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Alternative Explanations for Price Dispersion in the Online Book Market:
Loss Leader Marketing, Branding and Switching Costs
By
Bradley Aaron Morgan
Advisor: Maria Arbatskaya
A thesis submitted to the Faculty of Emory College
of Emory University in partial fulfillment
of the requirements of the degree of
Bachelor of Arts with Honors
Department of Economics
2000
Table of Contents
Forward 1
1. Introduction 2
2. Testable Hypotheses 4
3. Literature Review 9
3.1 Frictionless Commerce 9
3.2 Price Dispersion 10
Asymmetrically Informed Consumers and Search Costs 11
Product and Retailer Heterogeneity 13
Shipping & Handling Costs 14
4. Data Collection and Methodology 15
4.1 Why Books? 15
4.2 The Data Collection Process 17
4.3 Data Organization and Variable Creation 19
4.4 Biases in Data 20
5. Branding and Loss Leader Pricing Theory 22
5.1 The Internet and Branding 22
Coupons 23
5.2 Loss Leader Pricing Theory 24
6. Switching Costs 26
6.1 Switching Costs and the Borenstein Model 26
6.2 Switching Costs and Price Discrimination 28
6.3 Switching Costs and Intermediation 29
7. Econometric Framework and Modeling 32
7.1 Descriptive Statistics 32
7.2 Table of Variables 33
7.3 Formal Regression Model For All Books (Regression 1) 34
7.4 Formal Regression Model For All Paperbacks (Regression 2) 36
8. Econometric Analysis and Conclusions 38
8.1 The Relationship between Shipping & Handling and the
Posted Price
8.2 Market Power, Branding, and Pricing 40
8.3 The Posted Price, Availability, and Shipping Time 42
8.4 Amazon, Barnes & Noble, and Bestseller Loss Leader 43
Marketing Strategy
Works Cited and References 69
Index of Tables and Appendices
Tables
Descriptive Statistics 32
Table of Variables 33
Output of Regression 1 35
Output of Regression 2 37
Statistical Summaries 45
Table 1: All Books 45
Table 2: All Bestsellers 46
Table 3: All Paperbacks 47
Table 4: All Hardcovers 48
Table 5: Paperback Bestsellers 49
Table 6: Hardcover Bestsellers 50
Table 7: Total Bestsellers (Fiction) 51
Table 8: Total Bestsellers (Non-Fiction) 52
Table 9: Fiction Bestseller Paperbacks 53
Table 10: Non-Fiction Bestseller Paperbacks 54
Table 11: Fiction Bestseller Hardcover 55
Table 12: Non-Fiction Bestseller Hardcover 56
Output of Regression 3 66
Output of Regression 4 68
Appendices
Appendix A: Statistical Summaries of Posted Price in 51
Varying Categories
Appendix B: Visual Examples of Loss Leader Pricing Strategy 57
Amazon 57
Barnes & Noble 60
Appendix C: Personalized Product 63
Appendix D: Visual Examples of Consumer Choice and Market 64
Segmentation at Amazon
Appendix E: Intercept-Free Regression Modeling 65
Product pricing [for books] should be competitive on the Web, in part
because of the ease with which consumers will be able to compare prices.
Shoppers can compare prices within seconds by switching from Web site
to Web site. Provided that shipping costs are equal, in many instances
there should be little incentive for customers to order from higher-priced
providers.
-Morgan Stanley Dean Witter
1. Introduction
In their comparison of conventional and electronic book markets, Brynjolfsson
and Smith vehemently contest the notion and possibility of the Internet as a
“frictionless” marketplace (Brynjolfsson and Smith 1999). The authors conclude
that while total prices for the majority of books are lower on the Internet
compared to conventional retailers, Internet prices exhibit a dramatically higher
degree of price variation. They attribute high price variation primarily to
asymmetric consumer awareness, heterogeneity in retailer trust, and brand
market power. While their results are both relevant and significant, they fail to
closely examine other important variables that may produce the observed
variations in price. I extend Brynjolfsson’s and Smith’s analysis, focusing only on
the Internet sector, and emphasize what I argue are both critical and measurable
explanatory factors. I argue that the Internet bookseller’s leaders, Amazon and
Barnes & Noble, both employ similar marketing strategies which render their
prices strikingly similar, but different from their competitors. Specifically, I show
that both Amazon and Barnes & Noble attempt to capture market share by loss
leader pricing New York Times Bestsellers. I believe that the successful
deployment of this strategy, coupled with other variables allowing these two
leader firms to charge price premiums, substantially increases overall market
price variance.
The two critical variables I believe Brynjolfsson and Smith omit from their model
are the shipping & handling price and the total time between order placement
and order delivery. I argue in Chapter 2 that incorporating these variables into
my econometric model will significantly help explain how varying segments of
Internet consumers place different premiums on endogenous variables (e.g.,
posted price, shipping price, time, trust, etc.). Chapter 3 reviews and assesses
Brynjolfsson’s and Smith’s article, giving critical attention to their methodology
and explanations for price dispersion. A discussion of my data collection
process and methodology follows in chapter 4. Chapters 5 and 6 explore issues
Brynjolfsson and Smith fail to investigate in depth. In chapter 5 I argue branding
is key on the Internet because it allows market-leading firms to place price
premiums on their books and enables them to peruse marketing strategies that
proliferate their market shares. Chapter 6 examines both traditional and more
contemporary switching cost theories and discusses their applications in
electronic commerce. In chapter 7 I develop an econometric framework that
tests my hypotheses. Chapter 8 presents an examination of my econometric
results and conclusions.
My study is important given its assessment of the level of competition likely to
exist in the online book market over time. I argue that the barriers imposed by
Amazon, the Internet’s first mover in this market, and Barnes & Noble, the
second largest firm, will prevent the emergence of any significant competition.
This information is not only useful to consumers, but also to Amazon’s and
Barnes and Noble’s competitors, investment banks, capital markets
professionals, and marketing firms. While I do not claim the observed level of
price dispersion will continue indefinitely, I do hold that it will continue much
longer than most leading capital markets analysts predict.
2. Testable Hypotheses
Economic intuition coupled with my own observations from buying books online, lead
me to expect the following:
Hypothesis A: Amazon and Barnes & Noble draw consumers to
their site by employing a similar loss leader marketing strategy.
Most books sold by Amazon and Barnes & Noble are discounted twenty to thirty percent
off the retail (i.e., conventional) price. However, both firms offer all New York Times
Bestsellers at fifty percent off their retail price. Both firms, on and off the Internet,
heavily advertise New York Times Bestsellers. Nevertheless, while both sites heavily
emphasize this discount via advertising, neither site overtly advertises this discount once
the user visits the site1
. I believe that both firms use this strategy to send a signal to
consumers that all their books, not just the New York Times Bestsellers, are relatively
cheap. In turn, consumers visit these two sites and theoretically purchase books they
have not originally intended to purchase because of the perceived discount. Moreover, I
argue loss leader pricing contributes to price dispersion, as firms engaging in this strategy
are able to charge price premiums on non-New York Times Bestsellers partly because of
the signal these loss leader books send to the consumer. Small sites, while they may
price their bestsellers competitively, do not have the level of advertising necessary to
effectively render New York Times Bestsellers loss leader products. Therefore, they
must follow the leaders in pricing for these bestsellers, and still charge a discount on
books the leaders enjoy successfully charging at a higher price. Consequently, loss
leader pricing not only allows these firms to capture a higher market share, but also gives
1
See Appendix B
them a comparative advantage over competitors in that competitors are forced to mark
down New York Times Bestsellers and maintain comparatively higher discounts on other
books. They lose because despite offering the identical discount on the New York Times
Bestsellers, they do not draw in any further market share.
Hypothesis B: Books will be significantly more expensive at highly
branded, more trusted, and better known web sites.
Branding is a signal of quality that induces consumers to purchase a higher priced
product even if it is identical to another lesser-known product.2
Branding also signals
trust and establishes reputation. Amazon boasts the comparative advantage of being the
first mover in the Internet book business. They are therefore able to accumulate a loyal
customer base and establish a means to induce repeat purchases from retained customers.
Barnes & Noble, the Internet’s second largest book retailer, through its large chain of
conventional bookstores, also enjoys the great benefits of signaling trust and establishing
loyalty on the Internet. I believe the smaller, and lesser-known firms, must offer lower
prices to induce customers to switch from the branded sites or entice new price elastic
consumers who are privy to switching but only for striking price breaks.
Hypothesis C: Ceteris parabis, the sites offering a lower posted price will
have a higher availability and shipping time3
, thus indicating competitive
market equilibrium previously overlooked in the literature.
Many firms price the majority of their books below Amazon’s and Barnes &
Noble’s prices yet continue to operate without being forced to shutdown. Either a
continual stream of venture capital allows for the prolongation of their operations,
2
The power of branding induces consumers to buy brand name products over identical generics.
3
See chapter 4.2 or 7.2 (Table of Variables) for definitions of each of these variables.
or they are generating revenue from a source other than books themselves4
. I
believe, therefore, that many of the firms forced to compete with market leaders
by offering lower prices - and theoretically incurring continual losses -
compensate for lower posted prices by:
A. Not incurring the same costs realized by market leaders.
B. Using a lower grade of shipping, and consequently creating an expectedly
longer shipping period.
It follows from assumption (A) that leader firms incur costs not realized by smaller
firms since the latter do not have the same sophisticated and expensive
inventory systems. Consequently, I believe that the procedure of processing an
order, obtaining the book, and delivering it to the shipper, is more inefficient and
consequently more time intensive at smaller sites. I therefore expect the
availability time, the time between a customer’s order and the firm’s delivery of
that order to the shipping agent, to be significantly higher.
In accord with assumption (B), I expect more price sensitive consumers to
patronize the smaller, or cheaper sites, as they are most apt to engage in lowest
price searches or use a third-party search intermediary. Since these consumers
are relatively price sensitive, they are likely to be insensitive to the certainty of
delivery time, as they are mostly concerned with price. Most users find leader
4
Despite their high market capitalization, both Amazon’s and Barnes & Noble’s Internet business have
continually reported losses every quarter since each of their initial public offerings. It is puzzling that these
two Internet behemoths with tremendous brand name recognition, market capitalization, venture capital,
economies of scale, amongst other competitive advantages, have competitors that continue to operate.
firms “convenient” and “easy” and are willing to pay a price premium in exchange
for “certainty.” Uncertainty in delivery time comes not only from a wide range in
shipping time, but also a wider range in availability. Therefore, more price
sensitive consumers are most likely to tolerate a longer period of total time.
This phenomenon should not appear strange to those believing in frictionless
Internet markets. The presence of these endogenous variables should allow for
market segmentation and consequently motivate firms that cater to different
categories of tastes and preferences to enter the market. This type of market
segmentation benefits both consumers and firms, not by the presence of a single
price, but rather by variable pricing that fluctuates based on consumer
preferences.
Hypothesis D: When the posted price is higher, shipping cost will be
lower, indicating another form of price equilibrium.
Firms often market their books as having a lower cost than their competitors.
Often this claim is true. However, in most cases this claim does not refer to the
total cost of the books, but only their posted prices. It appears that many firms
touting relatively low prices compensate via comparatively high shipping &
handling prices. This, in essence, renders the firm not a purveyor of books, but
rather shipping & handling! Furthermore, one would expect that the firms
charging higher shipping prices are precisely those whose books have a higher
mean shipping time. The logic is that a longer shipping time should be
accompanied by a lower shipping cost to the firm. Higher than average shipping
prices and a lower grade of standard shipping may allow firms with comparatively
lower posted prices to compensate for losses.
I feel one internal mechanism allowing firms to market their books as such is the
time lag between the realization between the posted price and the shipping &
handling price. Most sites have a subtle hyperlink to information about shipping
& handling costs as well as shipping policies; however, none actively advertises
shipping prices alongside posted prices5
. Moreover, consumers are not as
sensitive to the shipping & handling prices as much as they are to the posted
price, as the consumers may perceive these charges, similarly to taxes, as given.
It is not only a lack of sensitivity to shipping prices that allows for this ostensibly
negative relationship between posted and shipping prices. At most sites, once a
consumer selects his basket of goods and proceeds to “check out,” he is required
to register. Registration not only involves entry of personal information (e.g.,
email address, name, home address, etc.) but also usually includes optional
questions about consumption patterns that firms often use for marketing
purposes. Sometimes registration also involves inputting credit card, billing, and
shipping information. The consumer realizes shipping and handling costs, and
therefore the total cost, only after this lengthy input process is finished. Even if
consumers are upset about what they believe to be high or unreasonable
shipping costs, they are not apt to switch to another firm. For most consumers,
unless they are hyper price sensitive, the cost expended during “registration”
often outweighs the cost of attempting to find a bookseller offering a lower total
cost. Therefore, most consumers proceed to confirm their orders.
5
See Appendix B
3. Review of Literature
3.1 Frictionless Commerce
The majority of my work expands upon studies conducted by Erik Brynjolfsson
and Michael D. Smith in their article Frictionless Commerce? Comparison of
Internet and Conventional Retailers. As per the title, this article deals
predominantly with testing for a frictionless market via a comparison of
conventional and Internet book and CD retailers. The authors first define the
state of “Internet efficiency” as an Internet market in which (i) location is irrelevant
and (ii) consumers are fully informed of both product selection and prices.
Brynjolfsson and Smith use an online bookseller list generated by Yahoo to
generate a list of Internet booksellers. They then track 10 bestsellers and 10
random books over time and compare prices to those at the conventional
retailers. The authors use Yahoo’s list because they believe it is both
comprehensive and unbiased. While I agree, they fail to consider the integrity of
the booksellers they include in their dataset. Yahoo lists all known online
booksellers, not necessarily those booksellers verified legitimate. This is
problematic because many of the booksellers Yahoo lists may not give accurate
price information or may not change their posted prices when actual prices
change. Finally, it is possible that many of the widely unknown booksellers
operate merely to steal credit card information. I argue that unbiased third-party
search intermediaries that monitor and evaluate web sites generate more
accurate “lists” of online booksellers.
To test for “Internet efficiency” Brynjolfsson and Smith conduct comparative t-
tests6
on mean store posted prices and measure the percentage of time Internet
prices are less than or equal to conventional prices. Variables such as
transportation costs (conventional stores only), shipping & handling prices, and
taxes are then incorporated and the t-tests are repeated. They ultimately
conclude that prices are lower on the Internet compared to brick and mortar
counterparts whether just prices alone are accounted for and when the t-tests are
repeated incorporating the other variables. In addition to lower prices, they find:
1) Internet prices are more dispersed.
2) Menu costs online are significantly lower and therefore prices change
more frequently and in smaller increments.
Clearly, this leads to the conclusion that frictionless commerce does not exist. In
the following sub-chapters, I give an overview of the conclusions reached by the
authors. My work builds upon these results.
3.2 Price Dispersion
A portion of price dispersion arising in online book markets is explained through
exogenous heterogeneous factors that are not part of the product per se, but are
rather complementary. Retailers use these service factors in an attempt to
6
A “t” random variable is formed by dividing a standard normal, Z ~ N(0,1), random variable by the
square root of an independent chi-square random variable, V ~ χ2
(m), that has been divided by its degrees
of freedom, m.
differentiate their product via a bundled package. Price dispersion online for
book and CD markets may be attributed to:
1) Differences in search costs
2) Asymmetrically informed consumers
Incorporating market share data into their model, they discover price is
concentrated around the market leader’s price at companies very close in market
share to the market leader. However, on average, the price spread between
each book is extremely low. Only when compared to companies holding
negligible market power does the price spread between books become
significant. They also find that despite setting prices significantly below the
market leader’s price, many firms, specifically those smaller retailers, do not
receive any significant portion of total sales per book. This leads Brynjolfsson
and Smith to a key conclusion:
Ironically, as we discuss below, a high market share may, in and of
itself, be an important “feature” that supports a price premium
because of the importance of network externalities in word-of-
mouth marketing and trust-building (21).
Asymmetrically Informed Consumers and Search Costs
The authors contend that producers will set prices above marginal cost when
positive search costs exists. They note that the ostensibly lower search costs on
the Internet will force book prices toward Bertrand marginal cost pricing7
.
Although search costs on the Internet appear to be trivially small due to search
intermediaries and other channels that allow for easy comparison of information,
7
The Bertrand model holds that a price war between firms ultimately results in P1=P2=MC. The presence
of search costs violates a primary assumption of the Bertrand model. However, if the Internet does
facilitate a movement toward lower or negligible search costs, a Bertrand price war should ensue.
they argue consumer ignorance ultimately increases search costs. Internet
consumers are figured as comparatively wealthier and therefore more price
insensitive. As a result, they are less apt to engage in costly searches.
The authors cite a number of different landmark search cost articles, which they
use to explain price dispersion via heterogeneous search costs.
Contrary to economic models of search costs (Salop and Stiglitz 1977) which
contend that assuming (1) consumers are imperfectly informed, (2) information is
costly to obtain, and (3) firms set prices to leverage consumer heterogeneity in
information and search costs, the authors do not find that the firm with the lowest
prices captures most sales. The firm with the lowest price should theoretically
capture all sales from informed consumers and some sales from uninformed
consumers. Despite Book’s8
consistently lower prices, Amazon continues to hold
a dramatically higher market share. This violation of the Salop and Stiglitz model
is attributed to asymmetric and heterogeneous consumer information coupled
with search costs greater than zero. They note that this explains why companies
with high market shares are able to charge higher price premiums but does not
explain the reason successful bookstores (e.g., Powell’s)9
are able to charge
8
Books is now Barnes & Noble
9
Powell’s, an online/conventional hybrid based out of Seattle, specializes in used, new, rare books. The
company distinguishes itself from Amazon and Barnes & Noble by boasting as large as a collection and the
ability to choose between different types of the same book (i.e., hardcover, paperback, used, new). One
reason Powell’s may continue to attract and retain customers despite comparatively higher prices is its
specialization in rare books. See discussion on switching costs for possible reasons consumers do not buy
rare books from Powell’s and then switch to cheaper sites for other, less expensive books. Finally, the
company distinguishes itself through its West coast eclecticism, and therefore tastes and preferences may
be the cause.
higher prices and yet retain a strong customer base. To explain this
phenomenon, they turn to heterogeneity in products and firms.
Product and Retailer Heterogeneity
Analysis is shifted to explain heterogeneous factors of service characteristics that
might account for price dispersion. Brynjolfsson and Smith use hedonic
regression10
analysis to test for price variation caused by service heterogeneity.
They discover that service factors do not vary across firms or are often negatively
correlated with price. This renders their hedonic regression model fruitless in
explaining residual price dispersion.
They also realize that product heterogeneity is fallible in explaining price
differences in that services are “informational” and therefore separable from the
homogenous product. Consumers are capable of employing a strategy in which
they use an information source from one web site and then switch to another
offering a lower posted price.
Unobservable variables, such as heterogeneous consumer measures of trust,
may play a key role in price dispersion. Studies have argued trust is an integral
component of any successful Internet marketing strategy (Urban 1999). Trust,
they contend, is important given the spatial and temporal demarcations between
10
A regression model that attempts to explain variation in the dependent variable via heterogeneity in
service characteristics and conveniences offered by firms.
the firm and the consumer. Trust is signaled via reliability, word of mouth
advertising, conventional channels of advertising, links from reputable sites, and
presence in non-Internet markets.
Shipping & Handling Costs
Brynjolfsson and Smith modify their regression model to include factors such as
shipping & handling costs.11
They note the number of items shipped and the
speed of delivery are determinant factors in shipping & handling charges. The
type of shipping is also important. For standard shipping, many firms, such as
Amazon charge a fixed, and relatively high standard fee for the first book and a
lower fee for each additional book ordered. This creates variable shipping prices
across companies and significantly contributes to price dispersion.
While the authors then proceed to merely state the necessity of branding along
with trust in Internet markets, they do not elaborate on the reasons for its
importance. In Chapter 5 I will pick up where Brynjolfsson and Smith leave off
and attempt to explain the reasons branding is crucial in electronic commerce. I
will discuss the importance of brand market power and its allowance for key
marketing strategies at market leading firms such as loss leader pricing. In
chapter 6 I will demonstrate how the imposition of switching costs allows for the
successful deployment of these strategies as market leading firms in the online
book industry are able to act monopolistically. Finally I will develop and analyze
my econometric models for evidence of loss leader pricing and test for the
possible variations of market equilibrium via testing hypotheses B, C, and D.
.
11
See discussion of Hypothesis D for further information regarding shipping & handling. Additionally, I
discuss shipping and handling costs at length in chapter 8.2.
4. Data Collection & Methodology
4.1 Why Books?
The decision to exclusively examine books is made for several reasons. Firstly,
books are homogenous goods and therefore do not vary in product
characteristics across retailers. This is a fundamental assumption in the model
of perfect competition - a state of zero market friction. Importantly, economists
traditionally tout homogenous goods as resilient to marketing forces because of
consumer indifference. Although consumers often receive utility in browsing
through a neighborhood bookstore or examining a book by flipping through its
pages, most consumers do not need a brick-and-mortar experience to decide
which book to purchase. Internet technology enables consumers to virtually
replicate many key components of the conventional book shopping experience
via the online medium. For example, many sites post mainstream critical reviews
of new books alongside reviews customers contribute. Moreover, many
booksellers allow consumers to read or listen to parts of a text.
This differs considerably from the shopping experience for heterogeneous, or
branded goods, requiring consumers to “feel,” “taste,” “experience,” or “sample,”
a product. Many consumers are reluctant to purchase goods such as clothes
and shoes from the Internet because they cannot test them for proper fit or
desired look before purchasing. This reluctance can be attributed, for example,
to heterogeneity in product sizes across brands. The cost of uncertainty for
some consumers to purchase goods online does not outweigh the utility from
savings. Moreover, many consumers enjoy shopping for heterogeneous
products for reasons such as stress relief and socialization (Landro 1999). The
problem of “testing” is also evident in shopping for furniture and bedding
(Fletcher 1999).
Another quintessential characteristic of the Internet book market is its relative
Internet maturity. Although the Internet is a new technology, bookselling is one
of the oldest and largest business to consumer industries on the web and
contains numerous companies selling the same book in the same market.
Additionally, there have been a myriad of recent legal disputes between the
leaders of the online book market (Amazon and Barnes & Noble). Their fierce
competitive battle to obtain market share is also indicative of an established and
competitive market.
Books are also very conducive to Internet retailing. In addition to the
aforementioned characteristics of books, they possess inherent qualities that
render their sale over the Internet relatively simple. Morgan Stanley notes the
four key properties of books that allow for their ease of sale:
1. Books are well-known commodities.
2. Books are inexpensive.
3. Huge variety translates into mass appeal.
4. Books are easy to ship (Morgan Stanley 1997).
I will also add that since books are ubiquitously sold, they should also be readily
available. Theoretically, given an efficient supply chain, this will allow online
firms to utilize the ability to not hold as much inventory and therefore reduce
costs.12
It is also important to note that books are only one of many commodities sold on
the Internet that are rated by a third party13
. While these rating sites are not
utilized by most users, they are readily available, easy to use, and plentiful in
number. In fact, Yahoo, a popular Internet site, has a shopping sub-category
page that allows users to compare prices. Furthermore, an entire Yahoo
directory is devoted to listing the fifteen third party web sites that rank books.
The ubiquitous presence of these third party book-ranking sites should increase
competitive behavior for books on the Internet.
4.2 The Data Collection Process
To collect data for bestsellers, I use the New York Times Bestsellers list, as
these are the most comprehensive and relevant to my study. The New York
Times Bestseller list is regarded as the most inclusive and exact measure of a
book’s popularity. I feel bestsellers are the best category of books to examine in
my study since they are the most likely to be sold by any given retailer because
they are the most widely purchased. I collect information from each of the four
categories of New York Times Bestsellers: paperback fiction, paperback non-
12
Traditional economic models of inventory state that inventory is beneficial to firms for reasons of
product smoothing, factors of production, stock-out avoidance, and work in process. When a firm holds
inventory, it forgoes the real interest it could have earned.
13
Of course many products on the Internet are “rated” or “reviewed.” However, along with other
homogenous commodities such as CD’s and software, books are unique in that they systematically rated
according to price, shipping costs, etc.
fiction, hardcover fiction, and hardcover non-fiction. The New York Times lists
the top fifteen books in each category.
Next, I use a “shop bot,” a web site that automatically compares prices, as the primary
mechanism through which I collect my data.14
Information is collected for each of the
observation's posted price, availability15
, shipping cost, and shipping time16
. When data
for availability and shipping time are in range form,17
I take the average value. The units
characterizing both variables are entered as days.
Excluded from my dataset are observations that are either classified as “out of stock” or
“on order.” The former observations cannot be converted into any definite value and
inclusion of the latter will cause my data to become skewed. “On-order” times typically
range from six to eight weeks and have been statistically calculated as an outlier due to
their abnormally high residuals and interference in the accuracy of my regression
results.18
I also exclude from my dataset used books, as they are not the concern of this
14
The specific "shop bot" I use for the collection of data is bestbookbuys. I feel this is the most
comprehensive, unbiased, and dependable bot on the web. Currently, the search capabilities of
bestbookbuys spans over 28 retailers and returns information about availability, price, shipping & handling
price and time, as well as relevant tax information. Furthermore, results are tabled and delivered in
ascending order of price. See also chapter 6.3.
15
Refers to the time between order processing and the time it takes the firm to ship order:
AVAIL=t(shipped out to delivery agent)-t(order processed) [measured in days]
16
Refers to the time between shipment and receipt of package:
SHTIME=t(receipt of order)-t(shipped out to delivery agent) [measured in days]
17
A range listing the lowest possible value through the highest possible value.
18
Although I cannot include these observations in my regression model, they are very interesting. Amazon
classifies many books and bestsellers as out of stock. The implications of this lack of availability are
striking. See Appendix A for a full discussion.
study. Finally, I exclude any retailer offering a special “club price” to paying members
only.19
Data is gathered for each of the twenty-eight retailers searched by bestbookbuys20
Observations from every site are not usually available for each book. Moreover, when a
search for a particular book returns less than seven observations, it is excluded from the
dataset and an alternate random book is chosen. I believe any such book is “rare” in that
it is not readily available and therefore violates my selection criteria21
and may skew my
dataset. In addition to the sixty bestsellers, data is collected for forty-five randomly
chosen titles, both paperback and hardcover.22
No special effort is made to gather a
proportionate amount of paperbacks and hardcover. Since I randomly generate ISBN
numbers, I assume that statistically, given my large sample size, I should finish with a
number of paperbacks relative to hardcovers equal to the true population proportion.
4.3 Data Organization and Variable Creation
All of the data is next appropriately organized in a Microsoft Excel file. Variables are
created for each of the characteristics relevant to the subsequent econometric analysis.
The non-dummy explanatory variables I examine are posted price, shipping & handling
19
The only retailer currently offering “club prices” is Booksamillion.com. For a 5-dollar annual fee,
members receive discounts on select books. These observations are excluded, as access to these prices is
limited to paying members. This may be argued as a form of second-degree price discrimination. See
Chapter 6.2 on switching costs and price discrimination.
20
Since the time of data collection thebigstore was added to bestbookbuys’ list.
21
See Chapter 4.1 Why Books?
22
I generate 10 random numbers in Microsoft Excel. If the number begins with a 1 or 0, a potentially
random ISBN, I feed it into the search engine of bestbookbuys. Potentially valid numbers are continuously
attempted until data from 45 valid ISBN’s are obtained.
price, shipping time, availability, and total time.23
Dummy variables24
are created for
each of the 105 observed books and the 14 retailers.25
Dummies are also created for
bestsellers and their different categories. Finally, a dummy variable is created for each
“type” of book. Specifically, I create a dummy for each of the 4 types of New York
Times Bestsellers26
: bestsellers (overall), paperbacks (overall) and hardcovers (overall).
There are a total of 136 dummy variables. I find, after summarizing overall statistics,
alllbooks4less, bookpool, classbook, and hamiltonbooks27
do not contain a significant
amount of observations and are therefore excluded from my regression model.
4.4 Biases in Data
Although I specifically use bestbookbuys to prevent biases in my data, numerous
variables cannot be determined until the book is actually ordered, processed, and
then shipped. As I mention in the footnote during my discussion of the returned
values associated with availability and shipping times, the range of the value
returned are only estimated ranges. While I take the arithmetic mean to best
estimate availability and shipping time, I have no way of determining the data’s
true statistical distribution. Consequently, I feel that precise knowledge of the
distribution of availability and shipping time will significantly alter my results as
my two most important explanatory variables are estimated in this manner.
23
See Table of Variables for descriptions of each variable.
24
Explanatory variables that are qualitative in nature. This study utilizes both intercept and slope
dummies.
25
For most books, bestbookbuys.com returns data on the same 10-14 top retailers. Although
bestbookbuys.com claims to be comprehensive, it never returns extensive observations. 9 of the 28
retailers it searches returned no data in any of the searches.
26
Paperback Fiction, Paperback Non-Fiction, Hardcover Fiction, Hardcover Non-fiction.
27
These companies specialize, respectively, in children’s books, technical books, college textbooks, and
surplus books. Their lack of overall coverage accounts for their small number of observations (<ten).
Nevertheless, these precise measures are unattainable unless each individual
book is ordered from each retailer - a process that is prohibitively expensive.
An additional problem arises when multiple books are purchased in a single
order. Unlike the “premium” sites that instantly convey to the user the availability
of each book, many smaller sites do not provide this information. Moreover,
smaller sites appear to have more variation in availability ranges for their books.
Consequently, the total range of availability for books on these sites may
increase with each additional book ordered. However, this is not applicable
when books are shipped gratis as they become available, which is the case at
many retailers.
5. Branding and Loss Leader Pricing Theory
The recent explosion in capital valuations of Internet related companies has
prompted many academics to explain the reasons for this tremendous growth
and its economic consequences. Additionally, much attention is spent attempting
to explain pricing and consumer behavior on the Internet.
5.1 The Internet and Branding
One factor prohibiting many consumers from utilizing electronic markets is a
general lack of trust. Fortunately for both firms and consumers, consumer lack of
trust is dissipating over time as consumers become more comfortable with
electronic commerce. Moreover, companies such as bizrate that systematically
rate sites according to trustworthiness strengthen consumer trust. Nevertheless,
consumer trust is still an issue and partly responsible for the millions of dollars
expended on advertising. However, trust is only one of many reasons a firm
needs to establish a strong brand name.
According to Morgan Stanley, studies show once users become familiar and
comfortable with the web, they limit themselves to the pages they are familiar
with. The consequence, according to Morgan Stanley, is a few leading brands in
each retail category (Morgan Stanley 1997). The obvious outcome of this
behavior is a lack of motivation to search, even if lower prices and better services
can be obtained. The importance of consumer familiarity furthers the need for
firms to aggressively brand themselves against their competitors. Aggressive
branding is evident on the web. Not only are a large percentage of firm revenues
reinvested in adverting out of the Internet realm, but also most Internet sites are
peppered with banners assertively promoting goods and services.
Morgan Stanley also argues that Amazon's strong brand name and customer
loyalty will create barriers to entry within the online book industry despite the
relative ease with which a company may enter the industry28
. Amazon has
become a “buzz word” that users have come to know and trust (Morgan Stanley
1997). Their ability, through branding and market power to exercise monopolistic
barriers to entry further debunks recent figurations of the Internet as frictionless
marketplace since free entry and exit are key assumptions in the economic
model for perfect competition.
Coupons
The striking quality of Internet coupon codes is their relatively large redeemable
values. For example, Vitamins.com offers fifteen dollars off any purchase of
fifteen dollars or more and free shipping. Moreover, nascent firms circulate
coupon codes redeemable for thirty to fifty percent off any purchase.29
An
interesting characteristic of Internet coupon codes is their lack of specificity.
Many third party sites, such as hotdeals, esmarts, and cleverclicksters, gather
coupon codes marketed to specific users via email marketing, newspapers, and
magazines, and make them available, at no cost, to visitors of their websites.
28
Legal or natural impediments protecting a firm from competition from potential new entrants.
Recently, many firms have been only accepting coupon codes accompanied by a
specific email address that may only be used only once. Nevertheless, despite
the drastic discounts these coupon codes offer consumers and the losses
theoretically incurred by the firms offering them, branding is paramount on the
web and firms, to avoid shutdown, must establish a brand name at any cost.
When non-discriminate30
coupon codes are considered, price variation increases
drastically. However, only savvy and price sensitive consumers are aware of the
ease and universality of coupon codes31
. Notwithstanding, the extreme
discounts offered by coupon codes contribute to online price dispersion.
5.2 Loss leader Pricing Theory
While loss leader pricing is most often observed in grocery stores, I believe it is
also evident in the online book industry. Loss leader pricing is a promotional
strategy but markedly different from others in this category. Firstly, retailers do
not price loss leader products to yield a profit. On the other hand, retailers often
price loss leader goods at or below marginal cost and therefore sustain a loss.
Secondly, loss leader goods are heavily advertised and intended to bring
consumers into a store [or a website] (Nagle 1987). Once consumers enter the
store [or the website] they purchase additional goods other than the loss leader
product. The firm therefore compensates for the loss on the loss leader product
via sales of other products that are priced well above average cost:
29
The magnitude of these savings can be more appreciated in light of Brynjolfsson’s and Smith’s work
demonstrating prices are usually less expensive on the Internet even without coupons.
30
Coupon codes that are not user specific.
31
I should note that at rating intermediaries, such as bestbookbuys, coupon codes for books are listed.
P>AC → π>0
Customers drawn in by the loss leader product also purchase complementary
products that are priced well above retail price. Companies are able to induce
the purchase of complementary products as they exercise monopoly power once
consumers enter the store due to the presence of high search costs (Gerntner
and Hess 1991). While high search costs are not characteristic of the Internet,
most consumers are not privy to searching for lower cost goods due to the
presence of high switching costs32
.
I believe the heavy discounts given to consumers by online book market leaders
on New York Times bestsellers and the large amount of advertising spent
promoting these books is evidence of a loss leader promotional strategy. The
relative cheapness of bestsellers is heavily advertised on the Internet, especially
by the firms leading the market. It appears that they lure customers to their web
site and entice them to purchase not only bestsellers but also other books and
products33
. This is evident as once consumers visit the site, they realize all
books are not nearly as heavily discounted as bestsellers. Furthermore, New
York Times Bestsellers are barely advertised within the web site. On the other
hand, once companies lure consumers in, they heavily advertise new or selected
titles34
. I believe this is precisely the marketing strategy employed by these two
firms in an aggressive attempt to encourage consumers to purchase other higher
priced books and products in addition to bestsellers. In effect, the figuration of
32
See Chapter 6. Switching Costs
bestsellers as a loss leader priced commodity acts a signal that all books the site
offers (and perhaps other commodities) are cheap.
33
Both Amazon and Barnes & Noble see products other than books.
34
See Appendix B for examples.
6. Switching Costs
6.1 Switching Costs and the Borenstein Model
It has been suggested that price discrimination can occur between customers
more apt to switch firms and those not likely to switch (Borenstein 1991).
Although Borenstein’s study focuses on the gasoline markets, its implications
extend far into the online book industry. According to Borenstein:
If most of a station’s marginal buyers are deciding between buying
from that station or reducing total purchases of gasoline, then the
seller is a monopolist for most of its buyers and faces a demand
elasticity close to or equal to the buyers elasticity of demand for the
good. Alternatively, if most of a station’s marginal customers are
deciding between buying from one station or switching to another
then the seller is competing with other stations for its marginal
customer and faces a demand elasticity that reflects the buyers’
cross elasticity of demand among sellers (Borenstein 1991).
I believe Borenstein’s model offers and alternative explanation for the
unexplained price variation found in Brynjolfsson and Smith’s (1999) model.
When consumers are unaware of market size, or unwilling to switch from one
Internet bookseller to another, which potentially may offer them books at a lower
cost, the firm to which they are loyal essentially becomes a monopoly. These
monopolistic capabilities arise because of the consumer’s unequivocal loyalty to
the site. However, in the latter case of Borenstein’s assertion, in which
consumers contemplate switching to another seller, the demand elasticity of both
sellers changes. Unlike Borenstein’s model, this does not necessarily reflect
upon the consumer’s cross-price elasticity of demand35
when considering online
35
Ceteris parabis, the responsiveness of the demand for a good relative to the price of a substitute or
complement. Calculated as the percentage change in the quantity demanded of the good divided by the
percentage change in the price of the substitute or complement.
markets because Internet consumers are not necessarily only concerned with
price. Numerous other factors play a key role in consumer's consumption
decisions36
. The Internet offers its users a personalized bundled product37
unparalleled by any homogenous counterpart in the conventional sector. Studies
have shown that the Internet’s unique service features, specifically those offered
by firms like Amazon, involve and engage their customers and retain them for
reasons not necessarily related to price (Morgan Stanley 1997). Customers also
return to utilize customer editorials, recommendations, and other services. This
violation of Borenstein’s model problematizes the actual costs a consumer faces
when contemplating switching from one online bookstore to another. Switching
costs are therefore not only a function of time, but also of the opportunity cost of
forgoing a relationship with a firm. One of the unique features of certain online
booksellers is their ability to guide tastes and preferences. Once a consumer is
registered with a firm (e.g., Amazon) and has explicated his tastes and
preferences via purchase history from that firm, he is subsequently given
recommendations for other titles in accord with his preferences. To switch to
another online bookseller he faces the opportunity cost of forgoing valuable book
recommendations for titles he may not otherwise have been aware of. This cost
must be weighed against the benefit of a lower cost.38
The consequence of the
switch is not only detrimental to the consumer but to the original seller as well.
The opportunity cost of switching becomes higher as more books are ordered
36
The factors being those discussed throughout this thesis.
37
See Appendix C.
38
Assuming a duopolistic model, the lower the cost at the other bookstore, the more apt the consumer is to
switch.
from a firm employing such a strategy. Therefore, when leaving a firm that
guides tastes and preferences:
Switching Costs=ƒ(time, price, buying pattern monitoring, δZ)
Where Z is all other service variables either lost or gained in the switch.
It is important to realize that “buying pattern monitoring” is not always a desirable
service. Many Internet consumers are concerned with privacy and may not trust
an online firm that closely monitors buying patterns and behavior and then
aggressively markets products. Observed marketing may actually cause a
consumer to switch to another site offering higher prices. Trust is of paramount
importance in Internet markets and any monitoring, whether it benefits the
consumer, may trigger switching (Brynjolfsson and Smith 1999).
6.2 Switching Costs and Price Discrimination
Joseph Bailey aptly notes that an essential requirement of price discrimination is
market power. However, on the Internet firms may not be able to charge higher
prices since consumers can easily choose another site. If a site’s prices are
noticeably higher than competitor prices, consumers are apt to switch (Bailey
1998). In accord with Bailey’s price discrimination model, we should expect to
see those firms with a higher market share possessing the capacity to price
discriminate. Therefore, based on his assumptions, firms with higher market
shares should be price makers and those with little market shares, price takers.
My econometric analysis loosely supports these assertions. Firstly, while the
dependent variable (post) increases when Amazon and Barnes & Noble are
present in my model, these two variables are not statistically significant.
Nevertheless, the smaller, and unbranded firms with significant p-values are
substantially lower in price than Amazon and Barnes & Noble and cause the
dependent variable (post) to fall. I believe issues of branding accounts for the
disparity Bailey seems unable to adequately explain. Moreover, Internet price
discrimination is theoretically possible given some firm’s abilities to exercise
monopoly power once a consumer has opted not to switch.
I have already discussed the advantages of buying pattern monitoring with
relation to product suggestion and awareness to otherwise product oblivion.
However, it may not always be in the consumer’s best interest to allow the
monitoring of purchase patterns and site navigation behavior as it may allow
firms to price discriminate against the consumer. Bailey notes that many firms
offer superior search services that direct consumers toward particular products
while forcing the customer to reveal invaluable marketing information to the firm.
Bailey argues the optimal consumer strategy is to gather the firm assisted
information and then anonymously switch to another firm offering a lower price.
He argues that if the consumer does not switch the firm will follow their optimal
strategy of discrimination against the consumer. He nevertheless notes that
price discrimination may harm a firm’s reputation and lead to mistrust.
6.3 Switching Cost and Intermediation
The myriad of online booksellers has given rise to intermediaries that search and
find queried books at the lowest cost. The presence of online intermediaries both
complicates and facilitates the ease of switching. An OECD study measures the
implications of financial intermediaries in the online book, CD, and software
industries (OECD 1998). The authors discover that while software intermediaries
have the capacity to help consumers obtain a lower price, some third-party rating
sites may be biased as some firms block pricing information. Although this
information is not observed for book or software intermediaries, the capacity for
price blocking exists. Price blocking probably does not exist because any firm
blocking prices from a third-party query might be rendered suspicious and
untrustworthy.
A second issue arising from financial intermediaries is the reliability of reported
information. Unless the intermediary derives prices directly from the company’s
web page, prices may be unreliable and the consumer may become vulnerable
to a “bait-and-switch” tactic (OECD 1998). According to this theory, the
customer is shown the low price for one good, and then, once the consumer
visits the web site, he is shown another higher priced substitute. A bait-and-
switch practice involves the purchase of substitute products when advertised, but
low-stocked brands, are unavailable. Their article states many reasons this tactic
is more effective in brick and mortar retailers and how it may be rendered
ineffective on the Internet for reasons of difficulty in execution. I partially
disagree with the OECD since sites offering their users book recommendations
may suggest products similar to the bait-and-switch product the consumer may
have been previously unaware of and privy toward purchasing.
Although a bait-and-switch strategy is similar to loss leader pricing I do not
believe it is practiced by market leading firms. At the time of data collection,
Barnes & Noble had every New York Time Bestseller “in stock.” Amazon,
however, had only ninety-five percent of its bestsellers “in stock” despite heavy
advertising. It is possible to argue Amazon uses a bait and switch tactic although
highly unlikely since any site engaging in such behavior might be perceived as
untrustworthy. It is interesting to note, however, that all the other non-bestseller
books I examine are in stock at Amazon. Furthermore, given the presence of
fifteen book intermediary search sites, those companies reporting unreliable
information or creating fallacious links should theoretically not survive in the long
run especially because search intermediaries are ranked by branded web sites
as well as popular Internet magazines and publications.
7. Econometric Framework and Modeling
7.1 Descriptive Statistics
Descriptive Statistics for all 1,406 Observations
Variable
Mean
Price
Standard
Deviation
Minimum
Value
Maximum
Value
AVAIL (Availability) 2.029 1.567 1 12
POST (Posted Price) 13.897 14.181 1.95 178.98
SHTIME (Shipping Time) 5.494 2.699 1.5 12.5
SHCOST (Shipping & Handling Cost) 3.637 1.255 0 4.95
TC (Total Cost) 17.526 14.138 2.59 183.93
TTIME (Total Time) 7.523 3.176 2.5 15
7.2 Table Of Variables
Table of Variables
Explanatory Variables (Non-Dummy)
avail The average time, measured in days, between the placement of
order and delivery to shipping provider.
shtime The average time, measured in days, between shipment and
delivery to customer.
ttime Total Time=avail+shtime
post The posted, or raw price of specified book (excludes shipping &
handling, taxes, and any other applicable charges).
shcost The cost of a firm’s standard/economy shipping for one book.
tc Total Cost=post + shcost
Dummy Variables39
Firm Dummies (14 Total)
amzn (Amazon.com) bandn (Barnes & Noble)
bigwrd (Bigwords.com) bkamil (Books A Million)
bkstrt (1 Book Street) border (Borders)
buycom (Buy.com) ecamp (Ecampus.com)
fatbra (Fatbrain.com) kngbok (Kingbooks.com)
pagone (Pageone.com) powell (Powells.com)
varsit (Varsitybooks.com) tbac (Textbooksatcost.com)
leaders amzn+bandn
Books Dummies (105 Total)
book001-105 Each book examined in study
book001-030 New York Times Bestsellers Paperback
book031-075 Randomly selected books
book076-105 New York Times Bestsellers Hardcover
paperback Paperback book
companies Group of all companies examined
companieslead Group of all companies examined less
leaders
39
All dummy variables assume a value of 1 if True and 0 if False
7.3 Formal Regression Models
Formal Regression Model For All Books (Regression 1)
POST = α + β1SHCOST + β2TTIME + β3AMZN + β4BANDN +
β5(NYTPF+NYTPNF) + β6(NYTHCF+NYTHCNF) + β7AMZN*(NYTPF+NYTPNF)
+ β8AMZN*(NYTHCF+NYTHCNF) + β9BANDN*(NYTPF+NYTPNF) +
β10BANDN*(NYTHCF+NYTHCNF) + β11PAPERBACK + β12BIGWRD +
β13BKAMIL + β14BKSTRT + β15BORDER + β16BUYCOM + β17ECAMP +
β18FATBRA + β19KNGBOK + β20POWELL + β21TBAC + δZ + ε
Where z is the vector of all other dummies, including 104 book dummies,
and δ is the corresponding vector of coefficients.40
40
I exclude the last book dummy (Book105) to prevent collinearity. Also, I exclude varsit for the same
reason.
Dependent Variable: POST (in dollars) N=1406 observations
Explanatory Variables Coefficient Std. Error t-Statistic Prob.
Intercept 33.057 3.887 8.505 0.000
SHCOST 0.473 0.180 2.630 0.009
TTIME 0.264 0.074 3.581 0.000
AMZN 0.168 0.432 0.389 0.698
BANDN -0.024 0.420 -0.057 0.955
NYTPF+NYTPNF -9.222 3.244 -2.842 0.005
NYTHCF+NYTHCNF -18.924 3.735 -5.066 0.000
AMZN*(NYTPF+NYTP
NF)
-2.641 0.640 -4.126 0.000
AMZN*(NYTHCF+NYT
HCNF)
-3.797 0.633 -6.001 0.000
BANDN*(NYTPF+NYT
PNF)
-2.279 0.622 -3.666 0.000
BANDN*(NYTHCF+NY
THCNF)
-3.110 0.621 -5.010 0.000
PAPERBACK -21.543 2.663 -8.090 0.000
BIGWRD -0.068 0.341 -0.200 0.841
BKAMIL -2.369 0.315 -7.531 0.000
BKSTRT 0.023 0.704 0.033 0.974
BORDER -2.540 0.310 -8.190 0.000
BUYCOM -2.668 0.333 -8.017 0.000
ECAMP -0.201 0.837 -0.241 0.810
FATBRA -0.294 0.335 -0.879 0.380
KNGBOK -0.387 0.536 -0.721 0.471
POWELL 1.037 0.734 1.412 0.158
TBAC -2.318 0.464 -4.992 0.000
R-squared 0.971270 Mean dependent var 13.89772
Adjusted R-squared 0.968464 S.D. dependent var 14.18615
S.E. of regression 2.519221 Akaike info criterion 4.771119
Sum squared resid 8123.485 Schwarz criterion 5.241469
Log likelihood -3228.097 F-statistic 346.1805
Durbin-Watson stat 1.163796 Prob (F-statistic) 0.000000
Formal Regression Model For All Paperbacks41
(Regression 2)
POST = α + β1SHCOST + β2TTIME + β3AMZN +β4BANDN +
β5(NYTPF+NYTPNF) + β6AMZN*(NYTPF+NYTPNF) +
β7BANDN*(NYTPF+NYTPNF) + β8BIGWRD + β9BKAMIL + β10BKSTRT +
β11BORDER + β12BUYCOM + β13ECAMP + β14FATBRA + β15KNGBOK +
β16POWELL + β17TBAC + δZ + ε
Where z is the vector of all other dummies, including 71 book dummies,
and δ is the corresponding vector of coefficients.42
41
Includes paperback bestsellers.
42
I exclude the last book dummy (Book075) to prevent collinearity. Also, I exclude varsit for the same
reason.
Dependent Variable: POST (in dollars) N=1406 observations
Explanatory Variables Coefficient Std. Error t-Statistic Prob.
Intercept 23.370 2.006 11.650 0.000
SHCOST -1.106 0.359 -3.080 0.002
TTIME 0.860 0.209 4.119 0.000
AMZN -1.635 0.683 -2.394 0.017
BANDN -1.364 0.652 -2.092 0.037
NYTPF+NYTPNF -13.111 3.471 -3.778 0.000
AMZN*(NYTPF+NYTP
NF) -0.856 1.132 -0.756 0.450
BANDN*(NYTPF+NYT
PNF) -1.225 1.099 -1.114 0.265
BIGWRD -2.826 0.612 -4.615 0.000
BKAMIL -4.607 1.360 -3.386 0.001
BKSTRT -2.370 0.615 -3.854 0.000
BORDER -3.123 0.645 -4.841 0.000
BUYCOM -7.493 1.738 -4.312 0.000
ECAMP -0.352 0.641 -0.548 0.584
FATBRA -0.934 1.154 -0.810 0.418
KNGBOK 2.684 1.688 1.590 0.112
POWELL -4.407 1.195 -3.689 0.000
TBAC 23.370 2.006 11.650 0.000
R-squared 0.889701 Mean dependent var 13.8977
Adjusted R-squared 0.882152 S.D. dependent var 14.1862
S.E. of regression 4.869964 Akaike info criterion 6.06658
Sum squared resid 31187.26 Schwarz criterion 6.40628
Log likelihood -4173.808 F-statistic 117.857
Durbin-Watson stat 0.455668 Prob (F-statistic) 0
8. Econometric Analysis and Conclusions
8.1 The relationship between shipping & handling and the posted price
The econometric analysis supports some of my theoretical ideas. The data
indicates that as the posted price increases, the shipping & handling price
increases. Therefore, with great confidence (p-value<.001), 43
I cannot reject the
null hypothesis that there is a negative correlation between the posted and
shipping & handling price of a given book (Hypothesis D). This demonstrates
that firms offering a lower posted price do not, on average, employ a strategy in
which they markedly increase the total price via imposing on the consumer a
comparatively higher shipping price during “checkout.” This may indicate that the
price retailers charge consumers for shipping & handling reflects firm costs and
does not reflect any firm’s profit strategy.
The correlation is problematic because the time in-between the realization of the
posted and shipping & handling price is never recorded. Asymmetry in consumer
awareness of components of total costs may potentially affect consumer price
sensitivity and therefore render my results somewhat problematic. Although I
believe incorporating such a measure will prove invaluable to this study,
accuracy and objectivity will be impossible to incorporate. Firstly, the actual
“time” lag between realizations is based upon connection technology as well as
43
The p-value or observed significance level of a statistical test is the smallest value of α for which H0 can
be rejected. It is the actual risk of committing a Type I error, if H0 is rejected based on the observed value f
the test statistic. The p-value measures the strength of the evidence against H0.
Internet congestion.44
Furthermore, realization of shipping & handling prices,
unless the site’s prices are overtly clandestine, (i.e., there is no way to know
them until checkout) are also difficult to measure because of subjectivity in
consumer awareness and sensitivity.
Unlike my analysis, which assumes a single book per order, Brynjolfsson and
Smith assume that customers purchase, on average, three titles per order. Their
quantity is derived via a study that finds consumers of Amazon purchase an
average of 2.8 titles per order. I believe that because the marginal shipping cost
approaches the variable shipping cost as the quantity of books ordered
increases, consumers should be enticed to purchase more books in the same
order:
SHCOST=FC(X) + (VC(Y))(QTY(Z))
As Z → ∞ , MCSHCOST → $0.99
I only assume one book per order because I am interested in the raw relationship
between the posted price and shipping & handling price. Furthermore, I test the
relationship between the posted price and availability and shipping time. Adding
multiple books to an order basket complicates my objective as:
1) Consumers may not necessarily be able to calculate the spread of
availability and shipping time at the time of order execution.
2) Dynamic shipping and availability times may result in multiple
shipments per order and bias any measure, assumption, or
analysis of consumer time sensitivities.
44
For example, those consumers with a modem will experience a greater time lag between realizations vs. a
consumer with a T1 or T3 network connection. In addition, those consumers “surfing” during business
hours, when Internet congestion is high, are also more apt to encounter longer lag times.
Therefore, a dynamic regression model that continually recounts for an
increasing number of books ordered, might yield different results. Each firm that
engages in two price shipping strategies charges a different amount for the fixed
cost and additional cost. Furthermore, a number of firms charge only a fixed
amount independent of quantity. A further study measuring consumer sensitivity
to different shipping pricing policies and their effects on total quantity may help
give a refined and more precise measure of the correlation. Regression 2, which
only includes paperbacks (including bestsellers), yields dramatically different
results. The strong negative coefficient (-1.06) and significance (p-value<. 001)
allows me to reject Hypothesis D. The results are somewhat enigmatic but do
support my theoretical ideas put forth in Hypothesis D. I posit that firms that are
more likely to sell a given hardcover, relative to paperbacks, are more apt to
charge a higher shipping price. Therefore, the exclusion of hardcovers from my
model yields the hypothesized relationship.
8.2 Market Power, Branding, and Pricing
Neither Amazon nor Barnes & Noble significantly contributes to a change in the
posted price in my model. Both variables are insignificant (p-value >.10) and
Barnes & Noble’s coefficient is negative, albeit only slightly. Given the market
share of the market leaders, which Brynjolfsson and Smith estimate is over
eighty percent for Amazon and fourteen percent for Barnes & Noble as of August
1999, (Brynjolfsson and Smith 1999) it is peculiar that prices are not significantly
higher on these two sites. Despite these high market shares, the data may
suggest both companies feel vulnerable to competition from each other and from
smaller retailers. Nonetheless, given their significantly lower prices on
bestsellers (p-value=0) coupled with every smaller site yielding negative
coefficients, it is arguable whether Amazon & Barnes & Noble do not, in fact,
charge higher than average prices.
I modify my regression model to include only paperbacks (Regression 2) to test
for any significant price differences. Both Amazon and Barnes & Noble are
significantly more expensive than all other retailers exhibiting significant results.
Therefore, I reject Hypothesis B and conclude, that for all paperbacks, books are
significantly more expensive at Amazon and Barnes & Noble. I believe these
results derive from the number of bestsellers in Regression 1 vs. Regression 2.
Regression 1 contains 60 bestsellers. Regression 2 only contains 30. As the
outcome of Regression 1 indicates, New York Times Bestsellers are priced
considerably cheaper at market leader’s sites. Consequently, I believe it is the
high percentage of bestsellers in my first model (57.14%) vs. (20.82%) that gives
the illusion books are no less expensive at Amazon and Barnes & Noble.
Another significant result arising from Regression 2 is the relationship between
paperback prices at Amazon and Barnes & Noble and other retailers. I contend
my econometric results indicate paperbacks are notably cheaper than
hardcovers at market leading firms when both Regressions 1 and 2 are run
without the inclusion of intercept terms. This observation indicates another form
of market segmentation as both firms discriminate between consumers preferring
different types of the same book. This is exemplified at both Amazon and Barnes
& Noble as both firms offer on option to purchase a hardcover version instead of
paperback or visa versa when a given book is selected (see Appendix D).
The second intercept-free regression (Regression 4 in Appendix E) demonstrates
that prices for paperbacks are significantly lower at Amazon versus Barnes &
Noble. In this sense, Amazon is relatively more discriminate between prices of
hardcovers and paperbacks. I argue these results indicate Amazon more
aggressively price discriminates between consumers preferring paperbacks and
those favoring hardcovers.
8.3 The Posted Price, Availability, and Shipping Time
Given the positive relationship between the posted price and total time in the
results, and their significance, I cannot reject the null hypothesis that books with
a higher posted price have longer processing and delivery times (Hypothesis C).
Although I find these results surprising, they are not without explanation.
Availability and shipping times are not usually posted on a retailer’s web site and
are not on all book search intermediary sites. Furthermore, my theoretical
assumption is inextricably tied to the idea that the market leader’s price should,
on average, be higher (Hypothesis B). This higher than average price should
have been, in part, the reason for the speedy processing and delivery time
characterizing books sold by Amazon and Barnes & Noble. However, since I
cannot reject Hypothesis B, my posited relationship between the posted price
and total time has been affected via a “domino effect” as the primary assumption
(Hypothesis C) is inextricably tied to not rejecting Hypothesis B.
Furthermore, consumers that are insensitive to price may also be insensitive to
time. A likely strategy adopted by retailers placing a price premium on the
posted price of their books may be tied to a realization that the average
consumer purchasing goods are time insensitive in addition to prince insensitive.
Additionally, sites affixing time premiums to their books, with the exception of
Amazon, are perhaps those that do not retain customers well and will be forced
to shutdown in the long run as customers realize that not only are prices
comparatively high, but service is also slow.
Loss leader pricing is not observed in Regression 2. The nature of paperbacks is
that they are intrinsically priced lower. This problematizes the results I discuss in
the next section.
8.4 Amazon, Barnes & Noble, and Bestseller Loss Leader Marketing Strategy
The strong negative and both statistically and economically significant
coefficients on bestseller variables for both leader firms in Regression 1 allows
me to reject the null hypothesis that bestsellers act as a loss leader product for
both leader firms (Hypothesis D). However, isolating my model to only
paperbacks, I observe different results. I find that for paperbacks alone, no
significant results exist. I believe these results stem from the nature of
paperbacks. Comparing hardcovers and paperbacks for bestsellers only, we
observe a comparatively significant price difference verses the difference
between hardcovers and paperbacks for all books. Since all bestsellers are
offered in either form, I claim retailers price discriminate between price sensitive
consumers. This is further evident as paperbacks are not released until after
hardcovers.
Loss leader pricing is evident given the web images presented in Appendix B.
Appendix B first shows examples of heavily advertised New York Times
Bestsellers. These advertisements appear on popular sites, including Yahoo,
which is one of the web’s most popular sites. In accord with loss leader pricing
theory and supported by my empirical results, once the consumer clicks on these
banner advertisements, they are transported to either Amazon’s or Barnes &
Noble’s web page where the New York Times Bestsellers luring the consumer to
the site are not immediately evident. The consumer must first click on the
bestseller hyperlink and is then transported to the “bestseller” page. Still, New
York Times Bestsellers do not “pop out” at the consumer. Rather, the firm’s own
bestsellers are marketed to the consumer and only upon careful scrutiny of the
page does the consumer find links to pages containing New York Times
Bestsellers. Nevertheless, a simple search upon transport from the banner to the
firm’s home page will forgo this obstacle of links. A savvy consumer will forgo
clicking on banners and shop via a reputable search site such as bestbookbuys.
While it is empirically evident that these leader firms incur a loss on this loss
leader product, the idea that bestsellers are a loss leader product is made
problematical by the fact that neither Amazon nor Barnes & Noble realize a profit
on the non loss leader books they sell. In the last fiscal year (1999), net loss at
Amazon totaled $720 million, up from $124.5 million (Yahoo Finance 2000). For
Barnes & Noble, net loss fell 42% to $48.2 million in the 1999 fiscal year.
Appendix A: Statistical Summaries of Posted Price in
Varying Categories
Table 1 – All Books (105)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn
45
100 95.24 13.05 18.26 0.71
bandn 105 100.00 13.24 18.24 0.73
bigwrd 86 81.90 14.47 17.23 0.84
bkamil 97 92.38 10.05 6.37 1.58
bkstrt 95 90.48 12.87 9.49 1.36
border 95 90.48 10.08 6.33 1.59
buycom 84 80.00 10.09 6.47 1.56
ecamp 66 62.86 15.71 17.95 0.88
fatbra 91 86.67 11.92 7.76 1.54
kngbok 96 91.43 11.40 9.07 1.26
pagone 76 72.38 14.94 8.14 1.83
powell 100 95.24 16.04 9.57 1.68
spree
46
105 100.00 13.24 18.24 0.73
tbac 105 100.00 12.60 18.36 0.69
varsit 85 80.95 11.22 8.82 1.27
TOTAL 1406 88.00 12.67 12.13 1.215
45
I find Amazon classifies five percent of New York Times Bestsellers as “out of stock” or “on order.”
This is ironic given their aggressive campaign pushing this genre of books. Furthermore, a market leader
boasting efficiency and speed should ideally hold an ample number of bestsellers in its inventory or have
efficient distribution channels that make them readily available. An interesting question this questionable
market failure raises is: do consumers shopping for bestsellers not available at Amazon wait for availability,
or buy from another retailer? I have demonstrated that it is dangerous to allow existing customers to switch
to another site as studies have shown customer retention is key on the Internet as consumers are particularly
choosy. Therefore, it is in Amazon’s best interest to hold an extra supply of its loss leader pricing
commodity to prevent such consequences. On the other hand, New York Times Bestsellers as a loss leader
product may act as a bait-and-switch good in which Amazon recommends alternative books “like” the on-
order bestseller that are priced higher. This tactic, albeit unethical, would greatly increase Amazon’s profit
margins.
46
Actually, Spree, as you may notice, has an identical value in every summary statistic table as Barnes &
Noble. Spree is a reward intermediary site that promotes brand loyalty. Registered users of Spree earn
financial rewards for shopping on branded web sites linked through their site. Although prices are identical
for a book purchased via spree or Barnes & Noble, registered users of spree receive identical benefits to
users of Barnes & Noble and also receive additional utility enjoyed via the free membership benefits of
Spree’s web site.
Table 2 – All Bestsellers (60)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 57 95.00 9.71 5.37 1.81
bandn 60 100.00 9.79 5.57 1.76
bigwrd 53 88.33 12.53 7.42 1.69
bkamil 59 98.33 9.38 4.94 1.90
bkstrt 56 93.33 11.83 6.32 1.87
border 58 96.67 9.29 5.28 1.76
buycom 56 93.33 9.08 5.09 1.79
ecamp 23 38.33 13.90 5.36 2.59
fatbra 55 91.67 11.33 6.27 1.81
kngbok 55 91.67 10.18 6.88 1.48
pagone 55 91.67 13.99 6.47 2.16
powell
47
66 110.00 15.56 8.39 1.85
spree 60 100.00 9.79 5.57 1.76
tbac 60 100.00 11.82 5.60 2.11
varsit 53 88.33 10.56 7.22 1.46
47
This ostensibly erroneous figure stems from Powell’s multiple book pricing strategy. Bestbookbuys
sometimes reports multiple observations per book for Powell’s. These are usually categorized into “special
new” or “special.” I opt to include these observations since, unlike Books A Million, prices are not limited
to paying members only.
Table 3 – All Paperbacks (72)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 69 95.83 10.00 8.85 1.13
bandn 72 100.00 9.98 9.32 1.07
bigwrd 57 79.17 9.88 9.00 1.10
bkamil 66 91.67 8.04 6.10 1.32
bkstrt 67 93.06 10.63 9.77 1.09
border 63 87.50 8.02 5.83 1.37
buycom 56 77.78 7.99 6.25 1.28
ecamp 40 55.56 13.05 10.37 1.26
fatbra 63 87.50 8.78 6.24 1.41
kngbok 69 95.83 9.57 8.95 1.07
pagone 47 65.28 12.67 8.10 1.57
powell 64 88.89 11.94 8.22 1.45
spree 72 100.00 9.98 9.32 1.07
tbac 72 100.00 8.53 8.71 0.98
varsit 57 79.17 8.88 7.52 1.18
TOTAL 934 86.48 9.75 8.167 1.19
Table 4 – All Hardcover (33)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 31 93.94 19.83 29.21 0.68
bandn 33 100.00 20.36 28.51 0.71
bigwrd 29 87.88 23.49 24.73 0.95
bkamil 31 93.94 14.34 4.61 3.11
bkstrt 28 84.85 18.23 6.17 2.96
border 32 96.97 14.15 5.25 2.70
buycom 28 84.85 14.31 4.63 3.09
ecamp 26 78.79 19.81 25.32 0.78
fatbra 28 84.85 18.97 6.07 3.12
kngbok 27 81.82 16.09 7.70 2.09
pagone 29 87.88 18.60 6.88 2.70
powell 36 109.09 23.32 7.25 3.22
spree 33 100.00 20.36 28.51 0.71
tbac 33 100.00 21.48 28.44 0.76
varsit 28 84.85 15.96 9.49 1.68
Table 5 – Paperback Bestsellers (30)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 28 93.33 5.19 1.51 3.45
bandn 30 100.00 5.09 1.50 3.38
bigwrd 27 90.00 7.06 2.84 2.48
bkamil 30 100.00 5.26 1.43 3.68
bkstrt 30 100.00 7.06 2.10 3.36
border 29 96.67 5.14 1.51 3.42
buycom 30 100.00 5.11 1.50 3.41
ecamp 0 0.00 N/A N/A N/A
fatbra 30 100.00 6.20 3.20 1.94
kngbok 30 100.00 6.05 3.32 1.82
pagone 28 93.33 10.09 3.38 2.99
powell 34 113.33 9.15 3.80 2.41
spree 30 100.00 5.09 1.50 3.38
tbac 30 100.00 6.67 2.19 3.05
varsit 27 90.00 6.50 2.33 2.79
Table 6 – Hardcover Bestsellers (30)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 29 96.67 14.07 3.91 3.60
bandn 30 100.00 14.48 3.89 3.72
bigwrd 26 86.67 18.20 6.36 2.86
bkamil 29 96.67 13.64 3.38 4.04
bkstrt 26 86.67 17.34 4.88 3.55
border 29 96.67 13.45 4.34 3.10
buycom 26 86.67 13.66 3.69 3.70
ecamp 23 76.67 13.90 5.36 2.59
fatbra 25 83.33 17.48 1.98 8.83
kngbok 25 83.33 15.14 6.79 2.23
pagone 27 90.00 18.03 6.47 2.79
powell 32 106.67 22.37 6.26 3.57
spree 30 100.00 14.48 3.89 3.72
tbac 30 100.00 16.97 2.05 8.27
varsit 26 86.67 14.78 8.16 1.81
Table 7 – Total Bestsellers (Fiction) (30)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 27 90.00 9.32 5.14 1.81
bandn 29 96.67 9.07 5.32 1.70
bigwrd 25 83.33 12.03 8.17 1.47
bkamil 28 93.33 8.82 4.98 1.77
bkstrt 27 90.00 10.81 5.40 2.00
border 28 93.33 8.48 4.92 1.72
buycom 26 86.67 8.26 4.85 1.70
ecamp 11 36.67 14.27 4.86 2.94
fatbra 25 83.33 10.50 6.37 1.65
kngbok 26 86.67 9.17 6.51 1.41
pagone 27 90.00 13.59 7.16 1.90
powell 32 106.67 15.40 8.43 1.83
spree 29 96.67 9.07 5.32 1.70
tbac 29 96.67 11.40 5.96 1.91
varsit 24 80.00 9.89 7.57 1.31
Table 8 – Total Bestsellers (Non-Fiction) (30)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 30 100.00 10.07 5.63 1.79
bandn 30 100.00 10.07 5.63 1.79
bigwrd 28 93.33 12.97 6.81 1.90
bkamil 31 103.33 9.89 4.92 2.01
bkstrt 29 96.67 12.78 7.03 1.82
border 30 100.00 10.05 5.58 1.80
buycom 30 100.00 9.79 5.26 1.86
ecamp 12 40.00 13.57 5.98 2.27
fatbra 30 100.00 12.02 6.21 1.94
kngbok 29 96.67 11.09 7.19 1.54
pagone 28 93.33 14.38 5.84 2.46
powell 34 113.33 15.71 8.47 1.85
spree 30 100.00 10.07 5.63 1.79
tbac 30 100.00 12.21 5.31 2.30
varsit 29 96.67 11.11 7.00 1.59
Table 9 – Fiction Bestseller Paperbacks (15)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 13 86.67 4.38 1.33 3.28
bandn 15 100.00 4.29 1.26 3.40
bigwrd 13 86.67 6.28 2.06 3.05
bkamil 15 100.00 4.49 1.30 3.47
bkstrt 15 100.00 6.35 1.55 4.10
border 14 93.33 4.34 1.30 3.34
buycom 15 100.00 4.35 1.31 3.33
fatbra 15 100.00 5.61 2.25 2.50
kngbok 15 100.00 5.67 2.25 2.52
pagone 13 86.67 8.84 2.63 3.36
powell 15 100.00 8.05 2.79 2.89
spree 15 100.00 4.29 1.26 3.40
tbac 15 100.00 5.90 1.68 3.52
varsit 12 80.00 5.78 1.61 3.58
Table 10 – Non-Fiction Bestseller Paperbacks (15)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 15 100.00 5.89 1.31 4.50
bandn 15 100.00 5.89 1.31 4.48
bigwrd 14 93.33 7.79 3.33 2.34
bkamil 15 100.00 6.03 1.14 5.30
bkstrt 15 100.00 7.77 2.38 3.26
border 15 100.00 5.89 1.31 4.50
buycom 15 100.00 5.87 1.31 4.47
ecamp 0 0.00 N/A N/A N/A
fatbra 15 100.00 6.79 3.92 1.73
kngbok 15 100.00 6.43 4.18 1.54
pagone 15 100.00 11.18 3.66 3.06
powell 19 126.67 10.02 4.32 2.32
spree 15 100.00 5.89 1.31 4.48
tbac 15 100.00 7.43 2.42 3.08
varsit 15 100.00 7.07 2.68 2.64
Table 11 – Fiction Bestseller Hardcover (15)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 14 46.67 13.90 2.06 6.75
bandn 14 46.67 14.20 2.16 6.59
bigwrd 12 40.00 18.26 7.71 2.37
bkamil 13 43.33 13.81 1.86 7.44
bkstrt 12 40.00 16.39 2.22 7.38
border 14 46.67 12.62 3.40 3.71
buycom 11 36.67 13.59 1.52 8.94
ecamp 11 36.67 14.27 4.86 2.94
fatbra 10 33.33 17.83 1.00 17.89
kngbok 11 36.67 13.95 7.43 1.88
pagone 14 46.67 18.00 7.26 2.48
powell 17 56.67 21.89 5.96 3.67
spree 14 46.67 14.20 2.16 6.59
tbac 14 46.67 17.29 1.18 14.68
varsit 12 40.00 14.00 8.97 1.56
Table 12 – Non-Fiction Bestseller Hardcover (15)
Firm Obs.
% Total
Availability
Mean
Price
Standard
Deviation
Coefficient
Of
Variation
amzn 15 100.00 14.24 5.15 2.76
bandn 10 100.00 14.24 5.15 2.76
bigwrd 14 93.33 18.15 5.23 3.47
bkamil 15 100.00 13.50 4.30 3.14
bkstrt 14 93.33 18.16 6.33 2.87
border 15 100.00 14.21 5.07 2.81
buycom 15 100.00 13.71 4.76 2.88
ecamp 12 80.00 13.57 5.98 2.27
fatbra 15 100.00 17.25 2.44 7.08
kngbok 14 93.33 16.08 6.37 2.52
pagone 13 86.67 18.07 5.80 3.12
powell 15 100.00 22.91 6.75 3.39
spree 15 100.00 14.73 5.01 3.14
tbac 10 100.00 16.70 2.60 6.42
varsit 14 93.33 15.44 7.67 2.01
Appendix B: Visual Examples of Loss Leader Pricing
Strategy
1) Amazon
Advertisements for Amazon
Amazon's Home Page
Amazon's Bestseller Page
2) Barnes & Noble
Advertisements For Barnes and Noble
Barnes & Noble’s Home Page
Barnes & Noble’s Bestseller Page
Appendix C: Personalized Product
Appendix D: Visual Example of Consumer Choice and
Market Segmentation at Amazon
Appendix E: Intercept-Free Regression Modeling
Modified Regression Model For All Books (Regression 3)
POST = β1SHCOST + β2TTIME + β3AMZN + β4BANDN + β5(NYTPF+NYTPNF) +
β6(NYTHCF+NYTHCNF) + β7AMZN*(NYTPF+NYTPNF) +
β8AMZN*(NYTHCF+NYTHCNF) + β9BANDN*(NYTPF+NYTPNF) +
β10BANDN*(NYTHCF+NYTHCNF) + β11PAPERBACK + β12BIGWRD +
β13BKAMIL + β14BKSTRT + β15BORDER + β16BUYCOM + β17ECAMP +
β18FATBRA + β19KNGBOK + β20POWELL + β21TBAC + δZ + ε
Where z is the vector of all other dummies, including 104 book dummies,
and δ is the corresponding vector of coefficients.
Dependent Variable: POST (in dollars) N=1406 observations
Explanatory Variables Coefficient Std. Error t-Statistic Prob.
SHCOST 3.050 0.431 7.079 0.000
TTIME 0.946 0.183 5.160 0.000
AMZN -0.472 1.144 -0.412 0.680
BANDN 1.053 1.095 0.961 0.337
NYTPF+NYTPNF 38.564 5.487 7.028 0.000
NYTHCF+NYTHCNF -3.665 2.745 -1.335 0.182
AMZN*(NYTPF+NYTP
NF) -1.444 1.680 -0.860 0.390
AMZN*(NYTHCF+NYT
HCNF) -2.958 1.662 -1.780 0.075
BANDN*(NYTPF+NYT
PNF) -2.352 1.622 -1.450 0.147
BANDN*(NYTHCF+NY
THCNF) -3.590 1.621 -2.215 0.027
PAPERBACK -52.807 2.639 -20.010 0.000
BIGWRD -1.734 0.890 -1.948 0.052
BKAMIL -2.222 0.821 -2.706 0.007
BKSTRT 2.585 1.835 1.409 0.159
BORDER -2.008 0.808 -2.483 0.013
BUYCOM -2.654 0.869 -3.055 0.002
ECAMP 12.209 2.008 6.079 0.000
FATBRA -0.021 0.872 -0.024 0.981
KNGBOK 0.319 1.401 0.228 0.820
POWELL -4.066 1.882 -2.161 0.031
TBAC -5.051 1.174 -4.303 0.000
R-squared 0.804151 Mean dependent var 13.89273
Adjusted R-squared 0.785178 S.D. dependent var 14.18828
S.E. of regression 6.576119 Akaike info criterion 6.689525
Sum squared resid 55354.03 Schwarz criterion 7.156411
Log likelihood -4574.39 F-statistic 1.532244
Durbin-Watson stat 0.804151 Prob (F-statistic) 13.89273
Modified Regression Model For All Paperbacks48
(Regression 4)
POST = β1SHCOST + β2TTIME + β3AMZN +β4BANDN + β5(NYTPF+NYTPNF) +
β6AMZN*(NYTPF+NYTPNF) + β7BANDN*(NYTPF+NYTPNF) + β8BIGWRD +
β9BKAMIL + β10BKSTRT + β11BORDER + β12BUYCOM + β13ECAMP +
β14FATBRA + β15KNGBOK + β16POWELL + β17TBAC + δZ + ε
Where z is the vector of all other dummies, including 71 book dummies,
and δ is the corresponding vector of coefficients.
48
Includes paperback bestsellers.
Dependent Variable: POST (in dollars) N=1406 observations
Explanatory Variables Coefficient Std. Error t-Statistic Prob.
SHCOST 2.635 0.242 10.892 0.000
TTIME 0.548 0.363 1.512 0.131
AMZN -3.660 1.193 -3.069 0.002
BANDN -0.954 1.133 -0.842 0.400
NYTPF+NYTPNF -14.814 6.043 -2.452 0.014
AMZN*(NYTPF+NYTP
NF) 0.588 1.977 0.297 0.766
BANDN*(NYTPF+NYT
PNF) -1.132 1.915 -0.591 0.554
BIGWRD -0.674 1.132 -0.595 0.552
BKAMIL -3.522 1.065 -3.306 0.001
BKSTRT -0.876 2.319 -0.378 0.706
BORDER -2.944 1.069 -2.754 0.006
BUYCOM -3.747 1.123 -3.337 0.001
ECAMP 10.761 1.405 7.657 0.000
FATBRA -1.057 1.118 -0.946 0.344
KNGBOK -4.576 1.984 -2.306 0.021
POWELL -10.164 2.450 -4.149 0.000
TBAC -2.088 2.066 -1.011 0.312
R-squared 0.665019 Mean dependent var 13.89273
Adjusted R-squared 0.642347 S.D. dependent var 14.18828
S.E. of regression 8.485175 Akaike info criterion 7.176431
Sum squared resid 94677.64 Schwarz criterion 7.512589
Log likelihood -4951.44 F-statistic 1.247258
Durbin-Watson stat 0.665019 Prob (F-statistic) 13.89273
Works Cited & References
Bailey, Joseph P. 1998. Intermediation and Electronic Markets: Aggregation
and
Pricing in Internet Commerce. Ph.D., Technology, Management and Policy,
Massachusetts Institute of Technology, Cambridge, MA.
Bailey, J. P. 1998. Electronic Commerce: Prices and Consumer Issues for Three
Products: Books, Compact Discs, and Software, Organization for Economic
Co-Operation and Development, OCDE/GD(98)4.
Bakos, J. Yannis; Brynjolfsson, Erik. 1999. Bundling Information Goods,
Management Science, Volume 45, Issue 11 (November).
Bakos, J. Yannis; Brynjolfsson, Erik. 1999. Bundling and Competition on the
Internet, Working Paper, MIT Sloan School.
Bakos, J. Yannis. 1997. Reducing Buyer Search Costs: Implications for
Electronic
Marketplaces, Management Science, Volume 43, Number 12, December.
Borenstein, Severin. 1991. Selling costs and switching costs: explaining retail
gasoline margins, RAND Journal of Economics, Volume 22, Number 3,
Autumn.
Brynjolfsson, E., Smith, M.D., 1999. Frictionless commerce? A comparison of
Internet and conventional retailers, Working paper. MIT Sloan School of
Management, Cambridge, MA (http://ecommerce.mit.edu/papers/friction).
Degeratu, Alexandru; Rangaswamy, Arvind; Wu, Jianan. 1998. Consumer Choice
Behavior in Online and Regular Stores: The Effects of Brand Name, Price, and
Other Search Attributes, Presented at Marketing Science and the Internet,
INFORM College on Marketing Mini-Conference. Cambridge, MA. 6-8 March.
Fletcher, Joel. 1999, November 15. Furniture Shopping? The Mall Still Beats the
Web, The Wall Street Journal. pp. B3-B5.
Ghemawat, Pankaj; Baird, Bret. 1998. Leadership Online: Barnes & Noble vs.
Amazon.com. Harvard Business School, Case N9-798-063, April 8.
Hess, James, and Eitan Gerstner. 1987. Loss Leader Pricing and Rain Check Policy,
Marketing Science, Vol 6, Fall, pp.358-374.
Lal, Rajiv, and Carmen Matutes. 1988. Price Competition in Multimarket Duopolies,
RAND Journal of Economics, vol.20, Number 4, pp. 516-537.
Landro, Laura. 1999, November 22. Nothing Like The Real Thing: Give up in-store
shopping for the cyber-version? You’ve got to be kidding, The Wall Street
Journal. pp. R25-R27.
Morgan Stanley. 1997. The Internet Retailing Report, May. http://www.ms.com.
Salop, S.; Stiglitz J. E. 1977. Bargains and Ripoffs: A Model of Monopolistically
Competitive Price Dispersion, The Review of Economic Studies., 44 (October),
pp. 493-510.
Salop, S.; Stiglitz J. E. 1982. The Theory of Sales: A Simple Model of Equilibrium
Price Dispersion with Identical Agents, The American Economic Review. 72:5
(December), pp. 1121-1130.
Stahl, Dale O. 1989. Oligopolistic Pricing with Sequential Consumer Search. The
American Economic Review, Volume 79, Issue 4, pp. 700-712.
Stahl, Dale O. 1996. Oligopolistic Pricing with Heterogeneous Consumer Search.
International Journal of Industrial Organization, Volume 14, Issue 2, pp. 243-
268.
Urban, Glen L.; Sultan, Fareena; Qualls, William. 1998. Trust-based Marketing on
the Internet, MIT Sloan School of Management.

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Price dispersion in the online book market

  • 1. Alternative Explanations for Price Dispersion in the Online Book Market: Loss Leader Marketing, Branding and Switching Costs By Bradley Aaron Morgan Advisor: Maria Arbatskaya A thesis submitted to the Faculty of Emory College of Emory University in partial fulfillment of the requirements of the degree of Bachelor of Arts with Honors Department of Economics 2000
  • 2. Table of Contents Forward 1 1. Introduction 2 2. Testable Hypotheses 4 3. Literature Review 9 3.1 Frictionless Commerce 9 3.2 Price Dispersion 10 Asymmetrically Informed Consumers and Search Costs 11 Product and Retailer Heterogeneity 13 Shipping & Handling Costs 14 4. Data Collection and Methodology 15 4.1 Why Books? 15 4.2 The Data Collection Process 17 4.3 Data Organization and Variable Creation 19 4.4 Biases in Data 20 5. Branding and Loss Leader Pricing Theory 22 5.1 The Internet and Branding 22 Coupons 23 5.2 Loss Leader Pricing Theory 24 6. Switching Costs 26 6.1 Switching Costs and the Borenstein Model 26 6.2 Switching Costs and Price Discrimination 28 6.3 Switching Costs and Intermediation 29 7. Econometric Framework and Modeling 32 7.1 Descriptive Statistics 32 7.2 Table of Variables 33 7.3 Formal Regression Model For All Books (Regression 1) 34 7.4 Formal Regression Model For All Paperbacks (Regression 2) 36 8. Econometric Analysis and Conclusions 38 8.1 The Relationship between Shipping & Handling and the Posted Price 8.2 Market Power, Branding, and Pricing 40 8.3 The Posted Price, Availability, and Shipping Time 42 8.4 Amazon, Barnes & Noble, and Bestseller Loss Leader 43 Marketing Strategy Works Cited and References 69
  • 3. Index of Tables and Appendices Tables Descriptive Statistics 32 Table of Variables 33 Output of Regression 1 35 Output of Regression 2 37 Statistical Summaries 45 Table 1: All Books 45 Table 2: All Bestsellers 46 Table 3: All Paperbacks 47 Table 4: All Hardcovers 48 Table 5: Paperback Bestsellers 49 Table 6: Hardcover Bestsellers 50 Table 7: Total Bestsellers (Fiction) 51 Table 8: Total Bestsellers (Non-Fiction) 52 Table 9: Fiction Bestseller Paperbacks 53 Table 10: Non-Fiction Bestseller Paperbacks 54 Table 11: Fiction Bestseller Hardcover 55 Table 12: Non-Fiction Bestseller Hardcover 56 Output of Regression 3 66 Output of Regression 4 68 Appendices Appendix A: Statistical Summaries of Posted Price in 51 Varying Categories Appendix B: Visual Examples of Loss Leader Pricing Strategy 57 Amazon 57 Barnes & Noble 60 Appendix C: Personalized Product 63 Appendix D: Visual Examples of Consumer Choice and Market 64 Segmentation at Amazon Appendix E: Intercept-Free Regression Modeling 65
  • 4. Product pricing [for books] should be competitive on the Web, in part because of the ease with which consumers will be able to compare prices. Shoppers can compare prices within seconds by switching from Web site to Web site. Provided that shipping costs are equal, in many instances there should be little incentive for customers to order from higher-priced providers. -Morgan Stanley Dean Witter
  • 5. 1. Introduction In their comparison of conventional and electronic book markets, Brynjolfsson and Smith vehemently contest the notion and possibility of the Internet as a “frictionless” marketplace (Brynjolfsson and Smith 1999). The authors conclude that while total prices for the majority of books are lower on the Internet compared to conventional retailers, Internet prices exhibit a dramatically higher degree of price variation. They attribute high price variation primarily to asymmetric consumer awareness, heterogeneity in retailer trust, and brand market power. While their results are both relevant and significant, they fail to closely examine other important variables that may produce the observed variations in price. I extend Brynjolfsson’s and Smith’s analysis, focusing only on the Internet sector, and emphasize what I argue are both critical and measurable explanatory factors. I argue that the Internet bookseller’s leaders, Amazon and Barnes & Noble, both employ similar marketing strategies which render their prices strikingly similar, but different from their competitors. Specifically, I show that both Amazon and Barnes & Noble attempt to capture market share by loss leader pricing New York Times Bestsellers. I believe that the successful deployment of this strategy, coupled with other variables allowing these two leader firms to charge price premiums, substantially increases overall market price variance. The two critical variables I believe Brynjolfsson and Smith omit from their model are the shipping & handling price and the total time between order placement
  • 6. and order delivery. I argue in Chapter 2 that incorporating these variables into my econometric model will significantly help explain how varying segments of Internet consumers place different premiums on endogenous variables (e.g., posted price, shipping price, time, trust, etc.). Chapter 3 reviews and assesses Brynjolfsson’s and Smith’s article, giving critical attention to their methodology and explanations for price dispersion. A discussion of my data collection process and methodology follows in chapter 4. Chapters 5 and 6 explore issues Brynjolfsson and Smith fail to investigate in depth. In chapter 5 I argue branding is key on the Internet because it allows market-leading firms to place price premiums on their books and enables them to peruse marketing strategies that proliferate their market shares. Chapter 6 examines both traditional and more contemporary switching cost theories and discusses their applications in electronic commerce. In chapter 7 I develop an econometric framework that tests my hypotheses. Chapter 8 presents an examination of my econometric results and conclusions. My study is important given its assessment of the level of competition likely to exist in the online book market over time. I argue that the barriers imposed by Amazon, the Internet’s first mover in this market, and Barnes & Noble, the second largest firm, will prevent the emergence of any significant competition. This information is not only useful to consumers, but also to Amazon’s and Barnes and Noble’s competitors, investment banks, capital markets professionals, and marketing firms. While I do not claim the observed level of
  • 7. price dispersion will continue indefinitely, I do hold that it will continue much longer than most leading capital markets analysts predict.
  • 8. 2. Testable Hypotheses Economic intuition coupled with my own observations from buying books online, lead me to expect the following: Hypothesis A: Amazon and Barnes & Noble draw consumers to their site by employing a similar loss leader marketing strategy. Most books sold by Amazon and Barnes & Noble are discounted twenty to thirty percent off the retail (i.e., conventional) price. However, both firms offer all New York Times Bestsellers at fifty percent off their retail price. Both firms, on and off the Internet, heavily advertise New York Times Bestsellers. Nevertheless, while both sites heavily emphasize this discount via advertising, neither site overtly advertises this discount once the user visits the site1 . I believe that both firms use this strategy to send a signal to consumers that all their books, not just the New York Times Bestsellers, are relatively cheap. In turn, consumers visit these two sites and theoretically purchase books they have not originally intended to purchase because of the perceived discount. Moreover, I argue loss leader pricing contributes to price dispersion, as firms engaging in this strategy are able to charge price premiums on non-New York Times Bestsellers partly because of the signal these loss leader books send to the consumer. Small sites, while they may price their bestsellers competitively, do not have the level of advertising necessary to effectively render New York Times Bestsellers loss leader products. Therefore, they must follow the leaders in pricing for these bestsellers, and still charge a discount on books the leaders enjoy successfully charging at a higher price. Consequently, loss leader pricing not only allows these firms to capture a higher market share, but also gives 1 See Appendix B
  • 9. them a comparative advantage over competitors in that competitors are forced to mark down New York Times Bestsellers and maintain comparatively higher discounts on other books. They lose because despite offering the identical discount on the New York Times Bestsellers, they do not draw in any further market share. Hypothesis B: Books will be significantly more expensive at highly branded, more trusted, and better known web sites. Branding is a signal of quality that induces consumers to purchase a higher priced product even if it is identical to another lesser-known product.2 Branding also signals trust and establishes reputation. Amazon boasts the comparative advantage of being the first mover in the Internet book business. They are therefore able to accumulate a loyal customer base and establish a means to induce repeat purchases from retained customers. Barnes & Noble, the Internet’s second largest book retailer, through its large chain of conventional bookstores, also enjoys the great benefits of signaling trust and establishing loyalty on the Internet. I believe the smaller, and lesser-known firms, must offer lower prices to induce customers to switch from the branded sites or entice new price elastic consumers who are privy to switching but only for striking price breaks. Hypothesis C: Ceteris parabis, the sites offering a lower posted price will have a higher availability and shipping time3 , thus indicating competitive market equilibrium previously overlooked in the literature. Many firms price the majority of their books below Amazon’s and Barnes & Noble’s prices yet continue to operate without being forced to shutdown. Either a continual stream of venture capital allows for the prolongation of their operations, 2 The power of branding induces consumers to buy brand name products over identical generics. 3 See chapter 4.2 or 7.2 (Table of Variables) for definitions of each of these variables.
  • 10. or they are generating revenue from a source other than books themselves4 . I believe, therefore, that many of the firms forced to compete with market leaders by offering lower prices - and theoretically incurring continual losses - compensate for lower posted prices by: A. Not incurring the same costs realized by market leaders. B. Using a lower grade of shipping, and consequently creating an expectedly longer shipping period. It follows from assumption (A) that leader firms incur costs not realized by smaller firms since the latter do not have the same sophisticated and expensive inventory systems. Consequently, I believe that the procedure of processing an order, obtaining the book, and delivering it to the shipper, is more inefficient and consequently more time intensive at smaller sites. I therefore expect the availability time, the time between a customer’s order and the firm’s delivery of that order to the shipping agent, to be significantly higher. In accord with assumption (B), I expect more price sensitive consumers to patronize the smaller, or cheaper sites, as they are most apt to engage in lowest price searches or use a third-party search intermediary. Since these consumers are relatively price sensitive, they are likely to be insensitive to the certainty of delivery time, as they are mostly concerned with price. Most users find leader 4 Despite their high market capitalization, both Amazon’s and Barnes & Noble’s Internet business have continually reported losses every quarter since each of their initial public offerings. It is puzzling that these two Internet behemoths with tremendous brand name recognition, market capitalization, venture capital, economies of scale, amongst other competitive advantages, have competitors that continue to operate.
  • 11. firms “convenient” and “easy” and are willing to pay a price premium in exchange for “certainty.” Uncertainty in delivery time comes not only from a wide range in shipping time, but also a wider range in availability. Therefore, more price sensitive consumers are most likely to tolerate a longer period of total time. This phenomenon should not appear strange to those believing in frictionless Internet markets. The presence of these endogenous variables should allow for market segmentation and consequently motivate firms that cater to different categories of tastes and preferences to enter the market. This type of market segmentation benefits both consumers and firms, not by the presence of a single price, but rather by variable pricing that fluctuates based on consumer preferences. Hypothesis D: When the posted price is higher, shipping cost will be lower, indicating another form of price equilibrium. Firms often market their books as having a lower cost than their competitors. Often this claim is true. However, in most cases this claim does not refer to the total cost of the books, but only their posted prices. It appears that many firms touting relatively low prices compensate via comparatively high shipping & handling prices. This, in essence, renders the firm not a purveyor of books, but rather shipping & handling! Furthermore, one would expect that the firms charging higher shipping prices are precisely those whose books have a higher mean shipping time. The logic is that a longer shipping time should be accompanied by a lower shipping cost to the firm. Higher than average shipping
  • 12. prices and a lower grade of standard shipping may allow firms with comparatively lower posted prices to compensate for losses. I feel one internal mechanism allowing firms to market their books as such is the time lag between the realization between the posted price and the shipping & handling price. Most sites have a subtle hyperlink to information about shipping & handling costs as well as shipping policies; however, none actively advertises shipping prices alongside posted prices5 . Moreover, consumers are not as sensitive to the shipping & handling prices as much as they are to the posted price, as the consumers may perceive these charges, similarly to taxes, as given. It is not only a lack of sensitivity to shipping prices that allows for this ostensibly negative relationship between posted and shipping prices. At most sites, once a consumer selects his basket of goods and proceeds to “check out,” he is required to register. Registration not only involves entry of personal information (e.g., email address, name, home address, etc.) but also usually includes optional questions about consumption patterns that firms often use for marketing purposes. Sometimes registration also involves inputting credit card, billing, and shipping information. The consumer realizes shipping and handling costs, and therefore the total cost, only after this lengthy input process is finished. Even if consumers are upset about what they believe to be high or unreasonable shipping costs, they are not apt to switch to another firm. For most consumers, unless they are hyper price sensitive, the cost expended during “registration”
  • 13. often outweighs the cost of attempting to find a bookseller offering a lower total cost. Therefore, most consumers proceed to confirm their orders. 5 See Appendix B
  • 14. 3. Review of Literature 3.1 Frictionless Commerce The majority of my work expands upon studies conducted by Erik Brynjolfsson and Michael D. Smith in their article Frictionless Commerce? Comparison of Internet and Conventional Retailers. As per the title, this article deals predominantly with testing for a frictionless market via a comparison of conventional and Internet book and CD retailers. The authors first define the state of “Internet efficiency” as an Internet market in which (i) location is irrelevant and (ii) consumers are fully informed of both product selection and prices. Brynjolfsson and Smith use an online bookseller list generated by Yahoo to generate a list of Internet booksellers. They then track 10 bestsellers and 10 random books over time and compare prices to those at the conventional retailers. The authors use Yahoo’s list because they believe it is both comprehensive and unbiased. While I agree, they fail to consider the integrity of the booksellers they include in their dataset. Yahoo lists all known online booksellers, not necessarily those booksellers verified legitimate. This is problematic because many of the booksellers Yahoo lists may not give accurate price information or may not change their posted prices when actual prices change. Finally, it is possible that many of the widely unknown booksellers operate merely to steal credit card information. I argue that unbiased third-party search intermediaries that monitor and evaluate web sites generate more accurate “lists” of online booksellers.
  • 15. To test for “Internet efficiency” Brynjolfsson and Smith conduct comparative t- tests6 on mean store posted prices and measure the percentage of time Internet prices are less than or equal to conventional prices. Variables such as transportation costs (conventional stores only), shipping & handling prices, and taxes are then incorporated and the t-tests are repeated. They ultimately conclude that prices are lower on the Internet compared to brick and mortar counterparts whether just prices alone are accounted for and when the t-tests are repeated incorporating the other variables. In addition to lower prices, they find: 1) Internet prices are more dispersed. 2) Menu costs online are significantly lower and therefore prices change more frequently and in smaller increments. Clearly, this leads to the conclusion that frictionless commerce does not exist. In the following sub-chapters, I give an overview of the conclusions reached by the authors. My work builds upon these results. 3.2 Price Dispersion A portion of price dispersion arising in online book markets is explained through exogenous heterogeneous factors that are not part of the product per se, but are rather complementary. Retailers use these service factors in an attempt to 6 A “t” random variable is formed by dividing a standard normal, Z ~ N(0,1), random variable by the square root of an independent chi-square random variable, V ~ χ2 (m), that has been divided by its degrees of freedom, m.
  • 16. differentiate their product via a bundled package. Price dispersion online for book and CD markets may be attributed to: 1) Differences in search costs 2) Asymmetrically informed consumers Incorporating market share data into their model, they discover price is concentrated around the market leader’s price at companies very close in market share to the market leader. However, on average, the price spread between each book is extremely low. Only when compared to companies holding negligible market power does the price spread between books become significant. They also find that despite setting prices significantly below the market leader’s price, many firms, specifically those smaller retailers, do not receive any significant portion of total sales per book. This leads Brynjolfsson and Smith to a key conclusion: Ironically, as we discuss below, a high market share may, in and of itself, be an important “feature” that supports a price premium because of the importance of network externalities in word-of- mouth marketing and trust-building (21). Asymmetrically Informed Consumers and Search Costs The authors contend that producers will set prices above marginal cost when positive search costs exists. They note that the ostensibly lower search costs on the Internet will force book prices toward Bertrand marginal cost pricing7 . Although search costs on the Internet appear to be trivially small due to search intermediaries and other channels that allow for easy comparison of information, 7 The Bertrand model holds that a price war between firms ultimately results in P1=P2=MC. The presence of search costs violates a primary assumption of the Bertrand model. However, if the Internet does facilitate a movement toward lower or negligible search costs, a Bertrand price war should ensue.
  • 17. they argue consumer ignorance ultimately increases search costs. Internet consumers are figured as comparatively wealthier and therefore more price insensitive. As a result, they are less apt to engage in costly searches. The authors cite a number of different landmark search cost articles, which they use to explain price dispersion via heterogeneous search costs. Contrary to economic models of search costs (Salop and Stiglitz 1977) which contend that assuming (1) consumers are imperfectly informed, (2) information is costly to obtain, and (3) firms set prices to leverage consumer heterogeneity in information and search costs, the authors do not find that the firm with the lowest prices captures most sales. The firm with the lowest price should theoretically capture all sales from informed consumers and some sales from uninformed consumers. Despite Book’s8 consistently lower prices, Amazon continues to hold a dramatically higher market share. This violation of the Salop and Stiglitz model is attributed to asymmetric and heterogeneous consumer information coupled with search costs greater than zero. They note that this explains why companies with high market shares are able to charge higher price premiums but does not explain the reason successful bookstores (e.g., Powell’s)9 are able to charge 8 Books is now Barnes & Noble 9 Powell’s, an online/conventional hybrid based out of Seattle, specializes in used, new, rare books. The company distinguishes itself from Amazon and Barnes & Noble by boasting as large as a collection and the ability to choose between different types of the same book (i.e., hardcover, paperback, used, new). One reason Powell’s may continue to attract and retain customers despite comparatively higher prices is its specialization in rare books. See discussion on switching costs for possible reasons consumers do not buy rare books from Powell’s and then switch to cheaper sites for other, less expensive books. Finally, the company distinguishes itself through its West coast eclecticism, and therefore tastes and preferences may be the cause.
  • 18. higher prices and yet retain a strong customer base. To explain this phenomenon, they turn to heterogeneity in products and firms. Product and Retailer Heterogeneity Analysis is shifted to explain heterogeneous factors of service characteristics that might account for price dispersion. Brynjolfsson and Smith use hedonic regression10 analysis to test for price variation caused by service heterogeneity. They discover that service factors do not vary across firms or are often negatively correlated with price. This renders their hedonic regression model fruitless in explaining residual price dispersion. They also realize that product heterogeneity is fallible in explaining price differences in that services are “informational” and therefore separable from the homogenous product. Consumers are capable of employing a strategy in which they use an information source from one web site and then switch to another offering a lower posted price. Unobservable variables, such as heterogeneous consumer measures of trust, may play a key role in price dispersion. Studies have argued trust is an integral component of any successful Internet marketing strategy (Urban 1999). Trust, they contend, is important given the spatial and temporal demarcations between 10 A regression model that attempts to explain variation in the dependent variable via heterogeneity in service characteristics and conveniences offered by firms.
  • 19. the firm and the consumer. Trust is signaled via reliability, word of mouth advertising, conventional channels of advertising, links from reputable sites, and presence in non-Internet markets. Shipping & Handling Costs Brynjolfsson and Smith modify their regression model to include factors such as shipping & handling costs.11 They note the number of items shipped and the speed of delivery are determinant factors in shipping & handling charges. The type of shipping is also important. For standard shipping, many firms, such as Amazon charge a fixed, and relatively high standard fee for the first book and a lower fee for each additional book ordered. This creates variable shipping prices across companies and significantly contributes to price dispersion. While the authors then proceed to merely state the necessity of branding along with trust in Internet markets, they do not elaborate on the reasons for its importance. In Chapter 5 I will pick up where Brynjolfsson and Smith leave off and attempt to explain the reasons branding is crucial in electronic commerce. I will discuss the importance of brand market power and its allowance for key marketing strategies at market leading firms such as loss leader pricing. In
  • 20. chapter 6 I will demonstrate how the imposition of switching costs allows for the successful deployment of these strategies as market leading firms in the online book industry are able to act monopolistically. Finally I will develop and analyze my econometric models for evidence of loss leader pricing and test for the possible variations of market equilibrium via testing hypotheses B, C, and D. . 11 See discussion of Hypothesis D for further information regarding shipping & handling. Additionally, I discuss shipping and handling costs at length in chapter 8.2.
  • 21. 4. Data Collection & Methodology 4.1 Why Books? The decision to exclusively examine books is made for several reasons. Firstly, books are homogenous goods and therefore do not vary in product characteristics across retailers. This is a fundamental assumption in the model of perfect competition - a state of zero market friction. Importantly, economists traditionally tout homogenous goods as resilient to marketing forces because of consumer indifference. Although consumers often receive utility in browsing through a neighborhood bookstore or examining a book by flipping through its pages, most consumers do not need a brick-and-mortar experience to decide which book to purchase. Internet technology enables consumers to virtually replicate many key components of the conventional book shopping experience via the online medium. For example, many sites post mainstream critical reviews of new books alongside reviews customers contribute. Moreover, many booksellers allow consumers to read or listen to parts of a text. This differs considerably from the shopping experience for heterogeneous, or branded goods, requiring consumers to “feel,” “taste,” “experience,” or “sample,” a product. Many consumers are reluctant to purchase goods such as clothes and shoes from the Internet because they cannot test them for proper fit or desired look before purchasing. This reluctance can be attributed, for example, to heterogeneity in product sizes across brands. The cost of uncertainty for some consumers to purchase goods online does not outweigh the utility from
  • 22. savings. Moreover, many consumers enjoy shopping for heterogeneous products for reasons such as stress relief and socialization (Landro 1999). The problem of “testing” is also evident in shopping for furniture and bedding (Fletcher 1999). Another quintessential characteristic of the Internet book market is its relative Internet maturity. Although the Internet is a new technology, bookselling is one of the oldest and largest business to consumer industries on the web and contains numerous companies selling the same book in the same market. Additionally, there have been a myriad of recent legal disputes between the leaders of the online book market (Amazon and Barnes & Noble). Their fierce competitive battle to obtain market share is also indicative of an established and competitive market. Books are also very conducive to Internet retailing. In addition to the aforementioned characteristics of books, they possess inherent qualities that render their sale over the Internet relatively simple. Morgan Stanley notes the four key properties of books that allow for their ease of sale: 1. Books are well-known commodities. 2. Books are inexpensive. 3. Huge variety translates into mass appeal. 4. Books are easy to ship (Morgan Stanley 1997). I will also add that since books are ubiquitously sold, they should also be readily available. Theoretically, given an efficient supply chain, this will allow online
  • 23. firms to utilize the ability to not hold as much inventory and therefore reduce costs.12 It is also important to note that books are only one of many commodities sold on the Internet that are rated by a third party13 . While these rating sites are not utilized by most users, they are readily available, easy to use, and plentiful in number. In fact, Yahoo, a popular Internet site, has a shopping sub-category page that allows users to compare prices. Furthermore, an entire Yahoo directory is devoted to listing the fifteen third party web sites that rank books. The ubiquitous presence of these third party book-ranking sites should increase competitive behavior for books on the Internet. 4.2 The Data Collection Process To collect data for bestsellers, I use the New York Times Bestsellers list, as these are the most comprehensive and relevant to my study. The New York Times Bestseller list is regarded as the most inclusive and exact measure of a book’s popularity. I feel bestsellers are the best category of books to examine in my study since they are the most likely to be sold by any given retailer because they are the most widely purchased. I collect information from each of the four categories of New York Times Bestsellers: paperback fiction, paperback non- 12 Traditional economic models of inventory state that inventory is beneficial to firms for reasons of product smoothing, factors of production, stock-out avoidance, and work in process. When a firm holds inventory, it forgoes the real interest it could have earned. 13 Of course many products on the Internet are “rated” or “reviewed.” However, along with other homogenous commodities such as CD’s and software, books are unique in that they systematically rated according to price, shipping costs, etc.
  • 24. fiction, hardcover fiction, and hardcover non-fiction. The New York Times lists the top fifteen books in each category. Next, I use a “shop bot,” a web site that automatically compares prices, as the primary mechanism through which I collect my data.14 Information is collected for each of the observation's posted price, availability15 , shipping cost, and shipping time16 . When data for availability and shipping time are in range form,17 I take the average value. The units characterizing both variables are entered as days. Excluded from my dataset are observations that are either classified as “out of stock” or “on order.” The former observations cannot be converted into any definite value and inclusion of the latter will cause my data to become skewed. “On-order” times typically range from six to eight weeks and have been statistically calculated as an outlier due to their abnormally high residuals and interference in the accuracy of my regression results.18 I also exclude from my dataset used books, as they are not the concern of this 14 The specific "shop bot" I use for the collection of data is bestbookbuys. I feel this is the most comprehensive, unbiased, and dependable bot on the web. Currently, the search capabilities of bestbookbuys spans over 28 retailers and returns information about availability, price, shipping & handling price and time, as well as relevant tax information. Furthermore, results are tabled and delivered in ascending order of price. See also chapter 6.3. 15 Refers to the time between order processing and the time it takes the firm to ship order: AVAIL=t(shipped out to delivery agent)-t(order processed) [measured in days] 16 Refers to the time between shipment and receipt of package: SHTIME=t(receipt of order)-t(shipped out to delivery agent) [measured in days] 17 A range listing the lowest possible value through the highest possible value. 18 Although I cannot include these observations in my regression model, they are very interesting. Amazon classifies many books and bestsellers as out of stock. The implications of this lack of availability are striking. See Appendix A for a full discussion.
  • 25. study. Finally, I exclude any retailer offering a special “club price” to paying members only.19 Data is gathered for each of the twenty-eight retailers searched by bestbookbuys20 Observations from every site are not usually available for each book. Moreover, when a search for a particular book returns less than seven observations, it is excluded from the dataset and an alternate random book is chosen. I believe any such book is “rare” in that it is not readily available and therefore violates my selection criteria21 and may skew my dataset. In addition to the sixty bestsellers, data is collected for forty-five randomly chosen titles, both paperback and hardcover.22 No special effort is made to gather a proportionate amount of paperbacks and hardcover. Since I randomly generate ISBN numbers, I assume that statistically, given my large sample size, I should finish with a number of paperbacks relative to hardcovers equal to the true population proportion. 4.3 Data Organization and Variable Creation All of the data is next appropriately organized in a Microsoft Excel file. Variables are created for each of the characteristics relevant to the subsequent econometric analysis. The non-dummy explanatory variables I examine are posted price, shipping & handling 19 The only retailer currently offering “club prices” is Booksamillion.com. For a 5-dollar annual fee, members receive discounts on select books. These observations are excluded, as access to these prices is limited to paying members. This may be argued as a form of second-degree price discrimination. See Chapter 6.2 on switching costs and price discrimination. 20 Since the time of data collection thebigstore was added to bestbookbuys’ list. 21 See Chapter 4.1 Why Books? 22 I generate 10 random numbers in Microsoft Excel. If the number begins with a 1 or 0, a potentially random ISBN, I feed it into the search engine of bestbookbuys. Potentially valid numbers are continuously attempted until data from 45 valid ISBN’s are obtained.
  • 26. price, shipping time, availability, and total time.23 Dummy variables24 are created for each of the 105 observed books and the 14 retailers.25 Dummies are also created for bestsellers and their different categories. Finally, a dummy variable is created for each “type” of book. Specifically, I create a dummy for each of the 4 types of New York Times Bestsellers26 : bestsellers (overall), paperbacks (overall) and hardcovers (overall). There are a total of 136 dummy variables. I find, after summarizing overall statistics, alllbooks4less, bookpool, classbook, and hamiltonbooks27 do not contain a significant amount of observations and are therefore excluded from my regression model. 4.4 Biases in Data Although I specifically use bestbookbuys to prevent biases in my data, numerous variables cannot be determined until the book is actually ordered, processed, and then shipped. As I mention in the footnote during my discussion of the returned values associated with availability and shipping times, the range of the value returned are only estimated ranges. While I take the arithmetic mean to best estimate availability and shipping time, I have no way of determining the data’s true statistical distribution. Consequently, I feel that precise knowledge of the distribution of availability and shipping time will significantly alter my results as my two most important explanatory variables are estimated in this manner. 23 See Table of Variables for descriptions of each variable. 24 Explanatory variables that are qualitative in nature. This study utilizes both intercept and slope dummies. 25 For most books, bestbookbuys.com returns data on the same 10-14 top retailers. Although bestbookbuys.com claims to be comprehensive, it never returns extensive observations. 9 of the 28 retailers it searches returned no data in any of the searches. 26 Paperback Fiction, Paperback Non-Fiction, Hardcover Fiction, Hardcover Non-fiction. 27 These companies specialize, respectively, in children’s books, technical books, college textbooks, and surplus books. Their lack of overall coverage accounts for their small number of observations (<ten).
  • 27. Nevertheless, these precise measures are unattainable unless each individual book is ordered from each retailer - a process that is prohibitively expensive. An additional problem arises when multiple books are purchased in a single order. Unlike the “premium” sites that instantly convey to the user the availability of each book, many smaller sites do not provide this information. Moreover, smaller sites appear to have more variation in availability ranges for their books. Consequently, the total range of availability for books on these sites may increase with each additional book ordered. However, this is not applicable when books are shipped gratis as they become available, which is the case at many retailers.
  • 28. 5. Branding and Loss Leader Pricing Theory The recent explosion in capital valuations of Internet related companies has prompted many academics to explain the reasons for this tremendous growth and its economic consequences. Additionally, much attention is spent attempting to explain pricing and consumer behavior on the Internet. 5.1 The Internet and Branding One factor prohibiting many consumers from utilizing electronic markets is a general lack of trust. Fortunately for both firms and consumers, consumer lack of trust is dissipating over time as consumers become more comfortable with electronic commerce. Moreover, companies such as bizrate that systematically rate sites according to trustworthiness strengthen consumer trust. Nevertheless, consumer trust is still an issue and partly responsible for the millions of dollars expended on advertising. However, trust is only one of many reasons a firm needs to establish a strong brand name. According to Morgan Stanley, studies show once users become familiar and comfortable with the web, they limit themselves to the pages they are familiar with. The consequence, according to Morgan Stanley, is a few leading brands in each retail category (Morgan Stanley 1997). The obvious outcome of this behavior is a lack of motivation to search, even if lower prices and better services can be obtained. The importance of consumer familiarity furthers the need for firms to aggressively brand themselves against their competitors. Aggressive
  • 29. branding is evident on the web. Not only are a large percentage of firm revenues reinvested in adverting out of the Internet realm, but also most Internet sites are peppered with banners assertively promoting goods and services. Morgan Stanley also argues that Amazon's strong brand name and customer loyalty will create barriers to entry within the online book industry despite the relative ease with which a company may enter the industry28 . Amazon has become a “buzz word” that users have come to know and trust (Morgan Stanley 1997). Their ability, through branding and market power to exercise monopolistic barriers to entry further debunks recent figurations of the Internet as frictionless marketplace since free entry and exit are key assumptions in the economic model for perfect competition. Coupons The striking quality of Internet coupon codes is their relatively large redeemable values. For example, Vitamins.com offers fifteen dollars off any purchase of fifteen dollars or more and free shipping. Moreover, nascent firms circulate coupon codes redeemable for thirty to fifty percent off any purchase.29 An interesting characteristic of Internet coupon codes is their lack of specificity. Many third party sites, such as hotdeals, esmarts, and cleverclicksters, gather coupon codes marketed to specific users via email marketing, newspapers, and magazines, and make them available, at no cost, to visitors of their websites. 28 Legal or natural impediments protecting a firm from competition from potential new entrants.
  • 30. Recently, many firms have been only accepting coupon codes accompanied by a specific email address that may only be used only once. Nevertheless, despite the drastic discounts these coupon codes offer consumers and the losses theoretically incurred by the firms offering them, branding is paramount on the web and firms, to avoid shutdown, must establish a brand name at any cost. When non-discriminate30 coupon codes are considered, price variation increases drastically. However, only savvy and price sensitive consumers are aware of the ease and universality of coupon codes31 . Notwithstanding, the extreme discounts offered by coupon codes contribute to online price dispersion. 5.2 Loss leader Pricing Theory While loss leader pricing is most often observed in grocery stores, I believe it is also evident in the online book industry. Loss leader pricing is a promotional strategy but markedly different from others in this category. Firstly, retailers do not price loss leader products to yield a profit. On the other hand, retailers often price loss leader goods at or below marginal cost and therefore sustain a loss. Secondly, loss leader goods are heavily advertised and intended to bring consumers into a store [or a website] (Nagle 1987). Once consumers enter the store [or the website] they purchase additional goods other than the loss leader product. The firm therefore compensates for the loss on the loss leader product via sales of other products that are priced well above average cost: 29 The magnitude of these savings can be more appreciated in light of Brynjolfsson’s and Smith’s work demonstrating prices are usually less expensive on the Internet even without coupons. 30 Coupon codes that are not user specific. 31 I should note that at rating intermediaries, such as bestbookbuys, coupon codes for books are listed.
  • 31. P>AC → π>0 Customers drawn in by the loss leader product also purchase complementary products that are priced well above retail price. Companies are able to induce the purchase of complementary products as they exercise monopoly power once consumers enter the store due to the presence of high search costs (Gerntner and Hess 1991). While high search costs are not characteristic of the Internet, most consumers are not privy to searching for lower cost goods due to the presence of high switching costs32 . I believe the heavy discounts given to consumers by online book market leaders on New York Times bestsellers and the large amount of advertising spent promoting these books is evidence of a loss leader promotional strategy. The relative cheapness of bestsellers is heavily advertised on the Internet, especially by the firms leading the market. It appears that they lure customers to their web site and entice them to purchase not only bestsellers but also other books and products33 . This is evident as once consumers visit the site, they realize all books are not nearly as heavily discounted as bestsellers. Furthermore, New York Times Bestsellers are barely advertised within the web site. On the other hand, once companies lure consumers in, they heavily advertise new or selected titles34 . I believe this is precisely the marketing strategy employed by these two firms in an aggressive attempt to encourage consumers to purchase other higher priced books and products in addition to bestsellers. In effect, the figuration of 32 See Chapter 6. Switching Costs
  • 32. bestsellers as a loss leader priced commodity acts a signal that all books the site offers (and perhaps other commodities) are cheap. 33 Both Amazon and Barnes & Noble see products other than books. 34 See Appendix B for examples.
  • 33. 6. Switching Costs 6.1 Switching Costs and the Borenstein Model It has been suggested that price discrimination can occur between customers more apt to switch firms and those not likely to switch (Borenstein 1991). Although Borenstein’s study focuses on the gasoline markets, its implications extend far into the online book industry. According to Borenstein: If most of a station’s marginal buyers are deciding between buying from that station or reducing total purchases of gasoline, then the seller is a monopolist for most of its buyers and faces a demand elasticity close to or equal to the buyers elasticity of demand for the good. Alternatively, if most of a station’s marginal customers are deciding between buying from one station or switching to another then the seller is competing with other stations for its marginal customer and faces a demand elasticity that reflects the buyers’ cross elasticity of demand among sellers (Borenstein 1991). I believe Borenstein’s model offers and alternative explanation for the unexplained price variation found in Brynjolfsson and Smith’s (1999) model. When consumers are unaware of market size, or unwilling to switch from one Internet bookseller to another, which potentially may offer them books at a lower cost, the firm to which they are loyal essentially becomes a monopoly. These monopolistic capabilities arise because of the consumer’s unequivocal loyalty to the site. However, in the latter case of Borenstein’s assertion, in which consumers contemplate switching to another seller, the demand elasticity of both sellers changes. Unlike Borenstein’s model, this does not necessarily reflect upon the consumer’s cross-price elasticity of demand35 when considering online 35 Ceteris parabis, the responsiveness of the demand for a good relative to the price of a substitute or complement. Calculated as the percentage change in the quantity demanded of the good divided by the percentage change in the price of the substitute or complement.
  • 34. markets because Internet consumers are not necessarily only concerned with price. Numerous other factors play a key role in consumer's consumption decisions36 . The Internet offers its users a personalized bundled product37 unparalleled by any homogenous counterpart in the conventional sector. Studies have shown that the Internet’s unique service features, specifically those offered by firms like Amazon, involve and engage their customers and retain them for reasons not necessarily related to price (Morgan Stanley 1997). Customers also return to utilize customer editorials, recommendations, and other services. This violation of Borenstein’s model problematizes the actual costs a consumer faces when contemplating switching from one online bookstore to another. Switching costs are therefore not only a function of time, but also of the opportunity cost of forgoing a relationship with a firm. One of the unique features of certain online booksellers is their ability to guide tastes and preferences. Once a consumer is registered with a firm (e.g., Amazon) and has explicated his tastes and preferences via purchase history from that firm, he is subsequently given recommendations for other titles in accord with his preferences. To switch to another online bookseller he faces the opportunity cost of forgoing valuable book recommendations for titles he may not otherwise have been aware of. This cost must be weighed against the benefit of a lower cost.38 The consequence of the switch is not only detrimental to the consumer but to the original seller as well. The opportunity cost of switching becomes higher as more books are ordered 36 The factors being those discussed throughout this thesis. 37 See Appendix C. 38 Assuming a duopolistic model, the lower the cost at the other bookstore, the more apt the consumer is to switch.
  • 35. from a firm employing such a strategy. Therefore, when leaving a firm that guides tastes and preferences: Switching Costs=ƒ(time, price, buying pattern monitoring, δZ) Where Z is all other service variables either lost or gained in the switch. It is important to realize that “buying pattern monitoring” is not always a desirable service. Many Internet consumers are concerned with privacy and may not trust an online firm that closely monitors buying patterns and behavior and then aggressively markets products. Observed marketing may actually cause a consumer to switch to another site offering higher prices. Trust is of paramount importance in Internet markets and any monitoring, whether it benefits the consumer, may trigger switching (Brynjolfsson and Smith 1999). 6.2 Switching Costs and Price Discrimination Joseph Bailey aptly notes that an essential requirement of price discrimination is market power. However, on the Internet firms may not be able to charge higher prices since consumers can easily choose another site. If a site’s prices are noticeably higher than competitor prices, consumers are apt to switch (Bailey 1998). In accord with Bailey’s price discrimination model, we should expect to see those firms with a higher market share possessing the capacity to price discriminate. Therefore, based on his assumptions, firms with higher market shares should be price makers and those with little market shares, price takers. My econometric analysis loosely supports these assertions. Firstly, while the dependent variable (post) increases when Amazon and Barnes & Noble are
  • 36. present in my model, these two variables are not statistically significant. Nevertheless, the smaller, and unbranded firms with significant p-values are substantially lower in price than Amazon and Barnes & Noble and cause the dependent variable (post) to fall. I believe issues of branding accounts for the disparity Bailey seems unable to adequately explain. Moreover, Internet price discrimination is theoretically possible given some firm’s abilities to exercise monopoly power once a consumer has opted not to switch. I have already discussed the advantages of buying pattern monitoring with relation to product suggestion and awareness to otherwise product oblivion. However, it may not always be in the consumer’s best interest to allow the monitoring of purchase patterns and site navigation behavior as it may allow firms to price discriminate against the consumer. Bailey notes that many firms offer superior search services that direct consumers toward particular products while forcing the customer to reveal invaluable marketing information to the firm. Bailey argues the optimal consumer strategy is to gather the firm assisted information and then anonymously switch to another firm offering a lower price. He argues that if the consumer does not switch the firm will follow their optimal strategy of discrimination against the consumer. He nevertheless notes that price discrimination may harm a firm’s reputation and lead to mistrust. 6.3 Switching Cost and Intermediation
  • 37. The myriad of online booksellers has given rise to intermediaries that search and find queried books at the lowest cost. The presence of online intermediaries both complicates and facilitates the ease of switching. An OECD study measures the implications of financial intermediaries in the online book, CD, and software industries (OECD 1998). The authors discover that while software intermediaries have the capacity to help consumers obtain a lower price, some third-party rating sites may be biased as some firms block pricing information. Although this information is not observed for book or software intermediaries, the capacity for price blocking exists. Price blocking probably does not exist because any firm blocking prices from a third-party query might be rendered suspicious and untrustworthy. A second issue arising from financial intermediaries is the reliability of reported information. Unless the intermediary derives prices directly from the company’s web page, prices may be unreliable and the consumer may become vulnerable to a “bait-and-switch” tactic (OECD 1998). According to this theory, the customer is shown the low price for one good, and then, once the consumer visits the web site, he is shown another higher priced substitute. A bait-and- switch practice involves the purchase of substitute products when advertised, but low-stocked brands, are unavailable. Their article states many reasons this tactic is more effective in brick and mortar retailers and how it may be rendered ineffective on the Internet for reasons of difficulty in execution. I partially disagree with the OECD since sites offering their users book recommendations
  • 38. may suggest products similar to the bait-and-switch product the consumer may have been previously unaware of and privy toward purchasing. Although a bait-and-switch strategy is similar to loss leader pricing I do not believe it is practiced by market leading firms. At the time of data collection, Barnes & Noble had every New York Time Bestseller “in stock.” Amazon, however, had only ninety-five percent of its bestsellers “in stock” despite heavy advertising. It is possible to argue Amazon uses a bait and switch tactic although highly unlikely since any site engaging in such behavior might be perceived as untrustworthy. It is interesting to note, however, that all the other non-bestseller books I examine are in stock at Amazon. Furthermore, given the presence of fifteen book intermediary search sites, those companies reporting unreliable information or creating fallacious links should theoretically not survive in the long run especially because search intermediaries are ranked by branded web sites as well as popular Internet magazines and publications.
  • 39. 7. Econometric Framework and Modeling 7.1 Descriptive Statistics Descriptive Statistics for all 1,406 Observations Variable Mean Price Standard Deviation Minimum Value Maximum Value AVAIL (Availability) 2.029 1.567 1 12 POST (Posted Price) 13.897 14.181 1.95 178.98 SHTIME (Shipping Time) 5.494 2.699 1.5 12.5 SHCOST (Shipping & Handling Cost) 3.637 1.255 0 4.95 TC (Total Cost) 17.526 14.138 2.59 183.93 TTIME (Total Time) 7.523 3.176 2.5 15
  • 40. 7.2 Table Of Variables Table of Variables Explanatory Variables (Non-Dummy) avail The average time, measured in days, between the placement of order and delivery to shipping provider. shtime The average time, measured in days, between shipment and delivery to customer. ttime Total Time=avail+shtime post The posted, or raw price of specified book (excludes shipping & handling, taxes, and any other applicable charges). shcost The cost of a firm’s standard/economy shipping for one book. tc Total Cost=post + shcost Dummy Variables39 Firm Dummies (14 Total) amzn (Amazon.com) bandn (Barnes & Noble) bigwrd (Bigwords.com) bkamil (Books A Million) bkstrt (1 Book Street) border (Borders) buycom (Buy.com) ecamp (Ecampus.com) fatbra (Fatbrain.com) kngbok (Kingbooks.com) pagone (Pageone.com) powell (Powells.com) varsit (Varsitybooks.com) tbac (Textbooksatcost.com) leaders amzn+bandn Books Dummies (105 Total) book001-105 Each book examined in study book001-030 New York Times Bestsellers Paperback book031-075 Randomly selected books book076-105 New York Times Bestsellers Hardcover paperback Paperback book companies Group of all companies examined companieslead Group of all companies examined less leaders 39 All dummy variables assume a value of 1 if True and 0 if False
  • 41. 7.3 Formal Regression Models Formal Regression Model For All Books (Regression 1) POST = α + β1SHCOST + β2TTIME + β3AMZN + β4BANDN + β5(NYTPF+NYTPNF) + β6(NYTHCF+NYTHCNF) + β7AMZN*(NYTPF+NYTPNF) + β8AMZN*(NYTHCF+NYTHCNF) + β9BANDN*(NYTPF+NYTPNF) + β10BANDN*(NYTHCF+NYTHCNF) + β11PAPERBACK + β12BIGWRD + β13BKAMIL + β14BKSTRT + β15BORDER + β16BUYCOM + β17ECAMP + β18FATBRA + β19KNGBOK + β20POWELL + β21TBAC + δZ + ε Where z is the vector of all other dummies, including 104 book dummies, and δ is the corresponding vector of coefficients.40 40 I exclude the last book dummy (Book105) to prevent collinearity. Also, I exclude varsit for the same reason.
  • 42. Dependent Variable: POST (in dollars) N=1406 observations Explanatory Variables Coefficient Std. Error t-Statistic Prob. Intercept 33.057 3.887 8.505 0.000 SHCOST 0.473 0.180 2.630 0.009 TTIME 0.264 0.074 3.581 0.000 AMZN 0.168 0.432 0.389 0.698 BANDN -0.024 0.420 -0.057 0.955 NYTPF+NYTPNF -9.222 3.244 -2.842 0.005 NYTHCF+NYTHCNF -18.924 3.735 -5.066 0.000 AMZN*(NYTPF+NYTP NF) -2.641 0.640 -4.126 0.000 AMZN*(NYTHCF+NYT HCNF) -3.797 0.633 -6.001 0.000 BANDN*(NYTPF+NYT PNF) -2.279 0.622 -3.666 0.000 BANDN*(NYTHCF+NY THCNF) -3.110 0.621 -5.010 0.000 PAPERBACK -21.543 2.663 -8.090 0.000 BIGWRD -0.068 0.341 -0.200 0.841 BKAMIL -2.369 0.315 -7.531 0.000 BKSTRT 0.023 0.704 0.033 0.974 BORDER -2.540 0.310 -8.190 0.000 BUYCOM -2.668 0.333 -8.017 0.000 ECAMP -0.201 0.837 -0.241 0.810 FATBRA -0.294 0.335 -0.879 0.380 KNGBOK -0.387 0.536 -0.721 0.471 POWELL 1.037 0.734 1.412 0.158 TBAC -2.318 0.464 -4.992 0.000 R-squared 0.971270 Mean dependent var 13.89772 Adjusted R-squared 0.968464 S.D. dependent var 14.18615 S.E. of regression 2.519221 Akaike info criterion 4.771119 Sum squared resid 8123.485 Schwarz criterion 5.241469 Log likelihood -3228.097 F-statistic 346.1805 Durbin-Watson stat 1.163796 Prob (F-statistic) 0.000000
  • 43. Formal Regression Model For All Paperbacks41 (Regression 2) POST = α + β1SHCOST + β2TTIME + β3AMZN +β4BANDN + β5(NYTPF+NYTPNF) + β6AMZN*(NYTPF+NYTPNF) + β7BANDN*(NYTPF+NYTPNF) + β8BIGWRD + β9BKAMIL + β10BKSTRT + β11BORDER + β12BUYCOM + β13ECAMP + β14FATBRA + β15KNGBOK + β16POWELL + β17TBAC + δZ + ε Where z is the vector of all other dummies, including 71 book dummies, and δ is the corresponding vector of coefficients.42 41 Includes paperback bestsellers. 42 I exclude the last book dummy (Book075) to prevent collinearity. Also, I exclude varsit for the same reason.
  • 44. Dependent Variable: POST (in dollars) N=1406 observations Explanatory Variables Coefficient Std. Error t-Statistic Prob. Intercept 23.370 2.006 11.650 0.000 SHCOST -1.106 0.359 -3.080 0.002 TTIME 0.860 0.209 4.119 0.000 AMZN -1.635 0.683 -2.394 0.017 BANDN -1.364 0.652 -2.092 0.037 NYTPF+NYTPNF -13.111 3.471 -3.778 0.000 AMZN*(NYTPF+NYTP NF) -0.856 1.132 -0.756 0.450 BANDN*(NYTPF+NYT PNF) -1.225 1.099 -1.114 0.265 BIGWRD -2.826 0.612 -4.615 0.000 BKAMIL -4.607 1.360 -3.386 0.001 BKSTRT -2.370 0.615 -3.854 0.000 BORDER -3.123 0.645 -4.841 0.000 BUYCOM -7.493 1.738 -4.312 0.000 ECAMP -0.352 0.641 -0.548 0.584 FATBRA -0.934 1.154 -0.810 0.418 KNGBOK 2.684 1.688 1.590 0.112 POWELL -4.407 1.195 -3.689 0.000 TBAC 23.370 2.006 11.650 0.000 R-squared 0.889701 Mean dependent var 13.8977 Adjusted R-squared 0.882152 S.D. dependent var 14.1862 S.E. of regression 4.869964 Akaike info criterion 6.06658 Sum squared resid 31187.26 Schwarz criterion 6.40628 Log likelihood -4173.808 F-statistic 117.857 Durbin-Watson stat 0.455668 Prob (F-statistic) 0
  • 45. 8. Econometric Analysis and Conclusions 8.1 The relationship between shipping & handling and the posted price The econometric analysis supports some of my theoretical ideas. The data indicates that as the posted price increases, the shipping & handling price increases. Therefore, with great confidence (p-value<.001), 43 I cannot reject the null hypothesis that there is a negative correlation between the posted and shipping & handling price of a given book (Hypothesis D). This demonstrates that firms offering a lower posted price do not, on average, employ a strategy in which they markedly increase the total price via imposing on the consumer a comparatively higher shipping price during “checkout.” This may indicate that the price retailers charge consumers for shipping & handling reflects firm costs and does not reflect any firm’s profit strategy. The correlation is problematic because the time in-between the realization of the posted and shipping & handling price is never recorded. Asymmetry in consumer awareness of components of total costs may potentially affect consumer price sensitivity and therefore render my results somewhat problematic. Although I believe incorporating such a measure will prove invaluable to this study, accuracy and objectivity will be impossible to incorporate. Firstly, the actual “time” lag between realizations is based upon connection technology as well as 43 The p-value or observed significance level of a statistical test is the smallest value of α for which H0 can be rejected. It is the actual risk of committing a Type I error, if H0 is rejected based on the observed value f the test statistic. The p-value measures the strength of the evidence against H0.
  • 46. Internet congestion.44 Furthermore, realization of shipping & handling prices, unless the site’s prices are overtly clandestine, (i.e., there is no way to know them until checkout) are also difficult to measure because of subjectivity in consumer awareness and sensitivity. Unlike my analysis, which assumes a single book per order, Brynjolfsson and Smith assume that customers purchase, on average, three titles per order. Their quantity is derived via a study that finds consumers of Amazon purchase an average of 2.8 titles per order. I believe that because the marginal shipping cost approaches the variable shipping cost as the quantity of books ordered increases, consumers should be enticed to purchase more books in the same order: SHCOST=FC(X) + (VC(Y))(QTY(Z)) As Z → ∞ , MCSHCOST → $0.99 I only assume one book per order because I am interested in the raw relationship between the posted price and shipping & handling price. Furthermore, I test the relationship between the posted price and availability and shipping time. Adding multiple books to an order basket complicates my objective as: 1) Consumers may not necessarily be able to calculate the spread of availability and shipping time at the time of order execution. 2) Dynamic shipping and availability times may result in multiple shipments per order and bias any measure, assumption, or analysis of consumer time sensitivities. 44 For example, those consumers with a modem will experience a greater time lag between realizations vs. a consumer with a T1 or T3 network connection. In addition, those consumers “surfing” during business hours, when Internet congestion is high, are also more apt to encounter longer lag times.
  • 47. Therefore, a dynamic regression model that continually recounts for an increasing number of books ordered, might yield different results. Each firm that engages in two price shipping strategies charges a different amount for the fixed cost and additional cost. Furthermore, a number of firms charge only a fixed amount independent of quantity. A further study measuring consumer sensitivity to different shipping pricing policies and their effects on total quantity may help give a refined and more precise measure of the correlation. Regression 2, which only includes paperbacks (including bestsellers), yields dramatically different results. The strong negative coefficient (-1.06) and significance (p-value<. 001) allows me to reject Hypothesis D. The results are somewhat enigmatic but do support my theoretical ideas put forth in Hypothesis D. I posit that firms that are more likely to sell a given hardcover, relative to paperbacks, are more apt to charge a higher shipping price. Therefore, the exclusion of hardcovers from my model yields the hypothesized relationship. 8.2 Market Power, Branding, and Pricing Neither Amazon nor Barnes & Noble significantly contributes to a change in the posted price in my model. Both variables are insignificant (p-value >.10) and Barnes & Noble’s coefficient is negative, albeit only slightly. Given the market share of the market leaders, which Brynjolfsson and Smith estimate is over eighty percent for Amazon and fourteen percent for Barnes & Noble as of August 1999, (Brynjolfsson and Smith 1999) it is peculiar that prices are not significantly higher on these two sites. Despite these high market shares, the data may
  • 48. suggest both companies feel vulnerable to competition from each other and from smaller retailers. Nonetheless, given their significantly lower prices on bestsellers (p-value=0) coupled with every smaller site yielding negative coefficients, it is arguable whether Amazon & Barnes & Noble do not, in fact, charge higher than average prices. I modify my regression model to include only paperbacks (Regression 2) to test for any significant price differences. Both Amazon and Barnes & Noble are significantly more expensive than all other retailers exhibiting significant results. Therefore, I reject Hypothesis B and conclude, that for all paperbacks, books are significantly more expensive at Amazon and Barnes & Noble. I believe these results derive from the number of bestsellers in Regression 1 vs. Regression 2. Regression 1 contains 60 bestsellers. Regression 2 only contains 30. As the outcome of Regression 1 indicates, New York Times Bestsellers are priced considerably cheaper at market leader’s sites. Consequently, I believe it is the high percentage of bestsellers in my first model (57.14%) vs. (20.82%) that gives the illusion books are no less expensive at Amazon and Barnes & Noble. Another significant result arising from Regression 2 is the relationship between paperback prices at Amazon and Barnes & Noble and other retailers. I contend my econometric results indicate paperbacks are notably cheaper than hardcovers at market leading firms when both Regressions 1 and 2 are run without the inclusion of intercept terms. This observation indicates another form
  • 49. of market segmentation as both firms discriminate between consumers preferring different types of the same book. This is exemplified at both Amazon and Barnes & Noble as both firms offer on option to purchase a hardcover version instead of paperback or visa versa when a given book is selected (see Appendix D). The second intercept-free regression (Regression 4 in Appendix E) demonstrates that prices for paperbacks are significantly lower at Amazon versus Barnes & Noble. In this sense, Amazon is relatively more discriminate between prices of hardcovers and paperbacks. I argue these results indicate Amazon more aggressively price discriminates between consumers preferring paperbacks and those favoring hardcovers. 8.3 The Posted Price, Availability, and Shipping Time Given the positive relationship between the posted price and total time in the results, and their significance, I cannot reject the null hypothesis that books with a higher posted price have longer processing and delivery times (Hypothesis C). Although I find these results surprising, they are not without explanation. Availability and shipping times are not usually posted on a retailer’s web site and are not on all book search intermediary sites. Furthermore, my theoretical assumption is inextricably tied to the idea that the market leader’s price should, on average, be higher (Hypothesis B). This higher than average price should have been, in part, the reason for the speedy processing and delivery time characterizing books sold by Amazon and Barnes & Noble. However, since I
  • 50. cannot reject Hypothesis B, my posited relationship between the posted price and total time has been affected via a “domino effect” as the primary assumption (Hypothesis C) is inextricably tied to not rejecting Hypothesis B. Furthermore, consumers that are insensitive to price may also be insensitive to time. A likely strategy adopted by retailers placing a price premium on the posted price of their books may be tied to a realization that the average consumer purchasing goods are time insensitive in addition to prince insensitive. Additionally, sites affixing time premiums to their books, with the exception of Amazon, are perhaps those that do not retain customers well and will be forced to shutdown in the long run as customers realize that not only are prices comparatively high, but service is also slow. Loss leader pricing is not observed in Regression 2. The nature of paperbacks is that they are intrinsically priced lower. This problematizes the results I discuss in the next section. 8.4 Amazon, Barnes & Noble, and Bestseller Loss Leader Marketing Strategy The strong negative and both statistically and economically significant coefficients on bestseller variables for both leader firms in Regression 1 allows me to reject the null hypothesis that bestsellers act as a loss leader product for both leader firms (Hypothesis D). However, isolating my model to only paperbacks, I observe different results. I find that for paperbacks alone, no
  • 51. significant results exist. I believe these results stem from the nature of paperbacks. Comparing hardcovers and paperbacks for bestsellers only, we observe a comparatively significant price difference verses the difference between hardcovers and paperbacks for all books. Since all bestsellers are offered in either form, I claim retailers price discriminate between price sensitive consumers. This is further evident as paperbacks are not released until after hardcovers. Loss leader pricing is evident given the web images presented in Appendix B. Appendix B first shows examples of heavily advertised New York Times Bestsellers. These advertisements appear on popular sites, including Yahoo, which is one of the web’s most popular sites. In accord with loss leader pricing theory and supported by my empirical results, once the consumer clicks on these banner advertisements, they are transported to either Amazon’s or Barnes & Noble’s web page where the New York Times Bestsellers luring the consumer to the site are not immediately evident. The consumer must first click on the bestseller hyperlink and is then transported to the “bestseller” page. Still, New York Times Bestsellers do not “pop out” at the consumer. Rather, the firm’s own bestsellers are marketed to the consumer and only upon careful scrutiny of the page does the consumer find links to pages containing New York Times Bestsellers. Nevertheless, a simple search upon transport from the banner to the firm’s home page will forgo this obstacle of links. A savvy consumer will forgo clicking on banners and shop via a reputable search site such as bestbookbuys.
  • 52. While it is empirically evident that these leader firms incur a loss on this loss leader product, the idea that bestsellers are a loss leader product is made problematical by the fact that neither Amazon nor Barnes & Noble realize a profit on the non loss leader books they sell. In the last fiscal year (1999), net loss at Amazon totaled $720 million, up from $124.5 million (Yahoo Finance 2000). For Barnes & Noble, net loss fell 42% to $48.2 million in the 1999 fiscal year.
  • 53. Appendix A: Statistical Summaries of Posted Price in Varying Categories Table 1 – All Books (105) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 45 100 95.24 13.05 18.26 0.71 bandn 105 100.00 13.24 18.24 0.73 bigwrd 86 81.90 14.47 17.23 0.84 bkamil 97 92.38 10.05 6.37 1.58 bkstrt 95 90.48 12.87 9.49 1.36 border 95 90.48 10.08 6.33 1.59 buycom 84 80.00 10.09 6.47 1.56 ecamp 66 62.86 15.71 17.95 0.88 fatbra 91 86.67 11.92 7.76 1.54 kngbok 96 91.43 11.40 9.07 1.26 pagone 76 72.38 14.94 8.14 1.83 powell 100 95.24 16.04 9.57 1.68 spree 46 105 100.00 13.24 18.24 0.73 tbac 105 100.00 12.60 18.36 0.69 varsit 85 80.95 11.22 8.82 1.27 TOTAL 1406 88.00 12.67 12.13 1.215 45 I find Amazon classifies five percent of New York Times Bestsellers as “out of stock” or “on order.” This is ironic given their aggressive campaign pushing this genre of books. Furthermore, a market leader boasting efficiency and speed should ideally hold an ample number of bestsellers in its inventory or have efficient distribution channels that make them readily available. An interesting question this questionable market failure raises is: do consumers shopping for bestsellers not available at Amazon wait for availability, or buy from another retailer? I have demonstrated that it is dangerous to allow existing customers to switch to another site as studies have shown customer retention is key on the Internet as consumers are particularly choosy. Therefore, it is in Amazon’s best interest to hold an extra supply of its loss leader pricing commodity to prevent such consequences. On the other hand, New York Times Bestsellers as a loss leader product may act as a bait-and-switch good in which Amazon recommends alternative books “like” the on- order bestseller that are priced higher. This tactic, albeit unethical, would greatly increase Amazon’s profit margins. 46 Actually, Spree, as you may notice, has an identical value in every summary statistic table as Barnes & Noble. Spree is a reward intermediary site that promotes brand loyalty. Registered users of Spree earn financial rewards for shopping on branded web sites linked through their site. Although prices are identical for a book purchased via spree or Barnes & Noble, registered users of spree receive identical benefits to users of Barnes & Noble and also receive additional utility enjoyed via the free membership benefits of Spree’s web site.
  • 54. Table 2 – All Bestsellers (60) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 57 95.00 9.71 5.37 1.81 bandn 60 100.00 9.79 5.57 1.76 bigwrd 53 88.33 12.53 7.42 1.69 bkamil 59 98.33 9.38 4.94 1.90 bkstrt 56 93.33 11.83 6.32 1.87 border 58 96.67 9.29 5.28 1.76 buycom 56 93.33 9.08 5.09 1.79 ecamp 23 38.33 13.90 5.36 2.59 fatbra 55 91.67 11.33 6.27 1.81 kngbok 55 91.67 10.18 6.88 1.48 pagone 55 91.67 13.99 6.47 2.16 powell 47 66 110.00 15.56 8.39 1.85 spree 60 100.00 9.79 5.57 1.76 tbac 60 100.00 11.82 5.60 2.11 varsit 53 88.33 10.56 7.22 1.46 47 This ostensibly erroneous figure stems from Powell’s multiple book pricing strategy. Bestbookbuys sometimes reports multiple observations per book for Powell’s. These are usually categorized into “special new” or “special.” I opt to include these observations since, unlike Books A Million, prices are not limited to paying members only.
  • 55. Table 3 – All Paperbacks (72) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 69 95.83 10.00 8.85 1.13 bandn 72 100.00 9.98 9.32 1.07 bigwrd 57 79.17 9.88 9.00 1.10 bkamil 66 91.67 8.04 6.10 1.32 bkstrt 67 93.06 10.63 9.77 1.09 border 63 87.50 8.02 5.83 1.37 buycom 56 77.78 7.99 6.25 1.28 ecamp 40 55.56 13.05 10.37 1.26 fatbra 63 87.50 8.78 6.24 1.41 kngbok 69 95.83 9.57 8.95 1.07 pagone 47 65.28 12.67 8.10 1.57 powell 64 88.89 11.94 8.22 1.45 spree 72 100.00 9.98 9.32 1.07 tbac 72 100.00 8.53 8.71 0.98 varsit 57 79.17 8.88 7.52 1.18 TOTAL 934 86.48 9.75 8.167 1.19
  • 56. Table 4 – All Hardcover (33) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 31 93.94 19.83 29.21 0.68 bandn 33 100.00 20.36 28.51 0.71 bigwrd 29 87.88 23.49 24.73 0.95 bkamil 31 93.94 14.34 4.61 3.11 bkstrt 28 84.85 18.23 6.17 2.96 border 32 96.97 14.15 5.25 2.70 buycom 28 84.85 14.31 4.63 3.09 ecamp 26 78.79 19.81 25.32 0.78 fatbra 28 84.85 18.97 6.07 3.12 kngbok 27 81.82 16.09 7.70 2.09 pagone 29 87.88 18.60 6.88 2.70 powell 36 109.09 23.32 7.25 3.22 spree 33 100.00 20.36 28.51 0.71 tbac 33 100.00 21.48 28.44 0.76 varsit 28 84.85 15.96 9.49 1.68
  • 57. Table 5 – Paperback Bestsellers (30) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 28 93.33 5.19 1.51 3.45 bandn 30 100.00 5.09 1.50 3.38 bigwrd 27 90.00 7.06 2.84 2.48 bkamil 30 100.00 5.26 1.43 3.68 bkstrt 30 100.00 7.06 2.10 3.36 border 29 96.67 5.14 1.51 3.42 buycom 30 100.00 5.11 1.50 3.41 ecamp 0 0.00 N/A N/A N/A fatbra 30 100.00 6.20 3.20 1.94 kngbok 30 100.00 6.05 3.32 1.82 pagone 28 93.33 10.09 3.38 2.99 powell 34 113.33 9.15 3.80 2.41 spree 30 100.00 5.09 1.50 3.38 tbac 30 100.00 6.67 2.19 3.05 varsit 27 90.00 6.50 2.33 2.79
  • 58. Table 6 – Hardcover Bestsellers (30) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 29 96.67 14.07 3.91 3.60 bandn 30 100.00 14.48 3.89 3.72 bigwrd 26 86.67 18.20 6.36 2.86 bkamil 29 96.67 13.64 3.38 4.04 bkstrt 26 86.67 17.34 4.88 3.55 border 29 96.67 13.45 4.34 3.10 buycom 26 86.67 13.66 3.69 3.70 ecamp 23 76.67 13.90 5.36 2.59 fatbra 25 83.33 17.48 1.98 8.83 kngbok 25 83.33 15.14 6.79 2.23 pagone 27 90.00 18.03 6.47 2.79 powell 32 106.67 22.37 6.26 3.57 spree 30 100.00 14.48 3.89 3.72 tbac 30 100.00 16.97 2.05 8.27 varsit 26 86.67 14.78 8.16 1.81
  • 59. Table 7 – Total Bestsellers (Fiction) (30) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 27 90.00 9.32 5.14 1.81 bandn 29 96.67 9.07 5.32 1.70 bigwrd 25 83.33 12.03 8.17 1.47 bkamil 28 93.33 8.82 4.98 1.77 bkstrt 27 90.00 10.81 5.40 2.00 border 28 93.33 8.48 4.92 1.72 buycom 26 86.67 8.26 4.85 1.70 ecamp 11 36.67 14.27 4.86 2.94 fatbra 25 83.33 10.50 6.37 1.65 kngbok 26 86.67 9.17 6.51 1.41 pagone 27 90.00 13.59 7.16 1.90 powell 32 106.67 15.40 8.43 1.83 spree 29 96.67 9.07 5.32 1.70 tbac 29 96.67 11.40 5.96 1.91 varsit 24 80.00 9.89 7.57 1.31
  • 60. Table 8 – Total Bestsellers (Non-Fiction) (30) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 30 100.00 10.07 5.63 1.79 bandn 30 100.00 10.07 5.63 1.79 bigwrd 28 93.33 12.97 6.81 1.90 bkamil 31 103.33 9.89 4.92 2.01 bkstrt 29 96.67 12.78 7.03 1.82 border 30 100.00 10.05 5.58 1.80 buycom 30 100.00 9.79 5.26 1.86 ecamp 12 40.00 13.57 5.98 2.27 fatbra 30 100.00 12.02 6.21 1.94 kngbok 29 96.67 11.09 7.19 1.54 pagone 28 93.33 14.38 5.84 2.46 powell 34 113.33 15.71 8.47 1.85 spree 30 100.00 10.07 5.63 1.79 tbac 30 100.00 12.21 5.31 2.30 varsit 29 96.67 11.11 7.00 1.59
  • 61. Table 9 – Fiction Bestseller Paperbacks (15) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 13 86.67 4.38 1.33 3.28 bandn 15 100.00 4.29 1.26 3.40 bigwrd 13 86.67 6.28 2.06 3.05 bkamil 15 100.00 4.49 1.30 3.47 bkstrt 15 100.00 6.35 1.55 4.10 border 14 93.33 4.34 1.30 3.34 buycom 15 100.00 4.35 1.31 3.33 fatbra 15 100.00 5.61 2.25 2.50 kngbok 15 100.00 5.67 2.25 2.52 pagone 13 86.67 8.84 2.63 3.36 powell 15 100.00 8.05 2.79 2.89 spree 15 100.00 4.29 1.26 3.40 tbac 15 100.00 5.90 1.68 3.52 varsit 12 80.00 5.78 1.61 3.58
  • 62. Table 10 – Non-Fiction Bestseller Paperbacks (15) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 15 100.00 5.89 1.31 4.50 bandn 15 100.00 5.89 1.31 4.48 bigwrd 14 93.33 7.79 3.33 2.34 bkamil 15 100.00 6.03 1.14 5.30 bkstrt 15 100.00 7.77 2.38 3.26 border 15 100.00 5.89 1.31 4.50 buycom 15 100.00 5.87 1.31 4.47 ecamp 0 0.00 N/A N/A N/A fatbra 15 100.00 6.79 3.92 1.73 kngbok 15 100.00 6.43 4.18 1.54 pagone 15 100.00 11.18 3.66 3.06 powell 19 126.67 10.02 4.32 2.32 spree 15 100.00 5.89 1.31 4.48 tbac 15 100.00 7.43 2.42 3.08 varsit 15 100.00 7.07 2.68 2.64
  • 63. Table 11 – Fiction Bestseller Hardcover (15) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 14 46.67 13.90 2.06 6.75 bandn 14 46.67 14.20 2.16 6.59 bigwrd 12 40.00 18.26 7.71 2.37 bkamil 13 43.33 13.81 1.86 7.44 bkstrt 12 40.00 16.39 2.22 7.38 border 14 46.67 12.62 3.40 3.71 buycom 11 36.67 13.59 1.52 8.94 ecamp 11 36.67 14.27 4.86 2.94 fatbra 10 33.33 17.83 1.00 17.89 kngbok 11 36.67 13.95 7.43 1.88 pagone 14 46.67 18.00 7.26 2.48 powell 17 56.67 21.89 5.96 3.67 spree 14 46.67 14.20 2.16 6.59 tbac 14 46.67 17.29 1.18 14.68 varsit 12 40.00 14.00 8.97 1.56
  • 64. Table 12 – Non-Fiction Bestseller Hardcover (15) Firm Obs. % Total Availability Mean Price Standard Deviation Coefficient Of Variation amzn 15 100.00 14.24 5.15 2.76 bandn 10 100.00 14.24 5.15 2.76 bigwrd 14 93.33 18.15 5.23 3.47 bkamil 15 100.00 13.50 4.30 3.14 bkstrt 14 93.33 18.16 6.33 2.87 border 15 100.00 14.21 5.07 2.81 buycom 15 100.00 13.71 4.76 2.88 ecamp 12 80.00 13.57 5.98 2.27 fatbra 15 100.00 17.25 2.44 7.08 kngbok 14 93.33 16.08 6.37 2.52 pagone 13 86.67 18.07 5.80 3.12 powell 15 100.00 22.91 6.75 3.39 spree 15 100.00 14.73 5.01 3.14 tbac 10 100.00 16.70 2.60 6.42 varsit 14 93.33 15.44 7.67 2.01
  • 65. Appendix B: Visual Examples of Loss Leader Pricing Strategy 1) Amazon Advertisements for Amazon
  • 68. 2) Barnes & Noble Advertisements For Barnes and Noble
  • 69. Barnes & Noble’s Home Page
  • 70. Barnes & Noble’s Bestseller Page
  • 72. Appendix D: Visual Example of Consumer Choice and Market Segmentation at Amazon
  • 73. Appendix E: Intercept-Free Regression Modeling Modified Regression Model For All Books (Regression 3) POST = β1SHCOST + β2TTIME + β3AMZN + β4BANDN + β5(NYTPF+NYTPNF) + β6(NYTHCF+NYTHCNF) + β7AMZN*(NYTPF+NYTPNF) + β8AMZN*(NYTHCF+NYTHCNF) + β9BANDN*(NYTPF+NYTPNF) + β10BANDN*(NYTHCF+NYTHCNF) + β11PAPERBACK + β12BIGWRD + β13BKAMIL + β14BKSTRT + β15BORDER + β16BUYCOM + β17ECAMP + β18FATBRA + β19KNGBOK + β20POWELL + β21TBAC + δZ + ε Where z is the vector of all other dummies, including 104 book dummies, and δ is the corresponding vector of coefficients.
  • 74. Dependent Variable: POST (in dollars) N=1406 observations Explanatory Variables Coefficient Std. Error t-Statistic Prob. SHCOST 3.050 0.431 7.079 0.000 TTIME 0.946 0.183 5.160 0.000 AMZN -0.472 1.144 -0.412 0.680 BANDN 1.053 1.095 0.961 0.337 NYTPF+NYTPNF 38.564 5.487 7.028 0.000 NYTHCF+NYTHCNF -3.665 2.745 -1.335 0.182 AMZN*(NYTPF+NYTP NF) -1.444 1.680 -0.860 0.390 AMZN*(NYTHCF+NYT HCNF) -2.958 1.662 -1.780 0.075 BANDN*(NYTPF+NYT PNF) -2.352 1.622 -1.450 0.147 BANDN*(NYTHCF+NY THCNF) -3.590 1.621 -2.215 0.027 PAPERBACK -52.807 2.639 -20.010 0.000 BIGWRD -1.734 0.890 -1.948 0.052 BKAMIL -2.222 0.821 -2.706 0.007 BKSTRT 2.585 1.835 1.409 0.159 BORDER -2.008 0.808 -2.483 0.013 BUYCOM -2.654 0.869 -3.055 0.002 ECAMP 12.209 2.008 6.079 0.000 FATBRA -0.021 0.872 -0.024 0.981 KNGBOK 0.319 1.401 0.228 0.820 POWELL -4.066 1.882 -2.161 0.031 TBAC -5.051 1.174 -4.303 0.000 R-squared 0.804151 Mean dependent var 13.89273 Adjusted R-squared 0.785178 S.D. dependent var 14.18828 S.E. of regression 6.576119 Akaike info criterion 6.689525 Sum squared resid 55354.03 Schwarz criterion 7.156411 Log likelihood -4574.39 F-statistic 1.532244 Durbin-Watson stat 0.804151 Prob (F-statistic) 13.89273
  • 75. Modified Regression Model For All Paperbacks48 (Regression 4) POST = β1SHCOST + β2TTIME + β3AMZN +β4BANDN + β5(NYTPF+NYTPNF) + β6AMZN*(NYTPF+NYTPNF) + β7BANDN*(NYTPF+NYTPNF) + β8BIGWRD + β9BKAMIL + β10BKSTRT + β11BORDER + β12BUYCOM + β13ECAMP + β14FATBRA + β15KNGBOK + β16POWELL + β17TBAC + δZ + ε Where z is the vector of all other dummies, including 71 book dummies, and δ is the corresponding vector of coefficients. 48 Includes paperback bestsellers.
  • 76. Dependent Variable: POST (in dollars) N=1406 observations Explanatory Variables Coefficient Std. Error t-Statistic Prob. SHCOST 2.635 0.242 10.892 0.000 TTIME 0.548 0.363 1.512 0.131 AMZN -3.660 1.193 -3.069 0.002 BANDN -0.954 1.133 -0.842 0.400 NYTPF+NYTPNF -14.814 6.043 -2.452 0.014 AMZN*(NYTPF+NYTP NF) 0.588 1.977 0.297 0.766 BANDN*(NYTPF+NYT PNF) -1.132 1.915 -0.591 0.554 BIGWRD -0.674 1.132 -0.595 0.552 BKAMIL -3.522 1.065 -3.306 0.001 BKSTRT -0.876 2.319 -0.378 0.706 BORDER -2.944 1.069 -2.754 0.006 BUYCOM -3.747 1.123 -3.337 0.001 ECAMP 10.761 1.405 7.657 0.000 FATBRA -1.057 1.118 -0.946 0.344 KNGBOK -4.576 1.984 -2.306 0.021 POWELL -10.164 2.450 -4.149 0.000 TBAC -2.088 2.066 -1.011 0.312 R-squared 0.665019 Mean dependent var 13.89273 Adjusted R-squared 0.642347 S.D. dependent var 14.18828 S.E. of regression 8.485175 Akaike info criterion 7.176431 Sum squared resid 94677.64 Schwarz criterion 7.512589 Log likelihood -4951.44 F-statistic 1.247258 Durbin-Watson stat 0.665019 Prob (F-statistic) 13.89273
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