Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets
Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets
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DOI：
10.1509/jmkr.47.3.485
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发表时间：
2010-06
影响因子：
6.1
通讯作者：
J. Hauser;Olivier Toubia;T. Evgeniou;R. Befurt;Daria Dzyabura
中科院分区：
文献类型：
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作者：
J. Hauser;Olivier Toubia;T. Evgeniou;R. Befurt;Daria Dzyabura
作者测试了基于认知上简单的决策规则的方法，这些方法预测消费者选择哪些产品作为他们的考虑集。在定性研究的基础上，作者提出了合取词(DOC)决策规则，这些规则概括了已有的决策模型，如析取、合取、词典和子集合取规则。他们提出了两种机器学习方法来估计认知上简单的DOC规则。他们观察消费者对全球定位系统校准和验证数据的考虑集。他们将提出的方法与机器学习和分层贝叶斯方法进行了比较，每种方法都基于现有的五个补偿和非补偿规则。对于验证数据，认知简单的基于DOC的方法在信息论测量和命中率方面比十种基准方法预测得更好。在衡量考虑、抽样和介绍概况的格式方面，结果是稳健的。本文最后举例说明了基于DOC的规则如何影响管理决策。
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-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.