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ABSTRACTA model for measuring subjective evaluations of multiple component alternatives is developed and tested in the context of business students' evaluations of professorial candidates for tenured positions. The model is based on a type of ANOVA formulation in which the response variable need only be rank ordered. That is, arbitrary monotonic functions of subjective responses can be made that optimize a well‐defined badness of fit measure.Application of the model indicates that metric analysis of the response data can exhibit a large number of configural (interaction) effects that may be a reflection of how subjects use rating scales rather than anything more basic in their combination of multiple component cues. Implications of these findings for the modeling of multiple component choices are discussed.

ABSTRACTProduct‐concept testing is a popular activity in marketing research. Often the number of new product/service concepts under study far exceeds the time available for any single respondent. Respondents therefore may receive only a subset of the concepts comprising the total design. Researchers are interested in making plausible imputations for the missing evaluations of any given respondent.This paper proposes a model and an iterative estimation procedure to impute missing entries for each evaluator. The model and the procedure incorporate (1) the internal structure of the response matrix and (2) an ancillary matrix of (nonmissing) respondent background data; they also (3) allow for individual differences in respondents' uses of the numerical rating scale. The model is applied to both real and synthetic data. Suggestions also are given on how the data imputations may be used in market segmentation and product‐line decisions.

ABSTRACTEarly formulations of conjoint models focused on part‐worth estimation at the individual level. As the methodology's popularity grew so did industry demands for increasingly larger numbers of attributes and levels. In response to these demands, new approaches, based on partial or full data aggregation (such as clusterwise/latent class conjoint and choice‐based conjoint), have appeared. This paper suggests that pooled‐data models will often be successful in predicting market shares when researchers employ monotonic attributes. In these cases more of a good attribute (or less of a bad attribute) is always more preferred. In the more realistic case, in which some of the attributes may be nonmonotonic, we find that data aggregation does not predict holdout sample preferences as well as individual part‐worth models.

ABSTRACTIn componential segmentation the researcher is interested in decomposing survey respondents' evaluations of conjoint‐designed product descriptions into separate contributions; these contributions are due to stimulus attributes, respondent attributes, and/or conjoint‐designed situation attributes. Previous research has employed either scalar product or ANOVA‐like decompositions.The present paper extends earlier research by presenting a procedure for finding optimal market segments for given products, and vice versa. The model is developed in the context of the ANOVA‐type formulation of componential segmentation.

Our reply to Curry, Louviere, and Augustine's critique of our earlier paper focuses on differences in motivation between our research and theirs. Our interest in the problem relates to the possible incorporation of self‐explicated evaluations in conjoint data collection methods; subsequent to the appearance of our original paper, we have developed hybrid models that combine elements of self‐explicated (compositional) and conjoint (decompositional) data collection procedures. As far as we can surmise from their critique, Curry, Louviere, and Augustine are concerned with much broader strategic issues relating share of choices in the consumer population to changes in the shape of attribute weight distributions, shape of the Pareto tradeoff boundary, and so on.

AbstractThis study compares, via brand‐choice simulation, the effect of various distortions in attribute importance weights on share of choices at the aggregate‐sample level. The results indicate that severe distortions in importance weights can occur with little change in aggregate brand share. The implications of this result for utility estimation in main‐effects conjoint analysis suggest that considerably simpler ways can be adopted to estimate part worths.