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Research Papers: Empirical Studies

Identifying Trends in Analogy Usage for Innovation: A Cross-Sectional Product Study

[+] Author and Article Information
Peter Ngo

George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: pngo@gatech.edu

Cameron J. Turner

Assistant Professor
Department of Mechanical Engineering,
Colorado School of Mines,
Golden, CO 80401
e-mail: cturner@mines.edu

Julie S. Linsey

Assistant Professor
Innovation, Design Reasoning, Engineering
Education and Methods Lab,
George W. Woodruff School
of Mechanical Engineering,
Georgia Institute of Technology,
Atlanta, GA 30332
e-mail: julie.linsey@me.gatech.edu

www.technologyreview.com bioinspired

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 20, 2014; final manuscript received June 29, 2014; published online October 8, 2014. Assoc. Editor: Robert B. Stone.

J. Mech. Des 136(11), 111109 (Oct 08, 2014) (13 pages) Paper No: MD-14-1065; doi: 10.1115/1.4028100 History: Received January 20, 2014; Revised June 29, 2014

Design-by-analogy, including bioinspired design, is a powerful tool for innovation. Engineers need better tools to enhance ideation. To support tool creation, an exploratory cross-sectional empirical product study of 70 analogy-inspired products is conducted to report trends and associations among factors in the analogy-inspired design process, giving a general account of real-world practices. Products are randomly sampled from three technology magazines and a bioinspired design database. Seven variables are developed and used to classify each example according to design team composition, analogy mapping approach, analogies used, and design outcomes. Results do not suggest significant differences between problem-driven approaches, which start from a design problem and find solutions in analogous domains, and solution-driven approaches, which begin with knowledge in an analog domain and find design problems to solve. For instance, results suggest that both approaches yield products at about the same frequency, and both yield products with improved performance at statistically indistinguishable rates—thus, neither approach can be concluded to be advantageous over the other for improving product performance at this time. Overall, few associations are detected between design outcome variables and other variables, thus precluding recommendations for how to compose design teams, what approaches to promote, and what number and source of analogies to support in order to achieve the outcomes measured in this study.

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Figures

Grahic Jump Location
Fig. 1

Results plots of the 11 contingency tables involving outcome variables, with Barnard's exact p-values for the null hypotheses of no association. Visualized using the “vcd” package [123,124] in R [125].

Grahic Jump Location
Fig. 2

Results plots of the remaining ten contingency tables not involving outcome variables, with Barnard's exact p-values for the null hypotheses of no association. Visualized using the “vcd” package [123,124] in R [125].

Grahic Jump Location
Fig. 3

Results plots of biological cross-disciplinarity versus additional function contingency tables for examples from AskNature (left) and technology

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