Why Aren’t the Prices of the Same Item the Same at Me.com and You.com?: Drivers of Price Dispersion Among e-Tailers

Frictionless e-commerce implies that price dispersion for identical products sold by various etailers should be smaller than it is offline, but some recent empirical evidence reveals the opposite. A study by Smith et al. (2000) suggests that such a phenomenon may be due to heterogeneity among etailers in such factors as shopping convenience, consumer awareness, and trust. These hypotheses, however, remain untested. In this paper, we extend previous research by developing a comprehensive framework of the drivers of online price dispersion that includes market characteristics in addition to e-tailer characteristics and product category uniqueness. We also empirically test our propositions in a more comprehensive manner than prior research by using a range of price dispersion measures covering over 6,739 price quotes for 581 products from 105 e-tailers in a variety of product categories including books, CDs, DVDs, desktop computers, laptop computers, PDAs, computer software, and consumer electronics. Specifically, we (1) identify the key dimensions of e-tailer heterogeneity using factor analysis; (2) identify clusters of e-tailers on these dimensions using cluster analysis; (3) analyze how market factors such as number of competitors in a market, consumer involvement, and product popularity affect price dispersion using regression analyses; and (4) examine how heterogeneity among e-tailers is related to their prices using hedonic regressions. Our results show that e-tailer services can be characterized by five underlying factors, namely, shopping convenience, reliability in fulfillment, product information, shipping and handling, and pricing policy. There are three clusters of e-tailers who target different consumer groups and position themselves differently along these five factors. Even after controlling for e-tailer characteristics, online price dispersion is large. Market characteristics drive a large portion of e-tailer price dispersion. etailers charge prices in line with their characteristics. E-taliers, however, do not command higher prices for better services. The models explain over 92% of the variance in price dispersion.