The relative importance of e-tailer website attributes on the likelihood of online purchase

The purpose of this paper is to present the results of an experiment in which participants view fictitious e-tailing web pages and indicate the likelihood of purchasing the products displayed by manipulating four attributes (familiarity with the e-tailer, product type, summary product review, and the number of customer reviews) in order to determine their relative importance.

Individual level conjoint models are estimated to determine the relative importance of the manipulated attributes. Furthermore, cluster analysis is used to group individuals into different segments.

The results suggest that the summary review star rating of the product and familiarity with the e-tailer are the two most important attributes. A three cluster solution is obtained and each segment is characterized by the derived relative influence each attribute has on likelihood of online purchase.

Understanding how consumers make choices among attributes especially when they are confronted with trade-offs has implications for e-tailers wishing to develop effective, targeted strategies for increasing the likelihood of online purchases.