Keywords: online market, Internet retailer, online branch, pricing, menu cost,
homogeneous goods
Copyright © 2001 Electronic Markets
Volume 11 (3): 171–185. www.electronicmarkets.org
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RESEARCH
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t
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Empirical analysis from several recent
studies has shown mixed results on the
‘frictionless market’ hypothesis, claimed by
many, about Internet retailing compared
to conventional channels. This study takes
a different approach to deal with this
apparent ‘paradox’ of online retailing.
We observe that the emergence of online
retailing threatens the business of conventional retailers. In response to this
challenge, some conventional retailers have
entered the online market to directly compete with those specialized ‘DotCom’ sellers.
We hypothesize that the pricing behaviour
of these online branches of conventional
retailers would be part of their parent companies’ integrated strategies of doing business in both the online and offline markets.
Focusing on a homogenous product
(CDs), we compared the pricing behaviour of
six major online branches of traditional CD
retailers with six top specialized Internet
retailers. We find that prices by the pure
Internet players are generally lower than
prices by the hybrids online. Further, price
changes by both types are few but adjustment magnitudes are large, indicating that
menu cost is not as negligible as many
claim for the online markets. Evidence
shows that price dispersion is lower among
the DotComs than among the hybrids online
for the popular titles but significantly
higher for the random titles.
We conclude that the online CD market is
far from perfectly competitive or frictionless. Market power and offline pricing
behaviour continue to influence the online
competition.
Pricing Patterns in the Online CD Market:
An Empirical Study
FANG-FANG TANG AND DING LU
INTRODUCTION
A
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t
h
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r
s
Fang-Fang Tang ([email protected])
is Assistant Professor of Applied
Economics and Industrial Analysis at
Nanyang Business School of Nanyang
Technological University in Singapore.
His research interests focus on
electronic markets and commerce,
online marketing and behavioural
studies in managerial decision-making
processes. He will be Associate Professor
of Internet Marketing in the Department of Marketing, Chinese University
of Hong Kong, from September 2001.
His recent working papers can be found
at http://www.ntu.edu.sg/nbs/ae/
Working-Papers.htm
Ding Lu ([email protected]) is
Assistant Professor of Economics at
National University of Singapore. His
main research interests are in industrial
organization, trade and investment, and
comparative economic systems. Recent
works can be found at http://
www.fas.nus.edu.sg/ecs/staff/ecslud
The emergence and explosive growth
of e-commerce through online trading
have ushered in a new era of retail
business. Despite the recent Žnancial
market adjustment, the long-term
prospect of e-commerce is still bright.
Online trading promises the potentials
of low barrier of entry, easy access to
information, and low transaction costs.
The nature of Internet decides that
online trading was ‘born global’, least
constrained by geographical and
political boundaries. The ‘death of distance’ in online trading can attenuate
the market power brought about
by geography. In terms of menu costs,
the cost of changing prices and informing customers of the new prices is
signiŽcantly lower in an electronic
market. With low menu costs, online
traders are more ready to compete on
price.
All the above features of online
trading imply that the growth of ecommerce has the potential of realizing
the economic ideal for an effectivecompetition market: low search
costs, strong price competition, low
margins, and weak market power. It
is thus expected to improve market
efŽciency and bring signiŽcant welfare
to consumers.
Empirical studies on online trading
efŽciency, however, provided mixed
results. Of the few known tests, evidence for improved market efŽciency
Online CD Pricing Patterns
compare the price sensitivity of groceries sold through
conventional and electronic outlets. They Žnd that price
sensitivity is lower among online grocery shoppers than
it is for conventional-world shoppers. The result does not
support the hypothesis that online market is more efŽcient.
In a simulated electronic market for wine, Lynch and Ariely
(1998) test customer price sensitivity by manipulating the
shopping characteristics. The authors Žnd that consumers
tend to focus on price when there is little other information
available to differentiate products. Providing better product
information to customers may soften price competition and
increases product–customer Žt.
As for menu costs, results are consistent between the
two conducted tests, Bailey (1998a) and Brynjolfsson
and Smith (2000), that both Žnd online menu costs to
be lower. These results lend support to the hypothesis of
online market efŽciency. As for price dispersion, Clemons
et al. (1998) study markets for airline tickets sold through
online travel agents. They Žnd that prices for airline tickets
can differ by as much as 20% across online travel agents
even after controlling for observable product heterogeneity. Further, Bailey (1998a, b) and Brynjolfsson and
Smith (2000) Žnd such dispersion not lower in Internet
markets as compared to conventional markets, particularly
for books and CDs. Brynjolfsson and Smith Žnd that prices
for identical books and CDs at different retailers differ by as
much as 50% and price differences average 33% for books
and 25% for CDs. However, when they weight these
prices by proxies for market share, they Žnd the dispersion
lower in Internet channels than in conventional channels.
They attribute this phenomenon to the dominance of
certain heavily branded retailers, in addition to several
other factors including asymmetric information and search
costs, heterogeneity in retailer attributes such as trust and
awareness.
In summary, so far empirical evidence for online market
efŽciency is only partially supportive to the hypotheses that
prices are lower and price dispersion is smaller in online
markets compared to conventional markets. It appears to be
a paradox that online retail markets have failed to produce
the level of pricing efŽciency predicted by many who claim
the ideal ‘friction-free’ commerce on the Internet (such
as Bill Gates).
Following the methodology in Tang et al. (2000) on the
book market analysis, this study takes a different approach
from the earlier ones to test online retailing efŽciency. We
notice that, with the rapid growth of e-commerce, more
and more conventional retailers have started selling online.
Bearing this in mind, it is interesting to see how these
‘hybrid’ retailers compete with specialized online traders.
This study thus differentiates itself from the study by
Brynjolfsson and Smith (2000) by focusing on the pricing
conduct comparison between those of the DotCom retailers
and the online branches of (established) conventional
retailers. With this focus, we hope to capture the nature of
competition in the online marketplace by verifying the
hypothesis that the pricing behaviour of the online
Fang-Fang Tang and Ding Lu
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appeared to be weak. According to Smith et al. (2000),
Internet market efŽciency can be empirically tested in four
dimensions. The Žrst is to compare the price level charged
online with that charged in physical retail markets. The
second is to test whether consumers are more sensitive to
small price changes online than otherwise. The result would
suggest whether the online demand is more elastic and
hence freer from market power. The third test is whether
the online traders adjust their costs more frequently than
their ofine counterparts. This would provide evidence
whether online trading does reduce the menu costs and
thus promote efŽciency. Finally, a relatively smaller spread
between the highest and the lowest online trading prices
would be another evidence for Internet market efŽciency.
Along the Žrst dimension of these tests, Lee (1998) and
Lee et al. (2000) studied prices in electronic and conventional auction markets for used cars sold from 1996
to 1997. They found that prices in the electronic markets
are higher than prices in the conventional markets and
that this price difference seems to increase over time. The
problems of their studies are twofold. First, the characteristics of the auction market that they studied are different from those of a retail market. Second, cars sold in
the electronic markets were, in general, newer than the
cars sold in the conventional markets. Bailey (1998a, b)
compared the prices for books, CDs, and software sold
on the Internet and in conventional channels in 1996
and 1997. In this study, the physical goods are entirely
homogeneous and are matched across the channels. Bailey
nevertheless Žnds higher prices in the electronic channel
for each product category during this time period. Bailey
speculates that the higher prices he observed could have
been caused by market immaturity. He notes that during
the 3 months following Barnes and Noble’s Internet
entry on 19 March 1997, Amazon.com dropped its prices
by nearly 10% to match the prices charged by their new
competitor. Clay et al. (1999) compare book prices for
107 titles sold by 13 online and two physical bookstores.
They found that prices in online and physical stores are
the same after controlling for book characteristics. In a
more comprehensive study, Brynjolfsson and Smith (2000)
examine prices for 20 books and CDs sold through Internet
and conventional channels (eight stores of each type)
in 1998 and 1999 and they Žnd that prices are 9–16%
lower on the Internet than in conventional outlets. Apart
from the differences in the methodologies, one possible
explanation for the differences in their Žndings is that
Internet markets efŽciency had improved between 1996
and 1999.
Along the dimension of testing price elasticity, Goolsbee
(1999) Žnds that online consumers are highly sensitive to
local tax policies. Consumers who are subject to high local
sales taxes are much more likely to purchase online (and
presumably avoid paying the local sales tax). This study,
however, does not address the issue whether the price
elasticity of online trading is higher than that of conventional ones. In another study, Degeratu et al. (1998)
172
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branches of conventional retailers would be part of their
parent companies’ integrated strategies of doing business
in both the online and ofine markets. The online marketplace is therefore ‘born with the birthmarks’ of market
power extended from the conventional market. As indicated in retail business literature the key drivers of retailing
include: 1) proŽt margin; 2) volume and the strategic asset;
3) brand; and 4) location. In the case of e-commerce,
all the four drivers remain valid with the third and fourth
ones (brand and location) being merged into one. This
may make market power and market dominance an even
stronger phenomenon in the online marketplace.
SpeciŽcally, the aim of this study is to test: a) whether
retailers who sell only through the Internet can offer
prices lower than the retailers who sell mainly through
traditional channels; b) whether the two types of online
retailers adjust their prices frequently and at the similar
frequency or magnitude; and c) whether the dispersion
of prices exhibited by online retailers is smaller than that
by the online branches of traditional retailers. Section 2
describes our data collection methodology. Results of
our empirical analysis are presented in Section 3. Section 4
concludes the paper.
Electronic Markets Vol. 11 No 3
DATA COLLECTION METHODOLOGY
173
We chose the CD market for the empirical analysis for
several reasons. First, the CD market is a traditional market
in which dominant retailers have considerable market
power. In particular, only very recently (10 May 2000,
http://www.ftc.gov/2000/05/cdpres.htm) has the US
Federal Trade Commission announced that it has reached
separate settlement agreements with the Žve largest distributors of recorded music who sell approximately 85% of
all CDs purchased in the US to end their allegedly illegal
advertising policies that affected prices for CDs. Second,
since the emergence of e-commerce, this market is one
of the most successful ones to migrate online and enjoy
considerable growth and sales. Third, the fact that the CD
market is relatively homogeneous makes data collection
tractable and price comparison meaningful. For a particular
CD title, product differentiation is almost nil, except
additional information services such as reviews and reader
feedback posted by the respective retailer.
Two types of retailers were selected: those that conduct
commerce only through the Internet and those that also
sell through traditional channels. Throughout the rest of
this paper, we use DotCom as the abbreviation for the
specialized online retailers, and OLB as the abbreviation
for the online branches of the conventional retailers. The
main criterion that a retailer must meet is that it sells a
general selection of titles and selling prices are posted on
its Web sites.
Following the music Web site popularity ranking and
100 hot shopping sites by Web21.com, ‘The most rigorous
and widely recognized web ranking service’ according to
Brynjolfsson and Smith (2000), six top retailers of each
type that sell a general selection of titles were selected (see
Table 1). We did not include the more specialized retailers
in speciŽc niche markets to minimize selection bias. At the
end of June 2000, we noticed that Forrester Research
had ranked Buy.com, a specialized online retailer, as one
of the top Žve music shopping sites although it was not
included in either of the Web21.com ranking lists. Thus
we included Buy.com in the survey from 1 July 2000,
as a further robustness check. With Buy.com reputed to
be low pricing shopping site, it would be interesting to
know whether including such a DotCom would change the
results qualitatively. We have found that all our main results
remain qualitatively robust even if we include Buy.com in
our data analyses.
Next, a selection of titles for comparison must be made.
A total of 34 titles were examined (see Appendix A for
details; our original goal was 30 titles, but to make sure that
all titles were sold by these retailers, we selected more titles
in case that some retailers might not have some of them
or out of stock temporarily). Half of them were selected
as an even mix of the top bestsellers among the retailers
when the study was initiated while the rest were chosen
randomly. The reason for such a combination is that if
all the titles were selected from a speciŽc bestseller list such
as Amazon’s Hot 100, which contains only popular titles by
a speciŽc retailer, the results may be biased as these titles are
likely to be loss leaders selected by the respective retailer.
However, if all the titles were selected randomly, one major
trend of pricing behaviour would be overlooked because
the bestsellers occupy a substantial market share in the CD
market and competition in the bestsellers’ niche is crucial
for any market structural analysis. To ensure that the titles
are sufŽciently random, we selected different titles from
each of several music types to be included in the analysis.
We refer to the Žrst category of titles as ‘popular’ and the
second as ‘random’ from now on. Further, during the data
collection process, we took extreme care to make sure that
the record label catalogue number, version and even the
CD cover are exactly the same for the same title.
Next, the frequency of temporal data collection must be
decided. The interval between data collection should not
be so long as to miss price changes that might occur but if
the interval chosen were too short, the overhead costs of
data collection would be prohibitive. From our research
experiences during the study of Tang et al. (2000), we
noticed that once every two days might be too short and
once every week might be too long for the online retailing
market. Thus we collected data once every four days in this
study. We started our data collection on 7 June 2000
and stopped after 9 July 2000 because several titles in our
survey went out of stock or only used CDs of these titles
became available by some retailers in our sample. It is also
noted during the writing that Bertelsmann announced its
decision to buy CDnow on 20 July 2000. That is, CDnow
is no longer a DotCom, but changed to an OLB. In total,
we have 3,774 price observations.
Shipping Rate
Number of Items per Order
Per
Shipment
Per
Item
1
2
3
4
5
6
7
8
DotComs
(Buy.com)
Amazon
CD Universe
CDnow
CompactDiscovery
ThinkCD
TheBigStore
2.00
1.99
2.97
2.99
2.50
2.99
3.20
0.95
0.99
1.00
0.99
0.50
0.50
0.00
2.95
2.98
3.97
3.98
3.00
3.49
3.20
1.95
1.99
2.49
2.49
1.75
2.00
1.60
1.62
1.65
1.99
1.99
1.33
1.50
1.07
1.45
1.49
1.49
1.74
1.13
1.25
1.08
1.35
1.39
1.19
1.59
1.00
1.10
0.86
1.28
1.32
1.00
1.49
0.92
1.00
0.72
1.24
1.27
0.85
1.42
0.86
0.93
0.77
1.20
1.24
0.75
1.36
0.81
0.87
0.68
OLBs
Borders
Barnes&Noble
Camelot
HMV(US$)
Musicland
Tower
3.00
1.99
2.99
4.94
1.99
2.95
0.95
0.99
1.00
0.00
1.00
0.00
3.95
2.98
3.99
4.94
2.99
2.95
2.45
1.99
2.50
2.47
2.00
1.98
1.95
1.65
2.00
1.65
1.66
1.32
1.70
1.49
1.75
0.00
1.50
1.24
1.55
1.39
1.60
0.00
1.40
0.99
1.45
1.32
1.50
0.00
1.33
0.83
1.38
1.27
1.43
0.00
1.28
0.71
1.33
1.24
1.37
0.00
1.25
0.62
Notes:
1. All rates are based on standard shipping in the US.
2. CD universe: $1 per item up to 3 items. Beyond that no more additional per item charges.
3. The BigStore: $3.20 per shipping for 1–3 items. $4.30 per shipping for 4–6 items. $5.40 for 7–8 items, etc.
4. HMV: $4.94 for up to 3 items. Free shipping for orders with 4 items or more.
5. Musicland: per item charge is up to 9 items; no per item charge for 10 items or more.
Observations were carried out by the authors and their
research assistant by accessing the Web sites of the selected
retailers and recording the prices of the selected titles, both
for the online retailers and for the online branches of
the conventional retailers. Note that the prices posted on
the Internet by ofine retailers need not necessarily concur
with prices found in the physical stores of these ofine
retailers. In addition, to ensure the independence of the
samples collected, the general price was selected instead
of membership price and prices that are dependent on the
prices charged by rivals. This is particularly important in the
online market analysis, where some online retailers might
allow consumers to compare their prices with those of their
main rivals. An algorithm could be used that offered consumers a lower price than the general price shown initially
when the latter was higher than those posted by the rivals.
Thus, if this price that is dependent on the prices charged
by its rivals were selected, it would seriously bias the results
of the analysis.
Table 1 also includes the standard shipping costs by each
retailer, that is, shipping costs for the normal delivery in
7–10 days rather than any special express delivery. Since the
shipping cost structure varies with different retailers, we
have calculated the per item shipping cost based on their
shipping cost tariff table for various baskets of typical
purchases. Figure 1 summarizes the mean and median per
item shipping costs by the two types of retailer (numbers
in brackets are the results for DotComs including Buy.com
for the July data; henceforth), with the DotComs being
slightly lower in this aspect. T-test shows that, on average,
the difference in per item shipping costs by either type of
retailer is not signiŽcant in any statistical sense. That is, any
consumer who shops randomly among these retailers with a
random basket of purchase would Žnd out that the per-item
shipping cost difference she would pay is in fact negligible.
Therefore we will concentrate on the analysis of the posted
prices in the rest of this paper.
DATA ANALYSIS
Relative Price Levels
We run several parametric and non-parametric statistical
tests to analyse the relative levels of prices between these
two types of online retailers. Figure 2 summarizes the mean
prices of our data sample at the most aggregate level.
DotComs’ posted prices are clearly lower than OLBs’, on
average by $0.31 ($0.35) or 2.4% (2.7%). It is interesting
to note that this price difference is much smaller than the
Online CD Pricing Patterns
6. Tower: $2.95 per shipping for 1 item, $3.95 per shipping for up to 3 items, $4.95 per shipping for 4+ items.
Fang-Fang Tang and Ding Lu
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Table 1. Retailers and their per Item Shipping Costs
174
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Electronic Markets Vol. 11 No 3
Figure 1. Analysis of per Item Shipping Costs
175
Figure 2. Retailer Type and Mean Prices
un-weighted Internet price and conventional outlet price
difference average in Bryjolfsson and Smith (2000), which
is $2.29 for CDs. For individual retailers, the average dollar
prices are: Amazon, 12.93, theBigStore, 10.94, (Buy.com,
11.82), CDnow, 12.93, CD Universe, 12.54, Compact
Discovery, 14.98, ThinkCD, 12.69; Borders, 13.20, B&N,
13.92, Camelot, 12.85, HMV, 11.79, MusicLand, 13.61,
Tower, 13.47. Note that both the lowest and highest
average prices come from DotComs (theBigstore and
Compact Discovery, respectively), although most OLBs
price higher than DotComs on average.
We also calculated the percentage of the posted prices
by each retailer for each title at each date, relative to the
list price of each title. The percentage price level is more
comparable across titles because it shows clearly how much
discount each retailer gives to each title compared to the
regular list price for each title. It seems that the DotComs
sampled in our survey gave 26.33% (26.63%) discount on
average compared to 24.88% by the OLBs, indicating lower
average price level in any sense. In the rest of this paper, we
will use ‘dollar-price’ as the abbreviation for the absolute
posted prices, and ‘percentage-price’ for the percentage
Price Changes
We measure the price changes by subtracting the previous
date data from the current date data (except the Žrst dataset), for each title by each retailer. In total, we Žnd 294
price changes out of 3,332 observations. In other words,
there was no price change for 88.24% of the time during
this period. There seems little difference between the
retailer types in the sense that the mean price change is
Online CD Pricing Patterns
below 0.07 level for the popular subsamples (with three
below 0.02). It can be claimed with more conŽdence that
the pricing differences seem indeed to be coming mainly
from the popular titles where the greater competition
pressure seems to drive the two types of retailers trying to
differentiate their pricing patterns.
In addition to the tests on mean and median prices,
we ran a third test on the pricing differences between these
two types of retailers. We compared the lowest prices found
among all the DotComs in our sample for each title with
the lowest prices found among all the OLBs sampled. We
Žnd that the minimum dollar-price among the DotComs is
$0.21 lower on average than the minimum dollar-price
charged by OLBs. Further, Figure 3 shows that during this
period the lowest price is found among the DotComs more
than half (53.3%) of the time. Including Buy.com in the
DotCom data sample would enhance this result to 61.1%
of the time. Note that Figure 3 summarizes both the dollarprice and percentage-price cases, which coincide in this
situation. For each day, if we form the null hypothesis that
minimum prices are the same for both DotComs and
OLBs, that is, half the time the lowest prices should be
found by either type of retailers, this statistic should follow
a binomial distribution. Then we can run the binomial test
against the alternative hypothesis that it is more likely to
Žnd minimum prices by the DotComs in each day. The
results are rather mixed, no matter whether one includes
Buy.com data in the analysis or not. For all titles pooled
together, Žve p-values (at 0.061) are below the weak signiŽcance level 0.10 while the other four p-values are above
0.196 to even 0.568. For the popular titles, except one at
0.072, all p-values are between 0.166 and 0.685, indicating
full-scale ambiguity. Only for the random titles, do all
results clearly reject the null hypothesis (one at p = 0.025,
three at 0.006 and Žve at 0.001) in favor of the alternative hypothesis that it is more likely to Žnd minimum prices
by the DotComs in each day. It seems that even for
the random titles, at least one would be able to Žnd lower
minimum prices by the DotComs although the mean and
median prices are not unanimously lower by DotComs in
the statistical sense for this category.
Overall, it seems clear from three possible tests of differences (t-test on means, median test and binomial test
on minimum prices) that the DotComs indeed price lower
than the OLBs for most times. This result is particularly
signiŽcant in the percentage-price sense.
Fang-Fang Tang and Ding Lu
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of the absolute posted prices relative to the list prices for
each title.
To control for the serial correlation problem, we have
run statistical tests for each data set day by day (see
Tables B1 to B3 in Appendix B). For both the dollar and
percentage prices, t-tests reject the null hypothesis that
the mean DotCom price is equal to the mean OLB price
in favour of the alternative hypothesis that it is lower by
DotCom than by OLB for six cases out of nine at 10%
signiŽcance level (one-tailed, henceforth throughout the
rest of this paper) with three being signiŽcant below 3%
level. If we include Buy.com in the analysis for July data,
all t-tests in July become highly signiŽcant as well. When
we further divide the data sets by the categorization of
popular titles versus random titles, this pattern is even more
clearly exhibited for popular titles while the test results
are rather indistinguishable for random titles between 7–19
June and 5–9 July. It seems that the pricing differences
mainly come from the popular titles, while both types
of retailers price more similarly for the random titles. This
phenomenon seems to indicate that price competition is
Žercer in the popular title niche and the DotComs that
solely depend on the online sales for survival price more
aggressively in the popular title niche.
Note that the power-efŽciency of the t-test decreases
when sample sizes decrease, and this problem may become
more severe with smaller sub-samples because of its distribution assumption. Thus, in order to control for the
possible bias associated with distribution assumptions,
we also run the median test (see Sheskin, 1996, 232–3),
which is a non-parametric test that does not require
any assumption on sampling distribution properties, to
ensure that this Žnding is robust. This test is a procedure
for testing whether two groups differ in central tendencies,
or more precisely, whether they have been drawn from
populations with the same median. By determining the
median value of the combined total sample, we can
dichotomize both types of retailers’ prices into a 2x2 table.
If both groups are samples from populations whose
medians are the same, we would expect about half of
each group to be above the combined median and about
half to be below. It can be shown, see Mood (1950), that
the sampling distribution under the null hypothesis that the
medians are the same is the hypergeometric distribution.
We also run the median test for each dataset day-byday as well (see Tables B1–B3 in Appendix B). For the
dollar-prices for all titles, the results are ambiguous but
all p-values are highly signiŽcant for the percentage-prices
(below 0.03 except 5 July at 0.05). Dividing the datasets
further by the categorization of popular titles versus random
titles for each day, for the percentage prices, all p-values are
highly signiŽcant again for the popular category (below
0.03 except 5 July data including Buy.com at 0.04) while
only four out of nine are so for the random category (below
0.03). For the dollar-prices, we Žnd that all the p-values are
above 0.15 for all random subsamples including the ones
with Buy.com data, but more than half remain signiŽcant
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Electronic Markets Vol. 11 No 3
Figure 3. Retailer Type and Minimum Prices
177
$0.01 for the DotComs and $0.03 for the OLBs. More
detailed categorizations are summarized in Figure 4.
It can be seen that there are far fewer cases of price
increases by DotComs (69, maximum increase of $4.0)
than by OLBs (112, maximum of $5.7), while the price
decrease cases are roughly the same by DotComs (56,
maximum of $-4.5) or by OLBs (57, maximum of
$-4.0). Further, apart from 10 price increases of $0.01 by
BarnesandNobel.com on 15 June, all other price changes
are at least $0.10 in magnititude, 284 cases out of 294 or
96.6%. It is interesting to notice that the one-cent-pricechange strategy by Books.com (see Tang et al., 2000) seems
to be continued by Amazon.com now, since Books.com
had been merged by BarnesandNobel.com, one major rival
of Amazon.com in the online book and CD markets.
Note also that both the DotComs and OLBs change their
selling prices rather sharply, out of all the price changes
respectively, 68.8% for DotComs and 81.7% for OLBs
being over half a dollar, with 41.6% for DotComs and
47.3% for OLBs over a whole dollar. It seems that both
types of online retailers do not change prices as frequently
nor as small as many researchers might have expected,
although menu costs are much lower in online channels than
conventional channels. This observation is an interesting
extension to the Žnding in Brynjolfsson and Smith (2000)
that ‘Internet retailers’ price adjustments over time are
up to 100 times smaller than conventional retailers’ price
adjustments – presumably reecting lower menu costs in
Internet channels.’ This evidence reinforces our observation in Tang et al. (2000) that, although theoretically
menu cost is close to zero for online price changes, real-life
retailers do not much follow this reasoning. Either they do
not change their prices, or if they do, they will adjust the
prices quite signiŽcantly, even for the online markets.
We have also measured the price changes in another way,
by subtracting the 7 June data from the 9 July data for each
title by each retailer, that is, the price change across the
whole period during which data were collected. (For
Buy.com, we use 9 July data minus 1 July data.) Similar
patterns as above can be observed (see Figure 5). In total,
we Žnd 95 price changes out of 442 observations, but
no price change remains the norm in 78.51% of time across
the Žve weeks’ time span. Further, the difference between
the retailer types is still small, in the sense that the mean
price change is $0.10 for the DotComs and $0.17 for the
OLBs. Both t-test (p = 0.19) and median test (0.45 < p <
0.495) fail to identify any clear signiŽcance. Note that on
average the price has increased at least ten cents for either
type of retailers (and close to twenty cents for OLBs), after
a mere Žve weeks. The hypothesis that competition will
drive the online price level lower over time does not seem
well supported by our data.
More speciŽcally, there are far fewer cases of price
increases by DotComs (18, maximum increase of $3.11)
than by OLBs (61, maximum of $5.7), and the price
decrease cases are also much fewer by DotComs (3,
maximum of $-1.5) than by OLBs (13, maximum of $-5).
Further, except for eight price increases of $0.01 by
BarnesandNobel.com, all other price changes are at least
$0.10 in magnitude, 87 cases out of 95 or 91.6%. Note
again that both the DotComs and OLBs change their selling prices rather sharply, out of all the price changes
respectively, 100% for DotComs and 81.1% for OLBs being
over half a dollar, with 81% for DotComs and 13.5% for
OLBs over a whole dollar. It seems particularly interesting
that the specialized DotComs adjust their prices so much
in magnitude during a mere Žve-week time span, which in
turn indicates that menu cost is not a negligible issue in
Price Dispersion
Following Sorensen (1998) and Bryjolfsson and Smith
(2000), we use both absolute and relative measures to analyse dispersion in posted prices by the DotComs and OLBs.
Absolute price dispersion refers to the range of either the
dollar-price or the percentage-price across our sampled
DotComs and OLBs, that is, the highest price minus the
lowest price for a particular title at each date for the
DotComs or the OLBs. The absolute dispersion statistics
for our data show a substantial range of prices available by
both the DotComs and the OLBs for the same CD title in
the same time period, with the DotCom case being slightly
larger. The range of dollar-prices across the DotComs
averages $4.12 ($4.17), see Table 2, which corresponds to
an average percentage-price range of 23.87% (24.18%), and
it is $3.96 or 22.07% across the OLBs, slightly smaller.
When we further examine the title categories of popular
versus random, this pattern is more clearly exhibited for the
random titles, in that the range of dollar-prices across the
DotComs averages $3.75 ($3.76), which corresponds to
an average percentage-price range of 23.32% (23.37%),
but it is $2.68 or 16.17% across the OLBs, signiŽcantly
smaller. For the popular titles, the pattern seems to be the
opposite – the price range across the OLBs is on average,
larger than the DotComs. Again, to control for serial
correlation, we have run the t-test for each data set dayby-day and furthermore both for the popular titles and
for the random titles in each day data (see Tables B4 to
B6 in Appendix B). All results for the random title category
are highly signiŽcant (p < 0.025) but not signiŽcant at all
for the other cases (except for popular titles on 5 July at
weak signiŽcance of 0.08). Qualitatively similar results are
exhibited with the case of the standard deviation (calculated
according to the standard formula).
To control for possible distribution assumption bias, we
Online CD Pricing Patterns
practice, even for the pure Internet retailers. We also noted
that there were 124 price changes by DotComs (plus 1 by
Buy.com between 1–9 July), much fewer than the 169
price changes by OLBs during the same period.
Fang-Fang Tang and Ding Lu
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Figure 4. Retailer Type and Four-Day Price Changes
178
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Electronic Markets Vol. 11 No 3
Figure 5. Retailer Type and Monthly Price Changes
179
apply the median test, as for relative price levels, and Žnd
that the null hypothesis cannot be rejected except for
most of the standard deviation results in the random title
category. It seems that the larger price dispersion within
DotComs than within OLBs mainly comes from the
random titles. This indicates more diverse pricing strategies
by DotComs than OLBs in the random category, while
the DotComs price the popular titles more closely to each
other for the intense online competition in the hot title
niche, resulting in similar price dispersions to the OLBs that
do not completely depend on online sales for survival.
For the relative dispersion in prices across OLBs and
DotComs, we compare measures of the price range and the
standard deviation in the following way. For each date, we
count the number of titles where a particular measure of
dispersion is larger for the DotComs than the OLBs (we
found between 22 to 23 usually, out of 34, except two at
20 for price range and standard deviation each and two
at 18 for standard deviation). The results are summarized
in Table 3, which presents the proportion of times our
measures of the price range and the standard deviation
are larger for the DotComs than the OLBs across this
period. Both measures of the price range and the standard
deviation are larger for the DotComs than the OLBs above
60% of the time.
As in the earlier relative price section, we have run the
binomial test against the alternative hypothesis that it is
more likely to Žnd larger price dispersion by the DotComs,
for each date. The results are qualitatively the same no
matter whether the Buy.com July data are included or
not. First, it is ambiguous for all titles pooled together
in the sense that for the price range, there are two cases of
p-values around 0.06, Žve around 0.12 and two around
0.20, while for the standard deviation, there are three cases
of p-values around 0.06, two around 0.12, two around
0.20 and two above 0.43. For the popular titles, all p-values
for either the price range or the standard deviation are
above 0.315. For the random titles, except for one case
(9 July) of p-value at 0.166 for the price range and 0.315
for the standard deviation, all p-values are below 0.072
with more than half of them being highly signiŽcant (below
0.025).This evidence further enhances our observation
that the higher price dispersion within DotComs than
within OLBs mainly comes from the random category. This
Mean
DotCom
OLB
Mean
DotCom
OLB
Dollarprice range
Dollarprice STD
4.12
(4.17)
1.44
(1.43)
3.96
Percentageprice range
Percentageprice STD
23.87%
(24.18%)
8.36%
(8.31%)
22.07%
Mean-popular
DotCom
OLB
Mean-popular
DotCom
OLB
Dollarprice range
Dollarprice STD
4.48
(4.59)
1.53
(1.54)
5.24
Percentageprice range
Percentageprice STD
24.42%
(24.99%)
8.42%
(8.47%)
27.97%
Mean-Random
DotCom
OLB
Mean-Random
DotCom
OLB
Dollarprice Range
Dollarprice STD
3.75
(3.76)
1.34
(1.32)
2.68
Percentageprice range
Percentageprice STD
23.32%
(23.37%)
8.29%
(8.16%)
16.17%
1.44
1.84
1.00
8.06%
10.08%
6.03%
Table 3. Proportion of Price Dispersion
Price Range: DotCom > OLB
Price STD: DotCom > OLB
60.45%
(61.77%)
61.11%
(60.13%)
Note: Numbers in parentheses are the results for DotComs including
Buy.com for the July data.
feature seems robust qualitatively, despite of the different
price dispersion deŽnition and different statistical tests we
use.
CONCLUSION
As we noted in the Žrst section, empirical results, so far, in
the e-commerce research literature constitute a paradox
that online retail market has failed to produce the level of
pricing efŽciency predicted by many. Our Žndings in this
study provide further evidence that the online CD market
is far from being perfectly competitive as well. They therefore reinforce the results of some earlier empirical studies
that have questioned the market-efŽciency hypothesis for
e-commerce retailing.
Furthermore, our study takes a methodology that is
unique in the literature. Instead of comparing online prices
with prices offered in conventional markets (e.g., like what
was done by Brynjolfsson and Smith (2000)), we investigate how the online branches of conventional retailers
compete with their DotCom counterparts. The purpose of
doing so is to test the hypothesis that the online branches’
pricing behaviour should be part of the conventional
retailers’ integrated strategies of excising their market
power both in the online and ofine markets.
Our Žndings lend signiŽcant support to such a hypothesis. First, we Žnd that the online branches of conventional
CD retailers tend to sell their products more expensively
than their DotCom rivals, particularly in the popular titles’
category. Second, our evidence shows that online price
changes are not as frequent nor as small as expected.
However, unlike the observation in Tang et al. (2000) on
the online book market, the OLBs in the online CD market
do not seem to exhibit greater pricing inertia than their
DotCom rivals in the sense that they made more price
changes during this period, although the magnitude of their
price adjustments was larger on average. Menu cost is not
negligible in practice even for the online markets. Third,
the price dispersion seems to be going in two opposite
directions – for the popular titles, the DotComs exhibit
lower price dispersions than the online branches of the
conventional retailers, although not greatly signiŽcant in
the statistical sense, but for the random category, price dispersion is much larger by the DotComs, highly signiŽcant
statistically.
Arising from our Žndings, an interesting phenomenon is
worth noting: the price difference we have found between
the DotComs and OLBs is much smaller than the price
difference between the Internet market and conventional
market revealed in an earlier study by Bryjolfsson and Smith
(2000). This phenomenon indicates that the conventional
CD retailers enjoy greater market power in their conventional outlets than in the online market. Meanwhile,
the online pricing behaviour of a conventional CD retailer
is indeed partly inuenced by its market power in the
conventional market since it has to treat the products
sold online as close substitutes for the products in its
Online CD Pricing Patterns
Note: Numbers in parentheses are the results for DotComs including Buy.com for the July data.
Fang-Fang Tang and Ding Lu
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Table 2. Absolute Price Dispersion
180
Electronic Markets Vol. 11 No 3
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181
conventional branches. It would therefore be more cautious
about cutting prices in online competition. A DotCom
retailer, on the other hand, does not have such constraints
and therefore could be more aggressive in the online price
competition. A simple theoretical model in Lu and Tang
(2000) interprets the business rationale for a conventional
retailer with market power to develop its online business in
the presence of a DotCom rival in a duopoly framework.
The main prediction of the model is consistent with our
Žndings in this empirical study.
As we mentioned in the introductory section, in the
online marketplace, market power and market dominance
may become even more phenomenal since the four drivers
in the conventional retailing business (namely, proŽt
margin, volumes and strategic assets, brand, and location)
not only persist but are also merged into three. In the short
run, the emergence of a ‘B2C’ (business-to-consumer)
online market may lower entry barriers relative to the
conventional market. In the longer run, however, as the key
drivers and strategic assets gradually clear the competitive
landscape, the online market is likely to succumb to the
market power of the dominant players. The recent merger
waves of the ‘Big Money’ conventional retailers buying
pure Internet players in Žnancial difŽculties seem to lend
further support to this hypothesis. As all the key drivers
(optimized volumes, margins, brand and access) move the
online marketplace towards long-term equilibrium, conventional retailers’ online branches tend to exhibit price
change and price dispersion patterns closer and closer
to their DotCom rivals and vice versa for the successful
DotCom retailers.
Our Žndings also suggest that the logistic infrastructure
of the two types of retailers matter in their online competitive strategies. On the one hand, we Žnd that the
average online price charged by DotComs is lower than
that charged by the OLBs while such a difference mainly
comes from the popular titles. On the other hand, we Žnd
that DotComs display the larger price dispersion than the
OLBs in random titles. These differences can be explained
by the two types of retailers’ different logistic infrastructure. Compared to conventional retailers, the DotComs are younger and most do not have large warehouse
capacity. They have to use their limited warehouse
resources in the market segment of popular titles. DotComs’ sole reliance on the online market for survival leads
to more aggressive price competition in popular titles.
That in turn results in their lower average price and smaller
price dispersion among themselves for the popular titles. As
for the random titles, the DotComs probably have to take
fresh orders from the publisher for their customers and thus
are unable to offer signiŽcant price discounts. In contrast,
the conventional retailers have more room to manoeuvre
between the online and ofine branches and between
popular-title market and non-popular-title market. Given
their large warehouse capacity, they are also more likely to
offer deep price discounts for the non-popular (random)
titles when they clear their inventories.
References
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Aggregation and Pricing in Internet Commerce’.
Ph.D. thesis, Technology, Management and Policy,
Massachusetts Institute of Technology.
Bailey, J.P. (1998b) Electronic Commerce: Prices and Consumer
Issues for Three Products: Books, Compact Discs, and
Software, Organisation for Economic Co-operation and
Development, OCDE/GD(98)4.
Brynjolfsson, E. and Smith, M.D. (2000) ‘Frictionless
Commerce? A Comparison of Internet and Conventional
Retailers’, Management Science 46(4), 563–85.
Clay, K., Krishnan, R., Wolff, E. and Fernandes, D. (1999)
‘Retail Strategies on the Web: Price and Non-price
Competition in the Online Book Industry’, working paper,
Carnegie-Mellon University.
Clemons, E.K., Hann, I.-H. and Hitt, L.M. (1998, June)
‘The Nature of Competition in Electronic Markets: An
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working paper, The Wharton School of the University of
Pennsylvania.
Degeratu, A., Rangaswamy, A. and Wu, J. (1998) ‘Consumer
Choice Behavior in Online and Regular Stores: The Effects
of Brand Name, Price and Other Search Attributes’, paper
presented at Marketing Science and the Internet, INFORM
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(6–8 March).
Goolsbee, A. (1999, July) ‘In a World Without Borders: The
Impact of Taxes on Internet Commerce’, working paper,
University of Chicago.
Lee, H.G. (1998) ‘Do Electronic Marketplaces Lower the
Price of Goods?, Communications of the ACM 41(1),
73–80.
Lee, H.G., Westland, J.C. and Hong, S. (2000)
‘The Impact of Electronic Marketplaces on Product
Prices: An Empirical Study of AUCNET’, The
International Journal of Electronic Commerce 14(2),
45–60.
Lu, D. and Tang, F. (2000) ‘Conventional Retailers’ Onlinebranch Behavior’, working paper, National University of
Singapore, (May).
Lynch, J.G., Jr and Ariely, D. (1998) ‘Interactive Home
Shopping: Effects of Search Cost for Price and Quality
Information on Consumer Price Sensitivity, Satisfaction
with Merchandise, and Retention’, paper presented at
Marketing Science and the Internet, INFORM College
on Marketing Mini-Conference, Cambridge, MA,
(6–8 March).
Mood, A.M. (1950) Introduction to the Theory of Statistics,
New York: McGraw-Hill.
Sheskin, D.J. (1996) Handbook of Parametric and
Nonparametric Statistical Procedures, New York,
CRC Press.
Smith, M.D., Bailey, J.P. and Brynjolfsson, E. (2000)
‘Understanding Digital Markets: Review and Assessment’,
in Brynjolfsson, E. and Kahin, B. (eds), Understanding the
Digital Economy, Boston, MIT Press.
APPENDIX B. DATE ANALYSIS BY DATE
Date
Dot Price
Mean
OLB Price
Mean
T-test (one- Median Test
tailed)
(one-tailed)
APPENDIX A. THE LIST OF CD TITLES (POPULAR: 1–17;
RANDOM: 18–34)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
12.81
12.84
12.84
12.84
12.79
12.79
12.80
(12.66)
12.88
(12.73)
12.92
(12.78)
13.04
13.02
13.13
13.16
13.20
13.19
13.19
(13.19)
13.11
(13.11)
13.22
(13.22)
0.12
0.17
0.07*
0.05*
0.02*
0.02*
0.02*
(0.002*)
0.12
(0.022*)
0.07*
(0.016*)
Date
Dot %-Price
Mean
OLB %-Price
Mean
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
73.54%
73.70%
73.71%
73.73%
73.39%
73.38%
73.46%
(72.62%)
73.95%
(73.04%)
74.13%
(73.26%)
74.57%
74.42%
75.01%
75.21%
75.46%
75.39%
75.44%
(75.01%)
75.01%
(75.01%)
75.57%
(75.57%)
0.16
0.25
0.10*
0.08*
0.02*
0.03*
0.03*
(0.002*)
0.16
(0.03*)
0.09*
(0.01*)
Table B1. Retailer Type and Mean Price Analysis by Date
(All 34 Titles)
Alternative hypothesis: Dot < OLB
Artist(s)
List
Price
(US$)
1
2
3
4
5
6
7
8
9
10
Santana
Moby
Red Hot Chili Peppers
Dixie Chicks
Sarah McLachlan
Marc Anthony
Dido
Goo Goo Dolls
Vertical Horizon
Eminem
18.99
21.99
18.99
18.99
18.99
18.99
17.99
17.99
17.99
18.99
Don Henley
18.99
Kid Rock
18.99
Andrea Bocelli
Carole King
Creedence Clearwater
Revival
Macy Gray
Buena Vista Social Club
(Producer: Ry Cooder)
Def Leppard
Steve Khan
The Gadjits
Skid Row
The O’jays
Joan Jett & The
Blackheart
Air
Soulfood
Sensational
Scratch Acid
Spice 1
Faith No More
Ras Michael
Chely Wright
Simple Minds
18.99
11.99
18.99
Secret Garden
Gathering
17.99
17.99
16
17
Supernatural
Play
Californication
Fly
Mirrorball
Marc Anthony
No Angel
Dizzy Up the Girl
Everything You Want
The Slim Shady LP (Explicit
Version)
Actual Miles: Henley’s
Greatest Hits
Devil Without a Cause
(Explicit)
Sogno
Tapestry [Remastered]
Chronicle: The 20 Greatest
Hits (Vol 1)
On How Life Is
Buena Vista Social Club
18
19
20
21
22
23
Slang
Got My Mental
At Ease
Slave To The Grind
Ship Ahoy
Bad Reputation
24
25
26
27
28
29
30
31
32
Moon Safari
Breathe
Corner The Market
The Greatest Gift
Hits
The Real Thing
Rally Round
Single White Female
Good News From The Next
World
Dawn Of A New Century
How To Measure A Planet
11
12
13
14
15
33
34
July 5
July 9
July 5
18.99
18.99
July 9
18.99
16.99
12.99
15.99
9.99
17.99
Note: * significant at level above 90%.
16.99
17.99
20.99
14.99
17.99
11.99
17.99
18.99
16.99
0.024*
0.03*
0.01*
0.01*
0.00*
0.00*
0.00*
(0.00*)
0.05*
(0.01*)
0.01*
(0.00*)
Numbers in parentheses are the results for DotComs including Buy.com for
the July data.
Table B2. Retailer Type and Mean Price Analysis by Date
(17 Popular Titles)
Alternative hypothesis: Dot < OLB
Date
Dot Price
Mean
OLB Price
Mean
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
13.03
13.07
13.08
13.08
13.09
13.08
13.09
(12.89)
13.45
13.40
13.58
13.59
13.55
13.50
13.50
(13.50)
0.08*
0.13
0.04*
0.03*
0.05*
0.07*
0.07*
(0.002*)
0.13
0.16
0.00*
0.00*
0.02*
0.05*
0.06*
(0.07*)
Online CD Pricing Patterns
No. Title
0.42
0.46
0.07*
0.08*
0.14
0.19
0.19
(0.03*)
0.50
(0.21)
0.35
(0.09*)
Fang-Fang Tang and Ding Lu
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Sorensen, A.T. (1998) ‘Equilibrium Price Dispersion in
Retail Market for Prescription Drugs’, MIT working
paper, October, [also Journal of Political Economy
(forthcoming)].
Tang, F.-F., Lu, D. and Ho, H.P. (2000) ‘Online Trading
Paradox: Evidence and Interpretation’, working paper,
June.
182
Table B2.—continued
Date
Dot Price
Mean
OLB Price
Mean
T-test (one- Median Test
tailed)
(one-tailed)
July 5
13.17
(12.96)
13.24
(13.04)
13.51
(13.51)
13.61
(13.61)
0.12
(0.027*)
0.1*
(0.026*)
Date
Dot %-Price
Mean
OLB %-Price
Mean
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
70.58%
70.91%
70.93%
71.39%
70.95%
70.92%
70.95%
(69.88%)
71.40%
(70.26%)
71.77%
(70.71%)
72.62%
72.33%
73.30%
73.84%
73.14%
72.89%
72.95%
(72.95%)
73.20%
(73.20%)
73.52%
(73.52%)
0.16
0.18
0.06*
0.05*
0.07*
0.10*
0.09*
(0.02*)
0.15
(0.04*)
0.13
(0.02*)
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July 9
July 5
July 9
0.13
(0.11)
0.13
(0.10)
0.025*
0.01*
0.00*
0.00*
0.01*
0.02*
0.03*
(0.00*)
0.01*
(0.04*)
0.02*
(0.01*)
Note: * significant at level above 90%.
Numbers in parentheses are the results for DotComs including Buy.com for
the July data.
Table B3. Retailer Type and Mean Price Analysis by Date
(17 Random Titles)
Alternative hypothesis: Dot < OLB
Electronic Markets Vol. 11 No 3
Date
183
Dot Price
Mean
OLB Price
Mean
T-test (one- Median Test
tailed)
(one-tailed)
12.60
12.60
12.60
12.61
12.50
12.50
12.51
(12.42)
12.60
(12.50)
12.60
(12.51)
12.64
12.64
12.67
12.73
12.85
12.87
12.88
(12.88)
12.71
(12.71)
12.82
(12.82)
0.44
0.44
0.38
0.32
0.08*
0.08*
0.08*
(0.034*)
0.33
(0.19)
0.20
(0.11)
Date
Dot %-Price
Mean
OLB %-Price
Mean
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
76.50%
76.50%
76.52%
76.52%
0.50
0.50
June 7
June 11
June 15
June 19
June 23
June 27
July 1
July 5
July 9
0.23
0.23
0.44
0.44
0.39
0.33
0.28
(0.15)
0.33
(0.50)
0.39
(0.22)
June 15
June 19
June 23
June 27
July 1
July 5
July 9
76.73%
76.58%
77.79%
77.88%
77.94%
(77.94%)
77.00%
(77.00%)
77.61%
(77.61%)
0.43
0.36
0.07*
0.06*
0.07*
(0.02*)
0.36
(0.18)
0.21
(0.08*)
0.29
0.20
0.03*
0.01*
0.01*
(0.00*)
0.24
(0.23)
0.03*
(0.02*)
Note: * significant at level above 90%.
Numbers in parentheses are the results for DotComs including Buy.com for
the July data.
Table B4. Retailer Type and Price Range Analysis by Date
(All 34 Titles)
Alternative hypothesis: Dot > OLB
Date
Dot Mean
of Price
Range
OLB Mean
of Price
Range
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
4.17
4.10
4.13
4.13
4.11
4.10
4.03
(4.22)
4.10
(4.26)
4.18
(4.34)
3.93
3.82
3.77
3.84
3.94
3.98
3.96
(3.96)
4.15
(4.15)
4.24
(4.24)
0.32
0.28
0.22
0.27
0.36
0.40
0.45
(0.30)
0.46
(0.41)
0.45
(0.41)
Date
Dot Mean
of %-Price
Range
OLB Mean
of %-Price
Range
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
24.23%
23.81%
23.97%
23.97%
23.82%
23.80%
23.25%
(24.35%)
23.79%
(24.64%)
24.22%
(25.07%)
21.87%
21.32%
21.08%
21.46%
21.92%
22.13%
22.02%
(22.02%)
23.31%
(23.31%)
23.52%
(23.52%)
0.20
0.17
0.13
0.16
0.22
0.25
0.31
(0.17)
0.43
(0.31)
0.39
(0.27)
July 5
July 9
July 5
July 9
0.34
0.34
76.50%
76.08%
75.83%
75.83%
75.97%
(75.35%)
76.50%
(75.81%)
76.50%
(75.81%)
0.23
0.23
0.40
0.40
0.40
0.40
0.40
(0.31)
0.40
(0.40)
0.40
(0.40)
0.23
0.23
0.23
0.40
0.40
0.40
0.40
(0.23)
0.40
(0.40)
0.31
(0.23)
Note: * are significant at level above 90%.
Numbers in parentheses are the results for DotComs including Buy.com for
the July data.
Table B5. Retailer Type and Price Range Analysis by Date
(17 Popular Titles)
Alternative hypothesis: Dot < OLB
July 1
Date
Dot Mean
of Price
Range
OLB Mean
of Price
Range
T-test (one- Median Test
tailed)
(one-tailed)
July 9
June 7
June 11
June 15
June 19
June 23
June 27
July 1
4.63
4.48
4.54
4.23
4.48
4.48
4.42
(4.75)
4.47
(4.80)
4.63
(4.96)
5.41
5.19
5.07
4.85
5.13
5.21
5.17
(5.17)
5.53
(5.53)
5.57
(5.57)
0.18
0.17
0.24
0.21
0.20
0.17
0.17
(0.29)
0.08*
(0.17)
0.12
(0.21)
Dot Mean
of %-Price
Range
OLB Mean
of %-Price
Range
T-test (one- Median Test
tailed)
(one-tailed)
25.21%
24.37%
24.69%
23.19%
24.40%
24.36%
24.07%
(25.78%)
24.33%
(26.04%)
25.20%
(26.91%)
28.84%
27.74%
27.11%
26.08%
27.37%
27.78%
27.57%
(27.57%)
29.55%
(29.55%)
29.71%
(29.71%)
0.20
0.20
0.27
0.24
0.23
0.19
0.19
(0.33)
0.10*
(0.19)
0.13
(0.24)
Date
June 7
June 11
June 15
June 19
June 23
June 27
July 1
July 5
0.012*
(0.008*)
0.011*
(0.011*)
0.021*
(0.021*)
Date
Dot Mean
of %-Price
Range
OLB Mean
of %-Price
Range
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
23.24%
23.24%
23.24%
24.75%
23.24%
23.24%
22.42%
(22.92%)
23.24%
(23.24%)
23.24%
(23.24%)
14.91%
14.91%
15.05%
16.84%
16.48%
16.48%
16.48%
(16.48%)
17.06%
(17.06%)
17.33%
(17.33%)
0.001*
0.001*
0.002*
0.003*
0.004*
0.004*
0.005*
(0.004*)
0.017*
(0.017*)
0.01*
(0.01*)
July 5
July 5
July 9
0.36
(0.24)
0.37
(0.37)
0.36
(0.36)
0.24
0.24
0.24
0.25
0.24
0.49
0.24
(0.24)
0.50
(0.50)
0.37
(0.37)
Note: * are significant at level above 90%.
0.50
0.37
0.50
0.25
0.50
0.50
0.50
(0.37)
0.50
(0.50)
0.37
(0.25)
Numbers in parentheses are the results for DotComs including Buy.com for
the July data.
Table B7. Retailer Type and Price Standard Deviation Analysis by
Date (All 34 Titles)
Alternative hypothesis: Dot > OLB
Date
Dot Mean
of Price STD
OLB Mean
of Price STD
T-test (one- Median Test
tailed)
(one-tailed)
the July data.
June 7
June 11
June 15
June 19
June 23
June 27
July 1
Table B6. Retailer Type and Price Range Analysis by Date
(17 Random Titles)
Alternative hypothesis: Dot > OLB
July 5
1.47
1.43
1.44
1.45
1.43
1.44
1.42
(1.39)
1.43
(1.42)
1.46
(1.45)
1.43
1.39
1.38
1.40
1.43
1.45
1.44
(1.44)
1.50
(1.50)
1.54
(1.54)
0.418
0.404
0.355
0.389
0.494
0.467
0.441
(0.388)
0.357
(0.318)
0.325
(0.288)
July 9
Note: * are significant at level above 90%.
Numbers in parentheses are the results for DotComs including Buy.com for
July 9
Date
June 7
June 11
June 15
June 19
June 23
June 27
Dot Mean
of Price
Range
OLB Mean
of Price
Range
T-test (one- Median Test
tailed)
(one-tailed)
3.73
3.73
3.73
4.04
3.73
3.73
2.44
2.44
2.47
2.82
2.76
2.76
0.001*
0.001*
0.001*
0.005*
0.007*
0.007*
0.14
0.14
0.14
0.25
0.24
0.24
0.404
0.404
0.404
0.404
0.404
0.404
0.404
(0.404)
0.404
(0.404)
0.404
(0.404)
Date
Dot Mean of OLB Mean of
%-Price STD %-Price STD
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
8.51%
8.30%
8.37%
8.39%
8.32%
0.29
0.28
0.25
0.28
0.35
7.98%
7.78%
7.74%
7.86%
7.99%
0.404
0.404
0.404
0.404
0.404
Online CD Pricing Patterns
Downloaded By: [Schmelich, Volker] At: 13:02 16 March 2010
July 9
2.76
(2.76)
2.77
(2.77)
2.91
(2.91)
Fang-Fang Tang and Ding Lu
July 5
0.50
0.37
0.50
0.37
0.50
0.24
0.50
(0.15)
0.50
(0.50)
0.50
(0.50)
3.63
(3.69)
3.73
(3.73)
3.73
(3.73)
184
Table B7.—continued
July 5
Dot Mean of OLB Mean of
%-Price STD %-Price STD
T-test (one- Median Test
tailed)
(one-tailed)
July 9
June 27
July 1
8.32%
8.19%
(8.06%)
8.32%
(8.20%)
8.48%
(8.36%)
0.39
0.43
(0.50)
0.45
(0.40)
0.45
(0.40)
Note: * are significant at level above 90%.
July 9
8.08%
8.05%
(8.05%)
8.44%
(8.44%)
8.59%
(8.59%)
0.404
0.404
(0.404)
0.404
(0.404)
0.404
(0.404)
Note: * are significant at level above 90%.
Numbers in parentheses are the results for DotComs including Buy.com for
Table B8. Retailer Type and Price Standard Deviation Analysis by
Date (17 Popular Titles)
Alternative hypothesis: Dot < OLB
Date
Dot Mean of OLB Mean of
Price STD
Price STD
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
1.61
1.53
1.56
1.46
1.53
1.44
1.52
(1.55)
1.54
(1.58)
1.60
(1.64)
0.13
0.11
0.16
0.15
0.13
0.47
0.11
(0.13)
0.053*
(0.065*)
0.072*
(0.087*)
July 5
Electronic Markets Vol. 11 No 3
July 9
1.94
1.86
1.83
1.75
1.84
1.45
1.86
(1.86)
1.99
(1.99)
2.00
(2.00)
0.50
0.25
0.50
0.50
0.50
0.50
0.50
(0.50)
0.50
(0.50)
0.50
(0.50)
Date
Dot Mean of OLB Mean of
%-Price STD %-Price STD
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
8.78%
8.36%
8.49%
8.02%
8.35%
8.36%
8.29%
(8.41%)
0.16
0.13
0.18
0.17
0.14
0.12
0.12
(0.13)
10.33%
9.93%
9.81%
9.42%
9.85%
10.00%
9.97%
(9.97%)
0.37
0.50
0.50
0.50
0.50
0.50
0.50
(0.50)
0.057*
(0.068*)
0.078*
(0.091*)
0.25
(0.50)
0.25
(0.25)
Numbers in parentheses are the results for DotComs including Buy.com for
the July data.
Table B9. Retailer Type and Price Standard Deviation Analysis by
Date (17 Random Titles)
Alternative hypothesis: Dot > OLB
Date
Dot Mean of OLB Mean of
Price STD
Price STD
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
1.33
1.33
1.33
1.43
1.33
1.33
1.31
(1.24)
1.33
(1.26)
1.33
(1.26)
0.004*
0.004*
0.004*
0.013*
0.015*
0.016*
0.02*
(0.055*)
0.02*
(0.050*)
0.051*
(0.11)
the July data.
Downloaded By: [Schmelich, Volker] At: 13:02 16 March 2010
10.65%
(10.65%)
10.73%
(10.73%)
Date
July 5
185
8.40%
(8.56%)
8.71%
(8.88%)
July 5
July 9
0.92
0.92
0.93
1.05
1.02
1.03
1.02
(1.02)
1.01
(1.01)
1.08
(1.08)
0.01*
0.01*
0.02*
0.25
0.02*
0.02*
0.02*
(0.02*)
0.09*
(0.25)
0.09*
(0.25)
Date
Dot Mean of OLB Mean of
%-Price STD %-Price STD
T-test (one- Median Test
tailed)
(one-tailed)
June 7
June 11
June 15
June 19
June 23
June 27
July 1
8.24%
8.24%
8.24%
8.75%
8.28%
8.28%
8.09%
(7.70%)
8.24%
(7.85%)
8.24%
(7.85%)
0.005*
0.005*
0.005*
0.009*
0.008*
0.008*
0.009*
(0.029*)
0.027*
(0.059*)
0.026*
(0.064*)
July 5
July 9
5.63%
5.63%
5.67%
6.30%
6.13%
6.15%
6.12%
(6.12%)
6.23%
(6.23%)
6.45%
(6.45%)
0.25
0.25
0.25
0.50
0.09*
0.17
0.09*
(0.25)
0.25
(0.25)
0.09*
(0.25)
Note: * are significant at level above 90%.
Numbers in parentheses are the results for DotComs including Buy.com for the
July data.