eBusiness
Research Lunch Seminars
NE20-336 (3 Cambridge Center)
Wednesday, October 3rd, 12-1:30 pm
Lunch Provided
Visiting Scholar Venkatesh Shankar, Associate Professor, University
of Maryland
Why are Prices of the Same Item not the Same at My.com &
Your.com?:Drivers of e-tailer Price Dispersion
Abstract
Frictionless
e-commerce implies that price dispersion for identical products
sold by various e-tailers should be smaller than it is offline,
but some recent empirical evidence reveals the opposite. A study
by Smithet al. (2000) suggests that such a phenomenon may be due
to heterogeneity among e-tailers in such factors as customer service,
shopping convenience,consumer awareness, and brand name. 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 factors relating
to e-tailer heterogeneity. In the conceptual framework, we analyze
how online or web characteristics, product category characteristics,
e-tailer characteristics, and market characteristics affect price
dispersion. We also empirically test our propositions in a more
comprehensive manner than prior research by using a range of 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 video devices. 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 consumer involvement, product
popularity, and number of competitors in a market affect price
dispersion using regression analysis; and (4) examine how heterogeneity
among e-tailers is related to their prices using hedonic regression.
Our results show that there are three clusters of e-tailers who
target different consumer groups and accordingly have different
overall prices. We find that market characteristics and e-tailer
characteristics, especially the former one, drive online price
dispersion and that e-tailers charge prices in line with their
characteristics. E-taliers, however, do not command higher prices
for better services. Our models explain over 92% of the variance
in price dispersion. We discuss the managerial implications of
our results.
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Students
may request materials from Robynne DeCaprio at <decaprio@mit.edu>
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