CHOICE‐CONSTRAINED CONJOINT ANALYSIS
提出选择约束联合分析新方法，通过迭代惩罚函数估计，使联合分析得出的效用函数能更准确预测消费者在独立选择情境中的实际选择，并用合成数据和电信公司数据验证。
ABSTRACT Choice‐constrained conjoint analysis (CCCA) is a new method for metric conjoint analysis studies. It computes part‐worth utility functions that account for “revealed preference”—those products a respondent actually selects in an independent choice situation. CCCA uses an iterative penalty function estimation procedure that successively modifies initial regressionderived part worths so that respondent choices (either actual or intended) of real brands are predicted as accurately as possible. The paper first describes the motivation and rationale for CCCA and presents the mathematics of the algorithm. As an illustration, it applies the CCCA model and penalty function estimation procedure to a limited set of synthetic data. A second application of the technique is presented that uses data obtained by a major telecommunications firm that used conjoint analysis to examine the importance of several features of residential communication devices. The paper also discusses potential extensions of the CCCA model and the kinds of marketing applications for which it might be useful.