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Do We Fully Understand the Critical Success Factors of Customer Satisfaction with Industrial Goods? - Extending Festge and Schwaiger’s Model to Account for Unobserved Heterogeneity

Abstract

This paper extends Festge and Schwaiger’s (2007) model of customer satisfaction with industrial goods by accounting for unobserved heterogeneity. The application of a novel response-based segmentation approach in partial least squares path modeling (PLS-PM) - the finite mixture partial least squares (FIMIX-PLS) methodology - opens the way for the effective identification of distinctive customer segments. In comparison to previous studies in this field, group-specific path model estimates reveal each customer segment’s particular characteristics as well as other differentiated findings. Hence, this contribution demonstrates that structural equation modeling studies on the aggregate data level can be seriously misleading and makes a strong case for segment-specific customer satisfaction analyses.

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Notes

  1. 1.

    “Note that this presentation slightly differs from Hahn et al.’s (2002) original presentation”

  2. 2.

    Even though recent research shows that single items lag significantly behind multi-item measures in terms of criterion validity (Sarstedt and Wilczynski 2009), a single item may still be used as a measure to assess a construct on an aggregate level, as all respondents can simultaneously consider all those parts of the construct that they consider important (Nagy 2002).

  3. 3.

    The authors would like to thank the anonymous reviewer for this helpful remark.

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Correspondence to Marko Sarstedt.

Appendix

Appendix

Table 3 Evaluation results of the formative measurement models

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Sarstedt, M., Schwaiger, M. & Ringle, C. Do We Fully Understand the Critical Success Factors of Customer Satisfaction with Industrial Goods? - Extending Festge and Schwaiger’s Model to Account for Unobserved Heterogeneity. J Bus Mark Manag 3, 185 (2009). https://doi.org/10.1007/s12087-009-0023-7

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Keywords

  • Customer satisfaction
  • Structural equation model
  • PLS path modeling
  • Segmentation
  • Finite mixture
  • Latent class
  • Unobserved heterogeneity