Research Papers: Design Theory and Methodology

An Approach to Study Associations Between Design Concepts and Design Outcomes

[+] Author and Article Information
Shun Takai

Department of Technology,
Northern Illinois University,
DeKalb, IL 60115
e-mail: stakai@niu.edu

Marcos Esterman

Department of Industrial
and Systems Engineering,
Rochester Institute of Technology,
Rochester, NY 14623-5603
e-mail: mxeeie@rit.edu

Ashok Midha

Department of Mechanical
and Aerospace Engineering,
Missouri University of
Science and Technology,
Rolla, MO 65409-0500
e-mail: midha@mst.edu

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 22, 2012; final manuscript received August 30, 2014; published online February 6, 2015. Assoc. Editor: Jonathan Cagan.

J. Mech. Des 137(4), 041101 (Apr 01, 2015) (12 pages) Paper No: MD-12-1219; doi: 10.1115/1.4028992 History: Received April 22, 2012; Revised August 30, 2014; Online February 06, 2015

This paper proposes an approach to investigate associations between design concepts and design outcomes. In the proposed approach, three deliverables (concept sketches, proof-of-concept (POC) prototypes, and final products) are evaluated using metrics called creative product semantic scale (CPSS). CPSS scores are analyzed using two methods: correlation analysis and classification tree analysis. Correlation analysis is used to investigate associations between the concept sketches or the POC prototypes and the final products. Classification tree analysis (together with leave-one-out cross-validation (LOOCV) and permutation test) is used to investigate relationships between CPSS scores and the retention of design concepts (i.e., concepts that are embodied in the final products). The proposed approach is illustrated using deliverables in a project-based design class.

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Fig. 1

Data acquisition and analysis procedure

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Fig. 4

Project testing setup

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Fig. 6

Design concepts and testing results

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Fig. 5

Project deliverables: (a) concept sketch, (b) POC prototype, and (c) final product

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Fig. 12

Effectiveness of decision rules: (a) number of evaluation, (b) misclassification rate, (c) LOOCV error, and (d) permutation-based

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Fig. 10

CPSS scores of concepts: (a) CPSS dimensions, (b) CPSS subscales, and (c) CPSS items

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Fig. 11

Classification trees: (a) CPSS dimensions, (b) CPSS subscales, and (c) CPSS items

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Fig. 7

Correlations of performance scores and CPSS: (a) CPSS dimensions, (b) CPSS subscales, and (c) CPSS items

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Fig. 8

Examples of correlations, regression lines, and p-values: (a) final products, (b) POC prototypes, and (c) concept sketches

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Fig. 9

Correlations of CPSS: (a) CPSS dimensions, (b) CPSS subscales, and (c) CPSS items

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Fig. 14

Performances of final products and the number of cognitive modes

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Fig. 15

Intraclass Correlations of CPSS evaluations

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Fig. 13

Creativity product semantic scale




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