Hostname: page-component-76fb5796d-25wd4 Total loading time: 0 Render date: 2024-04-26T02:28:51.462Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigating the Influence of the Decoy Effect in Pairwise Comparison in Terms of Idea Selection in the Product Development Process

Part of: Design Management

Published online by Cambridge University Press:  26 July 2019

Narucha Tanaiutchawoot ,
Nikola Bursac ,
Simon Rapp  and
Albert Albers
Narucha Tanaiutchawoot*
Affiliation:
IPEK: Institute of Product Engineering, Karlsruhe Institute of Technology (KIT);
Nikola Bursac
Affiliation:
TRUMPF GmbH+Co. KG
Simon Rapp
Affiliation:
IPEK: Institute of Product Engineering, Karlsruhe Institute of Technology (KIT);
Albert Albers
Affiliation:
IPEK: Institute of Product Engineering, Karlsruhe Institute of Technology (KIT);
*
Contact: Tanaiutchawoot, Narucha, Karlsruhe Institute of Technology, IPEK: Institute for Technology, Germany, Narucha.Tanaiutchawoot@partner.kit.edu

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.

Many activities in the new product development requires the decision making to find the final solution from multiple alternatives and make an evaluation. Even methods to support decision maker are available, the decision can go to the wrong direction because of heuristics. “Decoy effect” is a heuristic that appears in a comparison when 2 of 3 alternatives are similar but different in quality. The alternative that is similar but better in quality is possibly selected. The paper aims to understand the decoy effect by investigating it in the pairwise comparison that is a powerful technique for comparing alternatives. In an experiment, 3 ideas for the next generation of apple peeler are compared in pairs with different sequences. An impact of the decoy alternative on the comparison between other two alternatives, is investigated. Results show low impact of the decoy effect in the pairwise comparison, but this effect induces a high chance of selecting the decoy alternative when comparing the results from this study and the previous study by proposing 3 alternatives in the same sequence. Applying pairwise comparison to avoid decoy effect is thus an idea that will be further investigated.

Keywords

Decision makingDecoy effectPairwise ComparisonNew product developmentEvaluation
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 1195 - 1204
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

Albar, F. M. and Jetter, A. J. (Hg.) (2011), “An investigation of fast and frugal heuristics for new project screening”, Portland International Conference on Management of Engineering & Technology (PICMET).Google Scholar
Albers, A., Reiß, N., Bursac, N., Breitschuh, J. (2016), “15 Years of SPALTEN Problem Solving Methodology in Product Development”. In: DS 85-1: Proceedings of NordDesign 2016, Vol. 1, Trondheim, Norway, 10th-12th August 2016.Google Scholar
Albers, A., Simon, R., Clemens, B. and Nikola, B. (Hg.) (2017), “Die Frühe Phase der PGE–Produktgenerationsentwicklung”, Stuttgarter Symposium für Produktentwicklung.Google Scholar
Alian, S. (Hg.) (2014), “The behavioral economics guide 2014”. Vol. 1. Aufl. Online verfügbar unter www.behavioraleconomics.com, zuletzt geprüft am 25,04,2017.Google Scholar
Ariely, D. (2008), “Predictably Irrational”, The hidden forces that shape our decisions/Dan Ariely. HarperCollins, New York.Google Scholar
Becker, G.S. (1976), “The economic approach to human behavior”. University of Chicago Press, Chicago.Google Scholar
Bursac, N., Tanaiutchawoot, N., Rapp, S., Albers, A., Breitschuh, J., Eckert, C. (Hg.) (2018), “Decision heuristic in pge-product generation engineering”. TMCE 2018. Las Palmas de Gran Canaria, Spain, 7-11 May.Google Scholar
Dhar, R., Simonson, I. (2003), “The effect of forced choice on choice”, In: Journal of marketing research, Vol. 40 No. 2, S. 146–160.Google Scholar
Erdmann, D., Sichel, B., Yeung, L. (2015), “Overcoming obstacles to effective scenario planning”, In: McKinsey Quarterly, Vol. 55.Google Scholar
Feldhusen, J., Grote, K.-H. (2013), “Pahl/Beitz Konstruktionslehre”, Methoden und Anwendung erfolgreicher Produktentwicklung: Springer-Verlag.Google Scholar
Kahneman, D., Tversky, A. (1979), “Prospect Theory”, An Analysis of Decision under Risk. In: Econometrica Vol. 47 No. 2, S. 263. http://doi.org/10.2307/1914185.Google Scholar
Kahneman, D. (2011), “Thinking, fast and slow”, 1st ed. Farrar Straus and Giroux, New York.Google Scholar
Lipshitz, R., Strauss, O. (1997), “Coping with uncertainty”, A naturalistic decision-making analysis. In: Organizational Behavior and Human Decision Processes, Vol. 69 No. 2, S. 149–163.Google Scholar
Lockton, D. (2012), “Cognitive biases, heuristics and decision-making in design for behaviour change”.Google Scholar
Mike, P. (2017), “16 cognitive biases that can kill your decision making”. Online access under https://www.boardofinnovation.com/blog/2017/08/02/16-cognitive-biases-that-kill-innovative-thinking/.Google Scholar
Montibeller, G. and von Winterfeldt, D. (Hg.) (2015), “Biases and debiasing in multi-criteria decision analysis”, System Sciences (HICSS), 2015 48th Hawaii International Conference on: IEEE.Google Scholar
Muenzberg, C., Stingl, V., Geraldi, J. and Oehmen, J. (Hg.) (2017), “Identifying product development crises”, The potential of adaptive heuristics. DS 87-2 Proceedings of the 21st International Conference on Engineering Design (ICED 17) Vol 2: Design Processes, Design Organisation and Management, Vancouver, Canada, 21-25.08. 2017.Google Scholar
Tversky, A., Kahneman, D. (1974), “Judgment under uncertainty”, Heuristics and biases. In: science Vol. 185 No. 4157, S. 1124–1131.Google Scholar
Weick, K.E. (1995), “Sensemaking in organisation: Sage”.Google Scholar