«ABSTRACT Conjoint Analysis (CA) is a very popular class of methods for measuring consumer preferences, both in research and practice. However, since ...»
Several empirical studies have shown that AHP at least can keep up with traditional CA. To increase the practical impact of such empirical comparisons, we run AHP (with slight modifications with regard to the hierarchy, the scale and the number of pairwise comparisons conducted) against the popular Choice-Based Conjoint Analysis in an online survey. The empirical study focuses on the comparison in a real online consumer research setting with non-academic respondents to investigate the applicability as well as predictive and convergent validity of the two approaches.
The measures used in this study show that AHP is a good alternative for estimating market shares.
AHP outperformed CBC in market share prediction and was almost as accurate as CBC with respect to first choice hit rates. This is an important finding which suggests AHP as a promising tool for preference measurement. Moreover, the results show that AHP is slightly better with respect to face validity as well as the subjective evaluation of the approaches by the respondents. The AHP survey was rated as being more realistic, less difficult and more enjoyable. No significant differences were found concerning the average interview length.
M. Meißner, R. Decker/ Measuring Consumer Preferences with CBC and AHP As outlined in the theoretical part of the paper, Harker’s (1987) technique for incomplete pairwise comparison matrices can be used to reduce respondents’ burden and with that the total interview length. In this context future research should investigate how many pairwise comparisons are needed to ensure a high predictive validity of the results.
Hierarchical structuring of decision problems has been found to be one common theme in the way humans deal with complexity (Forman and Gass, 2001). In this study, we used a simple 3-level hierarchy for modeling the product evaluation problem. However, further empirical investigations are needed to determine the influence different hierarchical structures have on preference measurement.
By using hierarchies with more levels, the number of pairwise comparisons required could be further reduced. Albeit, in consumer online surveys respondents might have problems with the comparison of product categories consisting of multiple attributes. We leave this aspect for future research.
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