towards a model of online auction deception

Towards a Model of Online Auction Deception

By Alex Nikitkov1 and Dan N. Stone2

Contact Author:

Alex Nikitkov

Brock University 243 Taro Hall

St. Catharines, ON L2S 3A1 Tel. (905) 688-5550 ext. 3272 E-mail: [email protected]

December 24, 2005

Alex Nikitkov thanks Brock University for the grant supporting this study. Dan Stone thanks the University of Kentucky and the Gatton College of Business and Economics for grants supporting his research.

1 Assistant Professor, Brock University 2 Gatton Endowed Chair, University of Kentucky

Towards a Model of Online Auction Deception

Abstract

The phenomenal growth of e-commerce and online auctions is exceeded only by the

corresponding growth in e-commerce and online auction fraud. Herein, we report two pilot

studies that use eBay data, and the Bell & Whaley (1991) taxonomy of cheating, to investigate

the nature and frequency of online auction deception. Study one (n = 24) categorizes a one-week

sample of online transactions into deception strategies in two general classes (i.e., hiding versus

showing) and three strategic domains (i.e., product, seller, and transaction characteristics).

Results indicate more frequent use of hiding than showing strategies, and, more frequent

deception in product and seller than transaction characteristics. Study 2 (n = 31) replicates the

results of Study 1 and presents a nascent model of the effects of deception strategies on

transaction characteristics and outcomes. Results indicate that showing deception strategies are

associated with marginally higher sales prices, higher levels of seller experience, and, higher

levels of unusual or undesirable transaction outcomes (e.g., no sale, buyer dropped from market,

inexperienced (falsified?) buyer). In contrast, hiding strategies are associated with less

experienced sellers but have no apparent effects on transaction outcomes. Limitations of both

studies include high levels of beta error and a small sample of online markets. The results

indicate that experienced sellers use different deception strategies than do novice sellers, and,

that the strategies used by more experienced (but not novice) sellers change transaction

outcomes.

I. Introduction

While e-commerce has recently grown 25% per year, evidence suggests that

internet fraud is growing at an even faster rate (Laudon & Traver, 2004). Internet fraud

has many subcategories. However, Internet auction fraud is the most frequently reported

online offense, comprising 71.2% of reported complaints (Internet Fraud Complaint

Center (IFCC), 2005). Online auction fraud is also among the most expensive forms of e-

commerce fraud. In 2003, the U.S. Federal Trade Commission (FTC) reported $100

million in losses due to online auctions (Wingfield, 2004). Further, the incidence of

online fraud is increasing. For example, IFCC reports a 66.6% increase in complaints

from 2003 to 2004 (158% increase for 2003).

E-commerce and online auction fraud impose multiple costs on individuals,

companies, and society. For seller’s, fraud costs include the direct financial losses, lost

sales due to fraud risk, costs of preventive and detective internal controls, and discounts

to attract online buyers (cf. Grazioli and Jarvenpaa, 2000). For buyer’s, fraud costs

include monetary and time losses as well as loss of trust in the particular seller and in e-

commerce viability as a whole. Further, high fraud rates can increase regulation (e.g. the

Sarbannes-Oxley (SOX) act) which increases business costs of business and as a result

decrease the competitiveness of online relative to brick-and-mortar merchants.

Online auctions are more vulnerable to fraud than are brick-and-mortar

transactions due to increased information asymmetry between sellers and buyers

(Kauffman & Wood, 2000). Considerable evidence highlights the importance of trust in

online commerce (McKnight et al. 2002, Pennington et al. 2003). For example, Grazioli

and Jarvenpaa (2000) find a strong negative relationship between deception and trust in

Online Auction Deception Page 2

online trading. Research also suggests that increasing trust in online environments

increases consumer purchasing intentions and behavior (Ba et al. 2002, Pennington et al.

2003, McKnight et al. 2002, Nikitkov 2006). Building trust in online auctions is an

important antecedent to the continued growth of such markets. Grazioli & Jarvenpaa

(2000) argue for the importance of research directed towards understanding the predictors

of fraud and deception. Indeed, the ability to successful identify online fraud is an

important corollary of improving market security, privacy, and trust.

This paper reports the results of two pilot studies that explore the type, frequency,

and effects of online auction deception. We next describe the eBay auction market,

introduce online deception, and discuss the Bell and Whaley deception framework.

Study 1 then explores the feasibility of grounding the Bell and Whaley framework in

online auction seller deception strategies. Following this, study 2 investigates the

relationships among seller experience, deception strategies, and transaction outcomes.

We conclude by discussing the limitations and contributions of the study.

II. Online Seller Deception in eBay Auctions

A. The eBay Auction Market

eBay is the world’s largest online auction. Daily, it lists more than sixteen million

items for sale and recorded $24 billion in sales in 2004 (Wingfield, 2004). Federal Trade

Commission (FTC) data and eBay’s counter-measures to fraud suggest that fraud is an

increasing problem for eBay. For example, eBay spokesperson Kevin Pursglove says that

the company’s published fraud rate is about one-tenth of one percent of transactions; in

contrast, some online “vigilante” groups argue that in some categories, eBay fraud rates

approximate 75% of listings, and, that the incidence of fraud is growing 35-40% a year


(Sullivan 2005). Because evidence suggests that online seller fraud is more pervasive

and problematic than buyer fraud (IFCC 2003), we focus on seller misrepresentations in

online auctions.

Existing research investigates issues of security and trust in the eBay market. For

example, research investigates the ability of reputation feedback mechanisms to increase

consumer trust in online purchases (Dellarocas 2004, Ba et al. 2002, Lee et al. 2000,

McDonald et al. 2002). Research also investigates the usefulness of information

assurance seals provided by eBay or third parties in building customer trust (Nikitkov

2006). We contribute to this body of knowledge by seeking to build a model of cues that

help in diagnosing potentially deceptive eBay listings.

B. Detecting Online Deception

Deception is a misrepresentation by an opportunistic agent that is designed to

change the target’s behavior in a way that increases the agent’s payoff(s) or probability of

payoff(s) (Hyman 1989). Deception and its detection have been studied for decades. One

approach to detecting deception is to identify verbal and nonverbal cues as to its presence

(Buller et al. 1994, DePaulo et al. 2003, Zuckerman et al. 1985). For example, the

identification and systematic analysis of deception cues is one mechanism for improving

the detection of lies (Vrij et al. 2004). Training in deception cue identification, including

in electronic contexts, has been shown to increase deception detection accuracy (George

et al. 2004).

The strategy of detecting deception through cue identification is problematic in

the online auction environment. Potential online buyers cannot observe cues that are

present in face-to-face interactions that are known to be associated with higher deception


rates (e.g., increased eye blinking, changes in voice pitch, increased self-grooming)

(George et al. 2004). We speculate that the detection of deception through cue

observation can be extended to the online auction environment. However, this requires

identification and validation of cues that successfully predict online deception.

C. The Bell & Whaley Deception Framework

Categorical (i.e. nominal) measurement is the starting point of science (Shadish at

al. 2002). Well-specified categories provide a skeleton of characteristics that enable

generalization and progress from ideographic to nomothetic science. Herein, we build

upon Bell (1982) and Bell & Whaley (1991) framework of cheating and deception. This

framework has been applied to military tactical planning (Hughes 1990, Parker Jr. 1991),

to preventing and safeguarding against information systems hacker attacks (Cohen at al

2005), to model fraud prevention and detection in financial statement analysis (Johnson

et al, 2001), and to study the effects of training and warning on sensitivity to deception

(Biros et al. 2002).

Bell (1982) and Bell & Whaley (1991) posit two broad deceit strategies: (1)

hiding the real (i.e., concealment) and (2) showing the false (i.e., simulation).

Concealment influences the target by preventing the target’s access to otherwise

observable phenomenon. In contrast, simulation influences the target by offering a

distorted (i.e., simulated) image of reality. Bell (1982) suggests three concealment tactics:

masking, repackaging, dazzling, and three simulation tactics: mimicking, inventing,

decoying. Stech & Elsasser (2004) suggest two additional tactics: red flagging as a

concealment strategy, and, double play as a simulation strategy. Bell and Bell and

Whaley (1991) argue that rational deceivers choose from these deceit strategies based on


their feasibility, and, the joint likelihood (i.e., in combination with other strategies) of

their success in increasing the likelihood and amount of desired payoffs (cf. Dellarocas

2004).

Within the online auction environment, we speculate that the strategies and tactics

of deceit may be applied to product, seller, and transaction characteristics. Product

characteristics relate to the product or services offered for sale. Seller characteristics

relate to displayed characteristics such as the seller’s location, reputation feedback,

displayed assurance seals, and commitment to support honest trade. Transactions

characteristics include hiding or showing bidder identities, responses to buyer queries,

and acceptable payment methods.

Figure one presents a proposed model deception strategies and tactics in online

auctions. The first column summarizes the available strategies (hiding and showing) and

related tactics. The second column relates to the characteristic to which the strategy and

tactic is applied. The third column illustrates the ultimate goal of the deception strategies

and tactics: i.e., an illusion that effectively deceives the buyer.

Insert Figure 1 about here

III. Study 1 – A Qualitative Pilot Study of Seller Deception in Online Auctions

Study 1 is a primarily qualitative investigation of the presence of the strategies

identified in Bell and Whaley (1991) deception framework among sellers in the eBay

online auction market. One goal of the study is to generate a “grounded theory” that

applies the Bell and Whaley (1991) framework to online auction market.


A. Method

Study 1 employed a theory-based qualitative sampling strategy (Miles and

Huberman 1994) that was based on the Bell and Whaley deception framework. One of

the authors searched for examples of each of the Bell and Whaley deception strategies

and tactics in one week of listings during April 2005 in two eBay product categories:

Computer and Networks or Consumer Electronics categories. We chose these product

categories because of evidence that they have relatively high rates of deception compared

with many other product categories (IFCC 2004; Warner 2003).

For listings that employed identified deception strategies, one of the authors

categorized the strategy into the following categories:

1. strategy: hiding or showing

2. tactic within the strategy: hiding (masking, repackaging, dazzling, red-

flagging), showing (mimicking, inventing, decoying, double-play)

3. domain: product, seller, or transaction

B. Results

We identified twenty-four listing cases that showed evidence of deception

strategies and tactics. Table 1 summarizes the frequency of the observed deception

strategies and their related domains in the identified cases. Hiding strategies were twice

as frequent as were showing strategies. In addition, deception in the product and seller

domains was 33% more frequent than was deception in the transaction domain. A Chi-

square test for independence between strategy and domains approaches significance

(χ2(2) = 4.0, p = .14). The data suggest that hiding strategies may be more frequent in the

product and seller domains, while showing strategies may be more frequent in the


transaction domain. However, given the small sample and corresponding high levels of

Beta error, the significance and importance of this effect is unclear.

Insert Table 1 about here

Table 2 categorizes the twenty-four cases of deception by strategies, tactics, and

domain. The second column of this table includes speculations about possible uses of

each tactic by eBay sellers, although several of these potential tactics were not observed

in the reported data. Table 2 Panel A categorizes the sixteen cases of hiding strategies by

tactics and domain. Table 2 Panel B categorizes the eight cases of showing strategies by

tactics and domain.


B.1 Hiding Strategies (See Table 2 Panel A)

B.1.1 Hiding Strategy: Masking Tactic. Masking hides the real through

invisibility (Bell 1982). The data revealed six cases of masking tactics in the product

domain. These six cases were:

Case #1: including an image from the Original Equipment Manufacturer’s (OEM)

web site but providing little or no information about actual unit offered for

sale,

Case #2: providing no image(s) of the product,

Case #3: providing an obscured image of the product,

Case #4: providing a description copies from the OEM web site,

Case #5: providing a vague product description,

Case #6: promising to add information at a later time or asking buyer to email the

seller for more product information.


We observed one case of masking related to seller characteristics. In this case

(#9) the seller blocked buyer access to the seller feedback profile. We also observed one

case of masking related to transaction characteristics. In this case (#18), the seller hid the

identities of bidders on the transaction.

B.1.2 Hiding Strategy: Repackaging Tactic. Repackaging disguises the real (Bell

et al. 1991). In online auctions, repackaging tactics may attempt to disguise potential cues

as to a problem product, seller, or transaction. We found no examples of repackaging

tactics related to products. We identified six cases of repackaging related to seller

characteristics. These cases included:

Case #10: providing false reasons for changing seller user identification,

Case #11: building reputation feedback by purchasing rather than selling,

Case #12: building reputation feedback from small transactions,

Case #13: falsely displaying information assurance seals (Square Trade, Verified),

Case #14: Reserving an eBay user identification in advance, in order to give the

appearance of a longer tenure as an eBay user,

Case #15: providing false information about the seller’s location.

We did not identify the application of a repackaging tactic to the transaction

domain.

B.1.3 Hiding Strategy: Dazzling Tactic. Dazzling hides the real through

confusion. For example, when masking and repackaging ruses fail, dazzling covers the

retreat. The purpose of dazzling is to confuse the victim, to make victim loose clear

vision of underlying reality or possibly set a blame on someone else, or win time, or

minimize the consequences of the deceit (Bell 1982). We identified one case of dazzling


in the product domain. Case 7 provided a confusing (i.e., dazzling) text description of the

product. We did not observe the dazzling tactic in the seller or transaction domains.

B.1.4 Hiding Strategy: Red Flagging Tactic. Red flagging hides the real by

displaying potentially negative characteristics, but in a light that suggests that the

negative characteristics are innocuous. In the product domain, this strategy might result

in the seller identifying a problem with the product but claiming that the seller lacks

sufficient the knowledge to diagnose it. We observed one case (#19) of red flagging in

the transaction domain. In this case, the seller claimed that they do not accept Paypal

because they have encountered problems with it in the past and their account was frozen.

The seller then suggested the use of BidPay instead of Paypal for electronic payment.

While BidPay is a legitimate online transfer system, evidence suggests that it is more

frequently prone to seller abuse than is PayPal.

B.2 Showing Strategies (See Table 2 Panel B)

B.2.1 Showing Strategy: Mimicking Tactic. Mimicking shows the false through

imitation. In this strategy, the deceiver does not hide or repackage but rather goes on the

offense. We identified one case (#8) of a mimicking tactic in the product domain. In this

case, the seller displayed an image taken from another seller which did not match the

actual product of the seller. The intent of this deception appeared to be to create the false

impression that the seller had a better, more expensive product for sale. We also

identified one case of a mimicking tactic in the seller domain. In this case (#16), the

seller used an eBay identity that closely approximated that of a well-established brand

name. We also observed one case (#20) of a mimicking tactic applied to the transaction

domain. In this case, the seller advertised a non-existent manufacturer’s warranty.


B.2.2 Showing Strategy: Inventing Tactic. Inventing shows the false by

displaying a different, newly created, reality. In deception, the inventing strategy

involves showing the false in such a way that it has no analogy in genuine (typical or

legitimate trade) practice. We observed one case of inventing in the seller domain, and,

two cases in the transaction domain., The case in the seller domain (#17) involved the

“invention” of an innocent seller, i.e., claiming innocence related to negative feedback

(despite evidence to the contrary). One case of inventing related to transaction

characteristics (#21) involved attempting to lure buyers off of eBay to purchase an item

by email, thereby avoiding eBay transaction fees and eBay protections to the buyer. The

second case of inventing related to transaction characteristics (#23) involved attempting

to lure buyers off of eBay by using “Submit best offer” option based on the same

motivation as is given for case #21.

B.2.3 Showing Strategy: Decoying Tactic. Decoying shows the false by diverting

attention from the real. We found one example of decoying in the transaction domain

(#22). In this case, the buyer indicated that PayPal was an accepted payment method, but

later “decoyed” the buyer by claiming that, “I cannot accept Paypal because my account

has been frozen two days ago, so other methods of payment are personal check, money

order, or wire transfer”. These methods put the buyer at greater risk of seller fraud.

B.2.3 Showing Strategy: Double Play Tactic. A double play tactic involves

minimizing suspicious information. For example, a seller might employ a double play in

the product domain by stating a product’s faults but also claiming that they are too minor

to be considered (e.g. scratches are very minor or the item is refurbished but passed

manufacturer’s quality control for new merchandise). We observed one case of a double


play tactic in the transaction domain (#24). In this case, the seller claimed that the listing

“was only a test” but also provide all of the information to the buyer needed to contact

the seller for an actual sale.

C. Discussion

Study 1 provides evidence that eBay sellers use deception strategies and tactics

that are found in the Bell and Whaley (1991) deception framework. In addition, Study 1

provides evidence of the usefulness of the Bell and Whaley framework as a lens for

understanding online auction deception among sellers.

One limitation of Study 1 however, is its small sample size. In addition, Study 1

does not investigate whether sellers with differing levels of experience may use different

deception strategies, or, how deception strategies affect transaction outcomes. Study 2

attempts to provide some evidence related to the relationship among seller characteristics,

deception strategies, and transaction outcomes.

IV. Study 2 – A Pilot Study of Seller Characteristics, Deception Strategies, and Transaction Outcomes

Study 2 investigates the relationship among seller characteristics, deception

strategies, and transaction outcomes. Our goal in Study 2 is to ground the nascent theory

developed in Study 1 in quantitative data from an online auction market. Accordingly,

we collected quantitative data about the relationships among seller characteristics,

deception strategies, and transaction outcomes. Our intuitive speculations about the

relationships among these variables, which are not informed by specific theory, are as

follows:

Speculation #1: Experienced and novice eBay sellers will use different deception

strategies.


Speculation #2: The deception strategies used by experienced eBay sellers will be

more successful at deceiving buyers than will the deception strategies used by novice

eBay sellers.

A. Method

One author collected data on all offers (listings) for Apple PowerBook G4 laptop

computers with 1.67 GHz processor speed for one day in November 2005. We chose this

market because of potentially high rates of deception in the laptop computer market, and,

because unambiguous market prices are available for this product from its manufacturer.

The resulting sample produced 31 observations.

For each listing, we collected data on:

1. the number of deception cues in the listing using the method described in

Study 1,

2. if present, the domain of deception cues (product, seller, transaction), using

the method described in Study 1,

3. if present, the deception strategy (hiding or showing), using the method

described in Study 1,

4. two measures of seller experience: (a) the ratio of seller sell to buy

transactions, and (b) the total number of seller eBay transactions,

5. ending sales price,

6. transaction outcome, which measures the success or failure of a listing. We

coded this variable as:

a. 1: sale occurred and buyer posted positive feedback (successful

transaction)


b. 0.5 if a sale took place, the buyer did not post feedback, and none of

the condition listed in item “c” below obtained (ambiguous

transaction)

c. 0 if (unsuccessful transaction):

i. The listing closed prematurely: this can occur because eBay

suspects fraud, or, because the seller is dissatisfied with

existing offers

ii. Item received no bids, potentially indicating a lack of buyer

trust in the seller

iii. Sale occurs but buyer is delisted (removed) by eBay within 2

weeks of the sale

iv. Sale occurs but buyer has a feedback rating < 15 which

potentially indicates a seller using a buyer pseudonym to

“purchase” his or her own product.

v. Highest bid was entered after the end of the auction (which

violates eBay market rules).

B. Results

B.1 Descriptive Results

We identified forty-nine deception cues in the thirty-one listing in our sample.

The listings averaged 1.10 hiding and 0.48 showing deception cues. Six listings had no

deception cues while two listings had four deception cues. Table 3 summarizes the

frequency of the observed deception cues by strategy and domain. Consistent with Study

1, we observe a higher frequency of hiding than showing strategies. Also, consistent with


Study 1, we observe higher deception rates in the product and seller domains than in the

transaction domain. A Chi-square test of independent approaches significance, (χ2 (2) =

5.72, p = .06) which may suggest dependences in the relationship among strategies and

domains. If reliable when verified with a larger sample, the data suggest that hiding

strategies may be more frequent in the product domain, while showing strategies are

more common in the seller and transaction domains.


B.2 Tests of Speculations

Table 4 presents correlation results. Speculation #1 argues that experienced and

novice eBay sellers will use different deception strategies. Consistent with this

speculation, sellers with higher sell-to-buy transaction ratios use more showing deception

strategies than do sellers with lower sell-to-buy transaction ratios. Also consistent with

this speculation, sellers with fewer total eBay transactions use marginally more hiding

deception strategies than do sellers with more total eBay transactions. These results

provide some support for speculation #1.


Speculation #2 argues that the deception strategies used by experienced eBay

sellers will be more successful at deceiving buyers than will the deception strategies used

by novice eBay sellers. Consistent with this speculation, showing deception cues (which

are used more frequently by experienced sellers) positively (though marginally) correlate

with higher sales prices. In addition, showing cues positively correlate with successful

transaction outcomes.


V. Discussion, Limitations, and Conclusion

Study 2 provides evidence that online auction sellers may differentially use

deception strategies depending on their levels of experience. In addition, we find some

evidence that showing deception strategies, which are used more frequently by

experienced sellers, are more successful at deceiving buyers than are hiding deception

strategies. Although preliminary, the data suggest that experienced deceivers use

different, more successful, strategies than do inexperienced deceivers.

Our studies are subject to several important limitations. Our samples are both

small, and from a small set of eBay markets that are thought to have relatively high levels

of deceptive practices. Accordingly, our results are unlikely to generalize to markets

with fewer opportunities, and smaller payoffs, for deception. In addition, our small

samples, and correspondingly high levels of Beta error, may result in our failing to reject

null hypotheses that, with higher levels of statistical power, we would reject. We plan to

expand our samples for both studies. In addition, our pilot data were coded by only of

the authors. Hence, our coded variables may contain higher levels of noise and bias than

if we used a multi-coder approach and tested for agreement among coders.

Our studies investigate an important, but difficult to investigate, phenomenon:

online auction fraud. Our research strategy is to conceive of deception as a general and

generalizable phenomenon, and to therefore seek a general theory of deception. Our

goals in this research are to create a grounded theory of online seller deception, to

identify a set of information cues that are valuable in detecting this deception, and to

empirically validate these cues. The pragmatic goal of such research is to enable online

buyers to identify the strategies and tactics of seller deceit, and to take appropriate


countermeasures. However, fraud strategies and tactics are an evolutionary phenomenon.

Today’s successful deceit tactics, if overused (e.g., the Nigerian fraud letter scheme)

become tomorrow’s ubiquitous, though harmless annoyances. Though an uncomfortable

realization for buyers and consumers, the evolving nature of deception at least promises

to provide deception researchers with an unending set of strategies and tactics to

investigate in future research.


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Hiding (Concealing) Masking Repackaging Dazzling Red-Flagging Showing (Simulating) Mimicking Inventing Decoying Double-Play

Deception Strategies and Tactics

Product

or (and)

Seller

or (and)

Transaction

Applied to Characteristics of

Selected tactic is

applied to

Illusion (false perception of reality)

Figure 1

Proposed Model of Deception Strategies and Tactics in Online Auctions (Adapted

from Bell & Whaley, 1991)

creates

Goal


Table 1 Study 1 - Frequency of Observed Strategies & Domains

Domain

Strategy Product Seller Transaction Row

Totals % by Row

Hiding 7 7 2 16 66.7% Showing 2 2 4 8 33.3% Column Totals 9 9 6 24 % by Column 37.5% 37.5% 25.0% 100.0%

χ2(2) = 4.0, p = .14


Table 2 Panel A - Hiding (Concealing) Strategies Deception Strategies and Tactics and Example Application to eBay

Hiding Tactics Potential Application to eBay Sellers

Case # Domain

Displaying image from the original equipment manufacturer (OEM) Web site that does not represent the actual item for sale 1

Not including an image of the product 2 Displaying an obscure image of the product 3

Cut-and-paste description from OEM Web site 4

Providing vague textual description 5

Promising to add information at a later date or asking to email for information as a form of a lure 6

Product

Preventing access to the seller's feedback 9

Providing no or not meaningful information on the location of the seller Seller

Masking

Hiding bidder identities 18 Transaction Providing subjective product descriptions

Selling counterfeit product under a name that resembles a known brand.

Product

Suggesting a false reason for ID change 10 Collecting positive feedback primarily from buying not selling 11

Collecting positive feedback on primarily small transactions 12 Using multiple eBay IDs to falsely create self-delivered positive feedback Falsely using assurance seals 13 Reserving ID with eBay in advance 14

Providing unsupported statements of seller's commitment to honest trade Providing false information about seller location 15

Making false claims about affiliation with a brick-and-mortar (established) company

Seller Repackaging

Delaying shipment in order to gain time to defraud other potential buyers

Transaction

Providing confusing text description 7

Including an image of the item that does not match the textual description

Product

Claiming that seller was away from home and now will ship product Seller

Claiming that the drop-shipping company had a problem

Claiming that the merchandise is no longer available and payment will be returned

Dazzling

Not responding to buyers’ inquiries after receiving a payment

Transaction

Stating that there is a problem with the product, but claiming that seller lacks the needed knowledge to diagnose the problem Product Stating that the price for the item is abnormally low because the seller is new to eBay and is seeking to build a quick, positive reputation Seller Red

Flagging Claiming that the seller does not accept a common method of payment because seller has problems with it 19 Transaction


Table 2 Panel B - Showing (Simulating) Strategies Deception Strategies and Tactics and Example Application to eBay

Showing Tactics Potential Application to eBay Sellers

Case #

Domain of Application

Displaying an image from another seller 8 Selling stolen merchandise

Showing pictures of similar but not the described merchandise (alternative model, lack of options, absence of accessories)

Product

Using an ID that resembles an established and known seller 16 Falsely claiming to be an authorized dealer

Seller Mimicking

Offering a non-existent manufacturing warranty or not-honoring seller's personal guaranty 20

Transaction

Mailing different merchandise than that displayed in the listing Product Claiming innocence about negative feedback 17 Seller Luring a buyer off eBay to purchase an item through e-mail 21 Restricting bidders to pre-approved list

Inventing

Using "Submit best offer" option to lure buyers off eBay 23 Transaction

Focusing buyer's attention not on the item, but on a bonus that comes with the item

Product

Claiming that seller's account was compromised by hackers and that the seller did not sell disputed item

Seller

Listing conventional methods of payment on eBay page, but stating later in invoice that only Western Union or Money Gram payment will be accepted. 22

Decoying

Threatening the buyer with negative feedback, or, revealing negative information to eBay, the INS, IRS, or another governmental agency.

Transaction

Falsely minimizing merchandise's faults Product Claiming that the seller recently had trouble with one dishonest buyer who unjustly spoiled all the reputation feedback record to justify changing identities.

Seller Double Play

Stating in the description that listing is a test, but providing all the information and inviting buyers to contact the seller 24

Transaction


Table 3 Study 2 Results - Frequency of Observed Strategies & Domains

Domain

Strategy Product Seller Transaction Row

Totals % by Row

Hiding 18 13 4 35 71.4% Showing 2 9 3 14 28.6% Column Totals 20 22 7 49 % by Column 40.8% 44.9% 14.3% 100.0%

χ2 (2) = 5.72, p = .06

Table 4 Study 2 Results – Pearson Correlations Among Variables (n = 31) Significant and marginally significant results (p ≤ .10) shown in bold

Hiding Cues Showing Cues Sales Price

Seller Ratio of Sell to Buy Transactions

Seller # of eBay Transactions

Showing Cues -0.18

Sales Price 0.02 0.32*

Seller Ratio of Sell to Buy Transactions

-0.33 0.59*** 0.20

Seller # of eBay Transactions

-0.33* 0.11 0.23 0.76***

Transaction Outcome (1 = success, 0 = not success)

0.00 0.37** 0.06 0.18 0.03

*** significant at p ≤ .01 ** significant at p ≤ .05 * significant at p ≤ .10

towards a model of online auction deception

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