Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research

Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint an...

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Published in:	European journal of operational research Vol. 197; no. 1; pp. 340 - 348
Main Authors:	Karniouchina, Ekaterina V., Moore, William L., van der Rhee, Bo, Verma, Rohit
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 16.08.2009 Elsevier Elsevier Sequoia S.A
Series:	European Journal of Operational Research
Subjects:	Applied sciences Bayesian analysis Choice-based conjoint analysis Conjoint analysis Conjoint analysis in operations management research Exact sciences and technology Firm modelling Mathematical programming Operational research and scientific management Operational research. Management science Operations management Operations research Product and service design Ratings-based conjoint analysis Ratings-based conjoint analysis Choice-based conjoint analysis Conjoint analysis in operations management research Product and service design Studies Product and service design Ratings-based conjoint analysis Conjoint analysis in operations management research Choice-based conjoint analysis Bayes estimation Operations research Similarity Trademark Modeling Open market Compatibility Service quality Mathematical programming
ISSN:	0377-2217, 1872-6860
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Abstract	Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint analysis for this research. We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models. The biggest systematic difference between ratings-based (RB) and choice-based (CB) parameters is consistent with the compatibility effect, i.e., some enriched attributes like brand name tend to be more important in RB models and some comparable attributes like price are likely to be more important in CB models. Still, there were reasonably small differences between choice- and ratings-based parameters. Parameter similarity was also seen in the lack of differences both in the choice share validations when the “keep on shopping” alternative was not considered and in the profiles that were predicted to maximize choice shares. This suggests that the two approaches will produce similar estimates of the relative importance of various attributes. In spite of demonstrated success with each method, several reasons lead us to recommend the use of hierarchical Bayesian choice-based conjoint models. First, the slightly higher individual hit rate validations give us greater confidence in predictive accuracy overall as well as an increased ability to target individual customers. Additionally, the greater ease of modeling both changes in market size and competitive reactions are attractive benefits of choice-based models.
AbstractList	Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint analysis for this research. We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models. The biggest systematic difference between ratings-based (RB) and choice-based (CB) parameters is consistent with the compatibility effect, i.e., some enriched attributes like brand name tend to be more important in RB models and some comparable attributes like price are likely to be more important in CB models. Still, there were reasonably small differences between choice- and ratings-based parameters. Parameter similarity was also seen in the lack of differences both in the choice share validations when the "keep on shopping" alternative was not considered and in the profiles that were predicted to maximize choice shares. This suggests that the two approaches will produce similar estimates of the relative importance of various attributes. In spite of demonstrated success with each method, several reasons lead us to recommend the use of hierarchical Bayesian choice-based conjoint models. First, the slightly higher individual hit rate validations give us greater confidence in predictive accuracy overall as well as an increased ability to target individual customers. Additionally, the greater ease of modeling both changes in market size and competitive reactions are attractive benefits of choice-based models. Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint analysis for this research. We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models. The biggest systematic difference between ratings-based (RB) and choice-based (CB) parameters is consistent with the compatibility effect, i.e., some enriched attributes like brand name tend to be more important in RB models and some comparable attributes like price are likely to be more important in CB models. Still, there were reasonably small differences between choice- and ratings-based parameters. Parameter similarity was also seen in the lack of differences both in the choice share validations when the “keep on shopping” alternative was not considered and in the profiles that were predicted to maximize choice shares. This suggests that the two approaches will produce similar estimates of the relative importance of various attributes. In spite of demonstrated success with each method, several reasons lead us to recommend the use of hierarchical Bayesian choice-based conjoint models. First, the slightly higher individual hit rate validations give us greater confidence in predictive accuracy overall as well as an increased ability to target individual customers. Additionally, the greater ease of modeling both changes in market size and competitive reactions are attractive benefits of choice-based models. Conjoint analysis has played an important role in helping make a number of operations management decisions including product and service design, supplier selection, and service operations capacity. Many recent advances in this area have raised questions about the most appropriate form of conjoint analysis for this research. We review recent developments in the literature and provide new evidence on how the choice between ratings- and choice-based conjoint models might affect the estimates of customer demand used in operations management models. The biggest systematic difference between ratings-based (RB) and choice-based (CB) parameters is consistent with the compatibility effect, i.e., some enriched attributes like brand name tend to be more important in RB models and some comparable attributes like price are likely to be more important in CB models. Still, there were reasonably small differences between choice- and ratings-based parameters. Parameter similarity was also seen in the lack of differences both in the choice share validations when the "keep on shopping" alternative was not considered and in the profiles that were predicted to maximize choice shares. This suggests that the two approaches will produce similar estimates of the relative importance of various attributes. In spite of demonstrated success with each method, several reasons lead us to recommend the use of hierarchical Bayesian choice-based conjoint models. First, the slightly higher individual hit rate validations give us greater confidence in predictive accuracy overall as well as an increased ability to target individual customers. Additionally, the greater ease of modeling both changes in market size and competitive reactions are attractive benefits of choice-based models. [PUBLICATION ABSTRACT]
Author	Moore, William L. Karniouchina, Ekaterina V. Verma, Rohit van der Rhee, Bo
Author_xml	– sequence: 1 givenname: Ekaterina V. surname: Karniouchina fullname: Karniouchina, Ekaterina V. email: karniouc@chapman.edu organization: Chapman University, Orange, CA, USA – sequence: 2 givenname: William L. surname: Moore fullname: Moore, William L. email: mktbm@business.utah.edu organization: David Eccles School of Business, University of Utah, Salt Lake City, UT, USA – sequence: 3 givenname: Bo surname: van der Rhee fullname: van der Rhee, Bo email: B.vdRhee@nyenrode.nl organization: Cluster Marketing & Supply Chain Management, Nyenrode Business University, Breukelen, The Netherlands – sequence: 4 givenname: Rohit surname: Verma fullname: Verma, Rohit email: rohit.verma@cornell.edu, rv54@cornell.edu organization: School of Hotel Administration, Cornell University, 338 Statler Hall, Ithaca, NY 14853, USA
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Keywords	Product and service design Ratings-based conjoint analysis Conjoint analysis in operations management research Choice-based conjoint analysis Bayes estimation Operations research Similarity Trademark Modeling Open market Compatibility Service quality Mathematical programming
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SubjectTerms	Applied sciences Bayesian analysis Choice-based conjoint analysis Conjoint analysis Conjoint analysis in operations management research Exact sciences and technology Firm modelling Mathematical programming Operational research and scientific management Operational research. Management science Operations management Operations research Product and service design Ratings-based conjoint analysis Ratings-based conjoint analysis Choice-based conjoint analysis Conjoint analysis in operations management research Product and service design Studies
Title	Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research
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