Document Type

Article

Publication Date

5-1-2016

Abstract

Design fixation is a factor that negatively influences the generation of novel design concepts (Jansson & Smith, 1991). When designers fixate, they tend to reproduce example features or features from their initial ideas. In order to mitigate design fixation, it is crucial to identify the factors that influence the extent of design fixation. This paper investigates two such factors: the modality of examples and the familiarity of designers with the example features. To investigate this, an experiment is conducted with mechanical engineering students who were asked to generate ideas to solve a peanut sheller design problem. The students generated ideas in five different experimental conditions: control, where no example was given; the first example given in a sketch form; the first example given as a nonfunctional prototype; a second example in sketch form; and the second example in a working prototype form. The first example was a nonfeasible solution, but it contained several features familiar to the participants. The second example was a feasible solution, but it contained less familiar features. In order to understand the extent of fixation triggered by the examples, three metrics were utilized to compare across the experimental conditions: the quantity of nonredundant ideas generated by the participants, the presence of example features in their solutions, and their fixation to the example's energy source. The results showed that in the case of the familiar example, the example modality did play an important role in the extent of design fixation. Across the examples, it was found that the first example containing several familiar features caused more fixation than the second one. Overall, this paper shows that the modality in which the example was communicated and the presence of familiar features in an example influenced the fixation caused by those examples.

Comments

This is an Accepted Manuscript of an article whose Version of Record was published by Cambridge University Press in AI EDAM (Artificial Intelligence for Engineering Design, Analysis and Manufacturing) on April 18, 2016, available online: https://doi.org/10.1017/S0890060416000056. This version is free to view and download for private research and study only. Not for re-distribution, re-sale or use in derivative works. © copyright holders.

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