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Disjunctions of conjunctions, cognitive simplicity, and consideration sets

Author: Hauser, John R. ; Toubia, Olivier ; Evgeniou, Theodoros ; Befurt, Rene ; Dzyabura, DariaINSEAD Area: Technology and Operations ManagementIn: Journal of Marketing Research, vol. 47, no. 3, June 2010 Language: EnglishDescription: p. 485-496.Type of document: INSEAD ArticleNote: Please ask us for this itemAbstract: The authors test methods, based on cognitively simple decision rules, that predict which products consumers select for their consideration sets. Drawing on qualitative research, the authors propose disjunctions-ofconjunctions (DOC) decision rules that generalize well-studied decision models, such as disjunctive, conjunctive, lexicographic, and subset conjunctive rules. They propose two machine-learning methods to estimate cognitively simple DOC rules. They observe consumers' consideration sets for global positioning systems for both calibration and validation data. They compare the proposed methods with both machine learning and hierarchical Bayes methods, each based on five extant compensatory and noncompensatory rules. For the validation data, the cognitively simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates. The results are robust with respect to format by which consideration is measured, sample, and presentation of profiles. The article closes with an illustration of how DOC-based rules can affect managerial decisions
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The authors test methods, based on cognitively simple decision rules, that predict which products consumers select for their consideration sets. Drawing on qualitative research, the authors propose disjunctions-ofconjunctions (DOC) decision rules that generalize well-studied decision models, such as disjunctive, conjunctive, lexicographic, and subset conjunctive rules. They propose two machine-learning methods to estimate cognitively simple DOC rules. They observe consumers' consideration sets for global positioning systems for both calibration and validation data. They compare the proposed methods with both machine learning and hierarchical Bayes methods, each based on five extant compensatory and noncompensatory rules. For the validation data, the cognitively simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates. The results are robust with respect to format by which consideration is measured, sample, and presentation of profiles. The article closes with an illustration of how DOC-based rules can affect managerial decisions

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