Preference Measurement in Complex Product Development: A Comparison of Two-Staged SEM Approaches

  • Jörgen EimeckeEmail author
  • Daniel Baier
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


Since many years, preference measurement has been used to understand the importance that customers ascribe to alternative possible product attribute-levels. Available for this purpose are, e.g., compositional approaches based on the self-explicated-model (SEM) as well as decompositional ones based on conjoint analysis (CA). Typically, in SEM approaches, customers evaluate the importance of product attributes one by one whereas in decompositional approaches, they evaluate possible alternative products (attribute-level combinations) followed by a derivation of the importances. The SEM approaches seem to be superior when products are complex and the number of attributes is high. However, there are still improvement possibilities. In this paper two innovative two-staged SEM approaches are proposed and tested. The complex products under study are small remotely piloted aircraft systems (small RPAS) for German search and rescue (SAR) forces.


Rank Order Online Survey Predictive Validity Complex Product Conjoint Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Akaah, I. P., & Korgorgaonkar, P. K. (1983). An empirical comparison of the predictive validity of self-explicated, huber-hybrid, traditional conjoint, and hybrid conjoint models. Journal of Marketing Research, 20(2), 187–197.CrossRefGoogle Scholar
  2. Dorsch, M. J., & Teas, R. K. (1992). A test of the convergent validity of self-explicated and decompositional conjoint measurement. Journal of the Academy of Marketing Science, 20(1), 37–48.CrossRefGoogle Scholar
  3. Eckert, J., & Schaaf, R. (2009). Verfahren zur Präferenzmessung – Eine Übersicht und Beurteilung existierender und möglicher neuer Self-Explicated-Verfahren. Journal für Betriebswirtschaft, 59(1), 31–56.CrossRefGoogle Scholar
  4. Green, P. E., Goldberg, S. M., & Montemayor, M. (1981). A hybrid utility estimation model for conjoint analysis. Journal of Marketing, 45(1), 33–41.CrossRefGoogle Scholar
  5. Green, P. E., & Krieger, A. M. (1993). Conjoint analysis with product positioning applications. In Eliashberg, J. & Lilien, G. (Eds.), Handbook in operations research and management science (Vol. 5, pp. 467–515). Amsterdam: Elsevier.Google Scholar
  6. Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: New developments with implications for research and practise. Journal of Marketing, 54(4), 3–19.CrossRefGoogle Scholar
  7. Green, R. E., Krieger, A. M., & Agarwal, M. K. (1993). A cross validation test of our models for quantifying multiattribute preferences. Marketing Letters, 4(4), 369–380.CrossRefGoogle Scholar
  8. Höpfl, R. T., & Huber, P. H. (1970). A study of self-explicated utility models. Behavioral Sciences, 15(5), 408–414.CrossRefGoogle Scholar
  9. Meissner, M., Decker, R., & Adam, N. (2011). Ein empirischer Validitätsvergleich zwischen adaptive self-explicated approach (ASE), pairwise comparison-based preference measurement (PCPM) und adaptive conjoint analysis (ACA). Zeitschrift für Betriebswirtschaft, 81, 423–446.CrossRefGoogle Scholar
  10. Myers, J. H. (1999). Measuring customer satisfaction: Hot buttons and other measurement issues. Chicago: American Marketing Association.Google Scholar
  11. Netzer, O., & Srinivasan, V. (2011). Adaptive self-explication of multi-attributed preferences. Journal of Marketing Research, 48(1), 140–156.CrossRefGoogle Scholar
  12. Pullman, M. E., Dodson, K. J., & Moore, W. L. (1999). A comparison of conjoint methods when there are many attributes. Marketing Letters, 10(2), 125–138.CrossRefGoogle Scholar
  13. Sattler, H. (2006). Methoden zur Messung von Präferenzen für Innovationen. Zeitschrift für betriebswirtschaftliche Forschung, 54(6), 154–176.Google Scholar
  14. Scholz, S., Meissner, M. & Decker, R. (2010). Measuring consumer preferences for complex products: A compositional approach based on paired comparisons. Journal of Marketing Research, 47(4), 685–698.CrossRefGoogle Scholar
  15. Srinivasan, V. (1988). A conjunctive-compensatory approach to the self-explication of multiattributed preferences. Decision Sciences, 19(2), 295–305.CrossRefGoogle Scholar
  16. Srinivasan, V., & Park, C. S. (1997). Surprising robustness of the self-explicated approach to customer preference structure measurement. Journal of Marketing Research, 34(2), 286–291.CrossRefGoogle Scholar
  17. Torgerson, W. S. (1958). Theory and method of scaling. New York: Wiley.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Institute of Business Administration and EconomicsBrandenburg University of Technology CottbusCottbusGermany

Personalised recommendations