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The effect of explicit instructions in idea generation studies

Published online by Cambridge University Press:  28 May 2018

Luis A. Vasconcelos Open the ORCID record for Luis A. Vasconcelos [Opens in a new window] ,
Maria A. Neroni  and
Nathan Crilly
Luis A. Vasconcelos*
Affiliation:
Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
Maria A. Neroni
Affiliation:
Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
Nathan Crilly
Affiliation:
Engineering Design Centre, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK
*
Author for correspondence: Luis A. Vasconcelos, E-mail: lalv4401@gmail.com
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Abstract

In inspiration and fixation experiments, example designs are often provided along with the instructions for how participants should treat them. However, research has not reached a consensus about the influence of such instructions, leading to difficulties in understanding how the examples and the instructions each affect idea generation. We conducted an experiment in which 303 participants designed for the same design problem, while given different examples and instructions, which ranged from strongly encouraging copying the examples to strongly discouraging copying. Exposure to the examples affected the number and type of ideas generated, whereas exposure to the instructions did not. However, instructions did affect how participants incorporated features of the examples in their ideas. Encouraged groups incorporated many features of the examples, while also incorporating structural features more than conceptual ones. Surprisingly, the incorporation of features in discouraged groups was not different from that of groups given no instructions or even no stimulus. This indicates that concrete features may be easier to recognize and reproduce than abstract ones, and that encouraging instructions are more effective than discouraging ones, despite how strict or lenient those instructions are. The manipulation of different features also allowed us to observe how similar approaches to solving a design problem can compete for attention and how the calculation of feature repetition can be misleading depending on how common or obvious the features might be. These findings have implications for the interpretation of results from fixation studies, and for the development of design tools that present stimuli to assist idea generation.

Keywords

Design fixationexperimental instructionsfeature repetitioninspirationstimuli introduction
Type
Research Article
Information
AI EDAM , Volume 32 , Issue 3 , August 2018 , pp. 308 - 320
Copyright
Copyright © Cambridge University Press 2018 

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References

Aleman, V (2009) ECO 07 – Compactable Urban Bicycle, accessed February 10, 2017. Available at https://www.behance.net/gallery/293563/ECO-07-Compactable-Urban-Bicycle.Google Scholar
Atilola, O and Linsey, J (2015) Representing analogies to influence fixation and creativity: a study comparing computer-aided design, photographs, and sketches. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29(2), 161–171.CrossRefGoogle Scholar
Cardoso, C and Badke-Schaub, P (2011) The influence of different pictorial representations during idea generation. The Journal of Creative Behavior 45(2), 130–146.Google Scholar
Chakrabarti, A, Sarkar, P, Leelavathamma, B and Nataraju, BS (2005) A functional representation for aiding biomimetic and artificial inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19(2), 113–132.Google Scholar
Cheng, P, Mugge, R and Schoormans, JPL (2014) A new strategy to reduce design fixation: presenting partial photographs to designers. Design Studies 35(4), 374–391.CrossRefGoogle Scholar
Cheong, H, Hallihan, G and Shu, LH (2014) Understanding analogical reasoning in biomimetic design: an inductive approach. In Gero, JS (ed.). Design Computing and Cognition ‘12. Dordrecht, The Netherlands: Springer Netherlands, pp. 21–39.Google Scholar
Chrysikou, EG and Weisberg, RW (2005) Following the wrong footsteps: fixation effects of pictorial examples in a design problem-solving task. Journal of Experimental Psychology: Learning, Memory, and Cognition 31(5), 1134–1148.Google Scholar
Dahl, DW and Moreau, P (2002) The influence and value of analogical thinking during new product ideation. Journal of Marketing Research 39(1), 47–60.Google Scholar
Dinar, M, Shah, JJ, Cagan, J, Leifer, L, Linsey, J, Smith, SM and Hernandez, NV (2015) Empirical studies of designer thinking: past, present, and future. Journal of Mechanical Design 137(2), 021101.Google Scholar
Eckert, CM (1997) Design inspiration and design performance. In Proceedings of the 78th World Conference of the Textile Institute, Thessaloniki, Greece, pp. 369–387.Google Scholar
Editorial board of IJDCI (2013) Perspectives on design creativity and innovation research. International Journal of Design Creativity and Innovation 1(1), 1–42.Google Scholar
Feng, T, Cheong, H and Shu, LH (2014) Effects of abstraction on selecting relevant biological phenomena for biomimetic design. Journal of Mechanical Design 136(11), 111111.Google Scholar
Floss, GH (2010) Zee-K Ergonomic Bike, accessed February 10, 2017. Available at http://www.coroflot.com/gabrielfloss/zee-k-ergonomic-bike1.Google Scholar
Fu, K, Cagan, J and Kotovsky, K (2010) Design team convergence: the influence of example solution quality. Journal of Mechanical Design 132(11), 111005.Google Scholar
Gonçalves, M, Cardoso, C and Badke-Schaub, P (2012). Find your inspiration: exploring different levels of abstraction in textual stimuli. In 2nd International Conference on Design Creativity (ICDC2012), Glasgow, UK.Google Scholar
Gonçalves, M, Cardoso, C and Badke-Schaub, P (2014) What inspires designers? Preferences on inspirational approaches during idea generation. Design Studies 35(1), 29–53.Google Scholar
Jansson, DG and Smith, SM (1991) Design fixation. Design Studies 12(1), 3–11.Google Scholar
Kudrowitz, B and Dippo, C (2013) Getting to the novel ideas: exploring the alternative uses test of divergent thinking. In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, V005T06A013.Google Scholar
Kudrowitz, B, Te, P and Wallace, D (2012) The influence of sketch quality on perception of product-idea creativity. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26(3), 267–279.Google Scholar
Landis, JR and Koch, GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1), 159.Google Scholar
LeFevre, J-A and Dixon, P (1986) Do written instructions need examples? Cognition and Instruction 3(1), 1–30.Google Scholar
Liikkanen, LA and Perttula, M (2008) Inspiring design idea generation: insights from a memory-search perspective. Journal of Engineering Design 21(5), 545–560.Google Scholar
Linsey, J, Tseng, I, Fu, K, Cagan, J, Wood, K and Schunn, C (2010) A study of design fixation, its mitigation and perception in engineering design faculty. Journal of Mechanical Design 132(4), 041003.Google Scholar
Linsey, J and Wood, K (2007) Wordtrees: a method for design-by-analogy. In ASEE Annual Conference, AC 2008-1669.Google Scholar
Linsey, J, Wood, KL and Markman, AB (2008) Modality and representation in analogy. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22(2), 85–100.Google Scholar
Lujun, Z (2011) Design fixation and solution quality under exposure to example solution. In IEEE 2nd International Conference on Computing, Control and Industrial Engineering (CCIE), 2011, vol. 1. IEEE, pp. 129–132.CrossRefGoogle Scholar
Neroni, MA, Vasconcelos, LA and Crilly, N (2017) Computer-based “mental set” tasks: an alternative approach to studying design fixation. Journal of Mechanical Design 139(7), 071102.Google Scholar
Nijstad, BA, Stroebe, W and Lodewijkx, HF (2002) Cognitive stimulation and interference in groups: exposure effects in an idea generation task. Journal of Experimental Social Psychology 38(6), 535–544.CrossRefGoogle Scholar
Page, MM (1981) Demand compliance in laboratory experiments. In Tedeschi, JT (ed.). Impression Management Theory and Social Psychological Research. New York, NY: Academic Press, pp. 57–82.CrossRefGoogle Scholar
Perttula, M and Liikkanen, LA (2006). Exposure effects in design idea generation: unconscious conformity or a product of sampling probability. In Proceedings of NordDesign. Reykjavik, Iceland: The Design Society, pp. 42–55.Google Scholar
Perttula, M and Sipilä, P (2007) The idea exposure paradigm in design idea generation. Journal of Engineering Design 18(1), 93–102.Google Scholar
Purcell, AT and Gero, JS (1992) Effects of examples on the results of a design activity. Knowledge-Based Systems 5(1), 82–91.Google Scholar
Purcell, AT and Gero, JS (1996) Design and other types of fixation. Design Studies 17(4), 363–383.Google Scholar
Sarkar, P and Chakrabarti, A (2008) The effect of representation of triggers on design outcomes. AI Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22(2), 101–116.Google Scholar
Shah, JJ, Smith, SM and Vargas-Hernandez, N (2003) Metrics for measuring ideation effectiveness. Design Studies 24(2), 111–134.CrossRefGoogle Scholar
Shneiderman, B (2000) Creating creativity: user interfaces for supporting innovation. ACM Transactions on Computer-Human Interaction 7(1), 114–138.Google Scholar
Siangliulue, P, Chan, J, Gajos, KZ and Dow, SP (2015) Providing timely examples improves the quantity and quality of generated ideas. In ACM SIGCHI Conference on Creativity and Cognition. Glasgow, UK: ACM Press, pp. 83–92.Google Scholar
Smith, SM, Ward, TB and Schumacher, JS (1993) Constraining effects of examples in a creative generation task. Memory & Cognition 21(6), 837–845.Google Scholar
Töre Yargin, G and Crilly, N (2015) Information and interaction requirements for software tools supporting analogical design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29(2), 203–214.Google Scholar
Tseng, I, Moss, J, Cagan, J and Kotovsky, K (2008) The role of timing and analogical similarity in the stimulation of idea generation in design. Design Studies 29(3), 203–221.Google Scholar
Tsenn, J, Atilola, O, McAdams, DA and Linsey, JS (2014) The effects of time and incubation on design concept generation. Design Studies 35(5), 500–526.Google Scholar
Vasconcelos, LA, Cardoso, CC, Sääksjärvi, M, Chen, C-C and Crilly, N (2017) Inspiration and fixation: the influences of example designs and system properties in idea generation. Journal of Mechanical Design 139(3), 031101–031101-13.Google Scholar
Vasconcelos, LA and Crilly, N (2016) Inspiration and fixation: questions, methods, findings, and challenges. Design Studies 42, 1–32.Google Scholar
Vasconcelos, LA, Neroni, MA and Crilly, N (2016). Fluency results in design fixation experiments: an additional explanation. In The 4th International Conference on Design Creativity. Atlanta, GA: The Design Society.Google Scholar
Vattam, S, Wiltgen, B, Helms, M, Goel, AK and Yen, J (2011) DANE: fostering creativity in and through biologically inspired design. In Taura, T and Nagai, Y (eds). Design Creativity 2010. London, UK: Springer London, pp. 115–122.Google Scholar
Viswanathan, V, Atilola, O, Esposito, N and Linsey, J (2014). A study on the role of physical models in the mitigation of design fixation. Journal of Engineering Design 25(1–3), 25–43.Google Scholar
Viswanathan, V, Tomko, M and Linsey, J (2016) A study on the effects of example familiarity and modality on design fixation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30(2), 171–184.CrossRefGoogle Scholar
Yilmaz, S, Seifert, CM and Gonzalez, R (2010) Cognitive heuristics in design: instructional strategies to increase creativity in idea generation. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(3), 335–355.Google Scholar
Youmans, RJ (2011 a) Design fixation in the wild: design environments and their influence on fixation. The Journal of Creative Behavior 45(2), 101–107.Google Scholar
Youmans, RJ (2011 b) The effects of physical prototyping and group work on the reduction of design fixation. Design Studies 32(2), 115–138.Google Scholar
Youmans, RJ and Arciszewski, T (2014) Design fixation: classifications and modern methods of prevention. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28(2), 129–137.Google Scholar
Zahner, D, Nickerson, JV, Tversky, B, Corter, JE and Ma, J (2010) A fix for fixation? Rerepresenting and abstracting as creative processes in the design of information systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24(2), 231.CrossRefGoogle Scholar
Zhang, HZ, Xie, C and Nourian, S (2017) Are their designs iterative or fixated? Investigating design patterns from student digital footprints in computer-aided design software. International Journal of Technology and Design Education, 1–23. https://doi.org/10.1007/s10798-017-9408-1Google Scholar
Zhao, M (2013) Seek it or let it come: how designers achieve inspirations. In CHI’13 Extended Abstracts on Human Factors in Computing Systems. ACM, pp. 2779–2784.Google Scholar