On-Line Auction Fraud: A Case Study
of a ‘Successful’ Seller
By Alex Nikitkov and Dan Stone
Contact Information:
Alex Nikitkov
Tel. (905) 688-5550 ext. 3272
E-mail address: anikitko@brocku.ca
June 15, 2016
Alex Nikitkov thanks Brock University for the grant supporting this study. Dan
Stone thanks the University of Kentucky and the Gatton College of Business and
Economics for grants supporting his research.
Data are available from public sources.
Keywords: On-line auctions, fraud, e-commerce, case study
On-Line Auction Fraud: A Case Study of a ‘Successful’ Seller
Abstract
Research investigating on-line fraud and deception is rare because well-designed fraud is
designed to be undetectable. But facilitating trustworthy e-commerce markets demands
understanding the strategies of on-line fraud perpetrators. Herein, we present a primarily
qualitative case analysis of an on-line auction seller who apparently deceived more than 250
buyers over eight years. Using Bell and Whaley’s (1991) taxonomy, we identify the sellers’
“sustainable fraud strategies”, evidenced in years 1 through 6 of the fraud, as including
“dazzling” and “mimicking”. Quantitative evidence also suggests that the seller actively
“managed” feedback ratings to avoid a too-low user rating. In contrast, the “exit fraud strategy”,
evidenced in years 7 and 8, included indifference to increasingly shrill buyer complaints (e.g.,
“Awful, avoid this seller”) and correspondingly increasing negative feedback. We conclude by
discussing the limitations of our research, strategies for detecting “sustainable” versus “exit
strategy” frauds, and suggestions for research on fraud detection and prevention.
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1. Introduction
Development of new e-commerce-driven business models presents a challenge to
information systems, for it opens a door to previously unrelated risks. Sound information system
controls such as software applications, procedures, trained personnel, and physical hardware are
required to provide assurance that exchange of business information and transactions conducted
in electronic form do not subject organization to the risks arising from use of Internet as a new
medium for data interchange.
On-line selling brings many opportunities but also substantial risks. These include risks
specific to Internet data interchange, like integrity, non-repudiation, authenticity, confidentiality,
privacy, and availability (Laudon et al, 2004) and risks coming from inability to inspect the
product or directly observe the vendor (Grazioli et al 2000). Academic research finds that
customers perceive security of transaction as the most acute risk, however legitimacy of seller
and quality of product closely follow (Odom et al. 2002). Further, while recent technological and
service developments address some of the Internet shopping safety issues, Grazioli and
Jarvenpaa (2000) rightfully suggest that Internet security technologies can create a false sense of
safety, since on-line consumers may have secure communication with deceivers.
A large and growing body of research investigates the properties of on-line auctions.
While fraud and trust are frequent topics of this literature, a lacuna exists with respect to
explicating the strategies of successful long-term fraud perpetrators. We explicate a successful,
multi-year strategy used by a successful fraud perpetrator. We discuss controls implemented by
eBay in order to minimize risk exposure. We also, discuss a weakness in control activities in
relation to legitimacy of seller and product misrepresentation risks and suggest that lack of
control for such risks may have undesirable ramifications for eBay. We illustrate the
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aforementioned control weakness with a case of “eBay_Seller” seller on eBay1. Finally, we
deliberate on the controls that may help limit the occurrence of seller fraud on eBay.
2. Research Method
2.1 Design
Well-designed frauds are intended to be undetectable. Accordingly, studying on-line
fraud is problematic using conventional research methods, since along with other criminal
behavior, successful perpetrators avoid detection. The genesis of this case study investigation
was one author’s knowledge of eBay gleaned from studying the eBay feedback system. This
knowledge included an understanding of the properties of “normal” eBay seller’s feedback and a
heightened awareness of outlier sellers, whose feedback properties suggested unusual or aberrant
behavior. Many months of observing the behavior of a set of outlier sellers led to the discovery
of the seller who is the basis of this case study.
The purpose of this inquiry is to articulate the strategy of one successful, long-term eBay
fraud perpetrator. We seek to understand both the ‘why’ and the ‘how’ of this fraud. The
research method is primarily qualitative. Our methods include documenting the seller’s on-line
profile, characteristics, and accumulated feedback. In addition, one of the authors conducted an
“action research” experiment consisting of an on-line transaction with the seller. We also
conducted e-mail interviews with some buyers. We use quantitative methods where useful in
analyzing the seller’s posted feedback.
Our data do not form the basis for an objective test of pre-specified hypotheses. Instead,
we seek a grounded theory of on-line auction fraud that both emerges from and informs our data.
Studying long-term fraud cannot be done in an experimental laboratory; such research affords
few experimental controls. In compensation for this lacuna, our data offer a rich fraud
1
We use pseudonym to identify seller’s ID in this paper.
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description that is sustained over an 8-year period. Consequently, we do not seek to test
hypotheses with our data; we instead seek to generate them (cf. Shadish, Cook, and Campbell,
2002; Van Maanen, 1998).
2.2 Sample Selection
We chose eBay as the market focus of the study since it is the largest on-line auction
market maker. We chose “eBay-seller” as the seller focus of this case study in order to
synergistically combine three elements of sample selection in qualitative inquiry (Patton and
Patton, 1990; Miles & Huberman, 1994). These three sample selection characteristics are crucial
to studying a successful, long-term fraud:
1. Selection of a critical case. As a long-term, successful deceiver, this seller is a ‘critical’
case for market-makers, sellers, and buyers seeking to limit the extent of on-line auction
market fraud. Accordingly, this case should provide logical generalizations and
applicability to the population of successful long-term fraudulent eBay seller.
2. Selection of a deviant case. The case is deviant in that:
a. the large majority of eBay sellers receive almost uniformly positive feedback (cite
here) and are presumably honest,
b. fraudulent eBay sellers rarely sustain multi-year frauds,
Accordingly, that the seller documented herein is a “double” outlier in that this seller
is both dishonest and able to sustain this dishonesty for 8 years.
3. Selection of an opportunistic case. The case is opportunistic in that it arise from a
research strategy of data mining. Sleuthing for a long-term fraudulent seller provided no
guarantee that a case would be identifying. That one emerged is reflective of an
“opportunistic” (i.e., sleuthing) sampling strategy.
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2.3 Data
The data for this case include an 8-year, quarterly summary of the seller’s feedback, and,
posted feedback comments from 3,193 buyers who traded with this seller. Although not yet
reported, we also plan an interview with the seller, and, have obtained email interviews with 25
of the last 50 buyers who traded with this seller.
3. Case Context: On-line Auction Markets and eBay
eBay, which is the largest on-line auction maker, originated shortly after the first
commercial Web browser became available, along with some smaller auction sites including
Yahoo-auctions and Ubid. Fueled by both the private and public investment, many on-line
auction makers were profitable almost from the inception. Today, only ten years into existence,
eBay and Yahoo have become international on-line marketplaces, offering goods and services by
a diverse community of individuals and small businesses.
3.1 An Introduction to eBay Fraud Risk
We chose to study e-commerce system’s risks and controls for eBay for several reasons.
First, eBay is the largest online auction. It daily lists more than sixteen million items for sale and
recorded $24 billion in sales for 2004 (Wingfield, 2003). Second, eBay has state-of-the-art
technology that addresses issues of availability, confidentiality, privacy, and security of
transaction. Specifically, eBay has chosen Sun’s Solaris Operating System, which claims to be
the most advanced operating system, Solaris servers and Java software to power its business.
eBay has strict controls over access to customer account information. eBay is a licensee of
TRUSTe program meaning that privacy policies and practice meet strict requirements
established by TRUSTe. Finally, eBay has acquired Paypal, the most advanced and trusted epayments system, and makes its interface available for all transactions initiated on eBay. Further,
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the majority of products sold via eBay are brand name products. Academic literature shows that
consumers are more likely to purchase branded products on Internet since brand name alleviates
concerns regarding quality of the product (Lee et al. WP 2004). Thus, it appears that eBay
instituted controls for risks related to information interchange, privacy, and transaction security
that are beyond reproach. However, eBay’s e-commerce system is open to criticism when it
comes to controls over product misrepresentation and seller authentication.
There is ample evidence that seller fraud increasingly becomes a problem for eBay
despite the state-of-the-art information and payment security. We define online seller fraud as the
manipulation of Internet information to induce consumers to act in ways that illegally benefit the
provider of the manipulated information. This definition follows more general definition of fraud
as the intentional perversion of truth in order to induce another to part with something of value
(Webster’s Dictionary, 2005).
While eBay admits that only a small fraction of sales turn out to be fraudulent, the US
Federal Trade Commission (FTC) statistics and eBay’s counter-measures to fraud suggest a
different picture. According to Internet Fraud Complaint Center (IFCC, 2004) Internet auction
fraud is the most frequently reported offence, comprising 71.2% of reported complaints. Online
auction fraud is also among the most expensive forms of e-commerce fraud. Incidence of online
fraud is increasing. IFCC reports 66.6% increase in reported complaint from 2003 to 2004 (158%
increase for 2003). While eBay spokesperson Kevin Pursglove says fraud rates are still a tiny
fraction of eBay’s sales, specifically the company’s published fraud rate is about one-tenth of
one percent, eBay “vigilante” groups pose that in some categories on eBay up to 75% of item
offers are scam and incidence of fraud on eBay is growing 35-40% a year (Sullivan 2005). In
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2003, FTC reported $100 million in losses due to online auctions (Wingfield, 2003); this figure
(obviously) ignores unreported cases.
While eBay community values statement articulates that “eBay is built on a belief that
people are basically good”, management acknowledges presence of fraud and attempts to limit
its pervasiveness. The first anti-fraud mechanism implement by eBay was reputation feedback
system. It was followed with the creation of Trust and Safety department in 2002, headed by
former federal prosecutor with the US Attorney Office Rob Chesnut.
3.2 eBay Responsibility for Fraud
eBay poses that the company is a passive provider of virtual marketplace. It neither sells
nor purchases, but provides interface connecting sellers with buyers. Such position so far was
successful in shielding eBay from legal liability claims. Digital Millennium Copyright Act (the
new copyright law for the Internet) immunizes companies who merely provide a venue for
electronic activities like Internet service providers (Waldmeir 2004). The very thing that makes
eBay highly successful, its hands-off business model, also leaves it vulnerable to fraud. Because
the site is not so much an auction house as it is a swap meet, the company is purposely not
watching everything that happens. It provides the virtual campground and then steps out of the
way. Everything else depends on good will between users (Warner 2003).
One of the reasons for this position may be a concern over legal liability issue. Reducing
the incidence of fraud would increase trust and business. Yet ironically, if eBay becomes too
active or vocal about fraud the company could be vulnerable to lawsuits. “Were eBay to get
more actively involved in fraud, “ says Scott Feldmann of law firm Crowell & Moring, “it
[would] run the risk of losing this immunity, because they [would] start becoming more of an
agent for one side or the other” (Warner 2003). So far eBay successfully defended itself against
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liability claims on the ground of impartiality to the seller or buyer. However, opinion of the court
may change should eBay start taking more proactive stand in controlling fraud. But even if a
court decides that seller fraud is not eBay’s fault, it is still a problem. Any risk that gets out of
control cannot be good for a business that relies almost entirely on trust.
The second reason for such a position may come from a disincentive to scrutinize sellers.
eBay’s revenue model is based on assessing transaction and listing fees, which are paid by the
sellers. Thus, seller attraction and retention is the key component of financial success for eBay.
As for the eBay buyers, as long as their number exceeds the number of posted on eBay
transactions, they produce little impact on the bottom line of the business profits.
3.3 The eBay Trust and Safety department
eBay’s Trust and Safety department is responsible for the formation and coordination of
eBay’s trust and safety strategy worldwide, for changes to site rules, and, for policies to improve
the eBay marketplace. Existence of the department indicates that eBay realizes the need to limit
fraudulent and abusive activity on the site and works to detect and prevent fraud. eBay’s fraud
identification program began in 1999. Angela Malacari’s eBay fraud investigation team was
appointed to gather evidence on suspects and deliver it for to law enforcement for further
prosecution. Data-mining software developed by InfoGlide (TX) helped pinpoint sellers’
identities and compile a record of fraudulent transactions (Goff 2001). The team assists law
enforcement by providing eBay records, general investigative assistance, and testimony at trial.
Also, Trust and Safety department used to have a dedicated email address
(timesencitive@ebay.com) to report violations of eBay rules. It was later substituted with a Web
form, which is now available through the Help button at the top of each eBay’s page.
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The increased occurrence of fraud in 2004 led eBay CEO Meg Whitman to announce
new data mining software, designed to identify suspected fraudulent transactions (McGee et al.
2004). She also expanded the Trust & Safety department to around 1,000 employees with an
authorized $11 million in this unit (Shaughnessy 2004).
3.4 The eBay Reputation Feedback System as Fraud Control
eBay’s reputation feedback system is one, among many possible, systems for using
reputation to constrain seller and buyer mis-behavior (Dellarocas, 2003a, 2003b). Almost from
inception, eBay has heavily relied on reputation feedback to mitigate fraud. After conducting a
transaction, buyer and seller can rate one another’s performance. The history of such ratings is
displayed on the member’s profile page and intended to help transacting parties develop a
judgment how trustworthy is the intended partner. On-line feedback mechanism fosters
cooperation between strangers; for it insures that the behavior of a trader toward any other trader
becomes publicly known and may therefore affect the behavior of the entire community toward
that trader in the future. Knowing this, traders have an incentive to behave well toward each
other, even if their relationship is a one-time deal (Dellarocas 2003).
A growing body of empirical research suggests that reputation feedback systems have
managed to provide some stability to otherwise risky trading environments (Dellarocas 2003a
2003b; Ba et al. 2002, Resnick et al. 2002). These studies suggest that feedback profile seems to
affect both prices and probability of sale; impact of feedback is higher for the riskier and high
value transactions; negative feedback is more influential in affecting buyer behavior.
3.5 Weaknesses and Risks in the eBay Reputation Feedback System
eBay’s reputation feedback system facilitates the collection and presentation of
information that helps buyers assess risk in dealing with a seller. Comparing the degree of
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inherent risk, buyers can choose which offer to bid on and the maximum amount of bid.
Unfortunately, there are ways to manipulate the system. By “cooking” the reputation feedback
record a deceptive seller may alternate buyers’ choices: ill-intentioned seller appears as
trustworthy and attracts multiple offers on his pseudo-offer. Forms of feedback manipulation
include: providing feedback in absence of substance of transaction (e.g individual provides
feedback to himself using two accounts or asks a friend to bid and provide feedback, buying or
exchanging feedback), collecting feedback on buy transactions only (or buy and sell small-ticket
items) and citing it as evidence of seller dependability, and using someone else previously
cracked account.
eBay permits any individual create more than one account. This opens an opportunity to
acquire reasonably long feedback history at a low cost. One can arrange offers of small ticket
items and purchase them from the self incurring only a small listing fee and 3% charge from
eBay. Money never changed hands and goods offered never have been shipped. Similarly, one
trader may acquire feedback by asking a friend to bid and win his offers. No transaction occurs
in such case also. However, in both cases eBay permits “transacting” parties to leave feedback
for each other. There are also cases when reputation feedback have been purchased or traded
between the parties.
An alternative feedback manipulation strategy may be performed without creating a new
(additional) account. An individual may acquire a history of positive feedbacks by purchasing
goods only. The cost of such feedback will be low as purchase items may be of small value or
obtained for personal consumption. Being aware of this potential problem, eBay began indicating
the type of transaction completed, sell or buy, in reputation feedback profile. An extension of this
feedback manipulation is a case when feedback consists of seemingly balanced proportion of buy
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and sell feedbacks. However, under the scrutiny all the sell feedbacks are for a small value items.
If such reputation record presented as an evidence of seller dependability to sell substantially
more expensive items, it is a misrepresentation and deception takes place.
Identity theft of eBay user accounts is another possible weakness in eBay’s reputation
feedback system. The popular press documents cases of perpetrators stealing eBay identities to
list fraudulent items for sale (Graham 2004). In such a scam, the perpetrator poses as the account
holder and attempts to divert communication around eBay to a separate email address. Members’
login information may have been previously acquired through ‘phishing’ (i.e., direct email
solicitation of one’s logon or password) or purchased from a Web site.
3.6 eBay Control Failures and “Vigilanteeism” as Fraud Control
The increasing prevalence of fraud could harm eBay’s standing as the premier on-line
market maker. Disillusionment with, and loss of trust in, eBay would have lasting consequences.
Victims who lost their money either stop purchasing on eBay at all or limit their purchase
amounts. “On a scale one of ten, I would give eBay a two for dealing with fraud. I got nothing
from eBay—no support, nothing,” says victim Mindy Bollinger conned for $1,400 (Warner
2003, page 2).
Weaknesses in the eBay reputation feedback system have led to market alternatives to
control auction fraud. For example, some former victims post fraud stories to Web sites or chatrooms (e.g. auctionbytes.com, auctionblacklist.com, ebaythatsuck.com). These victims share
stories and write articles suggesting the inadequacy of eBay’s fraud controls. There are even
some “vigilantes” who proactively counter apparently fraudulent offers on eBay by bidding
unrealistically high amounts and failing to pay after winning the auction (Steiner 2005).
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4 A Case Study of a ‘Successful’ On-line Fraudster
4.1 Case History
eBay_Seller was registered with eBay between September 1997 and August 2005 when
the seller was either voluntarily or forcibly de-listed from eBay. During this period, eBay lists
3479 transactions for this user of which 2677 (76.9%) were sales and 802 (23.1%) were
purchases. For the sales transactions, 239 (8.9%) received negative feedback. Most eBay sellers
receive negative feedback postings rarely. For example, Bajari and Hortacsu (2002) studied
eBay sellers of U.S. mint and proof coin sets in 1998. For sellers in their sample, 99.8% of the
posted feedback was positive, while only 0.2% was negative. Accordingly, eBay_Seller’s rate of
~ 9% negative feedback postings is very high. eBay_Seller’s overall feedback score, which is
inflated by the uniformly positive feedback on purchase transactions, is 94%, which is still
considerably below the eBay average. For example, Resnick and Zeckhauser (2002) find that
eBay users provide, on average, 99.1% positive comments, 0.6% negative comments, and 0.3%
neutral comments.
eBay_Seller’s account was active for 8 years (32 quarters). Figure 1 plots eBay_Seller’s
percentage of negative feedback by quarter. This analysis suggests two distinct fraud strategies.
The percentage of negative feedback in years 1 through 6 (i.e., quarters 1 through 24) is 4.3%
while the percentage of negative feedback in years 7 through 8 (i.e., quarters 25 through 32) is
30.5%. We refer to years 1 through 6 of eBay_Seller’s account tenure as the “sustainable” fraud
strategy. We refer to years 7 and 8 of eBay_Seller’s account tenure as the “exit” fraud strategy.
4.2 Years 1 Though 6: The Sustainable Fraud Strategy
4.2.1 Mimicking Legitimate Sellers: Rates of Negative Feedback. Bruce et al. (2004)
suggest there are sellers whose fraud strategy is to build reputations that enable them to extract
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the maximum surplus and then default on all agreements one period prior to exiting. Observation
of the history of reputation feedback for “eBay_Seller” suggests that negative feedbacks appear
in clusters during the sustainable period of the fraud. For the first three years, clusters of negative
feedbacks occur in the fourth quarter of each year (i.e., in 1997, 1998, and 1999), possibly
related to the higher eBay volume that occurs in the Christmas season.
Years four through six evidence relatively lower rates of negative feedback (2.9%) than
years one through three (5.7%), and years seven and eight (30.5%). However, the year four
through six rates of negative feedback are still considerably above normal eBay negative
feedback rates. Evidence also implies the existence of the temporal (i.e., between-quarter)
management of feedback. Lagged one-quarter negative feedback predicts next quarter positive
feedback (r = .53, p < .01)). One deception strategy that would explain this result is increase
next quarter positive feedback in response to higher than average last quarter negative feedback.
4.2.2 Characteristics of Products Sold. Attempting to determine the value of the items
that were either not shipped or misrepresented by the “eBay_Seller” we encountered a limitation.
eBay retains this information only for 90 days, making specific information older than Spring
2005 inaccessible. However, there are multiple references captured in reputation feedback
suggesting that eBay_Seller’s sales included Mavica and Cybershot digital cameras, headphones,
A/C adapters, batteries, cellular phones, laptop computers—consumer electronic items. Several
feedbacks state exact values: “$490 item received damaged-Seller changed phone # TOOK
MONEY & RAN” (feedback provided by pi**nk2),” “$1525 DEFAULTER ** 5day/23 Bid
Auction Destroyed (for gre**sa).”
4.2.3 Mimicking: Manipulating Feedback. There is evidence that the seller engaged in
purchasing feedback ratings. That is, the seller offered to post multiple positive feedbacks for
2
Buyers’ actual IDs are protected by substituting from 2 to 8 actual characters with **.
14
buyers in exchange for received multiple positive feedback postings. For example: “Offers 2 for
1 positive feedbacks, rating is questionable, email for details (gr**k.com)”. Second, many
positive feedbacks for eBay_Seller are from the same registrant, e.g. transactions ##
1375076925, 2047760894 from old**39; 2058313982, 2058605098, 2058565404, 2058031740,
2056411354, 2056948569 from di**rn; 2025649584, 2026850705, 2024284442, 2023590274,
2023570848, 2023579865, 2024261850 from uca**2. While it is conceivable that the same
individual won many auctions from eBay_Seller and then provided positive feedback, it is
perhaps more likely that the seller self-provided or exchanged falsified feedbacks for many
transactions.
By default, every 25 new feedbacks would amount to a new page in the reputation feedback
record. This new page may effectively work as a cover up for the previous negative record. Most
buyers do click on reputation feedback score link and review the feedback. Unfortunately, if the
feedback amounts to 100 or more pages, probably no buyers take time to analyze the feedback in
entirety, a few spend time reviewing most recent 4-5 pages, and most review the last page of the
feedback. Such lack of use of available information exemplifies that human’s rationality is
indeed bounded. This in-turn opens an opportunity for a seller to rebuild previously spoiled
reputation feedback or to exercise a fraudulent strategy as in “rip off 10 customers and then have
30 satisfied”. With the buyers that review only the last page of feedback, this strategy may work
to simulate dependable seller, for it would present a spotless reputation record on the first page
but have a caveat on the following.
4.2.4 Hiding: The Use of Multiple Identities. At least three allegedly defrauded buyers
argue that eBay_seller used multiple identities: “Communications poor (one word emails),
waited 6 weeks, changes alias a lot”(za**er), “Cashed the check 2 months ago, no merchandise,
15
no response. Changes EBay ID.” (ce**ter), “WARNING: eBay_Seller used to be ZebraT-changes name often! self-praising.” (gr**k.com).
Finally, as indicated previously, it is highly possible that the seller self-provided feedback
using a second account or purchased feedback through transactions in appearance but not in
substance. Several defrauded customers indicated in their feedback response that “eBay_Seller”
works under different aliases.
4.3.5 “Dazzling #1:” Rationalizations for Deception. During the sustainable period of
the fraud, the seller responded to negative feedbacks with excuses. Previous array of negative
feedbacks received a different response: “Reply by eBay_Seller: # 1 Priority Status >>> call
208-676-8989 >>>leave message+ your ebay auction#”. As it is evidenced by the feedbacks
left by the defrauded individuals, calling and leaving a message resulted in no remedy:”Received
N returned 2 bad batteries and Co. would not honour refund of money” (skl**ca), “Received
faulty merch, returned item, but no replies!! Uncoop, elusive!!! Beware!” (ba**5). “Still waiting
3 months for item or refund. Phone # no help. Seller is a thief!”(wak**ne).
4.3.5 “Dazzling #2”: Insults and Blame as Replies. Responses from the allegedly
defrauded individuals indicate that eBay_seller also frequently made rude and offensive
statements in reply to customer inquiries. For example: “Oh my god! Impossible to deal with!
DANGER!!! email me if you want specifics..” (who**man). “Rude & GUILE- I tried to pay
FULL bid$$ w/ him keep item, replied with insults!”(kn**l). “Thirty days, still no shipment.
Payment made but only hostile reply from seller” (ai**ra).
eBay Seller also frequently cited a failure to deliver as a fault of the buyer. For example:
“Inaccurate & Inappropriate Negative Feedback from a Naive, Inexperienced Newbie!” and
“This "CONFUSED NEWBIE" did not read and/or understand the auction description”
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(comments by eBay_Seller in response to negative feedbacks). Third, in providing unjustified
offensive feedback in return for a negative feedback. For example, csp**08 apparently
purchased, paid, and received a product from “eBay_Seller”. Being dissatisfied with the
purchase, csp**08 provided the following feedback: “Seller hides behind "AS IS" in descriptions.
Email me for the full story.” In response to this feedback, the seller provided a feedback which
should be considered as retaliatory, for csp**08 has paid for the purchase and there is apparently
no ground for the following accusation: “Ebay PowerSeller Alert - I am pursuing a Fraud
Investigation against this bidder.”
4.3.6 “Dazzling #3:” Selling the Same Item to Multiple Bidders. eBay_Seller also
engaged in selling the same item to multiple buyers. For example, the seller had seven identical
listings for a pair of Sony headphones (##5779314214, 5779076003, 5780248991, 5780152231,
5780529042, 5778614706, 5779320451). The winners of all seven auctions indicated that they
paid through Paypal, waited for the phones, and then obtained a refund through Paypal. The
following feedback is indicative of this case: “After 27 days-no CDROM, no response to email,
yet still lists (same) items for sale” (w**). Also, the seller deliberately ignored communication
with the customers: “Took the money, did not ship, blamed the mail, and now does not answer
my mail” (che**ohn). “I REPORTED THIS SELLER TO EBAY FOR FRAUD: TAKING $, NO
DELIVERY, IGNORING EMAIL” (my**dane). “Not shipped; no resp-Email or phone. PayPal
did refund. Don't waste your time!!” (bi**g) Interestingly, at the same time “eBay_Seller” wrote
the following reply to negative feedbacks: “Bidders make your concern our #1 Priority by calling
480-783-8325 Thanks”. The phone number is currently not in service. Finally, as the following
feedback indicates, the seller was reported to eBay administration but apparently was found to be
below the threshold of recognition for fraud: in October 2001 “my**dane” member attracted
17
attention of Trust & Safety department to the cases where “eBay_Seller” failed to deliver
products, the member writes that “EBAY UPHELD THIS THEFT"BASED ON SELLER'S
FEEDBACK STATUS AND OTHER FACTORS, HMMMM.”
A mix of positive and negative feedbacks also confuses some buyers. Recent negative
feedbacks scare away some buyers relieving buyer competition for the auctioned product and
leading to an abnormally low price. This sets an incentive for the risk-prone or inexperienced
buyers—they hope to purchase the product cheap, gambling that in their case the product will be
shipped and received. For example, one buyer writes: “I purchased some headphones with BuyIt-Now - paid immediately and he never sent the headphones, never responded to e-mails,
nothing. I got my money refunded through Paypal. The same day I bought mine, someone else
bought some and they got theirs. I have no idea who he chooses to actually send items to and
who he decides to cheat. Yes, I got my money back, but it takes 10-14 days to get your refund.”
4.3 Years 7 and 8: The Exit Fraud Strategy
In the three months before eBay_Seller was deregistered, “eBay_Seller” offered for sale
smaller value items like headphones, computer accessories and parts, carrying bags for laptops
and cameras, lithium batteries, small construction equipment. A peculiar trait of these offers was
that just about every item for sale had more than one listing as if the seller had three identical
Fudjitsu tablet PC cases, forty identical Motorola batteries, eight identical sets of Sony
rechargeable batteries, eight identical Sony sport headphone sets.
In the end, the seller received negative feedbacks for the items of smaller value ($20 - $200),
but the frequency of failure to deliver increased 258%3. Such change in strategy may come
through experience in resolving buyers’ complaints. Failure to ship a dozen of $50 items is likely
3
258% increase was calculated as the following ratio: (SUM of non-positive feedbacks for the last 16 quarters) /
(SUM of non-positive feedbacks for the first 16 quarters). The seller was registered with eBay for 32 quarters.
18
to be under the eBay’s threshold of materiality for recognition, unlike a failure to ship $5,000
plasma TV. Also, buyers are less likely to even file a complaint with eBay—the pay back for the
effort will be less than $25 (assuming $50 paid) due to Paypal policies. In such case buyers often
limit their dishonest seller prosecution to providing a negative feedback only.
The last page of reputation feedback profile shows 27 of 50 feedbacks as negative and
two more neutral feedbacks--all of them indicating that the seller either did not ship the product
or substantially misrepresented it. For example, to cover up for the last cluster of failure to ship
transactions, the seller responded to 22 negative feedbacks citing the following: “Reply by
eBay_Seller: We were down >>> We are 100% up now >>>Thanks for your bid!” The first of
these replies was posted on March 4, 2005. After this date, the seller failed to deliver on 42
transactions despite the claim of “being 100% up now”.
5. Lessons from the Case Study
5.1 Analysis: Is This Fraud?
Does eBay_Seller’s pattern of transactions present evidence of negligence in commercial
relationships, or, evidence of fraudulent strategies that extract value from the customers and at
the same time stay below the threshold of recognition for action by eBay security or law
enforcement? Legally, fraud is defined as "an intentional perversion of truth" or a "false
misrepresentation of a matter of fact" which induces another person to "part with some valuable
thing belonging to him or to surrender a legal right" (criminal-law.freeadvice.com 2005, page 1).
Fraud is the intentional misrepresentation of facts (distortion of truth) calculated to prejudice
another (Botha 2005). We find the following evidence of fraud, captured in reputation feedback
pattern and content.
19
“eBay_Seller” (hereafter the seller) had economic incentive to fail fulfilling orders. eBay
policies make buyers prepay the purchase and thus the seller received payment and neither
mailed the promised merchandise not paid for shipping. Reputation feedback contains many
complains where various buyers state that they paid for the item and did not receive it: “Yes I
have a problem He got my money and He slanders me. Where is my order ” (lw**kes). “Cashed
the check 2 months ago, no merchandise, no response. Changes EBay ID.”(ce**ter), “This
Clown has my money I have NOTHING but bad feedback and threats BEWARE”( lw**kes ),
“BUYER BEWARE OF FRAUD! Never sent the item although promise to do so thru phone”
(ter**law).
Accepting a payment and failing to ship promised merchandise or supplying the buyer
with a product that is substantially different from the described is unlawful for such act
constitutes a breach of a contract. There is evidence that the seller committed these acts
intentionally. He sold products known to be defective: “Battery would not hold charge, seller
will not do nothing to rectify this issue” (ks**pard). “ALOT OF HYPE, item arrived DOA, stock
answers, no recourse Be Careful ! ! “(wc**000). “No response to mail, battery a dud, took
forever... All negatives are true...” (b**le). “Disappointed that the battery didn't work but they
did say no guarantees” (gwe**q9q). Also, in some cases the product was substituted without
customer authorization: “Sent item not in auction. Said he had sold my item "out the front
door"(b**ter1).
The seller misrepresented product: “Misleads buyers. Will forward all e-mail between
buyer and seller if interested” (gre**sa). “Seller hides behind "AS IS" in descriptions. Email me
for the full story.” (cs**08) “Did not get what was advertised in the PIC and misled that's what i
was getting” (ter**14).
20
5.2 Lessons from the Fraud
Internet auctions introduce a number of key differences with traditional auctions
increasing the information asymmetry gap. The differences include: inability to view actual item
for sale, take item immediately, have item professionally evaluated, promptly complete the
auction (Bywell et al. 2001), and requirement to prepay for goods. Firstly, observation of listing
for sale on eBay suggests that sellers often include pictures from OEM Web site in the
description of product. Pictures portraying actual item for sale are rare and even those are made
under the strong incentive for the seller to represent the product in the best possible light.
Secondly, item’s delivery time upon completion of the auction depends on the method of
shipping, method of payment, and seller’s diligence. Usual sale fulfillment time ranges from
three days to three weeks for a flawless transaction. Thirdly, traditional auctions provide buyers
with some assurance that products for sale were at least initially screened by the auctioneers,
supposingly experienced valuers. eBay employees fully automated listing process; thus, no
screening, unless there is a complaint, takes place. Finally, whereas the traditional auctioning of
a product would be completed within hours, eBay auctions typically run for seven days 4.
Ill-intentioned sellers exploit these features of on-line auctions. The problem is amplified by
the seller’s anonymity, remote location, ability to use several aliases, and reach to millions of
potential consumers. eBay and other on-line auctions are well-aware of the incidence of fraud.
They attempt to control fraud by making reputation feedback systems available for members’
use, adopting decision support systems to identify suspicious transactions, and through the work
of “anti-fraud” departments investigating and prosecuting fraud. However, as the case indicates,
there is still a place for a strategy that subverts these controls.
4
Auctions for three and ten days are possible but rare in occurrence.
21
Further, the case demonstrates that reputation feedback mechanism can be manipulated at a
reasonably low cost to the perpetrator. The arsenal of means for manipulation includes: setting
blame on the buyer, providing retaliatory feedback, citing excuses, and trying to persuade that
“we now are up and running”. Feedback manipulation shaped in a crafty strategy may allow
fraud to go on for years.
The case suggests that consumers can detect on-line fraud through careful consideration
of information captured in the seller’s reputation feedback. A seller always has economic
incentive to commit fraud and on-line auction system in its current stage gives the seller an
opportunity to commit fraud, either by requirement to prepay or by imperfections of controls. By
reviewing the responses left by other members and also by writing to the seller via eBay, a
prospective buyer should attempt to collect evidence about the seller’s personal code of ethics or
presence of attitude that may allow him to intentionally commit a dishonest act. Objective
description of the merchandise, straightforward and timely response to emailed questions,
absence of feedbacks that describe the seller as dishonest altogether contribute to the seller’s
ethical profile. For the buyer with no experience in fraud recognition, this is the only line of
defense prior to commencement of transaction. Also, this idea highlights the importance of
leaving an objective, truthful, and informative feedback; for reputation feedback system is only
as effective as the quality of information entered in the feedback.
It is difficult to overstate the value of fraud recognition for the individuals that purchase
on eBay often. Academic literature (Nikitkov & Stone WP2005) suggested taxonomy of on-line
auction fraud incorporating multiple antecedents of fraud. They suggest that fraud is subject to
taxonomy of deceit (Bell & Whaley 1989) classification and may apply to either product, or
22
seller, or transaction characteristics. Experiences eBay sellers and tech-guru shows (Komando
2005) are known to provide tips on how to recognize and avoid on-line fraud.
In order to prevent on-line auction fraud, eBay need to acquire focus on seller
authentication. Presently eBay requires either a credit card number or a bank account number to
register as a seller. However, cases are plentiful when provided credit card number is the stolen
one. Also, eBay accepts third party email addresses (e.g. hotmail, yahoo) easing sellers’ ability to
create multiple identities. eBay began talks with VeriSign in order to assure user identification.
This may be a promissing development leading to “one individual-one user ID” policy or precise
user identification which would have a profound effect on incedence of fraud on eBay.
The case also suggests a need for eBay Trust & Safety department to look not only on the
percentage of negative feedbacks, or even the value of the transaction, but to consider the
substance of the complaint against the seller. Current position of eBay suggest that it is
acceptable for a seller to have a mix of negative and positive feedbacks: every good seller may
have a certain number of negative feedbacks. However, on one hand, the infrequency of fraud is
not an indicator of the lack of severity. For example, a seller with 98% positive feedback could
have defrauded 20 individual for $10,0005. On the other hand, diminutive value of the claim
should not be the ground for its rejection also. While ten small value transactions may be below
the threshold of materiality recognition for eBay, loss of $50 in ten transactions may have more
lasting consequences for eBay reputation than a loss of $500 in one. Since eBay’s business
model has trust in its core, amassing the community of individuals experienced a possibility of
deception on eBay is contrary to the company’s interests.
Academic research in the area of on-line auction fraud detection and prevention is scarce.
Future research should attempt to create a comprehensive model for recognition of fraud. This
5
Assuming the seller conducted 1,000 transactions with average item value $500.
23
model has to have a dynamic nature to be able to incorporate ever-changing and evolving
strategies of deceit.
6. Summary, Limitations, and Conclusion
6.1 Summary
This case documents an eBay seller who appears to have manipulated feedback
information in order to defraud buyers. This strategy was successful over a multi-year period
despite multiple buyer complaints. Further, the eBay Safety and Trust department took no action
against the seller during this period. In August, 2005, “eBay_Seller” abandoned his user ID.
This case demonstrates a successful fraud seller that exploits loopholes in the current frauddetection system.
With the help of this case we attempted to highlight the importance of information system
controls relevant to legitimacy of seller and quality of product risks. The case demonstrates that
while eBay employs state-of-the-art information system, current means of control for these risks,
including reputation feedback and Trust & Safety department, can still be subverted by an illintentioned seller. The study provides insights into the eBay policies for fraud detection and
prosecution, fraudulent strategy to manipulate reputation feedback that escaped attention of eBay
administration and many buyers, methods how consumers can detect and eBay, academia, and
merchants prevent on-line auction fraud.
6.2 Limitations
The study is subject to the following limitations. First, the study discussed one seller.
Discussing this case we make no attempt to generalize from this experience. However, since this
is an actual case, we assert that such and other methods of feedback manipulation are possible.
Second, while we believe we collected sufficient evidence that the seller committed fraud, there
24
is a small possibility that the pattern of negative feedbacks reflects on highly negligent, rather
then a fraudulent seller. Third, the discussion of the fraudulent strategy involves elements of
design specific to eBay Web site. Thus, applications of the discussion to other auction Web sites
should be made with caution.
6.3 Conclusion
Most eBay sellers have reputation feedback score above 99%. Conventional logic
exemplified in the recent studies on antecedents of trust assumes that. In this study we
considered a case of an eBay seller with reputation feedback quantitatively well below average
and abounding with warnings “Paid, never received” and “Awful, avoid this seller”? We show
that a seller may devise a strategy that will allow him to consistently defraud buyers, accumulate
in excess of 250 negative feedbacks, and still remain in business on eBay for eight years.
25
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28
Time
Q3-05
Q2-05
Q1-05
Q4-04
Q3-04
Q2-04
Q1-04
Q4-03
Q3-03
Q2-03
Q1-03
Q4-02
Q3-02
Q2-02
Q1-02
Q4-01
Q3-01
Q2-01
Q1-01
Q4-00
Q3-00
Q2-00
Q1-00
Q4-99
Q3-99
Q2-99
Q1-99
Q4-98
Q3-98
Q2-98
Q1-98
Q4-97
%
Figure 1
Percentage of Negative Feedback for “eBay-Seller” by Quarter
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Table 1
Distribution of Feedback for “eBay-Seller” by Quarter
QuarterYear
# Feedback
Postings
%
Positive
%
Negative
%
Neutral
Q4-97
Q1-98
Q2-98
Q3-98
Q4-98
Q1-99
Q2-99
Q3-99
Q4-99
Q1-00
Q2-00
Q3-00
Q4-00
Q1-01
Q2-01
Q3-01
Q4-01
Q1-02
Q2-02
Q3-02
Q4-02
Q1-03
Q2-03
Q3-03
Q4-03
Q1-04
Q2-04
Q3-04
Q4-04
Q1-05
Q2-05
Q3-05
Total
13
11
48
110
61
98
50
39
41
2
30
76
163
160
294
193
238
247
228
219
264
330
384
223
74
120
101
247
113
129
41
13
4360
69.23%
90.91%
91.67%
92.73%
73.77%
96.94%
92.00%
76.92%
82.93%
100.00%
93.33%
89.47%
90.80%
93.75%
95.58%
96.37%
96.64%
93.93%
96.05%
95.43%
95.83%
92.73%
91.41%
93.27%
71.62%
77.50%
80.20%
95.95%
84.07%
72.09%
53.66%
23.08%
90.89%
23.08%
9.09%
6.25%
4.55%
18.03%
2.04%
6.00%
10.26%
12.20%
0.00%
6.67%
6.58%
3.68%
3.75%
2.04%
1.55%
1.68%
4.05%
2.19%
0.91%
2.65%
2.42%
5.47%
3.59%
25.68%
20.83%
15.84%
2.02%
11.50%
23.26%
43.90%
76.92%
6.10%
7.69%
0.00%
2.08%
2.73%
8.20%
1.02%
2.00%
12.82%
4.88%
0.00%
0.00%
3.95%
5.52%
2.50%
2.38%
2.07%
1.68%
2.02%
1.75%
3.65%
1.52%
4.85%
3.13%
3.14%
2.70%
1.67%
3.96%
2.02%
4.42%
4.65%
2.44%
0.00%
3.00%
29