Research paper
A conceptual research
framework for analyzing
online auctions in a B2B
environment
Diane H. Parente
Ray Venkataraman
John Fizel and
Ido Millet
The authors
Diane H. Parente is Assistant Professor, Ray Venkataraman is
Associate Professor of Management, John Fizel is Professor of
Economics, and Ido Millet is Associate Professor of MIS, all in
the School of Business, Pennsylvania State University, Erie,
Pennsylvania, USA.
Keywords
Electronic commerce, Supply chain management, Auctions,
Research
Abstract
The rapid growth of online auctions underscores the need to
analyze the mechanism of online auctions and to establish a
theoretical research framework based on the business models
adopted by successful organizations. While the theoretical and
empirical research bases for traditional auctions are well
established, current understanding of online auctions is still very
limited. A broad conceptual model is developed that can form
the basis for future research in online auctions. A review of prior
research and use systems theory and empirical analysis is
presented to identify the potential antecedents to online auction
success. Then dimensions of the input, process, and output
factors are discussed to develop the conceptual model. The
conceptual model provides an impetus and direction for future
research into online auctions, taking advantage of existing
tradition but also forming the basis for the development and
testing of research hypotheses that will expand the frontiers of
knowledge in online auctions.
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Supply Chain Management: An International Journal
Volume 9 · Number 4 · 2004 · pp. 287-294
q Emerald Group Publishing Limited · ISSN 1359-8546
DOI 10.1108/13598540410550037
Introduction
Auctions have long been a popular method for the
allocation and procurement of products and
services. With the advent of the Internet and the
proliferation of Web users, auctions are moving
online. Online auctions are also gaining in
popularity because they reduce transaction costs
for both the suppliers and buyers and, hence, can
have a significant impact on the profitability for
both the buying and selling firms (Klein, 1997;
Van Heck, 1998).
The rapid growth of online auctions
underscores the need for analyzing the mechanism
of online auctions and for establishing a theoretical
research framework based on the business models
adopted by successful organizations (Mahadevan,
2000). While the theoretical and empirical
research bases for traditional auctions are well
established (see, for example, EngelbrechtWiggans, 1980), current understanding of online
auctions is still very limited (Van Heck, 1998).
In this paper, we present a review of prior
research. Then we identify the potential
antecedents to online auction success within a
framework based on systems theory and our work
with a multi-national firm analyzing thousands of
online auctions. We then discuss dimensions of
each of the input, process and output factors and
propose a broad conceptual model that can form
the basis for future research in online auctions.
Prior research
Auctions are the preferred methods of commerce
for non-standard products or when the true value
or market price of the good is uncertain (McAfee
and McMillan, 1987). They offer trading
opportunities for both buyers and sellers and
assure prudent execution of contracts (Turban,
1997). Unlike online auctions, traditional auctions
have well-established theoretical and empirical
research bases. For example, a framework for
traditional auctions has been presented by
Engelbrecht-Wiggans (1980) for classifying and
describing various auctions and bidding models
based on the assumptions made for the various
parameters of the models.
Several past studies on traditional auctions have
also explored the use and importance of models for
linking auction theory to real transactions.
Rothkopf and Harstad (1994) stress the
importance of enriched models in bridging the gap
between theory and practice in both competitive
bidding and auction design. Furthermore, issues
such as predicting the “winner’s curse” when
quantity uncertainty exists, time and effort to be
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spent in preparing a bid in a first-price sealed-bid
auction, and the problem of determining a
revenue-maximizing set of non-bids in
simultaneous auctions have been addressed
extensively in past research on traditional auctions
(see, for example, Lederer, 1994; Pfeifer and
Schmidt, 1990; Rothkopf et al., 1998).
Traditional auctions, however, have several
limitations. These auctions have time and place
constraints which limit the participation level of
the bidders. Electronic auctions overcome these
limitations of traditional auctions, and the Internet
provides an infrastructure for conducting these
auctions in a cost-effective manner with many
more participants (Klein, 1997; Shaw, 1999;
Turban, 1997).
While the business-to-consumer (B2C)
category of online auctions has been the most
popular, the business-to-business (B2B) category
of online auctions has become a significant model
for businesses to auction their products and
services to each other (Rupley, 2000). In fact, in
1999 alone, B2B transactions in online auctions
totaled $109 billion, and that figure was expected
to grow to $2.7 trillion by 2004 (Blackmor, 2000).
B2B auctions can benefit both buyers and sellers.
For example, the Dow Chemical Company Co., as
a seller, uses online auctions to meet new
customers. As a buyer, the company routinely
saves 2-5 percent, and sometimes as much as
20 percent, on purchases of raw materials and
packaging materials needed to make and ship its
products (Gaudin, 2000).
The majority of the B2B online reverse auctions
are supplier-initiated. However, for many large
companies that buy thousands of items, it is costeffective to open their own electronic marketplace.
Auctions conducted in such buyer-provided
marketplaces for the purpose of purchasing goods
and services are called reverse auctions. While the
buying company can conduct these auctions either
through public or private auction sites, it is
estimated that 93 percent of B2B e-commerce in
the year 2000 was conducted through private sites
(Karpinski, 2000).
In a reverse auction, suppliers compete for
contracts online in real time by lowering the prices
as they see bids from other suppliers. This
frequently leads to significant cost savings. The
navy officials in NAVSUP, for example, estimate a
28.9 percent saving in the purchase price for their
components through reverse auctions (NAVSUP,
2000, pp. 31-33). Since 1997, Quaker Oats has
realized $8.5 million in savings through online
reverse auctions (Brunelli, 2000).
Reverse auctions exhibit several unique
characteristics. In addition to savings in purchase
price, reverse auctions can enable buyers to
respond to market fluctuations more quickly, and
also save the time that would have been required
for the buying company in sourcing individual
suppliers (Dash, 2000; Vigoroso, 1999). In a
reverse auction, in order to win the contract,
suppliers are more committed and involved in the
auction process (Karras, 1995).
Despite the rapid growth of online auctions,
there is a dearth of empirical research on the
performance of online auctions and the factors that
contribute to their success. Barua et al. (1999)
have proposed a four-layer framework to measure
the Internet economy as a whole. This framework
is comprised of layers of Internet infrastructure,
Internet applications, Internet intermediary and
Internet commerce. Reiley (2000) conducted a
comprehensive survey on Internet auctions, and
his research focused on the details of online
auction mechanisms and their relationship to the
existing body of literature on traditional auction
theory. The majority of auctions in Reiley’s survey,
however, included auctions targeted towards
individual consumers and not the B2B variety of
online auctions. Other business models have been
proposed for Internet-based e-commerce (see, for
example, Parkinson, 1999; Schlacter, 1995;
Timmers, 1998). These models, however, were
narrow in scope and did not cover all aspects of an
Internet-based business.
Mahadevan (2000) proposed a framework for
defining a business model for the B2B type of
business transactions over the Internet. His
framework identifies three streams that are critical
to a business. Mahadevan (2000) contends that his
framework can be adapted to describe specific
aspects of an Internet-based business. However,
the Mahadevan model is limited in that it does not
consider product characteristics, nor does it
distinguish successful and unsuccessful auctions.
Online auctions are similar to traditional
auctions in many respects (Reiley, 2000). From a
practical perspective, there are buyers, sellers,
transactions and outcomes. A forward auction is
typically operated by a seller and has many buyers.
In contrast, a reverse auction has one buyer and
many sellers. While the dimensions are the same
for both forward and reverse auctions, the relative
importance of individual components, as well as
the relationship between buying and selling firms,
may be different. Klein (1997) proposes a
framework for online auctions and identifies the
probable import of the online aspects of the
auction on each phase in the auction process. He
discusses his model by focusing on the elements of
auctions and how the Internet technology is
changing the auction conditions (Klein, 1997).
Auctions may be studied from the buyer’s or
seller’s perspective or at different levels (i.e. the
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transaction, product or auction level). The auction
is the unit of analysis that is the basis of the
proposed framework. Each of the components in
the model should be analyzed with respect to the
difference between traditional and online auctions.
dyad as an additional input to the proposed model
(Schopler, 1987). Finally, in a traditional
exchange, characteristics of the product or
products are hypothesized to contribute to the
outcome (Ruekert and Walker, 1987), and so, too,
must the characteristics of products contribute to
online auction outcomes.
In the following sections, we develop the
dimensions of each factor in the model shown in
Figure 1. The result is a research framework
suitable for evaluating both forward and reverse
auctions in a B2B environment.
Conceptual framework
One basis for analysis of online auctions may be as
straightforward as answering four simple
questions: who?, what?, how?, and why?.
Who is involved in the auction? Since an auction
is an exchange between buyer and seller, it is
appropriate to define the input portion of the
conceptual model by describing those factors or
characteristics of both the suppliers and buyers
that may affect the process or the output of the
auction. These factors include the attributes of the
firms of both buyers and suppliers. Additionally,
the dyad or the relationship between buyer and
supplier is also a significant factor in the
transaction.
What is being exchanged (or the product) is also
proposed as a major component of the input
portion of the conceptual model. Thus, we
hypothesize that product characteristics are
important in the model.
How the exchange or transaction takes place is
represented by the “Process” portion of the
conceptual model. The characteristics of the
auction define the process.
Finally, auction success is why two firms choose
to take part in a business transaction. Thus a
positive effect from each of the participant’s
perspectives is clearly the output of the model.
Systems theory is a logical basis to explain
online auctions. It has been used to explain many
phenomena in science, technology and
management. According to systems theory,
systems are made up of related and interdependent
components, including boundaries, outputs,
inputs, transformation mechanisms (ways of
converting inputs to outputs) and interfaces. Such
systems may also interact with their environment
(Lederhaus, 1984; Schopler, 1987). In our auction
example, as shown, the output component is
represented by the auction outcome, while the
transformation process is represented by the
auction itself. The input components of our
auction system are the firms involved in the
auction (i.e. buyers and suppliers). It is the
characteristics of the firms engaged in both the
purchasing and the selling aspects of the B2B
transactions that may influence the outcome of an
auction. The interfaces between the subsystems
are also critically important in the operation of the
whole system. We consider the buyer-supplier
Outcomes
The rationale for participating in any transaction
or exchange between two parties is the potential
benefit for buyers and suppliers (Lee and Corbitt,
2001). However, the goals of the buyers and sellers
are at odds, and must be dealt with concurrently in
the transaction process (Lee and Corbitt, 2001).
We will simply refer to the outcome of the auction
as success. There are several measures of success in
auctions. One of these may be either the net
increase in price or the net cost savings, depending
on whether one uses the seller or buyer
perspective. This measure is commonly used in
business today (Emiliani and Stec, 2001).
Online auctions may also reduce transaction
costs (Garicano and Kaplan, 2000; Rindfleisch
and Heide, 1997) or search costs (Kwak, 2001;
Lynch and Ariely, 2000). Electronic auctions are
purported to significantly reduce the total time in
which buyers and sellers are engaged in a
transaction (Emiliani and Stec, 2001). Total
transaction or cycle time includes the process from
customer identification through payment for
goods or services. Because online auctions may
reduce the time associated with identifying
customers, evaluating customers, receiving
Figure 1 Conceptual framework for the analysis of online auctions
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customer bids, finalizing contracts, shipping
product and receiving payment, transaction and
search costs can be reduced.
Outcomes are often described as the tangible
results of the process. While cost savings are
important, the concept of satisfied customers is
another outcome measure that is well-grounded in
the literature and critical to successful businesses
(see, for example, Churchill and Surprenant,
1982; Perkins, 1993; Rust and Zahorik, 1993).
Thus, customer and/or supplier satisfaction is a
possible outcome in the proposed framework.
Finally, perceived benefits to the participants
are also important. For example, Mahadevan
(2000) proposed descriptors of the value stream as
auction outcomes. The value stream is simply
presented as the worth, either perceived or real, of
the business transaction to both the buyer and the
seller. While Mahadevan’s focus is on the “selling”
relationship such as that employed by Lands End
and Dell, identification of the value stream is also
appropriate for the “buying” relationship or that
employed in the reverse auction process.
Whether we are analyzing a forward or a reverse
auction, both buyers and suppliers should have
benefits that add value and revenue/profit to the
organization. While we can presume that the
benefits are greater for sellers in a forward auction
(and buyers in a reverse auction), we must identify
benefits for both in order for the online auction to
have any chance of longevity in business (Emiliani,
2000; Lee and Corbitt, 2001).
Auction formats and characteristics
There are a variety of auction formats and many
characteristics that define auctions. These formats
and factors are valid for traditional or online
auctions. Klein (1997) clusters auctions into four
types and identifies the motivations for each type
of auction, specifically regarding the
determination of price. He suggests that the
preferred auction format is a function of the
motivation for determining the price. The number
of bidders and pattern of bidding is determined by
the rules of the auction and its surrounding
environment. For example, auctions may be held
for either fixed lots or split lots. Potential
environmental factors include the type of the good
being sold, risk preferences of bidders, the total
value of the auction, the number of items included
in the auction, and the available information
concerning the bidding process.
Auction length (in days or minutes) and the
auto-extension of the bidding period are additional
auction characteristics that may influence auction
success. Most auctions are initiated with advanced
notice of a specific closing time. The fixed end time
poses an incentive problem: the early bid serves no
benefit to the bidder but reveals information to
rivals. Many auctions with fixed end times are
experiencing “sniping” or submission of bids in the
final minute of an auction. Late bidding deprives
rivals the ability of seeing one’s bid and
undercutting it. Late bidding facilitates collusions
or interdependent pricing well above that
predicted by auction theory.
Auction “overtimes” can restore the desirable
bidding properties of reverse auctions. An
overtime or extension to the auction is invoked if
any bidding occurs in a designated final phase of
the auction (e.g. bids in the last two minutes). The
additional time allows bidders the opportunity to
react to “snipers” and minimize the potential for
pricing rings. A disadvantage of overtime is that it
obligates serious bidders to return to the auction at
closing time and remain through subsequent
extension periods. The effect on bidder
participation has yet to be examined.
Finally, the total number of suppliers
(Brannman et al., 1987), and the number of
invited suppliers or the number of participants
may affect the outcome of the auctions.
Process
In this section, we discuss the types of auctions,
bidding formats, and some of the attributes of
auctions that may impact the outcome of our
auction system. The process portion of Figure 1 is
the auction itself.
Historically, auctions emerge when competitive
prices do not exist (Gora, 1999). Auctions are a
transaction format that allows individuals/
organization to sell (or procure) goods and services
at the highest (lowest) possible price. While
forward auctions feature increasing incremental
bidding, reverse auctions feature decreasing
incremental bidding. The format lets participants
submit bids where the bidder with the most
advantageous bid to the firm will win. In the case of
a forward auction, the highest price will win. In a
reverse auction, the supplier bidding the lowest bid
will typically win. In other words, in a reverse
auction, prospective buyers can list any items they
wish to buy, and then sellers bid to provide the best
price. The consumer decides the exact
specifications of each item, instead of the
specifications being dictated by the seller.
Input or environment
Input in the auction system consists of the
characteristics of both buying and selling firms, the
relationship between these firms, and the
characteristics of the product involved in the
exchange. Input factors are also shown in Figure 1.
Whether it is the buying or the selling firm, the
characteristics of the firm are still the same.
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However, the characteristics of the buying firm
and selling firms will take on differing importance
depending on their respective roles in the auction.
In this section we will discuss those factors or
characteristics of the firm on which we will build
the model. Additionally, in this section we will
discuss the buyer-supplier dyad, or the
relationship characteristics between the buying
and the selling firms.
success, it is perhaps the interaction between buyer
and seller that is most critical.
Firm characteristics
While both buying and selling firms may have
many characteristics that are the same, differing
perspectives may elicit different responses. Past
research on traditional procurement has identified
that factors such as quality, delivery reliability,
trust, economic performance, and financial
stability are important criteria for selecting
suppliers (Choi, 1996; Ellram, 1990; Min, 1994).
Although these factors are equally important in an
online auction environment, there are additional
supplier characteristics that can have a significant
influence in the success of online auctions.
The information technology (IT)
sophistication, the familiarity and the comfort level
of the suppliers for conducting business online will
have an impact on their participation level in
online auctions. In addition, the sourcing practices
of the direct supplier, such as the number of
downstream suppliers, the degree of channel
control and dependence, and the extent of buyer
dependency, are all supplier-related criteria that
will have significant impact on the participation
level and success of an online auction.
The firm’s own resources, experience and
involvement in e-commerce-related activities can
also play an important role in online auction
success. The critical buying mass, as well as the
resources that large firms have, enable them to
invest in development of private online auction
sites and perhaps incur more risks and initial losses
that cannot be afforded by smaller and less wellcapitalized firms. Since conducting business online
through the auction mechanism is a gradual
learning process, more experienced firms are likely
to be high performers.
Another firm characteristic that can influence
the company’s involvement and commitment to
online auctions is the culture of the organization.
The culture of an organization may be defined in
practical terms, as simply, “the way things are
done” (Denison, 1996; Gordon, 1991; Hatch,
1993; O’Reilly et al., 1991). If familiarity and
commitment to e-commerce-related activities
pervade the entire organization, there is a higher
probability that the firm will be actively involved in
online auction mechanisms.
While there are many characteristics that are
important in both traditional and online auction
Dyad or relationship characteristics between buyers
and sellers
In research in the manufacturing marketing
interface, Ruekert and Walker (1987), Kohli and
Jaworski (1990), Jaworski and Kohli (1993),
Narver and Slater (1990), Parente (1998) and
others have identified a number of factors that
influence the interaction between manufacturing
and marketing. Similar factors are present in the
buyer-supplier dyad in a B2B environment.
We define two major areas of interest in the
buyer-seller relationship:
(1) the relationship between the firms; and
(2) each firm’s “stake” in the transaction.
The “relationship” aspect of the dyad takes on two
forms. The first is a factor we call the “dyad delta”.
The relationship between buyer and seller in either
a forward or a reverse auction will be influenced by
the differences, or deltas, between each of two
members in the dyads in the auction. In a forward
auction, if the seller is significantly larger than the
buyer(s), the seller probably has significantly more
power in controlling the outcome of the auction
transaction. In a reverse auction, a buyer may be
the proverbial “500 pound gorilla”. In other
words, if a supplier wants to do business with the
buyer, he will do so on the buyer’s terms (Emiliani,
2000).
The relative influence and power of the two
companies may also influence the interaction
(Schopler, 1987). The buyer in a forward auction
or the supplier in a reverse auction may feel that
they have been treated unfairly in negotiating
situations. It is even possible that one company
may attempt to withhold information or sabotage
the efforts of the larger company in order to
compete on a more even basis.
The interface between buyer and seller may also
be affected by physical distance. Companies
located in the same city interact differently owing
to expanded social and business opportunities.
Another “distance” factor may be the degree of
information and technology sharing. If both
companies have access to an online order system
where updates are instantly available, we propose
that there will be less difficulty in the interaction.
Communication distances are another factor to
consider in this environment, and they may be
measured by the extent of electronic
communications between the two functions, such
as electronic mail, voice mail, or electronic data
interchange.
There are also a variety of operational factors in
the dyad that may affect the auction process or the
auction outcome. These may include quality and
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delivery reliability experienced by the buyer when
purchasing from a specific supplier. Other
dimensions of the dyad may include customer
service on the part of the selling firm and the longterm relationship potential between the firms.
While these “deltas” are not inherently different in
an online versus traditional auction, we suggest
that the way that the dyad firms deal with them in
an online auction may be different.
Other dyad characteristics are factors such as
the longevity of the relationship and the individual
and personal interaction between the buyer and
seller. Once again, the relationship between buyer
and seller is not different in an online versus
traditional auction. However, the impact on an
online auction may be very different from a
traditional auction. For example, if we
conceptualize the dyad relationship on a
continuum, a good relationship between buyers
and sellers in a dyad will be less likely to influence
the outcome of a transaction in an online auction.
On the other side of the continuum, a firm that
chooses to participate in an auction with no history
at all with the principal member of the dyad will be
able to compete in the online environment without
the significant effort usually necessary in building a
relationship. This apparent “hands off” approach
could imply a lower importance on the relationship
and the supplier development process in the online
environment. However, in discussions with several
purchasing agents involved in online reverse
auctions, they report that they spend more rather
than less time in supplier development. Thus, we
should treat supplier development as an
independent factor separate from the relationship
in the proposed framework (Handfield et al., 2000;
Krause et al., 1998).
The second dyad component, the “stake” that
the competing firms have in the outcome, will also
affect the auction process and the outcome.
“Stake” may be defined as how much a firm has
involved in the outcome. As an example, if one of
the buying firms in a forward auction uses the
product being purchased as a large component of
its final product, the buyer may bid more
aggressively to ensure supply. In a reverse auction,
if one of the competing selling firms is the
incumbent supplier and has a large proportion of
the business with the buyer, they may choose to
“pull out all the stops” to maintain the business
(Emiliani, 2000).
Referring to Porter’s (1980) Five Forces
Industry analysis, the bargaining power of buyers
and suppliers is a good framework with which to
analyze the stake of the firms involved in the
auction. This includes such factors as the
importance of industry to suppliers, the criticality
of individual materials to industry success, the
percentage of the total product, and the number of
buyers or suppliers.
Product characteristics
Finally, a major factor in the input portion of the
proposed model is the characteristics of the
product being exchanged. A number of researchers
have identified product characteristics which affect
the manufacturing marketing relationship or, in
this example, the outcome of the transaction
(Kahn, 1997; Konijnendijk, 1993, 1994; Parente,
1998; Whybark, 1994). However, the challenge in
this discussion is to identify the difference in the
importance of product characteristics between
online and traditional auctions.
While the characteristics of the organization
may be the same, the product characteristics may
cause differing auction dynamics. Klein (1997)
suggests that specific auction formats may be
better for certain types of products (i.e. auctions as
a distribution mechanism for excess inventory
such as airline seats or outdated parts). For
example, a product that is manufactured in a
process environment with limited fixed inventory
storage capacity may cause a different interaction
between buyers and sellers than a customerdesigned product.
We suggest that the degree of customization or,
alternately, the notion of make-to-stock (MTS),
make-to-order (MTO), and engineered-to-order
(ETO) have a significant impact on the outcome of
an auction. However, we further suggest that there
are other aspects, such as the amount of risk in the
production of the product, the dependence of one
firm on the product for its livelihood, and the
market dynamics that are also contributing factors
to the outcome of an auction.
We define product risk as the certainty of
purchase (in a forward auction) or supply (in a
reverse auction) of a product with the
specifications desired. The more standard the
product, the more likely the buyer gets what he
needs in a forward auction. Likewise, the more
standard the product, the more likely it is that
the buyer is able to develop specifications and
have a wider array of suppliers compete for the
business.
Product dependence is another consideration in
the model. The ease with which a customer can
switch to a substitute product is important. Note
that the definition of substitute products is not one
of changing suppliers, but rather one in which a
different product might be used. (For example, we
may use oil or natural gas for home heating.
However, conversion of the furnace system would
be necessary.) If the threat of substitute products is
high, the relationship between the buying and
selling firms might be changed owing to a desire to
please the customer. However, if it would be
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difficult for a customer to replace the current
product with another, the attitude of the
selling firm might be less flexible in customer
demands.
Market dynamics relate to both the industry
structure and the market for the product (Porter,
1980). One major area to consider in the definition
of market is the current supply/demand situation.
If a product is in short supply and manufacturing
rates are high, the selling firm is less likely to be
successful with specific customer requests,
creating tension in the interface. If one customer
leaves, they can be easily replaced by another
customer, perhaps even at a higher price. The
converse is also true. The environment and
dynamics of the auction are likely to be changed
owing to the supply/demand situation.
In fact, it may be the interaction between the
type of product and each of the aforementioned
factors that impacts an online auction differentially
from a traditional auction. The real-time and
online nature of the auctions under discussion may
suppress or exacerbate the impact of, for example,
the interaction between a product in short supply
that is highly customized. The relationship aspect
is minimized in this circumstance owing to the
ability to do business without being face-to-face.
The potential combinations and permutations of
the interactions suggest significant opportunities
for research on online auctions.
interaction between the characteristics of the
supplier, the buyer and the auction environment
and thereby provides a framework for the
development of research questions. This
conceptual model can provide an impetus for
future research, structuring it along the lines of a
traditional interaction between buyers and sellers.
Online auctions will be studied incrementally from
traditional auctions. Not only will the research
build on and take advantage of existing tradition,
but it will form the basis for the development and
testing of research hypotheses that will expand the
frontiers of knowledge in online auctions.
Summary of findings and future research
directions
The next step in the development of this model is
to develop hypotheses to be tested for both forward
and reverse online auctions. Each of the factors
identified in the previous discussion will form the
basis for analysis in the study of both forward and
reverse auctions. The availability of data in online
auctions will allow researchers to adequately test
hypotheses in the electronic era – different from
the traditional auctions, where limited data was
available in both scope and volume.
The research framework presented in this paper
is unique, as there is no comprehensive theoretical
and practical model for analyzing online auctions
at present. Although the Mahadevan (2000) model
discusses various streams of online auctions, most
other models approach the auction process from a
limited perspective. None of the prior models have
taken into consideration the interaction between
suppliers, buyers and the auction process itself,
and how the triad has an impact on the outcome of
the auction.
In this paper, we propose a conceptual model
for analyzing online auctions that shows the
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