An empirical comparison of conjoint and best-worst scaling case III methods
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School of Natural Resources and the Environment, University of Arizona,Issue Date
2023-10
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Elsevier Inc.Citation
Cheng, H., Zhang, T., Lambert, D. M., & Feuz, R. (2023). An empirical comparison of conjoint and best-worst scaling case III methods. Journal of Behavioral and Experimental Economics, 106, 102049.Rights
© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
The Best-Worst Scaling (BWS) case III method, also called the BWS ‘multi-profile case.’ has been widely used to characterize survey respondent preferences for market goods. The BWS method is similar to conjoint analysis methods in that respondents select from a set of hypothetical item profiles with different attribute levels. Unlike conjoint methods, which allow respondents to select their best/most preferred profile, the BWS case III method asks respondents to select ‘best’ and ‘worst’ profiles in each choice set. This study compares consumer willingness to pay (WTP) estimates from conjoint and BWS case III survey formats. Data on consumer preferences for single-use eating-ware products made from biobased materials were collected. Results suggest that for the most preferred attribute levels, WTPs estimates are similar in magnitude and consistent for signs across methods. For least-preferred attributes, WTP estimates from the conjoint method are higher than those of the BWS method. However, the BWS WTP estimates have smaller confidence intervals. © 2023Note
Open access articleISSN
2214-8043Version
Final Published Versionae974a485f413a2113503eed53cd6c53
10.1016/j.socec.2023.102049
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Except where otherwise noted, this item's license is described as © 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/).