Hostname: page-component-745bb68f8f-hvd4g Total loading time: 0 Render date: 2025-01-27T14:45:54.483Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigating novelty–outcome relationships in engineering design

Published online by Cambridge University Press:  26 April 2010

V. Srinivasan  and
Amaresh Chakrabarti
V. Srinivasan
Affiliation:
Centre for Product Design and Manufacturing, Indian Institute of Science, Bangalore, India
Amaresh Chakrabarti
Affiliation:
Centre for Product Design and Manufacturing, Indian Institute of Science, Bangalore, India
Get access Rights & Permissions [Opens in a new window]

Abstract

Design creativity involves developing novel and useful solutions to design problems. The research in this article is an attempt to understand how novelty of a design resulting from a design process is related to the kind of outcomes, described here as constructs, involved in the design process. A model of causality, the SAPPhIRE model, is used as the basis of the analysis. The analysis is based on previous research that shows that designing involves development and exploration of the seven basic constructs of the SAPPhIRE model that constitute the causal connection between the various levels of abstraction at which a design can be described. The constructs are state change, action, parts, phenomenon, input, organs, and effect. The following two questions are asked. Is there a relationship between novelty and the constructs? If there is a relationship, what is the degree of this relationship? A hypothesis is developed to answer the questions: an increase in the number and variety of ideas explored while designing should enhance the variety of concept space, leading to an increase in the novelty of the concept space. Eight existing observational studies of designing sessions are used to empirically validate the hypothesis. Each designing session involves an individual designer, experienced or novice, solving a design problem by producing concepts and following a think-aloud protocol. The results indicate dependence of novelty of concept space on variety of concept space and dependence of variety of concept space on variety of idea space, thereby validating the hypothesis. The results also reveal a strong correlation between novelty and the constructs; correlation value decreases as the abstraction level of the constructs reduces, signifying the importance of using constructs at higher abstraction levels for enhancing novelty.

Keywords

ConceptIdeaNoveltyNovelty Assessment MethodVarietyVariety Assessment Method
Type
Special Issue Articles
Information
AI EDAM , Volume 24 , Issue 2: Creativity: Simulation, Stimulation, and Studies , May 2010 , pp. 161 - 178
Copyright
Copyright © Cambridge University Press 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Amabile, T.M. (1996). Creativity in Context: Update to the Social Psychology of Creativity. Westview, CO: Westview Press.Google Scholar
Andreasen, M.M. (1980). Syntesemetoder pa systemgrundlag. PhD Thesis. Lund Technical University, Sweden.Google Scholar
Berliner, C., & Brimson, J.A. (1988). Cost Management for Today's Advanced Manufacturing: The CAM-I Conceptual Design. Cambridge, MA: Harvard Business School Press.Google Scholar
Blessing, L., & Chakrabarti, A. (2009). DRM, a Design Research Methodology, pp. 120–121. London: Springer.CrossRefGoogle Scholar
Blessing, L., Chakrabarti, A., & Wallace, K. (1995). A design research methodology. Proc. Int. Conf. Engineering Design, pp. 502–507.Google Scholar
Boden, M.A. (1999). Computer models of creativity. In Handbook of Creativity (Sternberg, R.J., Ed.), pp. 351–372. New York: Cambridge University Press.Google Scholar
Cambridge University Press. (2009). Cambridge Dictionaries Online. New York: Cambridge University Press. Accessed at http://www.dictionary.cambridge.org/Google Scholar
Candy, L. (1996). Understanding creativity: an empirical approach. Proc. Creativity and Cognition, pp. 45–54.Google Scholar
Chakrabarti, A. (2002). Editor's introduction. In Engineering Design Synthesis: Understanding, Approaches and Tools (Chakrabarti, A., Ed.), pp. vii–xvi. London: Springer–Verlag.CrossRefGoogle Scholar
Chakrabarti, A. (2003). Towards a measure for assessing creative influences of a creativity technique. Proc. Int. Conf. Engineering Design [CD].Google Scholar
Chakrabarti, A., & Bligh, T. (1994). Functional synthesis of solution-concepts in mechanical conceptual design. Part I: knowledge representation. Research in Engineering Design 6 (3), 127–141.CrossRefGoogle Scholar
Chakrabarti, A., & Khadilkar, P. (2003). A measure for assessing product novelty. Proc. Int. Conf. Engineering Design [CD].Google Scholar
Chakrabarti, A., Johnson, A., & Kiriyama, T. (1997). An approach to automated synthesis of solution principles for microsensor designs. Proc. Int. Conf. Engineering Design, pp. 125–128.Google Scholar
Chakrabarti, A., Sarkar, P., Leelavathamma, B., & Nataraju, B.S. (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.CrossRefGoogle Scholar
Cross, N. (1996). Creativity in design: not leaping but bridging. Proc. Creativity and Cognition, pp. 27–35.Google Scholar
Eder, W.E. (1995). Introduction. Proc. Int. Workshop on Engineering Design and Creativity, pp. 16–18.Google Scholar
Franzosi, M. (2006). Novelty and non-obviousness—the relevant prior art. Accessed at www.law.washington.edu/casrip/Symposium/Number7/Google Scholar
French, M. (1999). Conceptual Design for Engineers, 3rd ed.Berlin: Springer–Verlag.CrossRefGoogle Scholar
Hubka, V. (1976). Theorie der Konstruktionsprozesse. Berlin: Springer–Verlag.CrossRefGoogle Scholar
Koyama, T., Taura, T., & Kawaguchi, T. (1996). Research on natural law database. Proc. Joint Conf. Knowledge Based Software Engineering, pp. 242–245.Google Scholar
Kreyszig, E. (2001). Advanced Engineering Mathematics, 8th ed.Singapore: Wiley.Google Scholar
Kurtoglu, T., Campbell, M., & Linsey, J. (2009). An experimental study on the effects of a computational design tool on concept generation. Design Studies 30 (6), 676–703.CrossRefGoogle Scholar
Lopez-Mesa, B., & Vidal, R. (2006). Novelty metrics in engineering design experiments. Proc. DESIGN, pp. 557–564.Google Scholar
Microbiologybytes. (2004). Critical values of correlation coefficient. Accessed at http://www.microbiologybytes.com/maths/rtable.htmlGoogle Scholar
Molina, A., Al-Ashaab, A.H., Ellis, T.I.A., Young, R.I.M., & Bell, R. (1995). A review of computer-aided simultaneous engineering systems. Research in Engineering Design 7 (1), 38–63.CrossRefGoogle Scholar
Murakoshi, S., & Taura, T. (1998). Research on the systematization of natural laws for design support. Proc. 3rd IFIP Workshop on Knowledge Intensive CAD, pp. 141–160.Google Scholar
Osborn, A. (1979). Applied Imagination. New York: Scribners.Google Scholar
Ottosson, S. (1995). Boosting creativity in technical development. Proc. Workshop on Engineering Design and Creativity, pp. 35–39.Google Scholar
Pahl, G., & Beitz, W. (1996). Engineering Design: A Systematic Approach. London: Springer–Verlag.CrossRefGoogle Scholar
Sarkar, P. (2007). Development of a support for effective concept exploration to enhance creativity of engineering designers. PhD Thesis. Indian Institute of Science.Google Scholar
Sarkar, P., & Chakrabarti, A. (2007 a). Understanding search in design. Proc. Int. Conf. Engineering Design, pp. 319–320.Google Scholar
Sarkar, P., & Chakrabarti, A. (2007 b). Development of a method for assessing design creativity. Proc. Int. Conf. Engineering Design, pp. 349–350.Google Scholar
Sarkar, P., & Chakrabarti, A. (2008). Studying engineering design creativity. Workshop on Studying Design Creativity, France.Google Scholar
Saunders, R. (2002). Curious design agents and artificial creativity—a synthetic approach to the study of creative behaviour. PhD Thesis. University of Sydney, Faculty of Architecture.Google Scholar
Shah, J.J., Smith, S.M., & Vargas-Hernandez, N. (2003). Metrics for measuring ideation effectiveness. Journal of Design Studies 24 (2), 111–134.CrossRefGoogle Scholar
Shalley, C.E. (1991). Effects of productivity goals, creativity goals, and personal discretion on individual creativity. Journal of Applied Psychology 76 (2), 179–185.CrossRefGoogle Scholar
Srinivasan, V., & Chakrabarti, A. (2008). Design for novelty—a framework? Proc. DESIGN, pp. 227–232.Google Scholar
Srinivasan, V., & Chakrabarti, A. (2009). SAPPhIRE—an approach to analysis and synthesis. Proc. Int. Conf. Engineering Design [CD].Google Scholar
Sternberg, R.J., & Lubart, T.I. (1999). The concept of creativity: prospects and paradigms. In Handbook of Creativity (Sternberg, R.J., Ed.), pp. 3–15. New York: Cambridge University Press.Google Scholar
Thring, M.W., & Laithwaithe, E.R. (1977). How to Invent. New York: Macmillan Company.CrossRefGoogle Scholar
Umeda, Y., Ishii, M., Yoshioka, M., Shimomura, Y., & Tomiyama, T. (1996). Supporting conceptual design based on the function–behavior–state modeler. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 10 (4), 275–288.CrossRefGoogle Scholar
Unsworth, K.L. (2001). Unpacking creativity. Academy of Management Review 26 (2), 286–297.CrossRefGoogle Scholar
Westwood, A.R.C., & Sekine, Y. (1988). Fostering creativity and innovation in an industrial R & D laboratory. Research-Technology Management 31 (4), 16–20.CrossRefGoogle Scholar
Wikipedia. (2009). Correlation. Accessed at http://en.wikipedia.org/wiki/CorrelationGoogle Scholar
Yoshioka, M., & Tomiyama, T. (1997). Pluggable metamodel mechanism: a framework of an integrated design object modelling environment. Proc. Lancaster Int. Workshop on Engineering Design CACD '97, pp. 57–70.Google Scholar
Zavbi, R., & Duhovnik, J. (2000). Conceptual design of technical systems using functions and physical laws. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14 (1), 69–83.CrossRefGoogle Scholar
Zavbi, R., & Duhovnik, J. (2001). Analysis of conceptual design chains for the unknown input/known output pattern. Proc. Int. Conf. Engineering Design, pp. 53–60.Google Scholar
Zimmerman, L.W., & Hart, G.D. (1998). Value Engineering—A Practical Approach for Owners, Designers and Contractors. New York: Van Nostrand Reinhold.Google Scholar