Online price dispersion – more than imperfect information

Abstract

PurposeThe objective of this study is to analyze price dispersion in the context of internet based shopping malls and assess which of the main informational elements available online about sellers affect online price dispersion.Design/methodology/approachThe author collected data for 100 models of watches from seven major brands and 100 models of cameras from nine major brands available for sale on Amazon. Statistical regression of price dispersion against average price, number of stores, shipping charge variation, average number of customer reviews, variation in the percentage of positive reviews and a dummy variable of product type were performed.FindingsIt is shown that price dispersion is present even in an online shopping mall. The conclusion significantly adds to the online pricing research, by showing that price dispersion is influenced by the average product price and product type and shipping charges. The number of stores does not affect price dispersion because there is no obstacle impeding the customer from finding the prices for all stores. A key finding is that the number of customer reviews significantly influences online price dispersion.Research limitations/implicationsThe study only analyzes price dispersion for two products using secondary data. Future studies can analyze different types of goods and focus on which elements affect the buyers' store selection by using primary data.Practical implicationsManagers can see that there are numerous other elements of information besides price that influence buying decisions. Practitioners need to note the importance of customer reviews in online settings, including the significance of the number of reviews as signal of experience and reliability.Originality/valueThe paper shows that online price dispersion exists even in the context of practically inexistent search costs. Even though the buyers have instant access to all prices of a product, they use a combination of elements in order to decide the seller selection. A key contribution of the study shows that the amount of information available about the seller and the customer reviews significantly influence online price dispersion.