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Cited by 478

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Designing Conjoint Choice Experiments Using Managers' Prior Beliefs

Open access
SÁndor, Zsolt, Wedel, Michel·2001·Journal of Marketing Research

Abstract

The authors provide more efficient designs for conjoint choice experiments based on prior information elicited from managers about the parameters and their associated uncertainty. The authors use a Bayesian design procedure that assumes a prior distribution of likely parameter values and optimizes the design over that distribution. The authors propose a way to elicit prior information from managers and show in Monte Carlo studies that the procedure provides more efficient designs than the current procedures. The authors provide an empirical application, in which they elicit prior information on the parameter values and the associated uncertainty from managers. Here, the Bayesian design provides 30%-50% lower standard errors of the estimates and an expected predictive validity that is approximately 20% higher.

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Crossref · 10.1509/jmkr.38.4.430.18904

Cites

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Metadata

Authors
SÁndor, Z., & Wedel, M.
Venue
Journal of Marketing Research
Published
2001-11
Volume
38
Issue
4
Pages
430-444
ISSN
  • 0022-2437 (ISSN-L)
  • 1547-7193
OpenAlex
W2155825816
Cited by
478
References
23
FWCI
19.69
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Designing Conjoint Choice Experiments Using Managers' Prior Beliefs. https://app.cochat.ai/papers/10.1509/jmkr.38.4.430.18904
The paper
SÁndor, Z., & Wedel, M. (2001). Designing Conjoint Choice Experiments Using Managers' Prior Beliefs. Journal of Marketing Research. https://doi.org/10.1509/jmkr.38.4.430.18904

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.