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Tracing the Birth and Evolution of Mundane Online Crime: Routine Activity
Theory (RAT), Management Control Systems (MCSs), and the Sustainable
Online Auction Con
By Alexei N. Nikitkov, Dan N. Stone, and Timothy C. Miller
Alexei N. Nikitkov
Brock University
Taro 231, Faculty of Business
St. Catharines, ON L2S 3A1
Phone: (905) 688-5550 ext. 3272
Fax: (905) 688 9779
Contact Author: Dan N. Stone
University of Kentucky
Von Allmen School of Accountancy
355F Gatton Business and Economics Building
Lexington, KY 40506
Phone: 859-257-3043
Fax: 859-257-3654
Timothy C. Miller
Kent State University
College of Business Administration
P.O. Box 5190
Kent, OH 44242
Phone: 513-310-1059
Fax: 330- 672-2548
March 14, 2011
Alexei Nikitkov thanks Brock University for a grant supporting this study. Dan Stone and Tim
Miller thank the University of Kentucky, the Gatton College of Business and Economics, and, the Von
Allmen School of Accountancy for grants supporting this research. Thanks to Wei-Cheng Shen for
assistance with the data, and, Jacqueline Thompson and Amanda Jo Hall for assistance with the
manuscript. For valuable comments and suggestions on previous drafts, thanks to workshop participants
at the American Accounting Association 2006 annual meeting, the University of California – Riverside,
the University of Montana, and, to an anonymous reviewer for the 2011 AOS Fraud conference.
The archival data are available from public sources. The primary data are available to scholars
willing to sign agreements that protect the confidentiality of the sources.
Keywords: routine activity theory (RAT), case study, deception, management control system, electronic
auctions, electronic markets, longitudinal research.
Tracing the Birth and Evolution of Mundane Online Crime: Routine Activity
Theory (RAT), Management Control Systems (MCSs), and the Sustainable
Online Auction Con
Abstract
This article adapts and extends routine activity theory (RAT) to investigate the co-
evolution of eBay‘s management control system (MCS) with mundane crime in the rapidly
growing online auction market of 1997-2005. Qualitative and quantitative data, including from a
suspected deceptive seller‘s eight-year account history, indicates the presence of the three market
characteristics that RAT identifies as endemic to deception and crime: (1) a motivated offender,
(2) suitable targets, and, (3) an absence of capable guardians. The results provide evidence that,
until endgame, the seller‘s tactics embedded within eBay‘s emergent online MCS to enable a set
of ―sustainable‖ deception strategies. In addition, while the largely eBay-regulated online auction
market embedded mundane crime, this did not appear to inhibit the market‘s remarkable growth,
nor lessen eBay‘s dominance of it. Contributions include: (a) tracing the growth and evolution of
the nascent and evolving eBay online MCS, (b) reporting the embedding of seller deception
within this MCS, and (c) extending investigations of fraud and MCS to a new market context,
with new methods, theory, and data. The article concludes by arguing that the eBay MCS
successfully balanced control with ―innovation‖ among traders, which pragmatically meant
embedding ―acceptable‖ levels of mundane crime in the market.
―Most people are honest. … But some people are dishonest. Or deceptive. … But here, those
people can't hide. We'll drive them away. Protect others from them.‖ (eBay founder Pierre
Omidyar, Letter to the eBay community, February 26, 1996)
―We believe people are basically good.‖ (eBay, Code of Business Conduct and Ethics,
2010a).
INTRODUCTION
Evidence suggests that in early August 2005 the eBay Trust and Safety department, i.e.,
the eBay unit charged with monitoring suspicious account activity, ―delisted‖(removed) the
account of ―eBay_Seller‖ (a pseudonym), temporarily interrupting the account‘s eight-year
history. 1
In the account‘s final year, almost 25% of buyers posted negative feedback about the
account to the eBay site; over the account‘s history, almost 400 buyers posted either negative or
neutral feedback, a significant departure from the ~ 99% positive norm for eBay feedback. How
was such a failure to meet buyer expectations possible in the ―world‘s largest online trading
community‖ in which sellers must, ―… consistently provide service that results in a high level of
buyer satisfaction‖ (eBay 2009). What methods allowed this seller to avoid account removal
(i.e., delisting) by the eBay Trust and Safety department for eight years, and to then have the
account closed only temporarily?
Online retail sales grew from ~ $0, in the early 1990s, pre-internet era, to $41.5 billion in
the US in the third quarter of 2010 (US Commerce Department 2010). The online auction
market, a large component of online retail sales, provides a unique context for investigating the
birth and evolution of an online MCS. One online auction market facilitator, i.e., eBay,
dominates the market sufficiently that their main competitors (i.e., YAHOO! Auctions & Amazon
1 Evidence that eBay delisted this account includes: (a) correspondence with buyers subsequent to the account
closing indicating that buyers believed that eBay pressured the seller to make restitution for disputed transactions in
which eBay held for the buyer and (b) that the seller‘s account was reinstated after the seller achieved resolution of
some portion of the disputed transactions.
2
Auctions) closed their auction sites (Yahoo in 2007 and Amazon in 2008). Prior to the exit of
their main competitors, and at the time of this case, eBay held a dominant (~ 60%) market share
(Skogøy 2010).
Within the laissez-faire online consumer auction market, eBay dominated, and was the
industry leader in establishing a MCS (Cohen 2002). eBay‘s dilemma in constructing a MCS was
a unique variant of a familiar problem: how to balance the dynamic tension of control versus
flexibility (Mundy, 2010; Simons, 1995a, 1995b), but in a market setting and using technologies
that were new in the history of business. The nascent online auction market affords a unique
opportunity to investigate the birth and growth of a MCS in the online, consumer-products
market, which differs in important ways from most previous investigations of MCS. The MCS
literature largely investigates relations among traditional hierarchical organizations engaged in
supply chain alliances between corporate partners. In contrast, eBay‘s emergence as the
dominant online auction facilitator affords the opportunity to investigate the growth of an online
market facilitator‘s MCS in a largely unregulated, online market, with a large, international
clientele of small business and individual traders, and in which one organization, i.e., eBay,
almost single-handedly created an emergent global market.
During the period of this case (1997-2005), eBay‘s growth was explosive (See Table 1).
In 1997, eBay had fewer than ½ million registered sellers, ~ 4.4 million listings, and sales of ~
$95 million. Eight-years later, registered users, listings and sales grew to about 50 times their
1997 levels, to 181 million registered users, 1.9 billion listings, and, $44 billion in sales. Wedded
to this remarkable success and growth however, was a parallel growth in online fraud and
deception that resulted from the unregulated nature of online activity and ecommerce (Baker
2002). The Internet Crime Complaint Center (ICCC), a partnership between the US FBI and the
3
White Collar Crime Center, began tracking online crime in 2001, four years after the start of this
case. Between 2001 and 2005, online auction fraud complaints grew by over 500%, from 21,576
to 133,380.2 In all years of this case, online auction fraud was the most frequent complaint filed
with the ICCC.
Insert Table 1 about here
Consistent with the largely unregulated nature of online activity (Dilla, Harrison,
Mennecke, and Janvrin 2011), the growth of the emergent online auction market exceeded the
capacity and ability of US law enforcement to enforce contracts and punish deceivers and
fraudsters. As one detective (who requested anonymity) stated in an interview:
I can either try to catch murders, rapists, and robbers, or I can spend 20
hours trying to get back the $100 that some guy lost to some jerk wad in Russia
while bidding on a camera on eBay. And if I find the jerk wad in Russia, I won‘t
have any jurisdiction to go after him. What would you do (Anonymous, 2010)?
Absent law enforcement, contracts in the online auction market were governed by private
enforcement, which pragmatically, meant the eBay MCS, and, user ―policing‖ through vigilante
and online ―neighborhood-watch‖ groups (Goldsborough 2003; Chua, Wareham et al. 2007).
Hence, the eBay MCS became a surrogate for law enforcement (Walton 2006). But as Baker
(2002, p. 9) notes: ―Online auctions typically take no responsibility for the quality, the
suitability, or event the existence of the merchandise offered for sale. Fraudulent sales of
products using on-line auctions, such as eBay, have occurred on a regular basis.‖ Hence, the
emerging online auction market offers a unique case study in which a private firms‘ MCS
became the predominant market control, a partial substitute for law enforcement, and a means for
permitting some, but not all, deceitful practices. This investigation seeks to situate the unique
2 In addition, between 2001 and 2005, the rate of growth in online auction complaints (518%) exceeds that of the
rate of growth of all online fraud complaints (323%).
4
and evolving eBay MCS in relation to the activities of a deceptive seller with an eight-year eBay
account tenure.
This investigation aims to introduce new methods, theory, and data to the accounting
fraud research literature. Our synthesis of methods includes the use of mixed -- i.e., ―thick and
thin‖, quantitative and qualitative -- data. The methodological contribution is the introduction
and synthesis of three disparate research methods: forensic, benchmarking and qualitative.
Longitudinally tracing the focal seller‘s behavior, and, identifying its potential legal implications,
constitutes the ―forensic‖ method investigation. The quantitative, large-sample portion of the
investigation consists of a quantitative event-study method, commonly used in capital markets
research, to examine market-reactions to company announcements, and to compare the focal
versus benchmark vendors‘ reactions to changes in the eBay control environment. Finally,
qualitative methods, adapted to the unique online data set, permit investigation of the focal
seller‘s account history.
We next present the theory that underlies the investigation.
THEORY AND LITERATURE
Routine Activity Theory (RAT)
Early criminology research largely focused on the individual attributes and motives of
potential criminals as predictors of criminal inclinations and behavior (Felson and Cohen 1980).
Cressey‘s (1953) fraud triangle (opportunity, individual non-sharable financial problem, and
individual rationalization), derived from interviews with convicted felons, illustrates a theory
from this era focused on individual criminal personality and attributes. Despite limited evidence
supporting its structure and inferences, much accounting research and practice have adopted the
fraud triangle as a framework for considering fraud and deception (cf. Donegan and Ganon
5
2008). For example, the Auditing Standards Board (AICPA 2002) promulgated the fraud triangle
in Statement on Auditing Standards (SAS) 99. The fraud triangle is also an organizing framework
for forensic accounting and fraud auditing textbooks (Singleton and Bologna 2006; Singleton
and Singleton 2010). However, as Mitchell, Sikka et al. (1998, p. 593) note, a theoretical focus
on the personality traits of potential fraudsters is relatively unhelpful in situating crime and
deception within its social and organization locus:
This focus upon the personality of individual criminals may be of some
help in differentiating those individuals who are most vulnerable to the attraction
of activities that are defined as criminal. But it simultaneously obscures the extent
to which institutional structures and norms provide both opportunity and motive
for engaging in activities that are prescribed as criminal.
This article proposes RAT as an alternative model for considering organizational and
market controls and fraud. With over 850 citations in the ―Web of Science‖ database (Soyer
2009), RAT is among the most enduring and important criminology theories (Boetig 2006).
Since its 1979 introduction, its creators have expanded and adapted aspects of the theory to
evolving markets and social conditions (Felson and Clarke 1998). This article applies the original
exposition of RAT (Cohen and Felson 1979; Felson and Cohen 1980), augmented by its
originators‘ subsequent proposed methods for reducing crime, e.g., Clarke (1997) and Felson and
Clarke (1998), and, more recent speculations regarding RAT‘s applicability to online crime
(Felson 2006).3 Additionally, the article adapts one model component, the Principle of Least
Effort (POLE), introduced in Felson (1987).
RAT‘s principal concern is the ecology of crime, which occurs when three factors
converge: (1) motivated offenders, (2) suitable targets (in economic crimes, product
characteristics), and, (3) the absence of capable guardians. Analysis of the situated assembly in
3 For example, we omit Felson‘s (1986) extension of the theory to include the construct of ―intimate handlers‖ as
lacking relevance to the online auction deception context.
6
space and time, of these elements, can explain and predict crime rates and their locales. More
specifically, observation of changes in the routine activities of mundane, daily life can predict the
extent of convergence of these factors, and therefore, crime (Cohen and Felson 1979). The core
RAT calculus of crime is: (1) the frequency and nature of contact between potential offenders,
and, (2) targets, (3) in the absence of capable guardians, predicts (4) the likelihood of
―predatory‖ crime, i.e., crime that includes a victim and perpetrator.
Figure 1 illustrates the relation between the elements of RAT and the fraud triangle. RAT
and the fraud triangle are both contingency theories; in addition, both contain three variables that
are proposed as diagnostic of crime-related outcomes. But the predictor variables, and the
predicted outcomes, differ between the theories. The predicted outcome of the fraud triangle is
the likelihood that an individual will commit a crime; in contrast, RAT predicts the likelihood of
crime in a market or other social context. Regarding predictor variables, within RAT, crime
happens -- motivated offenders are assumed to exist – and their individual cognitions and
motivations are considered largely irrelevant. In contrast, the central focus of the fraud triangle is
an individual criminal‘s motivations, rationalizations, and perceptions of criminal opportunity.
Hence, while motivated offenders are a shared element of RAT and the fraud triangle, the focus
on this element differs between theories; RAT assumes the presence of the motivated offender
while the fraud triangle‘s focus is analysis of the offender‘s perceptions, i.e., his or her
cognitions about, and motivations for, crime.
Insert Figure 1 about here
In contrast, RAT‘s primary focus is the environment: i.e., the localized, situated assembly
of offenders, targets, and capable guardians, within a market or social ecology. Suitable targets,
which can refer to either victims, or in economic crimes, the characteristics of products that are
7
stolen, are potential prey in a symbiotic relation to motivated offenders. Thoughtful ecological
design of social and market spaces can reduce crime by lessening the convergence of offenders,
targets and lack of capable guardians, and make its ―displacement‖, i.e., redirection towards
other targets unlikely (Felson and Clarke 1998). For economic crime, RAT posits that target-
objects vary in attractiveness to offenders based on four characteristics: Value, Inertia, Visibility
and Access (VIVA) (Felson and Cohen 1980a). Goods with higher symbolic or economic value,
that are easy to physically move (i.e., are low in inertia), are more visible, and, are easier to
access, are more desirable criminal targets.
Felson (1987) argues that, ―detailed local analysis is the best way to learn how crime
reaches people (p. 921).‖ Consistent with this approach, this case aspires to a ―local‖, i.e.,
situated and forensic, online auction market analysis of the focal Seller (the likely offender) in
relation to a benchmark set of comparison vendors, buyers (some of whom are target-owner
victims), products (some of which are target-products), and the evolving eBay market feedback
and control system (as a guardian). RAT‘s Principle of Least Effort (POLE) integration into the
analysis permits examination of issues related to offender‘s efforts to manage crime; according
to the POLE, potential perpetrators will minimize the cognitive and physical effort expended on
a crime, given a goal, target, and guardian; hence, POLE predicts that offenders commit
minimally effortful crimes that achieve the required criminal intent.
RAT, Cybercrime, and, Online Auction Deception
How does ―meatspace‖ 4
, i.e., physical, crime differ from virtual or ―cyber‖ crime (cf.
Baker 2002)? Yar (2005) explored the usefulness of RAT principles and constructs to
cybercrime activities; he argued that the two of the core constructs of RAT, i.e., motivated
4 The term ―meatspace‖ entered the Oxford English Dictionary (OED 2009) in 2001. It is defined as, ―noun: The
physical world, as opposed to cyberspace or a virtual environment.‖
8
offenders and capable guardians, directly and easily generalize to cyberspace; however, the
construct of suitable targets is more problematic; specifically, the VIVA constructs differ, and
assume differential relevance and complexity, in cyber- compared with meatspace. Specifically,
Yar (2005, p. 422) argued that while value remains relevant, ―… the remaining three sub-
variables <i.e., inertia, visibility and accessibility> exhibit considerable divergence between real
and virtual settings‖ including differences in ―… distance, location and movement‖.
RAT assumes that crime facilitation and prevention are dynamic, ecologically situated
processes. Given the extraordinary growth and rapid evolution of the eBay market and control
system, a dynamic theory – such as RAT -- holds promise for explaining the co-evolution of
deceptions and controls. In contrast, many theories, e.g., analytical models, have studied online
markets as static or single-period phenomenon. Alternatively however, RAT‘s focus on the
physical characteristics of potential targets -- for example, on an object‘s ―inertia‖ as predictors
of criminal likelihood -- seem unlikely to extend to the virtual environment. One goal of this
investigation is to determine the extent to which, and how, RAT constructs extend to online
auction seller deception, and to the constructs and elements of MCS.
The investigated research questions (RQs), organized around the three principle
constructs of RAT, are:
RQ1: The offender - Does the assumption of the motivated offender hold, i.e. was the
focal vendor deceptive?
RQ2: The guardian – did eBay‘s MCS influence the focal seller‘s behavior to alter his
deception strategies?
RQ3: The target - Do RAT‘s VIVA predictions regarding the characteristics of targeted
criminal products hold in relation to the focal vendor‘s deceptions?
9
At least three research literatures are relevant to these questions and the present
investigation:
1. the nature and evolution of MCSs, and,
2. the nature and evolution of crime and fraud in markets and organizations
3. eBay and the online auction market
We next consider the purpose of the present manuscript in relation to these literatures.
Management Control
The first body of research to which the present investigation relates applies field methods
to investigate the nature and practice of MCSs (Ahrens and Chapman 2006, 2007; Widener
2007). Most investigations within this work focus on manufacturing companies (e.g., Chenhall
and Langfield-Smith 1998; Lillis 2002; Wouters and Wilderom 2008; Henri and Journeault
2010), or, on control systems in hybrid organizations that include outsourcing, joint ventures or
partnering relations among companies (e.g., Boland, Sharma et al. 2008; Dekker 2004; Caglio
and Ditillo 2008). We are unaware of any previous investigations into a managerial control
system, such as that found in eBay, in a rapidly growing online market, in which a single,
dominant market facilitator emerged as the de facto industry control system standard, and, in
which comparatively small buyers and sellers operated at the permission of the market facilitator
(cf. Caglio & Ditillo, 2008). The market context of Frances & Garnsey (1996), perhaps, most
closely resembles that of the present investigation; they investigated technology-enabled changes
in relations between supermarkets and their suppliers, in particular, how technology-enabled
increases in accountability of suppliers to supermarkets, increased supermarket dominance in the
United Kingdom (UK) food market. The present investigation differs from Frances & Garnsey
(1996), however, in its focus on deception and fraud.
10
Accounting-Relevant Crime
The second relevant body of research investigates multiple aspects of accounting-relevant
crime and fraud. This research includes investigations of audit technologies for detecting fraud
(e.g., Lynch, Murthy et al. 2009; Brazel, Carpenter et al. 2010; Hammersley, Bamber et al. 2010;
Hunton and Gold 2010), the cognitive processes that underlie an auditor‘s detection of fraud
(Johnson, Grazioli et al. 1993, 2001; Jamal, Johnson et al. 1995), financial statement deception
by management (Jamal, Johnson et al. 1995; Hogan, Rezaee et al. 2008), and to a lesser extent,
white-collar crime perpetrated by executives (Mitchell, Sikka et al. 1998; Lehman and Okcabol
2005). This article contributes to this literature by exploring a different type of crime (theft
versus financial statement fraud), market (online consumer auctions), perpetrator (an online
seller), and MCS (eBay during a period of explosive growth).
eBay and the Online Auction Market
The third relevant body of research investigates online auctions, including deception.
Steiglitz (2007) investigates online auction market mechanisms and processes, including
deception. Duh, Jamal et al. (2002) provide a framework for evaluating the eBay MCS. Chua,
Wareham and Robey (2007) investigated the response of independent online communities to
auction frauds and their relationship to authorities. Some research has investigated online auction
markets using cross-sectional data at single time points. For example, previous large-sample,
quantitative research explores the mechanisms of price setting in online auctions, including the
influence of trust (e.g., Ba and Pavlou 2002), and, how feedback influences sellers‘ choices of
honest (versus deceptive) auction practices.
This academic research is supplemented by case-study, often first-person, narratives of
online deception. These include popular press accounts of one-time fraud and deception (e.g.,
11
Anonymous 2007; Warner 2003; Wingfield 2004), ―how-to‖ advice based on archival accounts
of one-time online deceptions (e.g., Hitchcock and Page 2002/2006; Silver Lake Editors 2006),
first-person accounts of one-time deceptions written by deceived buyers (e.g., Klink and Klink
2005, 2007; Willcox 2005).
Walton (2006) self-reports his fraudulent eBay sales tactics, following his conviction on
federal fraud charges. His seventeen-month account tenure – from 11/1998 through 5/2000 –
included shill bidding, collusive feedback, and, multiple tactics for creating and selling faked
paintings on eBay.5 It effectively ended due to wide-spread publicity, including front-page
articles in the Wall Street Journal, New York Times, and, the Los Angeles Times; comparison of
Walton‘s to single-incident online deceptions suggests that long-term online deceptions are
likely to be complex and to involve multiple deception tactics (cf. Xiao and Benbasat
forthcoming). In contrast, a small number of tactics appear to be used in single-event deceptions.
For example, we re-analyzed Grazioli and Jarvenpaa‘s (2003a) sample (n = 48) of online auction
deception news reports; re-analysis indicated that 75% of the detected and reported online
auction deceptions relied on a single deception strategy: an ―inventing‖ tactic in which a
fraudulent seller listed goods that he or she did not own.6
The absence of theory-based investigations of long-term deceptions in the online auction
market is an understandable lacuna in the accounting crime and MCS literatures; the online
auction market is young – eBay is less than 15 years old; in addition, multi-period deception is
only possible by effectively disguising the deception (Bell and Whaley 1982, 1991). In online
markets, as in nature, deceivers seek invisibility. In relation to this literature, this investigation
5 These deception strategies are also reported in Steiglitz, K. (2007). Snipers, shills, & sharks: eBay and human
behavior. Princeton, Princeton University Press. 6 Sincere thanks to Professors Grazioli and Jarvenpaa for sharing their data. Our re-analysis replicated their method.
Specifically, two, independent coders classified the deceptions in the data, using the same categories as the original
research paper. Coder agreement was 97.9%. Differences were resolved through discussion.
12
seeks to uncover the tactics by which an online deceiver successfully embedded within the eBay
MCS.
The eBay MCS
The eBay MCS included elements in common with other organizational MCS. For
example, applying Simon‘s (1995a, 1995b) model of the components of a MCS (i.e., belief,
boundary, diagnostic control system (DCS), interactive control system), the two quotations at the
beginning of this article can be characterized as a part of the eBay MCS belief system, which
includes a statement of guiding values and beliefs (eBay 2011). The evolving set of prohibited
buyer and seller behaviors (e.g., eBay 2009, 2010a, 2010b), constitute a boundary system for
eBay traders, though eBay‘s enforcement of these rules appeared to be selective (Cohen 2002;
Walton 2006). Because of the secrecy with which eBay guarded its internal operations, little is
known about its interactive control system, beyond its public forums (Cohen 2002; eBay 2008).
But among the most important innovations at eBay was its evolving diagnostic control system
(DCS), i.e., the feedback system for monitoring buyer and seller performance. Research has
often assumed that the eBay market, and its MCS, was static (e.g., Duh, Jamal et al. 2002;
Dellarocas 2003a, 2005; Gu 2007; Steiglitz 2007). In fact, the eBay MCS evolved in relation to
emergent seller and buyer abuses (Cohen 2002). For example, attorney Mark Walton (2006),
who began trading on eBay in the same year as this case study begins, argued that:
In 1998 it <eBay> had no effective mechanism to detect shill bidding
<bidding by a confederate to inflate prices> or determine if a single person was
registering many different user IDs. eBay considered itself a neutral platform
and let its users do pretty much as they pleased, perhaps fearing that if it
became too involved in policing its site it would someday be legally bound
to do so. Except when abuses were reported by users and easily verified, it
looked the other way. EBay was, after all, ‗all based on trust‘ (p. 14).
13
eBay‘s Trust and Safety Department used the feedback DCS to monitor and selectively
discipline traders (Cohen 2002). This system partially enabled the creation of a vast online
market that was limited only by the reach of the internet and potential traders‘ trust in the market
and the eBay MCS that enabled it (Dellarocas 2003a). But while the eBay feedback system
enabled vast market reach, it also obscured many traditional relations and contact points among
trading partners, leading some to characterize the online market as a ―hyperreality‖ where the
constructs of crime and deception no longer had clear meaning (see also Floridi and Sanders
2001):
Arguably, cyberspace is a realm of hyperreality where signs have become
detached from their referents. Clear definitions of terms such as ―crime‖, ―fraud‖
and ―deceit‖ may become problematic when faced with a situation of hyperreality
(Baker 2002, p. 4).
In relation to the eBay MCS, this article seeks to explicate the ways in which the eBay
MCS and DCS evolved, and allowed some, but not other, deceptions to embed within the
market.
We next describe the research method.
RESEARCH METHOD
The investigation employs three methods: (1) forensic analysis (2) benchmarking,
including an event-study analysis, and, (3) unobtrusive qualitative methods. The following
section introduces forensic methods, describes the selection of the focal seller, and, introduces
the data sources, quality, and classifications.
A “Forensic” Social Science Method
Unpacking the ―unseen hand‖ of routine crime, from it‘s embedding in mundane
economic activity, challenges traditional research methods. Forensic science concerns
investigations with potential legal implications. Nascent forensic investigation methods (Golden,
14
Skalak, and Clayton 2006; Hopwood, Leiner, and Young 2008; Vastrick and Institute of Internal
Auditors Research Foundation 2004; Weiner and Hess 2006) rely on the retrospective
reconstruction of events and behaviors to determine causality and culpability.7 For example,
computer, or online, forensic investigations determine whether and how computers or networks
facilitate incidents with legal, often criminal, implications (Sheetz 2007). Because their concern
is with legal issues in specific cases, forensic methods intensely examine contextually embedded,
i.e., ideographic, incidents.
A disadvantage of adapting and applying online forensic methods as social science tools
is that these methods afford few controls; consequently, their weakness is in generating
nomothetic (i.e., generalizable) explanations across units, treatments, outcomes, and settings
(Shadish, Cook, and Campbell 2002). To increase the generalizability of the account, we
supplement online forensic methods with an event-study method that tests for focal and
benchmark seller reactions to changes in the eBay control structure. Our longitudinal
investigation tracks the focal seller, and a set of benchmark sellers, over an eight-year period.
Focal Seller Selection
Clear selection criteria are important to establishing the contribution of research that
focuses on critical cases (Dube and Pare 2003; Eisenhardt 1989; Eisenhardt and Graebner 2007).
We chose eBay as the market and MCS focus of the study because of its unique position as the
dominant online auction market facilitator, and, because of one author‘s unique and extensive
eBay expertise. Selection of the focal seller of this case synergistically combines two elements of
sample selection in unobtrusive inquiry (e.g., Miles and Huberman 1994; Patton and Patton
7 Although beyond the scope of our investigation, we note the similarity of forensic to ―sensemaking‖ research
methods (Gioia and Chittipeddi 1991; Weick 1990, 1995; Weick, Sutcliffe, and Obstfeld 2005) and anticipate, with
enthusiasm, explorations of the similarities and differences of these methods, e.g., see Attfield and Blandford (2009)
for a sensemaking investigation of a systems-related, accounting fraud.
15
1990; Webb, Campbell et al. 1966) that we deemed crucial to studying a ―successful,‖ long-term
embedded deception:
1. Sustained, Embedded Deception. Evidence, presented in the results section, suggests that
the focal seller had unusually high rates of negative feedback and engaged in multiple
deceptive practices. As a long-term, successful deceiver, this seller is a ―critical‖ case for
market-makers, sellers, and buyers seeking to limit the extent of online auction market
deception. In addition, the case illustrates the processes whereby deception becomes
embedded in successful markets.
2. A Critical Test of the eBay MCS. EBay invested substantial resources in preventing theft
and defalcation (e.g., Duh, Jamal, and Sunder 2002; Shaughnessy 2004) during the case
period. Accordingly, a multi-year deception would suggest that eBay‘s control resources
were ineffective against the focal seller of this case. Hence, this analysis promises insight
into the processes by which mundane crime came to embed within the eBay MCS.
We next consider data quality, data sources, and the division of the account history into
phases.
Data Sources
Forensic research requires expert investigator knowledge (Golden, Skalak, and Clayton
2006); this case originated in one author‘s knowledge of seller deception in the eBay market,
including knowledge of the evolving properties of ―normal‖ eBay sellers and a heightened
awareness of sellers whose activity suggested deception. Months of qualitative ―data mining‖ of
unusual sellers and transactions led to the discovery of the focal seller.8
8 To preserve the privacy and anonymity of eBay users (cf. King 1996), including the focal seller, we use
pseudonyms for all eBay accounts.
16
Preliminary analysis suggested that the focal seller was deceptive; subsequent analysis,
including email interviews with buyers, suggested confirmation of deception. A mixed method,
forensic, event-study, qualitative analysis of a multi-year e-auction seller deception has multiple
potential benefits. Such analysis ―triangulates‖ (Dube and Pare 2003; Jick 1979), i.e., applies
differing and complementary research methods compared with previous approaches. In addition,
an eight-year deception, without prosecution, can be construed as a ―success‖ from the
perspective of the perpetrating seller. In contrast, Walton‘s (2006) seventeen-month deception
ended with his and his partner‘s prosecution and conviction on federal fraud charges. Hence, the
current account offers the possibility of studying ―sustainable‖ online deceptive seller practices.
Data Quality and the Evolving eBay MCS
The appendix summarizes the major changes in the eBay MCS between its inception in
1996 and early 2008. Two of these changes hold particular relevance to this case. In March 2000,
eBay restricted feedback to completed auction transactions. Before this date, eBay allowed self-
and friend-posted feedback that did not require a transaction. Accordingly, eBay data prior to
March 2000 are less reliable indicators of unusual and suspicious trading than are the subsequent
data.
In July 2001, eBay began designating buyers‘ and sellers‘ transaction roles in the US
market. In assessing the data prior to this date, we identify transaction buyers and sellers, where
possible, by analyzing feedback comments. The case focus is on the focal and benchmark
vendors‘ activities as sellers not buyers; hence, most transaction data prior to March 2000 are
excluded from benchmark comparisons. Specifically, transaction data prior to March 2000 help
establish the existence of phases, and, permit tests of strategy evolution by the focal seller.
17
The Evolving eBay MCS: Account Phases
We link the evolution of the eBay MCS to the focal seller‘s activity, by proposing four
phases of account activity, which delimit eBay MCS policy changes. Phase 1 begins with the
opening of the account (9/97) and ends with the eBay policy change, fully implemented in the
US market, on ~ 3/1/2000, that all posted feedback must relate to an auction transaction. Phase 2
begins on 3/2/2000 and ends on 7/11/2001, when eBay began designating transaction roles, i.e.,
separating buyer and seller, and, improved displays of the summary feedback profiles of traders.
Phase 3 begins on 7/12/2001, and ends on 2/9/2004, with the full implementation in the US
market, of the mutual feedback withdrawal policy; this policy stipulates that eBay will remove
negative comments from feedback profiles when buyers and sellers agreed to do so. Phase 4
begins on 2/10/2004, and ends on 8/2/2005, when eBay delisted the focal sellers‘ account. The
appendix summarizes these phases, and lists eBay control changes that precede and follow the
case period.
Focal Seller and Benchmark Vendors
Analysis included reviewing eBay transaction data for the focal seller related to
transaction volume, feedback sign (i.e., positive, negative, neutral), buyers‘ feedback comments,
and focal seller‘s replies to buyers‘ feedback comments. As a ―natural control‖ (Dube and Pare
2003; Lee 1989; Shadish, Cook et al. 2002), we compared the focal seller‘s feedback rates from
buyers, and frequency of repeated buyers, with the five best-matching contemporaneous sellers
in the same eBay sub-market. Matched sellers primarily sold used computers and electronic
equipment on eBay. The contemporaneous benchmark vendors completed between 23 and 2,726
total sales transactions over a comparable period (average = 972.0, SD = 1,220.6). These data
18
provide evidence as to whether the focal seller‘s behavior: (1) differed from that of benchmark
vendors‘ and, (2) suggests deception.
Focal Seller’s Products
EBay‘s long-term transaction records, i.e., greater than 90 days past auction close, do not
include product or sales price information. Because of this, two coders initially analyzed all
feedback comments and transaction records to identify the products sold in the focal seller‘s
auctions. Preliminary analysis suggested that the seller primarily sold in three categories: (1)
laptop computers, (2) cell-phone and laptop computer batteries, and (3) computer and electronic
components. Subsequently, two separate coders, who were blind to the purpose of the study,
categorized all identified product sales into these three categories. After training (Krippendorff
2004; Neuendorf 2002), the coders evidenced a high rate of agreement on the product codings
(99.8% agreement; Cohen‘s Kappa Coefficient: 0.985). The few differences among coders were
resolved by discussion.
Standardized (Canned) Focal Sellers’ Replies to Buyer Feedback
As a measure of the focal sellers‘ application of the POLE, we counted the frequency
with which he responded to buyers‘ feedback with repeated, standardized (i.e., ―canned‖) replies.
We deemed a reply to a feedback posting to be standardized and repeated if the seller reused the
reply five or more times.9 Canned replies also provide evidence of a seller‘s failure to address
buyers‘ concerns.
Additional Contextual Sources and Interview Data
Review of documents, intended to provide insight into the current and evolving nature of
online auction markets and seller deception within those markets, included:
9 As a test of metric sensitivity, we analyzed repeated replies that the seller used fewer than five times. The reported
results do not differ based on the cutoff used for defining repeated replies.
19
1. user postings to online auction list servers and discussion groups (i.e., eBay 2008;
Vendio Services 2008),
2. websites that evaluated and critiqued online auctions and eBay (i.e., Kenny 2008;
Klink and Klink 2005, 2007),
3. court and case records related to the Walton (2006) case, described previously, for
insights into Walton and his colleagues‘ deception tactics and their relations to the
eBay MCS.
Due to repeatedly denied requests for interviews to eBay Trust and Safety Department
personnel, the focal seller, and law enforcement, the data are almost entirely archival. EBay
declined these requests based on confidentiality agreements with account holders. The focal
seller also did not reply to repeated requests for interviews. Finally, requests for interviews with
law enforcement officials were declined, except in one case in which a retired detective agreed to
an anonymous interview. 10
Requests for semi-structured, email interviews with thirty buyers
from the focal seller resulted in fourteen completed interviews.
EXPECTATIONS AND RESULTS
Table 2 summarizes the research questions, data and analyses organized around the three
research questions.
Insert Table 2 about here
RQ1: The Likely Offender: Was the Focal Seller Deceptive?
Consistent with RAT, we assume, within the context of the eBay MCS, the existence of
deceptive sellers who seek to maximize financial gain and (obviously) avoid detection, including
10
The ethical requirements of forensic research, (e.g., Barnett 2001; Bowen 2010) mandated disclosing to the seller
that our investigation involved a ―forensic‖, i.e., legal, issue. Given this, his declining the requests for interviews is
unsurprising.
20
avoiding account delisting by eBay. This section considers the evidence that the focal seller
deceived buyers.
Comparing the negative feedback rates of the focal seller to the overall eBay market,
using the binominal probability distribution, tests the likelihood that the focal seller‘s feedback
rate did not differ from that of the overall eBay market. For the focal and benchmark vendors, we
also compared the frequency of repeat buyers, and, of positive and negative feedback posts.
Because the dependent measures were dichotomous, we used binomial logistic regression for
these individual comparisons, and, MANOVA as a test of the overall model significance.
Finally, we reviewed qualitative evidence suggesting deceptive tactics.
Benchmarking: Focal Seller Versus eBay Market-Wide Feedback
The focal account opened in September 1997, although buyer feedback posts allege that
this seller had pre-1997 accounts that were used to cross-post self-generated, positive feedback.
The account remained active until August 2005 when the evidence suggests that eBay delisted
(i.e., removed) the account. EBay lists 4,571 account transactions for eBay_Seller. For the period
from 2000–2005 (i.e., the more reliable data period), eBay lists 4,089 transactions for the focal
seller; analysis indicated that 3,947 (96.5%) of these transactions were sales. Among the sales
transactions, 217 (5.49%) received negative and 123 (3.1%) received neutral feedback. Most
eBay sellers rarely receive negative or neutral feedback (Resnick and Zeckhauser 2002; Walton
2006; Steiglitz 2007). For example, Bajari and Hortaçsu (2002) documented a positive feedback
rate among a sample of eBay auction coin sellers of 99.8%, which corresponds to one negative
feedback posting for every 500 transactions.
To test whether the focal seller‘s feedback rate was high compared with the overall eBay
market, we computed the likelihood of the observed (5.49%) negative feedback rate, where we
21
assumed that a normal negative feedback rate is 1%; this probability is less than 0.000001
(binominal probability: Lowry 2010); accordingly, the focal seller‘s negative feedback rate on
sales transactions is high relative to overall eBay market norms, even allowing for an unusually
high benchmark (comparison) negative feedback rate (1%).
Benchmarking: Focal Seller versus Used Electronic Benchmark Vendors
Although the negative feedback rates of the focal account are high relative to the overall
eBay market, used electronics equipment sellers on eBay may have similar, high-negative
feedback rates. To test whether the focal seller‘s feedback rating, and, percentage of repeat
customers, was normal for the used electronics auction market, we compared the feedback rates
and percentage of repeat customers of the focal and benchmark vendors using MANOVA and
binomial logistic regression (See Table 3). Compared with the benchmark vendors, eBay_Seller
received more negative feedback postings, fewer positive feedback postings, and, had fewer
repeat customers (MANOVA results: F(3, 7878), p < 0.0001, Wilks‘ Lambda = 91.7, Pillai‘s
Trace = 0.03, Hotelling‘s Trace = 0.04).
Insert Table 3 about here
Qualitative Forensic Analysis: Threats and Retaliation
eBay policy prohibits threatening others: ―Members can‘t threaten others with neutral or
negative Feedback or require that specific Feedback be left‖ (capitalization in original; eBay
2010b). The focal seller threatened buyers, sometimes publicly (source: buyer feedback posts),
sometimes privately (source: email interviews with buyers), with negative feedback to procure
payment or to retaliate for buyers‘ leaving negative feedback. Some buyers state that the focal
seller threatened them with negative feedback in email correspondence subsequent to their
purchase. For example, ―This Clown has my money I have NOTHING but bad feedback and
22
threats — BEWARE‖ (capitalization in original). The focal seller also posted standardized
replies to buyer comments that threatened retaliatory feedback, in violation of eBay‘s policies.
For example, ―NEWBIES >> NEGATIVE FEEDBACK INSTANTLY>> if you VIOLATE
eBay's Feedback Policy!‖ (capitalization in original).
Qualitative Forensic Analysis: Overnight Shipping … in Three Weeks
Buyer feedback posts complained that they paid for, but did not receive, accelerated
shipping from the focal seller. Buyer comments on this point include: ―. . . <he makes> quite a
profit on the shipping…‖; ―charges a 200% markup on shipping‖; ―$10.00 for shipping and it
takes 4–6 weeks. I even mentioned <that> we were in a hurry‖; and ―Thirty days, still no
shipment. Payment made but only hostile reply from seller.‖ The seller‘s replies to these postings
blamed the carrier, e.g., USPS or Fed Express, for shipping problems. Among the posts from
buyers to benchmark vendors‘ accounts, we found statements of seller refunds of shipping
charges in response to buyer complaints. In contrast, we found no evidence that the focal seller
refunded shipping overcharges, and, no evidence beyond the focal seller‘s claims, that the
shipping companies were responsible for these shipping delays.
Qualitative Forensic Analysis: Disguising Defects
Feedback posts from buyers allege that the focal seller used ambiguity in listing product
descriptions to deceive, by hiding the substandard nature of the listed products. Many buyers
observed that they were insufficiently ―mindful‖ (Butler and Gray 2006; Swanson and Ramiller
2004) of the auction description and therefore received substandard or inoperable products. For
example, according to one buyer (source: email interview), one auction featured ―used
headphones‖ with a 6-inch cord; the seller‘s description did not mention the abnormally short
cord. In many cases, the seller claimed that a used electronics product was sold ―as-is‖; in
23
feedback postings, buyers argued that the focal seller knew before the sale that the product was
unusable or substandard. For example, one post alleges: ―Seller hides behind ‗AS IS‘ in
descriptions. Email me for the full story.‖
Summary: Was the Focal Seller Deceptive?
In summary, the following evidence, across the account history, suggests that the focal
seller was deceptive.
1. The focal seller‘s negative feedback rates exceeded the eBay market average and a
sample of benchmark vendors in the used electronics market.
2. The focal seller received a lower positive feedback rate, and, had fewer repeat buyers,
than did the benchmark vendors.
3. Buyers allege that the focal seller threatened negative feedback, in contradiction to
eBay policy, overcharged for shipping, and, disguised (masked) product defects.
4. Evidence suggests that the benchmark vendors, but not the focal seller, sometimes
refunded disputed shipping charges.
RQ2: “Capable Guardians”: The Co-Evolution of Seller Tactics and eBay Controls
We next consider the co-evolution of the focal seller‘s tactics with the eBay MCS. This
analysis includes:
1. Event-study analysis of the effect of the 2/9/2004 eBay mutual feedback withdrawal
policy change on the focal and benchmark vendor‘s rate of negative feedback. As a
control, we also test three events for which we do not expect changes in the rate of
negative feedback.
2. Analysis of the focal vendor, by account phases, of transaction volume and sales
price.
24
3. A test of RAT‘s POLE, which would predict increased use of standardized replies by
the focal seller with increasing levels of transaction volume.
4. Qualitative analysis suggesting that the focal seller self-posted positive feedback
using multiple identities.
Event Analysis: Did the 2/9/2004 eBay Policy Change Increase the Focal Seller’s Negative
Feedback?
Theory. A central assumption, and testable hypothesis, of RAT is that deception tactics
evolve in relation to the ecological controls imposed by a ―capable guardian‖ (Felson and Cohen
1980). We test this assumption with analysis of the event that marks the end of phase 2 and the
beginning of phase 3: the mutual feedback withdrawal policy (event date: 2/9/2004). Negative
feedback from buyers for sellers on eBay is rare. During the period of this case, it included the
possibility of retaliatory negative feedback from the seller to the buyer, which, because of higher
seller transaction volume, was usually more damaging to the buyers‘ than the sellers‘ reputation
(Steiglitz 2007). Accordingly, buyers must be strongly dissatisfied to post negative feedback to a
seller‘s account. eBay introduced the mutual feedback withdrawal policy to provide a means for
dissatisfied buyers to negotiate a settlement with sellers without risking retaliatory seller negative
feedback.
Method. For honest sellers, and sellers who were responsive to buyer concerns, this
policy provided a means for resolving buyer dissatisfaction. However, for dishonest sellers, or
sellers who were unresponsive to buyer concerns or unwilling to negotiate with buyers, this
policy would likely increase negative, and lower positive, feedback. Higher rates of negative
feedback could occur among dishonest or nonresponsive sellers because of increased buyer
expectations, after the new policy, that sellers would successfully resolve buyers‘ complaints.
25
We also tested three additional events as control or benchmark events to compare with
the target date:
1. 9/9/2003: New feedback policy that bans users from restricting or limiting feedback
in listings, or, buying, selling, or trading feedback. We did not observe the focal seller
engaging in any of these practices following the March 2000 change in eBay policy.
Hence, we did not expect changes in the focal seller‘s feedback around this event. We
included this date to ensure that changes in focal seller‘s feedback rating are not
associated with all changes in eBay MCS policy.
2. 8/9/2004: Control date – 6 months after focal event date (of 2/9/2004),
3. 2/9/2005: Control date – 12 months after focal event date. We included this, and the
previous, date to ensure that changes in the focal seller‘s feedback were not
systematically associated, semi-annually and annually, with particular dates or times
that were unrelated to the focal event.
We do not expect changes in the focal seller‘s feedback around the control events. For the
event windows, we examined the number of negative feedback postings from buyers to the focal
seller‘s account for 30-day event windows, i.e., 15-day periods before and after the event dates.
We tested the events with four ANOVAs where the independent variables are thirty-day (pre- to
post-) event windows, and, the dependent measures are the number of negative feedback
postings by buyers to the focal seller‘s account.
We also examined changes in feedback for the benchmark vendors around the event
days; for the benchmark vendors, all feedback within the 30-day event windows was positive.
Accordingly, there are no event-window effects for the benchmark vendors. Table 4, columns 1,
26
2, and, 3 present the event dates, event description, and expected changed in the focal seller‘s
feedback, respectively.
Insert Table 4 about here
Results. Table 5 Panel A presents the ANOVA results for the event dates. Table 5 Panel
B presents the 30-day event window sample sizes, and, percentage of negative feedback for the
focal seller, in the pre-event and post-event periods. The results are consistent with predictions;
specifically, the 2-9-2004 event date is significant (p < 0.0001); as predicted, the percentage of
negative feedback increases from 11.4 to 66.7%, in the pre- to post-event periods. Also
consistent with predictions, no significant results obtain for the other three event dates (p ≥
0.213); the final column of Table 4 summarizes these results.11
Accordingly, the results suggest
changes in the focal seller‘s behavior that are contemporaneous with changes in the eBay MCS,
but no changes around control event dates, or, changes in benchmark vendor behavior.
Insert Table 5 about here
Quantitative Analysis – Did the Focal Seller’s Tactics Evolve with the eBay MCS and Market?
Theory. We next explore additional evidence of changing focal seller tactics across
account phases. The purpose of this analysis is to provide insight into how the seller‘s tactics
evolved to ―fit‖ or embed within the evolving eBay MCS and market. Within RAT, crime is
assumed to ―normalize‖, or become mundane, by its tacit embedding within social systems
(Cohen and Felson 1979). During the 1997-2005 period, eBay market volume grew rapidly but
the average sales price remained relatively constant (see Table 1). Hence, a tactic designed to
embed mundane crime within the rapidly expanding online auction market would likely consist
11
The event-study results obtain despite the focal event test having lower statistical power than do the
control event hypothesis tests. Statistical power is the long-run probability of correctly rejecting a false null, i.e., no
difference, hypothesis (Lindsay 1993, 1995). Setting α = .05, assuming a medium effect size (d = .5), and using the
achieved sample sizes, the statistical power for the hypothesis test of the focal event = 0.43, while the achieved
statistical power for the control events is 0.8 (for 9/9/2003), 0.77 (for 8/9/2004), and 0.59 (for 2/9/2005).
27
of seller movement towards a high transaction volume, coupled with a sales price similar to the
average eBay market price.
Method. To investigate this speculation, we ran two ANOVAs to test for changes in the
focal vendor‘s transaction volume and sales price, across the account phases. These are:
An ANOVA model with phase as a four-level fixed-effects independent variable. The
dependent measure was the focal seller‘s monthly sales volume. We expect the focal
seller‘s transaction volume to increase with the large increases in transaction volume of
the overall eBay market.
The sample of transactions that included sales prices was relatively small. Because of
this, we used an ANOVA model with phase as a two-level (1 and 2, versus, 3 and 4),
fixed-effect independent variable, to test for changes across phases in the focal seller‘s
average sales prices. We expect the focal seller‘s average sales prices to move towards
the average eBay transaction sales price, which is relatively constant across account
phases.12
Results. Table 6 (Panels A and B) reports the results of these analysis. Analysis indicates
increasing transaction volume across all phases of the focal seller‘s account activity (p < 0.001;
See Table 6 Panel A). The average monthly volume of sales transactions for the focal seller
increased from 15.3 in Phase 1, to 41.0 in phase 2, to 60.9 in phase 3, to 84.6 in phase 4.
Insert Table 6 about here
From eBay records and feedback comments, we obtained the sales prices for fifty-five of
the focal seller‘s transactions. Comparison of the focal seller‘s average sales prices across phases
indicates that sales prices are dramatically higher in phases 1 and 2 than phases 3 and 4 (p <
12
The test for post-hoc differences between phases used the Bonferroni correction.
28
0.001; See Table 6 Panel B). We also compared the focal seller‘s average sales prices with the
average auction listing value for the eBay market (See Table 1 for average auction listing
values). The average phase 3 and 4 sales price for the focal seller, i.e., $25.38 does not differ
from the average 2005 eBay sales price of $23.60 (t(51) = 0.161, p = 0.873). However, the
average phase 1 and 2 sales price for the focal seller, i.e., $528.21 exceeds the average eBay
sales price (t(10) = 2.711, p = 0.022). Hence, the evidence suggests that the focal seller began
with higher, but evolved to lower, product sales prices. Hence, the evolution of the seller‘s
tactics, i.e., from a low volume, high-sales price strategy in phases 1 and 2, to a high volume,
low-sales price strategy in phases 3 and 4, are consistent with a RAT account of the embedding
of mundane criminal activity in the evolving eBay market. Specifically, the focal seller‘s volume
increased with market volume, and, his sales prices dropped to be no different from the average
eBay produce sales price.
POLE Analysis: Coping with High Volume through Standardized Feedback Replies
Theory. RAT‘s POLE argues that perpetrators choose strategies that minimize effort; they
will execute a crime when minimally acceptable targets assemble, exerting minimal cognitive
and physical effort. We tested whether, with increases in volume, the focal seller minimized
transaction effort; specifically, we analyzed the frequency with which the focal seller used
standardized (i.e., reused) replies in response to buyer feedback.
Method. To test this hypothesis, we ran an ANOVA model with phase as a four-level,
fixed-effects independent variable. The dependent measure was the frequency of standardized
replies by the focal seller in response to buyer feedback postings. The ―POLE‖ prediction is that
the use of standardized replies will increase with the focal seller‘s transaction volume.
29
Results. The results are consistent with the POLE. The focal seller increased the use of
standardized replies, across phases, as transaction volume increased (p < 0.0001; see Table 6
Panel A). Specifically, the number of standardized replies to buyer‘s increased from 0 in Phase 1,
to 40.9% and 54.5% in Phases 2 and 3, respectively, to 83.2% in Phase 4. None of the
benchmark vendors used standardized replies. Hence, consistent with RAT‘s POLE prediction,
the focal seller‘s use of standardized replies exceeded that of the benchmark vendors, and
increased with transaction volume.
Qualitative Forensic Analysis: Phase 1 Deception - Inflated Positive Feedback
Evidence, from transactions before March 2000, suggests that the focal seller violated
eBay policy by artificially increasing his feedback ratings. For example, one buyer alleges that
the focal seller, despite eBay market rules, offered to ―purchase‖ feedback ratings (―Offers 2 for
1 positive feedback, rating is questionable, email for details‖; source: feedback post). That is, the
focal seller offered to post multiple positive feedbacks for buyers in exchange for multiple
positive feedback postings to the seller‘s account. We also observe that many of the focal seller‘s
positive feedback ratings, during this period, are from the same set of account holders. It is
conceivable that the same small group of account holders won many auctions from the focal
account and then provided positive feedback. However, the buyer allegations, coupled with the
frequency of repeated postings from a small set of accounts, suggest self-posting, exchanged
falsified feedbacks, or both, for some phase 1 transactions.
Qualitative Forensic Analysis: Phase 1 - Multiple Identities
The eBay (2010a) MCS has consistently banned the creation of multiple accounts for the
purpose of artificially self-posting positive feedback from one account to the other. However,
prior to March 2000, eBay permitted feedback postings that were unrelated to auctions. Three
30
buyer postings, all prior to March 2000, allege that the focal seller used multiple account
identities: ―Communications poor (one word emails), waited 6 weeks, changes alias a lot‖;
―Cashed the check 2 months ago, no merchandise, no response. Changes EBay ID‖;
―WARNING: ―eBay_Seller‖ used to be <previous eBay account name> —changes name often!
Self-praising.‖ We did not find buyer claims of self-posted feedback by the focal seller after the
March 2000 eBay rules change banning non-auction feedback postings.
Summary – Did eBay Seller Alter Strategies across Phases
Evidence supports multiple examples of changes in the focal seller‘s tactics that likely
evolved in response to changes in the eBay market and MCS. In phase 1, with the nascent eBay
MCS, we find evidence that the focal seller self-posted positive feedback using multiple
identities. Event-study analysis suggests that the 2/9/2004 mutual feedback withdrawal policy
change may have increased the focal seller‘s, but not the benchmark vendors, negative feedback.
As a control, we test three events for which we do not expect changes in the negative feedback
rate; consistent with expectations, the results for these three non-events indicated no change in
the negative feedback rate of the focal seller. In addition, we find evidence supporting RAT‘s
POLE predictions: as transaction volume increased, the focal seller increasingly posted
standardized, i.e., minimal effort, replies to buyer comments.
RQ3: Suitable Product Targets and RAT’s VIVA Predictions
The Used Online Electronics Sub-Market: a VIVA Analysis
Why did the focal seller choose to sell in the used electronics eBay submarket? In
economic crime, a RAT ―target‖ principally concerns how product characteristics influence the
nature and frequency of crime. This section investigates the focal seller‘s evolving choice of
product targets and the applicability of RAT to these products. Consistent with Yar (2005), the
31
product characteristic constructs advanced by RAT as relevant to ―meatspace‖, which are
summarized as VIVA, i.e., value, inertia, visibility, and accessibility, do not map easily or well
to seller deception in the online auction market.
Visibility: According to RAT, the likelihood of crime increases with target visibility,
e.g., jewelry that is displayed (visible) in a store window versus locked in a safe. Visibility and
market scope are important differences between terrestrial products versus those sold in the
online auction market. For example, eBay currently lists about 110 million products daily,
organized into 38 major categories, and, 447 subcategories (eBay 2010c); with a few mouse
clicks, any listed product is easily visible. Hence, product visibility – strictly constructed -- is
largely obsolete in the online auction market. However, we find evidence that three product
characteristics– popularity, reliability, and, complexity – hold relevance to sustaining an online
auction seller deception.
The focal seller appeared to use used electronic products as ―bait‖ to lure buyers. The bait
was nonrandom: it concentrated among the most popular eBay product categories -- electronics –
which is about 10% of the 110 million daily listings on eBay (2010c). When asked why he
robbed banks, Willie Sutton replied, ―Because that‘s where the money is‖ (FBI undated).
Similarly, the sustainable online seller con concentrates in a popular product category since, to
paraphrase Willie Sutton, ―that‘s where the buyers are.‖
Product reliability also appears to be relevant to the seller‘s strategies. Analysis of the
benchmark sellers suggests that the used ―as is‖ electronics market experiences slightly higher
rates of buyer dissatisfaction than the overall eBay market. For example, the average non-
positive, i.e., negative and neutral, feedback rate of the benchmarked sellers was 1.83% (SD
=.134, N=4,860), which exceeds the assumed eBay market average of ~ 1% (t = 4.318, p <
32
0.001). Operating in a market with higher than normal problem rates likely enabled the focal
seller to retain the account longer than would have been possible in a less problematic market.
Hiding frequently defective products among highly reliable products is impossible; hence, the
sustainable online con emerges in a product category with a higher than normal expected defect
rate – such as, herein, the used electronics market.
Product complexity also facilitates obscuring defects, which likely was a partial
motivation for the focal seller‘s electronics product line. Walton‘s (2006) choice of the online
market for art likely provided similar opportunities for deceiving buyers about complex products,
but included the risk of an unexpectedly high value, high visibility target, i.e., the faked Richard
Diebenkorn painting, that Walton sold on eBay for $135,805. Accurately assessing the condition
and value of used electronics products and art is considerably more challenging than are
assessments of, for example, a book, coat, DVD, or, CD. Hence, the sustainable online auction
seller con concentrates in complex product categories.
Value. An imbedded assumption of RAT is suspect in the online auction; specifically,
RAT‘s VIVA formulation assumes that individuals or businesses own goods, which they
physically expose to criminals who seek to steal them, by perhaps, shoplifting or breaking and
entering. In contrast, this case concerns an (allegedly) deceptive seller who lured potential buyers
to bid on frequently defective products. Hence, within the online auction market, goods with
higher symbolic or economic value are potentially less attractive to a deceptive seller, since they
create an undesirable salience of the seller to market regulators and vigilante groups (cf. Chua, et
al., 2007). Walton‘s (2006) conviction for the sale of a faked Diebenkorn painting on eBay –
with a sales price of ~ $136,000 -- illustrates the perils of high-value, high-visibility frauds in the
online auction market.
33
Hence, in contrast to meatspace, within which RAT assumes that criminals will seek
high-value targets, we find evidence that the focal seller sought an eBay submarket with
comparatively low valued targets, but within which his deceptive practices could be hidden, i.e.,
embedded, within otherwise normal market activity. The MANOVA of the products sold in 265
of the focal seller‘s sales transactions included phase as a four-level predictor variable and the
percentage of laptops, batteries, and components as the set of dependent variables, indicates
across-phase changes in product mix (MANOVA Wilks‘ Lambda = 0.4, F(9, 630.5) = 29.2;
Pillai‘s Trace = 0.6, F(9, 783) = 23.6, Hotelling‘s Trace = 1.2, F(9, 773) = 33.7, all p < 0.0001;
See Table 6 Panel C). Consistent with the analysis of sales prices, the percentage of sales of
higher value laptop computers declines after phase 1, while sales of lower-value items increases
in phases 3 (e.g., battery sales) and 4 (e.g., other components).
Hence, evidence suggests that the application of the RAT construct of value to seller
behavior requires modification: the sustainable online auction seller con that we identify has two
objectives in relation to target value: (1) selling valuable-appearing, target products whose (2)
value remains below an assumed threshold above which market regulators, or vigilantes (e.g.,
Chua, et al., 2007), will act to stop the deception. Hence, the sustainable seller online auction con
deceives with respect to low-value product targets. A corollary of the low-value product strategy
however, is the necessity of high sales volume, which was facilitated by large increases in
volume in the online auction market across the focal seller‘s account history (See Tables 1 and
6).
Inertia: RAT poses that products with low ―inertia‖, i.e., those that are physically easier
to move, are more likely targets of crime -- for example, a diamond ring is a more likely theft
target than is a recreational vehicle. However, economic exchange in the online market is under
34
―pay first then ship‖ rules; in addition, an independent, private carrier often delivers goods.
These market conditions would seem to lessen the importance of inertia in online, compared with
meatspace, crime. In the online environment, inertia is overcome simply by convincing a seller
to deliver a product, e.g., in an auction sale of an RV on eBay Motors. We find no evidence, in
the case, that inertia was a consideration in the seller‘s deceptions.
Access: RAT poses that crime increases for targets that are easier to access at, and
transport away from, the crime scene. The online auction removes the necessity of accessing and
removing a physical object. Hence, access is of less importance in the online auction than in
meatspace crime. However, the previous discussion mentions two product attributes, i.e.,
reliability and complexity, that we argue facilitated the focal seller‘s ability to escape detection
and prosecution for his deceptions. Hence, the case evidence would suggest that access was, at
most, a minor consideration in the focal seller‘s deceptions.
Summary – Are RAT’s VIVA Constructs Useful in Explaining Online Auction Deception
What product characteristics are associated with the focal seller‘s deceptions? Evidence
suggests that RAT‘s constructs of product visibility, inertia and access may be less important
predictors of deception in sustainable online auction deception than in meatspace deceptions.
Instead, we suggest that three product characteristics that are related to visibility – i.e.,
popularity, reliability and complexity, hold relevance to online auction deception; relatedly, the
seller sought to deceive with respect to low value, high volume products. Accordingly, we infer
that deceptive online auction sellers seek comparatively low-value, high-volume, popular,
unreliable, complex product categories within which to perpetrate deceptions.
35
Endgame and Delisting
The focal seller‘s final listings evidence a strategy shift towards non-delivery of sold
products. Investigation produced detailed transaction data, from buyer interviews or eBay
transaction data, on forty-two of the focal seller‘s final 50 listings. Most items for sale had more
than one listing for the exact same product. Consequently, unless the focal seller had 3 identical
Fujitsu tablet PC cases, 20 identical used Motorola batteries, 8 identical sets of used Sony
rechargeable batteries, and 8 identical Sony sport headphone sets, the seller was ―inventing‖
listings of products that he did not possess and could not deliver. The last page of the focal
seller‘s feedback profile shows 27 negative and 2 neutral postings out of a total of 50 feedback
postings. All 29 of the negative or neutral postings allege that the seller either did not ship the
product or misrepresented it. The seller‘s reply to twenty-two of these negative feedbacks does
not respond to the specific complaint; instead, it claims that technical problems prevented
satisfaction of the transaction: ―We were down; We are 100% up now; Thanks for your bid!‖.
In August, 2005, eBay_Seller‘s account was apparently delisted by eBay and the
statement ―Member since: 1997‖ was replaced with ―No longer a registered user (NARU).‖
Subsequent email interviews with buyers suggests that eBay may have pressured eBay_Seller to
resolve some, though not all, of the outstanding complaints. For example, one buyer received a
refund of the purchase price with a note from eBay_Seller stating: ―I am sorry that difficulties on
our end negatively affected this transaction. I sincerely apologize for any inconvenience this has
caused.‖ In November 2005, the eBay_Seller account was reinstated. Perhaps because of the
substantial (negative) feedback profile on the account, the focal seller has never traded on the
reinstated account.
36
The Sustainable Online Auction Con: Why Didn’t eBay Delist the Account Sooner?
Explaining a ―counterfactual‖, i.e., why an event didn‘t happen, is problematic –
particularly given the absence of interviews with eBay personnel; accordingly, we speculate as to
why the eBay MCS allowed the focal seller to trade with the same account for eight years despite
the seller‘s unusually high negative feedback rate. Buyer feedback makes clear that eBay was
aware of many of the problems with the focal seller. Many feedback postings indicate that buyers
obtained refunds through PayPal (an eBay subsidiary) and contacted eBay regarding alleged
transaction deceptions. For example, one buyer‘s negative feedback posting states: ―EBAY
UPHELD THIS THEFT BASED ON SELLER'S FEEDBACK STATUS AND OTHER
FACTORS, HMMMM‖ (capitalization in original).
EBay‘s business model (Cohen 2002) likely contributed to its failure to delist the focal
seller sooner (cf. Baker 2002). During the period of this case, fees charged to sellers for listing
products were an important eBay revenue source. In this business model, large volume sellers,
such as the focal seller in his later account phases, disproportionately contributed to eBay‘s
revenue. A published interview with an anonymous eBay security officer suggests that eBay‘s
business model weighs seller interests more than buyers when confronted with high volume,
long-term sellers (Brunker 2002). In contrast, the Walton (2006) case shows that eBay delisted a
high-volume seller‘s account only after a high-publicity fraud (cf. Gu 2007; Jewkes 2007;
Joinson 2007). In addition, eBay may have overlooked many deceptions, except for the most
egregious, through an intentional strategy of avoiding legal responsibility for transaction
deceptions by failing to act upon them (Walton 2006).
We speculate that, until endgame, the focal seller‘s restriction of deceptions to small
dollar amounts per transaction, and to less than ~ 10% of the seller‘s auctions, were sufficient to
37
prevent the eBay MCS from delisting the focal seller. The focal seller opportunistically deceived
buyers, but not sufficiently frequently, or in large-enough sales transaction amounts, to result in
account delisting. Until endgame, the focal seller was a mundane, ―small-time crook.‖ Prior to
the final endgame transactions, the seller primarily deceived buyers by delivering defective low-
cost goods, and by shipping overcharges.
SUMMARY, LIMITATIONS, AND, CONCLUSIONS
Case Summary
This case synthesizes forensic, benchmarking and event-study methods to investigate the
eight-year history of an eBay account characterized by evolving deceptions that link to changes
in the eBay system of feedback and control. The focal seller had unusually high negative
feedback rates and an unusually low numbers of repeat buyers throughout the account history.
Buyers allege that he threatened them with negative feedback, overcharged for shipping, and,
repeatedly disguised (masked) product defects.
Evidence suggests that the focal seller‘s deceptions evolved in relation to the eBay MCS.
In phase 1, we find evidence that the focal seller self-posted positive feedback using multiple
identities. Event-study analysis suggests that the focal seller‘s negative feedback increased
concurrent with eBay‘s implementation of the 2/9/2004 mutual feedback withdrawal policy. In
addition, the focal seller increasingly posted standardized ―canned‖ replies to buyer comments as
transaction volume increased.
Prior to endgame, the focal seller sought to deceive with respect to low-value, high
volume, popular, unreliable, complex product categories within which to perpetrate deceptions.
At endgame, the seller shifted to failing to deliver listed products. It seems likely that eBay failed
38
to delist the focal seller sooner due to his (successful) strategy of small-scale deception in the
high volume, high defect product category of used electronics goods.
Implications for Theory
Considerable high-quality research has investigated models of deception and, more
recently, applied this theory to online commerce. However, the modeling of online deception and
online auction case research has, with few exceptions (e.g., Walton 2006, Chua et. al., 2007),
largely focused on static models. Our analysis suggests that sustained, multi-year online
deception differs in diversity and complexity from the single-event deception observed in much
previous research. For example, we find evidence of multiple and shifting deception strategies,
which appear to evolve in relation to the eBay MCS.
Several of RAT‘s assumptions – i.e., the existence of motivated offenders, and, that crime
occurs in dynamic and evolving relations between potential offenders and capable guardians –
are well-suited to the investigation of seller deception in the online auction market. However,
consistent with Yar (2005), RAT‘s assumptions regarding products map poorly to the online
auction environment; the scope and reach of the online market, the inaccessibility of goods to
buyers except after payment is received, and, the shipment and delivery of goods by independent
third parties make RAT‘s product characteristic assumptions less relevant and useful in the
online auction market.
RAT‘s assumption that crime happens – that it is a ―natural‖ process that embeds and
normalizes in economic activity – is of great value and relevance in understanding the nearly
laissez-faire online auction market. Through sustaining our investigation across a multi-year
period – including the formation and explosion of the online auction market – we find evidence
of an evolution of tactics that mirror, and mask within, the evolving online auction market.
39
Our analysis suggests that RAT provides a useful, and perhaps complimentary,
framework to the much more commonly applied fraud triangle, for understanding accounting
crime and deception. RAT supplements the fraud triangle with a focus on the social and market
ecologies within which crime occurs. Alternatively, the fraud triangle compliments RAT by
loosening the assumption of the motivated offender and providing a framework to assess
potential perpetrator motivation.
Implications for Practice
Alibaba.com, the largest online business market facilitator in China, also operates an
eBay-like division, Taobao ("Alibaba to Invest in Taobao.com," 2008), for Chinese consumer
sales and auctions (Schepp & Schepp, 2010; Siegfried, 2009). The recent Alibaba.com fraud
(Chao & Lee, 2011), which led to the resignation of the CEO and COO, suggests that largely
unregulated online markets may be particularly susceptible to the embedding of fraud. In the
alibaba.com fraud, sellers, aided by alibaba.com employees, faked credentials that certified them
as ―Gold‖, i.e., highly reliable suppliers. Similar to the case presented herein, individual fraud
claim amounts in the alibaba.com case were relatively low compared to average sales at the site.
As a result of the fraud, alibaba.com delisted 1,200 sellers and fired about 100 employees. The
alibaba.com fraud suggests that online market deception is not restricted to the online consumer
market or eBay.
RAT assumes that the belief that all crime can or should be eliminated from a market is
an unhelpful myth. By following this assumption, we document the birth and evolution of one
seller‘s creation of a ―sustainable‖, online auction con. The focal seller‘s account tenure ended
only when his strategies switched from a sustainable, to a salient – to the eBay MCS –,
deception; his account was closed after he repeatedly failed to deliver sold products to buyers.
40
RAT principles suggest that MCS designers‘, regulators‘ and market stakeholders‘ choice is not
whether to eliminate online crime – it is instead which, and where, crimes will be allowed to
embed and sustain in a market and MCS. That allowing deception to embed is the theory-in-
practice, though not the espoused theory, of eBay is suggested by the current case; eBay
implicitly accepted the focal seller‘s sustained deception – through their failure to close his
account – until his deceptions apparently exceeded the threshold of ―acceptable,‖ mundane
deception. The extraordinary growth of the online auction market, and of eBay, during this
period, suggests that the embedding of these mundane deceptions did not impede market success.
While online market makers will accept and embed some deceptions, they are also
charged with designing and implementing feedback and control strategies that define acceptable
from prohibited deceptions. For example, we find several examples of shifts in the focal seller‘s
tactics, which appear to be reactions to changes in the eBay MCS. Hence, while some crime is
accepted and inevitable, market facilitators must mindfully choose where and how crime will
embed. Hence, the effective MCS in the online, laissez-faire market must balance controls
against permitted yet mundane crime.
That the espoused eBay MCS was at variance with the stated eBay MCS boundary rules,
is consistent with other research investigating control systems in large organizations. For
example, Alvesson and Kärreman (2004) investigated management controls at a large
management consulting firm. Investigation revealed widespread under-reporting of time worked
on engagements (called ―ghosting‖), meaning that the actual hours worked on an engagement
were never known or formally recorded in the system. Hence, while the control system of the
consulting firm purported to accurately record engagement hours worked, all who worked for the
firm recognized these hours as under-reported.
41
We find an analogous ―fiction‖ in the eBay MCS; specifically, the belief and boundary
systems of the eBay MCS espoused community honesty and a commitment to discipline
deceptive traders. But the MCS, in practice, allowed deceptive traders to embed, with discipline
invoked only in cases of extreme deception. eBay‘s balancing of control and mundane crime
included resisting calls to impose stronger controls over documented trading abuses (Albert
2002; Nikitkov and Bay 2008). Within RAT, this can be construed a rational response to the
assumption that crime will occur, and that the goal of controls must be to reduce, as opposed to
eliminate deception. The alternative approach, that the eBay MCS, without significant assistance
from law enforcement, must eliminate all deceptive transaction is at odds with RAT, and, almost
certainly unrealistic in the present online auction market.
Limitations and Conclusions
The strength of forensic methods is their ability to paint a rich, veracious, ideographic
portrait of the particular; their weakness is their (in)ability to generate nomothetic theory. Our
objective has been ―positioning data to contribute to theory,‖ (Ahrens and Chapman 2006) where
the data are from a single seller, in a single (eBay) market, during a fixed time period (1997–
2005). Our sample, of a single deceptive seller, confounds changes in the eBay market (see
appendix) with changes in the focal seller‘s deception tactics.
The investigation triangulates forensic, with event-study and benchmarking, methods.
However, these methods are executed within a sample of a specific set of vendors, in a specific
market, in a specific time period. The use of triangulation improves the strength and generality of
our inferences; but our inferences will be strengthened by future work testing their
generalizability and transferability to additional online and meatspace contexts, markets and
MCSs.
42
The profile feedback score of the average eBay seller exceeds 99%. The focal seller of
this case sustained a set of deceptions across an eight-year account history despite eBay‘s
promises that dishonest people ―can‘t hide‖ and will be driven away from the eBay market. The
focal seller in this case displayed impressive skills and protean tenacity; observed shifts in the
focal seller‘s deceptions are consistent with reactions to changes in the eBay MCS and the
expanding online auction market.
Many argue that the presence of predatory crime in the online auction market is
indicative of a breakdown of controls, and, a failure of market facilitators and regulators. These
results, and RAT, would suggest that, to the contrary, the existence of mundane, online-auction,
predatory crime is consistent with market success. The online auction markets make it possible
for Americans to collect rare, antique Russian nesting dolls, for Africans to buy newly released,
Italian designer clothing, and, for Brazilians to savor top-grade Iranian caviar. But the critical
lesson of RAT, and of this analysis, is that mundane, predatory crime will arise and embed in a
rapidly growing, unregulated, successful market (cf. Felson and Cohen 1980). Online auction
market controls co-evolved with creative deceptions; but ultimately, mundane deceptions
embedded in the eBay market because that is where they could best be hidden in plain ―site‖ –
carefully and cleverly embedded amongst almost 110 million, and growing, daily auction listings
(eBay 2010c).
43
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Figure 1
Comparison of Routine Activity Theory to the Fraud Triangle
Lack of Capable Guardian
Suitable Target
Perceived Opportunity
Perceived Non-Shareable Financial Need
Ex-Ante Rationalization
Motivated Offender
Fraud Triangle
(Offender’s Perception)
RoutineActivity Theory(Environment)
52
Table 1 - eBay Activity Volume and Listing Value -
Selected Years from 1997 to 2005
Year 1997 2000 2002 2005 1997 - 2005
% Change
Registered Users 0.34 22.5 61.7 180.6 52,862%
Listings 4.39 264.7 638.3 1,876.8 42,613%
Sales $95.3 $5,422 $14,868 $44,299 46,398%
Auction Listing
Value (Average) $21.68 $20.48 $23.29 $23.60 8.9%
Registered users, listings and sales in millions
Source: eBay SEC 10K filings and annual reports
Table 2 – Summary of Research Questions, Data and Analyses Panel A – RQ1: Motivated Offender – Was the Focal Seller Deceptive?
Quantitative (#) or
Qualitative (L)
Benchmarking (B) or
Forensic (F)
Summary /
Method
Variable / Data Source
# B = eBay Market Binomial
Probability
Negative Feedback
Rates
# B = Benchmark
Vendors
MANOVA /
Logistic Regression
Feedback Rates, Repeat
Buyers
L F Identify Threat
Claims
Buyer Feedback Posts
L F, B =Benchmark
Vendors
Identify Shipping
Overcharge Claims
Buyer Feedback Posts
L F Identify Claims of
Disguised Defects
Buyer Feedback Posts,
Email interviews with
buyers
53
Panel B – RQ2: Capable Guardian – Did Market and MCS Changes Influence the Focal
Seller?
Quantitative (#) or
Qualitative (L)
Benchmarking (B)
or Forensic (F)
Summary /
Method
Variable / Data Source
# B = Pre-Post
Comparison of
Events
ANOVA Positive & Negative
Feedback Rates
# B = Phases ANOVA: Vendor
Sales Volume
Monthly Sales Volume
L F Identify Inflated
Feedback Claims
Buyer Feedback Posts +
Pattern of Feedback Posts
Among Related Accounts
L F Identify Claims of
Multiple Identities
Buyer Feedback Posts
Panel C – RQ3: Product Targets – About Which Products Did the Focal Seller Deceive?
How Did the Focal Vendor’s Products Evolve within the eBay Market and MCS?
Quantitative (#) or
Qualitative (L) ,
Research Question
Benchmarking (B)
or Forensic (F)
Summary / Method Variable / Data
Source
#, L F, B = Used
Electronics vs.
Overall Market
Binomial Probability Negative Feedback
Rates
# B = Phases ANOVA Sales price
# B = Phases MANOVA / ANOVA Product Mix
# B = Phases ANOVA Standardized Replies
#, L F Description Final Auction Listings
L F, B = Walton case Identify Feedback
Posts, eBay Inaction
Against Focal Seller
Buyer Feedback Posts,
eBay Business Model,
Walton case
L F Account closure Account Activity,
Email interviews with
buyers
54
Table 3 – Logistic Regression Results Comparing
Focal and Benchmark Vendors
Dependent
Seller Mean
Std
Dev.
Exp
(B)
Wald’s
p
Cox
& Nagelkerke
Variable χ2(1)
Snell
R2 R
2
Repeat
Buyers
Benchmark 22.46% 0.417
1.572 60.372 < 0.0001 0.008 0.012 Focal 15.56% 0.363
Positive Benchmark 98.60% 0.117
6.653 165.825 < 0.0001 0.03 0.091 Feedback Focal 91.38% 0.281
Negative Benchmark 0.74% 0.086
0.128 107.041 < 0.0001 0.021 0.086 Feedback Focal 5.50% 0.228
MANOVA results: Wilks‘ Lambda = 91.7, Pillai‘s Trace = 0.034, Hotelling‘s Trace = 0.035, For
Wilks‘, Pillai‘s and Hotelling‘s analyses: F(3, 7878), p < 0.0001
Transaction Sample Sizes (post 3/1/2000 data): Benchmark = 3,936; Focal = 3,946
Table 4 - Event Dates, Predictions and Results Date Event Prediction: Focal
Seller’s Negative
Feedback
Result
9/9/2003 New Feedback Solicitation Policy, bans users from
listing any terms and conditions that restrict or limit
the ability of a member to leave feedback, also bans
selling, trading or buying of feedback (Steiner 2003c)
No change True
2/9/2004 Introduce mutual feedback withdrawal policy
(Steiner 2004)
Increased post-
event
True
8/9/2004 Control Date (6 months after last test date) No change True
2/9/2005 Control Date (6 months after last control date) No change True
Table 5 Panel A –ANOVA Results for 30-Day Event Window - Negative Feedback
Date F (df) P
9/9/2003 1.568 (1, 99) 0.213
2/9/2004 19.760 (1, 45) < 0.0001
8/9/2004 0.073 (1, 94) 0.788
2/9/2005 1.486 (1, 57) 0.228
(See footnote 13 for statistical power calculations related to these tests of hypotheses)
55
Panel B –Focal Seller Negative Feedback Means, SDs, and, Sample Sizes for Event Dates
Event Date Pre-Event Post-Event
9/9/2003 n 44 57
Mean 0.000 0.035
SD 0.000 0.186
2/9/2004 n 35 12
Mean 0.114 0.667
SD 0.323 0.492
8/9/2004 n 57 39
Mean 0.018 0.026
SD 0.132 0.160
2/9/2005 n 24 35
Mean 0.083 0.200
SD 0.282 0.406
Table 6 – General Linear Model (GLM) Results
Panel A –Volume and Standardized Replies by Account Phase
9/97-
12/31/99 (1)
1/1/00
-7/11/01 (2)
7/12/01-
2/09/04 (3)
2/10/04-End
(4)
Univariate Results
(F, p)
Feedback Postings (Avg
per month) (n = 4,409)
15.34a
(5.925)
(n = 462)
40.95b
(3.722)
(n = 790)
60.90c
(8.060)
(n = 2496)
84.60d
(4.024)
(n = 661)
11,333.73 < 0.001
% Standardized Replies
(n = 268)
0.0% a
(0.000)
(n = 0)
40.9% b
(0.503)
(n =22)
54.5% b
(0.504)
(n =44)
83.2% c
(0.374)
(n =185)
30.33 < 0.001
Analyzed by quarter, Standard deviations in parentheses
Panel B – Sales Price by Account Phase (n = 63)
Phases 1 & 2
(9/97-7/11/01) (n = 11) Phases 3 & 4
(7/12/01-End) (n = 52) Univariate Results
(F, p)
528.21a 25.38
b 33.77 < 0.001
Panel C – Product Mix by Account Phase (n = 265)
9/97-
12/31/99
(1) (n = 40)
1/1/00
-7/11/01
(2) (n = 30)
7/12/01-2/09/04
(3) (n = 158)
2/10/04-End
(4) (n = 37)
Univariate Results
(F, p)
Laptop Computers 70.0%
a
(0.464)
3.3%b
(0.183)
4.4%b
(0.206)
2.7%b
(0.164) 75.63 < 0.001
Computer Components 25.0%
a
(0.439)
53.3%b
(0.507)
17.7% a
(0.383)
5.4% a
(0.229) 9.31 < 0.001
Cell Phone & Laptop
Computer Batteries
0.0%a
(0.000)
30.0% b
(0.466)
58.2% c
(0.495)
45.9% c
(0.505) 18.62 < 0.001
Other Electronic Components 5.0%
a
(0.221)
13.31%a
(0.346)
19.6% a
(0.398)
45.9% b
(0.505) 7.76 < 0.001
MANOVA results for product mix: Wilks‘ Lambda = 0.4, F(9, 630.5) = 29.2; Pillai‘s Trace = 0.6, F(9, 783) = 23.6, Hotelling‘s Trace
= 1.2, F(9, 773) = 33.7, all p < 0.0001.
Post-hoc significant differences by row are indicated by superscript letter (a, b, c, d: Bonferroni correction; p .05).
Appendix: Chronology of the eBay MCS
and the Focal Seller (1995 – early 2008)
The major changes to the eBay MCS by year and approximate month appear below.
Dates for the eBay system changes noted below are approximate; roll-out dates differed by
region, e.g., Asia vs. US. Dates given below are best estimates for US completed revisions based
on web-searches (google news) related to eBay history and archival sources, most importantly,
Cohen (2002).
1995-1996
9/1995 – AuctionWeb (eBay precursor) opens for trading
2/1996 - eBay launches the Feedback Forum (EBay 2008)
11/1996 – eBay begins testing feedback ―star‖ system
9/1997 (Begin phase 1): Focal Seller begins trading on eBay
3/1/2000 (End phase 1): All feedback must be related to an auction transaction (Marino 2000)
7/11/2001 (End phase 2)
Distinguish transaction roles, i.e., separate buyer versus seller feedback (Steiner 2001)
View feedback left by the user from the Feedback profile (Steiner 2001)
―members may leave follow-up comments without waiting for response to original
feedback comment‖ (Steiner 2001)
9/9/2003: Members are now banned from: (1) including, in a listing, terms and conditions that
restrict or limit the ability of a member to leave feedback about the listing, or, (2) selling, trading
or buying feedback (Steiner 2003c).
2/9/2004 (End phase 3): Mutual feedback withdrawal policy implemented (Steiner 2004). eBay
will remove negative comments between buyers and sellers who resolve a transaction
disagreement.
8/2/2005 (Case ends): eBay delists focal seller.
9/2005 to 2/2008: eBay MCS Changes Subsequent to Case
10/2005 - Replaces Feedback solicitation policy with feedback manipulation policy: more
comprehensive policy allows eBay to take measures against questionable patterns, such
as "selling a items for 10 cents with free shipping, then moving to plasma TVs"(Steiner
2005)
11/2005 - Members with 10 or fewer feedback ratings must complete a tutorial before
leaving first negative or neutral feedback (Steiner 2005).
12/2005 - Feedback from nonpaying buyers no longer affects feedback ratings (Steiner
2005)
10/2006 - Sellers can no longer use the private feedback setting (Steiner 2006)
5/2007 – ―In 2007, eBay began using detailed seller ratings with four different categories.
When leaving feedback, buyers are asked to rate the seller in each of these categories
with a score of one to five stars, with five being the highest rating and one the lowest.
Unlike the overall feedback rating, these ratings are anonymous; neither sellers nor other
users learn how individual buyers rated the seller. The listings of sellers with a rating of
4.3 or below in any of the four rating categories appear lower in search results. Power
Sellers are required to have scores in each category above 4.5‖ (Wikipedia 2010).
2/2008 – Sellers can no longer leave negative feedback for buyers (Anonymous 2008;
World Law Direct 2008). Positive, repeat-customer feedback will count towards ratings -
feedback that is more than 12 months old will not. Negative and neutral feedback left by
a buyer will be removed for transactions in which a buyer does not pay for the item, or, if
the buyer is suspended.
