The Influence of Perceived Product Risk on Consumers’ e-TailerShopping Preference

Pradeep A. Korgaonkar Æ Eric J. Karson

Published online: 18 May 2007

� Springer Science+Business Media, LLC 2007

Abstract Increasingly, retailers are combining Internet

and store based operations to become ‘‘multi-channel’’ as

they attempt to attract and retain customers. This study

investigates how the type and level of perceived product

risk (specifically economic and psychosocial risk) influence

patronage preference for shopping from three types of

e-tailers. The e-tailer formats studied are: pure play

e-tailers (e.g., Overstock.com), value-oriented store based

e-tailers (e.g., Wal-Mart.com), and prestigious store based

e-tailers (e.g., Bloomingdales.com). The hypotheses,

based upon prior research in the area of perceived product

risk, show that type and level of risk do matter. Further,

e-tailers linked with prestigious stores have an advantage

over both other e-tailer types. Results also show an inter-

action between perceived product risk and the e-tail format.

Based on samples from the Northeast and Southeast

USA, the results are found to be similar in these diverse

regions, improving the generalizability of the findings.

Keywords Risk � Internet � e-Tailing � Online shopping �Store patronage

Introduction

The Internet has clearly revolutionized the way consumers

acquire and process, and marketers disseminate, informa-

tion. As online retail sales continue to increase at a slower

pace than expected, practitioners, and academics alike are

still searching for factors that influence consumer prefer-

ence for shopping on the Internet. Although published re-

search exists related to consumer Internet shopping, little is

known about how consumers shop from stores that have

added web sites to their ‘‘brick and mortar’’ retailing

(e.g., Jarvenpaa & Todd, 1996–97; Jones & Vijayasarathy,

1998). As technology increases consumer shopping alter-

natives, research is needed to uncover how the web sites of

multi-channel retailers such as Eddie Bauer compare vis-a-

vis pure play Internet retailers such as Shopzilla.com.

Specifically, this study attempts to provide insight into

which products are preferred by consumers using a par-

ticular e-tailer format.

Research to date suggests that perceived risk is likely to

be useful in understanding a variety of online consumer

behaviors, including e-tailing patronage (Donthu & Garcia,

1999; Ha, 2002). Still, little is known about how risk per-

ceptions influence patronage among the major variants of

Internet store formats e.g., pure play Internet retailers such

as ShopNBC.com, value oriented discount store based

‘‘click and mortar’’ retailers such as Target.com, or pres-

tigious department store based ‘‘click and mortar’’ retailers

such as Saksfifthavenue.com, henceforth called pure play,

value C&M, and prestigious C&M, respectively. Although,

past studies have investigated product categories that are

best suited for Internet retailing in general (e.g., Cheskin

Research and Studio Archtype/Sapient, 1999; Girard,

Silverblatt, & Korgaonkar, 2002; Peterson, Balasubrama-

nian, & Bronnenberg, 1997), published research on the

topic of different types of e-tailers is scant. Taking

advantage of the rich perceived risk paradigm literature,

this study empirically tests whether value and prestigious

C&M e-tailers have an advantage over strictly pure play

P. A. Korgaonkar

College of Business, Florida Atlantic University, University

Tower, 220 S.E. 2nd Avenue, Fort Lauderdale, FL 33301, USA

e-mail: Korgaonk@fau.edu

E. J. Karson (&)

Department of Marketing, Villanova School of Business,

Villanova University, Villanova, PA 19085-1678, USA

e-mail: eric.karson@villanova.edu

123

J Bus Psychol (2007) 22:55–64

DOI 10.1007/s10869-007-9044-y

e-tailers depending on the product risks perceived by on-

line shoppers for each e-tailer type.

Specifically, this study first tests whether consumers’

overall preference for online shopping differs based on the

perceived product risk. Second, we hypothesize that overall

shopping preference is highest for prestigious C&M

e-tailers followed by value C&M e-tailers, and the lowest

for pure play e-tailers, regardless of the product risk. Third,

hypotheses about the interaction effects of online e-tailer

types and perceived product risk types (e.g., economic risk

and psychosocial risk) on preference for shopping online

are formed and tested.

While we acknowledge that many factors are likely to

influence online shopping behavior, our focus on risk ex-

tends previous research on Internet retailing as risk is likely

to serve as a ‘‘catch-all’’ for consumers’ reservations to-

wards Web shopping, or their preference for one type of

e-tailer over another. As the perceived product risk concept

has been fruitful in explaining consumers’ choice of

products, retailers, catalog/telephone shopping, and inter-

net shopping in the past, we feel the extension of this

simple and useful concept will aid in understanding and

explaining patronage preferences between the three types

of e-tailers as well.

Product risk

Past studies suggest that the usefulness of the Internet as a

shopping medium is closely linked to the product that

consumers intend to purchase. For example, Rosen and

Howard (2000) propose what they term as e-potential for

different products to be sold on the Internet. Proponents of

the transaction cost paradigm suggest that product features

will influence transaction costs and, as such, play a key role

in e-tailer selection (e.g., Benjamin & Wigand, 1995). We

emphasize the perceived risk paradigm in our study as

the focal point of discussion as, for many consumers,

buying from the Internet is a new way of buying. In fact, a

May 2003 study by International Demographics, Inc.,

shows only 22.5% of US households were regular Internet

purchasers in 2002, making over four purchases. As a result

many consumers who buy online are often insecure and

perceive risk. This risk has two main sources: (a) risk re-

lated to the types of product purchased, and (b) the risk

associated with the type of online merchant they are pur-

chasing from.

Since Bauer’s (1960) seminal work, several studies in

marketing have explored the concept of perceived risk to-

wards understanding patronage behavior. The concept of

perceived risk has been used to explain and predict tradi-

tional store based shopping preferences as well as in-home

shopping behavior (e.g., Akaah & Korgaonkar, 1998;

Spence, Engel, & Blackwell, 1970). Studies by Dowling

and Staelin (1994), Shrimp and Bearden (1982), White and

Truly (1989), among others, suggest that perceived risk

toward a product category is inversely related to purchase

intentions. The literature also strongly suggests that con-

sumers are reluctant to patronize a retail store when they

are uncertain of the risks associated with purchase (Prasad,

1975).

There are different types of risks perceived by con-

sumers. Based on the early work of Roselius (1971) and

Jacoby and Kaplan (1972) the literature generally identifies

six different types of risks: financial, performance, physi-

cal, psychological, social, and time loss. However, rarely

are all six different types of perceived risk incorporated

into a single study. Given that Stone and Gronhaug (1993),

in their study of various dimensions of perceived risk,

suggest that the financial and psychosocial dimensions of

risks captured the majority of the overall risk perceptions

(compared to the time, performance and physical dimen-

sions of risks), and in line with past research (e.g.,

Korgaonkar, 1982; Prasad, 1975), we have selected the two

types of perceived product risk most likely to influence

behavior in an e-tail situation: economic and psychosocial.

Each is defined as follows: Economic risk refers to how the

choice of a product will affect the individual shopper’s

ability to make other purchases. Thus, it varies with the

financial considerations of price in relation to factors such

as the shopper’s income, ability to pay, and alternative uses

of money. Psychosocial risk relates to how the purchase

decision will affect the opinions other people hold of the

shopper. Thus, it varies with such factors as the social

conspicuousness and social relevance of the product.

In addition to the fact that economic and psychosocial

risks are reported to be more relevant than other types, we

maintain that the risk dimensions of time, product perfor-

mance, and physical dimensions of the product remain

largely invariant across the three e-tailer formats. In other

words, regardless of the type of outlet selling a product, the

features, performance, and physical dimensions of the

product do not change. Similarly, given the widespread

availability of overnight and express delivery and tracking

systems from companies such as Federal Express, UPS,

and USPS, except for rare situations, product acquisition

time also remains fairly consistent. Based on past research,

we suggest:

H1: Consumers will prefer low risk versus high-risk

products when shopping online. Hence,

(a) Consumers will prefer products of low levels of

psychosocial risk versus products of high psychoso-

cial risk when shopping online; independent of eco-

nomic risk.

56 J Bus Psychol (2007) 22:55–64

123

(b) Consumers will prefer products of low levels of

economic risk versus high economic risk when

shopping online; independent of psychosocial risk.

Literature suggests that, in addition to the level of risk,

the type of risk perceptions also influence shopping pref-

erences. Studies such as Perry and Hamm (1969), Peter and

Tarpey (1975), and Prasad (1975) suggest that the type of

product risk influences purchase decisions. Derbaix (1983)

and Mitchell (1999), studying food, cars, and TVs, show

that economic risks play an important role in influencing

purchasing decisions, while for clothing they suggest that

psychosocial risks play an important role in buying deci-

sions. Based on these past studies we hypothesize that:

H2: Consumers patronage preferences will be influenced

by the type of risk (economic and psychosocial) regardless

of the level of risks.

Online retail store type

Although a limited number of empirical studies exist on the

role consumer risk perceptions play in the selection of

traditional and non-store shopping channels, little attention

has been devoted to the role of perceived risks in online

shopping format preference. Due to this limited research,

the hypotheses of this study are based on the extant

empirical findings on the level of consumers’ perceived

risk in traditional in-store shopping channels (i.e., depart-

ment, specialty and discount retail stores, catalog show-

rooms) and non-in-store shopping channels (i.e., catalog

orders by mail, telephone, or internet. Namely: Bhatnagar,

Misra, & Rao, (2000); Cox & Rich, (1964); Festervand,

Snyder, & Tsalikis, (1986); Hisrich, Dornoff, &

Kernan, (1972); Korgaonkar, (1982); Korgaonkar &

Moschis, (1989); Miyazaki & Fernandez, (2001); Prasad,

(1975); Spence et al., (1970)). Results from non-online

retail studies indicate that the ‘‘perceived risk of a product

is transferable to the store that sells the product’’

(Korgaonkar, 1982, p 78). Previous findings also suggest

that in-store shopping is perceived as less risky than tele-

phone or mail catalog order shopping (Cox & Rich, 1964;

Festervand et al., 1986; Spence et al., 1970).

We propose that the work of Bettman (1973) provides a

theoretical base for prior findings. Bettman posits that risk

has two components: inherent risk that is endemic to a

product class, and handled risk, a clear derivative of

inherent risk, that varies with the amount of additional

information available about the purchase. When looking at

different retailer types (e.g., department stores, specialty

stores, discount stores, and non-in store retailing) each

certainly presents different types and amounts of infor-

mation to consumers. In the literature reviewed, generally,

the amount and type of information was greater for in-store

than ‘‘at-home’’ retailers, likely explaining the drop in

consumers’ perceptions of handled risk and increase in

shopping preference for those stores with a physical pres-

ence. The same is expected to hold true for e-commerce. In

general, we hypothesize that e-tailers without a physical

presence will be perceived as riskier places to shop com-

pared to clicks and mortar e-tailers.

Further, while Internet shopping does allow for 24/7 ac-

cess, easier price comparisons, and the ability to find rare

products, along with many other benefits, these advantages

are offset by a number of concerns. Among these concerns

are: privacy and security of the medium (e.g., Korgaonkar &

Wolin, 1999; Liebermann & Stashevsky, 2002), lack of

familiarity or experience with certain online retailers, and

generally, the risks associated with the intangible nature of

online shopping. Patronage of a pure play e-tailer such as

eBay poses the additional risks of getting a defective/

damaged product, delayed product arrival, the products not

matching descriptions posted on the seller’s Web site, etc.

Conversely, a store-based internet operation such as

Sears.com allows the consumer to physically check the

merchandise prior to purchase, or easily exchange or return

the merchandise to the store after purchase. Additionally a

physical presence provides a variety of available tangible cues

such as product displays supplemented with POP material, as

well as quality cues to help reduce perceived risk prior to and

post-purchase. Thus the store based C&M e-tailers are able to

offer the best of both worlds and reduce the risks associated

with shopping from a pure play e-tailer. Therefore:

H3: Consumer’s shopping preferences will be the lowest

for pure play e-tailers compared to store based C&M

e-tailers when shopping online, independent of product

risk.

Based on prior literature (Grewal, Iyer, & Levy, 2004),

we further speculate that among store-based C&M

e-tailers, the prestige C&M e-tailers, with their stronger

brand reputations, will be perceived as less risky than value

C&M e-tailers, as their brand equity communicates a better

selection of quality merchandise, as well as superior cus-

tomer service versus discount stores, again reducing han-

dled risk. Given this, our study predicts that online

shoppers will perceive the lowest risk for shopping from a

prestigious C&M Web site, medium amounts of risk

shopping from a value C&M Web site, and the highest risk

shopping from a pure play e-tailer.

The following hypotheses, drawing on the increased

levels of handled risk different e-tail formats allow, suggest

that consumers’ online shopping preference will vary by

the type of Internet store independent of the type of per-

ceived product risk. Specifically,

J Bus Psychol (2007) 22:55–64 57

123

H4: Consumers will prefer prestige C&M e-tailers over

value C&M e-tailers when shopping online, independent of

perceived product risks.

H5: Consumers will prefer value C&M e-tailers over

pure play e-tailers when shopping online, independent of

perceived product risks.

Interaction effects between product risk and online

store-type risks on online shopping preferences

Two way interactions

Past studies in traditional, as well as online, retailing sug-

gest that retailer type and product type have important

influences in determining retail patronage (e.g., Darden,

1979; Jones & Vijayasarathy, 1998; Kling & Palmer, 1997;

Sheth, 1983). As expected, the interaction between product

and retailer type is supported in studies incorporating the

congruency concept in e-tailing (De Figueiredo, 2000;

Jahng, Jain, & Ramamurthy, 2000), as patronage should

decrease as perceptions of risk increase. Previous research

has suggested that perceived product risks affect prefer-

ences not only for retail store selection, but between

product categories as well (Bhatnagar et al., 2000;

Miyazaki & Fernandez, 2001).

Based on these studies, we predict that online e-tail store

type and product risk interact as consumers choose the type

of Internet retailer they prefer to shop from. Simply stated,

for different levels of perceived risk in varied product cate-

gories, consumers will prefer different types of e-tailers, with

store type, and the information this signals, being more

important the greater inherent risk a product class has.

Compared to store based e-tailers, pure play e-tailers are

likely to pose higher economic, as well as psychosocial,

risks because of the limited information consumers can get

about these stores through physical inspection. Because

consumers are unable to personally experience/evaluate the

product or service prior to purchase, products that are high

in economic and/or psychosocial risk will be least preferred

by shoppers on pure play sites. Stated another way, pure

play e-tailers will have higher shopping preferences only

when risks are perceived to be low. Formally:

H6: Online shopping preference will be the highest for

pure play e-tailers for products with low inherent (eco-

nomic as well as psychosocial) risk.

Turning to the effects of economic risk on retailer

preference, online shopping from the Web site of value

C&M e-tailer should be preferred when it lowers economic

risk. In other words, value C&Ms’ positioning helps

‘‘handle’’ economic risk. Specifically, shoppers for high

economic risk products will view discounter’s ‘‘value ap-

peal,’’ a common discount/value store strategy, as lowering

the economic risk. This is evidenced as the online suc-

cesses of value C&M Web sites such as Bestbuy.com and

Wal-Mart.com, etc. are partly attributable to their capacity

to offer low prices, especially for expensive products. This

leads to the following hypothesis:

H7: For high economic risk products, online shopping

preference will be the highest for value oriented discount

store e-tailers.

Finally, the small number of research studies that have

investigated the role of perceived product risk in the selec-

tion of a shopping channel (Bhatnagar et al., 2000; Forsythe

& Shi, 2003; Hisrich et al., 1972; Korgaonkar, 1982;

Korgaonkar & Moschis, 1989; Prasad, 1975) indicate that

when shopping for high social risk products, consumers

perceive a lower amount of risk for department and specialty

stores versus discount stores. We expect similar relation-

ships in the context of online retailing. Online shopping from

the Web site of prestigious C&M e-tailers will be appealing

to shoppers for high psychosocial risk products as the

prestigious stores: offer more desirable brands, enable an

authentic view of the merchandise, provide higher security,

and are of superior graphic quality. These, and other po-

tential information cues, should reduce handled risk over

that of value C&M e-tailers. Thus we propose that:

H8: For high psychosocial risk products, online shopping

preference will be the highest for prestige C&M e-etailers.

Methodology

Pretests

Given our interest on risk perceived across different types

of Internet retailers, and the types of risks (economic and

psychosocial) with different product types as well, pre-

testing was done to establish several categories of products

that would satisfy all four combinations of high or low

economic and psychosocial risk. First, a group of 36 stu-

dents in a public southeastern university were given the

definitions of economic and psychosocial risk. They were

given a four quadrant diagram with high and low category

on one axis and economic and psychosocial risk on the

other axis. Then they were asked to develop a list of

products and/or services that would fit the four possible

categories. That task yielded 42 unique high economic and

high psychosocial risk products or services (henceforth

products), 56 low economic and high psychosocial risk

products, 77 high economic and low psychosocial

58 J Bus Psychol (2007) 22:55–64

123

risk products, and 96 low economic and low psychosocial

risk products. After combining like items (e.g., ‘‘cleaning

products,’’ ‘‘cleaning supplies,’’ and ‘‘glass cleaner’’) 34,

40, 45, and 50 major categories of each product, respec-

tively, remained.

Products such as cars, personal services, and tattoos that

appeared in more than one classification were eliminated.

Next, the authors selected 16 products thought to best

typify products in each of the four categories of interest.

This list, shown in Table 1, was then cross validated by

asking 99 respondents from a private northeastern univer-

sity to classify each of the 16 product categories as being

high or low on economic and psychosocial risk. These

ratings supported the classifications of product categories

used for the main study.

Main study

In order to ensure a more diverse sample, two groups of

Internet shoppers were used, one from the southeast (SE),

and one from the northeast (NE). The data were collected

by having undergraduate marketing students conduct face-

to-face survey interviews. The students were told to dis-

tribute surveys to ‘‘persons 18 years or older who are

regular internet users.’’ Students received extra-credit for

distributing the surveys. Two hundred and forty completed

surveys were gathered in the SE, while 276 surveys were

gathered in the NE. While this is, admittedly, a conve-

nience sample, the divergent populations and pre-selection

of Internet users is appropriate given the objectives of the

study. As can be seen from Table 2, the two samples were

only statistically similar on gender. Chi-square tests reveal

(p < .05, df = 5) that the NE sample was older (38.5%

were over 44, while in the SE only 16.6% were), and had

much higher income (41.1% of the NE sample recorded

income over $100,000, while only 10.4% of the SE sample

did). As for Internet buying, when asked if they had bought

on the Web in the last 6 months, 85.5% of the NE sample

had versus 74.6% of the SE sample. Further, the NE sample

was more satisfied with their ‘‘most recent online pur-

chase’’ reporting a mean of 4.26 (on a scale of 1–5), versus

the SE (3.6).

Survey instrument

In the main study, four different survey versions were

prepared. Not only did this allow for four different

groupings of product categories from Table 1, but coun-

terbalancing the ordering in which product risk categories

were presented. Prior to answering any questions, defini-

tions of e-tailer types in the study, and definitions of the

types of risk, were provided. During the survey respondents

were presented with each of the three e-tailer types one by

one. For each e-tailer type four examples of products for

the four risk combinations we presented and subjects were

asked to indicate their shopping preference for each of the

four types of products on a 1–5 scale (anchored by ‘‘may

never buy’’ and ‘‘may prefer buying’’). The statistical

design was a 3 (Type of e-tailer: Pure play, Value C&M

e-tailer, Prestige C&M e-tailer) · 2 (Perceived Level of

Economic Risk: Low, High) · 2 (Perceived Level of Psy-

chosocial Risk: Low, High) within subject design. Analysis

of variance was used to test the specific research hypoth-

esis. Table 3 shows the means and standard deviations for

each of the 12 cells for the combined sample, while Fig. 1

shows the overall preferences between e-tail type.

Table 1 Product service

classifications used in final

study

Ninety nine respondents in pre-

test to verify

Product category Economic risk Psychosocial risk

Accessories (friendship bracelet, watch < $40, costume jewellery) Low High

Personal grooming (deodorant, cologne, hair care) Low High

Apparel (fabric gloves, plastic sunglasses) Low High

Sundries (bottled water, greeting cards, wallets) Low High

Personal care items (soap, shampoo, toothbrush) Low Low

Office/school supplies (pen, pencil, notebooks) Low Low

Household products (cleaning supplies, detergent, napkins) Low Low

Toiletries (toothpaste, chap stick) Low Low

Home furnishings (floor covering, bedding) High Low

Home appliances (refrigerator, washing machine) High Low

Home entertainment (TV, stereo system, DVD player) High Low

Electronics (digital camera, camcorder, computer) High Low

Online services (education, health care) High High

Formal/dress apparel (dress shirts/blouses, shoes, suit) High High

Jewellery (diamond rings, formal wristwatch) High High

Durables (furniture, cars, boats) High High

J Bus Psychol (2007) 22:55–64 59

123

Analysis and results

Manipulation checks

To verify our manipulations for each of the four surveys,

means were tested for all combinations of high economic

risk (e.g., products with high economic risk and low psy-

chosocial risk summed with products with high economic

risk and high psychosocial risk) versus combinations of

low economic risk, as were high psychosocial risks versus

low psychosocial risks across the three e-tailer types. In all

cases there were significant differences across all com-

parisons of high and low economic and psychosocial risks

at the p < .001 level (t-values [economic risks]: 6.439,

8.900, 9.156, 12.352; [psychosocial risks]: 4.795, 19.399,

6.750, 6.197, with between 120 and 140 degrees of

freedom). These tests confirm our manipulations for the 12

product categories used in Table 1.

H1: shopping preference influenced by level of risk

Looking at the significant economic risk by type of e-tailer

(p < .001), and psychosocial risk by type of e-tailer

(p < .05) interaction in Fig. 2, one can clearly see that

across e-tailer type, products with low psychosocial risks

are preferred over those of high risk (supporting H1a), and

products of low economic risk are preferred over those of

high economic risk, supporting H1b (for prestigious C&M

e-tailers there is no significant difference at the p < .05

level in the difference between low and high economic risk

products). The direction of the relationship is suggested in

the table of means for the combined sample (Table 3).

The two samples were also analyzed independently for

cross validation (see Tables 4 and 5). As expected, some

differences are noted in the two regions, however, there is

more convergence than divergence among the sample results.

In both samples we find significant main effects for both

psychosocial risk, although its interaction with e-tailer type is

only significant in the NE sample, supporting H1a. On the

other hand, the main effect of economic risk is only significant

in SE sample (p < .05), although the NE sample is direc-

tionally correct (mean = 2.38 low, 2.76 high). Again, the

interaction between economic risk e-tailer type is significant

(p < .01) in the both samples. These results support H1b.

For H1 we are looking for the main effect of psycho-

social risk and economic risk that occurred in both sam-

ples. Thus, the results of the combined and separate

samples provide support for Hypotheses 1 (level of risks).

H2: shopping preference influenced by type of risk

Figure 3 displays the psychosocial · economic risk inter-

action. Coupled with the significant main effects just

discussed, we find support for H2, that the type of risk does

have an effect on shopping preference, with the possible

exception of economic risk in the NE sample.

H3–H5: difference in store preferences

The ANOVA analysis for both the combined and regional

data shows that the type of e-tailer is significantly related to

shopping preference (p < .01), with the sample means also

Table 2 Sample characteristics

Descriptor NE SE

Bought OL in last 6 months 85.50% 74.60%

Satisfaction with last OL buying

experience (1–5)

4.26 3.6

Gender (percent female) 50.40% 52.10%

Household income $50–74,999 $35–49,999

Age 25–34 20–24

N 275 240

Numbers in parenthesis indicate range of categorical answers

Table 3 Shopping preference

means (standard deviations in

parenthesis): combined sample

Cells report mean, standard

deviation

n = 511

Perceived product risk Pure play

e-tailer

Value C&M

e-tailer

Prestige C&M

e-tailer

Row mean

Low psychosocial, low economic 2.58 (1.351) 3.08 (1.366) 2.96 (1.398) 2.87

High psychosocial, low economic 2.64 (1.284) 2.99 (1.358) 3.14 (1.315) 2.92

High psychosocial, high economic 2.03 (1.215) 2.56 (1.371) 2.91 (1.355) 2.50

Low psychosocial, high economic 2.60 (1.247) 3.12 (1.241) 3.48 (1.180) 3.07

Column mean 2.46 2.94 3.12 2.84

2.4

2.5

2.6

2.7

2.8

2.9

3

3.1

3.2

Pure Play

Sh

op

pin

g P

refe

ren

ce

Value C&M Prestigious C&M

Fig. 1 Store preferences, combined sample

60 J Bus Psychol (2007) 22:55–64

123

supporting Hypothesis 3 about the relative preference for

e-tailer type. For the combined data, the overall shopping

preference mean for pure play e-tailers is 2.46, while it is

2.937, and 3.122 for value C&M and prestige C&M

retailers respectively. Further, consumer’s shopping pref-

erences are significantly (p < .01) lower for pure play

e-tailers compared to clicks and mortar stores. Adjusting

for multiple comparisons, all e-tailers have different levels

of shopping preferences from each other (p < .05).

The data also show support for the proposition that the

overall preference for prestige C&M e-tailers is higher than

the overall preference for value C&M e-tailers regardless

of type of risks (H4) for the combined as well as regional

data (p < .01). Similarly, support is found for Hypothesis 5

stating that overall shopping preference is higher for value

C&M e-tailers than for pure play e-tailers in all three data

sets (p < .01). Figure 1 clearly reflects the overall store

preferences hypothesized.

Hypothesis 6: pure plays preferred under low risk

The hypothesis that higher preferences will be demon-

strated for pure play e-tailers with low economic and/or

low psychosocial risk products is not supported. As seen in

Fig. 2 the results for the total sample show that the pref-

erence for low economic risk products was the lowest for

pure play (2.61), versus prestige C&M e-tailers (3.05), and

value C&M e-tailers (3.10), largely mirroring overall

preferences just reported. Similarly, for psychosocial risk

the mean preference score for pure plays (2.59) is lowest in

comparison to both value C&M e-tailers (3.04) and

2.2

2.4

2.6

2.8

3

3.2

3.4

Pure Play

Sh

op

pin

g P

refe

ren

ce

Lo Econ

Hi Econ

Lo psychosoc

Hi psychosoc

Value C&M Prestigious C&M

Fig. 2 Risk · type of retailer interaction, combined sample

Table 4 Analysis of variance

(ANOVA) of patronage

preference by type of perceived

risk (High–Low), and type of

e-tailer (Northeast sample)

a p \ .001

** p \ .01

* p \ .05

Source of variance Sum of

squares

Degrees of

freedom

Mean

square

F

Main effects 4

Economic risk 4.718 1 4.719 1.346

Psychosocial risk 68.123 1 68.123 38.817a

Type of e-tailer 205.276 2 102.638 56.065a

2-way effects 5

Econ risk · Psychosocial Risk 114.198 1 114.198 63.160a

Econ Risk · Type of e-tailer 56.330 2 28.165 32.105a

Psychosocial Risk · Type of e-tailer 4.892 2 2.446 5.123**

3-way effect 2

Econ Risk · Psychosocial Risk · Type of

e-tailer

5.886 2 2.943 5.273**

Table 5 Analysis of variance

(ANOVA) of patronage

preference by type of perceived

risk (High–Low), and type of

e-tailer. (Southeast sample)

a p \ .001

** p \ .01

* p \ .05

Source of variance Sum of squares Degrees of freedom Mean square F

Main effects 4

Economic risk 16.336 1 16.336 4.013*

PsychoSocial risk 34.084 1 34.084 20.761a

Type of e-tailer 303.292 2 151.645 87.672a

2-way Effects 5

Econ risk · Psychosocial risk 37.211 1 37.211 21.219a

Econ risk · Type of e-tailer 9.015 2 4.507 6.786**

Psychosocial risk · Type of e-tailer 1.338 2 .669 1.266

3-way effect 2

Econ risk · Psychosocial risk · Type

of e-tailer

.597 2 .299 .527

J Bus Psychol (2007) 22:55–64 61

123

prestige C&M e-tailers (3.22). A pairwise t-test shows that

the preference scores for each type of risks is significantly

lower (p < .01) for the pure play versus the C&M

e-tailers. Similar results are found for each region. Thus,

the results are opposite our stated hypothesis, suggesting

that even for low risk products consumers are reluctant to

patronize pure play e-tailers over both clicks and mortar

formats.

Hypothesis 7: higher preference for value C&M

e-tailers with high economic risk

The hypothesis suggesting that value C&M e-tailers will

be most preferred for high economic risk products was

partially supported. The mean scores of preferences for

high economic risk products for the total sample show that

value C&M e-tailers are preferred over pure play e-tailers

(2.78 vs. 2.31) at p < .001, but are less preferred over the

prestige C&M e-tailers (3.20) at p < .001. Similar results

are seen for each of the two regions. In the SE, for high

economic risk products, value C&M e-tailers are preferred

over pure plays (2.96 vs. 2.29), but less preferable to

prestige C&M e-tailers (3.06, p < .01). In the NE, the

prestige C&M e-tailers are, once again, most preferred

(3.00) followed by value C&M e-tailers (2.61) and the

pure play (2.37, all p < .001). Thus, overall, we see that

for high economic risk products, value C&M e-tailers are

preferred over pure plays, but not the prestige C&M

format.

Hypothesis 8: High psychosocial risks raises shopping

preference of prestige C&M e-tailers

Our last hypothesis states that for high psychosocial risk

products, shopping preference will be highest for presti-

gious C&M e-tailers. The results for the total sample, as

well as two regions, support this hypothesis. For the total

sample we find that prestige C&M e-tailers have the

highest mean score for high psychosocial risk products,

3.02, followed by a mean preference of 2.84 for value

C&M e-tailers, and 2.33 for pure plays. Pairwise tests show

the prestige C&M e-tailer’s preference is higher than other

e-tailers at p < .001. Similarly, in the SE, preference for

prestige C&M e-tailers is highest at 3.13 and marginally

higher than the preference for value C&M e-tailers (3.00,

p < .10), and higher than pure plays (2.28, p < .001). Fi-

nally, the NE preference is significantly higher for prestige

C&M e-tailers (p < .001) than the other two with the mean

preference scores of 3.23 for prestige, 2.69 for value, and

2.35 for pure play, respectively.

Discussion

Although the number and type of firms who sell products

online continues to increase, relatively small numbers of

consumers have embraced the e-tailing alternative (Cheung

& Lee, 2001). While e-commerce retail sales in the third

quarter of 2003 reached $13.3 billion, an increase of about

7% over the previous quarter, this still only accounted for

1.5% of total retail sales (U.S. Census Bureau, 2003).

While the press is enamored with the success of Ama-

zon.com, a pure play e-tailer, many other pure plays (e.g.,

eToys.com, Pets.com, Streamline.com and Webvan) have

met with failure and, some would say, helped fuel the

dot.com bust of the early 2000s.

Recognizing these difficulties, pure play e-tailers

increasingly opt for hybrid clicks and mortar approaches in

several product categories such as general merchandise

(Target at Amazon), clothing (with Sears’ acquisition of

Land’s End), travel (Marriott.com), electronics (Best Buy),

etc. It seems that multi-channel retailing is here to stay.

However, few published studies exist exploring which

e-tail format is suitable for various kinds of products. Al-

though scholars have suggested which products are best

suited for selling on the Internet (e.g., Rosen & Howard,

2000), little published information is available to e-tailers

of the three formats studied here. Our results suggest that,

overall, pure play e-tailers will continue to have a signifi-

cant disadvantage in comparison to the clicks and mortar

e-tailers, almost regardless of the type or level of inherent

risk. In this study, for each of the four categories of

products surveyed in each of two regions, the preference

for pure play e-tailers was always the lowest. This suggests

that pure play e-tailers have yet to fully earn the trust of

consumers. Additionally, our findings demonstrate the

substantial advantages that brand equity, visibility, and

multi-channel consumer options hold for C&M e-tailers

over pure play e-tailers.

2.2

2.4

2.6

2.8

3

3.2

3.4

Low Psychosocial

ecnereferP gnippoh

SLow Econ

High Econ

High Psychosocial

Fig. 3 Psychosocial risk · economic risk, combined sample

62 J Bus Psychol (2007) 22:55–64

123

The literature suggests that trust is essential for the

development of e-tailing. At the most basic level, trust

helps address concerns over factors such as privacy and

security essential to online transactions (Cheskin Research

& Studio Archtype/Sapient, 1999). Also, trust can mini-

mize feelings of risk and lack of control that are often

characteristic of e-tailing transactions (Bhattacherjee,

2000). Trust becomes especially pivotal in selecting

products or services that are already perceived as risky

(Mayer et al., 1995). As our results clearly demonstrate,

pure-play e-tailers need to overcome these trust issues to

reduce risk, and draw even with C&M e-tailers.

Several studies already suggest perceived risks as an

antecedent to trust (e.g., Corbitt, Thanasankit, & Yi, 2003;

Tan, 1999). One of the innovative ways of reducing risk

and building trust for pure play e-tailers is by providing

online sales help similar to live salespeople in retail envi-

ronments. Instead of just offering chat rooms as an option

to shoppers, a few e-tailers are monitoring Web shoppers

on their site, looking for opportunities to open a chat

window on the shopper’s screen to ask if they need any

help (Higgins, 2004). Another way of reducing the risk of

purchasing products from pure play e-tailers is to carry

well known brand names. Brands can communicate

valuable information to consumers, especially in online

environments where it is harder to physically inspect

products. Consumers may have personal experience or

knowledge about well known brands, lowering the risk of

purchasing them from a pure play e-tailer. A third way of

reducing risk, if possible, is to build the brand of the

e-tailer itself, either through heavy promotion or creation

of a very large e-tailer, such as Amazon. Generally, con-

sumers are less apprehensive purchasing products from

well known and/or large organizations. Finally, a seal of

approval from an organization such as eTrust may also go a

long way in alleviating consumers risk perceptions of

shopping from pure play e-tailers.

Limitations and future research

While statistics on Internet shopping vary widely, estimates

are that some 60–80% of all US adults are online, with 30–

50% of them buying online (ABC News Poll, 2003;

Pastore, 2001). This is, of course, US adults only, and our

sample, while diverse, does not represent all US shoppers.

Perhaps more significantly, if one looks at statistics

worldwide, one can see that online shopping penetration is

much lower than in the US. EMarketer (2004) reports that

only 16% of Internet users in the EU-15 buy online. Clearly

more representative samples in both the US and worldwide

are called for, with particular attention to the drastically

lower shopping rates in other countries.

Additionally, as this study identifies the challenges pure

play e-tailers face, branding—of either goods or sites, as

suggested—is likely to overcome many of these chal-

lenges. Studies on the effect of well-known versus less-

known brands’ ability to mitigate various risks are certainly

needed, and should provide useful insight as to additional

antecedents of online shopping risks.

Finally, much as catalogers removed perceived risk with

‘‘satisfaction guaranteed’’ pledges years ago, e-tailers must

fully understand all the risks perceived by potential online

shoppers, and how to address them. Once these risks, and

their interactions, are fully understood, consumer seg-

mentation based on online shopping risk perceptions is

possible, as well as insight into how to overcome these risk

perceptions. Given the potential for growth in online

shopping those firms that most fully recognize and address

consumers’ concerns will likely reap great benefits.

Our results show that the prestige clicks and bricks have

an advantage over other e-tail formats for three out of four

product categories, while value C&M e-tailers have an edge

over the other two online formats for products with low

economic risks. Thus, the results are largely supportive of

our study hypotheses in two different regions of the country.

This study shows that level and type of perceived risk

provides a good explanation for the congruity approach, and

the importance of handled risk provided by prestige C&M

e-tailers. This research helps suggest which types of prod-

ucts are most suitable for the three e-tail formats.

References

ABC News Poll. (2003). 2003 Holiday shopping season: Moreshoppers plan to go online. Retrieved November 23, 2005, from

abcnews.go.com/images/pdf/883a36Holiday Spending.pdf

Akaah, I. P., & Korgaonkar, P. K. (1988). Conjoint investigation of the

relative importance of risk relievers in direct marketing. Journalof Advertising Research, 28(August/September), 38–44.

Bauer, R. A. (1960). Consumer behavior as risk taking. In R. Hancock

(Ed.), Dynamic marketing for a changing world: Proceedings ofthe 43rd conference (pp. 389–398). Chicago, IL: American

Marketing Association.

Benjamin, R., & Wigand, W. (1995). Electronic markets and virtual

value chains on the information superhighway. Sloan Manage-ment Review, 36(Winter), 62–72.

Bettman, J. R. (1973). Perceived risk and its components: A model

and empirical test. Journal of Marketing Research, 19(May),

184–190.

Bhatnagar, A., Misra, S., & Rao, R. H. (2000). On risk, convenience,

and internet shopping behavior. Communications of the ACM,43(November), 98–105.

Bhattacherjee, A. (2000). Acceptance of e-commerce services: The

case of electronic brokerages. IEEE Transactions on Systems,Man, and Cybernetics: Part A, 32(July), 411-420.

Cheskin Research and Studio Archtype/Sapient. (1999). E-commerce

trust study. Retrieved from http://www.Sapient.com/cheskin

Cheung, C. M. K., & Lee, M. K. O. (2001). Trust in internet

shopping: Instrument development and validation through

J Bus Psychol (2007) 22:55–64 63

123

classical and modern approaches. Journal of Global InformationManagement, 9(July–September), 23–36.

Corbitt, B. J., Thanasanki, T., & Yi, H. (2003). Trust and e-

commerce: A study of consumer perceptions. Electronic Com-merce Research and Applications, 2(Autumn), 203–215.

Cox, D. F., & Rich, S. U. (1964). Perceived risk and consumer

decision making—the case of telephone shopping. Journal ofMarketing Research, 1(November), 32-39.

Darden, W. R. (1979). A patronage model of consumer behavior. In

R. Stampfl, E. C. Hirschman (Eds.), Competitive structure inretail markets: The department store perspective (pp. 43–52)

Chicago, IL: American Marketing Association.

Derbaix, C. (1983). Perceived risk and risk relievers: An empirical

investigation. Journal of Economic Psychology, 3(March),

19–38.

De Figueiredo, J. M. (2000). Finding sustainable profitability in

electronic commerce. Sloan Management Review, 41(Summer),

41–52.

Donthu, N., & Garcia, A. (1999). The internet shopper. Journal ofAdvertising Research, 39(May–June), 52–58.

Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and

intended risk-handling activity. Journal of Consumer Research,21(June), 119–135.

eMarketer. (2004). Internet users in the EU-15 who have ever

purchased products and/or services online by country, September

2003, (March 1), Retrieved from http://www.emarketer.com

Festervand, T. A., Snyder, D. R., & Tsalikis, J. D. (1986). Influence of

catalog vs. store shopping and prior satisfaction on perceived

risk. Academy of Marketing Science, 14(Winter), 28–36.

Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk

perceptions in internet shopping. Journal of Business Research,55(November), 867–875.

Girard, T. R. S., & P. K. Korgaonkar (2002). Influence of product

class on preference for shopping on the internet. Journal ofComputer-Mediated Communication, 8 (October), Retrieved

from ascusc.org/jcmc/.

Grewal, D., Iyer, G. R., & Levy, M. (2004). Internet retailing:

Enablers, limiters and market consequences. Journal of BusinessResearch, 57(July), 703–713.

Ha, H. (2002). The effects of consumer risk perceptions on pre-

purchase information in online auctions: Brand, word-of-mouth

and customized information. Journal of Consumer MediatedCommunication, 8(October), Retrieved from ascusc.org/jcmc/

Higgins, M. (2004). Pop-up sales clerks: Web sites try the hard sell.

The Wall Street Journal, (April 15) D1–D2.

Hisrich, R. D., Dornoff, R. J., & Kernan, J. B. (1972). Perceived risk

in store selection. Journal of Marketing Research, 9(November),

453–459.

Jahng, J., Jain, H., & Ramamurthy, K. (2000). Effective design of

electronic commerce environments: A proposed theory of

congruence and an illustration. IEEE Transactions on Systems,Man, and Cybernetics: Part A, 30(July), 456–471.

Jarvenpaa, S. L., & Todd, P. A. (1996–97). Consumer reactions to

electronic shopping on the world wide web. InternationalJournal of Electronic Commerce, 1(Winter), 59–88.

Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk.

In M. Venkatesan (Ed.), Proceedings, 3rd annual conferenceassociation for consumer research (pp. 382–393). Chicago, IL:

Association for Consumer Research.

Jones, J. M., & Vijayasarathy, L. (1998). Internet consumer catalog

shopping: Findings from an exploratory study and directions for

future research. Journal of Internet Research: ElectronicNetworking Applications and Policy, 8(4), 322–330.

Kling, R., & Palmer, J. W. (1997). Electronic commerce in retailing:Differences across retail formats. The Information Society,13(January–March), 75–91.

Korgaonkar, P. K. (1982). Consumer preferences for catalog show-

rooms and discount stores: The moderating role of product risk.

Journal of Retailing, 58(Fall), 76–88.

Korgaonkar, P. K., & Moschis, G. P. (1989). The effects of perceived

risk and social class on consumer preferences for distribution

outlets. In P. Bloom, R. Winer, H. H. Kassarjian, D. L.

Scammon, B. Weitz, R. Spekman, V. Mahajan & M. Levy

(Eds.), American marketing association educators’ proceedings:Enhancing knowledge development in marketing (pp. 39–43).

Chicago: AMA.

Korgaonkar, P. K., & Wolin, L. D. (1999). A multivariate analysis of

web usage. Journal of Advertising Research, 39(March/April),

53–68.

Liebermann, Y., & Stashevsky, S. (2002). Perceived risk as barriers to

internet and e-commerce usage. Qualitative Market Research,5(November), 291–300.

Mayer, R. C., Davis, J. H., & Schoorman, D. F. (1995). An integrative

model of organizational trust. Academy of Management Review,20(July), 709–734.

Mitchell, V. (1999). Consumer perceived risk: Conceptualizations

and models. European Journal of Marketing, 33½., 163–195.

Miyazaki, A. D., & Fernandez, A. (2001). Consumer perceptions of

privacy and security risks for online shopping. The Journal ofConsumer Affairs, 35(Summer), 27–44.

Pastore, M. (2001). Half of American adults now shop online, (April

24), Retrieved from http://www.clickz.com/stats/market/retail-

ing/article.php/6061_751021

Perry, M., & Hamm, C. (1969). Canonical analysis of relations

between socio-economic risk and personal influence in purchase

decision. Journal of Marketing Research, 6(August), 351–354.

Peter, J. P., & Tarpey, Sr. L. X. (1975). A comparative analysis of

three consumer strategies. Journal of Consumer Research,2(June), 29–38.

Peterson, R. T., Balasubramanian, S., & Bronnenberg, B. J. (1997).

Exploring the implications of the internet for consumer market-

ing. Journal of the Academy of Marketing Science, 25(Fall),

329–346.

Prasad, K. V. (1975). Socioeconomic product risk and patronage

preferences of retail shoppers. Journal of Marketing, 39(July),

42–47.

Roselius, R. (1971). Consumer rankings of risk reduction methods.

Journal of Marketing, 35(January), 56–61.

Rosen, K. T., & Howard, A. L. (2000). E-retail: Gold rush or fool’s

gold? California Management Review, 42(Spring), 72–100.

Sheth, J. N. (1983). An integrative theory of patronage preference and

behavior. In W. R. Darden & R. F. Lusch (Eds.), Patronagebehavior and retail management (pp. 9–28). North-Holland, NY,

NY: Elsevier Science Publicator Company.

Shimp, T. A., & Bearden, W. O. (1982). Warranty and other extrinsic

cue effects on consumers’ risk perceptions. Journal of ConsumerResearch, 9(June), 938–946.

Spence, H. E., Engle, J. F., & Blackwell, R. D. (1970). Perceived risk

in mail-order and retail store buying. Journal of MarketingResearch, 8(August), 364–369.

Stone, R. N., & Gronhaug, K. (1993). Perceived risk: Further

considerations for the marketing discipline. European Journal ofMarketing, 27(3), 372–394.

Tan, S. J. (1999). Strategies for reducing consumers’ risk aversion in

internet shopping. The Journal of Consumer Marketing, 16(2),

163–180.

U.S Census Bureau, Retail 3Q. (2003). E-commerce report (Novem-

ber 21).

White, J. D., & Truly, E. L. (1989). Price-quality integration in

warranty premium evaluation: A preliminary test of alternative

models of risk assessment. Journal of Business Research,19(September), 109–125.

64 J Bus Psychol (2007) 22:55–64

123

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.