Effects of Reputation Mechanisms on Fraud
Prevention in eBay Auctions
Florian Reichling, Stanford University
Faculty Advisor: Susan Athey
May 2004
Abstract
This paper shows empirically that eBay’s feedback system documents successful transactions, but often fails to inform users of
unsuccessful ones. A data set containing information from 3776
auctions I collected from eBay’s platform, gives reason to assume
that the feedback system exhibits a positive bias due to the nature of mutual feedback that is made public instantly. Sellers and
buyers are able to hold feedback hostages by refusing to leave feedback until the opposite party has provided a report. In cases of
problematic or fraudulent transactions, this behavior may lead to
false feedback reports, or no feedback-provision altogether for fear
of retaliation. In such cases, important information to the community about transactions’ efficiency is lost. In a simple model I
rationalize false feedback reports, after which I suggest a change
of the current feedback mechanism that circumvents the problem
of feedback-retaliation, while at the same time providing incentives to participate in the feedback-provision process.
1
Introduction
The essential decision consumers face is whether to buy or not to buy a certain product and from whom. When making this decision, consumers realize
that sellers possess private information about the object in question, e.g. the
seller knows the quality of the product. The inherent information asymmetries make some buyers reluctant to buy. Consequently, some transactions
that would be beneficial to both parties, sellers and buyers, are foregone.
By building a reputation for their trustworthiness, sellers can reduce the
inefficiencies created by asymmetric information and therefore are able to
induce consumers to buy from them. Electronic markets such as eBay offer
the advantage of rapidly conveying reputation to other market participants
by publicizing feedback. Consumers can then use this reputation to determine with whom to transact. For instance, a buyer may adjust his private
reservation price according to the feedback of a potential seller.
Electronic markets, however, pose new challenges to reputation building because the nature of transactions differs largely from sales conducted at
brick-and-mortar stores. While a conventional seller usually establishes a reputation by word-of-mouth within geographical boundaries, a seller on eBay
faces consumers who hardly interact among each other, and who are unlikely
to be repeat buyers. Furthermore, transactions do not involve face-to-face
contact, goods are rarely inspected before the transaction, and geographical boundaries do not exist. On the seller-side, the inherent incentive for
opportunistic behavior is obvious. A feedback system like eBay’s only reduces deficiencies resulting from asymmetric information to a certain extent,
because the system assumes that participants provide honest and truthful
information once they leave feedback. Interestingly, both parties have incentives to withhold feedback, which may lead to no provision of feedback at
all.
The goal of this paper is to examine how efficient eBay’s reputation mechanism is in reporting fraudulent transactions, to rationalize why market participants do not always provide truthful information when leaving feedback,
and to offer suggestions of how to improve the existing feedback mechanism. My data set containing information of 3776 auctions suggests that
users do not always provide truthful information about transactions’ efficiency. Specifically the timing of feedback indicates that users sometimes
withhold feedback in order to be able to retaliate negative feedback they
1
may receive. Additionally, given that all evaluations in transactions where
both parties provided feedback is positive, it is sensible to assume that some
transactions of low-quality received positive feedback. In a simple model,
I rationalize why under certain conditions users, specifically buyers, do not
disclose truthful information to eBay’s community. I conclude the paper by
offering two mechanism changes that get around the problems of the (1) low
participation rate, and (2) provision of false information.
2
eBay and its Reputation Mechanism
Since its launch in 1995, eBay has become the dominant auction and ecommerce site on the internet, functioning as an intermediary between buyers
and sellers who transact millions of items every day. In 2003, merchandise
worth more than $21 billion was transacted on eBay by almost 70 million
users. Ebay’s transaction-related revenue comes solely from seller-fees, which
are collected after the successful completion of a transaction. Ebay defines a
successful transaction as an auction that receives at least the minimum bid,
or as one in which the secret reserve price has been reached. The seller fee
is also collected if the transaction does not physically take place.
2.1
The Feedback System
Upon conclusion of a transaction, buyers and sellers have the option to leave
feedback for each other, which has to be left within 90 days of the auction
closing. The feedback consists of a numeric rating of +1 (positive), 0 (neutral) and —1 (negative); in addition, both parties have the option to leave
a comment, which gives insights about transaction qualities valued by market participants. Users comment, for example, on the speed of responses to
questions or concerns, or on the speed of payment and delivery. Participants
reduce the problem of asymmetric information by commenting on product
quality and the accuracy of product description.
The numeric values of feedback points add up to the user’s feedback
rating, which is shown in parentheses behind every user’s member name and
to which I will refer as “summary feedback.” This value is comprised only of
the amount of feedback points received from unique users. In other words,
if two users engage in 100 transactions with each other and leave positive
2
feedback for each other for all of the 100 transactions, only one feedback
point is added to the summary feedback rating in the parentheses. Every
user can click on that number in the parentheses to view a detailed feedback
history (Figure 1 in appendix). There, users can view feedback from previous
transactions, including comments left by the counterparts. Furthermore, the
detailed feedback page offers statistics such as negative feedback given within
the past 1, 6 and 12 months, as well as the percentage of positive feedback
(Figure 2).
2.2
Implications on Fraud
The feedback system is a mechanism specifically designed to spread information about transactions’ efficiency to market participants not directly involved in a particular trade. The information conveyed through this mechanism can be of different kind, ranging from praise about speed of payment/shipping to complaints about non-communication, and fraud. Users
may inform the eBay community about a cheating seller through the feedback system, or by submitting threads in the discussion forums. Hence,
the feedback system ideally not only informs market participants of successfully conducted transactions, but also unsuccessful ones, including buyercheating.1
Often, users strictly refer to another user’s summary feedback when considering whether to engage in a transaction. Frequently, for example, sellers
will post in their item description something similar to "No bidders with a
feedback rating less than 10." Ebay itself, limits certain functions with reference to users’ unique feedback ratings. The seller tool "Turbo Lister", for
instance, is a software tool, which can only be utilized by users who have
accumulated a minimum summary feedback score.
Ebay’s motivation to implement this limitation is probably to prevent
the software tool from being abused specifically for fraudulent activity. After
all, a user who has just signed up on eBay and has zero feedback points
could use Turbo Lister to insert hundreds of items at once just to collect the
1
A buyer can cheat by refusing to submit payment after having won an auction. In this
case, the seller cannot collect the highest bid, but still has to pay the eBay fees. A seller
can, however, turn to the second-highest bidder, and offer him the item at his submitted
bid. Therefore, in this paper I do not dwell on buyer cheating.
3
money after the auctions end and then disappear. Ebay assumes that the
minimum feedback score deters sellers from engaging in such scams because
the collection of such an amount of feedback is costly.
2.3
Definition of Fraud on eBay
Ebay defines sell-side fraud as the seller’s failure to deliver the sold merchandise, or the delivery of the item in physically bad condition. Throughout
this paper, however, I define fraud in much broader terms than eBay does.
I use the terms "fraudulent" and "problematic transaction" interchangeably,
because I assume that any deviation from the contractual agreement laid out
in the auction description comes at a cost to the buyer. That is, if the seller
ships the item two days later than agreed upon, without reimbursing the
buyer for these extra two days, late shipping constitutes fraud. Similarly, if
the merchandise differs slightly from the item’s auction description, the seller
commits fraud. Hence, when I refer to a transaction as being fraudulent, I
do not necessarily mean that the seller collected the buyer’s money and then
was never heard from again. Rather, the seller imposed a cost on the buyer,
of which other market participants should be aware when considering trading with that particular seller. Generally, any information related to sell-side
(buy-side) deviations from the auction description (requirements) should be
made public.
3
Related Literature
The literature on online auctions is quite numerous, especially empirical studies that focus on price formation. While I do not provide a comprehensive
review on the existing auction literature related to price determination and
bidding behavior, the interested reader may turn towards Bajari and Hortaçsu (2002a), Katkar and Lucking Reiley (2001), Lucking-Reiley, Bryan,
Prasad and Reeves (2000), Roth and Ockenfels (2002), Schindler (2003) to
get a brief overview of these issues. Bajari and Hortaçsu (2002b) provide a
survey of empirical studies that focus on internet auctions.
4
3.1
Literature on Reputation
A number of authors have conducted empirical studies of eBay’s reputation mechanism, which almost all focus on buyers’ response to published
feedback aggregates. A large number of studies estimate cross-sectional regressions of sale prices on seller feedback characteristics: Dewan and Hsu
(2001), Eaton (2002), Ederington and Dewally (2002), Houser and Wooders
(2003), Kalyanam and McIntyre (2003), Livingston (2002), Melnik and Alm
(2002), Resnick and Zeckhauser (2001). Surveys of these results are provided
by Bajari and Hortaçsu (2004), Dellarocas (2002) and Resnick, Zeckhauser,
Swanson and Lockwood (2003).
I am not the first to suggest that eBay’s reputation mechanism is imperfect. McDonald and Slawson (2002) note that eBay’s reputation system
reveals only part of the private information of participants due to some users’
unwillingness to report feedback. Users on eBay have little incentive to leave
feedback once a transaction has been completed and hence often they do not
bother to do so. According to McDonald and Slawson, participants have
incentives not to provide negative feedback when appropriate, simply for
fear of retaliatory feedback. This phenomenon is not explored in more detail, however, but rather classified as “noise.” Instead they show how market
participants with different feedback on eBay’s platform reduce asymmetric information costs by utilizing a quantifiable measure of reputation. For
this purpose, the authors collect data from 460 auctions of a limited-edition
Harley-Davidson Barbie doll held between January 1998 and July 1998. Two
advantages of this item are (1) a relatively limited product supply, since only
10,000 of this doll were produced; and (2) its value is independent of any team
or player, unlike sports memorabilia. The authors split up seller into highand low-reputation sellers. McDonald and Slawson are mainly interested in
how auction characteristics, especially reputation, influence the amount of
bids received, and the ending price realized. They hypothesize that sellers
with low reputations will receive fewer bids and realize a lower ending price
than high reputation sellers, which is confirmed by their data set.
Bolton, Katok and Ockenfels (2002) examine the implications of reputation mechanisms on transaction efficiency. The authors conduct an experiment in which they test cooperation in three different markets: (1) a
strangers market (without feedback), (2) a market with feedback (reputation market), and (3) a market in which traders face each other repeatedly
(partners market). The comparison between the reputation and partners
5
market, however, shows that reputation mechanisms suffer from a public
goods problem in that the benefits of trust and trustworthy behavior caused
by reputation mechanisms are available to the whole community and are not
fully internalized.
In their laboratory experiment, the authors had 16 traders interact with
each other over 30 rounds. At the beginning of each round, the traders are
matched in pairs, one assuming the role of the buyer and the other the role of
the seller. Each trader is a seller and a buyer for half of the time. The buyer
has the choice whether to buy a fictional commodity. If he chooses not to
buy, the payoff to both seller and buyer is $0.35. If the buyer chooses to buy,
the seller faces the choice whether to ship the commodity or not. If he ships,
seller and buyer receive a payoff of $0.50. If the seller should decide not to
ship, though, he receives a payoff of $0.70 and the buyer receives zero. Thus,
if a seller faces a buyer only once, he would have no incentive to send the
merchandise. This is exactly the kind of problem reputation mechanisms try
to solve. This kind of experiment is conducted in each of the three markets,
in three sessions. The participants received $5 plus the amount they gained
through the 30 rounds.
A theoretical discussion of reputation mechanisms and the incentives they
provide for successful transactions follows the introduction of the experiment.
One assertion made is that trading collapses in the last round in all markets,
which makes sense intuitively. No seller would have an incentive to send the
merchandise in the last round simply because this decision will have no effect
for the future. Consequently, buyers will not trust sellers in the last rounds.
Nevertheless, since traders seem to have trouble thinking ahead more than
two or three periods, trading does not collapse totally. In addition, sellers
have an incentive to ship the merchandise to receive positive feedback. In
the reputation and partners market, buyers are able to observe whether or
not a seller has shipped in previous transactions. Based on this information,
the buyer chooses to buy or not to buy. Therefore, a seller who ships in
early transactions increases his chance of sales in the future. In the strangers
market, this form of incentive does not exist. Thus, the authors expect more
transactions to take place in the partners and reputation market than in the
strangers market.
The results of the experiment confirm the authors’ expectations. In
the strangers market, the marginal effect conditional on whether the seller
shipped the last order is almost zero. This makes sense, because the buyers
cannot observe whether a seller has shipped the last item or not. In the
6
reputation market, however, buyers seem to condition their decision whether
to trust (buy) the seller’s last feedback: they trust only with a probability of
33% given the seller did not ship in the previous round, but buyers trust with
a probability 65% if the seller did ship the last order. In the partners market, buyers condition whether to buy even more strongly on past experience.
This is obvious, since buyers interact repeatedly with the same seller.
While the authors mentioned above focus, for the most part, on empirical
analyses of eBay’s reputation mechanism, a few authors have developed theoretical models to rationalize feedback effects and to improve the feedback
system. Cabral and Hortaçsu (2004) present a basic theoretical model of
eBay’s reputation mechanism that features both adverse selection and moral
hazard. Their model suggests that in equilibrium, a seller’s reputation is
positively correlated with seller effort. Interestingly, the model predicts that
a seller’s effort level decreases once he receives negative feedback for the first
time, and hence the rate at which negative feedback is received increases
after the first incidence of a negative evaluation. The authors’ model also
suggests that sellers, specifically honest sellers, have incentives to "buy" a
reputation by engaging in purchases rather than sales. An important conclusion of the paper is that eBay’s reputation system elicits noticeable strategic
responses from sellers and buyers, and hence the system has "bite". At the
same time, however, Cabral and Hortaçsu note that this does not mean that
eBay’s feedback system is optimal.
4
Empirical Insights
My empirical analysis of the effectiveness of eBay’s reputation mechanism is
based on auctions of four different items held on eBay between January and
February 2004. In my analysis of the collected data, I attempt to shed light
particularly on the following questions:
1. In how many transactions is feedback given?
2. Does the amount of reported feedback differ among items of different
value and different characteristics?
3. How accurate is feedback? Specifically, in how many transactions is
the same type of feedback reported?
7
4. Which party tends to leave feedback first? Does this depend on the
product’s value? Do other reasons exist?
4.1
Data Collection Procedure
I collected my sample data set using a computer software program, which
was specifically coded in Java for this research project.2 The program uses a
http connection, essentially clicking through eBay’s websites to gather predetermined data. In order to run the program and search for auctions listed
on eBay, I had to enter product key-terms, followed by the category identification number that specifies the category to be searched.
The program collected the data in two stages: in the first, it collected
identification numbers of auctions containing the entered search terms; in
the second stage, it ran through the list of auction identification numbers,
extracted relevant data, including the feedback left by both parties. The
separation of the data selection process into two stages was necessary because
users often provide feedback many days after the auction close, if at all. It was
therefore essential to first find and record relevant auctions, before they were
"de-listed," and revisit them at a later time to gather the information about
feedback3 . Since eBay keeps expired auctions on its servers for approximately
three months, I had enough time to extract data from auctions weeks after
the auctions had closed.
To be more precise about the programs collection procedure, in the first
stage it (1) searched for the key terms appearing in all open auctions in the
specified category, (2) copied the auction html page and auction bid history
in a prior specified folder onto the hard drive, and (3) created an excel file
with all the auction identification numbers that contained the search terms.
I had to manually start the second stage of the program, in which it (1)
revisited a specific auction by going to eBay’s internet site, given the auction
identification number, (2) extracted the data listed in table 1 in the appendix
into an excel file.
2
I am grateful for Usman Aijaz’s enormous support in programming the software, and
continuously adjusting the program to changing needs. Without his persistent and hard
work, I would not have been able to make it through the ups and downs of a difficult
data-gathering process.
3
Closed auctions do not appear in eBay’s search results, unless they just closed. The
only way to find closed auctions is through the feedback system, or by collecting the item
identification numbers before closing.
8
4.2
Sample Description
I collected information from a total of 3778 (3078) completed (transacted)
auctions: 998 (945) auctions of the Sony Playstation 2 game "Madden NFL
2004", 723 (615) Apple IPod auctions, 688 (566) Dell Latitude C600 laptop auctions, and 1367 (950) Dell 4600 desktop auctions. The reasons for
selecting these particular products were twofold:
1. The four products differ in their value, which enables me to explore
whether users’ feedback behavior changes among items of different
price. As a low-value item, I chose the Playstation 2 game, which
sale price averaged approximately $33 during January and February
of 2004. As a medium-value item, I selected an MP3-Player made by
Apple, the IPod with 20 GB memory capacity, with an average sale
price of approximately $290. As the high-value item I selected a Dell
Latitude C600 laptop computer with an average sale price of $448, as
well as a Dell 4600 desktop computer, which average sale price equalled
approximately $534.4
2. Two of the items are homogeneous, i.e. their configuration does not
change among different auctions, whereas two are heterogeneous, i.e.
items among auctions differ in product quality. The IPod and Playstation 2 game offer the advantage of being homogenous goods. Hence,
differences in provision of feedback can be attributed to behavior during
the transaction, rather than to a different configuration among items.
In contrast, given that the two Dell computers differ in their configuration from auction to auction, problematic transactions should be
more frequent. There is more room for product error in terms of its
advertised product quality. Therefore, one would expect more negative
and no feedback left for transactions within the computer category.
Table 2 below summarizes the results of the data collection.
4
Since the software I used collected data according to key-words, the collection of
low-value items, such as parts, among the high-value items was inevitable. Hence, the
calculations made here reflect some noise. By limiting the items to be considered in the
calculations to the ones sold at $100 or more (except for the Playstaytion 2 game), I
attempt to reduce this noise to a minimum.
9
Playstation Apple
2 Game
IPod
N
Mean Selling Price
Auctions without bids
% of all auctions
Feedback left by sellers
Positive
% of sold items
Negative
% of sold items
Neutral
% of sold items
None
% of sold items
Feedback left by buyers
Positive
% of sold items
Negative
% of sold items
Neutral
% of sold items
None
% of sold items
Dell
Latitude
C600
Dell
4600
Desktop
Total
998
$32.51
53
723
$290.25
108
688
$447.66
122
1367
$533.93
417
3776
5.31%
14.94%
17.73%
30.50%
18.54%
633
305
187
348
1473
66.98%
49.59%
33.04%
36.63%
47.89%
5
6
1
3
15
0.53%
0.98%
0.18%
0.32%
0.49%
700
0
6
1
0
7
0.00%
0.98%
0.18%
0.00%
0.23%
307
298
377
599
1581
32.49%
48.46%
66.61%
63.05%
51.40%
428
236
179
396
1239
45.29%
38.37%
31.63%
41.68%
40.28%
0
1
0
0
1
0.00%
0.16%
0.00%
0.00%
0.03%
0
0
0
0
0
0.00%
0.00%
0.00%
0.00%
0.00%
517
378
388
555
1883
54.71%
61.46%
68.37%
58.32%
59.69%
Table 2: Summary Statistics of Collected Data
10
Playstation Apple
2 Game
IPod
N
688
108
1367
287
3776
899
36.08%
26.50%
19.05%
30.21%
29.23%
341
163
108
287
899
100%
100%
100%
100%
100%
225
93
40
124
482
65.98%
55.69%
37.04%
42.76%
53.62%
116
70
68
163
417
34.02%
41.92%
62.96%
56.21%
46.38%
7.83
4.83
9.09
0.002
55.26
8.25
5.02
9.28
0.009
50.63
11.27
8.87
9.63
0.010
53.06
10.89
8.93
8.66
0.006
44.67
9.07
6.91
11.93
9.58
8.60
0.017
55.90
12.07
9.77
9.73
0.008
52.41
14.57
11.63
10.51
0.98
52.99
13.22
10.69
8.42
0.25
47.92
% of mutual feedback
Feedback provided first
Seller
Buyer
% of mutual feedback
Total
723
163
Identical Feedback
% of mutual feedback
Dell
4600
Desktop
998
341
Transactions w/ Mutual Feedb.
% of sold items
Dell
Latitude
C600
Days betw. close/seller feedb.
Average
Median
Std. Dev.
Min
Max
Days betw. close/buyer feedb.
Average
Median
Std. Dev.
Min
Max
Table 2: Summary Statistics of Collected Data (cont.)
4.3
Results
My data set suggests that users do not always provide truthful information
once they report feedback. The data I collected shows that, if feedback is
reported at all, most of the time it is positive. While only approximately
44% of users involved in a transaction reported feedback, 99.16% of those
who did left positive feedback for the opposite party. This result is very
similar to that of Resnick and Zeckhauser (2001), whose data set consists of
36,233 transactions, and therefore it does not come as a surprise. Negative
11
0.0068
50.91
12.74
10.42
0.31
52.30
feedback is reported by only 0.59% of users, which is almost identical to the
aforementioned study’s finding.
More surprising is the timing of the feedback: sellers provided feedback
first in 15.67%, whereas buyers reported first in 13.56% of all transactions. At
first glance, it seems puzzling that sellers leave feedback first more often than
buyers do. After all, one would expect sellers to withhold feedback in order
to be able to punish "bad" buyer-behavior. I discuss this kind of punishment,
or retaliation, in more detail in section 5. By inspecting the data a little more
carefully, however, one can argue that the decision to move first depends on
the item value, as I will discuss shortly. Another surprising fact, at first
glance, is that I cannot account for any possible case of feedback retaliation.
Of all the 899 transactions in which both parties reported feedback for each
other, feedback was positive. The few cases of negative feedback I found,
only a total of 16, remained unanswered by the receiving party. I discuss
retaliation and feedback withholding in more detail below.
4.3.1
Timing of Feedback
While in subsequent sections I assume that sellers always observe feedback
and then match the buyers report, on eBay this is not always the case. Sellers
provide feedback first in some instances, especially in the low-value category.
Nevertheless, the data indicates that sellers not only take a longer time to
provide feedback as the value of the item they sell increases, but they also
wait until they have received feedback more often.
The frequency of seller-feedback made before buyer-feedback, measured
as the percentage of all transactions within the particular category, declined
from approximately 24% in the Playstation 2 game category to roughly 15%
and 7% in the IPod and Dell Latitude category, respectively. In the Dell 4600
category, the percentage of transaction in which sellers gave feedback first
increased to about 13%, which is rather puzzling. Buyers, on the other hand,
did not seem to condition whether to report feedback first on the items value
as much. In the game category, buyers gave feedback first in approximately
12% of all transactions, which changed to 11% and 12% in the IPod and Dell
Latitude category, respectively. In 17% of the Dell 4600 transactions, buyers
provided feedback first. The following figure illustrates the aforementioned
characteristics in the feedback provision:
12
% of total total transactions in
respective category
25.00%
20.00%
15.00%
Sellers
Buyers
10.00%
5.00%
0.00%
Madden
NFL
Apple Ipod
Dell
Latitude
C600
Dell 4600
Desktop
Category
Figure 2: Feedback Provided First
The explanation for sellers’ decisions whether to provide feedback first
in the first three categories is straightforward. Due to risk-aversion, buyers
condition how much to bid more on a sellers’ feedback history in high-value
transactions than in low-value transactions. Therefore, a seller’s feedback
history is of greater importance in the IPod and Dell Latitude category than
in the Playstation 2 game category. Hence, sellers withhold feedback more
often in high-value categories. Since buyers’ choices in low-value categories
depend less on sellers’ feedback, sellers are less concerned about the possibility of receiving a negative evaluation, and are more willing to report first.
Another reason why sellers provide feedback first more frequently in lowvalue categories could be that they attempt to accumulate feedback quickly.
By moving first, sellers attempt to encourage buyers to respond with feedback, which may induce buyers to provide feedback more quickly than they
would do otherwise. This would imply that many of the first-moving sellers
in the low-value categories have a fairly low feedback score. Indeed, my data
set confirms this reasoning. Among all the sellers who provided feedback
first in the Madden NFL transactions, approximately 60% have a summary
feedback score below 100. More generally, in the two categories of low-value
the sellers who reported feedback first possess lower feedback scores, compared to those in the high-value categories. In the two computer categories,
seller’s decision to report feedback first does not seem to be dependent on
the feedback score they have, which the following graph illustrates.
13
Number of Sellers
100
80
60
40
20
0
0-49
50-99
100-149 150-199 200-249 250-499 500-749 750-999
>1000
Total Feedback Score
Madden NFL
IPod
Dell Latitude
Dell 4600
Figure 3: Reputation Distribution of Sellers who Provided Feedback First
Still, the fact that the percentage of sellers who provide feedback first
increases in the Dell 4600 category is surprising, and suggests that sellers
make choices dependent on other, unmentioned reasons. By observing the
frequency of auction listings per seller (Figure 4 below), one possible motivation for increased first-moving is obvious — sellers in the Dell 4600 category
engage in more transactions than sellers in the other categories do.
14
Number of Sellers
100
80
60
40
20
0
1
2
3
4
5
6-14
15-29
30-59
>60
Number of Auctions
NFL Madden
IPod
Dell Latitude
Dell 4600
Figure 4: Number of Auctions per Seller
If some sellers expect a return on positive feedback in subsequent transactions, they may be more willing to provide an evaluation first, in order
to speed up the feedback "exchange". In contrast, sellers who transact only
periodically are probably willing to wait longer. Figure 5 below seems to
verify this argument. Sellers list an average of 4.89 items in the Dell 4600
category, but only 1.67 Madden NFL games.
Number of Auctions
6.00
5.00
4.89
4.00
3.38
3.00
2.36
2.00
1.67
1.00
0.00
Madden NFL
Apple Ipod
Dell Latitude
C600
Dell 4600
Desktop
Category
Figure 5: Average Number of Auctions per Seller
15
4.3.2
Feedback Retaliation
In my data set, I was unable to find a single case in which feedback could
have been left as retaliation. All feedback that has been left mutually, i.e.
in which both parties reported feedback for each other, has been positive.
Consequently, I cannot make an assertion about the frequency of retaliation. Nevertheless, that does not mean that the problem of retaliation is
not persistent. Rather, it could be that all participants in the categories I
collected data from already consider the possibility of retaliation, and take
this into account when they make their decision of what feedback-type to
provide. Inexperienced eBay users would probably be oblivious to the threat
of feedback-retaliation, but the categories I focused on are likely to be comprised of market participants who are experienced with online trading. All
categories are related to electronics, and three of them directly to computers.
Therefore, most users within these categories are more likely to be acquainted
with eBay’s implicit trading rules than users in less technologically focused
categories. The user demographics may be another indication for level of
trading experience — buyers and sellers in my selected categories are probably younger than users in categories such as stamps, coins, or in the "Home
& Gardening" category. In retrospect, it may have been worthwhile to collect data from additional categories to explore retaliation. Additionally, my
data in regard to the timing of feedback may be somewhat noisy, since I may
have waited not long enough to collect all feedback that has been left. Given
Resnick and Zeckhauser’s (2001) results, however, which are very similar to
my findings in respect to the percentage of reported feedback, I doubt that
waiting longer would have yielded any additional insights.
Given that my data set does not validate the existence of feedbackretaliation, I manually searched the feedback history of some sellers contained
in my sample data. Due to eBay’s accessibility of a user’s feedback history, I
was able to record every possible feedback retaliation of these sellers. More
specifically, I clicked through a seller’s entire feedback history, searching for
negative reports from buyers. Once I found one, I clicked into the particular
buyer’s feedback history, looking whether the seller had reported feedback.
Once I found that both parties had left feedback, I did not only compare
the types of evaluations the two parties left for each other, but also the
time-difference between the report-filing.
16
One seller, who goes by the name happyzch, had a history with a number
of suspicious cases. He currently has a total feedback score of 4209, with 68
negatives to which he responded with negative feedback in 24 instances. A
very common theme in happyzch’s feedback statements is the exact opposite
claim the buyer has put forth in his negative evaluation. In addition, the time
difference between the reports suggests that happyzch was responding with
punishment: 13 have been left within one hour of buyer feedback, of which
five have been left within 15 minutes. Table 3 in the appendix summarizes
my findings of the 24 response-feedback transactions by happyzch.
Given that sellers do not seem to retaliate all the time, it is not surprising
that I found no cases of retaliation. Let’s assume for now that all sellers leave
a negative report in response to received negative feedback with probability
of 35% (taken from happyzch’s percentage of possible retaliation). Then the
probability that one of the 15 instances of negative seller-feedback I found in
my data set is potentially a case of retaliation is
µ ¶
15
· 0.35 · 0.6514 ≈ 0.0126
1
or 1.26%.
4.3.3
Examining Cases of Negative Feedback
Although the few cases of negative feedback I found are not left in retaliation,
they still provide other interesting information. Table 4 in the appendix summarizes the different cases of negative feedback for each category. First, note
that in almost all instances sellers’ reasons for reporting negative feedback
was non-payment on the buyer-side. Second, no buyer who received negative
feedback had a high feedback score. In fact, until May 2004 no buyer had a
summary-feedback score greater than one, whereas the sellers usually had a
significantly larger feedback score. This raises the question of whether sellers’ are more likely to provide negative feedback in bad transactions if buyers
have significantly lower reputation levels than they do. Consider seller i who
leaves negative feedback for buyer j, where j has a very low reputation level.
Also, let’s assume that buyer j returns negative feedback. Future buyers,
when evaluating whether to transact with i, may not weigh i’s negative feedback received from j very much, because the summary feedback score of both
users suggests that i is more trustworthy than j. In other words, seller i does
not need to be afraid of retaliation, because when received from buyers with
17
very low levels, negative feedback may have no impact on future buyers’
decisions.
Note also that 10 of the 15 buyers became an eBay member at the beginning of the year, but after having received a negative evaluation they hardly
engaged in any observable trades. This may also be the reason why buyers
did not retaliate. Instead of going through the trouble of providing feedback,
the negative feedback that they already received may have induced buyers
to switch identities simply by registering under a new user name. The lowfrequency trading of the buyers in the transactions from table 4 could be an
indication of the phenomenon of identity-switching.
5
Withholding Feedback and Retaliation
It is important to understand why a large portion of users chooses not to
provide feedback at all. Clearly, providing feedback is costly, and hence
there may not be sufficient incentive to leave feedback for the other party
once a transaction is completed. Especially low-frequency traders may have
little motivation to go through the trouble of providing evaluations. Yet, by
exploring eBay’s discussion boards, another reason seems to be very prevalent
— users withhold feedback in order to keep their feedback history clean, and
in order to be able to retaliate against negative feedback.
By observing eBay’s discussion boards, it seems that the phenomenon of
withholding feedback by sellers and buyers is quite common. A few feedbackrelated comments taken from the discussion board can be found in the appendix. The following comment left by user schimke confirms the problem
of feedback-withholding:
I have been told by sellers that when I leave them positive
feedback, they’ll know our "transaction is complete" and they’ll
then leave it for me. This is nothing more than holding my
feedback hostage until they are assured I won’t say something
negative about them. When I pay for an auction I have won
(usually immediately with PayPal) I have fulfilled my end of the
transaction as required. I have made it a practice to never bid on
anything again from a seller who practices this form of blackmail.
18
Although a buyer’s part of the transaction is over after submitting payment to the seller, sellers often choose not to report feedback first. They
seem to realize that they can exert pressure on buyers to give positive feedback, or no feedback at all. They can do so, because buyers care about the
feedback they receive, as I define in section 6.1.2. Consequently, a negative
reputation harms a buyer. By withholding feedback, a seller may try to send
a signal, implying that he will return the feedback he receives. Some sellers
even state this very explicitly in their auction description, as can bee seen in
figure 8 in the appendix.
Clearly, the decision whether to withhold feedback depends on one particular feature of eBay’s feedback system — that of instant publication of
feedback. When users report feedback for a particular transaction, they do
not only evaluate their trading partner’s behavior related to the transaction’s
quality, but also take post-transaction behavior into consideration.
For sellers, there may exist multiple reasons for wanting to retaliate
against negative feedback. One compelling explanation is that a seller may
consider it to be a rational response to return negative feedback, simply because it reduces the informational value of his received negative rating. To
be more precise, consider a seller i who receives justified negative feedback
from buyer j. If i denies any wrongdoing claimed by j, or alleges that j lied
in his comment, that particular negative feedback may not weigh very much
into subsequent buyers’ decision whether to transact with seller i. After all,
word stands against word, and subsequent buyers do not know who tells the
truth.
5.1
Characterization of Current Feedback Behavior
As I mentioned above, the current system provides strong incentives for users
to coerce other market participants into reporting positive feedback. This
essentially means that some transactions that deserve negative feedback actually receive positive evaluations. Consequently, positive reports may not
be very informative, because some users do not disclose all of their private
information.
To clarify this behavior in the feedback-giving process, consider a unit
interval that represents the quality of transactions on eBay. The upper bound
of this interval, Q, represents a high-quality transaction, while the lower
bound, Q, represents a low-quality transaction. Since feedback is supposed to
be indicative of a transaction’s quality, I can map feedback onto this interval
19
as I have done in figure 6 below. In the figure, the starting points of the arches
represent true transaction quality, whereas the end points correspond to the
quality associated with the transaction according to the feedback system.
Figure 6: Illustration of current feedback behavior
Since virtually all feedback is positive, it is reasonable to believe that
some information related to a transaction’s efficiency gets lost. A buyer, for
example, may give positive feedback to the seller, although the buyer received
the item he purchased much later than agreed upon. Similarly, a seller may
still provide positive feedback, despite the fact that he had to wait a long
time for payment of his sold item. In these examples, positive feedback would
not reflect true (negative) experience. Market participants who value time
would probably like to be informed of slow sellers or buyers.
Given that users report feedback, the frequency of the different types
of evaluations can be illustrated in figure 7 below. Again, the lower and
upper bound correspond to a low and high transaction quality, respectively.
Further, I divide the interval into three subintervals, which each represent
a transaction quality that receives negative (-1), neutral (0), and positive
feedback (1). As the data I collected indicates, of the users who chose to
evaluate their transaction, very few provided negative (< 1%) or neutral
feedback (< 1%), while many provided positive feedback (≈ 99%). Therefore,
the intervals representing the different types of feedback are of different size.
-1
0
1
Figure 7: Illustrated Frequency of Feedback Types
20
It is obvious that the informational value of positively reported transactions declines as more and more users choose to report positive feedback.
A transaction of relatively low quality, as marked in figure 7 above, is still
reported as a high-quality transaction in eBay’s reputation system. The inherent adverse selection problem is evident. Given that some eBay users are
of bad type, market participants not involved in the particular transaction
are unaware of the seller’s or buyer’s true type and may select either party as
trading partners in future transactions. Under conditions where the boundaries of the different intervals, which correspond to feedback type, reflect
users’ true experience, the problem associated with adverse selection might
be avoided.
The behavior characterized above provides the motivation for my model,
mainly (1) to rationalize why market participants do not always reveal their
private transaction-related information, (2) to define what the probability of
having a good transaction is, and given this definition, (3) to offer suggestions
of how to improve the current feedback system. But before I turn to my
model, it is important to define what users, specifically buyers, care about
in a transaction.
6
Rationalizing Feedback Reports
Having gained some empirical insights from above and having characterized
feedback behavior, it is worthwhile to think about why each party cares about
the feedback it receives, and what incentives either party has to provide feedback once a transaction is closed. For sellers this is pretty straightforward;
Livingston (2002) notes that reputable sellers receive a much larger expected
return than sellers who have no reputation. Using data from eBay, the author shows that auctions of sellers with a high reputation are more likely to
result in a sale by attracting more bidders, who are also willing to bid greater
amounts. Thus, the feedback a seller receives affects his future income. Since
buyers care about receiving feedback, as I will discuss shortly, they are likely
to buy from a seller who returns feedback. Consequently, a seller who wants
to build a lasting reputation has an incentive to report feedback.5
5
Since buyers can observe how much feedback a seller has left, and what type, it is
reasonable to assume that sellers report feedback to send a signal to future buyers that
21
Why buyers care about feedback is not quite as straightforward, since
buyers usually submit payment to the seller first, after they have won an
auction. Hence, a seller should not care much about a buyers feedback, and
consequently a buyer should not care either. It would be too simple, however,
to assume that buyers remain buyers forever — most eBay participants switch
back and forth between being a buyer and seller. Therefore, a buyer has to
worry about his feedback, because it may affect his future income as a seller.
It is thus understandable that negative feedback provides a disutility to both
parties, not just sellers.
Alternatively, it is reasonable to think that a buyer does not only get
disutility from receiving feedback because of the potential reduction in future
income as a seller. A user may think of himself as part of a community to
which he feels obligated to uphold the idealistic notions of safe-trading put
forth by eBay. The buyer may strive to become a respected member of
this community, which he can realize by increasing his feedback score. A
negative feedback posting would then provide a disutility to the member.
At the same time, he may feel the need to support safe-trading within the
community, which gives incentives to reward or punish sellers’ behavior by
reporting feedback.
There do not necessarily have to be exogenous variables that induce a
user, buyer or seller, to care about feedback, though. It may be that he
reacts to subliminal feelings of fairness and justice. If, for example, a user
feels that he behaved properly in a transaction, but still receives negative
feedback, he may feel treated unjustly. A resulting desire for retribution
may give enough incentives for users to leave feedback.
In summary, a buyer cares about giving feedback and the feedback he
receives, because
Assumption 1 A user may switch between being a buyer and being a seller.
Hence, feedback received as a buyer influences future income as a seller.
Assumption 2 A buyer may think of himself as part of a community of
which he would like to be a respected member and whose ideals it is imperative to uphold.
they will provide feedback. If a buyer, for instance, notices that a seller seldomly provides
evaluations in his transactions, he may be reluctant to buy from that particular seller.
22
Assumption 3 A buyer takes actions in response to strong feelings for fairness and justice.
Assumption 4 A seller may not accept the highest bidder’s bid if the winner’s feedback is too low.
Keeping these assumptions in mind, I turn to some general characteristics of
the utility functions.
6.1
Buyers’ Utility
My goal in this section is to provide some intuition for why buyers bother
about feedback at all. For this purpose, I define in rather general terms what
buyers care about, and how it relates to their decision to report feedback. To
keep subsequent steps simple, I assume that there are only four main factors
that affect buyers’ utility: (1) the ending price of an item, and the associated
payoff dependent on the buyer’s private valuation of an item, (2) the feedback
received from the seller, (3) the overall quality of the transaction, which is
determined by the speed of shipping and the quality of the product, and (4)
a vengeance function that represents the buyer’s feelings related to honesty
and the desire for retribution. In the following subsections, I extrapolate on
each of these issues.
6.1.1
Effect of the Ending-price on Utility
Given buyer j’s private valuation of an item, vj , his utility increases as the
difference of his private valuation and the item’s actual price, vj −p, increases,
given p to be the ending price in the auction. Obviously, the price p is a
function of the buyers willingness-to-pay (W T P ) for the item. Since eBay
uses a second-price sealed bid format, the buyer cannot fall victim to the
winner’s curse, and hence a buyer should always submit his true willingnessto-pay for any given item. 6 In other words, for any item, a buyer j should
always submit W T P = vj . This, however, only makes sense if he believes
6
Buyers do actually have an incentive not to bid their true valuation until the end of
the auction. Given the hard-closing format eBay uses, buyers have incentives to submit
bids late, typically in the last minutes or seconds of an auction, in order to catch a deal
at price below their true valuation. Schindler (2003) explores this phenomenon in more
detail. For simplicity, however, I will neglect this fact.
23
to receive the item with certainty, in exactly the condition he had expected
it. Of course, buyers do not know with absolute certainty what type of seller
they deal with, and whether the transaction will exactly turn out to be the
way they hope. Fortunately, the feedback system provides buyers with some
information related to a seller’s future performance.
It is reasonable to assume that buyers form beliefs about a seller’s type
based on his feedback history, which is publicly accessible on eBay’s website,
and determine their W T P accordingly. I will assume that there is a finite
set of sellers S = {s1 , s2 , ..., sn } from which a buyer can choose. Further,
I define a parameter fi representing seller i’s total positive feedback, and a
parameter Xi as the seller’s total feedback:
Definition 1 Let fi denote seller i’s total positive feedback and Xi total feedback received by unique users. Then, Xfii denotes seller i’s percentage of positive feedback.
It is reasonable to think that buyers, when evaluating a seller’s feedback history, pay relatively more attention to a seller’s most recent feedback.
These reports about the quality a particular seller provided in his recent
transactions, probably weighs heavier, because it may be an indicator for the
quality of next transaction. If, for example, seller i has received 10 negative
evaluations for his past 10 transactions, a buyer may assess a higher probability to the event of being cheated on. This should influence his decision
whether to bid, and how much money to bid. For this purpose, I define
a parameter λi ∈ [0, 1], which denotes the percentage of seller i’s positive
fN
feedback in his most recent transactions. Hence, λi = Ni , where N is the
amount of recent transactions. The greater λi , the better seller i’s feedback
from the past N observable transactions.
Definition 2 Let λi ∈ [0, 1] be a parameter that describes seller i’s most
recent feedback, including comments.
Let ri ∈ [0, 1] be buyer j’s belief that seller i will deliver the item after
he received payment. Clearly, ri should be a function of the seller’s total
feedback Xi , total positive feedback score fi and λi , which I define to be:
µ ¶
fi
ri (fi , Xi , λi ) =
λi
(1)
Xi
24
Equation 1 emphasizes the relative importance of recent feedback over total
positive feedback: if a seller has received mostly negative feedback in his
recent transactions, such that λi ≈ 0, a buyer will assess a low probability
to the event of receiving the item after having submitted payment. If, however, recent feedback has been mostly positive such that λi ≈ 1, a buyer will
assume that he will receive the item with probability close to Xfii .
Let B = {b1 , b2 , ..., bm } denote the set of m buyers. As already mentioned,
buyer j’s W T P for an item is a function of his private valuation for the item,
vj , and the post-payment probability of receiving the item from seller i, ri .
Assuming risk-neutrality, buyer j’s W T P for an item from seller i is his
expected payoff:
W T Pji (vj , ri ) = ri vj
The payoffs of any transaction to buyer j are then
uj (vj , p) = vj − p
which after plugging in W T Pji for p, yields
uj = vj (1 − ri ) for ri ∈ [0, 1]
After plugging in equation 1, the effect of the ending price of seller i’s item
on buyer j’s utility, Pji , is
¶
µ
fi λi
i
Pj (vj , ri , p) =
vj − p
(2)
Xi
I will revisit buyers’ assessment of the probability that a transaction is of
high quality, given a seller’s feedback history, in section 8.1.
25
6.1.2
Feedback Received
It makes sense to believe that the feedback score a buyer receives in return
for his behavior has an impact on his utility. If a buyer submits payment
quickly, but never receives the particular item, and on top receives negative
feedback, his utility from the transaction is lower than if no feedback had
been given at all. It is also important to note that feedback left by a seller has
a greater impact on the utility of the buyer if the total feedback received, Xj ,
is low. If, for example, buyer j already has 1000 feedback points, the marginal
utility-gain (-loss) from an additional positive (negative) feedback point may
be close to zero. If, on the other hand, the buyer has only a feedback score
of 2, the impact of one additional feedback point is more significant. Hence,
the functional form Fji (·) that relates feedback received from seller i, and
what impact it has on buyer j’s utility, is a concave function such that
∂Fji (·)
∂fij
∂ 2 Fji (·)
> 0, ¡ j ¢2 ≤ 0
∂ fi
Here, fij represents the feedback left by seller i to buyer j.
6.1.3
Transaction Quality
The quality of the transaction (Q) is generally determined by the effort level
seller i puts into the completion of the transaction. Two simple ways effort
can be measured in eBay auctions are (1) speed of shipment, and (2) the
item’s condition at arrival. The seller may ship very late, or he may not
package an item sufficiently well enough to avoid damage incurred during
transit. It makes sense that both of these factors do not necessarily increase
buyers’ satisfaction, but rather decrease their utility.
How much a transaction’s quality influences a buyer’s utility should depend on the price, since when the item never arrives, the buyer incurs a loss
of p, the ending price he already paid. This is, of course, assuming that he
will not be compensated by the seller.
Remark 1 It is important to note that utility is not affected by Q on the
upward end; that is, if a buyer pays for an item, he expects timely shipping
26
and a functional item. He does not gain any additional utility if the quality
of the transaction is high.
It is questionable, however, whether buyers uniformly value time and
item-condition the same; perceptions of both may widely differ, which should
be reflected in the functional form relating Q to buyers’ utility. For this
reason, let αj ∈ [0, 1] be a parameter that defines buyer j’s perception of
the transaction’s quality, where α reflects both the speed of shipping and the
item’s condition. Here, αj = 1 corresponds to a high-quality transaction and
hence the function Q (·) ∈ [−p, 0] possesses characteristics such that:
∂Q (p, αj )
>0
∂αj
which implies that Q (·) → −p as αj → 0.
6.1.4
Feelings of Vengeance and Honesty
So far, the utility components I defined do not give buyers an incentive to
provide negative reports at all. Instead, users would simply provide positive
feedback after every transaction. On eBay, however, buyers do provide negative feedback in some instances, which gives reason to the assumption that
there is some other factor market participants care about. This goes back to
the notion that users respond to subliminal feelings of justice, fairness and
retribution. Generally, people probably feel better about themselves if they
punish inappropriate behavior of the opposite party. To account for these
feelings, I introduce what I call a "vengeance function".
Let Vj (·) be a function that links buyer j’s feelings related to the desire
for punishment in any given transaction to his utility. This function should
depend on the quality of the transaction, Q, the ending price of the item, p,
and the feedback the buyer provides to the seller, fji . The lower the quality
of the transaction was, the greater is perhaps the desire for vengeance, and
if taken, the buyer derives positive utility from providing negative feedback.
At the same time, how strong the feeling for vengeance is likely depends on
the item’s value. If, for instance, the buyer bought a pen for a few dollars,
his desire to get back at the seller in response to a low Q may be negligible.
In a transaction where the buyer bought a laptop, on the other hand, Vj (·)
27
may be much greater than in the pen transaction, given the same level of
Q. Hence, for a fixed Q, Vj (·) generally increases with p, such that the first
derivative of Vj (·) in respect to p is positive:
∂Vj (·)
>0
∂p
In addition to feelings of vengeance, it is a reasonable to assume that buyers
care about being honest. To be more precise, buyers get disutility if they
do not provide truthful information when evaluating a transaction. If, for
example, the quality of the transaction was very low, and the buyer gives
positive feedback, he gets a disutility. This should be incorporated in the
vengeance function. If, for example, buyer j behaved appropriately in a
transaction (i.e. he submitted payment very quickly), but he never receives
the item, his value Vj (·) will be positive if he leaves negative feedback, but
negative if he leaves positive feedback.
For simplicity, consider Q to be of either high- or low-quality, denoted
by Q and Q, respectively. The following matrix summarizes the effect the
vengeance function has on a buyer’s utility, given the different types of feedback and Q:
Reported Feedback
Positive Negative None
∂V (·)
∂V (·)
Q ∂fj i > 0 ∂fj i < 0 0
Q
j
j
∂Vj (·)
<
∂fji
∂Vj (·)
>
∂fji
0
0
0
Since buyer j’s utility is composed of the four functions above, it can be
written as the general form
Uj (Pji , Fji , Qij , Vj )
where, to summarize, the impact of P (·), F (·), and Q (·) are such that
∂Uj (·)
∂ 2 Uj (·)
∂Uj (·)
∂Uj (·)
> 0,
> 0,
>0
2 ≤ 0,
∂P (·)
∂F (·)
∂Q (·)
∂F (·)
28
Now that I have defined buyers’ utility characteristics in very general terms,
let’s move on to the seller’s utility.
6.2
Sellers’ Utility
Since in subsequent sections I mainly focus on buyers’ decision of what type
of feedback to provide, I will assume that sellers’ utility depend only on their
expected lifetime profit. Let Wif denote seller i’s future revenue gained from
an infinite amount of transactions. Further, let ω be the discount factor and
let ρt be the probability that the seller receives payment in transaction t.
Then seller i’s expected future income at time t can be denoted as
Wif
=
∞
X
t=1
ωt (ρt pt − ct )
Where pt denotes the ending price the particular transaction in period t and ct
represents the total cost associated with a successfully conducted transaction
as eBay defines it.7 For simplicity I will assume no buyer-defect, such that
ρt = 1 for any t. Further neglecting the discount factor, Wif simplifies to
Wif =
∞
X
t=1
(pt − ct )
Following Houser and Wooders (2003), pt is the second highest bid in auction
t:
pt = rit vt2
(3)
where rit is the probability that seller i will deliver the item after receiving
payment in auction t, and vt2 is the second-highest bidder’s valuation of the
item in auction t. Using my definition of rit from (1) in (3), I get
pt = rtt vt2
7
Hence, in addition to a seller’s item-cost, this cost is comprised of the insertion fee
and a staged percentage cut of the ending price.
29
such that
Wif
∞
X
¡ t
¢
=
rt vt2 − ct
t=1
From this definition of future revenue, it is obvious why sellers care about
receiving positive feedback and try to avoid a negative evaluation. Clearly,
the value of a seller’s future revenue stream depends on how buyers will assess
the probability of receiving the item. By definition, they do this by referring
to the sellers feedback rating. This constitutes the primary reason for why
sellers care about feedback at all. Empirical studies suggest that the ending
price a seller realizes is positively correlated with his summary feedback rating, and hence a seller’s future revenue is affected by the feedback he receives.
The greater a seller’s negative feedback, as percentage of total feedback, the
lower the ending price he can realize will be. The seller may even be unable
to attract bidders at all, and hence not be able to transact. How strongly
feedback affects the seller’s utility and his future revenue depends on his total
amount of positive feedback, fi , and on his total feedback amount, Xi .
7
Rationalizing False Reports
Now that I have presented the empirical part of my paper and characterized
feedback behavior, naturally the question "Why exactly do users provide
false information?" arises. In order to rationalize the deficiencies of eBay’s
feedback system described so far, I model the process of providing feedback as
a repeated three-stage game between two distinct players, who transact with
each other for the first time.8 In this game, let player j be a buyer from a finite
set of J = {j1 , j2 , ..., jm } buyers. Similarly, let player i denote one seller from
a finite set of I = {i1 , i2 , .., in } sellers, where the subscripts 1, 2, ..., n denote
the rank of player i in terms of his reputation parameter, fXi λii , as defined in
section 6.1.1. That means, i1 has the highest reputation, i2 the second-highest
and so forth. For simplicity, I will assume that there exists no ambiguity in
the ranking of the sellers, i.e. the ranking is i1 > i2 > ... > in .9 In this game,
8
For reasons of convention, in this section I will refer to buyers and sellers as players.
The reason for this ranking is simply to provide clear intuition for why sellers care
about feedback.
9
30
where players can choose between reporting positive (P) and negative (N)
feedback, the strategy sets are Sj = {P, N} and Si = {P P, P N, NP, NN }
for player i and j, respectively.
In the first stage of the game, player j selects player i among all the
players contained in set I, from whom he wishes to buy a certain item. In
addition, he decides the price he is willing to pay for the item. Both decisions made by player j are contingent on the feedback of player i. The second
stage of the game represents the post-completion decision about what type
of feedback to provide. Player j decides whether to provide a positive (1), or
a negative (-1) evaluation of the transaction. This stage is followed by player
i’s decision to do the same. To keep this model simple, I assume that
Assumption 5 The goods being traded are homogenous in every period. That
is, they do not differ in characteristic or configuration. Additionally, the
transaction characteristics, such as shipping costs, choice of payment methods, and other transaction characteristics are the same in each transaction.
Assumption 6 Transactions are either of high-, or low quality.
Assumption 7 Sellers are either of high (H) or low (L) type.
Assumption 8 Both players will report either positive or negative feedback
in sequential moves, player j (the buyer) moving before player i (the seller).
Furthermore, it is common knowledge that the seller plays a matching strategy. This means, the seller will provide the same feedback he receives.
The game and its payoffs to both players can be represented diagrammatically by the following game tree:
31
1
i1
i
i2
1
-1
j
j
in-1
1
-1
i
in
-1
Clearly, the buyer’s decision what type of feedback to leave depends on
what he believes his response-feedback to be. To be more precise, if buyer j
believes that seller i is playing a matching strategy, he will provide negative
feedback if
Condition 1 Given a transaction of low quality, Q, buyer j will report
negative feedback for seller i if, and only if
¡
¢
¡
¢
¡
¢
¡
¢
V Q, p, fji = −1 −V Q, p, fji = 1 > F fij = 1, Xj , fj −F fij = −1, Xj , fj
In words, the buyer reports truthful negative feedback if the benefit from
taking vengeance outweighs his incremental feedback gain, Fji (·). Therefore,
given this condition, under certain circumstances the buyer would not provide
negative feedback, even if he experienced a low-quality transaction. To be
more precise, if the buyer expects that the seller will match whatever type
of feedback he provides, the buyer is sometimes better off reporting a lowquality transaction as being of high-quality, i.e. he provides positive feedback.
This is the case when the buyer cares a lot about the feedback he receives,
which by definition from section 6.1.2 means that Fji (·) is large, and when
his feelings for vengeance are low. By my definition of the vengeance function
V (·), this is the case when the item value was relatively low. Consequently,
positive feedback for bad transactions is often given when the buyer has few
positive feedback points, and the transaction value was fairly low.
32
To illustrate this, I map transactions where feedback has been given
falsely in a two dimensional space, where the x-axis represents the item price,
and the y-axis the buyer’s amount of positive feedback.
Figure 9: Illustration of Falsely Reported Transactions
Here, the two-dimensional space represents all positive feedback left by
a buyer, of which some unknown proportion was given although the quality
of the transaction was low. This graph illustrates the relationship between
a buyer’s decision whether to leave positive or negative feedback, given that
a transaction was of low quality, and given the buyer’s feedback and the
transaction value. By definition of buyers’ utility, buyers with low positive
feedback scores, who engage in low-value transactions, are more likely to
provide false information than buyers with high feedback scores.
It is obvious that this phenomenon distorts the transaction-related information on which buyers base their decision with which seller to transact. The
probability of receiving a low-quality transaction from a certain seller post
payment may be higher than the seller’s feedback history, h (·), suggests.
33
8
Enhancing eBay’s Reputation Mechanism
The primary purpose of this section is to show that predictions of getting a
high-quality transaction post-payment are harder to make under a mechanism that allows for feedback retaliation than under a mechanism that does
not allow for such behavior. Where exactly the boundary of the area representing falsely reported transactions is in figure 9, is of secondary interest.
Important is that the area will be smaller under the proposed mechanism.
Before I turn to suggestions of how to improve the existing feedback system,
it is important to define the chance of getting a bad transaction, given a
seller’s feedback history.
8.1
Current Risk of Getting a Low-Quality Transaction
Returning to the game defined in section 7, let’s assume buyers know that a
certain percentage of sellers is either of low- or high quality. Furthermore, it
seems reasonable that each type of seller sometimes provides a high-quality
transaction, but a low-quality transaction at other times. The intuition for
the fact that sellers’ transaction quality does not always reflect their true type
is straightforward. High—type sellers, who have accumulated a lot of positive
feedback have an incentive to take advantage of their good reputation every
once in a while by defecting in certain transactions [quote study that shows
that]. On the flip-side, low-type sellers have an incentive to build up a
positive reputation in order to cash in on it. For instance, a low-type seller
may accumulate positive feedback by providing high-quality transactions in
low-value categories, like computer games. He may then switch to a highvalue category and provide low-quality transactions.
Then, let µH ∈ [0, 1] represent the predefined probability that a seller is
of good type, and let α be the probability that a high-type seller provides
a high-quality transaction, Q. Furthermore, I denote β as the probability
that a low-type seller provides a high-quality transaction. The table below
summarizes these variable definitions.
34
Variable
µH
1 − µH
α
1−α
β
1−β
Probability that
the seller is of high type (H)
the seller is of low type (L)
an H-type seller will provide Q
an H-type seller will provide Q
an L-type seller will provide Q
an L-type seller will provide Q
In addition, let γ ∈ [0, 1] be the proportion of all low-quality transactions
reported as high-quality transaction, i.e. γ is the percentage of low-quality
transactions that end up receiving positive feedback under the current feedback system. Here, γ corresponds to the area drawn in the figure above.
Given these definitions, the percentage of all transactions that were reported
as high-quality transactions is:
Pr (fi = 1) = α · µH + (1 − µH ) · β + γ · [(1 − α) µH + (1 − µH ) (1 − β)]
Hence the probability that a transaction was of high quality, given positive
feedback, can be expressed as
¡
¢
Pr Q|fji = 1 =
αµH + (1 − µH ) β
α · µ + (1 − µH ) · β + γ · [(1 − α) µH + (1 − µH ) (1 − β)]
| H {z
|
}
{z
}
probability of Q=Q
probability of Q=Q
(4)
To simplify subsequent steps, let
δ = αµH + (1 − µH ) β
1 − δ = (1 − α) µH + (1 − µH ) (1 − β)
¡ ¢
To summarize, the probability that the transaction was of high quality Q ,
given the buyer reported positive feedback for that transaction is
¡
¢
Pr Q|fji = 1 =
35
δ
δ + γ · (1 − δ)
(5)
¡ ¢
And the probability that the transaction was of low quality Q , given the
buyer reported positive feedback for that transaction is
¡
¢
Pr Q|fji = 1 =
γ · (1 − δ)
δ + γ · (1 − δ)
(6)
Having established the likelihood of a transaction being of either highor low quality, given that the buyer has left positive feedback, I can define
a probability distribution over the seller’s transaction history. This probability distribution is a function of the number of reported transactions, N,
the amount of transactions reported as bad (negative), B, the proportion
of falsely reported transactions, γ, and the probability that a transaction
reported as Q actually is a high-quality transaction, δ.
e− denote the true number of total low-quality transactions seller i
Let Q
i
has. One should keep in mind that B, represents the amount of negative
e− ∈ [B, N]. This makes sense, since negative feedback
feedback, and hence Q
i
e+ ∈ [0, G] be the true number of
reflects true bad experience. Similarly, let Q
i
high-quality transactions, where Gi represents seller i’s amount of positive
feedback. Then the probability that seller i had x ∈ [0, G] high-quality
transactions, given his transaction history hi (·), can be expressed as
µ ¶µ
¶x µ
¶Gi −x
γ (1 − δ)
Gi
δ
∀x ∈ [0, G]
Pr (x|hi (·)) =
x
δ + γ (1 − δ)
δ + γ (1 − δ)
(7)
But the probability that a high-type seller has x out of N transaction of
high quality, or xQ, is
µ ¶
¢
¡
N x
α · (1 − α)N−x
(8)
Pr xQ|hi (·) =
x
Combining (9) and (10), the probability that a good seller i has G reported
good transactions can be expressed as
Πgi (hi (·)) =
¶µ
Gi µ
X
Gi
x=0
x
¶x µ
¶Gi −x µ ¶
γ (1 − δ)
N x
δ
α ·(1 − α)N−x
x
δ + γ(1 − δ)
δ + γ (1 − δ)
(9)
36
and similarly
Πbi (hi (·)) =
¶µ
Gi µ
X
Gi
x=0
¶x µ
¶Gi −x µ ¶
γ (1 − δ)
N x
δ
β ·(1 − β)N−x
δ + γ(1 − δ)
δ + γ (1 − δ)
x
(10)
x
Applying Bayes’ Formula, the probability that a seller is of high-type can
be estimated by
Pr(i = H|hi (·)) =
Πgi · µH
Πgi · µH + Πbi · (1 − µH )
(11)
And hence in expectation, the chance at a good transaction is
µ
¶
µ
¶
Πgi · µH
Πbi · (1 − µH )
Pr(Q = Q|hi (·)) = α
+β
Πgi · µH + Πbi · (1 − µH )
Πgi · µH + Πbi · (1 − µH )
(12)
In the current system, seller’s matching strategy constitutes an equilibrium, since neither party has incentives to deviate from playing the matching
strategy.
8.2
A Reputation Mechanism with Imperfect Information
Since retaliation against feedback depends on observed feedback, it would
make sense to keep information about reported feedback private until both
parties have left feedback. If seller i, for example, is given the information
that buyer j has left feedback for him, but is unable to observe the type of
feedback he received, his decision of what feedback to provide for buyer j
may be different than if he knew exactly what type of feedback he received.
Ebay’s current reputation system could be altered in such a way. An Email could be sent to a user communicating that the opposite party has left
37
feedback, although it’s type would only become publicized once the particular
user himself has left feedback. This mechanism would make it impossible
for sellers to retaliate, since they cannot directly observe the feedback they
receive. A seller could, however, attempt to guess what feedback he will
receive based on his judgement about his own performance in a particular
transaction. Hence, if a seller presumes he will receive negative feedback,
he may choose to leave a negative report. For simplicity, I will neglect this
behavior, and rather assume that a seller reports information that reflect his
true experience with the buyer. Furthermore, the mechanism must have a
time limit for reports, after which whatever feedback has been provided is
publicized, and no response is possible. If this time limit would not exist,
sellers could simply choose not to respond to whatever feedback they receive,
which would keep their reputation at their current level. Especially highreputation sellers would have an incentive to cash in on their feedback, and
then not respond to feedback.
It is clear that under this revised feedback mechanism buyers would no
longer take into account the feedback they expect in response when they
decide what type of feedback to report. More specifically, consider again the
same game as in section 7.1, with the slight change that players do not move
sequentially, but rather simultaneously. Since in this altered game, buyers
cannot condition their decision what feedback to give on the rating they receive, they decide to leave negative feedback if
Condition 2 Given a transaction of low quality, Q, buyer j will report
negative feedback for seller i if, and only if
¢
¡
¢
¡
V Q, p, fji = −1 − V Q, p, fji = 1 > 0
This condition is rather trivial: buyers provide negative feedback if the
quality of the transaction was sufficiently low, regardless of what type of
feedback they receive. Buyers do not need to fear sell-side retaliation any
longer, and hence do not condition their feedback reports on it. In the same
setting as in the previous section, this would mean that the probability that
a seller is of high-type, given his feedback history, is
µ ¶
N x
Pr (i = H|hi (·)) =
(13)
α · (1 − α)N−x
x
Without getting caught up in the messy algebra here, taking (13) and (14)
38
simplified, the following inequality holds if γ 6= 0, α 6= β, µH 6= 0, and µH 6= 1:
N
X
X
Πgi · µH
<
Πgi · µH + Πbi · (1 − µH ) x=0
N
Πgi
G=0
[Pr (i = H|hi (·))]2 · µH
µ ¶
N x
Pr (i = H|hi (·)) · µH +
β (1 − β)N−x (1 − µH )
x
(14)
All this says is that under the new mechanism, the probability that a buyer
will identify a high-type seller, given his feedback history, is greater under
the new system than under the current system. Intuitively this makes sense,
since market participants provide truthful information (more often) when
they leave feedback because they are not concerned about retaliation.
8.3
Inducing Participation by Monetary Incentives
A change of the reputation mechanism described above may elicit more honest feedback from users than the current system does, but it still does not
provide additional incentives for users to leave feedback in the first place.
As already discussed, users have little reason to care about leaving feedback
upon completion of a transaction, especially if they have received feedback
first. I have mentioned that users provide evaluations because they respond
to subliminal feelings of fairness and they may think of themselves as part of
a community to which they want to contribute by providing information to
other participants. Yet, the data I collected indicates that many users do not
respond to such feelings. Approximately 56% of all the users did not report
any feedback by the time I finalized my data set, which was at the end of
March 2004.
Given the high percentage of non-participation in the post-completion
evaluation process, it is important to provide incentives for market participants to leave feedback. In order to provide such incentives, one has to first
note that the process of providing evaluation is costly to users. A particular
user has to take the time to log onto eBay, go to the feedback forum, and
type in the feedback. Especially if a user has already received feedback in a
given transaction, incentives to undertake this costly action is reduced to a
minimum. After all, once a user has received feedback, there is no reward
from providing feedback any more.
It would make sense to compensate, or reward, market participants for
leaving feedback. By adjusting sellers’ listing placements on eBay to the
39
percentage of feedback they have given, these rewards can come in the form of
indirect monetary payments. Currently eBay gives shoppers different options
to filter their search results. Users can inspect search results by looking at
a list of items that (1) seems to be in random order, (2) is ascending in
time until auction close, and (3) is descending in time of item listing. This
way of filtering could be altered in that eBay lists items of sellers with the
highest feedback-participation rate highest among all listed items. With such
a mechanism, providing feedback, independently of its type, is rewarded in
the future by high placements of sellers’ items in users’ search results. This
in turn does not only increases the expected revenue of the transaction, since
it attracts more bidders, but also increases the probability that the item gets
sold at all.
Since this mechanism by itself does not induce market participants to
provide truthful information, it is sensible to combine it with the mechanism
I discussed in 8.2.
9
Conclusion
The goal of this paper is threefold: (1) to investigate how useful eBay’s
reputation mechanism is in reporting problematic or fraudulent transactions,
(2) to rationalize why users sometimes provide false information, and (3) to
provide suggestions of how to improve eBay’s current feedback mechanism.
A lot of the existing literature on online auctions mentions that a reputation
mechanism like eBay’s facilitates the rapid distribution of transaction-related
information to the whole market, which reduces problems associated with
adverse selection. My research, however, suggests that eBay’s reputation
mechanism mostly fails to propagate information about bad transactions by
enabling users to punish other members’ post-transaction behavior. The
implications of the feedback system’s inefficiencies for market participants is
that they cannot clearly distinguish between good or bad sellers.
The implication of my research is that the mechanism should be redesigned in order to increase members’ participation rate in the feedbackprovision process, while at the same time eliciting truthful transaction-related
information. I offer two mechanism-changes that, when applied in combination, may reduce the problems I defined. The first change imposes an uncertainty about reported feedback in that users cannot observe what type of
40
evaluation they received. Only when users who received feedback respond,
or after some time limit, does feedback get publicized. The effect of this
change is that market participants cannot condition their feedback-provision
on observed feedback, and hence retaliation is not possible. As a result,
eBay members would provide truthful information more often. The second
change provides indirect monetary incentives to market participants who report feedback frequently. Consequently, the gain from participating may
outweigh the time-cost associated with reporting feedback and overall the
participation rate may increase.
41
10
10.1
Appendix
Figures and Tables
Figure 1: Sample of Feedback History Page
Figure 2: Sample of Member Profile
42
Figure 8: Sample item description with announcement of future feedback
43
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
Item Number
Quantity
Number of bids
First bid amount
First bid date
Auction start date
Highest bid amount
Auction end date and time
Seller identification
Seller summary feedback score
Seller positive feedback %
Buyer identification
Buyer summary feedback score
Feedback left by seller
Date feedback left by seller
Feedback left by buyer
Date feedback left by buyer
But It Now
PayPal
Power Seller
Table 1: Data extracted from each auction
44
Seller: happyzch; Total Score: 4209; Negatives: 68
Buyer ID
akron004
crwilson
gibu2
mark_nikirk
j-olanin
paraduxx
xspolymers
califgirl101us
dfwbirdhtr
livefan123
seahawks2002
edbandj
stickfinger
subzilla
wkeil1981
umss420
kappyhappy
ljkiii
saveau
dadluvsdukie
ozmasgirl
oneboxman
drtybrdy2
tone_loke
Current
Buyer
Feedback
Total Buyer
Negatives
12
-5
1
0
-1
36
10
36
221
2
6
0
18
14
180
4
6
183
104
113
279
21
26
7
1
-5
1
1
2
1
2
1
2
1
1
2
1
1
3
1
1
2
3
1
1
3
6
2
Time Difference in
Minutes
(SellerBuyer)
3
8
12
13
13
16
23
25
35
36
55
56
59
90
101
110
226
233
243
315
455
492
1398
15666
Table 3: Example of Negative Seller-Response Feedback
45
Category
Seller
ID
Buyer
ID
Buyer
Member
Since
Buyer
Location
Seller Complaint
p s2 g a m e sa le s (2 7 )
1 a 2 j3 s (-1 )
3/25/2003
U .S .
N o p ay m e nt/ n o c o m m u n ic a tio n
d e a th sleep 6 6 6 (8 7 )
a n d relm o rris (-2 )
1/12/2004
APO /FPO
N o p ay m e nt
a d m ira llu ck y (1 8 2 )
anth o ny q b (-1 )
1/16/2004
U .S .
N o p ay m e nt/ n o c o m m u n ic a tio n
c ap t_ ra m o s (3 )
go ob lesh iz zer (-1 )
2/3/2004
U .S .
N o p ay m e nt/ n o c o m m u n ic a tio n
d e lla 1 2 3 (3 6 )
b a se 6 7 (-2 )
1/22/2004
U .S .
N o p ay m e nt
m tro p in (5 5 )
b ja ck so n 1 9 3 3 (-1 )
12/6/2002
U .S .
N o p ay m e nt
st8 k ny f (71 )
om cc o m b (-1 )
UK
N o p ay m e nt
lb lw h re (3 4 )
d o ctre ss_ d o o little (-3 )
1/12/2004
G erm a ny
N o p ay m e nt/ n o c o m m u n ic a tio n
rich lm o rris (1 7 )
d ru m m e r2 30 3 (1 )
1/23/2004
C anada
N o p ay m e nt/ n o c o m m u n ic a tio n
d m a son 8 98 (2 6)
ton m oy _ 1 9 (-1 )
9/20/2003
C anada
N o p ay m e nt
a lex an d ra h sco tt (1 5 )
sp iffi n g 7 0 (-1)
4/2/2003
UK
N o p ay m e nt/ n o c o m m u n ic a tio n
say rt (1 )
tika zi (0 )
2/7/2004
Tu rke y
N o p ay m e nt
b u lly _ jj (1 3 6 7 )
m a a rten 2 0 0 6 (-1 )
2/3/2004
U .S .
U n cle ar
h a p pyau ctin eer (2 68 )
jessica d iv in g (-1 )
1/28/2004
U .S .
N o p ay m e nt
h a p pyau ctin eer (2 68 )
jessica d iv in g (-1 )
1/28/2004
U .S .
N o p ay m e nt
M a d d en N F L
IPo d
7/7/2003
L a titu d e
D ell 4 6 0 0
Table 4: Summary Characteristics of Auctions with Negative Feedback
46
10.2
User Comments from eBay’s discussion board
1. bermuda_mohawk (445), 02/09/04 10:07 PM
I usually have a successful payment rating of about 80%, that means that
1 in 5 of my bidders don’t pay. A store with that type of loss would close, a
factory with 20% of it’s workers unproductive, would close. I feel that this
is a fairly poor mark to hold when it comes to the amount of people who
actually pay for their auctions. Now, the thing is, I am supposed to submit
feedback for these people, and if they do not pay, they deserve a negative.
And in the end, I don’t give it, for fear that I will get a retaliatory feedback.
2. immortaltemplar (34), 02/09/04 10:04 AM
2. NPB’s (non-paying bidders) should be BARRED from leaving feedback. I am a seller on eBay. This is how I pay my bills. EACH AND EVERY
negative can damage my reputation. If I get a NPB and file the forms they
should NOT be allowed to leave me any feedback. IE: the received negative
feedbacks that SO MANY powersellers have to endure SHOULD NOT BE
ALLOWED TO HAPPEN!
3. woods.n.water (536), 02/02/04 10:34 AM
I was given a negative feedback from a Newbie with 1 feedback. The
item was $3.25....In the feedback she claimed the aprons were not "vintage"
and were cheap. I do not feel that my auction was misleading....My biggest
gripe is that she never contacted me to let me know of her dislike....I would
have gladly refunded her the $3.25. (+ there was no charge for shipping)Any
suggestions?!
4. skywarp_party_hats_clueless! (0), 02/10/04 10:18 PM
cowboys: I would at least withhold feedback until the transaction has
been 100% completed, if nothing else. A lot of sellers would recommend you
withhold feedback until it has been left for you. Stick around the boards for
a while, and you will come to learn everyone’s opinion on the matter. It’s a
good way to come up with a method that works well for you
47
5. letzavit (185), 02/02/04 1:03 PM
Well, that’s the subject. He said he should receive it first because he did
his end to paying me. I said that’s fine, BUT I wait for a positive, that way
I know everything is satisfactory, plus he has nothing to lose. I received my
payment, he would definitely get a positive from me(before anyone says’well,
why dont you’)..here is my reasoning..I used to leave feedback first, whether
I was the buyer OR seller..however, as a seller, i left one, then the person
emails me, saying the product I listed was wrong, etc etc..couldnt deal with
the return policy, and wiped out a flawless feedback profile..hence, why I do
it this way..anyway, he stated if I did not leave feedback for him, that he was
going to leave a negative feedback in regards to my feedback ’policy’..kind
of iron, considering it seems he has a policy of his own..any suggestions? oh
yes, if I’m the buyer, I dont look to see if I got one first, I jsut leave positive
if i’m happy with what I got
6. countrysidemusic (31), 01/03/03 2:27 PM
I have avoided leaving negative feedback because of a fear that I will
receive negative feedback in retaliation. I think a review board with a mediation fee paid by the petitioner, if he loses would be one solution. The
way it is now is not useful to me because I’m reluctant to leave justified neg.
feedback because of retaliation.
7. stinky*felix (68), 04/17/04 9:53 AM (answering question: "How accurate do you think the eBay feedback system is?")
It depends on whether a seller withholds FB or not. If s/he does, their
FB is not a true representation of whether or not their buyers have been
satisfied with their transactions. If s/he doesn’t withhold FB (and they do
not participate in shilling, FB padding or other offenses), I believe that their
FB profile is much more accurate, since the buyer is free to leave earned FB
without fear of retaliation.
48
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