Abstract
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.
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Eimecke, J., Baier, D. (2015). Preference Measurement in Complex Product Development: A Comparison of Two-Staged SEM Approaches. In: Lausen, B., Krolak-Schwerdt, S., Böhmer, M. (eds) Data Science, Learning by Latent Structures, and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44983-7_21
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DOI: https://doi.org/10.1007/978-3-662-44983-7_21
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