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Design fixation: Classifications and modern methods of prevention

Published online by Cambridge University Press:  16 May 2014

Robert J. Youmans  and
Thomaz Arciszewski
Robert J. Youmans*
Affiliation:
Department of Psychology, George Mason University, Fairfax, Virginia, USA
Thomaz Arciszewski
Affiliation:
Department of Civil, Environmental and Infrastructure Engineering, George Mason University, Fairfax, Virginia, USA
*
Reprint requests to: Robert J. Youmans, Department of Psychology, David King Hall 2057, George Mason University, 4400 University Drive, Fairfax, VA 22030. E-mail: ryouman2@gmu.edu
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Abstract

The term design fixation is often used interchangeably to refer to situations where designers limit their creative output because of an overreliance on features of preexisting designs, or more generally, an overreliance on a specific body of knowledge directly associated with a problem. In this paper, we argue that interdisciplinary interest in design fixation has led to increasingly broad definitions of the phenomenon that may be undermining empirical research efforts, educational efforts to minimize fixation, and the acquisition and dissemination of transdisciplinary knowledge about fixation effects. To address these issues, we recommend that researchers consider categorizing fixation phenomena into one of three classifications: unconscious adherence to the influence of prior designs, conscious blocks to change, and intentional resistance to new ideas. Next, we distinguish between concept-based design fixation, fixation to a specific class of known design concepts, and knowledge-based design fixation, fixation to a problem-specific knowledge base. With these distinctions in place, we propose a system of orders of design fixation, recommend methods for reducing fixation in inventive design, and recommend areas that are in need of further research within the field of design science.

Keywords

Creativity and IdeationDesign CognitionDesign CreativityDesign MethodsDesign Theory
Type
Special Issue Articles
Information
AI EDAM , Volume 28 , Issue 2: Design Computing and Cognition (DCC'12) , May 2014 , pp. 129 - 137
Copyright
Copyright © Cambridge University Press 2014 

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