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THEORY AND REVIEW

PRODUCT-RELATED DECEPTION IN E-COMMERCE:

ATHEORETICAL PERSPECTIVE1

Bo Xiao

Department of Computer Science (Computing and Information Systems), Faculty of Science,

Hong Kong Baptist University, Kowloon, Hong Kong, CHINA {[email protected]}

Izak Benbasat

Sauder School of Business, University of British Columbia, 2053 Main Mall,

Vancouver, BC V6T 1Z2, CANADA {[email protected]}

With the advent of e-commerce, the potential of new Internet technologies to mislead or deceive consumers has

increased considerably. This paper extends prior classifications of deception and presents a typology of

product-related deceptive information practices that illustrates the various ways in which online merchants

can deceive consumers via e-commerce product websites. The typology can be readily used as educational

material to promote consumer awareness of deception in e-commerce and as input to establish benchmarks

for good business practices for online companies. In addition, the paper develops an integrative model and

a set of theory-based propositions addressing why consumers are deceived by the various types of deceptive

information practices and what factors contribute to consumer success (or failure) in detecting such deceptions.

The model not only enhances our conceptual understanding of the phenomenon of product-based deception and

its outcomes in e-commerce but also serves as a foundation for further theoretical and empirical investigations.

Moreover, a better understanding of the factors contributing to or inhibiting deception detection can also helpgovernment agencies and consumer organizations design more effective solutions to fight online deception.

Keywords: Product-based information practices, electronic commerce, typology, stimulusorganismresponse

framework, model of deception detection

Introduction1

The rapid growth of electronic commerce (e-commerce) has

created fertile ground for online fraud and deception (Federal

Trade Commission 2001; Grazioli and Jarvenpaa 2000). The

Federal Trade Commission (FTC), the U.S. Securities and

Exchange Commission (SEC), the Federal Bureau of Inves-

tigation (FBI), as well as consumer protection agencies, such

as the National Consumers League (NCL), have all voiced

concerns over Internet consumer fraud and have initiated

specialized programs targeted at detecting and prosecuting

such practices (Grazioli and Jarvenpaa 2003b). According to

the annual report released by the Internet Crime Complaint

Center (IC3)a partnership between the FBI, the National

White Collar Crime Center (NW3C), and the Bureau of

Justice Assistance (BJA)in 2008, the IC3 received a total of

275,284 complaints from consumers claiming to have been

defrauded online, which represents a 33.1 percent increase

over the previous year; the total dollar loss linked to online

fraud was U.S. $265 million, about $25 million more than in

2007; and the average individual loss amounted to $931

(Internet Crime Complaint Center 2008). In addition to finan-1M. Lynne Markus was the accepting senior editor for this paper. Choon

Ling Sia served as the associate editor.

MIS Quarterly Vol. 35 No. 1 pp. 169-195/March 2011 169


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Xiao & Benbasat/Product-Related Deception in E-Commerce

cial damage, victims of Internet deception often suffer from

the psychological damage of being victimized, the loss of

time for filing complaints and refund requests, and the loss of

private information (Grazioli and Jarvenpaa 2000). The

increases in online fraud can also adversely affect honest

online businesses. When consumers are reluctant to make

purchases online for fear of deception, online businesses as a

whole suffer from loss in sales and reputation.

Consumer deception is clearly an important concern for

e-commerce. Prior studies in offline and online deception

(Boyle 2003; Davies and Parasuraman 1982; Gilovich 1991;

Johnson et al. 1993; Johnson et al. 2001; Klein et al. 1997)

have focused on investigating factors contributing to indi-

viduals detection (or nondetection) of another partys

deliberate attempts to deceive. However, they have not

investigated the rather obvious question ofwhy individuals

may actually be deceived by deliberately deceptive practices.

In a world where deceptive practices are perpetrated viatelephone, in print, on radio, or on TV, deception theorists

may not have asked this question because of the commonly

held assumption that people are deceived because they do not

pick up the nonverbal behavioral cues leaked that can serve

as telltale signs of deception (i.e., changes in deceivers

behaviorsuch as pupil dilation, higher vocal pitch,

fidgeting, blinkingas a result of the physiological arousal,

emotional reactions, cognitive effort, and attempted control

associated with deception) (Ekman and Friesen 1969;

Zuckerman et al. 1981, 1986). Media richness theory (Daft

and Lengel 1986) and social presence theory (Short et al.

1976), which are often used as the theoretical foundations for

empirically examining deception detection across different

media, suggest that people should be more accurate in

detecting deception in richer media (e.g., face-to-face)

where more nonverbal cues are available for judgment. For

instance, Short et al. (1976) found that people are more

responsive to fraud attempts via phone than via face-to-face

communication because the lack of visual cues in the phone

medium makes people more vulnerable to strategic

manipulation.

However, with the advent of e-commerce, the potential of

new Internet technologies to mislead or deceive consumers is

considerable. As noted by Heckman and Wobbrock (2000),every new technology applicable to commerce (e.g., tele-

graph, telephone, radio, or television) helps the unscrupulous

to swindle the unwary. E-commerce is no exception. Al-

though many deceptive information practices in e-commerce

settings are variations of well-known deception types already

used in the traditional physical shopping contextsuch as the

misrepresentation of merchants, products, and return/refund

policiesthe advent of e-commerce has not only made

deception more likely and the perpetration of deceptive acts

easier, but has also introduced new avenues for deception

(Grazioli and Jarvenpaa 2001, 2003a).

First, the unique characteristics of the Internet, such as digital

environment, low entry barriers, spatial/temporal separation,

and anonymity, have made it a fertile ground for deception.

The Internet is a digital environment, which lowers the effort

for online companies to create and change information

content as well as to manipulate the presentation and produc-

tion of such information content in order to achieve deception.

For instance, web pages can be constructed to attract/distract

attention, encourage/discourage cross-comparisons, force

choices, and create pressure to buy immediately (Aditya

2001). The Internet also lowers the resources needed to set up

a genuine-looking online storefront, thus making the mer-

chants identity easy to falsify and difficult to authenticate.

In addition, the physical distance between the web merchant

and its customers, as well as the temporal separation ofpayment and product delivery, makes it harder for customers

to verify the truthfulness of the website or its claims. More-

over, since anonymity provides people with a low threat

setting, it may breed disinhibited antisocial behavior (Suler

2004, 2005), including deception.

Second, the various innovative technologies supporting

e-commerce have also given rise to novel forms of deceptive

practices. Prior research (e.g., Benassi 1999; Glover and

Benbasat 2006; Grazioli and Jarvenpaa 2000) has revealed a

number of information technology (IT) mechanisms that can

be used by online companies to increase consumers trust ine-commerce websites and/or mitigate their risk perceptions

associated with online shopping. However, as illustrated in

Appendix A, both trust-building mechanisms and risk-

reducing tools can be exploited by dishonest companies to

deceive consumers (Grazioli 2004; Grazioli and Jarvenpaa

2000). For instance, an online company may design a product

recommendation agent (PRA) that provides biased product

recommendations to serve the interests of the company. It

may provide a virtual product experience (VPE) (Jiang and

Benbasat 2005, 2007a) that does not represent consumers

real experiences with a product (Bloom et al. 1994). It may

also employ multimedia technologies (e.g., Flash, animations)

to excite consumers in a manner similar to what fast-talking

salespersons can do in physical shopping settings, thus

leading consumers to make impulse purchases they may regret

later (Bloom et al. 1994). In electronic marketplaces such as

eBay, a seller can collude with a group of buyers in order to

receive unfairly high ratings, thus allowing that seller to

receive more orders from buyers and at a higher price than

deserved (Dellarocas 2000).

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Concerned with the identity crisis in the IS research com-

munity, Benbasat and Zmud (2003) urged IS researchers to

pay close attention to the information technology artifact and

to incorporate it in IS theories and design recommendations.

In the case of e-commerce, there is significant potential for IT

(i.e., the Internet and the technologies supporting

e-commerce) to contribute to consumer deception. This paper

makes the following contributions to research and practice:

It extends prior classifications of deception and presents

a typology of product-related deceptive information

practices that illustrates the various ways online mer-

chants can deceive consumers via e-commerce product

websites. The typology can be readily used as educa-

tional material to promote consumer awareness of

deception in e-commerce and as input to establish bench-

marks for good business practices for online companies.

It develops an integrative model and a set of theory-basedpropositions addressing why consumers are deceived by

the various types of deceptive information practices and

what factors contribute to consumer success (or failure)

in detecting deceptions. It enhances our conceptual

understanding of the phenomenon of product-based

deception and its outcomes in e-commerce and serves as

a foundation for further theoretical and empirical investi-

gation. Moreover, a better understanding of factors

contributing to or inhibiting deception detection can also

help government agencies and consumer organizations

design more effective solutions to fight online deception.

This paper focuses onproduct-relateddeceptive information

practices. A 1999 survey by Indiana University and the

professional service organization KPMG revealed that

product information is the most important concern for online

customers over the age of 25 (Williams and Larson 2000).

The importance of product information to consumers has

motivated online merchants to perform deceptive manipu-

lations on such information so as to influence consumers

judgment and decision making in e-commerce settings.

According to Pavlou and Gefen (2005), product mis-

representation is one of the most common forms of Internet

fraud reported. The NCL has also consistently ranked product

misrepresentation among the top two Internet scams (e.g., NCLs Fraud Center 2007). Many consumers enjoy the

convenience and low prices offered by online shopping, yet

these benefits may be countermanded by the increased risk

associated with the products purchased online.

In the next section, we present a typology of product-related

deception information practices in e-commerce. Following

that, we introduce the conceptual model of why consumers ay

be deceived by deceptive practices embodied in websites, and

then go on to define the constructs that make up the model.

Next, we present propositions concerning the relationships

among the constructs. In the final section, we discuss

contributions to research and practice and suggest directions

for future work.

A Typology of Product-Related DeceptiveInformation Practices in E-Commerce

Deceptive practices revealed in prior research include:

(1) insufficient information disclosure of refund policies,

warranty information, and cancellation terms; (2) contract

default; (3) product-related deceptive information practices,

such as product misrepresentation; (4) nondelivery or late

delivery of product; and (5) misuse of personal and financial

information (Pavlou and Gefen 2005; Singsangob 2005).

To obtain an understanding of deceptive information practices

in e-commerce, we first identified the definitions of deception

suggested by psychologists and communication researchers.

They include

A communicators deliberate attempt to foster in others

a belief or understanding which the communicator

considers to be untrue (DePaulo and DePaulo 1989, p.

1553).

Message distortion resulting from deliberate falsifica-

tion or omission of information by a communicator with

the intent of stimulating in another, or others, a belief that

the communicator himself or herself does not believe

(Miller 1983, p. 92).

The deliberate attempt, whether successful or not, to

conceal, fabricate, and/or manipulate in any other way

factual and/or emotional information, by verbal and/or

nonverbal means, in order to create or maintain in

another or in others a belief that the communicator him-

self or herself considers false (Masip et al. 2004, p.

148).

Three characteristics of deception appear to be consistent

across the above definitions.

1. Deception is an intentional or deliberate act. The ele-

ment of intentionality is what distinguishes deception

(i.e., intentional distortion of messages) from misinfor-

mation (i.e., unintentional distortion of messages) (Masip

et al. 2004, p. 148).

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2. Deception is accomplished by manipulating information

in some way.

3. Deception has an instrumental end goalthat is, to create

or maintain a belief in another that the communicator

herself believes to be false.

In accordance with these characteristics, this paper defines

product-relatede-commerce deceptive information practices

as the deliberatemanipulation of product-related information perpetrated by online merchants to mislead consumers in

order to induce desired attitudinal and behavioral changes in

consumerschanges that are detrimental to consumers and

beneficial to the merchants.

Prior research has suggested many different ways of classi-

fying deception. Summarizing the multitude of previous

categorizations of deception, Buller, Burgoon, and theircolleagues (Buller et al. 1994; Burgoon et al. 1994) dis-

tinguished among three relatively distinct types of deception:

(1) concealment to withhold, omit, or disguise relevant

information; (2) equivocation to present information

vaguely and/or ambiguously; and (3)falsificationto present

false or exaggerated information. Extending Buller and

Burgoons concealmentequivocationfalsification classifica-

tion, which is generic enough to be applied to different

deception cases, we include a new dimension detailing the

specific operational-level deception techniques in which the

three types of deception are carried out in the e-commerce

context. This results in a 3 3 typology of deceptive infor-

mation practices that describes how deception works within

e-commerce. The typology has two dimensions: one repre-

senting the three deception types already examined in prior

deception research and the other representing the following

three specific implementation techniques:

1. The manipulation ofinformation content, which refers to

the direct alteration of the content of product information

provided at an e-commerce website.

2. The manipulation ofinformation presentation, which

refers to the manipulation of the design of how product

information is presented to consumers at an e-commerce

website.

3. The manipulation of information generation, which

refers to the manipulation of the dynamic production of

product information at an e-commerce website, based on

consumer interests, needs, and/or preferences obtained

explicitly or implicitly.

Since the dimension of these three different deception tech-

niques is the main contribution of this paper, in the remainder

of this section, we will discuss each in greater detail.2

It should be noted many similarities exist between deception

in e-commerce product websites and deception in contexts(e.g., advertising, personal selling, close relationship, employ-

ment), particularly in advertising. While an exact demarca-

tion between honest advertising and deceptive advertising is

often difficult to make, we consider a piece of advertising

deceptive when it employs any of the deceptive manipulations

specified in our 3 3 typology of deceptive information

practices in order to mislead the consumer acting reasonably

in the circumstances, to the consumers detriment (Ford and

Calfee 1986, p. 86).

Manipulations Performed on

Information Content

Prior studies in interpersonal deception have focused

predominantly on the nonverbal cues accompanying decep-

tion, such as pitches and tones, body gestures, and facial

expressions, rather than on the deceptive messages commu-

nicated by the deceivers (Buller and Burgoon 1996).

Research investigating deceptive messages has primarily

examined manipulations that can be performed on the content

rather than thepresentation of these messages. For instance,

McCornack (1992) and Buller and Burgoon (1996) propose

that individuals can manipulate the content of information

simultaneously along several different dimensions such ascompleteness, clarity, and veridicality, which correspond to

the three deception types concealment, equivocation, and

falsification, respectively.

For product-related information provided at an e-commerce

website, content can be concealed, equivocated, and/or falsi-

fied by online companies. For instance, an online company

can withhold negative information (e.g., a known safety

problem) about a product (i.e., concealment); provide vague

information about the total cost (e.g., selling price, tax,

shipping and handling fee) of a product (i.e., equivocation);

give ambiguous information concerning product return andrefund policies (i.e., equivocation); automatically filter out

negative consumer reviews (i.e., concealment); pose as con-

2While we will focus onpersonalizedinformation (i.e., information tailored

to the specific needs of the individual customer) when discussing the

manipulation ofinformation generation, our discussion ofinformation pre-

sentation and information contentmanipulation will center on nonperson-

alizedinformation.



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sumers to write positive reviews about products and services

received from the company (i.e.,falsification); or even sell a

nonexistent product (i.e.,falsification).

In sum, by altering the availability and quality of information,

online companies can manipulate the content of productinformation at an e-commerce website so as to enhance

consumers evaluation of those particular products.

Manipulations Performed onInformation Presentation

In addition to the direct alteration of the content of product

information, deceptive manipulations can also be performed

on the presentation of product information at an e-commerce

website. Kleinmuntz and Schkade (1993) note that the

presentation of information can be designed to encourage

effective decision making. In the same vein, the design of

information presentation and delivery can be manipulated to

lead to biased decision making. Since the number of potential

ways for presentation and delivery of certain information

content is vast, we focus on two important characteristics that

apply to a broad range of contexts:presentation media and

information organization.

Presentation Media

Deceptive manipulations can be performed on the presen-

tation of product information at an e-commerce website viathe manipulation ofpresentation media . Information content

can be presented using a variety of media in the e-commerce

context, such as text, graphics, audio, video, and animations,

so that users can make better sense of the information

available (Lim and Benbasat 2000). Heller and Martin (1995)

have categorized four different types of presentation media

with increasing complexity: text,graphics,sound, and motion.

We add to this classification a new media type, virtual experi-

ence (e.g., virtual reality), which involves active consumer

interaction with the media (Jiang and Benbasat 2005, 2007a,

2007b).

Online companies can manipulate presentation media in three

ways to achieve their deceptive ends. First, an online com-

pany can alter the individual features of a medium to either

inhibit correct product understanding or foster incorrect

product understanding; for instance, an online company may

use images (still images or videos) of small size and low

fidelity to present a product that has some exterior problems

(i.e., concealment). It can also manipulate the response rate

of an online product demonstration to mislead consumers

about a particular product feature; for example, it may

demonstrate a shorter delay between shots of a digital camera

(i.e.,falsification).

Second,by manipulating the level ofvividness of a presen-tationthat is, the extent to which the presentation is

emotionally interesting, imagery provoking, and inherently

appealing (Sundar and Kalyanaraman 2004)deceptive

online companies can direct consumers attention toward

irrelevant information, distract their attention from relevant

information, and shape their overall attitude toward and

judgment of certain products. For instance, an online com-

pany may choose a text-only presentation for a product with

desirable functionalities but unappealing appearance (i.e.,

concealment). The same company may use flashy animations

as a decoy to distract consumers from processing non-vivid

yet more useful and informative textual descriptions (i.e.,

concealment). In addition to attracting consumers attention,

the sensory stimuli supplied by a vivid presentation may also

trigger intense emotional responses (e.g., pleasure and

arousal) that can overwhelm consumers self-observation

during online shopping, leading to unregulated buying

behavior (e.g., impulse buy) (LaRose 2001).

Finally, a common deceptive practice utilized by online

companies is to present conflicting information via different

media. For instance, a digital camera retailer may state truth-

fully in the textual description that the interchangeable lens of

a single lens reflex (SLR) camera is not included in the

package; however, it may display an image of the camera withlens attached, with no annotation to indicate that the lens is

not included (i.e., concealmentand equivocation). Since

information conveyed in images is given greater weight in

consumer judgment than that conveyed in text, particularly

when the two types of information are in conflict (Argyle et

al. 1971; Bone and France 2001), consumers are likely to be

misled into believing that the lens featured in the image is

actually part of the package and may potentially make a

purchase decision to their own detriment.

Information Organization

The manipulation of information presentation can also be

achieved via the manipulation ofinformation organization.

Information content in an e-commerce website can be

organized meaningfully into groups (Jarvenpaa 1989;

Kleinmuntz and Schkade 1993), hierarchies, and/or sequences

(Kleinmuntz and Schkade 1993). Information organization

provides a cognitive incentive system for decision makers by



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influencing the effort and accuracy associated with infor-

mation processing strategies and, therefore, inducing the use

of different strategies (Kleinmuntz and Schkade 1993).

Unscrupulous online companies can manipulate the way

information is organized in their web pages to encourage or

discourage the use of certain information processing strategies

by consumers. For instance, to encourage consumers to com-

pare different products based on a certain attribute (e.g., the

only attribute on which the promoted products have advantage

over other products), online companies may provide the

functionality to sort by that attribute alone (i.e., concealment).

Likewise, since information at a deep level of navigation

requires more effort to access (Chau et al. 2000), online com-

panies can hide negative product information and consumer

reviews such that consumers have to traverse many levels of

navigation to locate such information (i.e., concealment).

The sequence in which information appears may influence

consumers judgment as to the relevance and/or importance ofthe individual pieces of information (Kleinmuntz and Schkade

1993; Schkade and Kleinmuntz 1994), particularly when

consumers hold certain expectations as to the order of infor-

mation presentation; for instance, when they expect that

products are arranged in order of decreasing popularity.

Deceptive online companies can thus influence consumers

decision making by manipulating the sequence of presented

information in accordance with their expectations. For

example, online companies may present a promoted product

at the top of their bestselling list in order to increase the

chance that consumers will choose that particular product

(i.e.,falsification).

In summary, the manipulation of information presentation can

be achieved in two ways. First, by a combination of the

different means of manipulating presentation media, online

companies can influence consumers product understanding

and attention as well as emotional responses conducive to

deception. Second, by manipulating the way product infor-

mation is organized at an e-commerce website, deceptive

online merchants can influence consumers information pro-

cessing strategies as well as the accessibility and the per-

ceived relevance of information.

Manipulations Performed onInformation Generation

The third type of deceptive manipulation is performed on

information generation, which refers to the dynamic produc-

tion of product-related information at an e-commerce website,

based on consumer interests, needs, and/or preferences

obtained explicitly or implicitly. Examples of e-commerce

technology supporting dynamic production of information

include search engines, product catalogs, and online product

recommendation agents (PRAs). Our discussion of the

manipulation of information generation focuses on PRAs

because they are likely to be the first technological artifacts

with which a consumer interacts at an e-commerce website

that has a vast number of products or choices.

PRAs are software artifacts that take as input individual

consumers product-related interests or preferences, obtained

either explicitly or implicitly, and subsequently provide

recommendations for products that match the consumers

expressed interests or preferences (Xiao and Benbasat 2007).

Appropriately designed PRAs can enable consumers to make

informed purchase decisions by reducing their information

overload and search complexity, while improving their

decision quality. However, the degree to which PRAs

actually empower consumers depends upon the veracity and

objectivity of the PRAs (Hill et al. 1996; King and Hill 1994).

Users of automated decision aids have been found to place

undue trust in such technologies, leading to the abusive use of

such systems and biased decision-making processes, par-

ticularly when the competence of the systems far exceeds that

of their users (e.g., Mosier et al. 1998; Skitka et al. 1999).

Two classes of errors that often emerge in highly automated

decision-making environments are omission errors (which

occur when people fail to respond to system irregularities/

events because the automated decision aid fails to detect or

indicate them) and commission errors (which occur when

people incorrectly follow the directive or recommendation of

the automated decision aid, despite contraindications from

other sources of information) (Mosier et al. 1998; Skitka et al.

1999). Likewise, consumers (particularly those with little

experience in the intended product category) may over-rely on

the PRAs to make decisions for them, rather than using the

PRAs as one component of a thorough monitoring and

decision-making process (Skitka et al. 2000). Moreover,

people have a tendency to trust experts or specialists. Just as

we trust advice from human experts rather than that offered

by nonexpert friends and relatives, we also trust IT artifacts

that claim specialty. People who lack expertise in the subject

matter are likely to perceive a technology labeled as

specialist (e.g., PRAs acting as online sales advisors) to becredible (Reeves and Nass 1996; Tseng and Fogg 1999).

Unscrupulous online companies can prey upon consumers

double vulnerabilities, that is, theirtrust inautomation and

trust in specialist, by designing deceptive PRAs that provide

recommendations biased toward their own interest. For

instance, a PRA can focus consumers attention only on

criteria on which the promoted products have a distinctive



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competitive advantage (Wagner et al. 2001) (i.e., conceal-

ment); provide false decision guidance to influence con-

sumers decision criteria (i.e.,falsification); give priority to

promoted products by manipulating the underlying algorithm

for generating recommendations (Aksoy and Bloom 2001)

(i.e.,falsification); mix promoted products (which do not go

through the PRAs filtering process) with those that actually

fit consumers preferences in an unordered set of recom-

mendations (i.e., equivocation); exclude products that best fit

consumers preferences from its recommendation list (i.e.,

concealment); and provide value or overly general explana-

tions on how the recommendations are generated (i.e.,

equivocation). There have already been some reported

incidents of companies generating false recommendations to

consumers. For instance, Amazon.com has admitted to using

faux recommendations to drive business to its new clothing

store partners. The false recommendations were positioned

right next to the legitimate recommendations for books,

music, and other items that were generated based oncustomers purchase histories (Wingfield and Pereira 2002).

In summary, by manipulating how personalized information

is generated at an e-commerce website (e.g., through product

recommendations), online merchants can influence con-

sumers product evaluation and subsequently their decision

making.

Summary

In this section, we have proposed a two-dimensional typology

of deceptive information practices in e-commerce. The threemajor types of deceptionconcealment, equivocation, and

falsificationexamined in prior deception research comprise

one dimension, while the other dimension consists of the three

operational-level deception techniquesthe manipulation of

information content, the manipulation of informationpresen-

tation, and the manipulation of information generationin

the e-commerce context, with the latter dimension being our

focus of discussion. Please see Table 1 for examples of

deceptive information practices in e-commerce based on our

new typology.

A Theory of Product-Related DeceptiveInformation Practices in E-Commerce

In the previous section, we presented a 3 3 framework of

the various ways by which online merchants can exploit the

capabilities of IT to deceive consumers with e-commerce

websites. In this section, we present our theoretical model of

product-related deceptive information practices in e-

commerce. Drawing primarily from thestimulusorganism

response framework in environmental psychology (Mehrabian

and Russell 1974) and the model of deception detection

(Johnson et al. 1993; Johnson et al. 2001), our model theo-

rizes (1) how the various types of deceptive information

practices exploiting IT capabilities (based on the typology that

we developed and presented in the previous section) con-

tribute to consumer deception, and (2) what factors contribute

to the success (or failure) of consumer deception detection at

an e-commerce website embodying deceptive information

practices.

Thestimulusorganismresponse framework posits that the

variousstimuliwithin a shopping environment together affect

a consumers cognitive and/or affective processes (organism),

which in turn determine the consumers behavioralresponses,

expressed as either an approach behavior or an avoidance

behavior. Whereasstimuli are cues external to the customer

that rouse or incite him consciously or subconsciously intoaction (Belk 1975), organism refers to the intervening internal

processes (e.g., the perceptual, feeling, and thinking activities)

between the stimuli and the reaction of the consumer (Bagozzi

1986). The concept ofapproachavoidance is defined in a

broad sense to include physical movement toward, or away

from, an environment or stimulusdegree of attention,

exploration, favorable attitudes such as verbally or non-

verbally expressed preference or liking, [and] approach to a

task (Mehrabian and Russell 1974, p. 96). Approach

behaviors are positive attitudes/actions toward a product/

brand, service, or shopping environment (such as staying in

the store, purchasing products, or returning to the store), while

avoidancebehaviors are the opposite (Parboteeah 2005). The

stimulusorganismresponse framework has been extensively

elucidated (Bagozzi 1986) and widely adopted in past

research, with promising results, to model the impact of

environmental stimuli on consumer responses in both offline

and online shopping contexts (e.g., Baker et al. 1994; Eroglu

et al. 2001; Fiore and Kim 2007; Sherman et al. 1997). In an

e-commerce context, the various types of deception tactics

can be deliberately employed by online merchants to create

the stimuli that can activate consumers internal cognitive/

affective processes, which, in turn, promote an approach

behavior toward the e-commerce website. As such, the

stimulusorganismresponseframework serves as an appro-priate overarching framework for our theoretical model.

Johnson and his colleagues (Johnson et al. 1993; Johnson et

al. 2001) developed a model of deception detection that

describes four subprocesses by which individuals, based on

their domain knowledge and the available information cues,

determine whether information provided by a sender is

deceptive (Grazioli 2004):



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Table 1. Examples of Product-Related Deceptive Information Practices in B2C E-Commerce

Deception

Types

Manifestations of Deception Types

Manipulation of Info

Content

Manipulation of Info Presentation

(Media and Organization) Manipulation of Info Generation

Concealment

The website withholds

negative information

(e.g., a known safety

problem) about a

product.

The website

automatically filters out

negative consumer

reviews.

The websites use flashy animations as a

decoy, that is, to distract consumers

from processing non-vivid yet more use-

ful and informative textual descriptions

( Presentation media).

To encourage consumers to compare

different products on a certain attribute

(e.g., the only attribute on which the

promoted products have advantage over

other products), the website provides the

functionality to sort by that attribute alone

( Information organization)

The PRA focuses consumers

attention only on criteria on which

the promoted products have a

distinctive competitive advantage.

The PRA excludes products that

best fit consumers preferences

from the recommendation list.

Equivocation

The website provides

vague information about

the total cost (e.g.,

selling price, tax,

shipping and handling

fee) of a product.

The website gives

ambiguous information

concerning product

return and refund.

The website shows conflicting informa-

tion about what is included in the pack-

age with different media. For instance,

while displaying a vivid image of an SLR

camera with lens attached (with no

annotation to indicate that the lens is not

included), the website states (truthfully)

in the textual description that the lens of

the camera is sold separately (

Presentation media).

The PRA presents an unordered

set of recommendations, mixing

promoted products (which do not

go through the PRAs filtering

process) with those that actually fit

consumers preferences.

The PRA provides vague or overly

general explanations as to how the

recommendations are generated.

Falsification

The website sells a

nonexistent product.

Staff of the online com-

pany pose as prior

consumers and write

positive reviews about

products and services

received from the

company.

The website manipulates the response

rate of an online product demonstration

to mislead consumers about a particularproduct feature (e.g., demonstrate a

shorter delay between shots of a digital

camera) ( Presentation media).

The website presents promoted products

at the top of its bestselling list (

Information organization).

The PRA gives priority to promoted

products by manipulating the

underlying algorithm for generatingrecommendations.

The PRA provides false decisional

guidance (i.e., guidance as to how

to choose a certain product) to

influence consumers decision

criteria.

1. The activation subprocess consists of allocating attention

to cues, based on the presence of discrepancies between

what is observed and what is expected.

2. The hypothesis generation subprocess is where indi-

viduals generate interpretive hypotheses to explain the

anomalies detected during the activation process.

3. The hypothesis evaluation subprocess is where pre-

viously generated deception hypotheses are evaluated (by

comparison with some criterion) to determine their

acceptability.

4. Theglobal assessmentsubprocess consists of combining

the accepted hypotheses into one synthetic assessment of

deceptiveness.

According to Johnson and his colleagues, individuals are

deceived because they cannot detect deception due to their

inability to identify anomalies/inconsistencies in their inter-

actional environment, generate deception hypotheses to

explain the detected anomalies/inconsistencies, evaluate

already generated deception hypotheses, or arrive at an appro-

priate global assessment of the other partys deceptiveness.



9/29


There is some empirical work in applying the model of

deception detection in the online shopping context. Inte-

grating the process-oriented model of deception detection

(Johnson et al. 1993; Johnson et al. 2001) with the broader

deception, trust, and risk model of Internet consumer behavior

developed by Grazioli and his colleagues (Grazioli 2004;

Grazioli and Jarvenpaa 2000; Grazioli and Wang 2001)

empirically investigated the determinants of deception detec-

tion success by comparing the information processing

behavior of successful and unsuccessful detectors during the

four processes of activation, hypothesis generation, hypoth-

esis evaluation, and global assessment. They found that

peoples competence at evaluating the hypothesis of decep-

tion was a strong differentiator between successful and

unsuccessful detectors. In addition, successful and unsuc-

cessful detectors relied on different types of cues when

evaluating the deception hypothesis. Whereas successful

detectors relied on assurance cues (e.g., third-party seals,

warranties, news clips, and physical location of the onlinestore) and discounted trust cues (e.g., seller reputation,

customer testimonials, and seller size), unsuccessful detectors

did the opposite.

In a similar vein, researchers in psychological contract

breach3 in organizational settings (Morrison and Robinson

1997; Robinson 1996; Robinson and Morrison 2000) have

specified a two-stage model for detecting psychological

contract breach, in which individualsperceive psychological

contract breach before attributing it to either purposeful

reneging, reneging due to inability, or misunderstanding. In

this model, the hypothesis generation and hypothesis evalua-

tion subprocesses of the model of deception detection are

combined into a single stage and the global assessment

subprocess is omitted.

Informed by the model of deception detection and in line with

the more parsimonious model for detecting psychological

contract breach, our model focuses on two sequential sub-

processes of individuals deception detection process, namely,

the noticing of anomaly and the attribution of anomaly.

Individuals competence in noticing anomalies and in making

proper attributions of such anomalies will likely help them

perform better in deception detection.

In the remainder of this section, we will introduce our

theoretical model and define the constructs in the model.

Then, we will present propositions concerning the relation-

ships among the constructs in the model.

Theoretical Model

Our theoretical model is presented in Figure 1. As shown in

Figure 1, the key constructs of the model (from right to left)

are consumers approach behavior toward target product(s);

consumers perception of website deceptiveness; affective

mechanisms; cognitive mechanisms; use of deceptive informa-

tion practices at e-commerce website; individual, product,

and situational characteristics; and deception detection

support mechanisms .

In the remainder of this section, whenever we mention the

terms anomaly, deception, and deceptiveness, we referspecifically to product-related anomaly, deception, and

deceptiveness.

Adopting the definition of approach behavior from

Mehrabian and Russell (1974), we define approach behavior

toward target product(s) as the positive attitude or action

toward the target product(s) (i.e., the product offering(s) on

which deceptive information practices have been performed).

It is the potential outcome, or the instrumental end goal, of

deceptive information practices. An approach behavior

toward target product(s) is exhibited when

A consumer generates an attitude toward the target

product(s), an attitude that she would not otherwise have

generated in the absence of the deceptive information

practices performed by the online merchants, or

A consumer makes a purchase of the target product(s)

that she would not otherwise have made in the absence of

the deceptive information practices performed by online

merchants, a purchase that may be to the detriment of the

consumer.

A consumer is deceived when she exhibits an approach

behavior toward the target product(s).

Perceived product-related deceptiveness in e-commerce

website is the extent to which a consumer believes that an

e-commerce website is deceptive in the content, presentation,

and generation of product-related information. It indicates the

consumers awareness of the online merchants intent to

deceive. Perceived deceptiveness is triggered by a negative-

valenced violation of consumers preconceived expectations,

3Research in psychological contract breach is closely related to deception

research in that deception can be considered as purposeful reneging of

psychological contract whereas incompetence can be considered as reneging

due to inability.



10/29


Figure 1. A Theoretical Model of Product-Related Deceptive Information Practices in E-Commerce

often as a result of the deceptive information practices per-formed by online merchants, or the recognition of cues

suggesting deceptive information practices. It is the outcome

of the consumers deception detection process.

Cognitive/affective mechanisms refer to the cognitive and/or

affective mediating processes through which deceptive infor-

mation practices influence consumer behavior. Cognitive

mechanisms consist of beliefs, thoughts, or perceptions about

products, brands, and e-commerce websites, formed either

through direct interaction with product offerings or through

the processing of secondary source information. This model

includes one cognitive mechanism promoting approach

behaviornamely, perceived productvalue, referring to a

consumers overall assessment of the utility of a product

based on an assessment of what is received and what is given

(e.g., the trade-off between quality and price) (Sweeney and

Soutar 2001; Zeithaml 1988). Various deceptive information

practices are employed to either enhance consumers positive

assessment or reduce/mitigate their negative assessment of the

value of the target product(s).

The model also includes a cognitive mechanism potentiallyleading to an avoidance behavior (i.e., a negative attitude/

action)namely, deception detection process at an

e-commerce website. Websites that manipulate product-

related information with deceptive information practices often

exhibit anomalies that violate consumers preconceived

expectations, thus triggering a two-step deception detection

process: the noticing of product-related anomalies in the

e-commerce website, followed by the attribution of product-

related anomalies noticedin the e-commerce website. The

deception detection process may result in consumers belief

that the e-commerce website is deceptive and subsequently

lead to an avoidance behavior on the part of the consumers.

Affective mechanisms refer to emotional reactions activated by

the stimuli in the shopping environment. This paper examines

two such emotional reactions:pleasureand arousal. Whereas

pleasure refers to a subjective feeling state in which a person

feels good, joyful, or happy about the target product(s),

arousalis a subjective feeling state in which a person feels

excited, stimulated, alert, or active about the target product(s).

P4, P6

Affective Mechanisms

Cognitive Mechanisms

Use of Deceptive

Information Practices at

E-Commerce Website

Types of Deception Concealment

Equivocation

Falsification

Types of Deception

Techniques

Manipulation ofinformation content

Manipulation of

information presentation

Manipulation of

information generation

Approach

Behavior

Toward Target

Product(s)

Individual, Product, and

Situational Characteristics

STIMULUS ORGANISM RESPONSE

Perceived Product Value

Pleasure

Arousal

Perceived

Product-Related

Deceptiveness in

E-Commerce

Website

Dominance

Deception Detection

Process

Noticing of Product-

Related Anomaly at

E-Commerce Website

Attribution of Product-

Related Anomaly

Noticed at E-Commerce

WebsiteDeception Detection

Support Mechanisms

P4-5

P18-19

P25-26

P13-17

P20-24

P27-28

P8-11

P7

P1b

P1a

P12

P2

P3a

P3b

Causal link

Process arrow



11/29


Deceptive information practices are sometimes performed to

induce pleasure and arousal in a consumer, which in turn

may result in an approach behavior on the part of the con-

sumer. Dominance was originally conceptualized as a third

emotional reaction to environmental stimuli (Mehrabian and

Russell 1974), defined as the extent to which a person feels in

control of or free to act in a situation. However, Russell and

Pratt (1980) proposed a modification of the Mehrabian

Russell theory that deletes the dominance dimension. They

argued that dominance required a cognitive interpretation by

the person and is therefore not purely applicable in situations

calling for affective responses. Moreover, prior empirical

studies have found the dominance factor to be of little

predictive value (Donovan and Rossiter 1982; Russell and

Pratt 1980). Hence, although dominance is included in our

theoretical model for the reason of completeness, we will not

state any propositions related to this factor.

Use of deceptive information practices at e-commercewebsite refers to the application of one or more types of

deceptive manipulations at an e-commerce website, based on

our newly developed typology of product-related deceptive

information practices presented in the previous section.

Individual, product, and situational characteristics refer to

the individual, product, and situational factors that moderate

whether or not the use of deceptive information practices

succeeds in deceiving customers. The individual factors

explored in this paper include consumers motivation (i.e.,

involvement with product and involvement with purchase

decision), experience with online/offline shopping in general,

product expertise,prior interaction(s) with the e-commerce

website,prior trust toward the e-commerce website, truth bias

(i.e., the predisposition to assume that others communication

is truthful) (Carlson et al. 2004), and the receipt of third-party

information about the e-commerce website. The product-

related factor isproduct type (i.e., whether the product is a

search product that can be assessed based on the values

attached to its attributes or an experience product charac-

terized by attributes that need to be experienced prior to

purchase). The situational factors include types of consumer

purchase (i.e., whether the purchase is a plannedone that

occurs when consumers have a specific purchase in mind or

an impulse purchase made with no pre-shopping intentions)(Beatty and Ferrell 1998; Moe 2003), task complexity, and

consultation of alternative resources (i.e., whether alternative

information resources, such as another website, are consulted

by consumers during online shopping).

Deception detection support mechanisms refer to support

mechanisms (IT-based or non-IT-based) that can help con-

sumers carry out the processes of deception detection,

namely, noticing or identifying anomalies (resulting from

deceptive information practices) in the e-commerce website

and attributing such anomalies to deception by online

merchants.

Next, we present propositions concerning the relationships

among the constructs in the theoretical model.

Propositions

Effects of Deceptive Information Practices

In this section, we theorize about the positive (i.e.,pleasure,

arousal, and perceived product value) and negative ante-

cedents (i.e.,perceived deceptiveness of the e-commerce web-

site) of consumers approach behavior toward target pro-

duct(s). We also examine the effects of deceptive informationpractices on these antecedents and the moderating effects of

situational (i.e., type of consumer purchases and task

complexity) and product (i.e.,product type) characteristics.

In accordance with the stimulus-organism-response model,

the environmental cues in e-commerce websites that embody

various deceptive information practices (as delineated in the

typology of e-commerce deceptive information practices)

create the stimuli that invoke different cognitive/affective

mechanisms by consumers. The cognitive mechanism ex-

amined in this paper is consumersperceived product value .

When consumers perceive higher value associated with a

particular product offered at an e-commerce website as aresult of the deceptive manipulation performed on product-

related information, they will be more likely to exhibit

positive attitude/action toward this product (an approach

behavior) compared to when deception does not exist.

Similarly, heightenedpleasure and arousal(the two affective

processes examined in this paper) triggered by sensory stimuli

created by deceptive information practices undermine con-

sumers self-control (LaRose 2001) and reduce their cognitive

attention (Cenfetelli 2006), thus leading to positive attitude/

action toward the productan approach behaviorwithout

active information processing (Forgas 1995). It is thus

proposed that

P1a: Perceived value of a target productis positively

associated with the likelihood that consumers will

exhibit approach behavior toward the target

product.

P1b: Affective reactions (i.e., pleasure and arousal)

toward a target product are positively associated



12/29


with the likelihood that consumers will exhibit

approach behavior toward target product(s).

However, deceptive information practices will not succeed in

inducing desired consumer responses if consumers, during

their interaction with the e-commerce website, are able to

perceive the online merchants intent to deceive and thus

make appropriate and adequate corrections. When product-

related anomalies in the e-commerce website are noticed by

consumers and subsequently attributed to deception by online

merchants, an approach behavior toward the target pro-

duct(s) is less likely to take place, even when consumers have

enhanced perception of the value of and/or heightened affec-

tive reaction (i.e., pleasure and arousal) toward the target

product (effected by deception). It is thus proposed that

P2: Perceived product-related deceptiveness in e-commerce

websites is negatively associated with the likelihood that

consumers will exhibit approach behavior toward targetproduct(s).

P3: The effect ofperceived product value (P3a) as well as

that ofpleasure and arousal (P3b) on consumers

approach behavior toward target product(s) will be

attenuated when consumers perceive product-related

deceptivenessin an e-commerce website.

Individuals often lack the requisite cognitive resources to

form well-defined preferences that are stable over time and

invariant to the context in which decisions are made (Bettman

et al. 1998). Instead, they tend to construct their preferences

on the spot when they must make a choice (Bettman et al.

1998; Payne et al. 1992). Since these preferences are con-

structed, rather than absolute, they are sensitive to the charac-

teristics of the decision environment. E-commerce deceptive

information practices are performed to influence consumer

preference construction, potentially to their detriment.

All three types of deception techniquesmanipulation of

information content, manipulation of information presen-

tation, and manipulation of information generationwork by

enhancing consumers perception of the valueassociated with

target products and/or triggering feelings ofpleasure and

arousaltoward those products. However, each type exerts adifferential effect on consumers cognitive/affective pro-

cesses. The manipulation of information contentoperates

directly on the content of product information and is thus

expected to exert greater influence on consumers cognitive

evaluation of the value of a product. The same is true with

the manipulation ofinformation generation, which influences

the relative attractiveness of a particular product offering via

biased product recommendations. The manipulation of

information presentation, however, often achieves deception

by activating affective (i.e.,pleasure and arousal) processes

that may accentuate the perceived value associated with

particular product offerings. For instance, online companies

can use vivid presentations to trigger intense emotional

responses in consumers that can overwhelm their self-

observation during online shopping, potentially to their

detriment. It is thus proposed that

P4: Websites that exhibit deceptive information practices are

more likely to enhance consumers perceived value

associated with a target product and trigger strong

feelings ofpleasure and arousalin consumers toward a

target product.

P5: Websites that employ the manipulation of information

content or informationgeneration are more likely to

enhance consumersperceived value associated with a

target product than websites that employ the manipu-lation ofinformationpresentation.

P6: Websites that employ the manipulation of information

presentation are more likely to trigger strong feelings of

pleasure and arousal in consumers toward a target

product than websites that employ the manipulation of

informationcontentor the manipulation ofinformation

generation.

Type of Purchase Situation as Moderator

The type of purchase situation interacts with different decep-tive information practices to influence consumers purchase

decision making. Prior research has dichotomized consumer

purchase intoplanned purchase and impulse purchase (e.g.,

Beatty and Ferrell 1998; Rook and Fisher 1995). Aplanned

purchase occurs when consumers have a specific purchase in

mind (Moe 2003), whereas an impulse purchase is a sudden

and immediate purchase with no pre-shopping intentions

(Beatty and Ferrell 1998). Research in impulse purchase has

shown that sensory stimuli in a retail environment may make

consumers less attentive to their purchasing behavior by

generating a sense of pleasure and arousal about the product

that overwhelms their self-control (LaRose 2001). Shoppingenvironments can be carefully crafted to facilitate impulse

purchases by undermining consumers self-control mech-

anisms (LaRose 2001). Moreover, positive emotions trig-

gered by sensory stimuli in the shopping environment may be

relied upon by consumers as a heuristic and thus lead to

behavior without active information processing (Forgas

1995). Positive emotions signal that the environment is favor-

able and, therefore, little cognitive attention is required



13/29


(Cenfetelli 2006). Insomuch as the manipulation of informa-

tion presentation exploits media characteristics that empha-

size attractive product stimuli (e.g., enlarged photos, anima-

tions, and interactive graphics), such tactics may trigger

feelings of pleasure and arousal in consumers, which can

reduce their self-control and information processing, thus

leading to impulse purchases (LaRose 2001). In contrast, the

instrumental nature of the planned purchase suggests that

consumers are likely to engage in focused, deliberate,

thoughtful search and evaluation during the shopping process.

They will scrutinize the content of product information and

assess the merit of the products, thus attending less to

peripheral cues (e.g., image vividness, relative position in a

list of recommendation by PRAs) (Petty and Cacioppo 1986).

Since the manipulation of information content operates

directly on the content of product information, it is expected

to exert greater influence on a consumers cognitive

evaluation of the value of a product than the manipulations of

information presentation orinformation generation. It is thusproposed that

P7: Forimpulse purchases, websites that employ the manipu-

lation of information presentation are more likely to

trigger strong feelings of pleasure and arousal in

consumers toward a target product than websites that

employ the manipulation of information contentor the

manipulation of information generation.

P8: Forplanned purchase s, websites that employ the manip-

ulation of information contentare more likely to enhance

consumers perceived value associated with a target

product than websites that employ the manipulation of

information presentation or the manipulation of infor-

mation generation.

Product Type as Moderator

Product type can also influence a consumers vulnerability to

deception at an e-commerce website. Products are generally

categorized into search products and experience products.

Whereas search products (e.g., cameras, calculators, or

books) are characterized by attributes (e.g., color, size, price,

and components) that can be assessed based on the valuesattached to them without necessitating the need to experience

them directly, e xperience products (e.g., movie, music,

clothing, or cosmetics) are characterized by attributes (e.g.,

taste, smell, softness, and fit) that need to be experienced

prior to purchase. Because of the inherent difficulty asso-

ciated with the evaluation of experience products prior to

purchasing online, consumers tend to feel uncertain as to

whether the products would meet their expectations (Spieker-

mann 2001). When faced with difficulties in making choices,

consumers may resort to heuristics, resulting in decision

biases (Bettman et al. 1998; Payne et al. 1993). Thus, the

manipulation of information presentation and the manipu-

lation informationgeneration, which increase the visual

salience as well as the perceived relevance/importance of the

target products, will likely exert greater effect on consumers

cognitive evaluation of experience products than on their

evaluation ofsearch products.

In addition, King and Balasubramanian (1994) found that

product type had a significant impact on consumers reliance

on a particular decision-making process. Consumers eval-

uating asearch productwere more likely to use own-based

decision-making processes (i.e., to rely on themselves for

product search, evaluation, and purchase); in contrast, those

evaluating an experience producttended to use other-based

decision-making processes (i.e., to subcontract either part or

all of their decision-making process). Since prior research hasshown that consumers who relied on others for decision

making are likely to make purchasing decisions in keeping

with salespersons recommendations (Formisano et al. 1982),

Xiao and Benbasat (2007) conclude that consumers eval-

uating experience products are more likely to rely on

salespersons assistance and adopt their recommendations.

As the role of product recommendation agents in online

shopping is similar to that of salespersons in a traditional

shopping environment, consumers shopping forexperience

products are likely to be influenced by the recommendations

provided by PRAs and thus render themselves vulnerable to

the manipulation of information generation performed by

online merchants via the design of biased PRAs. We thus

propose

P9: When the target product is an experience product,

websites that employ manipulation of information

generation are more likely to enhance consumers per-

ceived value associated with the target product than those

employing manipulation of information presentation or

manipulation of information content.

Task Complexity as Moderator

An additional situational characteristic that moderates the link

from deceptive information practice to the affective and

cognitive mechanisms is task complexity, typically deter-

mined by the number of attributes on which each alternative

is compared (Jacoby 1977). As consumers tend to evaluate

products on several dimensions (e.g., attributes or features)

before making a choice, the greater the number of alternatives

(or dimensions), the more complex the decision task will be



14/29


due to the increasing volume of relevant information that cus-

tomers must assess. When task complexity is low, consumers

are able to evaluate all the relevant information in detail prior

to making their final choice. Under such circumstances, the

manipulation of information contentwhich alters the con-

tent of product information to make the target products more

attractivewill likely exert greater influence on consumers

assessment of product value. In contrast, when task com-

plexity is high, due to the limitations in information pro-

cessing capacity, it is not feasible for consumers to evaluate

all relevant product information to determine the value of

each alternative. Instead, consumers are likely to attend to

and be influenced by peripheral cues in their decision-making

process (Petty and Cacioppo 1986). For instance, consumers

may choose a target product simply because it is presented at

the top of a websites bestselling list or is recommended as

the best-fitting product by a PRA at the website, without

processing available information thoroughly or searching for

additional information. As such, the manipulations of infor-mation presentation and information generationwhich

increase the visual salience and the perceived relevance/

importance of the target productswill have greater effect on

consumers assessment of the value of the target products and

hence their purchase decision making when the task com-

plexity is high. It is thus proposed that

P10: Websites that employ the manipulation of informa-

tion contentare more likely to enhance consumers

perceived value associated with a target product

when task complexity is low.

P11: Websites that employ the manipulation of informa-

tion presentation or the manipulation of information

generation are more likely to enhance consumers

perceived value associated with a target product

when task complexity is high.

In summary, while cognitive/affective mechanisms such as

perceived product value, pleasure, and arousalcontribute

positively to consumers approach behavior toward target

product(s), perceived deceptiveness of the e-commerce

websitehas a negative impact. Different deception techniques

exert differential impacts on the cognitive/affective mech-

anisms, with the manipulation of information content or

manipulation ofinformationgeneration having greater impact

on consumersperceived product value, and the manipulation

of informationpresentation on feelings ofpleasure and

arousal. Type of consumer purchase,product type, and task

complexity also interact with type of deception technique to

influence the cognitive/affective mechanisms.

Consumer Deception Detection

This section examines the effects of deceptive information

practices as well as individual (i.e., motivation, experience

with online/offline shopping in general, product expertise,

prior interaction(s) with the e-commerce website,prior trust

toward the e-commerce website,truth bias, and the receiving

of third-party information about the e-commerce website),

product (i.e., product type), and situational (i.e., type of

consumer purchase and consultation of alternative resources)

characteristics on the deception detection process and its

outcomes (i.e., hits ormisses).

Signal detection theory (Davies and Parasuraman 1982)

differentiates between two classes of eventsnoise (i.e., the

background) andsignal (i.e., stimulus that deviates from

background noise and thus may be detected)and explains

the performance of individuals who strive to determine the

presence of a signal. Signal detection theory specifies fourpossible outcomes in error detection tasks: (1) hits, (2) misses,

(3)false alarms, and (4) correct rejections. In the context of

deception detection, information that is free from manip-

ulation might be considered noise, whereas information that

is tampered by manipulation of its content, presentation,

and/or generation would provide a signal (Biros 1998). As

such, there are also four possible outcomes in a deception

detection task, which determine an individuals deception

detection performance:

1. A hitoccurs when consumers detect deception when it

does exist.

2. A miss occurs when consumers fail to detect deception

when it does exist.

3. Afalsealarm occurs when consumers report deception

when it does not exist.

4. A correctrejectionoccurs when consumers do not report

deception when it does not exist.

This paper focus on hits and misses as indicators ofdeception

detection success and deception detection failure, respec-

tively, since our theoretic model assumes the existence of

deception in the e-commerce website. Hits occur whenconsumers perceive the e-commerce website to be deceptive;

misses occur when consumers perceive the website to be free

from deceptive manipulations.

Prior research in deception detection (e.g., Johnson et al.

1993; Johnson et al. 2001; Morrison and Robinson 1997;

Robinson 1996; Robinson and Morrison 2000) suggests that



15/29


individuals follow two sequential subprocesses in detecting

deception, namely, the noticing of anomaly and the attribution

of anomaly. To be successful in detecting deception in an

e-commerce website, consumers must be able to (1) notice the

anomalies as a result of the deceptive information practices,

and (2) attribute the noticed anomaly to deception by online

merchants. Thus, we propose

P12: Hits are more likely to occur when consumers are

able to notice product-related anomalies in the

e-commerce website and attributesuch anomalies to

deception.

The extent to which a consumer is able to notice and correctly

attribute anomalies (as a result of deceptive information

practices) in an e-commerce website is influenced by a variety

factors and processes, including the amount and type of

deception deployed in the website, the availability of decep-

tion detection support mechanisms, and various individualand situational characteristics. In the remainder of this sec-

tion, we will first examine factors influencing the likelihood

of consumers to notice and attribute anomalies resulting from

deceptive information practices. We will then explore the

effects of deception support mechanisms on deception detec-

tion performance.

The Noticing of Product-Related Anomaly: Individual

Characteristics, Amount/Type of Deception, and

Situational Characteristics

People hold preexisting expectations regarding the behaviorof those with whom they are interacting (Burgoon and

Walther 1990). These expectations may stem from social

norms (Burgoon et al. 1995; Jones 1986), prior interactions

(Burgoon et al. 1995; Honeycutt 1991; Jones 1986), informa-

tion from a third party (Levine et al. 2000), or stereotypes

(Burgoon and Le Poire 1993; Jones 1986). According to the

model of deception detection (Johnson et al. 1993; Johnson et

al. 2001), an anomaly is an inconsistency between what is

observed and what is expected. When an interactants

observed behavior sufficiently deviates from what is ex-

pected, an expectation violation is said to occur (Levine et al.

2000), raising suspicion, creating doubt, and ultimatelyleading to judgments of deceptiveness.

However, for the occurrence of expectation violation to be

noted, people must have situation-relevant expectations

regarding an interactants behavior in the first place. For

instance, in an online shopping context, consumers may have

expectations regarding the selection and prices of products

available at the e-commerce website; pre-sale and post-sale

services provided by the online company; website func-

tionalities facilitating product search, evaluation, choice, and

purchase; or other relevant issues. As discussed above, such

expectations may derive from a variety of bases, such as the

consumers general experience in shopping online and/or

offline, their prior interaction with this particular website, and

information about the website from their friends and relatives,

all of which help consumers create expectations that serve as

criteria against which the behavior of the e-commerce website

is evaluated and inconsistencies, if any, are noted. Thus, we

propose

P13

P15:

Consumers who are experienced in online/offline

shopping in general (P13), who have had prior

interaction(s) with the e-commerce website (P14),

and/or who have received third-party information

about the e-commerce website (P15) are more likely

to generate expectations that enable them to notice

product-related anomalies in the e-commercewebsite (thus resulting in hits).

As posited by the signal detection theory, increased attention

and effort as a result of increased motivation enable indi-

viduals to better distinguishsignalfrom noise in a deception

detection task (Boyle 2003; Gilovich 1991; Klein et al. 1997).

Similarly, individuals with greaterexpertise in a task domain

are better equipped to detect a wider set of signals from

background noise. In an online shopping context, consumers

motivation is manifest in their involvement with a particular

product category and/or their involvement with a particular

purchase decision. Consumers who are highly involved either

with a product or with a purchase decision are likely to

engage in thorough information search and comprehensive

information processing (Clarke and Belk 1978; Zaichkowsky

1985), and thus are more likely to identify deception cues

when they are present. Likewise, the moreproduct expertise

the consumers possess, the more likely for them to notice

anomalies in their interaction with the e-commerce website

during the online shopping process (e.g., Biros 1998; Johnson

et al. 2001) and to distinguishsignals from noise. It is thus

proposed

P16: Motivated consumers are more likely to notice

product-related anomalies in the e-commerce web-site (thus resulting in hits) than unmotivated con-

sumers.

P17: Consumers with a high level ofproduct expertise are

more likely to notice product-related anomalies in

the e-commerce website (thus resulting in hits) than

consumers with a low level ofproduct expertise.



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The interactants observed behavior must sufficiently deviate

from what is expected in order for the inconsistency to be

noticeable. This is in line with the signal detection theory,

which states that strong signals are more likely to be detected

than weak signals (Davies and Parasuraman 1982). There-

fore, in an e-commerce context, the more deceptive infor-

mation practices are employed by the online merchant, the

more likely for consumers to take notice of the anomalous

behavior. Also, although empirical research in detection

performance associated with different types of deception is

scarce, there exists some evidence suggesting the differential

detectability of different deception tactics. Burgoon and

Buller (1994; see also Burgoon et al. 1996) found that of the

three types of deception (i.e., equivocation, concealment, and

falsification), equivocation produced the greatest degree of

detection accuracy. The vague and indirect nature of equiv-

ocal messages makes it more noticeable than the other two

types of deception. Fatt (2001) also suggests that those who

wish to deceive others should engage in falsification ratherthan equivocation, since the latter is more likely to result in

detection. While Grazioli and Jarvenpaa (2003a) argue that

falsification is hard for online consumers to identify because

detection requires assessing the content of an offer to transact

via the Internet, we believe that the same applies to conceal-

ment. It is thus proposed that

P18: Consumers are more likely to notice product-related

anomalies in an e-commerce website that employs

more deceptive information practices than in one

that employs fewer.

P19: Consumers are more likely to notice product-related

anomalies as a result of equivocation than those

resulting fromfalsification orconcealment.

The type of purchase situation also influences consumers

performance in noticing anomalies. Aplanned purchase is

characterized by focused, deliberate, thoughtful search and

evaluation (Moe 2003); in contrast, an impulse purchase

occurs after experiencing a compelling urge to buy (Beatty

and Ferrell 1998). As noted by Kacen and Lee (2002), the

rapidity of an impulse purchase decision precludes thoughtful,

deliberate consideration of all information and choice alter-

natives. This lack of reflection often renders consumersvulnerable to unwanted strategic influences exerted by online

merchants via the implementation of various deceptive

information practices. It is thus proposed that

P20: Product-related anomalies in the e-commerce web-

site are less likely to be noticedby consumers (thus

resulting in misses) when they are engaged in

impulse purchases than inplanned purchase.

Whether a shopper consults alternative information sources

during online shopping (a situational factor) also influences

the likelihood that he will notice anomalies. Since all three

types of deceptive information practices work (albeit to

various extent) by influencing the availability and quality of

information presented to consumers, when alternative externalinformation resources (e.g., product manufacturers websites,

third-party expert review or consumer review websites such

as CNET.com and ConsumerReports.org, or independent

product recommendation agents, such as those designed by

Active Decisions) are available to consumers and when

consumers actually make use of such resources to verify the

information presented at an e-commerce website, they are

more likely to uncover inconsistencies and anomalies in the

website. It is thus proposed that

P21: When shopping at websites that exhibit deceptive

information practices, consumers who consult alter-

native information sources are more likely to noticeproduct-related anomalies in the e-commerce web-

site (thus resulting in hits) than consumers who do

not.

The Attribution of Product-Related Anomaly:

Individual Characteristics, Type of Deception,

Type of Deception Techniques

Individuals frequently try to make sense of the world around

them by making attributions about the cause of events/

behaviors (Tomlinson and Mayer 2009), particularly when theevents/behaviors are negative in valence. According to

causal attribution theory (Weiner 1986), the perception of a

negative outcome (that has occurred when the trustor per-

ceives disconfirming evidence regarding her expectations)

leads the individual to identify the causes of the outcome.

Similarly, the model of deception detection specifies that,

upon noticing an anomaly, individuals will often make

attributions of the anomaly by generating potential hypotheses

to explain it (Johnson et al. 2001; Koonce 1993). Since

human behavior is frequently amenable to different inter-

pretations (e.g., Borkenau 1986; Higgins 1989), the same act

is quite often considered from multiple perspectives

(Wojciszke 2005). As such, several alternatives (e.g., inno-

cent mistakes, incompetence) are often available to explain

the observed anomaly, with deception being one of the

alternatives. In an e-commerce context, factors such as

individual characteristics (e.g., trustand truth bias) as well as

type of deception technique influence the likelihood for

consumers to attribute the observed anomalies to deception by

online merchants.



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Robinson (1996) defines trust as ones expectations, assump-

tions, or beliefs about the likelihood that anothers future

actions will be beneficial, favorable, or at least not detri-

mental to ones interests (p. 576). As a general positive

attitude toward another social entity, trust plays a significant

role in influencing ones interpretation of social behaviors

within a relationship (Robinson 1996). Two types of trust

relevant to the e-commerce deception context areprior trust

(a kind of knowledge-based trust) and calculative-based trust.

Individuals tend to maintain cognitive consistency by inter-

preting information in ways that reinforce their prior beliefs

and attitudes, while avoiding or ignoring interpretation that

disconfirms their prior beliefs (Fiske and Taylor 1984); this is

referred to asselective interpretation bias in judgment and

decision-making literature. In an online shopping context,

consumers with highprior trusttoward a website (probably

resulting from positive prior interaction with the website) will

expect the e-commerce website not to behave in a waydetrimental to their interests. Therefore, upon noticing an

anomaly, they will be less likely to attribute the anomaly to

deception and more apt to attribute it to extenuating circum-

stances (e.g., incompetence, honest oversight) (Robinson

1996). In contrast, consumers with lowprior trusttoward a

website will be more likely to interpret the identified

anomalies as cues of deception. This is done to reinforce and

maintain their prior theories and hypotheses about the

e-commerce website. It is thus proposed that

P22: Consumers with a high level ofprior trustare less

likely to attribute product-related anomalies noticed

in the e-commerce website to deception (thus

resulting in misses) than consumers with a low level

ofprior trust.

Calculative-based trustis shaped by the rational assessment

of t

ethics research 1

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