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Towards Understanding the Role of Interaction Effects in Visual Conjoint Analysis

[+] Author Affiliations
Brian Sylcott, Jeremy J. Michalek, Jonathan Cagan

Carnegie Mellon University, Pittsburgh, PA

Paper No. DETC2013-12622, pp. V03AT03A012; 12 pages
doi:10.1115/DETC2013-12622
From:
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3A: 39th Design Automation Conference
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5588-1
  • Copyright © 2013 by ASME

abstract

We investigate consumer preference interactions in visual choice-based conjoint analysis, where the conjoint attributes are parameters that define shapes shown to the respondent as images. Interaction effects are present when preference for the level of one attribute is dependent on the level of another attribute. When interaction effects are negligible, a main-effects fractional factorial experimental design can be used to reduce data requirements and survey cost. This is particularly important when the presence of many parameters or levels makes full factorial designs intractable. However, if interaction effects are relevant, a main-effects design creates biased estimates and potentially misleading conclusions. Most conjoint studies assume interaction effects are negligible; however, interactions may play a larger role for shape parameters than for other types of attributes. We conduct preliminary tests on this assumption in three visual conjoint studies. The results suggest that interactions can be either negligible or dominant in visual conjoint, depending on both consumer preferences and shape parameterization. When interactions are anticipated, it is possible in some cases to re-parameterize the shape such that interactions in the new space are negligible. Generally, we suggest that randomized designs are better than fractional factorial designs at avoiding bias due to the presence of interactions and/or the organization of profiles into choice sets.

Copyright © 2013 by ASME

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