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Relational Consequences of Perceived

Deception in Online Shopping:The Moderating Roles of Type of Product,

Consumer’s Attitude Toward the Internet

and Consumer’s Demographics Sergio Roma n

ABSTRACT. This study investigates the negative influ-

ence of consumer’s perceptions of online retailer’s

deceptive practices (perceived deception) on consumer’s

relational variables (satisfaction and loyalty intentions to

the online retailer). Also, the moderating role of product

type (goods versus services), consumer’s attitude toward

the Internet, and consumer’s demographics in the

deception-relational outcomes link is considered. Data

from 398 online consumers revealed that satisfaction

totally mediated the influence of deception on loyalty.

Furthermore, the deception-satisfaction link was moder-

ated by all the hypothesized variables. Interestingly, a

direct effect of deception on loyalty was found amongmore educated consumers, consumers who had a more

positive attitude toward the Internet and consumers who

had purchased a physical product. Implications for theory

and management are discussed.

KEY WORDS: perceived online deception, consumer 

satisfaction, loyalty intentions, type of products, moder-

ating effects

Introduction

Consider the following examples: An online services

provider makes ‘‘free trial’’ offers to consumers, yet it

does not make it clear that consumers have an affir-

mative obligation to cancel the service before the trial

period ends (the key information is available, but

buried in the fine print). As a result, consumers who

failed to cancel were enrolled automatically and began

incurring monthly charges. Another website shows

misleading information about what is included in the

final offer using various media. While displaying an

attractive image of a computer with a monitor, the

website states in very small text that the monitor is sold

separately. These are both examples of real consumer 

complaints drawn from two major consumer review

websites (epinions.com and bizrate.com).

The commercial use of the Internet is still

increasing, and online shopping more and more

becomes a part of our day-to-day life (Van Noort

et al., 2008). Unfortunately, fraudulent practices,

misleading advertisements, and misrepresentationsof information on the Internet also continue to

increase. The rapid rise in the number of consumer 

complaints related to online fraud and deception

bears this out: in 1997, the National Fraud Infor-

mation Center (www.fraud.org) received fewer than

1000 Internet fraud complaints. In 2005, it received

over 12,000 complaints. Furthermore, the average

loss in 2005 was $1917, much higher than the

average loss in 2004 ($895).

Many deceptive practices in e-commerce settings

(e.g., the exaggeration of product benefits andcharacteristics) are variations of well-known decep-

tion types already used in the traditional shopping

context. However, the opportunity to perpetrate an

online deception is increased by several reasons.

First, the Internet is inherently a representational

environment, i.e., an environment in which con-

sumers make decisions about products based on

cognitive representations of reality. The relatively

unfamiliar and impersonal nature of the Web, as well

 Journal of Business Ethics (2010) 95:373–391 Ó Springer 2010

DOI 10.1007/s10551-010-0365-9

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as the lack of opportunities for face-to-face interac-

tions reduces people’s ability to detect deception

(Ben-Ner and Putterman, 2003). For instance, in

traditional retail settings, the detection of deception

relies, among other things, on recognizing subtlechanges in a person’s nonverbal behaviors, such as

eye contact and body movements (DePaulo, 1992).

Second, compared to the brick and mortar world,

the Internet lowers the entry and set up costs for 

new sellers (Biswas and Biswas, 2004), making it

relatively easy for a deceptive online retailer to set up

a storefront on the Internet that is as genuine-

looking as its legitimate counterpart. For example,

the Internet can be used effectively by a small

company to appear deceptively large, as the webpage

on the computer screen does not distinguishbetween a large and a small company (Petty, 1998).

Third, the Internet makes the identity of the parties

involved in communications and transaction difficult

to verify. In particular, the Internet allows firms from

different legal and regulatory environments to pres-

ent their offerings without a strong international

legal and consumer protection system (Morris-

Cotterill, 1999).

Research in traditional settings shows that

deceptive company policies impact consumers’ atti-

tudes and behaviors in the marketplace (e.g., Ingram

et al., 2005; Jehn and Scott, 2008; Ramsey et al.,2007). However, relatively little attention has

explicitly been given to consumers’ reactions to

deceptive practices of online retailers (Biswas and

Biswas, 2004; Palmer, 2005; Roman, 2007). In the

light of these issues, this research has two main

objectives: (1) to analyze the direct and indirect

influence of consumer’s perceptions of online re-

tailer’s deceptive practices (perceived deception) on

consumer’s satisfaction and loyalty intentions to the

online retailer 1 and (2) to analyze to what extent the

hypothesized direct influence of perceived deceptionon satisfaction and loyalty intentions is moderated by

the type of product being purchased (goods versus

services), consumer’s attitude toward the Internet,

and consumer’s demographics (age, education, and

gender). This research does not intend to examine all

potential moderating variables, rather it represents an

initial step in the process of understanding the

moderating influence of the type of product, con-

sumer’s attitude toward the Internet and demo-

graphics. These variables were chosen because prior 

research has shown that they play a key role

in explaining consumers’ online purchasing behav-

ior (e.g., Hansen, 2005; Jayawardhena, 2004;

Korgaonkar et al., 2006; Sexton et al., 2002).

The remainder of the article consists of the fol-lowing sections. First, we provide a brief synthesis of 

the existing literature on deception in marketing,

with an emphasis on those studies focused on online

retailing. Then, the main constructs of the study

(online deception, consumer satisfaction, and loyalty

intentions) are defined, leading to the development

of hypotheses. Third, the methodology of the study

is described. Finally, the study results, managerial

implications, limitations, and future research oppor-

tunities are discussed.

Literature review

Deception is a general phenomenon that can occur 

in virtually any form of communication under 

conflict of interest (Johnson et al., 2001). Deception

comes in a wide array of forms other than the out-

right lie, and among the features that differentiate

them are amount and sufficiency of information,

degree of truthfulness, clarity, relevance, and intent.

Whatever the type of deception, it causes a number 

of ethical questions and issues for companies, con-sumers, and policy makers. Within business disci-

plines, deception has been extensively studied by

organizational (e.g., Fleming and Zyglidopoulos,

2008; Jehn and Scott, 2008), accounting (e.g.,

Gibbins, 1992; Zimbelman, 1997), and information

systems researchers (e.g., Biros et al., 2002).

In the marketing field, deception has received

special attention in the areas of advertising and

personal selling/traditional retailing. Deception in

the context of marketing practices is ‘‘unethical and

unfair to the deceived’’ (Aditya, 2001, p. 737). Prior research on deceptive advertising has focused largely

on identifying the specific types of claims that lead

consumers to make erroneous judgments and its

consequences on consumers’ beliefs, affect, and

behavioral intentions (e.g., Burke et al., 1988; Darke

and Ritchie, 2007). For instance, recent findings

from Darke and Ritchie (2007) showed that

deceptive advertising engenders consumers’ distrust.

Earlier research in retailing and personal selling

has identified ‘‘the exaggeration of the features and

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benefits of a product’’ and ‘‘selling items through

high-pressure selling techniques’’ as common

examples of deceptive or manipulative tactics

(Ingram et al., 2005; Ramsey et al., 2007; Roman and

Ruiz, 2005). Results from this stream of researchparallels those obtained by advertising researchers in

that deceptive selling actions have been found to

decrease customer satisfaction and trust.

Only recently researchers have paid attention to

the topic of deception in online retailing. In what

follows, we summarize the results of the empirical

studies that have addressed, to some extent, these

issues. Grazioli and Jarvenpaa (2000) conducted a

laboratory experiment with 80 MBA students. Half 

of the subjects accessed a real commercial site, and

the other half accessed a copy of the site forged bythe researchers. The forged site contained several

malicious manipulations (e.g., false quotes from

professional magazines were created, the site size and

sales were grossly exaggerated), designed to increase

trust, and ultimately increase the likelihood that

visitors would buy from it. Their results revealed

that deceptive manipulations can alter the decision-

making processes of individuals, and suggested that

even sophisticated, technologically competent indi-

viduals fail to detect the fraud manipulations. Later,

Miyazaki and Fernandez (2001) evaluated consum-

ers’ concerns regarding online shopping. Four major concerns emerged from a sample of 189 consum-

ers. One of them was online retailer fraud, which

referred to consumers’ concerns regarding fraudulent

behavior by the online retailer, such as purposeful

misrepresentation or non-delivery of goods. Also,

some effort has been devoted to examine consumers’

perceptions and reactions to online retailers’ safety

cues (e.g., privacy policies, security disclosures, and

warranties). These experiments, mostly conducted

with students, tend to show that online safety cues

(1) lower consumers’ risk perceptions (Van Noortet al., 2008) and (2) are stronger relievers of per-

ceived risks in online than in offline contexts (Biswas

and Biswas, 2004).

Interestingly, Grazioli and Jarvenpaa (2003) con-

ducted a content analysis of 201 cases of Internet

deception, which revealed that deceivers selected

deceptive tactics based on the characteristics of their 

targets as well as their own purported identities.

Among the four types of e-commerce deception

(i.e., B2C, B2B, C2B, and C2C), those by online

businesses against consumers was found to be the

most frequent. Roman (2007) developed a scale to

measure consumers’ perceptions regarding the ethics

of online retailers (CPEOR). His findings indicatedthat the CPEOR scale had four dimensions: security,

privacy, non-deception, and fulfillment/reliability.

The CPEOR scale was implemented initially with

two separate convenience samples of online con-

sumers. The scale demonstrated good psychometric

properties based on findings from a variety of reli-

ability and validity tests. Recently, Mitra et al. (2008)

analyzed how consumer’s beliefs are shaped by

online advertising (truthful versus misleading) claims

and affected by media richness. Their lab study,

conducted with students, showed that the deceptionpotential is greater when consumer’s involvement

is low.

Figure 1 represents our conceptual model. The

present study contributes to theory and management

in the following ways. First, we extend previous

studies in the context of online deception by ana-

lyzing the direct effects of perceived online decep-

tion on consumer’s satisfaction and loyalty intentions

to the online retailer. In doing so, we examine to

which extent the effect of deception on loyalty is

mediated by satisfaction. Several studies conducted

in traditional retail settings have recently called for acomprehensive analysis of the relationships between

deception and its consequences because these rela-

tionships may not always be simple and direct

(Ingram et al., 2005; Roman and Ruiz, 2005). A

thorough investigation of the complex interrela-

tionships will prove beneficial for a more complete

understanding of the mechanisms that lead from

deception to desfavorable relational outcomes.

Researchers have also called for the study of the

relationship between satisfaction and loyalty in the

online context (e.g., Anderson and Srinivasan, 2003;Shankar et al., 2003). For instance, Anderson and

Srinivasan (2003, p. 134) pointed out that: ‘‘learning

more about the critical relationship between e-sat-

isfaction and e-loyalty should be a top priority for 

scholars and practitioners.’’ In the offline context,

extant research has found that satisfaction leads to

loyalty (e.g., Bolton and Lemon, 1999). Yet, in the

online context (where many alternatives are only a

mouse click away), it is possible for a customer to be

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highly satisfied and yet not be loyal. Therefore, the

study of the relationship between satisfaction and

loyalty should provide a managerial contribution in

that online retailers could more precisely allocate

their online marketing efforts between satisfactioninitiatives and loyalty programs.

Finally, none of the previous studies has incor-

porated the analysis of moderating variables on the

consequences of online deception on consumer’s

relational variables. Yet, researchers have repeatedly

pointed out that it is important to investigate mod-

erating effects in consumer studies (e.g., Dabholkar 

and Bagozzi, 2002). Figure 1 proposes a more gen-

eral, encompassing theoretical model: the direct

effects are moderated by the type of product being

purchased, the consumer’s attitude toward the

Internet and consumer’s demographics. Importantly,evidence from studies carried out in traditional set-

tings tend to indicate that consumers’ perceptions of 

the severity of unethical/deceptive company prac-

tices increase with age, education and tend to be

higher for females than for males (e.g., McIntyre

et al., 1999; Ramsey et al., 2007; Weeks et al.,

1999). The analysis of the moderating effects in the

online context will provide online retailers a better 

understanding of the potential reactions of key

segments of consumers toward the firm’s (deceptive)

actions. This, in turn, may facilitate their relation-

ship-building efforts toward various demographic

groups (e.g., by explicitly addressing the ethical

concerns of those target groups who perceive higher severity of deceptive retail actions).

Hypotheses development

In what follows, the focal constructs of the study are

defined (perceived online deception, satisfaction,

and loyalty intentions). Then, the framework and

the hypotheses to be tested are developed. Internet

deception practices can have several manifestations:

making false claims about product characteristics,

failing to meet warranty obligations, selling defectivegoods or services without adequate disclosures,

fraudulently acquiring sensitive information, such as

usernames, passwords, and credit card details,2 etc.

This study particularly focuses on consumer’s per-

ceptions of product-related online deception.3 We

are drawing from early studies in advertising

deception (Carson et al., 1985; Gardner, 1975;

Hyman, 1990), as well as recent work on Internet

deception (Grazioli and Jarvenpaa, 2003; Roman,

Perceiveddeception

Consumersatisfaction

Consumerloyalty

intentions

χ2(32)=83.34 p<.01; GFI=.96; AGFI=.93 CFI=.99; RMSEA=.04; RMSR=.04; TLI (NNFI)=.99

Product type (goods vs. services) (H4a, H4b)

 H 1 ( - )γ  = -. 3 9 *

 *

H 2 ( -) γ  = -.0 1 ( ns) 

Consumer’s attitude toward Internet (H5a, H5b)

Consumer’s age (H6a, H7a), education (H6b, H7b) and gender (H6c, H7c)

Moderating variables

H3(+) β=.81**

Figure 1. The research model and results of direct effects (standardized coefficients). ** p< 0.01, ns Not significant.

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2007) to conceptualize our main variable. Perceived

online deception practices cause consumers to have

false beliefs about the nature of the products being

offered, and thereby their purchasing decisions may

differ from those that they would have had other-wise. In other words, perceived deception in this

study represents an unethical act perpetrated by

online companies to manipulate product informa-

tion content and/or presentation so as to induce

desired behavioral changes in consumer decision

making – changes that may be to the detriment of 

the consumers (e.g., purchasing an item based on

misleading representations of their characteristics

made by the online retailer). For example, the online

retailer can manipulate the information content by

withholding, equivocating, or falsifying the contentof information presented to consumers in the web-

site. Also, the online retailer can manipulate the

information presentation by: (1) altering individual

features (e.g., size, color, and interactivity) to either 

inhibit correct product understanding or foster 

incorrect product understanding and/or (2) manip-

ulating the level of presentation vividness so as to

focus consumers’ attention on irrelevant information

or distract their attention from relevant information

(Grazioli and Jarvenpaa, 2003).

Importantly, Internet fraud is a narrower term that

denotes a violation of the law, whereas not all per-ceived deceptive practices, as defined in this study,

constitute fraud.4 For example, as shown in one of 

the introductory vignettes, an online retailer dis-

played an attractive image of a computer and mon-

itor together. Only in very small text was it stated

that the computer and monitor were sold separately.

Though this practice does not constitute a violation

of the law, consumers perceived it as deceptive.

As for the dependent variables, satisfaction with

the online retailer is conceptualized as: ‘‘the con-

tentment of the customer with respect to his or her prior purchasing experience with a given electronic

commerce firm’’ (Anderson and Srinivasan, 2003,

p. 125). Loyalty intentions are defined as a combi-

nation of consumer’s intention to buy from the

website in the future, and to recommend it to other 

consumers. This covers the two aspects of loyalty

suggested most often in extant research: the inten-

tion of repurchase and the commitment echoing in

the intention to spread positive word-of-mouth

(e.g., Cronin et al., 2000; Wolfinbarger and Gilly,

2003). Furthermore, taking an intentions perspective

of loyalty rather than considering actual repurchase

behavior may avoid confusing spuriously loyals, who

only repurchase because of a lack of alternatives,

with genuinely loyal customers (Bell et al., 2005;Fassnacht and Kose, 2007).

Direct and indirect effects

We build on the expectancy disconfirmation para-

digm (e.g., Oliver and DeSarbo, 1988) to propose

the influence of deception on satisfaction. This

theory holds that consumers make a comparison

between product expectations and performance that

will result in either confirmation or disconfirmation.

Customers’ expectations are confirmed when prod-

uct performance exactly meets expectations. Dis-

confirmation will be the result of a discrepancy

between expectations and performance. Positive

disconfirmation occurs when product performance

exceeds prior expectations, and negative disconfir-

mation occurs when expectations exceed perfor-

mance. Confirmation and positive disconfirmation

will be likely to result in satisfaction, whereas neg-

ative disconfirmation leads to dissatisfaction.

Consumers’ expectations regarding the product

(either a physical product or a service) are highlydependent on the information displayed at the site

(Coupey, 2001). As discussed earlier, an online re-

tailer that implements deceptive techniques is more

likely to provide unrealistic expectations about the

product (among other things). This may result in

negative disconfirmation between expectations and

product performance, thus leading to customer dis-

satisfaction with the website. Earlier research in off-

line settings provides empirical evidence for the

negative effect of deceptive/manipulative selling

tactics on consumer satisfaction (e.g., Roman and

Ruiz, 2005). All the above leads us to propose that:

Hypothesis 1: Perceived deception will have a neg-

ative influence on consumer’s satisfaction with

the online retailer.

The relationship between online deception and

loyalty intentions can be explained using equity

theory (Adams, 1963). Equity theory involves the

norm of distributive justice in a dyadic relationship

(i.e., the desire on the part of the members involved

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to have a fair and just distribution of profit). Re-

search indicates that consumers often evaluate mar-

ketplace transactions by considering how equitable

each party has contributed to the exchange (Hup-

pertz et al., 1978). In particular, equity theory arguesthat if one party (consumer) perceives another party

benefiting unfairly (i.e., the online retailer sells the

product as a result of implementing deceptive

practices), the disadvantaged party views the situa-

tion as inequitable and attempts to regain balance or 

restore equilibrium. In such a case, actions may

consist of negative word-of-mouth to friends and

family, complaints to the company or third party

organizations, or no future purchases from the on-

line retailer (Ingram et al., 2005). Prior research in

traditional retail settings has linked consumers’ per-ceptions of deceptive practices (e.g., high-pressure

selling techniques) to loyalty (e.g., Whalen et al.,

1991). Accordingly, the following hypothesis is

formulated:

Hypothesis 2: Perceived deception will have a neg-

ative influence on consumer’s loyalty intentions

to the online retailer.

The positive influence of satisfaction on loyalty has

been well documented in the traditional retail context

(e.g., Bolton and Lemon, 1999; Ingram et al., 2005).Only recently this relationship has also been tested in

the online environment (e.g., Fassnacht and Kose,

2007). This relationship can be explained by the fact

that satisfied customers highly value the product

offered by the company. For this reason, they will be

more inclined to buy from the company in the future

and behave in a way that is beneficial to the company

(spreading positive word-of-mouth). Accordingly,

we expect that satisfaction with the online retailer 

increases loyalty intentions. Stated formally:

Hypothesis 3: Consumer’s satisfaction with the on-line retailer will have a positive influence on

loyalty intentions.

It is important to note that we predict the link to

be positive between satisfaction and loyalty as per the

marketing literature. We also predict the links to be

negative between deception and satisfaction and

loyalty intentions as per our previously stated theo-

rizing. Yet, as discussed at the beginning of the

article, we will also investigate the extent to which

deception has an indirect effect on loyalty through

satisfaction.

Moderating effects

In addition to testing for the aforementioned effects,

this article also takes an initial step toward assessing

the role of the type of product being purchased

(goods versus services), consumer’s attitude toward

the Internet, and consumer’s demographics that may

moderate the effect of perceived deception on

consumer satisfaction and loyalty intentions.

The moderating effect of the type of product purchased 

online 

While shopping for service products presents a range

of challenges for consumers, most of service prob-

lems are reduced during online shopping experi-

ences (Pitt et al., 1999). Through the advanced

technology created by the World Wide Web, con-

sumers can now experience the sights and sounds

related to particular service products. Thus, the

traditional problem of intangibility is virtually

reduced in some types of services (Smith and

Sivakumar, 2004). In traditional retailing, theproblem of intangibility is especially relevant for 

services high in experience attributes (e.g., travel

vacation packages). This implies that consumers are

generally unable to make a decision about the quality

of a service until they have purchased it. However,

online retailers can give consumers many tools that

can be used to evaluate the experience properties

associated with many service products. For example,

Disney.com ‘‘tangibilizes’’ the Disney dream vaca-

tion by allowing consumers to virtually experience

the Disney theme parks by meeting the characters,

viewing the rides, and hearing the music typically

associated with Disney (Pitt et al., 1999).

By contrast, Internet retailing, despite allowing

for some multimedia presentation, is inherently

deficient in offering pretrial experience and evalua-

tion for a majority of commonly bought items

(physical products), such as clothing, toys, and fur-

niture (Grewal et al., 2004). Consumers often require

high sensory evaluation and/or trial for products such

as clothing, but these can hardly be represented

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digitally (Grewal et al. 2004). Accordingly, consumers

in the online context have difficulty in evaluating

some physical/tangible products that are easily evalu-

ated in the traditional context. Then, quality uncer-

tainty becomes a problem. In these cases, e-retailerscan easily exaggerate product characteristics such as

quality, performance, size, or even color. On the

contrary, because services are intangible in traditional

retail settings, consumers will place a great amount of 

emphasis on browsing and information gathering

during on-line shopping experiences (Shankar et al.,

2003). In fact, information gathering online will

actually serve to reduce or alleviate the risks that are

typically associated with the purchase of services in

traditional retail settings (Frambach et al., 2007).

In short, online consumers are in a better positionto know what to expect from the service and are less

likely to be surprised or disappointed at the service

received (Shankar et al., 2003), than when they buy

a physical product online. This suggests more dis-

confirmation with expectations as a result of 

deceptive practices when the product purchased

online is a physical product, instead of a service.

Also, the consumer may view the situation as more

inequitable or unfair if deceptive practices have been

implemented when buying a physical product as

opposed to a service. Based on this reasoning, it is

expected that the negative effect of perceiveddeception on consumers’ satisfaction and loyalty

intentions will be stronger for physical products

(goods) than for services. Stated formally:

Hypothesis 4a: The negative influence of perceived

deception on satisfaction will be stronger when

the consumer has purchased a physical product

rather than when he/she has purchased a service.Hypothesis 4b: The negative influence of perceived

deception on loyalty intentions will be stronger 

when the consumer has purchased a physical

product rather than when he/she has purchased a

service.

The moderating effect of consumer’s attitude toward 

the Internet 

Building on Petty et al. (1991, p. 242), consumer’s

attitude toward the Internet is defined as consumer’s

global and relatively consistent evaluations, feelings,

and tendencies toward the Internet. Attitudes put

people into a frame of mind for liking or disliking

things, for moving toward or away from them. Prior 

research suggests that consumers with a more posi-

tive attitude toward the Internet have more positivebeliefs about the trustworthiness of the Internet and

feel more comfortable using it (George, 2002). In

fact, researchers drawing on the technology accep-

tance model (TAM) have shown that consumer’s

attitudes toward the Internet are strongly and

positively correlated with user acceptance (e.g.,

 Jayawardhena, 2004).

The above findings along with the expectancy

disconfirmation paradigm allow us to expect con-

sumer’s attitudes toward the Internet to have a

moderating effect on the influence of deception onsatisfaction and loyalty intentions. Consumers with a

more positive attitude toward the Internet are less

likely to expect deceptive/opportunistic practices

from online retailers than consumers with a less

positive attitude toward the Internet. In case such

practices take place, consumers with a more positive

attitude toward the Internet will evaluate them dif-

ferently than consumers with a less positive attitude.

More specifically, it is hypothesized that deceptive

practices are particularly harmful when unexpected

(unexpected because the consumer has a more

positive attitude toward the Internet), and conse-quently they will have stronger negative effects on

satisfaction and loyalty than when they are expected

(expected because the consumer has a less positive

attitude toward the Internet). Stated formally:

Hypothesis 5a: The negative influence of perceived

deception on satisfaction will be stronger when

the consumer has a more positive attitude toward

the Internet than when he/she has a less positive

attitude toward the Internet.Hypothesis 5b: The negative influence of perceived

deception on loyalty intentions will be stronger 

when the consumer has a more positive attitude

toward the Internet than when he/she has a less

positive attitude toward the Internet.

The moderating effect of consumer’s demographics

Only recently research has empirically addressed the

moderating role of consumer’s demographics in the

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online environment. For example, Hansen (2005)

found that perceived order accessibility had a signif-

icant positive effect on future online buying intention

for well educated consumers, but not for less educated

consumers. Findings from Garbarino and Strahilevitz(2004) suggested that positive word-of-mouth leads

to both greater reduction in perceived risk and

stronger increase in willingness to buy online among

women than men. Nevertheless, the moderating role

of demographics on the consequences of deception

has not been previously examined.

In the offline context, while the evidence linking

demographic groups with unethical/deceptive activ-

ities is not conclusive, the weight appears to rest on

the side of describing demographic segments that

consistently vary in their general evaluations of ethics, and thus, in the perceptions of the un/ethical

practices of firms. More specifically, prior research

(e.g., McIntyre et al., 1999; Ramsey et al., 2007)

suggests that consumers become more aware of the

severity of unethical practices as they obtain greater 

maturity (age and education). In fact, education and

age are theorized to result in higher levels of moral

reasoning (Rest, 1986). For instance, results from

Ramsey et al. (2007) showed that older consumers,

as compared to younger ones, evaluated unethical

practices using higher standards5 than those used by

 younger subjects.Additional evidence exists for greater ethical

awareness and sensitivity by females (e.g., Roxas and

Stoneback, 2004; Weeks et al., 1999). Weeks et al.

(1999) found that women adopted a more strict

ethical stance than males when assessing unethical

practices. This can be explained by the gender 

socialization approach (Kohlberg, 1969). The main

idea is that males and females will respond differently

to the same set of unethical/deceptive practices.

Roxas and Stoneback (2004) argue that men seek

competitive success and are more likely to breakrules, whereas women are more likely to adhere to

rules, as they are concerned about doing tasks well

and having harmonious relationships.

In summary, based on the above arguments we

expect older, more educated and female consumers

to be more critical of deceptive practices and con-

sequently to have a stronger negative reaction in

terms of lower levels of satisfaction and loyalty

intentions to the online retailer. Accordingly, it is

hypothesized that:

Hypothesis 6a – c : The negative influence of perceived

deception on satisfaction will be stronger for (a)

older (b) more educated and (c) female consumers

than for younger, less educated and male con-

sumers.Hypothesis 7a – c : The negative influence of perceived

deception on loyalty intentions will be stronger 

for (a) older (b) more educated and (c) female

consumers than for younger, less educated and

male consumers.

Method

Sample and data collection

A survey instrument was administered to a sample

of 398 real consumers. A marketing research firm

was hired to assist with the data collection.

Respondents were approached randomly among

individuals who passed the data collection point

located on the pedestrian walkway in a major 

metropolitan city (for a similar procedure see

Frambach et al., 2007, pp. 30–31). Screening

questions were administered before the respondent

was invited for an interview. An invitation only

followed if the respondent proved to be eligible for the study (that is, he/she should have purchased a

product online in the last 6 months). The latter 

condition to facilitate consumers’ evaluations of the

online retailer’s website. Then, subjects were taken

to the company office (conveniently located in the

metropolitan area). The procedure was to let sub-

 jects browse the website where they made their last

online shopping. After a certain period of time

(a maximum of 10 min), subjects were asked to

complete the questionnaire corresponding to that

site.The respondents were representative of online

consumers across numerous e-retailers, having

purchased a variety of items (e.g., travel, books,

CDS, and computers). A profile of the sample is

shown in Table I. The respondents were relatively

  young, generally highly educated and experienced

with the Internet. Prior research has found that

these characteristics are common among Internet

shoppers (Girard et al., 2003; Swinyard and Smith,

2003).

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Measures

Existing multi-item scales, adapted to suit the con-

text of the study, were used for the measurement of the constructs (all items of the questionnaire are

reported in Table II). All scales consisted of 5-point

Likert questions, ranging from ‘‘1 = strongly dis-

agree’’ to ‘‘5 = strongly agree.’’ Perceived deception

was measured with four items from Roman (2007).

These items refer to the extent to which the con-

sumer believes that the online retailer uses deceptive

or manipulative practices with the intent to persuade

consumers to purchase the website’s offerings.

Importantly, this scale focuses on consumer’s

perceptions of online retailer’s deceptive practices

rather than on the act of deceiving itself (Roman,

2007).

Consumer attitude toward the Internet was

assessed using a three-item scale adapted from Porter and Donthu (2006) and Schiffman et al. (2003). This

adaptation is consistent with previous studies

examining consumers’ attitudes toward the tradi-

tional retail context (e.g., Shim and Eastlick, 1998).

Three items from Anderson and Srinivasan (2003)

were used to measure satisfaction. Due to the evi-

dence that satisfaction is primarily an affectively

oriented construct (cf. Oliver, 1980), all items were

emotional in content and included references to the

respondent’s general feelings of outright satisfaction

and happiness about the purchase decision. Con-sumers’ loyalty intentions were measured using a

three-item6 scale adapted from Wolfinbarger and

Gilly (2003) and Fassnacht and Kose (2007). As

discussed earlier in this article, this scale measures the

two aspects of loyalty suggested most often in extant

research: the intention of repurchase and the inten-

tion to spread positive word-of-mouth.

Confirmatory factor analyses: reliability, convergent,

and discriminant validity

A confirmatory factor analysis (CFA) by means of 

LISREL 8.72 was conducted to assess measurement

reliability, convergent, and discriminant validity.

The measurement model had a good fit (v2(59) =

103.31, p< 0.01, GFI = 0.96, AGFI = 0.94,

CFI = 0.99, RMSEA = 0.02, RMSR = 0.03, TLI

(NNFI) = 0.98). In addition, the observed normed

v2 for this model was 1.75, which is smaller than 3

recommended by Fornell and Larcker (1981), indi-

cating a good model fit when we consider the

sample size.Reliability of the measures was confirmed with

composite reliability index higher than the recom-

mended level of 0.60 (Bagozzi and Yi, 1988) and

average variance extracted was higher than the rec-

ommended level of 0.50 (Hair et al., 1998) as shown

in Table III. Following the procedures suggested by

Fornell and Larcker (1981) and Bagozzi and Yi

(1988), convergent validity was assessed by verifying

the significance of the t  values associated with the

parameter estimates (Table II). All t  values were

TABLE I

Sample profile

Variable Percentage

Gender 

Male 51

Female 49

Age

<20 10.5

20–35 65.2

36–50 18.1

>50 6.3

Education

Low (primary school) 3.5

Middle (high school) 28.3

High (University; polytechnic)

a

68.2Occupation

Employed people 52.4

Self-employed workers 14.3

Students 29.3

Others (retired, homemaker,

and unemployed)

4.4

Internet experience (years)

<2 6.3

2–5 33.4

6–8 35.3

>8 25

Online purchases in the last year (e)

<120e 28.8120e –599e 41.8

600e –1199e 15.3

>1200e 14.2

aThese individuals had completed their university studies.

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positive and significant ( p< 0.01). Discriminant

validity was tested by comparing the average variance

extracted by each construct to the shared variance

between the construct and all other variables. For 

each comparison, the explained variance exceeded all

combinations of shared variance (see Table III).

Results

Direct and indirect effects

The hypothesized relationships were estimated via

LISREL 8.72. The key benefit of this methodology

TABLE II

Construct measurement summary: confirmatory factor analysis of multi-item measures

Item descriptiona SD loading (t -value)

Perceived deception

The site exaggerates the benefits and characteristics of its offerings 0.57 (10.34)

The site uses misleading tactics to convince consumers to buy its products 0.80 (16.30)

It is not entirely truthful about its offerings 0.82 (17.12)

This site attempts to persuade you to buy things that you do not need 0.72 (15.53)

Consumer satisfaction

I am satisfied with my decision to purchase from this site 0.57 (9.74)

My choice to purchase from this site was a wise one 0.89 (15.08)

I am happy I made my purchase at this website 0.95 (16.21)

Consumer loyalty intentions

I plan to do business with this website in the future 0.83 (12.91)

I would recommend the website to someone who seeks my advice 0.91 (16.43)I will advise friends and relatives to at least give this website a trial 0.95 (17.17)

Consumer attitude toward the Internet

The Internet enables me to do things I would not be able to do otherwise 0.70 (12.03)

I am positive toward the Internet 0.82 (13.41)

I feel comfortable using the Internet 0.62 (8.90)

v2(59) = 103.31, p< 0.01, GFI = 0.96, AGFI = 0.94, CFI = 0.99,

RMSEA = 0.02, RMSR = 0.03, TLI (NNFI) = 0.98

aAll scales consisted of 5-point Likert questions, ranging from ‘‘1 = strongly disagree’’ to ‘‘5 = strongly agree.’’

TABLE III

Mean, SD, scale reliability, AVE, and correlations

Mean SD AVE 1 2 3 4 5 6 7 8

1. Perceived deception 2.41 0.83 0.54 0.82 0.16 0.11 0.07

2. Satisfaction 4.03 0.85 0.67 -0.40 0.85  0.64 0.09

3. Loyalty intentions 4.06 0.79 0.80 -0.34 0.80 0.92 0.12

4. Attitude toward the Internet 4.08 0.64 0.51 -0.27 0.31 0.35 0.76 

5. Product type (0 = services, 1 = goods) na na na -0.13 0.03 0.05 -0.04 na

6. Gender (0 = women, 1 = men) na na na -0.01 -0.01 -0.02 0.00 -0.02 na

7. Education (0 = low, 1 = middle, 2 = high) na na na -0.04 -0.01 -0.01 0.18 0.09 0.19 na

8. Age (years) 30.29 9.76 na -0.10 -0.01 -0.01 -0.01 0.10 0.01 0.04 na

 AVE  average variance extracted, na not applicable.Scale composite reliability of multi-item measures is reported along the diagonal. Shared variances of multi-item measures

are reported in the upper half of the matrix. Correlations are reported in the lower half of the matrix. Correlations higher 

than 0.09 significant at 95%.

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in the context of our study is that it allows for a test of 

indirect effects. The results indicated a good fit

between the model and the observed data

(v2(32) = 83.34, p< 0.01, GFI = 0.96, AGFI =

0.93, CFI = 0.99, RMSEA = 0.04, RMSR = 0.04,TLI (NNFI) = 0.99). The model explained 19% and

68% of the variance in satisfaction and loyalty inten-

tions, respectively. The analyses provided strong

support for the direct negative influence of perceived

deception on satisfaction (c = -0.39, t -value =

-5.56), but not on loyalty (c = -0.01, ns). Thus,

supporting Hypothesis 1, but not Hypothesis 2.

In line with Hypothesis 3, satisfaction had a

highly significant influence on loyalty (b = 0.81,

t -value = 8.72).

The relationship between perceived deceptionand satisfaction was further studied. In particular, the

indirect influence of deception on loyalty intentions

via satisfaction was examined. The results indicated a

strong and significant indirect relationship between

deception and loyalty (SD coeff. = -0.32; t -value =

-5.32). In fact, as shown in Table III both variables

were highly and significantly correlated (r = -0.34,

 p< 0.01). This ‘‘compensates’’ for the insignificant

direct effect of deception on loyalty and shows

that its impact on loyalty is completely mediated by

satisfaction.

To further support the pivotal role of satisfactionwithin this model, the hypothesized model (M T) was

compared with a rival model (M U), where the

influence of satisfaction on loyalty intentions was not

estimated. The results of the rival model showed that

both satisfaction and loyalty were significantly

and directly influenced by deception (c = -0.47,

t -value = -6.33; c = -0.40, t -value = -6.02,

respectively). We used a chi-square difference test

(CDT) to test the null hypothesis: M T-M U = 0.

The relevant test statistics (M T has 32 df and a v2 of 

83.34, M U has 33 df and a v

2

of 301.70) lead to ahighly significant CDT (v2 difference is 218.36 at 1

df, p< 0.01). Consequently, the rival model had a

significantly worse fit to the data compared to the

hypothesized model. Together, these results clearly

indicate the mediating nature of customer satisfac-

tion: the negative influence of deception on satis-

faction, which in turn has an effect on loyalty. These

effects are strong and in the directions predicted.

Once these paths are estimated, any possible direct

effect of deception on loyalty intentions is minimal,

supporting a point of view that an assessment of 

satisfaction is the process through which deception

alters a consumer’s tendencies toward loyal brand

and firm behavior.

Moderating effects

Hypotheses 4a–b, 5a–b, 6a–c, and 7a–c examined

the effects of the moderating variables on the

deception-consequences link. We tested moderating

effects through multigroup LISREL analyses. The

samples were splitted into subsamples according to

whether consumers scored high or low on the

moderating variables (as far as gender and type of 

product are concerned, males versus females andgoods versus services were compared, respectively)

to ensure within-group homogeneity and between-

group heterogeneity. The subgroup method is a

commonly preferred technique for detecting mod-

erating effects (cf. Stone and Hollenbeck, 1989), and

has been extensively used in the literature (e.g.,

Brockman and Morgan, 2006; De Wulf et al., 2001;

Homburg and Giering, 2001).

Following the aforementioned procedures, for 

consumer’s attitude toward the Internet and con-

sumer’s age, the sample was median split in two

subgroups, respectively (consumers with a morepositive versus less positive attitude toward the

Internet and older versus younger consumers). For 

the remaining moderating variables, the sample was

split into goods and services subgroups, male and

female consumers and more educated (college edu-

cation or higher) versus less educated consumers (no

college education). Then, multiple-group LISREL

was performed comparing two subsamples. More

specifically, two models that are different only with

respect to the effect of deception on the dependent

variable (either satisfaction or loyalty intentions)were compared. One model restricts this parameter 

to be equal across groups (equal model), whereas the

more general model allows this parameter to vary

across groups. Because these are nested models, with

the general model having one degree of freedom less

than the restricted model, the chi-square value will

always be lower for the general model than for 

the restricted model. The question is whether the

improvement in chi-square when moving from

the restricted to the more general is significant.

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Significance can be assessed on the basis of the chi-

square difference between the two models with the

use of a chi-square distribution with one degree of 

freedom.

The results of the multi-group LISREL analysesare shown in Table IV. As anticipated, the negative

influence of deception on satisfaction was stronger 

among individuals who: had purchased a physical

product (c = -0.58, p < 0.05) versus a service

(c = -0.31, p < 0.05), had a more positive attitude

toward the Internet (c = -0.70, p < 0.05) versus a

less positive attitude (c = -0.19, p < 0.05), were

older (c = -0.56, p < 0.05) versus younger (c =

-0.31, p < 0.05), were more educated (c = -0.58,

 p < 0.05) versus less educated (c = -0.28,

 p<

0.05) and females (c =-

0.59, p<

0.05) ver-sus males (c = -0.34, p < 0.05). In all these cases

the decrease in chi-square when moving from the

restricted (equal) model to the more general model

was significant, providing support for Hypothesis 4a,

5a and 6a–c, respectively.

Interestingly, even though there was no direct

influence of deception on loyalty intentions for the

whole sample, we found some moderating effects

(although not statistically significant at the 0.95

confidence level). In particular, the negative influ-

ence of deception on loyalty was significant when

the product purchased was a physical product(c = -0.08, p < 0.05), but not when it was a ser-

vice (c = -0.01, ns). The findings also showed that

deception negatively influenced loyalty when con-

sumers had a more positive attitude toward the

Internet (c = -0.12, p < 0.05), but not when

consumers’ attitude toward the Internet was less

positive (c = -0.01, ns). Also, deception influenced

loyalty intentions among consumers who were more

educated (c = -0.08, p < 0.05), but not when they

were less educated (c = -0.01, ns). Overall, these

findings partially support Hypothesis 4b, 5b and 7b,respectively. As shown in Table IV, age and gender 

did not moderate the influence of deception on

loyalty. Therefore, Hypothesis 7a and 7c were not

supported.

Discussion and conclusions

While e-commerce has witnessed extensive growth

in recent years, so has consumers’ complaints

regarding deceptive practices in online shopping.

This study represents the first attempt to analyze

the influence of perceived online deception on

consumer satisfaction and loyalty intentions to the

online retailer. In doing so, this study begins toaddress recent calls for empirical research concerning

the effects of online retailers’ deceptive practices on

consumer’s relational variables (e.g., Biswas and

Biswas, 2004; Roman, 2007). Unlike previous

research related to Internet deception, that has

mostly been conducted with students being exposed

to artificial hypothetical scenarios, this study used a

sample of real consumers referring to their latest

online purchase, which increases the external valid-

ity of the findings. As predicted, perceived deception

had a strong and negative influence on satisfaction.In fact, perceived deception alone explained 19% of 

the variance in satisfaction. This is noteworthy be-

cause previous studies that have analyzed the influ-

ence of several key antecedents on e-satisfaction (i.e.,

convenience, product offerings, product informa-

tion, site design, and financial security) have not

been able to predict more than 27% (Szymanski and

Hise, 2000, p. 317) and 17% (Evanschitzky et al.,

2004, p. 243) of the variance in e-satisfaction.

Contrary to our expectations, perceived decep-

tion had no direct influence on loyalty intentions

when the relationship between satisfaction and loy-alty was estimated. Interestingly, further analysis

revealed that deception had a significant and direct

effect on loyalty when the path from satisfaction to

loyalty was not estimated. Overall, this highlights the

key mediating role of satisfaction in the perceived

deception-loyalty link. Also, these results are con-

sistent with those obtained by Ingram et al. (2005) in

a traditional retail context. They found that satis-

faction totally mediated the influence of consumer’s

perceptions of retailers’ un/fairness on consumer’s

behavioral intentions. As noted earlier in this article,marketing researchers have considered deceptive

practices as unfair practices.

The marketing literature has long evidenced the

positive influence of satisfaction on favorable

behavioral intentions in the traditional context.

Importantly, this study is one of the few to show the

strong and positive influence of satisfaction on loy-

alty intentions in the online context (Anderson and

Srinivasan, 2003; Fassnacht and Kose, 2007).

Drawing on this stream of research, the current study

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    T    A    B    L    E    I    V

    R   e   s   u    l   t   s   o    f   m   o    d   e   r   a   t    i   n   g   e    f    f   e   c   t   s

    R   e    l   a   t    i   o   n   s    h    i   p

    M   o    d   e   r   a   t   o   r   v   a   r    i   a    b    l   e

    C    h    i  -   s   q   u   a   r   e    d    i    f    f   e   r   e   n   c   e

    (      D    d    f   =    1    )

    H   y   p   o   t    h   e   s    i   s

   s   u   p   p   o   r   t   e    d

    G   o   o    d   s    (    n   =    1    9    8    )

    S   e   r   v    i   c   e   s    (    n   =    1    9    8    )

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    S    A    T

      c

   =  -    0 .    5    8    (      t   =  -    7 .    3    4    )

      c

   =  -    0 .    3    1    (      t   =  -    4 .    4    3    )

      D     v

       2

   =    7 .    3    3    *    *    *

    H    4   a   s   u   p   p   o   r   t   e    d

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    l   o   y   a    l   t   y

      c

   =  -    0 .    0    8    (      t   =  -    2 .    1    3    )

      c

   =  -    0 .    0    1    (      t   =  -    0 .    2    1    )

      D     v

       2

   =    3 .    1    5    (    p   =    0 .    0    7    )

    H    4    b   p   a   r   t    i   a    l    l   y   s   u   p   p   o   r   t   e    d

    M   o   r   e   p   o   s    i   t    i   v   e   a   t   t    i   t   u    d   e   t   o   w   a   r    d   t    h   e

    I   n   t   e   r   n   e   t    (    n   =    1    5    1    )

    L   e   s   s   p   o   s    i   t    i   v   e   a   t   t    i   t   u    d   e   t   o   w   a   r    d

   t    h   e    I   n   t   e   r   n   e   t    (    n   =    2    4    7    )

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    S    A    T

      c

   =  -    0 .    7    0    (      t   =  -    7 .    1    8    )

      c

   =  -    0 .    1    9    (      t   =  -    4 .    4    3    )

      D     v

       2

   =    2    5 .    6    7    *    *    *

    H    5   a   s   u   p   p   o   r   t   e    d

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    l   o   y   a    l   t   y

      c

   =  -    0 .    1    2    (      t   =  -    2 .    6    3    )

      c

   =  -    0 .    0    1    (      t   =  -    0 .    4    1    )

      D     v

       2

   =    3 .    3    4    (    p   =    0 .    0    6    )

    H    5    b   p   a   r   t    i   a    l    l   y   s   u   p   p   o   r   t   e    d

    O    l    d   e   r    (    n   =    2    0    4    )

    Y   o   u   n   g   e   r    (    n   =    1    9    4    )

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    S    A    T

      c

   =  -    0 .    5    6    (      t   =  -    7 .    2    7    )

      c

   =  -    0 .    3    1    (      t   =  -    4 .    3    6    )

      D     v

       2

   =    6 .    1    9    *    *

    H    6   a   s   u   p   p   o   r   t   e    d

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    l   o   y   a    l   t   y

      c

   =  -    0 .    0    5    (      t   =  -    1 .    2    7    )

      c

   =  -    0 .    0    3    (      t   =  -    0 .    7    3    )

      D     v

       2

   =    0 .    1    5    (   n   s    )

    H    7   a   n   o   t   s   u

   p   p   o   r   t   e    d

    M   o   r   e   e    d   u   c   a   t   e    d   u   n    i   v   e   r   s    i   t   y

   s   t   u    d    i   e   s    (    n   =    2    7    1    )

    L   e   s   s   e    d   u   c   a   t   e    d   n   o

   u   n    i   v   e   r   s    i   t   y   s   t   u    d    i   e   s    (    n   =    1    2    7    )

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    S    A    T

      c

   =  -    0 .    5    8    (      t   =  -    8 .    5    4    )

      c

   =  -    0 .    2    8    (      t   =  -    3 .    3    7    )

      D     v

       2

   =    8 .    5    1    *    *    *

    H    6    b   s   u   p   p   o

   r   t   e    d

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    l   o   y   a    l   t   y

      c

   =  -    0 .    0    8    (      t   =  -    2 .    1    8    )

      c

   =  -    0 .    0    1    (      t   =  -    0 .    3    5    )

      D     v

       2

   =    2 .    8    2    (   p   =    0 .    0    9    )

    H    7    b   p   a   r   t    i   a    l    l   y   s   u   p   p   o   r   t   e    d

    F   e   m   a    l   e   s    (    n   =    1    9    5    )

    M   a    l   e   s    (    n   =    2    0    3    )

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    S    A    T

      c

   =  -    0 .    5    9    (      t   =  -    7 .    2    5    )

      c

   =  -    0 .    3    4    (      t   =  -    5 .    1    4    )

      D     v

       2

   =    4 .    0    7    *    *

    H    6   c   s   u   p   p   o   r   t   e    d

    P   e   r   c   e    i   v   e    d    d   e   c   e   p   t    i   o   n   fi

    l   o   y   a    l   t   y

      c

   =  -    0 .    0    1    (      t   =  -    0 .    3    4    )

      c

   =  -    0 .    0    0    (      t   =  -    0 .    1    9    )

      D     v

       2

   =    0 .    8    (   n   s    )

    H    7   c   n   o   t   s   u

   p   p   o   r   t   e    d

    n    s    N   o   t   s    i   g   n    i    fi   c   a   n   t .

    *    *    p    <

    0 .    0    5 ,    *    *    *    p    <

    0 .    0    1 .

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also focused on behavioral loyalty (i.e., loyalty

intentions), rather than on attitudinal loyalty. This

distinction is particularly relevant since Shankar et al.

(2003) found that loyalty and satisfaction had a re-

ciprocal relationship such that each positively rein-forced the other in the online environment. In their 

research, both constructs were conceptualized and

measured as attitudinal variables. Therefore, our 

findings do not contradict Shankar et al.’s study.

Moreover, our results are consistent with the theory

of reasoned action TRA (Fishbein and Azjen, 1975)

that theorizes that consumer’s behavioral intentions

(e.g., loyalty intentions) are determined by attitudes

(e.g., satisfaction).

The study of the moderating effects represents the

first effort in the process of identifying the conditionsunder which the deceptive practices of online retailers

are likely to have the greatest negative effects on

consumer satisfaction and loyalty intentions. In par-

ticular, our results revealed that the negative influence

of perceived deception on satisfaction was stronger 

among individuals who had purchased a physical

product (instead of a service), had a more positive

attitude toward the Internet, were older, more edu-

cated and females. The analysis of the moderating

effects also produced some interesting and unex-

pected findings. Even though perceived deception

did not have a direct influence on loyalty intentionswhen the whole sample was considered, the multi-

group analyses revealed that deception had a direct

effect on loyalty among consumers who (a) had

purchased a physical product, (b) had a more positive

attitude toward the Internet, and (c) were more

educated.7 Also, it is important to note that, as shown

in Table IV, the moderating influence of type of 

product, attitude toward Internet and education on

the deception-satisfaction link was statistically stron-

ger ( p < 0.01) than when the moderating variables

were age and gender ( p<

0.05). Overall, thesefindings highlight the key moderating role of type of 

product, consumer’s attitude and education on the

influence of deception on satisfaction and loyalty

intentions. These results have key managerial impli-

cations that will be discussed in detail below.

Importantly, we found a direct influence of 

deception on loyalty only among individuals who

were more educated. In fact, education was the only

demographic factor that had a moderating role on

the deception-loyalty link. Upon reflection, one

possible reason for this finding may be the normative

view that the core of education itself is virtue or 

right conduct (Howard, 1989). Indeed, early re-

search by Rest (1979) provided substantial data to

support that moral judgment was more highly re-lated to formal education than to age. Accordingly,

we may speculate that more educated consumers are

more ethically sensitive, and consequently they are

more likely to take action (loyalty intentions) to

remedy an unethical/deceptive practice. Also, these

findings parallel, to some extent, those obtained by

Vitell et al. (2001) in a consumer ethics study in the

traditional context. They found that education was

the only demographic variable that moderated the

influence of consumer’s judgments of situations

involving ethical issues on consumer’s behavioralintentions.

Managerial implications

There are a number of managerial implications

derived from this study. Competing businesses are

only a mouse click away in e-commerce settings, so it

is critical for them to gain a better understanding of 

the factors affecting customer satisfaction and loyalty

intentions. Our results highlight the negative conse-

quences of perceive deception on consumer satisfac-tion and loyalty to the online retailer. Accordingly,

online retailers need to pay close attention to con-

sumers’ perceptions of deception. Derived from our 

conceptualization and measurement of deception,

online retailers need to be especially cautious not only

with the information content, but also with the

information presentation in their websites. As for 

information content, communication should be

credible and entirely truthful in order to avoid con-

sumers’ perceptions of deception. For example, on-

line retailers should provide realistic information onproduct characteristics and benefits. As for informa-

tion presentation, online retailers are encouraged to

pay particular attention to the persuasive power 

inherent in visuals and effects and their ability to

distract the consumers’ attention from relevant

information. Also, we advice online retailers to be

 judicious in their use of conflicting information via

different media (e.g., exploiting ‘‘picture-superiority-

effect’’ to present deceptive information in images

and truthful information in text).

386 Sergio Roma n

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Our findings revealed that loyalty intentions were

significantly and strongly associated with increased

satisfaction. Firms need to gain a better under-

standing of the relationship between satisfaction and

loyalty in the online environment to allocate their online marketing efforts between satisfaction initia-

tives and loyalty programs. Therefore, derived from

our results, we encourage online retailers to imple-

ment actions designed to enhance consumer’s satis-

faction.

Finally, this research indicated that while online

retailers should conduct their businesses in an ethical

manner with all consumers (as evidenced by the

direct and indirect effects of deception), the analyses

of the moderating variables revealed that actions

should be taken to explicitly address the ethicalconcerns of those target groups who perceive higher 

severity of deceptive online retail practices and

consequently punish such practices to a greater ex-

tent. In short, we encourage online retailers to

critically examine their communication approaches

(e.g., providing realistic information about their 

offerings that is easily accessible on the website) to

older, more educated, and female consumers as well

as consumers who have purchased a physical product

(instead of a service) and have a more positive atti-

tude toward the Internet. Managers must be aware

that any perceived deceptive practice may quicklyresult in dissatisfaction and loss of business, especially

in the cases mentioned earlier. Nevertheless, we do

not imply that online retailers could implement

deceptive practices in those segments of consumers

who are less ethically sensitive.

Limitations and future research directions

Substantively, building on the findings obtained in

this study, several suggestions can be offered for future researchers. Perceived online deception is a

complex and a highly elusive construct. The present

study focused on product-related deception. How-

ever, the intrinsic nature of the Internet medium

seems to enable several forms of deception, which

were previously virtually impossible to execute in

traditional retail settings. For example, pagejacking  – 

redirecting the browser from the target location

intended by the user to another location determined

by the deceiver – is a fraudulent scheme that does

not have an obvious equivalent in traditional chan-

nels. Therefore, the conceptualization and mea-

surement of perceived online deception needs

further attention from scholars. One additional

limitation and a need for further research concernsthe causality suggested in our findings. The research

design is cross-sectional in nature, and purely causal

inferences remain difficult to make. Hence, evidence

of causality through longitudinal studies is recom-

mended. In addition, the three items used to mea-

sure consumer loyalty intentions covered the two

aspects of loyalty suggested most often in extant

research (word-of-mouth and intended patronage)

and were derived from existing scales. Also the scale

showed satisfactory levels of reliability, convergent,

and discriminant validity. Nevertheless, the use of additional items, while increasing the survey length,

might improve the measurement properties of the

scale.

This study represents an initial step into the analysis

of the consequences of deception on satisfaction and

loyalty intentions. Further research is needed to ex-

tend the conceptual model. For example, it would be

interesting to analyze to what extent the differences

between the types of goods or the types of services

purchased by the various moderating groups (men

versus women, high education versus low education,

etc.) may be driving some of the differences foundbetween these groups in our research. Also, prior 

research has found that ethical ideologies and the

degree of machiavellianism play an important role in

consumer ethics (Winter et al., 2004). Future research

may analyze the extent to which such variables

moderate the influence of deception on the custom-

ers’ relational variables. Finally, this study focused on

consumers’ perceptions of online retailers’ deceptive

practices. Additional research may analyze to what

extent online retailers provide different information

(e.g., different levels of deception) to different seg-ments of consumers (e.g., men versus women, older 

versus younger consumers).

Notes

1 This research is focused on online shopping sites.

The article does not deal with other Internet sites – 

such as online newspapers, portals, free download sites,

customer to customer sites such as eBay or job sites

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 – that exist for purposes other than online shopping and

that are advertiser supported.2 All these examples of Internet deception generally

constitute fraud.3

For the sake of brevity, in the remaining of this arti-cle we refer to consumer’s perceptions of product-

related online deception as ‘‘online deception’’ or just

‘‘deception.’’4 Nevertheless, the extent to which deceptive prac-

tices constitute fraud is a complex issue. Early work in

advertising deception already advised about the difficul-

ties of defining, regulating, and establishing the relation-

ship between deception and fraud. For example,

Gardner (1975, p. 40) observed that: ‘‘Unfortunately,

even though the commission has issued many rulings

since 1914, it is not clear that the FTC, or anyone else,

has an adequate understanding of deceptive advertising.’’

Ten years later, Carson et al. (1985, p. 102) cautioned

that: ‘‘The FTC has recently enacted controversial new

standards pertaining to the regulation of deception.’’ This

issue is even more complex in the online context since

the legal definition of Internet fraud is changing and is

inconsistent across national boundaries (Morris-Cotterill,

1999). Authorities in a number of jurisdictions, including

the European Union, the Chinese Government, and

the United States Government have shown substantial

interest in limiting fraudulent practices conducted over 

the Internet. Unfortunately, all acknowledge that the

law has not kept pace with the technology and that

enforcement is problematic (Spinello, 2006; Nikitovand Bay, 2008).5 These higher standards were applied both to very

straightforward scenarios and to scenarios that were less

clear-cut in ethical terms. For example, ‘‘older consum-

ers were more likely to evaluate such behavior as exag-

gerating the benefits of a product/service, selling

products/services people do not need, and making ver-

bal promises that are not legally binding as unethical

selling behaviour’’ (Ramsey et al., 2007, p. 201).6 Very often, prior research has relied upon a limited

number of positive word-of-mouth and intended patron-

age items to measure loyalty. For instance, Sirohi et al.

(1998, p. 241) and Cronin et al. (2000, p. 213) used two

intended patronage items and one word-of-mouth item.

Wolfinbarger and Gilly (2003, p. 195) used two repeat

purchase intentions items and three word-of-mouth

items, whereas Johnson et al. (2006, p. 127) and Fassn-

acht and Kose (2007, p. 44) used three intended patron-

age items and two word-of-mouth items.7 As evidenced in the data, satisfaction and loyalty

were highly correlated (r  = 0.80), which reduces the

direct influence of deception on loyalty. Based on one

reviewer’s suggestions we ran additional multigroup

LISREL analyses comparing two subsamples (two mod-

els that are different only with respect to the effect of 

satisfaction on loyalty). More specifically, the objective

was to check if the influence of satisfaction on loyalty

was weaker among consumers who (a) had purchased aphysical product versus a service, (b) had a more posi-

tive attitude toward the Internet versus less positive atti-

tude, and (c) were more educated versus less educated.

  Yet, the influence of satisfaction on loyalty was not sta-

tistically different in either of the aforementioned cases.

In summary, the direct influence of deception on satis-

faction in theses cases is not caused by a weaker rela-

tionship between satisfaction and loyalty in those

groups.

Acknowledgments

This research was funded by the grant ECO2009-13170

from the Spanish Ministry of Science & Innovation. I

would like to thank Dawn Iacobucci and the two

anonymous reviewers for their many helpful comments

on previous drafts of this article.

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E-mail: sroman@um.es

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