Product Value Metrics and Value-Characteristic Modeling

DS 71: Proceedings of NordDesign 2012, the 9th NordDesign conference, Aarlborg University, Denmark. 22-24.08.2012

Year: 2012
Editor: Assoc. Prof. Poul Kyvsgaard Hansen, Professor John Rasmussen, Assoc. Prof. Kaj A. Jřrgensen, Assoc. Prof. Christian Tollestrup
Author: Withanage, Chathura; Park, Taezoon; Duc, Truong Ton Hien; Choi, Haejin
Series: NordDESIGN
Institution: 1: Aalborg University, Denmark; 2: Design Society, United Kingdom
ISBN: 978-87-91831-51-5


The mainstream product value-attribute models can be categorized in to two types, according to exclusion or inclusion of product price in the product attribute/characteristic vector. The characteristic price trade-off is included in popular preference/choice analysis methods, such as random utility analysis and discrete choice analysis, where price is considered as a controllable design variable. The other type of models, customer revealed value models, is focused on modeling a pure characteristic value. Product prices are used as an indicator of value, which is governed by competition and other exogenous factors. The both approaches can be used for the design decision support at the front-end product development. In the presented research study, partial least square path modeling (PLSPM) is used to get simplified meta-models of value-characteristic relationships to compare these two approaches. A data set containing US Sedan market 2008-2010 specifications, prices and sales was used to conduct the case study. The key findings of the research study are 1) using price as a value indicator is suggested in situations where customer attributes are unavailable, and 2) revealed value is a valid overall product value metric for the US Sedan market segment.

Keywords: Front-end decision support methods, product value-attribute modeling, revealed value, partial least square path modeling.


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