Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/103305
Title: A design strategy for improving adaptive conjoint analysis
Author: Huertas García, Rubén
Gázquez Abad, Juan Carlos
Forgas-Coll, Santiago
Keywords: Disseny d'experiments
Anàlisi conjunt (Màrqueting)
Anàlisi factorial
Experimental design
Conjoint analysis (Marketing)
Factor analysis
Issue Date: 2016
Publisher: Emerald
Abstract: Adaptive conjoint analysis (ACA) is a market research methodology for measuring utility in business-to-business and customer studies. Based on partial profiles, ACA tailors an experiment's design to each respondent depending on their previously stated preferences, ordered in a self-assessment questionnaire. The purpose of this paper is to describe advantages and disadvantages of using a partial-profile randomised experiment, the usual system, and to propose a new design strategy for arranging profiles in blocks that improve its performance.
Note: Versió postprint del document publicat a: https://doi.org/10.1108/JBIM-02-2013-0043
It is part of: Journal of Business & Industrial Marketing, 2016, vol. 31, num. 3, p. 328-338
Related resource: https://doi.org/10.1108/JBIM-02-2013-0043
URI: http://hdl.handle.net/2445/103305
ISSN: 0885-8624
Appears in Collections:Articles publicats en revistes (Empresa)

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