Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study

Green, Paul E.; Helsen, Kristiaan

doi:10.2307/3172906

Cited by 43 publications

(5 citation statements)

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“…After calculating the part-worth utilities and relative importance values, we identified consumer segments using cluster analysis. With cluster analysis, similar part-worth utilities are grouped together to form clusters (market segments) (Green and Helsen, 1989). There is a myriad of clustering techniques.…”

Section: Methodsmentioning

confidence: 99%

GMO Turfgrass Introduction to the Market: Acceptance and Market Simulations for Connecticut Consumers

Campbell¹,

Campbell²,

Berning³

2021

horts

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Using conjoint analysis and market simulations, the impact of the introduction of certified genetically modified organism (GMO)-free; GMO-free, not certified; and nonlabeled turfgrass was examined for Connecticut consumers. We categorized consumers into five distinct segments according to their preferences. The largest segment consisted of 38% of respondents (multifaceted), whereas the smallest consisted of 8% of respondents (extremely price sensitive). For most consumers GMO labeling was not a major driver for purchasing decisions, accounting for only 11% of purchasing decisions. However, holding all factors constant except GMO labeling and price, 66% of the market preferred a noncertified GMO-free label, with a significant number of consumers willing to pay for the certified GMO-free label. Based on market simulations, the noncertified GMO-free-labeled seed would maximize revenue at a 60% premium whereas the certified GMO-free label maximizes revenue when there is no premium.

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Section: Methodsmentioning

confidence: 99%

GMO Turfgrass Introduction to the Market: Acceptance and Market Simulations for Connecticut Consumers

Campbell¹,

Campbell²,

Berning³

2021

horts

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“…For example, researchers (Herman 1988;Krishnamurthi and Wittink 1989;Pekelman and Sen 1979) augment traditional part-worth modeling with mixtures of linear, quadratic, and part-worth parameters. Gains in reliability and validity can also be obtained by constraining part-worths to respect within-attrihute monotonicity (Srinivasan, Jain, and Malhotra 1983) or various partial aggregation methods, such as those proposed by Green and DeSarbo (1979). Hagerty (1985), andKamakura (1988).…”

Section: A Taxonomy Of Conjoint Methodsmentioning

confidence: 99%

“…In contrast, the hybrid models Green, Goldberg, and Montemayor 1981) and the Adaptive Conjoint Analysis (ACA) model (Johnson 1987) collect a limited number of full or partial profiles that serve as ways to refine self-explicated part-worths (ACA) or estimate additional group-level parameters (hybrid models).' Because these latter approaches have fewer data demands than the Bayesian methods, they have received extensive commercial application.…”

Section: A Taxonomy Of Conjoint Methodsmentioning

confidence: 99%

Psychometric Methods in Marketing Research: Part I, Conjoint Analysis

Carroll

Green

1995

Journal of Marketing Research

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114

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“…As an alternative to individual-level models, several researchers have developed group-level methods applicable to benefit segmentation (Hagerty, 1985;Kamakura, 1988;Wedel & Steenkamp, 19891, in addition to some group-level models noted earlier (DeSarbo & Hoffman, 1987;DeSarbo & Rao, 1986). However, Green and Helsen (1989) found that the Hagerty and Kamakura approaches do not result in better cross-validation correlations than individual-level regression models. (Green and Helsen did not directly test the Wedel and Steenkamp approach, but produced cross-validation correlations equal to or below those of Hagerty.)…”

Section: Limitationsmentioning

confidence: 99%

Interpreting multidimensional data with cognitive differentiation analysis

Perkins¹,

Reynolds²

1995

Psychology and Marketing

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The interpretation of multidimensional data in consumer research has traditionally required property fitting to determine the structure underlying the data. As an alternative, Cognitive Differentiation Analysis (CDA) regresses a matrix of pairwise product judgments on vectors of product‐attribute ratings. The estimated multiple regression equation indicates the degree of differentiation in the pairwise data that is accounted for by each attribute. In this article CDA is applied to the analysis of simulated multidimensional data and to actual data from a benefit segmentation study. The implications of improved interpretability of multidimensional data for understanding the competitive environment are discussed. © 1995 John Wiley & Sons, Inc.

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Cross-Validation Assessment of Alternatives to Individual-Level Conjoint Analysis: A Case Study

Cited by 43 publications

References 0 publications

GMO Turfgrass Introduction to the Market: Acceptance and Market Simulations for Connecticut Consumers

GMO Turfgrass Introduction to the Market: Acceptance and Market Simulations for Connecticut Consumers

Psychometric Methods in Marketing Research: Part I, Conjoint Analysis

Interpreting multidimensional data with cognitive differentiation analysis

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