Identifying Unacceptable Attribute Levels in Preference Measurement: Assessing the Methodological Differences between and Relative Performance of Common Methods

摘要

The measurement of customer preferences is a typical aspect of market research. Results can be used to segment markets or define prices. The increasing competitive pressure in many markets has led to preference measurement enjoying much attention in research and practice. However, some major factors that might distort estimates of preference measurement are still under-researched. For example, it is commonly known that unacceptable levels can distort the results of preference measurement. However, what remains unclear is how to identify unacceptable levels. In this paper, we focus on this problem. Before assessing preferences, researchers should first eliminate unacceptable attribute levels. Two types of approaches could be used to identify unacceptable attribute levels: a direct method and an indirect method. We show that the type of approach used influences the identification of unacceptable levels. When applying an indirect approach, respondents are more likely to accept a level. Furthermore, we assess the relative performance of these approaches using real purchase data (BDM mechanism) as a benchmark. The results show that indirect as well as direct methods providing consumers with information on the decision context perform well.