1944
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
business-to-consumer (B2C) auctions (Bapna,
Goes, & Gupta, 2001). In B2C auctions, large mer-
chants such as Dell, Disney, Home Depot, IBM,
Motorola, Sears, Sun Microsystems, and Sharper
Image have been able to use Internet auctions to
VHOOH[FHVVLQYHQWRU\IRUJUHDWHUSUR¿WWKDQWKH\
would receive from using a liquidator (Dholakia,
2005b; Gentry, 2003; Grow, 2002; Vogelstein,
Boyle, Lewis, & Kirkpatrick, 2004). As further
evidence of the growth of B2C Internet auctions,
E\WKH¿UVWTXDUWHURI,QWHUQHWDXFWLRQHHU
eBay alone hosted approximately 383,000 eBay
stores worldwide, including 171,000 on Web
sites other than their U.S. Web site (eBay, 2006).
$V¿UPVFRQWLQXHWRPDNHH[WHQVLYHXVHRI,Q-
ternet auctions, the interest in developing sound
guidelines for businesses as well as developing
theory to advance research will likely continue
to grow as well.
While many studies have examined the factors
W K DWGH W HU P L Q HDQD X FWLR QLWHP¶V ¿ Q DOE LGS U LFH W KH
number of bids an item receives, whether a sale
is completed, or the revenue earned by a seller,
the examination of price premiums (above-aver-
DJH¿QDOELGSULFHVLVUHODWLYHO\XQGHUVWXGLHG,Q
HFRQRPLFVSULFHSUHPLXPVDUHGH¿QHGDVSULFHV
WKDW\LHOGDERYHDYHUDJHSUR¿WV.OHLQ/HI ÀHU
1981; Shapiro, 1983). Price premiums within the
,QWHUQHW DXFWLRQ FRQWH[W KDYH EHHQ GH¿QHG DV

“the monetary amount above the average price
received by multiple sellers for a certain match-
ing product” (Ba & Pavlou, 2002, pp. 247-248).
Restated, a number of auctions exist where sell-
ers have earned above-average prices, or price
premiums, on the items they have auctioned. In
this study, we compare the group of auctions that
have achieved above-average prices with those that
KDYHQRWWRREVHUYHVLJQL¿FDQWGLIIHUHQFHV7R
our knowledge, only two studies have previously
examined price premiums (Ba & Pavlou, 2002;
Pavlou, 2002). Since it is only by maximizing
UHYHQXHDQGSUR¿WWKDWD¿UPFDQUHPDLQYLDEOH
in the marketplace (Seth & Thomas, 1994), an
increased focus on how businesses that rely upon
Internet auctions can earn price premiums may
SURYHEHQH¿FLDO7KHIRFXVRQSULFHSUHPLXPVLV
WKH¿UVWFRQWULEXWLRQRIWKLVVWXG\$VZHLQYHV-
tigate price premiums, we examine many of the
independent variables that have been considered
in previous studies to determine if they are also
predictive of price premiums. The second con-
tribution is the application of CART analysis to
Internet auctions as a tool to generate decision
rules. CART analysis is a tree-based method of
recursive partitioning for explaining or predict-
LQJDUHVSRQVHWRRUGHUYDULDEOHVE\VLJQL¿FDQFH
(Brieman, Friedman, Olshen, & Stone, 1984). It
generates decision trees and decision rules that
may be used as guidelines (by sellers in Internet

auctions, in this case). While electronic commerce
r e s e a r c h h a s d e m o n s t r a t e d t h a t C A R T a n a l y s i s c a n
be used to improve one-to-one Internet market-
ing (Kim, Lee, Shaw, Chang, & Nelson, 2001),
CART has not yet been applied to Internet auc-
tions. Thus, our study is, to our knowledge, the
¿UVWWRXVHDVWDWLVWLFDOO\EDVHGGHFLVLRQPDNLQJ
technique to demonstrate how sellers can use
quantitative data to decide how to sell products
LQ %& ,QWHUQHW DXFWLRQV 7KH WKLUG DQG ¿QDO
contribution of this study is the examination (by
CART analysis) of variables that have been found
(generally by multiple-regression analysis) to be
determinants of auction outcome in previous
VWXGLHV7KLVFRQ¿UPDWLRQRIYDULDEOHVLGHQWL¿HG
as critical factors in other types of analysis is the
third contribution of this study.
The article will be organized as follows. We
begin by reviewing literature on auctions, includ-
ing relevant research on both traditional auctions
as well as Internet auctions. Next, we present
literature on machine-learning techniques that
enable the induction of decision trees. Following
the literature review, we discuss our methods,
including our dataset, variables, and our research
GHVLJQ6SHFL¿FDOO\ZHGHVFULEH WKHFROOHFWLRQ
DQGDQDO\VLVRI¿HOGGDWDIURP,QWHUQHWDXFWLRQHHU
eBay. We then present the results of our analysis.
Following the presentation of our results, we
1945

Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
GLVFXVVRXU¿QGLQJVDQGQRWHWKHLPSOLFDWLRQVRI
RXUVWXG\)LQDOO\ZHFRQFOXGHE\EULHÀ\QRWLQJ
the limitations of our study and directions for
future research.
LITERATURE REVIEW
Literature pertinent to this study will be selectively
drawn from two areas of research. Given that one
of the objectives of this study is to investigate
factors enabling sellers to earn price premiums
LQ ,QWHUQHW DXFWLRQV WKH ¿UVW DUHD IURP ZKLFK
we draw theory is that of auction literature. An
additional objective—namely, describing a tech-
nique for developing decision rules for sellers in
Internet auctions—leads us to the second area
of research that is pertinent to the present study:
decision-tree induction techniques.
Auctions
$XFWLRQVKDYHEHHQGHVFULEHGDV³a market in-
stitution with an explicit set of rules determining
resource allocation and prices on the basis of
bids from the market participants” (McAfee &
McMillan, 1987, p. 701). A vast amount of research
addresses the topic of auctions. Numerous surveys
of auction literature can be found (Engelbrecht-
Wiggans, 1980; Klemperer, 1999, 2000; Krishna,
2002; McAfee & McMillan, 1987; Milgrom, 1985,
1986; Rothkopf & Harstad, 1994; Wilson, 1987),
including a bibliography of earlier literature (Stark
& Rothkopf, 1979) and a review of experimental

auction literature (Kagel, 1995).
Auction Mechanisms and Auction
Theory
Auction mechanisms are generally categorized
as: (1) English or ascending-price auctions; (2)
'XWFKRUGHVFHQGLQJSULFHDXFWLRQV¿UVWSULFH
sealed-bid auctions; or (4) second-price sealed bid
or Vickrey auctions (McAfee & McMillan, 1987).
A thorough description of these mechanisms can
be found in the recent work of Lucking-Reiley
(2000a). Internet auctions on eBay, the point of
data collection for this study, have been described
by scholars as a hybrid of the English and second-
price auctions (Lucking-Reiley, 2000a, 2000b;
Ward & Clark, 2002; Wilcox, 2000). Researchers
assert that eBay uses a hybrid auction type on
the grounds that the presence of a proxy-bidding
mechanism ensures that a winning bidder will pay
only one increment more than the second-highest
bidder’s price. Since this study examines only
auctions of the hybrid eBay type, a discussion
of how various types of auction mechanisms
impact auction outcome is beyond the scope of
the present study.
Auction theory is often centered around or
developed in response to the seminal work of
William Vickrey (1961), who described the In-
dependent Private Values Model (IPV). In this
model, each bidder formulates a valuation for
the item being auctioned without an awareness of

competing bidders’ valuations. Even if valuations
were shared among all bidders, each individual
bidder’s valuation would be unaffected by the
additional information that competing bidders’
v a l u a t i o n s w o u l d p r o v i d e . I n t h i s w a y, t h e b i d d e r ’s
YDOXHLVLQGHSHQGHQWRIWKHLQÀXHQFHRIFRPSHWLQJ
bidders and is privately determined. In contrast,
the Common Values Model (CV) posits that the
value of the item being auctioned is common to
all bidders, but incomplete information causes
each bidder to formulate a valuation for the item
that falls either above or below the common value
(Rothkopf, 1969; Wilson, 1969). If it is assumed
that bidders’ valuations are normally distributed
about the common value, the winner of the auc-
tion is the bidder with the valuation that is farthest
above the common value. This person incurs the
³ZLQQHU¶V FXUVH´EHFDXVH KH RU VKHKDVOLNHO\
overpaid for the item. An integrative approach,
UHIHUUHGWRDVWKH$I¿OLDWHG9DOXHV0RGHO$9
explains that bidder valuations depend upon the
bidder’s personal preferences, the preferences of
1946
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
others, and the intrinsic qualities of the item being
sold (Milgrom & Weber, 1982). Bidders’ valu-
DWLRQVDUHGHVFULEHGDVDI¿OLDWHGEHFDXVHDKLJK
valuation by one bidder makes a high valuation
by other bidders more likely (Milgrom & Weber,
1982). The AV model is a more general concep-

tualization of the valuation of items in auctions
than the IPV or CV models; both the IPV and CV
models can be understood as special cases of the
more general AV model (McAfee & McMillan,
1987). Recent studies of Internet auctions rely
upon and explicitly mention the merits of the AV
model (Dholakia & Soltysinski, 2001; Gilkeson &
Reynolds, 2003; Segev, Beam, & Shanthikumar,
2001; Wilcox, 2000). These studies empirically
validate the AV model in Internet auctions by
GHPRQVWUDWLQJ WKDW ELGGHUV PD\ EH LQÀXHQFHG
not only by their own valuation of the item, but
also by the behavior of other bidders.
Internet Auctions
Internet auctions have a relatively brief history.
Among the earliest electronic auctions were the
auctioning of pigs in Singapore (Neo, 1992) and
ÀRZHUV LQ +ROODQG YDQ +HFN  YDQ 'DPPH
1997) conducted over a LAN. Auctions on the
Internet, conducted via newsgroups and e-mail
discussion lists, were the next major development
in the Internet auction timeline (Lucking-Reiley,
1999, 2000a). The explosion in popularity of In-
ternet auctions, however, did not begin until the
1995 launches of U.S. Web sites Onsale and eBay
(Lucking-Reiley, 2000a). By 1999, there were
an estimated 200 auction sites on the Internet
(Crockett, 1999). The continued growth of Internet
auctions is demonstrated by the performance of
international industry leader eBay, a company

that operates auction Web sites in 24 countries,
includes over 180 million registered users, and
generated US$ 4.552 billion in sales in 2005 (eBay,
,QWHUQDWLRQDOFRPSHWLWLRQLQFOXGHV¿UPV
s u c h a s QX L . c o m i n E u ro p e , Ta o b a o .c o m i n A s i a ,
and MercadoLibre in Latin America. Following
0|OOHQEHUJSSZHZLOOGH¿QH
Internet auctions to mean virtual marketplaces
relying on Internet services (such as the World
Wide Web) and Internet protocols to conduct
auctions.
In spite of the relatively short history of Internet
auctions, they have begun to draw interest not
only from economists, but also from researchers
in marketing, information systems, and computer
science (see Appendix A for a selective listing of
recent studies in each of these disciplines). The
general questions that many of these studies seek
WRDQVZHUDUH³:KDWLVWKHRSWLPDOZD\WRDXFWLRQ
DQLWHP"´RU³+RZLVWKHPDUNHWSODFHFKDQJLQJ
DVDUHVXOWRI,QWHUQHWDXFWLRQV"´RU³:KDWIDF-
tors should be considered when buying or selling
in an Internet auction?” We will generally limit
our discussion of Internet auctions to empirical
studies that deal with variables that are under the
control of the seller (rather than variables under
the control of the other two parties to the auction
transaction, the auctioneer and the bidder). Since
this study focuses on developing decision rules for
sellers in single-item B2C Internet auctions, we

will reserve exploration of multi-unit auctions and
buyer behavior for other researchers. To organize
the list of variables that have been investigated
in previous studies, we introduce the categories
of: (1) selling information, (2) seller information,
(3) product information, and (4) delivery informa-
WLRQ:HZLOOGH¿QHDQGGLVFXVVHDFKRIWKHVH
categories in turn.
Selling information includes general infor-
mation about the auction and the terms of an
item’s sale. The initial bid price, the availability
of a buy-now option, the auction duration, and
the auction’s ending time are included as selling
information variables. Table 1 contains a list of
W K HVH Y D U LDE OH V  W KHL U G H¿QLW L RQV  D QGD O L VWRIV W X G -
ies in which they have been investigated. There
KDYHEHHQDQXPEHURILPSRUWDQW¿QGLQJVLQWKLV
DUHD,WKDVEHHQREVHUYHGWKDWDQLWHP¶V¿QDOELG
S U LF HF D QE H VLJQL ¿FD QWO\D I IH FW H GE \L WVL Q LW L DOELG 
price (Brint, 2003). Bidders have been found to
1947
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
sometimes ignore a buy-now option even when
buy-now prices are set below prevailing market
SULFHV 6WDQGL¿UG 5RHORIV  'XUKDP 
Setting a buy-now price may, however, enhance
revenue for sellers (Budish & Takeyama, 2001)
in some situations. The time of day or week that
an auction ends, and the duration of an auction
are frequently used as either control variables

or dependent variables (Bruce, Haruvy, & Rao,
2004; Dholakia & Soltysinski, 2001; Gilkeson
& Reynolds, 2003; McDonald & Slawson, 2002;
6W DQGL ¿UG 6W DQGL¿UG5RHORIV'X UKDP
2004; Subramaniam, Mittal, & Inman, 2004), but
have not, to our knowledge, been conclusively
linked to higher closing prices.
Seller informationLVGH¿QHGDVWKHYDULRXV
facets of the seller’s feedback rating. The ease
with which buyers are able to provide feedback
has made a seller’s feedback rating one of the most
VLJQL¿FDQWSUHGLFWRUVRIDXFWLRQFORVLQJSULFH
Feedback mechanisms can help sellers earn higher
prices (Bruce, Haruvy, & Rao, 2004; McDonald
& Slawson, 2002; Ottaway, Bruneau, & Evans,
2003) and have been shown in one previous study
to play a role in generating price premiums for
reputable sellers (Ba & Pavlou, 2002). The number
of positive feedback ratings and the number of
negative feedback ratings are included as seller
information variables in this study (see Table 1).
We investigate both positive as well as negative
feedback, because it has been found that positive
and negative feedback have an asymmetrical
HIIHFWXSRQWKH¿QDOELGSULFH6SHFL¿FDOO\SRVL-
WLYHIHHGEDFNLVPLOGO\LQÀXHQWLDOLQGHWHUPLQLQJ
¿QDOELGSULFHZKLOHQHJDWLYHIHHGEDFNLVKLJKO\
LQÀXHQWLDO6WDQGL¿UG7KXVLWKDVEHHQ
clearly demonstrated that seller information is
also an important subset of variables to examine

when researching Internet auctions.
Product information r e f e r s t o t h e i n f o r m a t i o n
provided by the seller or by other bidders about
the item being auctioned. Frequently, product
information is measured by recording the num-
ber of pictures of an item and the number of bids
which an item receives (see Table 1). One study
has explained that pictures of an item being
auctioned on the Internet may affect information
SURFHVVLQJDQGXOWLPDWHO\WKHLWHP¶V¿QDOFORVLQJ
price (Ottaway, Bruneau, & Evans, 2003). An-
other found that detailed descriptions of the item
ZHUHVLJQL¿FDQWSUHGLFWRUVRIDFRPSOHWHGVDOH
(Gilkeson & Reynolds, 2003)
1
. Other researchers
have included product description as a control
variable in their studies (Bruce, Haruvy, & Rao,
2004; Dholakia & Soltysinski, 2001; Gilkeson &
5H\QROGV6WDQGL¿UG5RHORIV'XUKDP
2004), giving at least informal credence to the
notion that product information, such as pictures
RIDQLWHPFDQLQÀXHQFHDQLWHP¶V¿QDOFORVLQJ
price. Finally, the number of bids and the number
of bidders has been shown to be factors leading
to higher closing prices (Dholakia & Soltysinski,
2001; Gilkeson & Reynolds, 2003; Wilcox, 2000).
Following the lead of these scholars, and in order
WRUHDFKDPRUHGH¿QLWLYHFRQFOXVLRQUHJDUGLQJWKH
possible impact of product description on auction

prices, we also include product information in our
analysis of Internet auctions.
Finally, delivery information simply refers to
the cost of shipping and to the available delivery
options. The availability of expedited delivery,
international delivery, and the item’s shipping
cost are included here as variables (see Table 1).
Relatively few researchers have included this
subset of variables within their models. However,
one study argues that high seller reputation and
GHOLYHU\ HI¿FLHQF\ PD\ FRYDU\ 0F'RQDOG 
Slawson, 2002), while another includes shipping
cost as a control variable (Gilkeson & Reynolds,
2003). We introduce the examination of interna-
tional delivery because we believe that, with the
increasing level of international activity in Inter-
net retailing and Internet auctions, international
shipping will become more important to sellers
wishing to ensure the largest possible set of poten-
tial bidders. To gain a more complete perspective
on all factors impacting auction prices, we will
include each of the aforementioned delivery at-
1948
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
Variable Description Source
Initial Bid Price Starting bid price
(Gilkeson & Reynolds, 2003; McDonald & Slawson,
6WDQGL¿UG6WDQGL¿UG5RHORIV'XU-
ham, 2004)
Buy-Now Option

Presence or absence of
option for bidder to end
auction early by purchas-
ing at a seller-determined
¿[HGSULFHH%D\¶V%X\
it-Now option)
6WDQGL¿UG5RHORIV'XUKDP
Auction Duration Length of auction in days
(Dholakia & Soltysinski, 2001; Gilkeson & Reynolds,
2003; McDonald & Slawson, 2002; Mehta, 2002;
6WDQGL¿UG6WDQGL¿UG5RHORIV'XUKDP
2004; Subramaniam, Mittal, & Inman, 2004)
Auction Ending
Time
Time of day auction ends
(Dholakia & Soltysinski, 2001; Gilkeson & Reynolds,
2003; McDonald & Slawson, 2002; Mehta, 2002;
6WDQGL¿UG
Table 1a. Previous empirical studies measuring selling information variables
Variable Description Source
Number of Posi-
tive Feedback
Ratings
Total number of eBay
positive feedback ratings
(Ba & Pavlou, 2002; McDonald & Slawson, 2002;
6WDQGL¿UG
Number of Nega-
tive Feedback
Ratings

Total number of eBay
negative feedback ratings
(Ba & Pavlou, 2002; McDonald & Slawson, 2002;
6WDQGL¿UG
Product Information Variables
Number of Pic-
tures
Number of pictures (Ottaway, Bruneau, & Evans, 2003)
Number of Bids
Total number of bids sub-
mitted for item
(Dholakia, 2005b; Dholakia & Soltysinski, 2001;
Gilkeson & Reynolds, 2003; McDonald & Slawson,
6WDQGL¿UG6XEUDPDQLDP0LWWDO,Q-
man, 2004; Wilcox, 2000)
Delivery Information Variables
Availability of
Expedited De-
livery
Availability of express
delivery
Availability of
International
Delivery
Possibility to Deliver
Internationally
Shipping Cost
Amount of shipping and
handling charges
(Gilkeson & Reynolds, 2003; McDonald & Slawson,

2002)
1949
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
tributes in our analysis.
5HFHQWVFKRODUO\FRPPHQWDU\LGHQWL¿HVWKUHH
approaches that researchers have taken in their
studies of Internet auctions: (1) concept discov-
ery, which explores new phenomena; (2) process
explanation, which seeks to provide an economic,
psychological, or social explanation for behavior;
and (3) theory deepening, which uses electronic
markets to develop and test theories (Dholakia,
2005a). It has been noted that concept discovery
and process explanation have received the majority
of researchers’ attention, while theory-deepening
approaches are relatively few in number (Dhola-
kia, 2005a). In the absence of established theory,
continued exploratory work such as this study
seems warranted.
:KLOH WKH IRUHJRLQJ ¿QGLQJV IURP ,QWHUQHW
auction research are noteworthy in their own
right, they have a limited usefulness even when
taken in sum. Without being able to ascertain
ZKLFKYDULDEOHVZLOOSURYLGHWKHJUHDWHVWEHQH¿W
relative to other variables, businesses are left
without guidance for generating price premiums
in Internet auctions. In light of this need, we will
capitalize upon previous work in a novel way.
Rather than simply searching among the myriad
DWWULEXWHVRIDQ,QWHUQHWDXFWLRQWR¿QGWKRVHWKDW

DUHSUHGLFWLYHRIWKH¿QDOFORVLQJSULFHZHSURSRVH
a descriptive model based upon empirical data
which ranks the attributes of Internet auctions
E\WKHLULPSRUWDQFH$FODVVL¿FDWLRQDQGUHJUHV-
sion tree will be produced which can be used to
guide businesspeople who are making decisions
regarding how to auction their products in B2C
auctions. At this point, we will turn our attention
to decision-tree induction, a technique capable of
producing decision rules for sellers.
Decision-Tree Induction
Techniques
'HFLVLRQUXOHVRUUXOHVRIFODVVL¿FDWLRQFDQEH
deduced from data by using various machine-
learning techniques (Tsai & Koehler, 1993).
Information gained by analyzing data with these
inductive learning techniques can be represented
in various forms, including mathematical state-
ments, logical expressions, formal grammar,
decision trees, graphs, and networks (Kim, Lee,
Shaw, Chang, & Nelson, 2001). Decision trees
are essentially visual presentations of sets of
nested if-then statements. One advantage of using
decision trees is that they depict rules that can
be readily expressed in words, thus facilitating
comprehension by decision-makers (Kim, Lee,
Shaw, Chang, & Nelson, 2001).
Several algorithms for building decision trees
H[LVW WKH\ LQFOXGH &$57 &ODVVL¿FDWLRQ DQG
Regression Trees), QUEST (Quick, Unbiased

DQG(I¿FLHQW6WDWLVWLFDO7UHH6/,46XSHUYLVHG
Learning In Quest), CHAID (Chi-squared Auto-
matic Interaction Detector), IC (Interval Classi-
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Mehta, Shcafer, & Srikant, 1996; Mehta, Agar-
wal, & Rissanen, 1996; Quinlan, 1990). While
decision-tree induction allows data analysts to
deduce decision rules for both continuous and
discrete variables, not all algorithms are equally
well-suited for use with both types of variables.
For instance, CHAID and C5.0 are restricted to
the analysis of categorical variables only (Berry
& Linoff, 1997; Zanakis & Becerra-Fernandez,
2005). CART, on the other hand, can analyze
either categorical or continuous variables. Clas-
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criterion
2
variables; regression-tree analysis is
used for continuous criterion variables (Brieman,
Friedman, Olshen, & Stone, 1984). Because of this
characteristic of the CART algorithm, and because
we intend to make binary splits of our dataset into
price premium and non-price premium groups at
each node, CART is ideally suited to our study.
We now turn to a brief description of the CART
decision-tree induction process.
&ODVVL¿FDWLRQDQG5HJUHVVLRQ7UHH$QDO\VLV
(CART) is a nonparametric procedure that deter-
mines the optimal decision tree for classifying

observations on the basis of a large number of
1950
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
predictive variables (Brieman, Friedman, Ol-
shen, & Stone, 1984). CART recursively splits
a dataset into non-overlapping subgroups based
upon the independent variables until splitting is
no longer possible (Kim, Lee, Shaw, Chang, &
Nelson, 2001). One of the principal advantages
of CART is that it tends to be less-biased than
other data analysis methods (Lhose, Biolsi,
Walker, & Reuter, 1994; Sorensen, Miller, & Ooi,
2000; Zanakis & Becerra-Fernandez, 2005). For
instance, multiple discriminant analysis (MDA)
and LOGIT methodologies need to satisfy the
assumption of multivariate normality for inde-
pendent variables; in addition, MDA requires
that the groups’ covariance structure be equal.
Thus, if the variables follow some distribution
other than the multivariate normal distribution,
MDA and LOGIT will give biased results. The
assumptions of multivariate normality and equal
covariance can be easily violated in empirical
GDWDVHWVELDVHGFODVVL¿FDWLRQFDQUHVXOW,QVXFK
a situation, CART is preferable because it rests
upon more realistic, less-frequently violated as-
sumptions. CART assumes only that the groups
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(Brieman, Friedman, Olshen, & Stone, 1984).
Thus, CART is a data analysis technique that may

be well-suited to real-world electronic commerce
datasets. Now that some of the merits of CART
have been described, we turn to an explanation
of the process of decision-tree induction with
CART.
The decision-tree induction technique begins
as a dataset is subdivided into N sub-datasets.
N-1 subsets are used as training datasets, and
the remaining dataset is used to test the model.
7KH¿UVWWUDLQLQJGDWDVHWLVDQDO\]HGWR¿QGWKH
single most important independent variable for
classifying the observations into two groups.
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at the root node (Berry & Linoff, 1997; Zanakis
& Becerra-Fernandez, 2005). Each subgroup is
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the next-most important variable for classifying
observations. After this partition, the process
continues until only inconsequential variables
remain (Berry & Linoff, 1997). The possibility
of erroneously classifying some observations is
computed by summing the predictive error rate
at each split (Zanakis & Becerra-Fernandez,
$WWKLVSRLQWWKHWUHHLV³SUXQHG´WRUH-
PRYHEUDQFKHVWKDWLQÀDWHWKHHUURUUDWHZLWKRXW
providing substantial improvements in predictive
power (Berry & Linoff, 1997). After the decision
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the subsequent training datasets are analyzed to
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validation. Analysis of the training datasets thus
generates a decision tree—a predictive model for
classifying observations. Finally, the test dataset is
a n a l y z e d t o ve r i f y t h a t t h e d e c is i o n t r e e g e n e r a t e d
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remainder of the data as well.
To our knowledge, the use of decision-tree
induction techniques to analyze Internet auction
data and generate decision rules has not been
undertaken. The application of the decision-tree
analysis technique to Internet auction data may
help to unify and bring coherence to the disparate
H[WDQW¿QGLQJVLQ,QWHUQHWDXFWLRQUHVHDUFK,WPD\
also provide perspective on the relative importance
of the numerous factors that have been proven to
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METHOD
We present the following analysis in order to
answer questions about the variables enabling
merchants to earn price premiums in Internet
auctions and also to describe the decision rules
for these variables.
Sample
Data was collected over a one-month period
in 2005 from eBay’s U.S. Web site. Data from
international industry leader eBay has been
1951
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
frequently used as the point of data collection
for studies of Internet auctions (Ba & Pavlou,

2002; Brint, 2003; Bruce, Haruvy, & Rao, 2004;
Dholakia, 2005b; Dholakia & Soltysinski, 2001;
*LONHVRQ5H\QROGV6WDQGL¿UG5RHORIV
& Durham, 2004; Ward & Clark, 2002; Wilcox,
2000). Data from eBay is used for three reasons.
First, eBay data is often used because the realism
of such data is often preferable to data collected
in an experimentally-controlled laboratory set-
ting. Field experiments with auctions present an
obvious trade-off between experimental control
and realism (List & Lucking-Reiley, 2000).
Laboratory experiments of auctions have been
criticized on the grounds that subjects’ behavior
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be exactly the same as it would be in real-world
conditions (Lucking-Reiley, 1999). It has been
argued that experimental subjects have no in-
centive to develop optimal bidding strategies
or apply experience gained from bidding (Ward
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setting reduces questions regarding its general-
izability to the marketplace. For these reasons,
our goal of developing a guideline for selling
in Internet auctions that is both descriptive and
prescriptive leads us to follow the precedent of
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experimental data.
The second reason that researchers often use
eBay data is simply that eBay continues to be the
Internet auctioneer of choice. EBay continues to

lead the industry because of the circular effect of
high seller volume eliciting high bidder interest,
which in turn motivates sellers to continue to uti-
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substantial numbers of auctions to observe and
numerous points of measurement.
7KHWKLUGDQG¿QDOUHDVRQIRUWKHXVHRIH%D\
data is that eBay is the largest and most interna-
tional of the Internet auctioneers. Their auction
mechanism and terminology are used more widely
than any other auctioneer’s. Thus, in an endeavor
to provide the most generalizable results, we have
selected eBay as the point of data collection for
this study.
The items examined in this study are a DVD
movie (404 auctions) and a popular MP3 player
(366 auctions). All DVD auctions were for the
same, new, identically-packaged movie title (the
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and all MP3 player auctions were for the same,
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the device (the 4 GB Apple iPod). All items were
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³EUDQGQHZ´:HLQFOXGHGWKHVHLWHPVWRVDPSOHD
reasonably-broad spectrum of items, ranging from
inexpensive (DVD) to relatively expensive (MP3
player). We collected data during a three-week
window of time to guard against effects due to
changes in the market price (due to the release of
new versions of the products, or due to a reduc-

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these items were examined because their value
should not change with the fortunes of a team
or individual (as sports collectibles or celebrity
memorabilia might). Finally, the high sales volume
of these items facilitates data collection.
Variables
The variables for this study are those listed and
GH¿QHGHDUOLHULQ7DEOH$VZHQRWHGHDUOLHU
variables studied in previous research as predictors
RIDXFW LRQRXW FRPH FD QEHFOD VVL ¿HGL QWRIRX UFDW-
egories: selling information, seller information,
product information, and delivery information.
In addition, the dependent variable of interest is
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highest bid submitted for a given item.
Measurement of Variables
Table 2 reports our coding scheme for the vari-
ables in the Internet auction. Table 3 reports the
descriptive statistics of the data for 404 DVD
auctions and 366 MP3 player auctions.
1952
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
Research Design
This study uses CART to determine the most
important variables that sellers should consider
to earn price premiums. The reader will recall
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“the monetary amount above the average price
received by multiple sellers for a certain matching

product” (Ba & Pavlou, 2002, pp. 247-248) and
second, that CART is a nonparametric procedure
that determines the optimal decision tree for
classifying observations on the basis of a large
Variables Coding
Criterion (Dependent) Variable:
Final Bid Price Continuous: dollars and cents
Independent Variables:
Selling Information Variables
(1) Initial Bid Price Continuous: dollars and cents
(2) Buy-Now Option Binary: 0—not available, 1—available
(3) Auction Duration Continuous: duration of auction in days
(4) Auction Ending Time
Categorical:
1: Weekday before 4 PM
2: Weekday after 4 PM
3: Weekend before 4 PM
4: Weekend after 4 PM
Seller Information Variables
(5) Number of Positive Feedback
Ratings
Continuous: number of positive ratings
(6) Number of Negative Feedback
Ratings
Continuous: number of negative ratings
Product Information Variables
(7) Number of Pictures Continuous: number of pictures
(8) Number of Bids Continuous: total number of bids submitted
Delivery Service Information Variables
(9) Availability of Expedited

Delivery
Binary: 0—not available, 1—available
(10) Availability of International
Delivery
Binary: 0—not available, 1—available
(11) Shipping Cost Continuous: dollars and cents
Table 2. Data coding scheme
1953
Exploring Decision Rules for Sellers in Business-to-Consumer (B2C) Internet Auctions
number of predictive variables (Brieman, Fried-
man, Olshen, & Stone, 1984).
We perform two analyses with CART: classi-
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:H¿UVWXVH¿QDOELGSULFHDVWKHFULWHULRQYDULDEOH
IRU FO D V VL¿F D W LRQ  W U HHD QDO \VL V7 K H FO D V VL ¿ F DW L R Q
WUHHDOJRULWKPLGHQWL¿HVWKHSUHGLFWRUVWKDWEHVW
separate our data into categories where an auction
yields a price premium (denoted in subsequent
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(denoted as NPP). Second, we use number of
bids as a criterion variable for regression-tree
analysis. We use number of bids as criterion
variable because the number of bids is highly and
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the results should be substantially similar to those
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DVD Movie
(N=404)
MP3 Player (N=366)
Mean Std. Dev. Mean Std. Dev.

Criterion (Dependent) Variable:
Final Bid Price 9.74 2.94 187.58 19.34
Independent Continuous variables:
(1) Initial Bid Price 4.58 3.89 34.98 69.39
(3) Auction Duration 4.47 2.20 2.93 2.05
(5) Number of Positive Feedback
Ratings
849 2616 2374 3176
(6) Number of Negative Feedback
Ratings
5.70 9.35 20.04 32.38
(7) Number of Pictures 0.58 0.56 2.58 1.71
(8) Number of Bids 6.14 4.23 23.28 12.60
(11) Shipping Cost 4.20 1.07 16.32 5.50
Independent Categorical Variables
Frequencies Frequencies
(2) Buy-Now option No: 384,Yes: 20 No: 357, Yes: 9
(4) Auction Ending Time
Weekday Morning 98 164
Weekday Afternoon 153 48
Weekend Morning 50 20
Weekend Afternoon 103 134
(9) Availability of Expedited
Delivery
No: 354, Yes: 50 No: 256, Yes: 110
(10) Availability of International
Delivery
No: 187, Yes: 217 No: 130, Yes: 236
Table 3. Descriptive statistics