Research Papers: Design Theory and Methodology

The Effect of Product Representation in Visual Conjoint Analysis

[+] Author and Article Information
Brian Sylcott

Department of Engineering,
East Carolina University,
Greenville, NC 27858
e-mail: sylcottb15@ecu.edu

Seth Orsborn

School of Management,
Bucknell University,
Lewisburg, PA 17837
e-mail: seth.orsborn@bucknell.edu

Jonathan Cagan

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

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received November 12, 2015; final manuscript received July 1, 2016; published online August 30, 2016. Assoc. Editor: Katja Holtta-Otto.

J. Mech. Des 138(10), 101104 (Aug 30, 2016) (8 pages) Paper No: MD-15-1758; doi: 10.1115/1.4034085 History: Received November 12, 2015; Revised July 01, 2016

When most designers set out to develop a new product, they solicit feedback from potential consumers. These data are incorporated into the design process in an effort to more effectively meet customer requirements. Often these data are used to construct a model of consumer preference capable of evaluating candidate designs. Although the mechanics of these models have been extensively studied, there are still some open questions, particularly with respect to models of aesthetic preference. When constructing preference models, simplistic product representations are often favored over high fidelity product models in order to save time and expense. This work investigates how choice of product representation can affect model performance in visual conjoint analysis. Preference models for a single product, a table knife, are derived using three different representation schemes: simple sketches, solid models, and three dimensional (3D)-printed models. Each of these representations is used in a separate conjoint analysis survey. The results from this study show that the choice model based on 3D-printed photopolymer prototypes underperformed. Additionally, consumer responses were inconsistent and potentially contradictory between different representations. Consequently, when using conjoint analysis for product innovation, obtaining a true understanding of consumer preference requires selecting representations based on how accurately they convey the product details in question.

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Fig. 1

Typical table knife

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Fig. 2

Perspective view of knife model

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Fig. 3

Slope attribute levels

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Fig. 4

Edge attribute levels

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Fig. 5

End attribute levels

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Fig. 6

Screenshot of CAD trial

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Fig. 7

Screenshot of sketch trial

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Fig. 8

Photograph of prototype trial

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Fig. 9

Screenshot of prototype trial

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Fig. 10

Experimental procedure example

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Fig. 11

Mean participant choice consistency

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Fig. 12

Mean pairwise representation consistency

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Fig. 13

End attribute part-worth values for each representation

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Fig. 14

Slope attribute part-worth values for each representation

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Fig. 15

Edge attribute part-worth values for each representation



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