000			01794pab a2200205 454500
008			140923b0 xxu||||| |||| 00| 0 eng d
040			_cWelingkar Institute of Management Development & Research, Mumbai _aWelingkar Institute of Management Development & Research, Mumbai
041			_aENG
082			_a _bHau
100			_aHauser John R
245			_aDisjunctions of Conjunctions Cognitive Simplicity and Consideration Sets
250			_a3
260			_a _bJune 2010 _c0
300			_a485-496 Pp.
490			_vXLVII
520			_aThe 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-of-conjunctions (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.
650			_aConsumer Heuristics, Conjoint Analysis
856			_uhttp://192.168.6.13/libsuite/mm_files/Articles/AR11974.pdf
906			_a40331
999			_c31741 _d31741