Effectiveness of generic-parts technique in idea generation
- Maho Akao, Nagoya University, Nagoya, Japan
- Mayu Yamakawa, Nagoya University, Nagoya, Japan
- Sachiko Kiyokawa, Nagoya University, Nagoya, Japan
AbstractGeneric-parts technique (GPT), a method developed by McCaffrey (2012), involves repeatedly breaking an object into parts and rephrasing their descriptions to not imply fixed functions. A previous study showed that GPT facilitates insight problem solving. We investigated this method’s effectiveness in idea generation. Ninety-four undergraduates were assigned to either an experimental group using GPT or a control group. In the training phase, the GPT-group participants were explained how to create a generic-parts diagram with an example of a bell, and they drew two diagrams for other objects by themselves. The control-group participants were given a word association test of 180 words and were instructed to write the first word that came to their mind. All participants then engaged in an unusual uses task with an umbrella. The results showed that the GPT-group generated less ideas than the control group. We concluded that GPT is not particularly effective in idea generation.
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