Since it is only by maximizing UHYHQXHDQGSUR¿WWKDWD¿UPFDQUHPDLQYLDEOH in the marketplace Seth & Thomas, 1994, an increased focus on how businesses that rely upon Internet auctions can ea
Trang 1business-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
VHOOH[FHVVLQYHQWRU\IRUJUHDWHUSUR¿WWKDQWKH\
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¿UVWTXDUWHURI,QWHUQHWDXFWLRQHHU
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¿UPVFRQWLQXHWRPDNHH[WHQVLYHXVHRI,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
WKDWGHWHUPLQHDQDXFWLRQLWHP¶V¿QDOELGSULFHWKH
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¿QDOELGSULFHV LVUHODWLYHO\XQGHUVWXGLHG,Q
HFRQRPLFVSULFHSUHPLXPVDUHGH¿QHGDVSULFHV
WKDW\LHOGDERYHDYHUDJHSUR¿WV OHLQ /HIÀHU
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
KDYHQRWWRREVHUYHVLJQL¿FDQWGLIIHUHQFHV7R
our knowledge, only two studies have previously
examined price premiums (Ba & Pavlou, 2002;
Pavlou, 2002) Since it is only by maximizing
UHYHQXHDQGSUR¿WWKDWD¿UPFDQUHPDLQYLDEOH
in the marketplace (Seth & Thomas, 1994), an
increased focus on how businesses that rely upon
Internet auctions can earn price premiums may SURYHEHQH¿FLDO7KHIRFXVRQSULFHSUHPLXPVLV WKH¿UVWFRQWULEXWLRQRIWKLVVWXG\$VZHLQYHV-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-LQJDUHVSRQVHWRRUGHUYDULDEOHVE\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 research has demonstrated that CART analysis can
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
¿UVWWRXVHDVWDWLVWLFDOO\EDVHGGHFLVLRQPDNLQJ 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 VWXGLHV7KLVFRQ¿UPDWLRQRIYDULDEOHVLGHQWL¿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 GHVLJQ6SHFL¿FDOO\ZHGHVFULEHWKHFROOHFWLRQ DQGDQDO\VLVRI¿HOGGDWDIURP,QWHUQHWDXFWLRQHHU eBay We then present the results of our analysis Following the presentation of our results, we
Trang 2RXUVWXG\)LQDOO\ZHFRQFOXGHE\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
$XFWLRQVKDYHEHHQGHVFULEHGDV³a 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)
'XWFKRUGHVFHQGLQJSULFHDXFWLRQV ¿UVWSULFH
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’ valuations would provide In this way, the bidder’s YDOXHLVLQGHSHQGHQWRIWKHLQÀXHQFHRIFRPSHWLQJ 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 valuaauc-tion 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, UHIHUUHGWRDVWKH$I¿OLDWHG9DOXHV0RGHO $9 explains that bidder valuations depend upon the bidder’s personal preferences, the preferences of
Trang 3others, and the intrinsic qualities of the item being
sold (Milgrom & Weber, 1982) Bidders’
valu-DWLRQVDUHGHVFULEHGDVDI¿OLDWHGEHFDXVHDKLJK
valuation by one bidder makes a high valuation
by other bidders more likely (Milgrom & Weber,
1982) The AV model is a more general
concep-tualization of the valuation of items in auctions
than the IPV or CV models; both the IPV and CV
models can be understood as special cases of the
more general AV model (McAfee & McMillan,
1987) Recent studies of Internet auctions rely
upon and explicitly mention the merits of the AV
model (Dholakia & Soltysinski, 2001; Gilkeson &
Reynolds, 2003; Segev, Beam, & Shanthikumar,
2001; Wilcox, 2000) These studies empirically
validate the AV model in Internet auctions by
GHPRQVWUDWLQJ WKDW ELGGHUV PD\ EH LQÀXHQFHG
not only by their own valuation of the item, but
also by the behavior of other bidders
Internet Auctions
Internet auctions have a relatively brief history
Among the earliest electronic auctions were the
auctioning of pigs in Singapore (Neo, 1992) and
ÀRZHUV LQ +ROODQG YDQ +HFN YDQ 'DPPH
1997) conducted over a LAN Auctions on the
Internet, conducted via newsgroups and e-mail
discussion lists, were the next major development
in the Internet auction timeline (Lucking-Reiley,
1999, 2000a) The explosion in popularity of
In-ternet auctions, however, did not begin until the
1995 launches of U.S Web sites Onsale and eBay
(Lucking-Reiley, 2000a) By 1999, there were
an estimated 200 auction sites on the Internet
(Crockett, 1999) The continued growth of Internet
auctions is demonstrated by the performance of
international industry leader eBay, a company
that operates auction Web sites in 24 countries,
includes over 180 million registered users, and
generated US$ 4.552 billion in sales in 2005 (eBay,
,QWHUQDWLRQDOFRPSHWLWLRQLQFOXGHV¿UPV
such as QXL.com in Europe, Taobao.com in Asia,
and MercadoLibre in Latin America Following
0|OOHQEHUJ SS ZHZLOOGH¿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 WRDQVZHUDUH³:KDWLVWKHRSWLPDOZD\WRDXFWLRQ DQLWHP"´RU³+RZLVWKHPDUNHWSODFHFKDQJLQJ DVDUHVXOWRI,QWHUQHWDXFWLRQV"´RU³:KDWIDF-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:HZLOOGH¿QHDQGGLVFXVVHDFKRIWKHVH 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 WKHVHYDULDEOHVWKHLUGH¿QLWLRQVDQGDOLVWRIVWXG-ies in which they have been investigated There KDYHEHHQDQXPEHURILPSRUWDQW¿QGLQJVLQWKLV DUHD,WKDVEHHQREVHUYHGWKDWDQLWHP¶V¿QDOELG SULFHFDQEHVLJQL¿FDQWO\DIIHFWHGE\LWVLQLWLDOELG price (Brint, 2003) Bidders have been found to
Trang 4sometimes 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;
6WDQGL¿UG 6WDQGL¿UG5RHORIV 'XUKDP
2004; Subramaniam, Mittal, & Inman, 2004), but
have not, to our knowledge, been conclusively
linked to higher closing prices
Seller informationLVGH¿QHGDVWKHYDULRXV
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
HIIHFWXSRQWKH¿QDOELGSULFH6SHFL¿FDOO\SRVL-WLYHIHHGEDFNLVPLOGO\LQÀXHQWLDOLQGHWHUPLQLQJ
¿QDOELGSULFHZKLOHQHJDWLYHIHHGEDFNLVKLJKO\
LQÀXHQWLDO 6WDQGL¿UG 7KXVLWKDVEHHQ
clearly demonstrated that seller information is
also an important subset of variables to examine
when researching Internet auctions
Product information refers to the information
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 numnum-ber 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 SURFHVVLQJDQGXOWLPDWHO\WKHLWHP¶V¿QDOFORVLQJ price (Ottaway, Bruneau, & Evans, 2003) An-other found that detailed descriptions of the item ZHUHVLJQL¿FDQWSUHGLFWRUVRIDFRPSOHWHGVDOH (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¿UG5RHORIV 'XUKDP 2004), giving at least informal credence to the notion that product information, such as pictures RIDQLWHPFDQLQÀXHQFHDQLWHP¶V¿QDOFORVLQJ 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 WRUHDFKDPRUHGH¿QLWLYHFRQFOXVLRQUHJDUGLQJWKH 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 InterInter-net 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
Trang 5at-Variable Description Source
Initial Bid Price Starting bid price
(Gilkeson & Reynolds, 2003; McDonald & Slawson,
6WDQGL¿UG 6WDQGL¿UG5RHORIV 'XU-ham, 2004)
Buy-Now Option
Presence or absence of option for bidder to end auction early by purchas-ing at a seller-determined
¿[HGSULFH H%D\¶V%X\
it-Now option)
6WDQGL¿UG5RHORIV 'XUKDP
Auction Duration Length of auction in days
(Dholakia & Soltysinski, 2001; Gilkeson & Reynolds, 2003; McDonald & Slawson, 2002; Mehta, 2002;
6WDQGL¿UG 6WDQGL¿UG5RHORIV 'XUKDP
2004; Subramaniam, Mittal, & Inman, 2004) Auction Ending
(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
Number of Bids Total number of bids sub-mitted for item
(Dholakia, 2005b; Dholakia & Soltysinski, 2001;
Gilkeson & Reynolds, 2003; McDonald & Slawson,
6WDQGL¿UG 6XEUDPDQLDP0LWWDO ,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)
Trang 6tributes in our analysis.
5HFHQWVFKRODUO\FRPPHQWDU\LGHQWL¿HVWKUHH
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
ZKLFKYDULDEOHVZLOOSURYLGHWKHJUHDWHVWEHQH¿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
DWWULEXWHVRIDQ,QWHUQHWDXFWLRQWR¿QGWKRVHWKDW
DUHSUHGLFWLYHRIWKH¿QDOFORVLQJSULFHZHSURSRVH
a descriptive model based upon empirical data
which ranks the attributes of Internet auctions
E\WKHLULPSRUWDQFH$FODVVL¿FDWLRQDQGUHJUHV-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
'HFLVLRQUXOHVRUUXOHVRIFODVVL¿FDWLRQFDQEH
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¿FLHQW6WDWLVWLFDO7UHH 6/,4 6XSHUYLVHG Learning In Quest), CHAID (Chi-squared Auto-matic Interaction Detector), IC (Interval
Classi-¿HU ,'DQG& $JDUZDO$UQLQJ%ROOLQJHU 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-VL¿FDWLRQWUHHDQDO\VLVFDQEHXVHGIRUFDWHJRULFDO criterion2 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¿FDWLRQDQG5HJUHVVLRQ7UHH$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
Trang 7predictive 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 ELDVHGFODVVL¿FDWLRQFDQUHVXOW,QVXFK
a situation, CART is preferable because it rests
upon more realistic, less-frequently violated
as-sumptions CART assumes only that the groups
DUH GLVFUHWH QRQRYHUODSSLQJ DQG LGHQWL¿DEOH
(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¿UVWWUDLQLQJGDWDVHWLVDQDO\]HGWR¿QGWKH
single most important independent variable for
classifying the observations into two groups
&$57WKXVPDNHVLWVPRVWVLJQL¿FDQWVSOLW¿UVW
at the root node (Berry & Linoff, 1997; Zanakis
& Becerra-Fernandez, 2005) Each subgroup is
WKHQH[DPLQHGDJDLQZLWKWKHDOJRULWKPWR¿QG
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,
$WWKLVSRLQWWKHWUHHLV³SUXQHG´WRUH-PRYHEUDQFKHVWKDWLQÀDWHWKHHUURUUDWHZLWKRXW providing substantial improvements in predictive power (Berry & Linoff, 1997) After the decision WUHHLVJHQHUDWHGIURPWKH¿UVWWUDLQLQJGDWDVHW the subsequent training datasets are analyzed to UH¿QHWKHWUHH7KLVSURFHVVLVNQRZQDVFURVV validation Analysis of the training datasets thus generates a decision tree—a predictive model for classifying observations Finally, the test dataset is analyzed to verify that the decision tree generated XVLQJWKHWUDLQLQJGDWDVHWDFFXUDWHO\FODVVL¿HVWKH 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¿QGLQJVLQ,QWHUQHWDXFWLRQUHVHDUFK,WPD\ also provide perspective on the relative importance
of the numerous factors that have been proven to VLJQL¿FDQWO\LPSDFWDXFWLRQRXWFRPH
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
Trang 8frequently 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¿UG5RHORIV
& 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
LQDQDUWL¿FLDOODERUDWRU\HQYLURQPHQWPD\QRW
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
&ODUN &ROOHFWLRQRIGDWDIURPD¿HOG
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
WKHVHUHVHDUFKHUVLQXVLQJ¿HOGGDWDUDWKHUWKDQ
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-OL]HH%D\ :LQJ¿HOG 7KXVH%D\SURYLGHV
substantial numbers of auctions to observe and
numerous points of measurement
7KHWKLUGDQG¿QDOUHDVRQIRUWKHXVHRIH%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 SRSXODU DQLPDWHG IHDWXUH ³7KH ,QFUHGLEOHV´ and all MP3 player auctions were for the same, QHZ¿UVWTXDOLW\LGHQWLFDOO\SDFNDJHGPRGHORI the device (the 4 GB Apple iPod) All items were GHVFULEHGDV³QHZ´³QHYHUXVHG´³QHZLQER[´RU
³EUDQGQHZ´:HLQFOXGHGWKHVHLWHPVWRVDPSOHD 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-WLRQLQFRVWLQ¿[HGSULFHPDUNHWV $GGLWLRQDOO\ these items were examined because their value should not change with the fortunes of a team
or individual (as sports collectibles or celebrity memorabilia might) Finally, the high sales volume
of these items facilitates data collection
Variables
The variables for this study are those listed and GH¿QHGHDUOLHULQ7DEOH$VZHQRWHGHDUOLHU variables studied in previous research as predictors RIDXFWLRQRXWFRPHFDQEHFODVVL¿HGLQWRIRXUFDW-egories: selling information, seller information, product information, and delivery information
In addition, the dependent variable of interest is
¿QDOELGSULFH:HGH¿QH¿QDOELGSULFHDVWKH 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
Trang 9Research Design
This study uses CART to determine the most
important variables that sellers should consider
to earn price premiums The reader will recall
¿UVWWKDWSULFHSUHPLXPVKDYHEHHQGH¿QHGDV
“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
Criterion (Dependent) Variable:
Independent Variables:
Selling Information Variables
(1) Initial Bid Price Continuous: dollars and cents
(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
(6) Number of Negative Feedback
Product Information Variables
(7) Number of Pictures Continuous: number of pictures
Delivery Service Information Variables
(9) Availability of Expedited
(10) Availability of International
Table 2 Data coding scheme
Trang 10number of predictive variables (Brieman,
Fried-man, Olshen, & Stone, 1984)
We perform two analyses with CART:
classi-¿FDWLRQWUHHDQDO\VLVDQGUHJUHVVLRQWUHHDQDO\VLV
:H¿UVWXVH¿QDOELGSULFHDVWKHFULWHULRQYDULDEOH
IRUFODVVL¿FDWLRQWUHHDQDO\VLV7KHFODVVL¿FDWLRQ
WUHHDOJRULWKPLGHQWL¿HVWKHSUHGLFWRUVWKDWEHVW
separate our data into categories where an auction
yields a price premium (denoted in subsequent
¿JXUHVDV33 RUIDLOVWR\LHOGDSULFHSUHPLXP (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 GLUHFWO\FRUUHODWHGZLWKWKH¿QDOELGSULFH7KXV the results should be substantially similar to those LQWKHFODVVL¿FDWLRQWUHHDQDO\VLV
DVD Movie (N=404) MP3 Player (N=366) Mean Std Dev Mean Std Dev.
Criterion (Dependent) Variable:
Independent Continuous variables:
(5) Number of Positive Feedback
(6) Number of Negative Feedback
Independent Categorical Variables
Frequencies Frequencies
(4) Auction Ending Time
(9) Availability of Expedited
(10) Availability of International
Table 3 Descriptive statistics
... 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... literature (Stark
& Rothkopf, 1979) and a review of experimental
auction literature (Kagel, 1995)
Auction Mechanisms and Auction
Theory
Auction... 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