus at the Ross School of Business, University of Michigan. Robert K. Rayner is Vice President, Market Strategies, Inc. The authors appreciate financial support for this work from the Richard Ivey School of Business, the Katz Graduate School of Business, and the Social Sciences and Humanities Council of Canada. We also appreciate the very helpful comments made on earlier versions of the paper by Michel Wedel and Fred Feinberg. Finally, we are extremely grateful for the study design and estimation assistance provided by Sungjin Hong.

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Authors and Affiliations

  1. Katz Business School, University of Pittsburgh, Pittsburgh, PA, 15260, USA

    John Hulland

  2. Ross School of Business, University of Michigan, 105, Bass Harbor, ME, 04653, USA

    Michael J. Ryan

  3. Market Strategies International, 20255 Victor Parkway, Suite 400, Livonia, MI, 48152, USA

    Robert K. Rayner

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  1. John Hulland
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Correspondence to John Hulland .

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Editors and Affiliations

  1. Dept. Inf. Systems & Decision Sciences, ESSEC Business School of Paris, av. Bernard Hirsch, Cergy Pontoise CX, 95021, France

    Vincenzo Esposito Vinzi

  2. Bauer College of Business, University of Houston, Houston, 77204-6282, U.S.A.

    Wynne W. Chin

  3. Nijmegen School of Management, Radboud University, Nijmegen, Netherlands

    Jörg Henseler

  4. School of Economics & Management, BeiHang University, XueYuan Road 37, Beijing, 100083, China, People's Republic

    Huiwen Wang

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Hulland, J., Ryan, M.J., Rayner, R.K. (2010). Modeling Customer Satisfaction: A Comparative Performance Evaluation of Covariance Structure Analysis Versus Partial Least Squares. In: Esposito Vinzi, V., Chin, W., Henseler, J., Wang, H. (eds) Handbook of Partial Least Squares. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-32827-8_15

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