– 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.
