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Research Papers: Design Theory and Methodology

Comparing Ideation Techniques for Beginning Designers

[+] Author and Article Information
Shanna R. Daly

Assistant Professor
Mechanical Engineering,
University of Michigan,
2350 Hayward Avenue,
Ann Arbor, MI 48109
e-mail: srdaly@umich.edu

Colleen M. Seifert

Professor
Department of Psychology,
University of Michigan,
530 Church Street,
Ann Arbor, MI 48109
e-mail: seifert@umich.edu

Seda Yilmaz

Associate Professor
Department of Industrial Design,
Iowa State University,
146 College of Design,
Ames, IA 50011
e-mail: seda@iastate.edu

Richard Gonzalez

Professor
Department of Psychology,
University of Michigan,
530 Church Street,
Ann Arbor, MI 48109
e-mail: gonzo@umich.edu

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received January 25, 2016; final manuscript received June 1, 2016; published online August 30, 2016. Assoc. Editor: Julie Linsey.

J. Mech. Des 138(10), 101108 (Aug 30, 2016) (12 pages) Paper No: MD-16-1073; doi: 10.1115/1.4034087 History: Received January 25, 2016; Revised June 01, 2016

Concept generation techniques can help to support designers in generating multiple ideas during design tasks. However, differences in the ways these techniques guide idea generation are not well understood. This study investigated the qualities of concepts generated by beginning engineering designers using one of three different idea generation techniques. Working individually on an open-ended engineering design problem, 102 first year engineering students learned and applied one of three different ideation techniques—design heuristics, morphological analysis, or individual brainstorming (using brainstorming rules to generate ideas working alone)—to a given design problem. Using the consensual assessment technique, all concepts were rated for creativity, elaboration, and practicality, and all participants' concept sets were rated for quantity and diversity. The simplest technique, individual brainstorming, led to the most concepts within the short (25 minute) ideation session. All three techniques produced creative concepts averaging near the scale midpoint. The elaboration of the concepts was significantly higher with design heuristics and morphological analysis techniques, and the practicality was significantly higher using design heuristics. Controlling for number of concepts generated, there were no significant differences in diversity of solution sets across groups. These results demonstrate that the use of design heuristics does not limit the creativity of ideation outcomes, and helps students to develop more elaborate and practical ideas. Design heuristics show advantages in the initial idea generation phase for beginning engineering students. These findings point to specific strengths in different ideation techniques, and the value of exposing beginning designers to multiple techniques for idea generation.

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Copyright © 2016 by ASME
Topics: Creativity , Design , Students
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Silvia, P. J. , Winterstein, B. P. , Willse, J. T. , Barona, C. M. , Cram, J. T. , Hess, K. I. , Martinez, J. L. , and Richard, C. A. , 2008, “ Assessing Creativity With Divergent Thinking Tasks: Exploring the Reliability and Validity of New Subjective Scoring Methods,” Psychol. Aesth., Creativity, Arts, 2(2), pp. 68–85. [CrossRef]
Jansson, D. G. , and Smith, S. M. , 1991, “ Design Fixation,” Des. Stud., 12(1), pp. 3–11. [CrossRef]
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Chen, C. , Kasof, J. , Himsel, A. J. , Greenberger, E. , Dong, Q. , and Xue, G. , 2002, “ Creativity in Drawings of Geometric Shapes: A Cross-Cultural Examination With the Consensual Assessment Technique,” J. Cross-Cultural Psychol., 33(2), pp. 171–187. [CrossRef]

Figures

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Fig. 1

Heuristic card example: attach independent functional components

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Fig. 2

Heuristic card list

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Fig. 3

Students' work shown on transcribed and adjusted concept sheets

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Fig. 4

Examples of low and high scoring concepts

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Fig. 5

Example solutions using each ideation technique

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Fig. 6

Average CAT scores for concepts generated with each ideation technique

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Fig. 7

Average number of concepts generated and CAT scores for diversity of concept

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Fig. 8

Correlation analysis of number of ideas and diversity score

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