사례기반추론을 이용한 전통적 컨조인트 분석의 불완전한 응답 자료의 효용추정방법에 관한 연구

The conjoint analysis has been a representative methodology to find out consumer’s preference. When carrying on the conjoint analysis, the most challenge task for the subjects is to evaluate more than eight profiles. When the respondent evaluate the profiles based on a ranking scale, as the predictive validity was greater compared to rating, this ranking scale have been usually applied with traditional conjoint analysis. However, several mistakes by respondents occurred such as the omitted ranking and same ranking answers. These incomplete answers are exempted from estimating utilities step. However, this study tried to find out the utility of subject who incompletely responds in traditional conjoint analysis based on the case based reasoning. The Kendall’s and Spearman’s ranking correlation coefficient are applied to search the similar cases to complement the incomplete response, also the utilities are estimated based on the preference data evaluated in regular sequence. The result shows that the hit-ratio predicted by data with higher correlation coefficient is higher than it done by the data with lower correlation coefficient. Also, in terms of the reasoning methodology, the Kendall’s coefficient rather than the Spearman’s coefficient shows higher predictive accuracy. In addition, the interaction between correlation coefficient and the number of profiles has positive impact on the predictive accuracy.