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Research Papers: Design Automation

Experiential Conjoint Analysis: An Experience-Based Method for Eliciting, Capturing, and Modeling Consumer Preference

[+] Author and Article Information
Noah Tovares

Mem. ASME
Department of Mechanical Engineering,
Carnegie Mellon University,
5000 Forbes Avenue,
Pittsburgh, PA 15213
e-mail: ntovares@cmu.edu

Peter Boatwright

Tepper School of Business,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: pbhb@andrew.cmu.edu

Jonathan Cagan

Mem. ASME
Department of Mechanical Engineering,
Carnegie Mellon University,
5000 Forbes Avenue,
Pittsburgh, PA 15213
e-mail: cagan@cmu.edu

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received October 19, 2013; final manuscript received June 24, 2014; published online July 31, 2014. Assoc. Editor: Bernard Yannou.

J. Mech. Des 136(10), 101404 (Jul 31, 2014) (12 pages) Paper No: MD-13-1473; doi: 10.1115/1.4027985 History: Received October 19, 2013; Revised June 24, 2014

Traditionally, consumer preference is modeled in terms of preference for the aesthetic and functional features of a product. This paper introduces a new means to model consumer preference that accounts for not only for how a product looks and functions but also how it feels to interact with it. Traditional conjoint-based approaches to preference modeling require a participant to judge preference for a product based upon a static 2D visual representation or a feature list. While the aesthetic forms and functional features of a product are certainly important, the decision to buy or not to buy a product often depends on more, namely, the experience or feel of use. To address the importance of the product experience, we introduce the concept of experiential conjoint analysis, a method to mathematically capture preference for a product through experience-based (experiential) preference judgments. Experiential preference judgments are made based upon the use, or simulated use, of a product. For many products, creating enough physical prototypes to generate a preference model is cost prohibitive. In this work, virtual reality (VR) technologies are used to allow the participant an interactive virtual product experience, provided at little investment. The results of this work show that providing additional interaction-based (interactional) information about a product through a product experience does not affect the predictive ability of the resulting preference models. This work additionally demonstrates that the preference judgments of virtual product representations are more similar to preference judgments of real products than preference judgments of 2D product representations are. When examining similarity of modeled preference, experiential conjoint is found to be superior to visual conjoint with respect to mean absolute error (MAE), but with respect to correlation no significant difference between visual and experiential is found.

FIGURES IN THIS ARTICLE
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Copyright © 2014 by ASME
Topics: Preferences , Design
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References

Figures

Grahic Jump Location
Fig. 1

Virtual dashboard with components

Grahic Jump Location
Fig. 2

Virtual dashboard with virtual hand

Grahic Jump Location
Fig. 5

Mug design parameterization

Grahic Jump Location
Fig. 6

Mug handle parameterization

Grahic Jump Location
Fig. 7

Average MAE for the nine model designs (designs 2, 4, 5, 8, 9, 14, 15, 17, and 18)

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