| Title | Adaptive CBC: Are the Benefits Justifying its Additional Efforts Compared to CBC? |
| Authors | Brand, Benedikt M. and Baier, Daniel |
| Year | 2020 |
| Volume | Archives of Data Science, Series A 6(1) / 2020 |
| Abstract | Currently, there is a big discussion ongoing among both practitioners and scientists whether the benefits of the Adaptive Choice-Based Conjoint (ACBC) analysis in comparison to (standard) Choice-Based Conjoint (CBC) analysis are justifying the additional costs and efforts of ACBC. To answer this question, recent studies in literature are reviewed and a conducted ACBC (n=205) about e-commerce in an international context is analyzed with regards to several aspects, e.g. excluded attribute levels and stimuli used for the Choice Tasks section. The results indicate that CBC is generally able to provide the main information about the most preferred attribute levels with less effort compared to ACBC. However, ACBC is very suitable for more complex products or services and for gaining deeper insights, such as information about the second-best options or completely unacceptable features. Furthermore, CBC requires a bigger sample size and is often less precise. Still, the related context will remain the main factor for or against the usage of one or the other method. |