Segmentation for path models and unobserved heterogeneity: The finite mixture partial least squares approach
Partial least squares-based path modeling with latent variables is a methodology that allows to estimate complex cause-effect relationships using empirical data. The assumption that the data is collected from a single homogeneous population is often unrealistic. Identification of different groups of consumers in connection with estimates in the inner path model constitutes a critical issue for applying the path modeling methodology to form effective marketing strategies. Sequential clustering strategies often fail to provide useful results for segment-specific partial least squares analyses. For that reason, the purpose of this paper is fourfold. First, it presents a finite mixture path modeling methodology for separating data based on the heterogeneity of estimates in the inner path model, as it is implemented in a software application for statistical computation. This new approach permits reliable identification of distinctive customer segments with their characteristic estimates for relationships of latent variables in the structural model. Second, it presents an application of the approach to two numerical examples, using experimental and empirical data, as a means of verifying the methodology's usefulness for multigroup path analyses in marketing research. Third, it analyses the advantages of finite mixture partial least squares to a sequential clustering strategy. Fourth, the initial application and critical review of the new segmentation technique for partial least squares path modeling allows us to unveil and discuss some of the technique's problematic aspects and to address significant areas of future research.
|Date of creation:||2006|
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- Claes Fornell & Peter Lorange & Johan Roos, 1990. "The Cooperative Venture Formation Process: A Latent Variable Structural Modeling Approach," Management Science, INFORMS, vol. 36(10), pages 1246-1255, October.
- Claes Fornell & William T. Robinson & Birger Wernerfelt, 1985. "Consumption Experience and Sales Promotion Expenditure," Management Science, INFORMS, vol. 31(9), pages 1084-1105, September.
- Vikas Mittal & Eugene W. Anderson & Akin Sayrak & Pandu Tadikamalla, 2005. "Dual Emphasis and the Long-Term Financial Impact of Customer Satisfaction," Marketing Science, INFORMS, vol. 24(4), pages 544-555, August.
- Jarvis, Cheryl Burke & MacKenzie, Scott B & Podsakoff, Philip M, 2003. " A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research," Journal of Consumer Research, University of Chicago Press, vol. 30(2), pages 199-218, September.
- 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.
- Viswanath Venkatesh & Ritu Agarwal, 2006. "Turning Visitors into Customers: A Usability-Centric Perspective on Purchase Behavior in Electronic Channels," Management Science, INFORMS, vol. 52(3), pages 367-382, March.
- Sargeant, Adrian & Ford, John B. & West, Douglas C., 2006. "Perceptual determinants of nonprofit giving behavior," Journal of Business Research, Elsevier, vol. 59(2), pages 155-165, February.
- Karl Jöreskog, 1978. "Structural analysis of covariance and correlation matrices," Psychometrika, Springer, vol. 43(4), pages 443-477, December.
- McLachlan, Geoffrey J. & Krishnan, Thriyambakam & Ng, See Ket, 2004. "The EM Algorithm," Papers 2004,24, Humboldt-Universität Berlin, Center for Applied Statistics and Economics (CASE).
- Venkatram Ramaswamy & Wayne S. Desarbo & David J. Reibstein & William T. Robinson, 1993. "An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data," Marketing Science, INFORMS, vol. 12(1), pages 103-124.
- Carsten Hahn & Michael D. Johnson & Andreas Herrmann & Frank Huber, 2002. "Capturing Customer Heterogeneity Using A Finite Mixture Pls Approach," Schmalenbach Business Review (sbr), LMU Munich School of Management, vol. 54(3), pages 243-269, July.
- Tenenhaus, Michel & Vinzi, Vincenzo Esposito & Chatelin, Yves-Marie & Lauro, Carlo, 2005. "PLS path modeling," Computational Statistics & Data Analysis, Elsevier, vol. 48(1), pages 159-205, January.
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