search query: @indexterm CONJOINT ANALYSIS / total: 133
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| Author: | |
| Title: | Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs |
| Journal: | Marketing Science
1996 : VOL. 15:2, p. 173-191 |
| Index terms: | CONJOINT ANALYSIS CONSUMER BEHAVIOUR CONSUMER RESEARCH MODELS |
| Language: | eng |
| Abstract: | The drive to satisfy customers in narrowly defined market segments has led companies to offer wider product and services arrays. Conjoint analysis aims to unravel the value, or partworths, that customers place on the product or service's attributes from experimental subjects' evaluation of profiles based on hypothetical products or services. The problems associated with long questionnaires call for experimental designs and estimation methods that recover heterogeneity in the partworths with shorter questionnaires. Unlike more popular estimation methods, Hierarchical Bayes (or HB) random effects models do not require that individual design matrices be of full rank. That leads to the possibility of using fewer profiles per subject than currently used. It is tested if this possibility can be practically implemented. Results indicate that HB methods can recover heterogeneity and estimate individual-level partworths. |
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