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Online Auctions: A Closer Look*
Alok Gupta
Associate Professor
Information and Decision Sciences Department
Carlson School of Management
University of Minnesota
3-365 Carlson School of Management
321 - 19th Avenue South
Minneapolis, MN 55455
Ravi Bapna
Assistant Professor
MIS Area
College of Business Administration
214 Hayden Hall
Northeastern University, Boston, MA 02115,
(May 2001)
Published in: Handbook of Electronic Commerce in Business and Society, Boca
Raton, FL: CRC Press, 2002.
*First author’s research is supported by NSF CAREER grant IIS-0092780.
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1. Introduction to Online Auctions
Online auctions represent a model for the way the Internet is shaping the new
economy. In the absence of spatial, temporal and geographic constraints these
mechanisms provide many benefits to both buyers and sellers. They are now an
important component of the portfolio of mercantile processes that are
transforming the economy from traditionally hierarchical to market oriented
structures [see Kauffman and Walden (2001) for an exhaustive review]. A broad
and deep body of economics literature exists that investigates the theoretical
properties of traditional auctions. However, significant differences in the cost
structures, to both buyers and sellers, participating in online auctions, have
resulted in a need to revisit much of the existing theory. This chapter provides a
broad context, derived from an overview of the current research and practice in
this field, and provides insights into this interesting sphere of economic activity.
Online auctions fall under the ambit of web based dynamic pricing
mechanisms. In these mechanisms, consumers become involved in the price-
setting process. Consumers can now experience the thrill of ‘winning’ a product,
potentially at a bargain, as opposed to the typically relatively tedious notion of
‘buying’ it. For sellers these mechanisms are likely to bring access to newer
markets, help clear aging or perishable inventory, and provide experiential and at
times viral marketing capabilities.
Nowhere are these trends as visible as in the hugely popular online
auction site, eBay. Among other things eBay has resulted in dramatically
improving the efficiency of secondary markets that were typically associated with
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garage sales and flea markets. eBay’s legion of 10 million monthly visitors
provides the necessary critical mass of buyers and sellers to set market prices
for their goods. The more bids that come in, the more competition there is, and
chances are that higher the price. In retrospect, eBay had and continues to have,
both the positive network externality effect of a growing user base as well as the
first mover advantage, necessary and sufficient conditions for success in today’s
economy [Varian (2000)].
The impact is even more dramatic in business-to-business (B2B) markets
where Forrester Research predicts an increase in sales from $19.3 billion last
year to $52.6 billion by 2002. A full suite of dynamic pricing mechanisms is in use
in B2B markets, including standard auctions where there is a single seller and
multiple buyers, reverse auctions where a single buyer receives bids from
multiple sellers and multiple buyer, and multiple seller exchanges that resemble
the bid-ask markets for stocks and commodities. Mollman (2000) presents an
overview of the top performing B2B auctions.
Beginning with the dotcom euphoria of 1999, one can observe the
emergence of a myriad collection of price-setting processes, such as traditional
first-price auctions for single items (e.g. eBay.com), multi-item auctions selling
multiple identical units (e.g. Onsale.com and eBay’s Dutch auction), reverse
auctions for goods and services (e.g. eLance.com and FreeAgent.com), name-
your-price mechanisms (e.g., Priceline.com), quantity discounters (e.g.,
Mercata.com), and methods that used derivative based pricing for consumer
goods (e.g., Iderive.com). Not surprisingly, some continue to flourish (e.g. eBay
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and Onsale) while others have floundered (Mercata and Iderive). Despite the
innovativeness of these pricing approaches and the initial dotcom buzz
surrounding them, little attention has been paid to the their effectiveness.
Instead, directionless entrepreneurship, at times by fueled by overzealous
venture capitalists, replaced scientific enquiry and rigor when it came to
examining the efficacy and viability of candidate mechanisms.
We contend that significant research is still needed in designing new and
better mechanisms, as well as examining the efficacy of existing ones in the
contexts of the markets they serve. In this chapter, we touch upon issues of
mechanism design, secondary market creation, bidding costs and strategies,
incentive compatibility, bid taker cheating (shilling), simultaneous substitutability,
and associated research methodologies.
Interestingly, the advent of auctions over open Internet Protocol based
networks, such as the Internet, has also facilitated the pursuance of a richer set
of empirically derived methodologies by today’s researchers. Most pre-Internet
based auction research was either purely theoretical in nature [see McAfee and
McMillan (1987), Milgrom (1989), and Myerson (1981) for a thorough review), or
involved laboratory experimentation [see Kagel and Roth (1997) for an excellent
overview]. Empirical research was rare due the lack of meaningful data sets,
which in turn could be attributed to the lack of mainstream appeal of auctions.
Lucking-Reiley (1999) acknowledges the difficulty in obtaining field data for
testing long-standing hypotheses, such as the supposed revenue equivalence
between the basic auction formats. The best data set available prior to the arrival
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of the web-based auctions covered US Forest Service sales of contracts for
harvesting timber in the Pacific Northwest during 1977 [see Hansen (1985)]. The
earlier lack of empirical and/or realistic experimental test environments is
increasingly disappearing with the technological advancements in online auction
technology. The widespread popularity of online auctions, coupled with the open
computing paradigm upon which Internet applications are built, together present
a golden opportunity for researchers to revisit the various branches of auction
theory in a setting that is more realistic and has higher inductive value.
In the remainder of this chapter we share the insights of these recent
research developments in online auctions.
2. A Review of Major Online Auction Mechanisms
A key factor that makes electronic markets, such as online auctions, interesting is
the potential for achieving higher efficiency. On surface e-markets such as eBay,
with millions of registered users, would appear to be a close approximation to an
economist ’s idealization of a frictionless, efficient market. One thing for certain
is that using information technology has brought this sphere of economic activity
out of the domain of specialists to that of the common man. The success of eBay
notwithstanding, we contend that the frictionless efficient market is still an ideal to
strive for. We review the popular types of online auctions, with the caution that
this is by no means an exhaustive list of current online auctions. Our objective
here is to isolate mechanisms that are interesting, being currently researched
and in which the online environment influences the strategic spaces of the
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participants. We begin with the classic single item, open, ascending, English
auction.
2.1 Single item Auctions
This asset-exchange mechanism has been extensively studied
theoretically, beginning with the seminal article by Nobel laureate William Vickrey
(1961). More recent coverage can be found in Rothkopf and Harstad (1994).
Researchers studying this auction commonly make use of assumptions, such as
the independent private values (IPV) assumption to derive its equilibrium
characterization. Such an assumption implies that a single indivisible object is to
be sold to one of several bidders. Each bidder is risk-neutral and knows the value
of the object to himself, but does not know the value of the object to other
bidders. It also implies that there is a finite population of bidders, each of which
draws his valuation independently from some given continuous distribution (see
Milgrom, 1989 for a detailed description).
Consider the applicability of this assumption to common single item online
auctions such as the ones conducted on eBay. Note, eBay’s multi-item Dutch
auction are discussed in the next subsection. For most goods being auctioned,
the IPV assumption is robust. However, for collectibles, a popular category on
eBay, it is reasonable to assume that an individuals’ valuation will be dependent
on the valuations of fellow bidders. Presumably, a collector will have the
objective of at least recovering the cost of the item purchased and thus will
implicitly carve her valuation distribution to be dependent on that of other bidders.
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The presence of winners curse in auctions, namely that bidders win items
only because they pay too much, has long been of interest to researchers. Bajari
and Horta (2000), in an empirical study of eBay auctions, find that a bidder’s
surplus falls by 3.2 percent when the expected number of bidders increases by
one. In section 6, we describe two recent studies that have compared online
auctions with posted price mechanisms selling the same goods (using matching
SKU’s). The evidence for winners curse from those two studies is mixed.
A common assumption related to IPV, especially popular in experimental
studies in the lab, is that the number of bidders in an auction is exogenously
determined. That somehow this population is known a priori and the design of the
auction itself does not influence the number of participants. Several, recent
empirical studies challenge this notion and point to interesting issues regarding
the choice of a reserve price by the seller. On eBay the seller can set an open
minimum bid that serves as the starting price for the auction. Additionally, the
seller also has the option of setting a hidden reserve price below which she is not
obligated to sell. Once the bidding level exceeds the reserve, an indication of
“reserve met” is displayed to the bidders. The questions are: a) Should the seller
use the hidden reserve at all? b) Does setting a low minimum bid attract bidders
to the auction, thereby increasing competition? And c) Is there an optimal mixed
strategy that can be employed by sellers with respect to these two parameters?
Bajari and Hortacsu (2000) find that items with higher book value tend to be sold
using a secret as opposed to posted reserve price with a low minimum bid. They
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also find that the minimum bid is the most significant determinant of whether a
bidder enters an auction.
Lucking-Reiley (1999b) describes controlled experiments, conducted on
the Internet, to verify a variety of theoretical properties of electronic auctions by
manipulating the reserve prices in these auctions as an experimental treatment
variable. His findings indicate that bidders consider their bid submission to be
costly and that bidder participation is indeed an endogenous decision.
Additionally, the data shows that a zero reserve price provides higher expected
profits than a reserve price greater than or equal to the auctioneer’s salvage
value for the good. In contrast, Reily and Samuleson (1981) showed that for an
optimal, highest clearing price, English auction the reserve price is a function of
the sellers' valuation of the product.
Perhaps, the most cited property of single item auctions is the Theorem of
Revenue Equivalence [Vickrey (1961), Myerson (1981) and Bulow and Roberts
(1989)]. The idea is that under a set of restrictive assumptions (IPV and risk-
neutral bidders), the expected revenue from a variety of auction types, namely:
English, Dutch, first price and second price sealed bid auctions, is equivalent.
Revenue equivalence results are known not to be robust with respect to the
slightest deviation from the restrictive assumptions of the independent private
values model [Myerson (1981)] or bidder risk preferences [Maskin and Riley
(1984)], which are notoriously difficult to observe. Lucking-Reiley (1999a) tests
revenue equivalence through his field experiments auctioning Magic game cards.
He finds that the Dutch auction produces 30 percent higher revenues than the
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first-price auction format, a contradiction to the theoretical prediction and a
reversal of previous laboratory results that the English and second price formats
produce roughly equivalent revenues.
It will be interesting to see whether similar trends are observable in online
auctions of a more general nature. Future research in this area promises to bring
interesting new insights into these age old topics of interest.
2.2 Multi-unit (item) auctions
The online environment has spawned a variety of auctions that sell
multiple identical units of the same item or good. These range from auctions of
consumer goods, mostly aging hardware and electronics, through sites such as
Onsale.com and Ubid.com, to auctions of fixed-income and equity securities by
Muniauction.com, to OpenIPO.com -- which allows individual and institutional
investors to bid online for shares of an IPO -- giving both types of investors a
level playing field in the IPO market for the first time in the history. A cursory
examination of the above mentioned sites reveals that what was historically a
sealed-bid dominated market, as in the auction of Treasury bonds, now supports
the a wide range of auction mechanisms, namely ascending English, descending
Dutch, and eBay’s so called "Dutch" -- which is really an ascending open uniform
price auction.
Rothkopf and Harstad (1994) point out that single-item results do not
carry over into multiple-item settings and that this has been a vastly neglected
area of auction theory research. Of late there is evidence of research spawning
in multi-item auctions. List and Lucking-Reiley (1999) examine the case when
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consumers are allowed to bid for more than one item under two different types of
two-unit, two-person sealed bid auctions. When consumers are allowed to bid for
more than one-item in an m-item auction, Vickrey’s original proposition -- full
demand revelation occurs in a sealed-bid auction -- does not hold [Ausubel and
Crampton (1999)]. Instead, the rule has to be modified such that for an m-item
Vickrey auction bidders can submit as many individual unit bids as they like.
Further, the top m bids are declared winners and for the jth unit won by a bidder,
she pays an amount equal to the jth highest of the rejected bids submitted by
others [Groves (1973) and Clarke (1971)]. Hence this revised mechanism offers
discriminating prices in contrast to the original mechanisms’ uniform pricing.
Importantly, this mechanism is incentive compatible. Bidders gain nothing by not
revealing their true valuations, as they never have to pay what they bid.
In the two-item case, List and Lucking-Reiley (1999) indicate that there is
evidence of demand reduction, i.e. lowering of the second bid below the true
valuation, when the uniform pricing rule is applied. This is a cause for concern
and leads to lower allocative efficiency. In the case of real-world B2C online
multi-item auctions consumers are allowed to bid for more than one-item but
these bids cannot be discriminating, i.e. they all have to be of the same amount.
For instance, a given individual can bid for 3 items at $100 each but cannot bid
for 2 items at $110 and 1 item for $80. Whether this constraint is designed to
prevent demand reduction in auctions that sell multiple (far greater than 2 units)
is an open and interesting research question.
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Bapna, Goes and Gupta (2000, 2001) present an analytical modeling
approach, to analyze multi-item progressive English auctions, also known as
Yankee auctions, such as those conducted by Onsale.com. Their analytical
modeling is subsequently validated by empirical investigation using data
collected by automated agents which track real-world web auctions, adding a
new methodological dimension to auction theory research.
They focus on hitherto undescribed discrete and sequential nature of the
revenue realization process of such auctions, caused by the presence of the bid
increment. In such auctions, say of ten Palm V PDAs, the ten highest bidders win
and the price they pay is equivalent to their highest bids, technically making this
a discriminatory auction. Usually, a very low opening bid such as $1 is set by the
auctioneer as a way to attract web traffic. In addition, all auctions have a bid
increment that defines the minimum step size for bidders. Bids that fall in
between bid increments are automatically rounded down to the nearest step.
This discretization of the process challenges the common auction theory
assumption that individuals' valuations can be drawn from a known, continuous
distribution. The bid increment also helps determine the minimum required bid at
any time during the auction. This is equal to the lowest winning bid plus the bid
increment.
The list of current winning bidders, the bid increment, the minimum
required bid, and the auction closing time are all continuously updated on the
web. Auction durations’ typically range from one-hour express auctions to day-
long regular auctions. Bids are ranked by bid amount and by time within amount.
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Unlike traditional, single-item English auctions, a new bidder's high bid does not
automatically displace the existing winner from the winner's list. In fact if the
current number of bidders is less than the lot size than the new high bid does not
affect any of the existing winners.
An interesting observation revealed by their data collection process is that
online auctioneers experiment with the design parameters they can control for
such auctions. For instance, they sell the same goods using a $20 bid increment
one day, followed by using a $15 bid increment on another day. There exists a
great opportunity for researchers in determining how to optimally set the control
factors such as the bid increment, lot size and the opening bid that influence the
efficiency of such auctions.
There have been other attempts to compare the efficiency of different
auction mechanisms both theoretically and empirically. The focus has been on
comparing single-item sealed-bid competitive auctions with sealed-bid
discriminatory auctions. In the former mechanism, the highest bidder wins,
however, the price paid is the second highest bid; whereas in the latter the
highest bidder wins with the price being the highest bid. Competitive auctions
were first suggested by Vickrey (1961) in his seminal article; the special property
of this mechanism is that all the bidders have incentive to bid their true valuation.
Plot and Smith (1978) were among the first to design a controlled laboratory
experiment to compare competitive auctions with discriminatory auctions. Actual
bidding data has also been analyzed by Baker (1976). The key results of these
empirical investigations have been inconclusive with respect to sellers' revenue.
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Harris and Raviv (1981) compare the efficiency and expected revenue of the
uniform price (Vickrey-like) auction mechanism with that of the discriminating
(first price sealed-bid) mechanism when a fixed quantity of divisible goods are to
be sold to many buyers. Their results indicate that the sellers' revenue under a
specific mechanism depend on the risk characteristics of the bidders.
Given the growing theoretical and empirical interest in multi-item auctions,
it will be interesting to see work that examines the relative efficacy of
mechanisms that are available to the consumers today. Does eBay’s so called
“Dutch” mechanism, which has little theoretical basis and unexplored incentive
characteristics, lead to higher clearing prices than say an equivalent Yankee
auction? The extension of the single-item results, such as revenue equivalence,
to the online setting of multi-item auctions is an interesting area of research. This
will help us understand which mechanism should be adopted under what
circumstance. For instance if there is a pre-dominance of risk-averse bidders
who prefer a certain outcome to an uncertain one, than would a descending
Dutch auction yield higher expected revenue? Of course, this analysis is not
trivial even with the most simplistic of assumptions regarding the consumer type.
2.3 Combinational Auctions
If we allow multiple units of non-identical goods to be sold through online
auctions, then we get into the realm of combinational auctions. Such auctioning
schemes are desirable to sell complementary goods that can be “bundled”
together. A good example is the FCC spectrum auction for different regional
licenses, where the value of having, say Boston increases if the bidder can also
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acquire neighboring New York. These auctions present many interesting
challenges to practitioners, both sellers as well as bidders, as well as
theoreticians. In general, the auctioneer's problem of determining an optimal set
of bids in a combinatorial auction is an NP-Complete problem, a class of
problems that are tough to solve in a reasonable amount of time. Additionally,
there are the issues of the (i) exposure problem: an unsuccessful attempt to
acquire a collection of assets, when combinational bidding is not allowed, may
lead to paying more for some individual assets than they are worth, and (ii)
threshold problem: a bidder on item A and a bidder on item B may not be able to
coordinate to displace a bid on package AB in the presence of diseconomies of
scale.
Several interesting approaches are being proposed to overcome some of
the above mentioned, computational and mechanism design, difficulties in
combinatorial auctions. One such apporach is the iBundle mechanism of Parkes
(1999). The basic idea of the iBundle mechanism is to use software to calculate
the maximal allocation of products among various users who can bid on bundles.
Each bidder can bid for any number of bundles, so a bidder can offer $10 for A,
$20 for A and B. The iBundle software then calculates the combination of
bundles that maximize total transaction value and notifies the bidders of the
provisional winners. The bidders are then able to make higher bids, and the
process repeats until bidders are satisfied. The contribution of iBundle is to use
IT to quickly solve an optimal allocation problem that would be computationally
infeasible for human agents in real time.
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Jones and Koehler (2001) are developing another interesting
combinatorial auction mechanism. Their approach accepts incompletely specified
bids that provide a framework to guide, rather than dictate, the choice of goods
that satisfy bidder needs. Their incompletely specified combinatorial auction
mechanism is designed to facilitate large and complex problems commonly
relegated to negotiated sales, where the allocation of goods requires solving a
complex combinatorial problem. A representative example is the complex multi-
dimensional process of media buying, specifically, the sale of television
advertising airtime. Allowing a bid in the form of high-level rules relieves the
buyer from the burden of enumerating all possible acceptable bundles.
Further research is needed to harness the enormous computational power
available to us to make combinatorial auctions mainstream for corporations keen
to optimize their complex logistical decisions, such as determining optimal freight
patterns fro moving goods from manufacturing sites to wholesale sites onwards
to retail sites.
2.4 Multi-dimensional Auctions, Reverse Auctions
In certain cases, for example in many procurement situations, it is not
sufficient to conduct auctions where only the price dimension matters. Often
price and quality go hand in hand and a jointly determine the winning bid -- not
necessarily the lowest price bid. Majority of the literature on auction theory has
focused on the analysis of auctions of a well-defined object or contract so that
the price to be paid is the unique strategic dimension with the exception of
Branco (1997), Che (1993) and Thiel (1988). For example, in the auction for a
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department of defense contract, say for the construction of an aircraft, the
specification of its characteristics is as important as the price. In such multi-
dimensional auctions bidders submit bids with relevant characteristics of the
project, price being just one such characteristic, and the procurement agency
uses a scoring mechanism to select amongst the bids. Branco (1997) and too
some extent Che (1993) recommend a two-stage auction mechanism where in
the first stage the procurer selects one firm and in the second stage she bargains
to re-adjust the level of the quality to be provided.
Multiple dimensions also exist in the reverse auctioning of service
contracts, such as those conducted by Elance.com and Freeeagent.com, where
price is not the only differentiating factor. For instance, a client who posts a
requirement for a web design project could receive bids from all over the world,
ranging from Bangalore, India to Yugoslavia to the US with hourly rates of $24,
$10 and $50 respectively, as we casually observed on Freeagent.com. It would
be naïve to think that the client would necessarily go with the lowest bid in this
case as service quality may be widely varying, and perhaps even difficult to
assess. Snir (2000) studies Internet based spot markets for service contracts. His
analysis confirms the fact that the transaction costs of posting a project, bidding
on a project and evaluating bids are all significant.
3. Consumer Bidding Strategies
The global scope and reach of the online environment makes it feasible
for “armchair” bidders [Kauffman and Walden (2001)], to be active players in
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auction markets. While much of the theoretical development assumes buyers
and sellers to be rational, profit maximizing individuals, a tenuous assumption to
begin with, the reality of the online markets landscape indicates a wide disparity
in user experience and information levels. Researchers such as Lucking-Reiley
and List (2000) and Bapna, Goes and Gupta (2001) have been quick to capitalize
on the availability of real auction data, obtained through field experiments and
automated-agent based real-time tracking of online auctions respectively, for
examining consumer bidding strategies on the Internet. Among other things they
test the behavior of uninformed bidders, who exhibit different behavior than
theory predicts.
Lucking-Reiley and List (2000) auctioned 4 types of trading cards ranging
from $3 to $70. They examined whether dealers of such cards would bid more
rationally than non-dealer, less experienced, individual card collectors. They find
that dealers exhibit more of the predicted strategic behavior than do nondealers,
for both lower and higher priced cards and that the predicted strategic behavior is
considerably greater when the auctioned sportscards have higher values,
confirming prior theory [Smith and Walker (1993)] that suggest that rationality is
more likely to be exhibited when the stakes are higher.
Bapna, Goes and Gupta (2001) also find support for the notion that
rationality becomes more evident as the expected payoff is higher. Their
empirical investigation of 90 such auctions identified three distinct types of
bidders. They are summarized in Table 1 [Bapna (1999)]:
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Evaluators • Early one time high bidders who have a clear idea of their
valuation
• Bids are, usually, significantly greater than the minimum
required bid at that time
• Rare in traditional auction settings - high fixed cost of
making a single bid
• Violates the assumption of rational participatory behavior
described earlier
Participators • Derive some utility (incur a time cost) from the process of
participating in the auction itself
• Make a low initial bid equal to the minimum required bid
• Progressively monitor the progress of the auction and
make ascending bids never bidding higher than minimum
required
Opportunists • Bargain hunters
• Place minimum required bids just before the auction
closes.
Table 1. Bidder Classification
In order to compare the performance of these strategies they introduced a
metric based on loss of surplus. This is the difference between an individual's
winning bid and the minimum winning bid. Loss of surplus evaluates the
performance of an individual or a group with respect to the bidder who had the
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minimum winning bid in a given auction. Bapna, Goes and Gupta (2000)
compared the relative performance of these three groups with respect to loss of
surplus in the auctions tracked on the WWW. They found that the evaluators as a
group fared worst, the participators were best off, and the opportunists lay in
between.
Much like Lucking-Reiley and List (2000) they also find evidence for the
fact that as the stakes get higher, a larger percentage of the population behaves
strategically. This is evident in Figure 1 below where the percentage of rational,
non-evaluator type, bidders is plotted against the dollar values of the auctions
tracked.
% Rational Bidders
-20%
0%
20%
40%
60%
80%
100%
120%
0 500 1000 1500 2000
$ Value of Auction
Figure 1 – Rationality Increases with the Dollar Value of the Auctions
It is easy to see an increasing trend in the percentage of bidders who
behave in a non-evaluator mode as the auction stakes get higher. Evaluators, as
we know were found to be the worst off of the three bidder categories.
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5. Opportunism and Trust in Online Auctions
Typically, ignorance of what price to post is a reason for negotiating or
holding an auction. Rothkopf and Harstad (1994) provide a behavioral reason for
holding auctions. They assert that one of the critical reasons for the use of
bidding is that the formality of the auction process provides legitimacy, in a way
that other economic means cannot. Contradicting this belief from academia, is
the practical notion that the lnternet makes the likelihood of fraud detection and
punishment low. Thus, one is not surprised that opportunistic behavior in online
auctions, by both sellers and buyers, is constantly in the media. According to
Internet Fraud Watch, online auction fraud has become the number one type of
Internet fraud over the last two years.
Brandabur and Saunders-Watson (2001) discuss this issue in depth. For
instance, they talk about an auction for a painting that was claimed to by the late
Bay Area painter Richard Diebenkorn. The auction soared to $135,805 with the
winning bid coming from Holland, however, the painting turned out to be a fake.
The seller gave the appearance of being a novice, never actually mentioning the
artist by name. In reality, it turned out this seller along with two accomplices
often used more than 20 eBay screen names for the purpose of shilling -- a
strategy of placing phony bids to run up the closing prices of auctions!
Recently researchers have begun to attempt to model important aspects
of trust and reputation in online auctions. Much of this stream relies on analytical
modeling backed up empirical investigation using automated agents to capture
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data from real-world auctions. This is an important area of research as it
addresses many of the issues raised by the critics of online auctions and helps
formalize the concept and measurement of trust and reputation of sellers.
There is increasing concrete evidence of shilling by bid-takers in online
auctions. Kauffman and Wood (2000) present an analytical model and provide
empirical evidence of ‘questionable bidding behavior (QBB)’ by sellers on eBay.
They operationalize QBB as bidding on an item when the same or a lower bid
could have been made on the exact same item in a concurrent auction ending
before the bid-upon auction. QBB can be considered irrational, since the buyer
has a greater level of utility if she were to bid on another item for the same or
lower cost. Their research highlights the difficulties associated with identifying
opportunistic sellers using QBB in online environments, when many auctions are
going on in parallel. First, non-reputable sellers try to remain anonymous.
Because they are attempting to hide their identity, it is difficult to identify them.
Second, it is difficult to track multiple Internet auction identities and tie them
together. Third, QBB needs to be reviewed over time in multiple auctions. For
instance, if a bidder (using the same name) consistently rates a particular seller
higher and exhibits active bidding behavior (for shilling purposes) while never
winning many of the items, it would be easy to identify suspicious behavior,
however, in practice finding such behavior is difficult. Using intelligent data
gathering agents Kauffman and Wood (2000) track a number of eBay auctions of
coins, and their initial findings suggest that indeed a significant amount of QBB is
evident.
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Opportunism can also be exhibited by the bidders, in the form of collusion,
through fictitious identities. Wang, Hidvegi and Whinston (2000) bring out the
importance of appropriate mechanism design to counter such undesirable
behavior. They focus on sealed bid auctions and propose an alternative
mechanism to the Generalized Vickrey Auction (GVA), called the Sealed-bid
Multi-Round Auction Protocol (S-MAP). They give instances when the GVA is no
longer incentive compatible. Their examples show that in GVA under collusion,
truth-telling is not longer a dominant strategy and illustrate how a bidder can
reduce her payment by submitting bids under her false names. This limitation is
overcome in S-MAP, however, the mechanism itself induces a higher cognitive
load on the players and it remains to be seen whether it can become
mainstream.
Pavlou and Ba (2000) recognize that trust is an essential component of
online auctions, and that buyers pay a price premium to transact with reputable
sellers, particularly for expensive products. Results showed a significant
correlation between trust and price premiums for all products. Moreover, this
correlation became increasingly more significant for more expensive products.
In another interesting empirical study Dewan and Hsu (2001) examine the
economic value of trust in electronic markets, based on a comparison of prices
across generalist (eBay) and specialty sites (Michael Rogers, Inc.) in the arena of
person-to-person online auctions. Generally, the two types of sites have very
different mechanisms for providing trust in the marketplace – whereas generalist
sites do not inspect the merchandise and rely instead on a reputation reporting
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system, specialty sites typically take possession of auction items and provide a
variety of value-added services directed at reducing information asymmetry and
other sources of transaction risk. Their empirical findings appear to confirm that
the observed price differences between eBay and the specialist are driven
primarily by the relative effectiveness of trust mechanisms in the two markets.
6. Simultaneous Substitutable Mechanisms: Auctions v. Posted Price
Another promising stream of IS research in the area of dynamic pricing
deals with comparing auctions with posted price mechanisms for the sale of
identical goods. Seidmann and Vakrat (1999) compared online catalog prices
with online auction prices. They obtained data from 473 online auctions, such as
SurplusAuction (www.surplusauction.com) and OnSale.Com (www.onsale.com).
They compared prices received in these auctions with prices from Internet
catalog sellers, such as Egghead (www.egghead.com) and PriceScan.Com
(www.pricescan.com). Their data analysis revealed that consumers expect
greater discounts for more expensive items. In their studies, Seidmann and
Vakrat employed Internet agents as a data collection tool. Using a similar
methodological approach Lee and Mehta (1999) investigated the existence of
winner's curse using theoretical modeling and empirical validation. Their
preliminary results confirm the existence of the winners’ curse in electronic
auction. The amount overbid is especially pronounced for items where potential
information asymmetries exist as a result of the nature of the product, and it is
24
further augmented in cases where the product is relatively new and not much
information regarding it exists in the retail channels
Interesting current research in this area is being carried out both from the
buyers’ and the sellers’ perspective. Barua and Tomak (2000) are studying under
what conditions should buyers use auctions in contrast to posted price
mechanism?
Aron, Croson and Lucking-Reliey (2000) investigate when should auctions
be used by sellers instead of posted prices? They are developing a theoretical
model of markets with uncertain demand, in an attempt to understand what types
of demand uncertainty make it worthwhile for a seller to consider investing in an
auction mechanism in order to gain more price flexibility.
7. Conclusions and Future Trends
The above review indicates that interesting developments are happening in both
the practice and research of online auctions. The current dotcom shakeout not
withstanding, dynamic pricing mechanisms, such as online auctions, will continue
to be an important component in the portfolio of mercantile processes that will be
deployed by businesses to transact with their customers and suppliers, and for
consumers to transact with other consumers.
We call for greater interaction between the practitioners and researchers in this
area. In many cases. practitioners, fueled by over zealous venture capitalists,
would do well to resist carrying out costly field experiments in the name of
innovation. They can enlist the research community to examine the design of the
dynamic pricing mechanisms they propose to adopt in a give market. Is the
25
mechanism suitable for the targeted market? Would an alternative mechanism fit
the bill? Will it achieve the desired liquidity to sustain itself, and will it achieve
higher allocative efficiency than its current counterparts. The large numbers of
failed real-life experiments in dynamic pricing (mercata.com, priceline.com for
groceries!), and the associated loss of social capital, could have been avoided if
interactions between practitioners and researchers were de rigueur.
We look forward to more research that examines the relevant issues in online
auctions without the baggage of the traditional assumptions made in earlier
auction theory. In the absence of the physical constraints of traditional auctions
the behavior of the different economic agents in auctions is heavily influenced by
the (online) context in which they take place. For instance, the presence of
simultaneous substitutable online auctions - which allows an individual shopping
for, say a computer, to simultaneously bid at Onsale.com or Yahoo.com -
impacts the efficiency of not just the isolated auction under consideration but also
the external market in which it takes place. Auction portals like
www.biddersedge.com are specifically designed to make tracking such
simultaneous substitutable online auctions easy for the consumer.
We firmly believe that the emerging practice and research in this area has high
inductive value and will lead to a significant enhancement to the body of
knowledge dealing with dynamic pricing and auctions.
26
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