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Research Papers: Design Theory and Methodology

Deciphering the Influence of Product Shape on Consumer Judgments Through Geometric Abstraction

[+] Author and Article Information
Gunay Orbay, Luoting Fu

Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213

Levent Burak Kara

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

A demonstration of this method, examples, and supplemental material can be found at: http://vdel.me.cmu.edu/co-abstraction-of-shape-collections

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received December 8, 2014; final manuscript received March 12, 2015; published online June 8, 2015. Editor: Shapour Azarm.

J. Mech. Des 137(8), 081103 (Aug 01, 2015) (10 pages) Paper No: MD-14-1775; doi: 10.1115/1.4030206 History: Received December 08, 2014; Revised March 12, 2015; Online June 08, 2015

Understanding and tailoring the visual elements of a developing product to evoke desired perceived qualities and a positive response from the consumer is a key challenge in industrial design. To date, computational approaches to assist this process have either relied on stiff geometric representations, or focused on superficial features that exclude often elusive shape characteristics. In this work, we aim to study the relationship between product geometry and consumers' qualitative judgments through a visual decomposition and abstraction of existing products. At the heart of our investigation is a shape analysis method that produces a spectrum of abstractions for a given three-dimensional (3D) computer model. Our approach produces a hierarchical simplification of an end product, whereby consumer response to geometric elements can be statistically studied across different products, as well as across the different abstractions of one particular product. The results of our case study show that consumer judgments formed by coarse product “impressions” are strongly correlated with those evoked by the final production models. This outcome highlights the importance of early geometric explorations and assessments before committing to detailed design efforts.

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Figures

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Fig. 1

(a) The nine-level simple-to-complex abstraction of a Mustang model. Note that several distinguishing characteristics of a Mustang such as the front bumper, grill, and air intake on the hood start to emerge over abstractions. (b) Various real car images used in our user studies. The Mustang model in (a) corresponds to the top-left image in (b).

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Fig. 2

A typical survey question in study I. The answer to this question is the Subaru at the top right.

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Fig. 3

(a) Average participant recognition accuracies in study I as a function of the abstraction level. For each plot, the left-most point corresponds to the simplest abstraction, while point FM corresponds to the full models, (b) full models, (c) DB models.

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Fig. 4

A typical survey question in study II

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Fig. 5

The differences between consumers' relative assessments within a set of final products (condition F) and that within a set of debranded abstracted models of the same products (condition D) for the six tested attributes. The numbers (1–7) in circles denote the brand. Each edge between two circles corresponds to a pairwise comparison. In each graph there are 21 such edges. The color of each edge represents the magnitude of the mean difference between the assessments of full models and that of the debranded abstractions. See the legend for the color map. Edges are drawn as solid lines if the mean difference is statistically significant, and dashed otherwise. The percentage of solid edges for each attribute is 24%, 19%, 5%, 14%, 10%, and 0%, respectively.

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Fig. 6

The positioning maps of the debranded abstractions and full models by their AHP scores in terms of two attributes. The numbers in or near the circles denote the brand. The solid lines connect the feature scores of the debranded model (blue/solid circles) and its corresponding full model (red/hollow circles) of the same car. The models, debranded and full, are shown at the top.

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Fig. 7

The AHP scores with respect to the abstraction levels for all cars and attributes. The lines with circle markers denote the AHP scores, the green (short dashed) lines show the recognition accuracy calculated in study I, and the red (long dashed) and black (solid) lines show the correlation values between the AHP scores for attributes and the brand recognition accuracy in increasing and decreasing directions of abstraction levels, respectively. The two correlation values suggest potential connections between the bulk features or brand-apparent abstractions and the attribute scores.

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Fig. 8

Our studies have identified brand-apparent abstraction for all the models in our examples. Certain features impact brand recognition rates more than others. (a) The recognition of the Mustang by the majority was highly affected by the front bumper. (b) Similarly, headlight sockets were the most distinguishing details for the firebird. In (a) and (b) the DB models and the next level in the abstraction spectrum are shown on the left and right, respectively. Our studies have also analyzed the sensitivity of consumer responses with respect to the geometric features: (c) the drop in utility scores for the Impreza can be explained by a class shift from an SUV to a compact sedan in the abstractions.

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