Paul E. Green & V. Srinivasan
Conjoint Analysis in Marketing: New Developments With Implications for Research and Practice The authors update and extend their 1978 review of conjoint analysis. In addition to discussing several new developments, they consider alternative approaches for measuring preference structures in the presence of a large number of attributes. They also discuss other topics such as reliability, validity, and choice simulators.
INCE the early 1970s, conjoint analysis has received considerable academic and industry attention as a major set of techniques for measuring buyers' tradeoffs among multiattributed products and services (Green and Rao 1971; Johnson 1974; Srinivasan and Shocker 1973b). We presented a state-ofthe-art review of conjoint analysis in 1978 (Green and Srinivasan 1978). Since that time many new developments in conjoint analysis and related methods have been reported. The purpose of this article is to review those developments (with comments on their rationale, advantages, and limitations) and propose potentially useful avenues for new research. We assume the reader is familiar with our previous review as background for a detailed study of this article.' In subsequent sections we describe a variety of de-
velopments that have been achieved since the 1978 review. Topics include: • choosing conjoint models to minimize prediction error, • collecting conjoint data via the telephone-mail-telephone method, • experimental designs that incorporate environmental correlations across the attributes, • methods for improving part-worth estimation accuracy, • new techniques for coping with large numbers of attributes and levels within attribute, • issues in measuring conjoint reliability, • recent findings in conjoint validation, • coping with the problem of "unacceptable" attribute levels, • extending conjoint to multivariate preference responses, • trends in conjoint simulators, and • new kinds of industry applications of conjoint analysis.
Industry Acceptance of Conjoint Techniques
'Readers who are new to conjoint analysis may first want to read the article by Green and Wind (1975). Paul E. Green is the Sebastian S. Kresge Professor of Marketing, Wharton School, University of Pennsylvania. V. Srinivasan is the Ernest C. Arbuckle Professor of Marketing and Management Science, Graduate School of Business, Stanford University. The authors thank four anonymous JM reviewers and the Editor for their comments on a previous version of the article.
Conjoint analysis continues to be popular. Witdnk and Cattin (1989) estimate that about 4()0 commercial applications per year were carded out during the early 1980s. Some of the highlights of their study are: • The large majority of conjoint studies pertain to consumer goods (59%) and industrial goods (18%), with financial (9%) and other services (9%) accounting for most of the rest. • New product/concept evaluation, repositioning, com-
Conjoint Analy»s in Mwiceting / 3
pedtive analysis, pricing, and market segmentation are the principal types of applications.^ e Personal interviewing is the most popular data-gathering procedure, though computer-interactive methods are gaining favor. • The full-profile method, using rating scales or rank orders with part-worths estimated by least squares regression, is the most conmion type of application.
Part of the reason for conjoint's growing usage in the 1980s has been the introduction of microcomputer packages for p f e n n i n g commercial conjoint studies.^ The availability of these packages makes conjoint analysis easier and less expensive to apply, leading us to expect its increased use (and misuse) in years to come. Scope of the Review As defined in our 1978 review, conjoint analysis is any decompositional method that estimates the structure of a consumer's preferences (i.e., estimates preference parameters such as part-worths, importance weights, ideal points), given his or her overall...
References: Conjoint Analysis in Marketing / 1 7
Krishnamurthi, Lakshman (1988), 'Conjoint Models of Family Decision Making," International Journal of Research in Marketing, 5, 185-98
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