Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-27T04:49:54.688Z Has data issue: false hasContentIssue false
  • English
  • Français

EXPLORING THE IMPACT OF GENERATIVE STIMULI ON THE CREATIVITY OF DESIGNERS IN COMBINATIONAL DESIGN

Published online by Cambridge University Press:  19 June 2023

Da Wang  and
Ji Han
Da Wang*
Affiliation:
University of Liverpool
Ji Han
Affiliation:
University of Liverpool
*
Wang, Da, University of Liverpool, United Kingdom, [email protected]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The ideation process has a significant impact on the initial concept generation and final product creativity of the design. Visual stimuli play an important role in the process of innovative product design. With the increase in computing capability, generative design methods are widely implemented. In this paper, features of design targets and combinational objects in 2 combinational design tasks are fused using adversarial neural generative networks to form the generated stimuli. It is also used with combinational object pictures to investigate the impact on creativity in design ideation. The study invited designers to use and subjectively self-evaluate the two stimuli in a design task. Through analysis of participant data (n=20), the results showed that the generative stimuli had an advantage over the combinational image stimuli in terms of the smoothness of creativity in the design ideation of outcomes. And there is a positive correlation between designers' years of design education and their tendency to prefer generative stimuli. Based on the results obtained, ideas are provided for the study of the influence of visual and generative stimuli on the designer's ideation process.

Keywords

CreativityDesign ideationComputational design methodsArtificial intelligenceStimuli
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1805 - 1814
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Agkathidis, A. (2016) Generative Design. Hachette UK.Google Scholar
Bacciotti, D., Borgianni, Y. and Rotini, F. (2016) “An original design approach for stimulating the ideation of new product features”, Computers in Industry, Vol. 75, pp. 80–100. https://doi.org/10.1016/j.compind.2015.06.004.CrossRefGoogle Scholar
Benami, O. and Jin, Y. (2008) “Creative Stimulation in Conceptual Design”, in. ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers Digital Collection, pp. 251–263. https://doi.org/10.1115/DETC2002/DTM-34023.CrossRefGoogle Scholar
Bettaieb, D. (2022) “Sources of Inspiration in The Interior Design Process: How Ideation is Affected by Modality”, Proceedings of DARCH, 2022(2nd). https://doi.org/10.46529/darch.202206.Google Scholar
Cardoso, C. and Badke-Schaub, P. (2011) “The Influence of Different Pictorial Representations During Idea Generation”, The Journal of Creative Behavior, Vol. 45, No. 2, pp. 130–146. https://doi.org/10.1002/j.2162-6057.2011.tb01092.x.CrossRefGoogle Scholar
Cash, P. and Štorga, M. (2015) “Multifaceted assessment of ideation: using networks to link ideation and design activity”, Journal of Engineering Design, Vol. 26, No. 10–12, pp. 391–415. https://doi.org/10.1080/09544828.2015.1070813.CrossRefGoogle Scholar
Chen, L., Wang, P., Dong, H., Shi, F., Han, J., Guo, Y., Childs, P. R. N., Xiao, J., & Wu, C. (2019) “An artificial intelligence-based data-driven approach for design ideation”, Journal of Visual Communication and Image Representation, Vol. 61, pp. 10–22. https://doi.org/10.1016/j.jvcir.2019.02.009.CrossRefGoogle Scholar
Chen, M., Zhao, T., Zhang, H., & Luo, S. (2018) “A Study of the Influence of Images on Design Creative Stimulation”, in Meiselwitz, G. (ed.) Social Computing and Social Media. User Experience and Behavior. Cham: Springer International Publishing (Lecture Notes in Computer Science), pp. 3–18. https://doi.org/10.1007/978-3-319-91521-0_1.CrossRefGoogle Scholar
Chulvi, V., Mulet, E., Chakrabarti, A., López-Mesa, B., & González-Cruz, C. (2012) “Comparison of the degree of creativity in the design outcomes using different design methods”, Journal of Engineering Design, Vol. 23, No. 4, pp. 241–269. https://doi.org/10.1080/09544828.2011.624501.CrossRefGoogle Scholar
Dorst, K. and Cross, N. (2001) “Creativity in the design process: co-evolution of problem–solution”, Design Studies, Vol. 22(5), pp. 425–437. https://doi.org/10.1016/S0142-694X(01)00009-6.CrossRefGoogle Scholar
Du, P., Miller, C., MacDonald, E., & Gormley, P. (2015) “Review of supporting and refuting evidence for Innovation Engineering practices”, Design Science, 1, p. e5. https://doi.org/10.1017/dsj.2015.5.CrossRefGoogle Scholar
Duan, Y. and Zhang, J. (2022) “A Novel AI-Based Visual Stimuli Generation Approach for Environment Concept Design”, Computational Intelligence and Neuroscience, 2022, pp. e8015492. https://doi.org/10.1155/2022/8015492.CrossRefGoogle ScholarPubMed
El-Zanfaly, D. (2015) “Imitation, Iteration and Improvisation: Embodied interaction in making and learning”, Design Studies, Vol. 41, pp. 79–109. https://doi.org/10.1016/j.destud.2015.09.002.CrossRefGoogle Scholar
Flavell, J.H. (1979) “Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry”, American Psychologist, Vol. 34, pp. 906–911. https://doi.org/10.1037/0003-066X.34.10.906.CrossRefGoogle Scholar
Gentner, D. (1983) “Structure-mapping: A theoretical framework for analogy”, Cognitive Science, Vol. 7, No. 2, pp. 155–170. https://doi.org/10.1016/S0364-0213(83)80009-3.Google Scholar
Gero, J.S. and Kannengiesser, U. (2004) “The situated function–behaviour–structure framework”, Design Studies, Vol. 25, No. 4, pp. 373–391. https://doi.org/10.1016/j.destud.2003.10.010.CrossRefGoogle Scholar
Goldschmidt, G. and Smolkov, M. (2006) “Variances in the impact of visual stimuli on design problem solving performance”, Design Studies, Vol. 27, No. 5, pp. 549–569. https://doi.org/10.1016/j.destud.2006.01.002.CrossRefGoogle Scholar
Gonçalves, M., Badke-Schaub, P. and Cardoso, C. (2011) “Searching for inspiration during idea generation”, In DS 70: Proceedings of DESIGN 2012, the 12th International Design Conference, Dubrovnik, Croatia.Google Scholar
Gonçalves, M., Cardoso, C. and Badke-Schaub, P. (2014) “What inspires designers? preferences on inspirational approaches during idea generation,” Design Studies, Vol. 35, No. 1, pp. 29–53. Available at: https://doi.org/10.1016/j.destud.2013.09.001.CrossRefGoogle Scholar
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A & Bengio, Y. (2014) “Generative Adversarial Networks”. arXiv preprint arXiv:1406.2661 https://doi.org/10.48550/arXiv.1406.2661.CrossRefGoogle Scholar
Goucher-Lambert, K. and Cagan, J. (2019) “Crowdsourcing inspiration: Using crowd generated inspirational stimuli to support designer ideation”, Design Studies, Vol. 61, pp. 1–29. https://doi.org/10.1016/j.destud.2019.01.001.CrossRefGoogle Scholar
Han, J., Forbes, H. & Schaefer, D (2021). An exploration of how creativity, functionality, and aesthetics are related in design. Res Eng Design Vol. 32, pp. 289–307. https://doi.org/10.1007/s00163-021-00366-9CrossRefGoogle Scholar
Han, J, Park, D, Shi, F, Chen, L, Hua, M, Childs, PR (2019). Three driven approaches to combinational creativity: Problem-, similarity- and inspiration-driven. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science,.Vol. 233, No. 2, pp.373–384. https://dx.doi.org/10.1177/0954406217750189Google Scholar
Han, J., Shi, F., Chen, L. and Childs, P. R. N. (2018a) “A computational tool for creative idea generation based on analogical reasoning and ontology,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing. Cambridge University Press, Vol. 32, No. 4, pp. 462–477. https://dx.doi.org/10.1017/S0890060418000082.CrossRefGoogle Scholar
Han, J., Shi, F., Chen, L. and Childs, P. R. N. (2018b) “The Combinator – a computer-based tool for creative idea generation based on a simulation approach,” Design Science. Cambridge University Press, 4, p. e11. https://dx.doi.org/10.1017/dsj.2018.7.CrossRefGoogle Scholar
Howard, T., Culley, S. and Dekoninck, E. (2009) “Creative Stimulation in Conceptual Design: An Analysis of Industrial Case Studies”, in. ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers Digital Collection, pp. 161–170. https://doi.org/10.1115/DETC2008-49672.CrossRefGoogle Scholar
Howard, T.J., Culley, S.J. and Dekoninck, E. (2008) “Describing the creative design process by the integration of engineering design and cognitive psychology literature”, Design Studies, Vol. 29, No. 2, pp. 160–180. https://doi.org/10.1016/j.destud.2008.01.001.CrossRefGoogle Scholar
Howard, T.J., Dekoninck, E.A. and Culley, S.J. (2010) “The use of creative stimuli at early stages of industrial product innovation”, Research in Engineering Design, Vol. 21, No. 4, pp. 263–274. https://doi.org/10.1007/s00163-010-0091-4.CrossRefGoogle Scholar
Jansson, D.G. and Smith, S.M. (1991) “Design fixation”, Design Studies, Vol. 12, No. 1, pp. 3–11. https://doi.org/10.1016/0142-694X(91)90003-F.CrossRefGoogle Scholar
Jonson, B. (2005) “Design ideation: the conceptual sketch in the digital age”, Design Studies, Vol. 26, No. 6, pp. 613–624. https://doi.org/10.1016/j.destud.2005.03.001.CrossRefGoogle Scholar
Leite De Vasconcelos, L., Neroni, M., Cardoso, C., & Crilly, N. (2018) “Idea representation and elaboration in design inspiration and fixation experiments”, International Journal of Design Creativity and Innovation, Vol. 6, No. 1–2, pp. 93–113. https://doi.org/10.1080/21650349.2017.1362360.CrossRefGoogle Scholar
Li, H. and Lachmayer, R. (2018) “Generative Design Approach for Modeling Creative Designs”, IOP Conference Series: Materials Science and Engineering, Vol. 408, No. 1, p. 012035. https://doi.org/10.1088/1757-899X/408/1/012035.CrossRefGoogle Scholar
Li, X., Su, J., Zhang, Z., & Bai, R. (2021) “Product innovation concept generation based on deep learning and Kansei engineering”, Journal of Engineering Design, Vol. 32, No. 10, pp. 559–589. https://doi.org/10.1080/09544828.2021.1928023.CrossRefGoogle Scholar
Massetti, B. (1996) “An Empirical Examination of the Value of Creativity Support Systems on Idea Generation”, MIS Quarterly, Vol. 20, No. 1, pp. 83–97. https://doi.org/10.2307/249543.CrossRefGoogle Scholar
McCoy, J.M. and Evans, G.W. (2002) “The Potential Role of the Physical Environment in Fostering Creativity”, Creativity Research Journal, Vol. 14, No. 3–4, pp. 409–426. https://doi.org/10.1207/S15326934CRJ1434_11.CrossRefGoogle Scholar
Moreno, D., Blessing, L., Wood, K., Vögele, C., & Hernández, A. (2016) “Creativity Predictors: Findings from Design-by-Analogy Ideation Methods “Learning and Performance”, in. ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, American Society of Mechanical Engineers Digital Collection. https://doi.org/10.1115/DETC2015-47929.CrossRefGoogle Scholar
Nijstad, B.A., Stroebe, W. and Lodewijkx, H.F.M. (2002) ‘Cognitive stimulation and interference in groups: Exposure effects in an idea generation task’, Journal of Experimental Social Psychology, Vol. 38, No. 6, pp. 535–544. Available at: https://doi.org/10.1016/S0022-1031(02)00500-0.CrossRefGoogle Scholar
Obieke, C.C., Milisavljevic-Syed, J. and Han, J. (2021) ‘Data-Driven Creativity: Computational Problem-Exploring in Engineering Design’, Proceedings of the Design Society, Vol. 1, pp. 831–840. Available at: https://doi.org/10.1017/pds.2021.83.CrossRefGoogle Scholar
Oppenlaender, J. (2022) “The Creativity of Text-to-Image Generation”, in 25th International Academic Mindtrek conference, pp. 192–202. https://doi.org/10.1145/3569219.3569352.CrossRefGoogle Scholar
Osborn, A.F. (1953) Applied imagination. Oxford, England: Scribner's (Applied imagination), pp. xvi, 317.Google Scholar
Perttula, M. and Sipilä, P. (2007) ‘The idea exposure paradigm in design idea generation’, Journal of Engineering Design, Vol. 18, No. 1, pp. 93–102. Available at: https://doi.org/10.1080/09544820600679679.CrossRefGoogle Scholar
Piya, C., V., Chandrasegaran, S., Elmqvist, N., & Ramani, K. (2017) “Co-3Deator: A Team-First Collaborative 3D Design Ideation Tool”, in Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery (CHI “17), pp. 6581–6592. https://doi.org/10.1145/3025453.3025825.CrossRefGoogle Scholar
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022) “Hierarchical Text-Conditional Image Generation with CLIP Latents” arXiv. https://doi.org/10.48550/arXiv.2204.06125.CrossRefGoogle Scholar
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., & Norouzi, …, M. (2022) “Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding”. arXiv. https://doi.org/10.48550/arXiv.2205.11487.CrossRefGoogle Scholar
Sarkar, P. and Chakrabarti, A. (2008) “The effect of representation of triggers on design outcomes”, AI EDAM, Vol. 22, No. 2, pp. 101–116. https://doi.org/10.1017/S0890060408000073.Google Scholar
Sarkar, P. and Chakrabarti, A. (2011) “Assessing design creativity”, Design Studies, Vol. 32, No. 4, pp. 348–383. https://doi.org/10.1016/j.destud.2011.01.002.CrossRefGoogle Scholar
Sarica, S., Han, J., Luo, J., 2023. Design Representation as Semantic Networks. Computers in Industry. 144, 103791. https://dx.doi.org/10.1016/j.compind.2022.103791.CrossRefGoogle Scholar
Shi, F., Chen, L., Han, J., and Childs, P. (2017). “A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval.” ASME. J. Mech. Des. November 2017; Vol. 139, No. 11, 111402. https://doi.org/10.1115/1.4037649Google Scholar
Tseng, W.S.-W. (2018) “Can visual ambiguity facilitate design ideation?”, International Journal of Technology and Design Education, 28, No. 2, pp. 523–551. https://doi.org/10.1007/s10798-016-9393-9.CrossRefGoogle Scholar
Wang, D., Li, J., Ge, Z., & Han, J. (2021) “A COMPUTATIONAL APPROACH TO GENERATE DESIGN WITH SPECIFIC STYLE”, Proceedings of the Design Society, Vol. 1, pp. 21–30. https://doi.org/10.1017/pds.2021.3.CrossRefGoogle Scholar
Yang, M.C., Wood, W.H. and Cutkosky, M.R. (2005) “Design information retrieval: a thesauri-based approach for reuse of informal design information”, Engineering with Computers, Vol. 21, No. 2, pp. 177–192. https://doi.org/10.1007/s00366-005-0003-9.CrossRefGoogle Scholar
Yu, S., Dong, H., Wang, P., Wu, C., & Guo, Y. (2019) “Generative Creativity: Adversarial Learning for Bionic Design”, in Tetko, I.V. et al. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. Cham: Springer International Publishing (Lecture Notes in Computer Science), pp. 525–536. https://doi.org/10.1007/978-3-030-30508-6_42.CrossRefGoogle Scholar