Table of Content
Background of research at University of Groningen .. 2
Introduction ..................................................................................................................... 2
Methodology ................................................................................................................... 7
Empirical survey ......................................................................................................... 7
Experiments ................................................................................................................ 7
Testing the reputation system ..................................................................................... 8
Trust: Definitions, Antecedents and Consequences ... 9
Trust from multiple perspectives .................................................................................... 9
Literature review on trust ................................................................................................ 9
Trust constructs ............................................................................................................. 12
Importance of trusting beliefs ....................................................................................... 14
Summary ....................................................................................................................... 17
Risk-reduction strategies (RRS) .............................. 19
Description and types of Auction markets: .............. 22
Consumer motivations and concerns in online auctions ............................................... 23
Prior research on auction studies .................................................................................. 24
Research gaps: .............................................................................................................. 34
Product catageories sold in online auctions .................................................................. 35
B2B versus B2C ........................................................................................................ 35
Fee and commissions ................................................................................................ 36
Useful input for simulations..................................... 36
Glossary ................................................................... 38
Links: ....................................................................... 43
1
Background of research at University of Groningen
Introduction
In recent years the Internet contributed to a growth of consumer-to-consumer (C2C)
business (Ba and Pavlou 2002). Several auction sites are available, facilitating the
exchange of goods between individual consumers. Auction sites may be very generic
concerning the products being offered and operate on a global scale (e.g., E-Bay), or may
focus on specific products on a national scale (e.g., national car auction sites). At the
moment, virtually all consumer products are being auctioned on the Internet, ranging
from used toys and CDs to cars and houses.
The online environment is more uncertain due to the difficulty of assessing the
seller’s reputation and product quality; lack of trust is one of the most frequently cited
reasons that prevent consumers from making online purchases (Lee and Turban 2001).
This study examines how customers use reputation mechanisms1 to assess the seller
reputation and product quality with the aim to reduce their perceptions of risk and
facilitate satisfying transactions. Next, it is investigated which reputation mechanisms are
most effective in promoting trust in consumer-to-consumer (C2C) transactions under
varying circumstances (availability of similar products, product type, and customer
experience/expertise) and across different stages within the bidding process.
Reputation is particularly important when engaging in online purchasing given the
anonymous nature of the Internet (Grabner-Kräuter et al., 2003). On auction sites most
transactions occur between parties that have never met (Ba and Pavlou, 2002). As such,
this may give rise to opportunistic behavior (Akerlof, 1970; Grabner-Kräuter et al.,
2003), as it is relatively easy for a dishonest seller to lure an unsuspected buyer into a
fraudulent transaction (Neumann, 1997); the detrimental effects on the seller’s
reputational equity are much less strong than, for instance, for retailers who have invested
heavily in physical buildings and facilities (Doney and Cannon, 1997). Apart from the
difficulty in assessing the seller’s expected behavior, consumers may also face difficulties
in assessing the quality of physical products due to the limited ability to (physically)
examine the product prior to purchase (Alba et al., 1997; Standifird, 2002). Finally,
online transactions often involve asynchronous exchange of goods and money –i.e.
spatial and also temporal separation between exchange partners is common (Grabner and
Kräuter et al., 2003). Consumers are frequently required to share sensitive personal
information (e.g. mailing address, telephone number) and financial information (e.g.
credit card numbers), although they are located in different parts of the country or even in
In this study, we use the term ‘reputation mechanisms’ to refer to all cues that bidders can use to infer the
reputation or trustworthiness of a seller.
1
2
other countries and have limited history of prior online transactions (Bhattacherjee,
2002). In online auctions, consumers are confronted with a myriad of offerings and
(contradicting) marketing messages. One effective heuristic or means to reduce the
complexity of the decision is by means of trust (Luhmann, 1989).
Trust is often viewed as a catalyst in many buyer-seller transactions (Doney and
Cannon, 1997; Shapiro 1987) but seems to be particularly important in the case of ECommerce due to the less verifiable and less controllable business environment (Ba and
Pavlou 2002; Gefen 2000; Reichheld and Schefter 2000). Trust generally is a crucial
enabling factor when there is uncertainty, interdependence, incomplete information, and
fears of opportunism (Bhattacherjee 2002; Hoffman, Novak and Peralta 1999; Mayer,
Davis and Schoorman 1995, McKnight and Chervany 2002); it plays a key role in
markets that involve high uncertainty and lack of legal protection, of which C2C markets
are a typical example (Luo 2002).
Reputation is often used synonymously with trust. However, reputation differs
from trust2; reputation refers to the public evaluation of the credibility and accountability
of sellers, whereas trust refers to an individual’s willingness to rely on the other party.
Reputation emerges as a result of social network effects when information on an object or
individual in on one relation spreads (i.e., shared voice) to others via an information
network (Granovetter, 1985; Sabater, Paolucci and Conte 2006). Trust is more personal
than reputation, and is, for example, also influenced by direct experiences and the
individual’s trust propensity. Some authors conceptualized trust in conative or behavioral
terms (i.e., relying on another party) (Einwiller, 2003; Ganesan 1994; Mayer, Davis and
Schoorman 1995); others use more cognitive or evaluative definitions of trust, arguing
the link between trust evaluations and behavioral response should be open to empirical
investigation and is likely subject to the influence of other contextual factors (Doney and
Cannon 1997; Morgan and Hunt 1994). Following Morgan and Hunt (1994, p. 23), we
define trust as existing when “one party has confidence in the exchange partner’s
reliability and integrity.”
Sellers on the Internet may actively try to communicate their trustworthiness to
potential buyers. Trust can facilitate transactions by mitigating the perceptions of risk,
and may generate price premiums for reputable sellers. Although empirical studies have
shown conflicting results, on balance, it seems that trustworthy sellers can ask for higher
price premiums (Ba and Pavlou 2002; Melnik and Alm 2002). Sellers –but also auction
2
Reputation is also sometimes used synonymously with image (Gotsi and Wilson, 2001), but authors (e.g.,
Einwiller 2003; Sabater, Paolucci and Conte 2006) mention the difference between reputation and image.
In contrast to the collective nature of reputation (i.e., meta-evaluation), an image is the mental picture a
person has stored in his or her memory, or –in psychological terms – it can be defined as the cognitive
schema a person has of an object. Image does not necessarily have to relate to the predictive and benevolent
behavior of a person, it can also contain other aspects, such as the novelty of the merchandise from this
seller, or the friendliness during interactions.
3
markets such as eBay– can facilitate transactions by making use of reputation
mechanisms that inspire trust, such as feedback mechanisms, third-party escrow services
(e.g., Paypal.com and Escrow.com), and credit card guarantees (Pavlou and Gefen 2004).
An increase in the perceived effectiveness of these mechanisms engenders trust, reduces
risk and, consequently increases intentions to transact online. Many online services have
emerged that provide feedback on sellers’ reputation, such as Bizrate.com, eBay’s
Feedback Forum, Epinions.com, and Rapleaf.com (cf. Ba and Pavlou, 2002). To reduce
the information asymmetry for potential buyers, the eBay Feedback Forum site3, for
instance, shows potential buyers the performance ratings or trustworthiness of sellers
(e.g., sellers can become Platinum Power Sellers).
Reputation mechanisms reduce the chances of opportunistic (i.e. fraudulent)
behavior, as traders are motivated to maintain good reputation records in order to
maximize their profits (Conte and Paolucci 2003; Lin et al. 2006). However, such
reputation feedback systems apply to sellers that sell on a regular basis, and such
indicators of trust do not apply to first time or very irregular sellers. Here potential buyers
need other indicators and/or additional information to estimate the trustworthiness of a
seller, such as the information provided by the seller about the goods for sale (e.g.,
quality of the pictures, detail of description), information on the seller (e.g., address,
email address, level of experience), arrangement for payment (upfront or afterwards), and
interactions with the seller (by email or telephone).
Consumers may differ in their use (type and degree) of mechanisms to assess the
transaction risks involved and trustworthiness of sellers, depending on product factors
(e.g., homogenous vs. heterogeneous goods, level of product involvement), consumer
characteristics (e.g., prior experience/expertise, trust propensity), market conditions (e.g.,
number of similar items offered at auction site) and seller characteristics and
interventions (e.g., reputation feedback score, minimum opening bid, information
provided about product, interactions with bidder), and these are likely to differ according
to the bidding phases (i.e., entering the bid, during the bid, end of bid). This study focuses
on understanding how and to what extent consumers use (a variety of) reputation
mechanisms in C2C online auction exchanges. We expect product involvement and
perceived risk to be key factors determining consumers’ use of indicators for sellers’
reputation. Risk is often viewed as antecedent of involvement, as the consequences of
making a bad decision influence the importance of the decision and subsequent search
behavior (Chaudhuri 2000; Choffee and McLeod, 1973; Zaichkowsky, 1986). When a
decision is less important (in terms of consequences) decision-makers are more likely to
use a simpler heuristic instead of using all information available (Tversky 1969; 1972).
3
In contrast to the eBay Feedback Forum where bidders and sellers can only provide feedback about the
other party in terms of a positive or negative evaluation, open forum sites display posted messages from
individuals about sellers and bidders without a predetermined format.
4
For expensive products, risk perceptions will be higher, and buyers will spend more time
and effort to reduce their transaction-specific risk (Bettman 1973). The more expensive a
product is, the less incentive the seller will have to cooperate since the benefits of
cheating are greater. Given the greater risk involved with expensive products, buyers
need to develop more trust before they engage in an exchange. Hence, we hypothesize
that buyers spend more cognitive effort in estimating the trusting beliefs (e.g.,
benevolence, integrity, ability, predictability) of a seller when they are confronted with
more complex and expensive products, and that the trustworthiness of a seller becomes
more critical in facilitating these transactions. Apart from the monetary expenses of a
product, its complexity plays a role in influencing buyer’s use of reputation mechanisms.
In contrast to search goods (e.g., CDs, books, etc.), experience goods are less amenable to
be sold through the Internet, as the product quality cannot easily be assessed prior to
purchase (Alba et al. 1997; Girard et al. 2003), and hence increases buyer’s perceptions
of risk. In circumstances where intrinsic product attributes are absent, consumers tend to
rely stronger on extrinsic attributes, such as the seller’s reputation (Teas and Agarwal,
2000; Zeithaml, 1988), and hence engage in more extensive search. In sum, we
hypothesize that the type of product category influences both the type of and the degree
to which reputation mechanisms are used.
Additional factors that are expected to relate to evaluation and use of reputation
information are (1) the experience/expertise of buyers, (2) the relative opening price of
the good, (3) level of product differentiation, and (4) the number of competing items
being sold. First, the level of experience or expertise buyers have is an important factor in
explaining online auction behaviors (Wilcox 2000); the more experience an auction
buyers is, the better they will be able to judge when and how much to bid for an item.
Consumers’ expertise on the product domain and on the bidding process is likely to
influence the way they evaluate sellers. Compared to experienced buyers, inexperienced
buyers are more likely to depend on extrinsic cues (e.g., seller’s reputation) when
intrinsic cues are absent (Ariely and Simonson, 2003; Zeithaml, 1988). In line with this
reasoning, Einwiller (2003) demonstrated that experienced online buyers rely less
strongly on third-party evaluations (i.e., reputation) in determining their trust. As such,
we expect that buyers having more experience/expertise with auctions and the product
category are less likely to rely on impersonal reputation feedback mechanisms, and rely
more strongly on their own prior experiences in developing their trust. Second, we expect
that the lower the minimum price of a product is (i.e., initial price to similar items on
auction site), the lower the initial trust of the buyer, as price discounts are viewed as
compensation to buyers for bearing higher than average risks (cf. Ba and Pavlou, 2002)4.
4
In the study of Ariely and Simonson (2003) lower opening bids drew more bidders (lower initial risk
levels), but in the end led to lower final prices, which they explained as the result of anchoring effects of
the opening bid.
5
We therefore hypothesize that the lower the relative price, the more distrust consumers
develop and the higher their risk perceptions, which stimulates them to make more use of
reputation mechanisms to infer the seller’s trustworthiness. However, this effect of
relative opening price is expected to be attenuated in highly competitive markets (see
below), as consumers focus more strongly on evaluating the product rather than the
supplier. Thirdly, for differentiated products (e.g., second-hand products, products that
vary in product quality) individuals generally rely stronger on reputation mechanisms.
Lee et al. (2001) found that for new products (i.e. sealed products), the influence of
negative feedback scores was negligible. Yet, when bids were placed for second-hand or
refurbished goods, feedback scores had a great influence in the purchase decision and
size of the bid. Fourthly, we hypothesize that in a highly competitive market5 buyers
make less use of reputational indicators. The reputation mechanisms may function as a
filter to select a subgroup of interesting sellers; however, as the market is more
transparent, buyers can more easily compare offerings and rely on the offerings itself. In
a scarce market (very few items are offered), they are less capable of addressing whether
they get good value for their money, and hence rely more strongly on the reputation
mechanisms to reduce their risk. The study of Ariely and Simonson (2003) shows the
interaction effect between the market’s competitiveness and the opening price on final
prices; they found that when participants can compare the prices of similar items (i.e.,
competitive market), high or low initial starting prices do not seem to have an effect on
final price; in contrast, when there are few comparative alternatives (i.e., scarce market)
high opening price leads to higher final prices.
5
The number of competitive offerings (and competitive bidders) is highly dependent on the product
category (Ariely and Simonson 2003).
6
Methodology
The proposed research will combine an empirical survey of actual C2C business on
auction sites, experimental study of the use of reputation indicators, and the development
and experimental testing of reputational systems.
Empirical survey
In an empirical survey, we plan to study the buyer’s use and perceived effectiveness of
reputation indicators. We will select four types of markets differing on the average price
of products offered (relative cheap versus relative expensive) and the degree to which
information on the relevant/dominant attributes can be distributed through the Internet
(e.g., tactile versus non-tactile/differentiated versus standardized products). In a pilot
study we will conduct in-depth interviews with a sample of consumers that have
considerable experience in C2C auctions to (1) select archetypical markets that
significantly differ on involvement/perceived risk, and (2) get an indication which
reputation mechanisms are used and how frequently. After selecting four archetypical
markets, we will study the search behavior of consumers in a natural (nonmanipulated)
setting (e.g., eBay). Respondents will be assigned to a shopping task. The search behavior
of these respondents will be followed using a click-track following methodology. These
data will reveal the actual use of reputation indicators in the selection process.
Additionally, through a questionnaire we will measure background variables (sociodemographics, auction experience/expertise), attitudinal variables (product involvement,
trust propensity), and transaction-specific variables (perception of risk, effectiveness of
reputation mechanism). Next, we will select a sample of sellers in the four markets, and
(1) rate them on their explicit use of reputation mechanisms, and (2) question them on
their motives to use certain of these indicators. This sample of sellers will be controlled
for seller experience. The results of this empirical survey will provide information on
how different reputation indicators are being used –by both consumers and sellers– in
different markets.
Experiments
While the empirical survey produces an overview of the relevant reputation indicators,
the experiments will focus on the specific trade-offs buyers make in a well-controlled
auction environment. For different product categories varying on involvement and
perceived risk (as selected on the basis of the survey), we will offer respondents an
experimentally designed auction site. Respondents will be confronted with this auction
site and will be assigned to select one seller to conduct business with. Different variables
7
will be tested experimentally. First we will vary the type and number of reputation
indicators as used by the sellers (e.g. all positive, mixed positive and negative, all
negative). Sellers of similar goods will systematically differ on the reputation indicators
as provided. These experiments will reveal which reputation indicators are the most
important in what type of market. Next we will vary the available product information, as
to experimentally test the trade-off function between risk and reputation indicators. Here
the sellers will systematically differ on critical reputation indicators as presented (as
identified in the previous experiment) and the level of detail in the product description.
Following that we will introduce variations in relative product prices in the experimental
design in order to identify the relation between the use of reputation information and the
relative prices of goods offered. The final experiment will vary the number of sellers in
the market to test if reputation indicators play a less role in highly competitive markets.
The experiments will use a click-tracking methodology along with measuring
background, attitudinal and transaction-specific variables. The proposed experiments will
reveal what reputation indicators are being used and appear most effective under differing
market conditions.
Testing the reputation system
The previous experiments provide useful information about the effectiveness of
reputation indicators under varying conditions. Based on this, we will develop a system
allowing sellers to indicate their reputation through a number of mechanisms (e.g.,
escrow, feedback). Respondents will be challenged to sell their goods in an experimental
context, where buyers are represented by computer-simulated agents as developed by the
simulation project. Sellers are prompted to obtain a maximum of profit from a series of
selling actions. Artificial buyers can choose varying reputation systems to select the
sellers they want to buy from. The performance of the reputation system will be measured
in terms of effectiveness by measuring the outcome quality of the transactions.
8
Trust: Definitions, Antecedents and Consequences
Trust from multiple perspectives
Prior research has sought to identify valid and relevant dimensions (Ganesan 1994; Smith
and Barclay 1997). Trust is a complex and multidimensional (i.e., it comprises multiple
components) concept (Lewis and Weigert 1985). Many theorists and researchers of trust
focus on interpersonal relationships; however, the analysis of trust in the context of
online auctions should also consider impersonal forms (e.g., systems/technologies) of
trust (Grabner-Kräuter et al. 2003).
Multiple disciplines/perspectives have defined trust:
 Personality psychologists: a belief, or expectancy, or feeling that is deeply
rooted in the personality and has its origins in the individual’s early
psychological development (e.g., Rotter, 1967;1971): personal trait or belief
 Social psychologists: an expectation about the behavior of others in
transactions, focusing on the contextual factors that serve either to enhance or
inhibit the development and maintenance of trust (Lewicki and Bunker, 1995):
social structure.
 Economists/socialists: how institutions and incentives are created to reduce the
anxiety and uncertainty associated with transactions (e.g., Granovetter, 1985;
Zucker, 1986; Williamson, 1993): economic-choice mechanism.
Literature review on trust
Table 1 shows the definitions, antecedents or components and consequences of trust
derived from several studies.
Table 1: Prior studies on trust
Author(s)
McKnight and
Chervany
(2001, p. 42)
Definition of trust
To willingly become
vulnerable to the
trustee, whether
another person, an
institution or people
generally, having
taken into
consideration the
characteristics of the
trustee.
Antecedents/components of
trust
1. Competence: belief that
other party has the ability
or power to do for one
what one needs done.
2. Benevolence: belief that
other party cares about one
and is motivated to act in
one’s interest.
3. Integrity: belief that the
other party makes good
faith agreements, tells the
truth, acts ethically, and
fulfills promises.
4. Predictability: belief that
the other party’s actions
(good or bad) are
9
Consequences of trust



Trusting behaviors:
Purchasing
Cooperating
Information sharing
consistent enough that one
can forecast them in a
given situation.
Urban et al.
(2000)
Mayer et al.
(1995, p. 712)
Moorman,
Zaltman and
Deshpandé
(1992, p. 315)
(1993)
Morgan and
Hunt (1994, p.
23)
Sirdeshmukh 
et al. (2002)
GrabnerKräuter et al.
2003
Trust is not being
afraid even if you
are vulnerable.
The willingness of a
party to be
vulnerable to the
actions of another
party based on the
expectation that the
other will perform
particular action
important to the
trustor, irrespective
of the ability to
monitor or control
that other party
A willingness to rely
on an exchange
partner in whom one
has confidence.



Ability
Benevolence
Integrity
Trust exists when
one party has
confidence in the
exchange partner’s
reliability and
integrity.
the expectations held
by the consumer that
the [service
provider] is
dependable and can
be relied on to
deliver its promises.
Risk taking in relationship.
Outcomes of trusting
behaviors are updated of prior
perceptions of the ability,
benevolence and integrity
(dynamic nature of trust)
Trust creates value by (1)
providing relational benefits
derived from interacting with a
service provider that is
operationally competent,
benevolent toward the
consumer, and committed to
solving exchange problem, and
(2) reducing exchange
uncertainty and helping the
consumer form consistent and
reliable expectations of the
service provider in ongoing
relationships.
In case of little trust, there will
be a need for more
information. The more trust in
Trust is a
mechanism to reduce
the complexity of
10
human conduct in
situations where
people have to cope
with uncertainty
(based on Luhmann
1989). Trust is a
very effective
complexity
reduction method;
although it does not
really enable people
to control or even
anticipate without
error the behavior of
others.
a specific transaction, the less
the need for more complete
information.
1.
Reduce uncertainty
2.
Comprehensive
organization of the
activities performed
Source: McKnight and Chervany (2001, p.42)
11
Trust
Interpersonal trust:
Institutional trust:
Dispositional trust:
The trustor trusts the trustee
The bidder trusts the seller
The bidder trusts the auction site/The bidder trusts the feedback system
The bidder trusts others generally
Source: McKnight and Chervany (1996)
Trust constructs
Some authors have conceptualized trust in conative/behavioral terms (Coleman, 1990;
Deutsch, 1962; Ganesan 1994; Giffin, 1967; Mayer, Davis and Schoorman 1995;
Schlenker, Helm and Tedeschi, 1973): trusting behaviors (or behavioral intentions).
Other researchers use cognitive/evaluative definitions of trust (trusting beliefs), arguing
the link between trust evaluations and behavioral response should be open to empirical
investigation and likely subject to the influence of other contextual factors (Doney and
Cannon 1997; Morgan and Hunt 1994). Here we distinguish between the cognitive
evaluations and behavioral outcomes of trust.
12
Trusting Behavior: the extent to which one person voluntarily (Lewis & Weigert 1985)
depends on another person in a specific situation with a feeling of relative security, even
though negative consequences are possible. Depends is a behavioral term, which
distinguishes Trusting Behavior from the intentional term Trusting Intention (willingness
to depend). Trusting Behavior construct implies acceptance of risk (i.e., placing a bid,
making a payment).
Trusting Intention: the extent to which one party is willing to depend on the other party
in a given situation with a feeling of relative security, even though negative consequences
are possible. Trusting Intention is situation-specific, intentional, personal (originating in a
person) and (one-way) directional: one person is willing to depend on the other.
Trusting Beliefs: the extent to which one believes (and feels confident in believing) that
the other person is trustworthy in the situation. The most prevalent trusting beliefs in the
literature involve benevolence, honesty/integrity, competence, and predictability.
Benevolence means one cares about the welfare of the other person and is therefore
motivated to act in the other person's interest. A benevolent person does not act
opportunistically toward the other person. Honesty/integrity means one makes good faith
agreements (cf. Bromiley & Cummings, 1995), tells the truth, and fulfills any promises
made. Competence means one has the ability to do for the other person what the other
person needs to have done. So the essence of competence is efficacy. Predictability
means one's actions are consistent enough that another can forecast what one will do in a
given situation; it embodies an element of temporal continuity that can be related to the
other trusting beliefs. Examples in auction market: trusting beliefs can be developed by
evaluating the seller based on personal (communication with the seller) and impersonal
(reputation feedback mechanism, quality of picture, information on product/seller) cues;
these beliefs are influenced by individual customer characteristics (trust propensity; level
of experience/expertise).
System Trust (=institution-based trust): the extent to which one believes that proper
impersonal structures are in place to enable one to anticipate a successful future endeavor
(Luhmann 1991; Lewis & Weigert 1985; Shapiro 1987). Personal attributes of the other
are not at issue with System Trust. Hence, it does not support Trusting Beliefs about the
other; but it does support Trusting Intention. Two types of impersonal structures can be
differentiated: (a) structural assurances, and (b) situational normality. Structural
assurances include such safeguards as regulations, guarantees, or contracts (Shapiro,
1987; Zucker, 1986) (i.e. reduction of the impact of negative consequences). Situational
normality may include one's own role and others' roles in the situation (Baier, 1986); it is
based on the appearance that things are normal (Baier, 1986; Garfinkel, 1967) or in
13
"proper order" (Lewis & Weigert, 1985a: 974). System Trust supports Trusting Intention
in that it makes it feel safe to depend on that person because of type (a) System Trust's
safeguards, which act like a 'safety net,' or type (b) System Trust's reduction of
uncertainty, which enables one to feel more secure in taking risks with other people.
Examples in auction market: System trust can refer to eBay as a whole, but also at the
lower levels of the reputation systems, including (1) buyer-driven feedback systems, (2)
escrow services (auction house authorizes payment only after the buyer is satisfied), (3)
credit card guarantees, and (4) trust in intermediary (Pavlou and Gefen 2002).
Dispositional Trust (trust propensity): the extent that an individual has a consistent
tendency to trust across a broad spectrum of situations and persons (noncontext-specific).
Dispositional Trust is directed toward people (i.e., that others generally should be
trusted).
Situational Decision to Trust: the extent to which one intends to depend on a nonspecific other party in a given situation. It means that one has formed an intention to trust
every time a particular situation arises, irrespective of one's beliefs about the attributes of
the other party in the situation (Riker 1971). For instance, when intending to buy an
auction item below €30, it is better to just trust the seller automatically (without searching
for additional information, or contacting the person). Like Dispositional Trust, it means
that one has decided to trust without regard to the specific persons involved -because the
benefits of trusting in this situation outweigh the possible negative outcomes of trusting.
Kee and Knox (1970:p. 360) suggested that this may occur “when there is much to gain
from trusting..., but little attendant risk.” Situational Decision to Trust differs from
Dispositional Trust in that it is an intentional construct and relates only to specific
situations, not across situations generally. It differs from System Trust in that it does not
imply structural safeguards. It is simply an individual, situational strategy. Because it
does not concern specific individuals, Situational Decision to Trust does not support
Trusting Beliefs about specific individuals. But because it encourages a willingness to
depend on others in the situation, Situational Decision to Trust supports Trusting
Intention directly.
Importance of trusting beliefs
All four trusting beliefs (benevolence, integrity, ability, and predictability) are important,
as they complement and are likely to reinforce each other; a bad score on a belief may
also the negatively impact the scores on other beliefs. If a person fears that a seller is
incompetent to protect his credit card number, then this person is more likely to think that
the seller will also sell personal information to other companies (i.e., integrity) relative to
14
competent sellers. Next, the asymmetric impact of negative and positive attribute-level
performance on intentions (Mittal, Ross and Baldasare 1998) is also likely to apply to the
trusting beliefs. This asymmetric impact implies that a negative performance (below a
certain threshold) on a trusting belief (e.g., competence) has a greater impact on purchase
intentions than a positive performance (above a certain threshold) on that same belief.
Mayer et al. (1995) argued that the importance of the trusting beliefs is also
dependent on the stage within the relationship. In the initial stages, the effect of
honesty/integrity on trustworthiness is very strong. Next, the effect of benevolence on
trust will increase in time, as the relationship between the parties develops.
15
Cognitive
processes
16
A similar trust model (based on Mayer et al. 1995)
Mayer et al. (1995) argue that trustworthiness is based on ability, benevolence and
integrity (omitting predictability). They also take into account the trust propensity
(disposition to trust). The level of risk is dependent on the situation (e.g., buying a
second-hand car vs. new CD) and affects the reliance on trust in risk taking. The level of
trust will affect the amount of risk the trustor (i.e., bidder or seller) is willing to take; the
more risk involved the stronger the relationship between trust and risk-taking behavior.
Outcomes of trusting behaviors (positive or negative) will lead to updating of prior
perceptions of the ability and benevolence, and integrity of the trustee (Note: this is also
likely to update institution-based beliefs).
Summary
The trust literature shows that consumers develop trusting beliefs, based on the trust
propensity (personal, general disposition towards people), situational-based trust
(context), institution-based trust (impersonal, related to structures) and seller
characteristics (impersonal feedback mechanisms, information on seller and product) and
interventions (personal communications). Consumers can actively engage in search
activities (using Google, asking other buyers that bought from the seller), or ‘passively’
in order to develop the trustworthiness of a seller. In developing these trusting beliefs,
consumers update prior beliefs with new information. Figure 2 (below) shows the trust
constructs and their hypothesized relationships.
17
Figure 2: Modeling trust-related behaviors in an online auction context
Institutionbased trust
Consumer
characteristics


Prior
experience/
expertise
Trust propensity


Structural
assurance
(i.e., eBay)
Situational
normality
Trusting beliefs
about seller
 Competence
 Benevolence
 Integrity
 Predictability
Trusting intentions


Willingness to
depend
Subjective
probability of
depending
Perceived risk
Situational trust

Trust based on
specific
situations (e.g.,
below €30)
Trust-related (risk
taking) behaviors




Seller characteristics
and interventions







Information about product
Minimum/relative opening
bid
Reputation (feedback
score)
Payment options &
guarantees
Communication with
bidder (email, phone)
Third party seals
Escrow services
Purchasing
Paying ‘premium’ price
Cooperating
Information sharing
Market conditions

18
Number of
competitive bids at
auction
Product factors


Price
Homogeneity/
heterogeneity
Risk-reduction strategies (RRS)
Consumers usually rely on risk relievers to allay perceived risk to a tolerable level by (1)
reducing the amount at stake, and (2) increasing the degree of certainty that consequences
would be favorable (Akaah and Korgaonkar 1988; Mitchell and McGoldrick 1996).
Consumers may seek information from formal and informal sources, rely on information
acquired in the past (Cox and Rich, 1964), use brand image or price as quality guide, or
shop only in stores with high quality image (Akaah and Korgaonkar 1988). Among
various risk-reduction strategies, information seeking has been identified as the most
desirable strategy for reducing uncertainty in a purchase situation (Lutz and Reilly, 1973;
Jasper and Oullette, 1994). Information seeking has been found to be a direct function of
perceived risk (Hisrich, Dornoff and Kernan 1972).
In a ‘risky’ situation, the consumer may have a variety of information needs to satisfy
(Cox, 1967); the type of perceived risk being faced by the consumer may dictate his or
her information needs. Past research has shown a rather weak positive relationship
between perceived risk and tendency to seek word-of-mouth information regarding a
product (Cunningham, 1967; Sheth and Venkatesan, 1968). When faced with increasing
perceived performance risk, consumers tend to use more sources of information. Over a
wide range of products low or moderate in risk, the most frequently used method of
information acquisition is simply to buy the product, presumably on a trial basis.
However, when product performance risk is high, trial purchase is the least likely form of
information acquisition (Lutz and Reilly, 1974). In these circumstances, persons are
likely to acquire information from observation and own experience (Lutz and Reilly,
1974; Locander and Hermann, 1979).
Mitchell and McGoldrick (1996) reviewed the literature concerning risk-reduction
strategies (RRS). They distinguish between personal (direct contact with person) and
impersonal risk-reduction strategies, which can simplify, clarify or do both. Clarifying
strategies involve looking for additional information and asking experts, whereas
simplifying strategies refer to buying from a well-known seller, buying from a reputable
seller, buying through the same seller. Sometimes they include both (e.g. asking family
and friends, past experience, use price information as indication of quality). For example,
past experience may provide the buyer an insight into the buying problem: what and how
to evaluative (clarify), or may lead to copying past behavior (simplify).
19
Rely on seller
without thinking
Reputation of
seller
Ask other buyers
to evaluate seller
Interaction
with seller
Price as an
indicator of
product quality
and
trustworthiness
Source: Mitchell and McGoldrick (1996).
Risk-reduction strategies in auction markets will predominantly involve impersonal
strategies, as personal sources (friends, families and other buyers) that can assess the
seller in question are difficult to find.
Varying risk levels during the bidding stages
Ariely and Simonson (2003) argue that in English auctions (ascending prices), there is
little risk involved in the initial entry decision, because the initial bidding prices is
typically low relative to the value of the item. However, after the initial decision
consumers are likely to experience escalation of commitment to the action (e.g., Staw
1976). Auctions might be particularly susceptible to escalation of commitment because
participation in an online auction may often trigger an intense emotional response (“I
20
don’t want to lose this bidding”). Many bidders see other bidders as “competitors” and
referred to outcomes as “winning” or “losing”. In addition, to escalation of commitment
early bids, by a consumer might be later interpreted as a signal that they value the
particular item (Bem 1972; Ariely et al. 2001; Drolet, Simonson and Tversky 2001). For
example, an early decision to bid $10 on a watch might be interpreted a few days later as
an indication that the watch was attractive (and not just for $10), thus providing a
justification to submit a higher bid.
A related way of thinking about this process is through the endowment effect
(e.g., Kahneman, Knetsch & Thaler 1990). In the standard demonstration of the
endowment effect, a person’s value for item X increases once it is owned. One
explanation of this effect is that the ownership causes increased attachment to the item,
which, in turn, increases its subjective value (e.g., Carmon and Ariely 2000). Of course,
as long as auctions are in progress, no bidder owns the product. However, during the
process of an auction, psychological ownership could take place (Strahilevitz and
Loewenstein 2001). For instance, a consumer who was the highest bidder for an item on
the first day of an auction and does not visit the auction site for the next four days might
get more attached to the item during this time, as it becomes a part of his or her
psychological endowment. When this consumer returns to the auction site on the fifth day
of the auction they might be sorely disappointed to see that their items has been “lost”.
This reaction might increase the consumer’s willingness to raise the previous bid to
reclaim the endowment.
21
Description and types of Auction markets:
Market settings for auctions (Anandalingam, Day and Raghavan, 2005)
Number
of Type of items
Number of items
Differentiation
participants
One seller, many Divisible
Single-item
Identical items
buyers
Discrete
Multiple items
Distinct items
One buyer, many
sellers
Many buyers and
sellers
Some auction sites use an ascending bid protocol with a fixed end time (e.g., eBay and
Yahoo.com). At these sites the practice of last minute bidding is prevalent (Matsubara
2001; Teich, Wallenius, Wallenius and Zaitsev 1999). On other sites, which more closely
resemble traditional auctions (going, going, gone), the auction closing time is received
within 10 minutes of the current closing time. In general, auctions range from a few days
to three weeks.
Bidding stages and assessments (Ariely and Simonson 2003):
1. Auction entry decisions: value assessments (low opening biddings can be
positive and negative on end price: attactiveness of entering the bid vs. low
anchoring may signal low value)
2. Bidding during the auction: value assessments (more biddings can have both
positive and negative effects: getting a bargain vs. perceived quality (everybody
sees it is of high quality) Decision dynamics (behavior of one stage influences
behavior at later stage). There is likely a disproportional attention and weight
attributed to price relative to other product attributes (color, quality,etc.).
3. Bidding at the end of an auction: decisions are final and often irreversible.
Decision dynamics. At the end the winner may experience the winner’s curse
(regretting having paid too much), and the loser may experience the loser’s curve
(regretting having bid sufficiently high). It seems that the loser’s curse plays a
more significant role.
22
Consumer motivations and concerns in online auctions
Benefits/motivations for buyers
Costs/concerns for buyers
Purchase related costs and benefits
Access to products otherwise unavailable
Potential fraudulent transactions
Lower prices than through retail
Comparison price points
Information on competition
Information on product availability
Entertainment (thrill of the hunt, bargaining, ( Anticipated) Frustration of lost bid
getting a bargain)
Nonpurchase related costs and benefits
Friendship and community feelings
Lower self-esteem
Source: Bosnjak et al. (2006); Cameron and Galloway (2005)
23
Prior research on auction studies
Ariely and
(2003a)
Research aim/content
Simonson Performed three studies
of consumers value
assessments
1st study: search and
price comparison with
online retailers
2nd study: determinants
of final prices
3rd study: comparative
search and starting price
on magnitude of bid
Type of research
1st Study: Empirical
field study, 500 auctions
for music CDs, books
and movies (VHS
&DVDs): comparison
prices with online
retailers
2nd study: Empirical
field study for RoseBowl game on eBay:
275 participants
3rd study: Empirical
study, experiment,
DVDs, VHS, webcams,
keyboards, trackballs: 48
auctions: 2X2 (starting
price high-low)X
(comparability highlow)
24
Results
 1st study: auction site was cheapest in 1.2% of
the cases, and more expensive than online
retailers in 98.8% of the cases. Consumers paid
on average 15.3% more in auctions compared
with the lowest regular online retail prices.
Consumers fail to look and search for cheaper
(and more reliable) alternatives. Consumers
believe they are getting cheaper products
through auction sites, but they do not search.
 2nd study: Starting prices, total number of bids
and total number of bidders are positively
related to final prices, but seller’s reputation is
not significantly related to final price. Auction
duration and date started (later bidders are less
interest in getting the ticket) had a negative
impact on final price.
 3rd study: Higher starting price caused
participants to bid higher for the goods, but only
when there were no immediate comparisons. In
case of high comparability (other items) there
was no effect for the starting price. Low opening
bids led to more bidders (and more bids per
bidder) compared with high opening price.
Ariely and
(2003b)

Simonson

Bapna et al. 2001b)
Experience of buyers
Black (2005)
Determine the impact of
socio-economic
variables on the degree
of online participation
(auction purchasing
behavior)

Empirical, 753
transactions of two eBay
clothing sellers in the
USA: (1) a pure online
auction seller (Platinum
Power Seller), (2)
25



However, while lower starting prices draw more
bidders, these bidders bid relatively low
(presumably because of the anchoring effect of
the staring bid) and hence this strategy is not
always successful. On the other hand, to the
extent that there are bidding wars (as reflected
by the multiple bids of individuals), the
magnitude of bids increase.
Starting prices, lower comparability are
positively related to final prices.
Significant two-way interaction: higher starting
prices lead to higher prices, but only when there
were no immediate comparisons. When
participants could compare the prices of the two
items, there was no effect of starting price.
Uninformed consumers are poor auction
decision makers and have been found to pay an
average of 18./5 percent more than experienced
bidders
Regions play a role
People from rural areas are more likely to make
a purchase than those of urban areas. However,
urban people are more willing to pay higher
prices per eBay purchases.
Females are more likely to make an eBay
multichannel retailer
who complements sales
through eBay
Empirical, 294
individuals completed
the first questionnaire
prior to bidding
(INTENTION), 188
individuals also
completed second
questionnaire (USE)
Bosnjak, Obermeier and Predicting and
Tuten (2006)
explaining the
propensity to bid in
online auctions: a
comparison of two
action-theoretical
models (Theory of
Planned Behavior (TPB)
and Technology
Acceptance Model
(TAM)
Cameron and Galloway Consumer motivations Empirical, exploratory
(2005)
and concerns in online in-depth interviews
auctions








26
purchase than are males. However, males are
more willing to pay higher prices per eBay
purchases.
TAM: PEOU and PU significant predictors of
intentions to bid (R2= .87), and intention is
significant predictor of # of bids (R2=.10)
TPB: Attitude and PBC significant predictors of
intention to bid (R2=.84), and # of bids (R2=.10)
TAM was somewhat better in explaining
intentions but equal in ³ of bids.
Auction users strongly favor one auction site.
Then they know their way around, and how the
system works, and it is easy to find what they
are looking for.
Auction users reveal obsessive behavior traits.
Obsessive behavior like gambling; becomes a
daily routine.
Online auctions complement traditional retail
stores. Buying limited editions and discontinued
items.
No clear preference between new and used
items.
Last-minute bidding as a key strategy. “Winning
the game”. “Sniping: bidding at the very last




27
second”
User experience influences bidding success rate.
More winnings, and being more able to
distinguish between genuine and fraudulent
offerings.
Feedback system impacting on purchase
decision. Feedback systems were vital input for
bidding. Especially for more expensive
products. Also the other way around: not selling
to not trustworthy buyers. There were some
doubts based on the quality of the feedback
system. It can be manipulated.
Mixed views on the importance of user
interaction. Several users found interacting with
other auction users important (community
message boards), but only during the first
months because they learned how to operate on
eBay; after that it became less vital. The main
motivations for interacting with other users was
for practical reasons (additional information and
receiving suggestions on how specified
problems could be solved.
Auction benefits outweigh threat of fraud. There
is always a risk of becoming caught up in a
fraudulent transaction, whether as a buyer or a
seller. However, almost without exception
respondents felt that the benefits of using the
online auction system far outweigh the potential
threat of fraud.


Diekmann and Wyder
(2002)
Ding, Eliashberg, Huber Effects of anticipated
and Saini (2005)
regret and anticipated
excitement in multiple
biddings at a reverse
auction
Experiment, empirical 
setting, computer, 87
students


Gilkeson and Reynolds How certain online
(2003)
features affect the
success and final price
of auctions.
Pieces of sterling silver 
flatware
28
Starting prices are not related to final prices
Reputation and minimum increase required to
participate are related to final price
A bidder changed her bids after each round
(dynamic behavior of bidding behavior!). The
direction (increase or decrease) of such change
is conditional on the outcome of the previous
bid.
Emotions are an integral component of a
bidder’s decision state (mood) and bidding
strategy.
A bidder in a relatively favorable environment
(higher chances of winning) emphasizes more
the amount of gain if she wins (thus make lower
bids), but gradually put more emphasis on the
probability of winning as she becomes more
interested in the bidding. In a hostile
environment (lower chances of winning), she
emphasizes more the probability of a win (and
thus overbid), although this effect is not as
strong as in the favorable environment.
Relative opening price, number of bids, and
whether or not the auction format chosen
included a reserve price, were positively
associated with final price, but were negatively

Gregg
(2006)
and
Walczak Developing an online
auction advisor
Ha (2002)
Heyman, Orhun
Ariely (2005)

Simulation
The way information
affects the risk
assessment prior to
bidding in online
auctions
and Quasi-endowment
2 experimental designs:
(feeling of owning the  2x2 (high/low
product during bedding)
competition)x(low
and opponent (winning
vs. high duration of
the competition) effect
relationship)

2x2x2x4: same+
(order of auctioned
goods)x(2 gift
certificates, t-shirt,
chocolates)
29

associated with auction success.
Shipping and handling charges, duration of
auction, daytime of ending, and sellers’
feedback rating… (not related to auction
outcomes)
Both buyers and sellers achieve tangible benefit
from the information acquired by and
recommendations made by the Auction Advisor
Agents
Both quasi-endowment and the opponent effect
are apparent
Houser
(2006)
and
Wooders Reputation from bidders Empirical, use additional
and sellers in auctions at data about list item
eBay
(quality, second-hand,
including warranties,
etc. to account for
heterogeneity) and
actual feedback profile
scores (at moment of
purchase).
Kshetri (2002)
Factors
influencing Experiment, actual
consumers’ reaction to a auction users (student
price and intention to bid sample, N=80),
in a C2C Internet competitive bidding for
auction
a low-risk calculator
with varying buyer and
seller reputation ratings
Kwon et al. (2002)
To what extent do
design
factors
and
customer characteristics
influence evaluations of
web-based auctions



Consumer’s reaction to feedback scores and
comments has a significant positive influence on
the (final) auction price.

Overall evaluations has positive effect on
intentions to bid
Design has strongest impact on overall
evaluation, followed by usability and
information comprehensiveness.
Higher risk-seeking respondents are more
willing to bid. Financial status and previous
bidding experience does not influence bidding
intentions


30
Bidders pay a statistically and economically
significant premium to sellers with better
reputations (seller reputation).
Bidder reputation also has impact on price.
Lin, Li, Janamanchi and Role of reputation at
Huang (2006)
macro level: how the
distributions of online
traders’
reputation
scores
reflect
the
structure of the C2C
auction market, and how
the distribution changes
reflect the transition of
the market status
Empirical data of buyers 
(N1=293 & N2=1061)
and sellers (N1=352 &
N2=1745)
Lin, Ramanathan and Agent-based simulation
Kandula (2001)
of C2C Internet auctions
with online escrow
services
Lucking-Reiley (1999)
(2000)
ABM, 2 tier system 
(buyer-seller) and 3 tier
system (buyer, escrow,
seller)
Use spider (computer 
program) to analyse data
on eBay coin auctions

Lucking-Reiley, Bryan,
Prasad
and
Reeves
(2001)
Melnik and Alm (2002) Does
a
seller’s
reputation matter on
final price

Significant effect of seller’s reputation on final
price.

A seller’s e-commerce reputation is an
influencer in bidding prices; however, it is only
one of a number of factors taken into account,
hence only has a small impact on the price of the
winning bid.


31
Seller reputation rather than buyer reputation is
lognormally distributed. (Gibrat’s law). It seems
that both seller and buyer reputation are rightskewed.
There is a tendency that reputation level rises;
i.e. overall the reputation of the market as a
whole goes up.
A higher percentage of sellers leave the market
than buyers, while a higher rate of new buyers
entered the market than sellers.
No results yet. Working paper
Negative feedback has a significant effect on
price, but positive feedback does not.

Ockenfelds and Roth The effect of end rules
(2001)
(hard vs. soft end rules)
Rafaeli and Noy (2002)
The influence of social
interaction
(“virtual
social presence”) offered
by auction sites on
auction behavior

Experiment with 65
students, who took part
in a number of
simulation auctions on
specifically designed
website with increasing
levels of social
interaction: (1) only
number of bidders, (2)
number and names and
pics of bidders, (3)
direct communication
access to bidders, (4)
full access/full
communication
32



With regard to whether or not consumers want
to rate sellers: Not every transaction results in a
feedback comment, there is lttle economi
motivation for buyers to provide feedback after
a transaction has been completed, sellers can
change their Internet identities…(and) the
measures do not provide a complete indicator of
seller quality.
The end rule has large effects on bidding
behavior.
Social facilitation also occurs online: virtual
presence.
In experimental examination, participants
improve their results and stay longer in the
auction under conditions of higher virtual
presence.
Participants indicate preference for auction
arrangements with higher degrees of virtual
presence.

Resnick et al. 2000
Resnick and Zeckhauser
Stafford
(2002)
and
Standifird (2001)
Wilcox (2000)
Data from eBay, two
types of homogeneous
products

Reputation systems help to foster better
behavior in both buyers and sellers, because
they seek to enhance their online-auction
reputation.
Positive and negative feedback do not have
significant effect on final price.

PEOU (ease of use), PU (usefulness), Intention
to use, affinity with computers and involvement
are significantly related to propensity to bid
online
Demonstrating
the Systematic
and 
asymmetrical effect of unobtrusive observations
positive versus negative
reputation
Influence of level of

experience on auction
behavior

Positive reputation has mildly positive effect on
final price, whereas negative reputation has
strong negative effect on final price and auction
success
More experienced bidders are better able to
judge when and how much to bid for an item.
Experienced bidders tend to wait until the
moments before an auction closes to submit a
bid and tend to go for items with few or no
bidders.
Stern Explaining propensity to
bid online, using TAM
33
Research gaps:



The literature on the willingness to bid and the actual bidding behavior of consumers
in online auctions is currently dominated by approaches based on the economic
decision making and information processing paradigm and are primarily focused on
what influences auction outcomes (e.g., opening bids (Gilkeson and Reynolds, 2003)
or reserve prices (Häubl and Popkowski Lesczcyc 2001), bid increments (Diekmann
and Wyder, 2002), and seller reputation (Standifird, 2001) or herding behavior
(Dholokia and Soltysinski 2001). No serious attempts have been made to stringently
test and compare existing models derived from the action-theoretical perspective to
predict and explain consumers’ propensity to use online auctions (Bosnjak et al.
2006).
Research on the subject to date has largely concentrated on monitoring and collecting
descriptive information on the processes involved in Internet auction, and although
this is an important area, very little concern has been given to consumers’ motivations
and concerns over using the online auction system. With millions of people using
auction sites every day, it is essential for traditional retailers to know why consumers
are turning to online auctions in increasingly large numbers (Cameron and Galloway
2005, p. 181)
Research in the field of auction sites has investigated the effect of seller reputation on
end prices and on the propensity to bid. However, little is known about which
reputation mechanisms consumers use and to what degree they rely on them -during
each of the stages of the bidding. process. Additionally, relatively little is known
about the effect of experience/expertise on the use of reputation mechanisms.
34
Product catageories sold in online auctions
Category
Collectibles
 Antiques
 Celebrity memorabilia
 Stamps
 Coins
 Toys
 Trading cards
Electronics and computers
Jewellery
Computer software
Used equipment
Sporting goods
Travel services
Real estate
Wine
Source: Lucking-Reilly (2000, p. 233)
# sites featuring that category
90
40
16
11
17
17
14
48
17
16
15
13
7
4
3
# sites specializing in that category
56
10
7
5
2
0
0
9
1
0
7
4
5
2
2
Lucking-Reily (2000) showed that electronics and computers, as well as antiques, were the major product groups involved in C2C
transactions. Additionally, even for these product categories, the number of specialist sites would appear to be low, showing that the
general auction sites are the preferred option for consumers, regardless of the product type they wish to buy.
B2B versus B2C
While a large proportion of online auction participants are individuals, there is evidence that businesses themselves are both selling
and marketing by utilizing auctions such as eBay (Cameron and Galloway 2005).
35
Fee and commissions
From the three major auction sites (Yahoo!,eBay and Amazon), only Yahoo! did not charge a fee and a commission (Lucking-Reilly
2000). Yahoo! tended to have very high minimum bids (or reserve prices) compared to eBay and Amazon; the reason could be that
eBay and Amazon sellers know that they have to pay the fee, even if they do not sell, and so they lower the minimum price levels to
increase the chances of a sale. As of May 2002, Yahoo! alleged with eBay in Europe.
Useful input for simulations
Houser and Wooders (2006) have modeled the following in a market with one seller and n buyers:
Bidder’s reputation:
Seller’s reputation:
Bidder i’s value:
rB ε (0,1] (this reputation is publicly known; bidder pays once he has won the item)
rS ε (0,1] (this is publicly known; seller delivers the item once he has received payment)
vi (with vi > 0) (this is only known to the bidder)
If Bidder i wins the auction and is to pay the price b then with the probability rBi he delivers payment and his expected payoff is
(rSvi –b) with probability (1-rBi) he defaults and his payoff is zero. His expected utility therefore is: rBi (rSivi - b)
Jaiswal, Kim and Gini (2004) develop a multi-agent marketplace (MAGNET) (Multi-AGent Negotiation Testbed). They develop a
software agent that can help bidders. The trust model of MAGNET is somewhat different from other online auction systems since the
marketplace, which mediates all communications between agents, acts as a partially trusted third party.
36
Lin, Ramanathan and Kandula (2001) designed an agent-based model for online marketplaces with online escrow services (OES).
There are three architectures (in the three tier architecture) for the experimental system if different configurations are defined:
(1) buyer
(2) marketplace
(3) seller.
The market module has six processes:
(1) the market module sends current information about the item for bidding to the buyer module;
(2) the buyer module bids on the item until auction closes
(3) the winner decides if online escrow to be used:
(4) all these messages: agreed price, buyer parameters, and OES decisions are sent from the buyer modulate to the market module
(5) the market module finalizes the auction by disseminating all information to both buyer and seller in the trade
(6) the market module let a seller initialize a new auction (cheat or no cheat). Input variables: OES fee rate, cheating rate
37
Glossary
Auction: an auction is a mechanism of information submission, together with rules for
assigning items and payments to participants based on this submitted information
(Anandalingam, Day and Raghavan, 2005). A market with an explicit set of rules to
determine resource allocation, based on the bids of participants (McAfee and McMillan,
1987 cited in Brosnjak et al, 2006)
Auction market: Auction markets: market where buyers and sellers and buyers come
together to bid on products/services. They have characteristics that distinguish them form
general forms of consumer decision making: (1) multi-stage process (no fixed price
purchase, auctions take place of time, forming sequence with bidding activities leading to
a final bid), (2) value signals (the online environment provides different types of value
cues (i.e. intangible) that bidders can rely on), (3) decision dynamics – the fact that
multiple viding decisions are made during the auction process suggests that earlier
decisions can dynamically impact subsequent decisions. (Ariely and Simonson 2003).
Benevolence: A trusting belief that the other party cares about the welfare of the other
person and is therefore motivated to act in the other person's interest. A benevolent
person does not act opportunistically toward the other person. (McKnight and Chervany
2001).
Bidder: someone who bids on an auction market. This is different from a buyer: someone
who wins the bid (Lin et al. 2006)
Bid increment: the minimum amount of money that can be bid in top of the currently bid
price.
Combinatorial auctions: multiple item auctions for which bids may be placed on
packages of items, and are often referred to as auctions with package bidding.
Competence: trusting belief that the other party has the ability or power to do for one
what needs to be done. Here, the essence of competence is efficacy. (McKnight and
Chervany 2001).
Disposition to trust: (see also trust propensity)
Double auctions: auction with many sellers and many buyers. Open market in which
transactions have the opportunity to occur at any time and require only the agreement of
the transactos, as in the stock market (Anandalingam, Day and Raghavan, 2005)
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Dutch auction: auction where bids are publicly called out in descending order and the
bidder who calls first wins
English auction: auction where bids are publicly called out in ascending order and the
bidder offering the highest price wins (also on eBay).
eBay’s feedback forum: A trader’s reputation is measured by an overall rating, which is
counted and saved a number (e.g., 6442, or 46). After an auction, both the buyer and the
seller can rate each other using the services provided by Feedback forum. A positive
comment from a unique trading partner adds one point to the net reputation score, and a
negative comment deducts one point from the net reputation score; a neutral comment has
no effect on the net reputation score. (Lin et al. 2006). For each transaction, only the
buyer and seller can rate each other by leaving feedback. Each feedback left consists of a
positive, negative, or neutral rating, and a short comment. Leaving honest comments
about a particular eBay member gives other Community members a good idea of what to
expect when dealing with that member. Once it is left, the feedback becomes a permanent
part of the member's profile (eBay.com). The percentage of traders that give a rating are
52.1% from buyers to sellers, and 60.6% from sellers to buyers (Resnick and Zeckhauser
2002).
Endowment effect: This effect is a hypothesis that people value a good or service more
once their property right to it has been established. In other words, people place a higher
value on objects they ‘own’ relative to objects they do not (e.g., Thaler 1980). The effect
is related to loss aversion and status quo bias in prospect theory.
Escrow service: A trusted third party (examples: Paypal) acts as an intermediary between
seller and buyer.
Faith in humanity: one assumes that others are usually competent, benevolent,
honest/etical, and predictable. (McKnight and Chervany 2001; Mayer et al. 1995). This is
an underlying construct of disposition to trust.
Fee: Sellers (for example from eBay and Amazon) need to pay an auction fee, even if
they do not sell the product.
Fishing: illegal activity to withdraw private information of sellers and buyers (on auction
sites).
Forward auction: auction with a single seller (Anandalingam, Day and Raghavan (2005)
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Hard stopping rule: an auction that has been set to end at a fixed time (e.g., 12 p.m.
November 5). eBay uses this approach. (the opposite is soft stopping rule)
Honesty: see integrity
Institution-based trust: one believes that favorable conditions are in place that are
conducive to situational success. Behaviors are situationally constructed (socialist
perspective): action or behaviors are not determined by factors within the person but by
the environment or situation. E.g. one trusts the auction market eBay (or at a higher level,
the Internet), or credit card company.
Integrity: a trusting belief that the other party makes good faith agreements (cf. Bromiley
& Cummings, 1995), tells the truth, acts ethically, and fulfills any promises made.
(McKnight and Chervany 2001). Also referred to as humanity honesty.
Interpersonal trust: the trust one has in a specific other (seller, bidder, etc.). See also
trust.
Platinum Power Seller: Power Sellers are eBay top sellers who sustain a consistent high
volume of monthly sales and a high level of total Feedback --with 98% or better positive
rating by other eBay users (eBay.com).
Predictability: trusting belief that the other party’s actions (good or bad) are consistent
enough that one can forecast them in a given situation; it embodies an element of
temporal continuity that can be related to the other trusting beliefs. (McKnight and
Chervany 2001).
Procurement auction: auction with a single buyer and several sellers (also reverse
auction)
Propensity to trust (see trust propensity)
Proxy bidding: a bidder enters the maximum amount that he is willing to bid, and then
‘the system will bid for you as the auction proceeds, bidding only enough to outbid other
bidders. If someone outbids you, the system immediately ups your bid. This continues
until someone exceeds your maximum bid, or the auction ends, or you win the
auction.’(eBay)
Reverse auction: auction with single buyer and several sellers (also procurement auction)
(Anandalingam, Day and Raghavan, 2005)
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Reputation: the public evaluation of the credibility and accountability of individuals or
sellers (Einwiller 2003). The second-hand rumor that one has positive general traits
(McKnight and Chervany 2001). The public information on the hitherto trustworthiness
of an individual (Picot et al 2001).
Reputation system: a system that collects, distributes, and aggregates feedback about
participants’ past behaviors. (Resnick et al. 2000). Online reputation systems record and
report an online trader’s reputation accorfding to other traders’ feedback (Lin et al 2006).
They help to build trust and elicit cooperation among loosely connected and
geographically dispersed economic agents (Dellarocas 2003)
Reserve prices: minimum (opening) bids (Cameron and Galloway 2005)
Reverse auction: Rather than requiring consumers to find the supplier with the lowest
offering, a company (e.g. Priceline.com) takes a bid from a consumer and then searches
to find suppliers who match that bid. (Ding et al. 2005). Suppliers are actually bidding for
consumer’s business.
Self-efficacy: belief of individuals that they have the capabilities and resources to perform
the behavior (e.g., making an online auction purchase) (Bandura, 1977). The level of
experience of sellers and bidders is positively associated with their level of expertise
(Alba and Hutchinson, 1985), and hence increases their efficacy levels (the belief
becomes stronger that they are able to perform the behavior in case of successful
attempts).
Shilling: illegal practice of driving up prices by using bids with fake email addresses
Sniping: the legal practice of bidding at the very last second (i.e., this frequently occurs
with hard stopping rules).
Situational normality: the belief that the situation in a purchase is normal or favorable or
conducive to situational success (Chervany and McKnight 2001). The perception that
things are normal, proper, customary, fitting, or in proper order (Garfinkel 1963).
Soft stopping rule: an auction that has not been set to stop at a specific time.
Amazon.com, for example, has a system whereby an auction is scheduled to stop at a
certain time, only if no bids were accepted within the last 10 min. of the auction.
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Structural assurance: the belief that protective structures –guarantees, contracts,
regulations, promises, legal recourse, process, or procedures- are in place that are
conducive to situational success (McKnight and Chervany, 2001; Shapiro 1987). This is
part of institution-based trust. For example, belief of protection of encryption from credit
card theft.
Trust: The willingness of a party to be vulnerable to the actions of another party based on
the expectation that the other will perform particular action important to the trustor,
irrespective of the ability to monitor or control that other party (Mayer et al. (1995). A
willingness to rely (or depend) on an exchange partner in whom one has confidence
(Moorman et al. 1993).
Trust propensity: a general disposition to trust people in life (this is a consumer trait that
is relatively stable: “faith in humanity” & “trusting stance”). People differ in this trait;
some are always distrusting others, whereas others believe that people can be trusted.
Disposition to trust is a general, i.e. not situation specific, inclination to display faith in
humanity and to adopt a trusting stance toward others (Mayer et al. 1995; McKnight et al.
1998), and is perceived as a personality trait (Grabner-Kräuter et al., 2003). This
tendency is not based upon experience with or knowledge of a specific trusted party (ref),
but is the result of an ongoing lifelong experience and socialization. As an antecedent of
trust, disposition to trust is most effective in initial phases of a relationship when the
parties are still mostly unfamiliar with each other (Mayer et al. (1995) and before
extensive ongoing relationships provide a necessary background for the formation of
other trust-building beliefs, such as integrity, benevolence and ability.
Trusting stance: regardless of what one assumes about other people generally, one
assumes that one will achieve better outcomes by dealing with people as though they
were well-meaning and reliable. (McKnight and Chervany 2001). This is an underlying
construct of disposition to trust.
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Links:
http://www.internetauctionlist.com/Default.asp A list of online auction sites (contains
more than 2,500 auction sites)
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