Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T16:57:17.319Z Has data issue: false hasContentIssue false
  • English
  • Français

Exploring the Application of Network Analytics in Characterizing a Conceptual Design Space

Part of: ICT for Design

Published online by Cambridge University Press:  26 July 2019

Joshua T. Gyory ,
Kosa Goucher-Lambert ,
Kenneth Kotovsky  and
Jonathan Cagan

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 ability to effectively analyse design concepts is essential for making early stage design decisions. Human evaluations, the most common assessment method, describe individual design concepts on a variety of ideation metrics. However, this approach falls short in creating a holistic representation of the design space as a whole that informs the underlying relations between concepts. Motivated by this shortcoming, this work leverages network theory to visualize and characterize features of a conceptual design space. To illustrate the utility of network theory for these purposes, a network composed of a corpus of solutions to a design problem and their semantic similarity is derived, and its design properties (e.g., uniqueness and innovation potential) are studied. This network-based approach not only characterizes features of individual designs themselves, but also uncovers more nuanced properties of the design space through studying emerging clusters of concepts. Overall, this work expands on developing research in design, demonstrating the value in applying network analytics to a conceptual design space as an engineering support tool to aid design decision-making.

Keywords

Conceptual designEvaluationDesign methods
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1953 - 1962
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) 2019

References

Ahmed, F. and Fuge, M. (2018), “Creative Exploration Using Topic Based Bisociative Networks”, Design Science, Vol. 4 No. 12, pp. 1–30.Google Scholar
Ahmed, F., Fuge, M., Hunter, S. and Miller, S. (2018), “Interpreting Idea Maps : Pairwise comparisons reveal what makes ideas novel”, Journal of Mechanical Design.Google Scholar
Borgatti, S., Mehra, A., Brass, D.J. and Labianca, G. (2009), “Network Analysis in the Social Sciences”, Science, Vol. 323 No. April, pp. 892–896.Google Scholar
Borgatti, S.P. and Halgin, D.S. (2011), “On Network Theory”, Organization Science, Vol. 22 No. 5, pp. 1168–1181.Google Scholar
Brandes, U., Robins, G., Mccranie, A.N.N. and Wasserman, S. (2016), “Science : What is network science?”, Vol. 1 No. April 2013, pp. 1–15.Google Scholar
Carley, K.M. (2014), “ORA - A Toolkit for Network Analysis and Visualization”.Google Scholar
Chen, W., Heydari, B., Maier, A.M. and Panchal, J.H. (2018), “Network-based Modeling and Analysis in Design”, Design Science, Vol. 4, p. e16.Google Scholar
Freeman, L.C. (1978), “Centrality in Social Network Conceptual Clarification”, Social Networks, Vol. 1 No. 3, pp. 215–239.Google Scholar
Fu, K., Cagan, J. and Kotovsky, K. (2010), “Design Team Convergence: The Influence of Example Solution Quality”, Journal of Mechanical Design, Vol. 132 No. 11, p. 111005.Google Scholar
Ulu, Gercer, Messersmith, N., Goucher-Lambert, M., Cagan, K. and Kara, J.L.B. (2018), “Wisdom of Micro-Crowds in Evaluating Solutions to Esoteric Engineering Problems”, Journal of Mechanical Design.Google Scholar
Gosnell, C.A. and Miller, S.R. (2015), “But Is It Creative? Delineating the Impact of Expertise and Concept Ratings on Creative Concept Selection”, Journal of Mechanical Design, Vol. 138 No. 2, p. 021101.Google Scholar
Goucher-Lambert, K. and Cagan, J. (2019), “Crowdsourcing Inspiration: Using crowd generated inspirational stimuli to support designer ideation”, Design Studies, Elsevier Ltd., Vol. 61, pp. 1–29.Google Scholar
Goucher-Lambert, K., Gyory, J.T., Cagan, J. and Kotovsky, K. (2019), “Computationally Adaptive Stimuli for Real-Time Design Support”, (Working Paper).Google Scholar
Gyory, J.T., Cagan, J. and Kotovsky, K. (2019), “Are you better off alone? Mitigating the underperformance of engineering teams during conceptual design through adaptive process management”, Research in Engineering Design, Springer London, Vol. 30 No. 1, pp. 85–102.Google Scholar
Kittur, A., Chi, E.H. and Suh, B. (2008), “Crowdsourcing User Studies With Mechanical Turk”, CHI ’08 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 453–456.Google Scholar
Landauer, T. and Foltz, P. (1998), “An introduction to latent semantic analysis”, Discourse Processes, Vol. 25, pp. 259–284.Google Scholar
Ma, S., Jiang, Z. and Liu, W. (2017), “A design change analysis model as a change impact analysis basis for semantic design change management”, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol. 231 No. 13, pp. 2384–2397.Google Scholar
Maher, M. and Fisher, D. (2012), “Using AI to evaluate creative designs”, Proceedings of the 2nd International Conference on Design Creativity, No. September, pp. 45–54.Google Scholar
Opsahl, T., Agneessens, F. and Skvoretz, J. (2010), “Node centrality in weighted networks: Generalizing degree and shortest paths”, Social Networks, Elsevier B.V., Vol. 32 No. 3, pp. 245–251.Google Scholar
Redi, J. and Povoa, I. (2014), “Crowdsourcing for Rating Image Aesthetic Appeal”, Proceedings of the 2014 International ACM Workshop on Crowdsourcing for Multimedia - CrowdMM ’14, pp. 25–30.Google Scholar
Song, B., Srinivasan, V. and Luo, J. (2017a), “Patent stimuli search and its influence on ideation outcomes”, Design Science, Vol. 3, p. e25.Google Scholar
Song, H.I., Lopez, R., Fu, K. and Linsey, J. (2017b), “Characterizing the Effects of Multiple Analogs and Extraneous Information for Novice Designers in Design-by-Analogy”, Journal of Mechanical Design, Vol. 140 No. 3, p. 031101.Google Scholar
Sosa, M., Mihm, J. and Browning, T. (2011), “Degree Distribution and Quality in Complex Engineered Systems”, Journal of Mechanical Design, Vol. 133 No. 10, p. 101008.Google Scholar
Toh, C.A. and Miller, S.R. (2016), “Creativity in design teams: the influence of personality traits and risk attitudes on creative concept selection”, Research in Engineering Design, Springer London, Vol. 27 No. 1, pp. 73–89.Google Scholar
Verhaegen, P.-A., Vandevenne, D. and Duflou, J.R. (2012), “Originality and Novelty: A different Universe”, International Design Conference - Design 2012, Dubrovnik - Croatia, pp. 1961–1966.Google Scholar
Walsh, H.S., Dong, A. and Tumer, I.Y. (2018), “The role of bridging nodes in behavioral network models of complex engineered systems”, Design Science, Vol. 4, p. e8.Google Scholar
Wang, M., Chen, W., Huang, Y., Contractor, N.S. and Fu, Y. (2015), “A Multidimensional Network Approach for Modeling Customer-Product Relations in Engineering Design”, ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Boston, Massachusetts, USA, available at: https://doi.org/10.1115/DETC2015-46764Google Scholar
Wang, M., Sha, Z., Huang, Y., Contractor, N., Fu, Y. and Chen, W. (2016), “Forecasting Technological Impacts on Customers’ Co-Consideration Behaviors: A Data-Driven Network Analysis Approach”, ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Charlotte, North Carolina, p. V02AT03A040.Google Scholar
Wasserman, S. and Faust, K. (1994), Social Network Analysis: Methods and Applications, Cambridge University Press.Google Scholar