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Effects of E-Tailer and Product Type on Risk Handling in Online ShoppingHyun-Joo Leea; Patricia Huddlestona

a Department of Human Environment and Design, Michigan State University, USA

To cite this Article Lee, Hyun-Joo and Huddleston, Patricia(2006) 'Effects of E-Tailer and Product Type on Risk Handlingin Online Shopping', Journal of Marketing Channels, 13: 3, 5 — 28To link to this Article: DOI: 10.1300/J049v13n03_02URL: http://dx.doi.org/10.1300/J049v13n03_02

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Effects of E-Tailer and Product Typeon Risk Handling in Online Shopping

Hyun-Joo LeePatricia Huddleston

ABSTRACT. This study aims to increase knowledge of consumer-perceived risk (CPR) and ways of relieving perceived risk in onlineshopping. A factorial design was used for investigating differences inCPR and risk-reduction strategies (RRS) by e-tailer (single and multi-channel) and product type (music CDs and apparel). Using multiple re-gression analysis, the effects of consumer factors on CPR and the effectsof convenience and CPR on RRS were identified. Results indicate thate-tailer type has a significant influence on CPR. Innovativeness and fre-quency of online shopping were inversely related to CPR. Convenienceand CPR are positively associated with RRS. Managerial implicationsare provided. [Article copies available for a fee from The Haworth DocumentDelivery Service: 1-800-HAWORTH. E-mail address: <docdelivery@haworthpress.com> Website: <http://www.HaworthPress.com> © 2006 by The HaworthPress, Inc. All rights reserved.]

KEYWORDS. Electronic commerce, consumer risk, online shopping

Hyun-Joo Lee and Patricia Huddleston are affiliated with the Department of HumanEnvironment and Design, Michigan State University.

Address correspondence to: Patricia Huddleston, 112 Human Ecology, East Lansing,MI 48824-1030 (E-mail: huddles2@msu.edu).

Journal of Marketing Channels, Vol. 13(3) 2006Available online at http://www.haworthpress.com/web/JMC

© 2006 by The Haworth Press, Inc. All rights reserved.doi:10.1300/J049v13n03_02 5

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INTRODUCTION

In recent years, many companies have gravitated to the Internet asa channel through which they can efficiently carry out their market-ing activities and sell products. Jupiter Communications predicts thatshoppers will spend more than $831 billion on online purchases andwhat they refer to as “web-influenced offline purchases” by 2005 (Enos& Greenberg, 2000). Online sales in the leading retail categories couldgrow from $34 billion in 2000 to $168 billion by 2005 (Boston Consult-ing Group, 2001). The number of online shoppers continues to grow,and handheld and portable computing devices will most likely increaseshopper access to online retail venues (Vigoroso, 2002). Online shop-ping in North America has spiked during the holiday shopping season inrecent years: In 2003, online holiday shopping grew 35% from 2002(Rosencrance, 2004). At the same time that online shopping has spiked,however, we have also witnessed the rise and fall of a variety of dot.coms–pets.com and its emblematic dog puppet being one of the mostmemorable of the dot.coms to disappear.

Clearly, careful plans and approaches are needed for companies asthey either emerge online or as they transition from a purely brick-and-mortar establishment to companies complemented with an online pres-ence and online sales. A variety of companies have launched onlineretail sites without establishing physical stores–perhaps the most fa-mous example of this is Amazon.com. As more people shop online, tra-ditional retail firms have started to offer sales through the Internet.Increasingly, traditional retailers–such as Walmart.com, Jcpenney.com,and Gap.com–are gaining market share and attaining number one posi-tions in online retail sites (Laudon & Traver, 2001). Despite the excep-tionally fast growth of the online retail market, some consumers are stillreluctant to shop online. Blocked from physical contact with productsand barred from inspecting items before purchase, some consumersmay be uncertain about their decision-making–or even unclear as tohow to begin to select items while shopping online (Jarvenpaa & Todd,1996/1997). Consumers may thus associate online shopping with greaterrisk–risk of being dissatisfied with a purchase, risk of difficulty in re-turning an item, etc. (Bhatnagar, Mirsa, & Rao, 2000). Perceived risk isgenerally higher online than in-store (Akaah & Korgaonkar, 1988;McCorkle, 1990; Tan, 1999), and can act as a strong deterrent to onlineshopping (Siu & Cheng, 2001).

Traditionally, consumers will engage in a risk-handling process andwill utilize various risk-reduction strategies (RRS) until the level of

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perceived risk reaches a tolerable level (Cox, 1967; Cox & Rich, 1964).E-tailers need to understand the salient types of perceived risk for con-sumers and the types of RRS important in online shopping. This knowl-edge will be helpful in developing approaches to attract and retainonline customers. Therefore, the objectives of our study are to: (1)examine how consumer-perceived risk (CPR) and the importance ofRRS in online shopping differ by e-tailer and product type; (2) identifyrelationships between consumer innovativeness, experience with onlineshopping, and CPR; (3) distinguish the relationship between onlineshopping convenience, CPR, and the importance of RRS; and (4) exam-ine how each of four types of CPR and each of three types of RRS differaccording to e-tailer and product type.

CONCEPTUAL FRAMEWORK

Roselius (1971) assumed that variations in perceived risk resultedfrom variations in the purchase decision and employed utility theory toinvestigate ways of relieving CPR: As buyers vary in their perceptionsof risk in a given buying situation, they are expected to vary in attitudetoward methods of resolving that risk. Our study suggests that CPRlevel is influenced by the particular purchase situation, as well as byconsumer factors. Differences in CPR level and perceptions of onlineshopping convenience may generate differences in the importance ofRRS. As shown in Figure 1, our study postulates that consumer factors(i.e., consumer innovativeness and experience with online shopping)

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FIGURE 1. Conceptual Framework of CPR and RRS in Onlines Shopping

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and purchase situation factors (i.e., e-tailer and product type) influenceCPR. The importance of RRS can be identified by CPR and byconsumer perception of online shopping convenience.

LITERATURE REVIEW

Perceived Risk

Some degree of risk is involved in any consumer purchase. AfterBauer’s (1960) seminal work, which suggested that consumer behaviorcould be viewed as a risk-taking behavior insofar as consumers cannotanticipate the possibly unfortunate consequences of a purchase deci-sion, perceived risk has been conceptualized in a variety of ways.Cunningham (1967) viewed perceived risk in terms of uncertainty andpotential consequences; Bettman (1973) considered perceived risk acombination of inherent risk and handled risk, inherent risk referring torisk associated with a specific product category, and handled risk refer-ring to risk caused by a particular brand in a buying situation. In earlystudies, perceived risk was measured uni-dimensionally as overall riskwith regard to certain products; in the 1970s and 1980s, perceived riskwas recognized by researchers as a multi-dimensional phenomenon(Bettman, 1973; Garner, 1986; Jacoby & Kaplan, 1972). Jacoby andKaplan described five risk categories: financial, performance, psycho-logical, physical, and social; Garner measured social, financial, physi-cal, performance, time, and psychological risk. McCorkle (1990)presented two additional types of perceived risk in relation to catalogshopping–time-loss and overall source risk–as well as financial, perfor-mance, and social risk. Time-loss risk stems from the possibility of timeloss in making the product purchase. Previous studies have acknowl-edged that consumers perceive more risk when they make non-store-based purchases rather than store-based purchases (Akaah & Korgaonkar,1988; Cox & Rich, 1964; Hawes & Lumpkin, 1986; Spence, Engel, &Blackwell, 1970). Cox and Rich found that consumers experienced ahigher level of perceived risk with telephone shopping and that per-ceived risk was a main determinant of consumers who did not shopover the telephone. Similarly, Spence et al. found that consumers asso-ciated a greater risk with shopping by mail because of the inability toexamine products before purchasing, difficulty in returning defectivemerchandise, and distrust of the business practices of some mail-ordercompanies.

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Perceived Risk in Online Shopping

Previous research has indicated that although there is some degree ofrisk with any consumer purchase, there are risks specific and unique toonline shopping. For instance, a primary concern of online shoppers isprotection of privacy. According to Jarvenpaa and Todd (1996/1997),loss of privacy is perceived as a risk in part because consumer informa-tion is collected while shopping (e.g., online customers are asked tologin to a site to begin shopping, or are asked to provide gender or ageinformation as they enter different areas of an online store). Liebermannand Stashevsky (2002) also found that privacy and security were pri-mary risk components for online shopping; security risk often manifestsin customer fear related to transmitting credit card and other personal/financial information over networks. These perceived privacy- andsecurity-related risks influence both current and future online shoppers(Weber & Roehl, 1999)–current shoppers because they may be wary ofshopping at certain sites, and future shoppers because they may behesitant to shop online at all.

Bhatnagar and colleagues (2000) considered two types of riskpredominant in Internet shopping: product category risk (especially re-lated to technologically complex, expensive, ego-related products), andfinancial risk (closely associated with loss of money through credit cardfraud). Technological hesitancy–resulting from a fear of computers orcommunication technologies–can manifest as risk (Dekimpe, Parker, &Sarvary, 2000). Due to these studies and speculations, online shoppinghas been associated with a higher level of risk than in-store shopping(Bhatnagar et al., 2000; Tan, 1999), consistent with previous studies onperceived risk related to non-store-based purchases. These higher levelsof perceived risk discourage some consumers from making onlinepurchases (Siu & Cheng, 2001).

Risk-Reduction Strategies (RRS)

Risk handling is the process by which consumers try to reduce per-ceived pre-purchase risk in an attempt to increase certainty. Consumerrisk-handling processes typically continue until the level of perceivedrisk is acceptable and the consumer is willing to make a purchase (Cox& Rich, 1964; Stem, Lamb, & MacLachlan, 1977). Risk-reductionmethods consumers rely upon include advertising, word of mouth infor-mation, brand and/or store loyalty, the price/quality relationship of an

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item, and money-back guarantees (Arndt, 1967; Barach, 1969; Bauer,1960; Cunningham, 1967; Roselius, 1971).

Roselius (1971) proposed 11 risk relievers and found that buyersresponded most favorably to brand loyalty and the image of majorbrands. Studies of risk relievers related to in-home shopping found thatprior satisfactory purchase experiences, money-back guarantees, andmanufacturer reputation significantly diminished the level of perceivedrisk (Akaah & Korgaonkar, 1988; Festervand, Snyder, & Tsalikis,1986). For online shopping, Tan (1999) found that reference group ap-peal was the most preferred risk reliever, followed by retailer reputationand brand image. Warranties, such as money-back guarantees or freetrial periods, were the least preferred risk relievers. This finding is con-trary to research that has established the importance of warranties asrisk relievers (Akaah & Korgaonkar, 1988; Van den Poel & Leunis,1999). These inconsistent research findings require a more detailedstudy of consumer risk-reduction strategies related specifically to on-line shopping.

HYPOTHESES DEVELOPMENT

E-Tailer Type

As discussed in the introduction, many traditional retailers are mov-ing into e-commerce. Multi-channel retailers such as Wal-Mart, JCPenney, and Sears have physical operations as their primary retail chan-nel, but also have expanded onto the Internet, thus becoming “click-and-mortar” companies. “Virtual merchants”–single-channel compa-nies that operate exclusively on the Internet–such as Amazon.com,iBaby.com, and Buy.com have also emerged (Laudon & Traver, 2001).

Multi-channel retailers have benefited more from the exceptionallyrapid growth of the online retail market. Multi-channel companies aremore attractive to consumers because they provide well-known brandnames, local store delivery, and return/exchange facilities (Nataraj &Lee, 2002; Rushe, 2000), and offer trust, personal privacy, technicalreliability, and fast delivery (Schoenbachler & Gordon, 2002). Multi-channel shoppers are able to cross channels easily; consumers cansearch for products online, purchase them, and pick them up at the near-est store; if dissatisfied, consumers can return the products purchasedonline to the store (Lawson, 2001). Multi-channel e-tailers are thus

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likely to lead to diminished levels of consumer risk related to onlineshopping. Hence, the following hypothesis is proposed:

H1a: Consumers will perceive lower levels of risk when theypurchase a product from a multi-channel e-tailer than from asingle-channel e-tailer.

Product Type

Most online purchases are small-ticket items, such as books andmusic. Apparel, software, and toys also fall into the top online sales cat-egories, while furniture, eyewear, art, and collectibles make up thelow-sales categories (Jupiter Media Metrix, 2001). Based on this infor-mation, there are product characteristics especially well suited to onlinesales. Products and services that are intangible and relatively high indifferentiation, for instance, are more amenable to online purchase(Phau & Poon, 2000).

One way to classify products is to classify them as search goods andexperience goods. When consumers can evaluate the quality of an itembefore purchase without actually touching or using the item, it is asearch good (e.g., music CD, DVD, camera). Items that must be worn,touched, tried on, or consumed before purchase are experience goods(e.g., gourmet chocolate, shoes; Nelson, 1974). Search goods with lowtangibility seem to have a bigger advantage in online sales because theyare often information-based products that can be evaluated based onexternally available information rather than physical inspection (Poon& Joseph, 2001). Experience goods, however, are typically dependentupon physical inspection and are more likely to be associated with ahigher level of risk, leading to the following hypothesis:

H1b: Consumers will perceive lower levels of risk when theypurchase a search good rather than an experience good online.

Consumer Innovativeness

Innovators are more willing to adopt new ideas and products and tendto be younger, above average in education, and able to cope with finan-cial risk or a high degree of uncertainty arising from an innovationadoption. “Venturesomeness” is the salient value of the innovator(Rogers, 1983). Darian (1987) found that profiles of in-home shoppersmatched the characteristics of innovators. Marketing strategies forproducts and services were revolutionized by the Internet, which

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allowed innovative shoppers to purchase goods online, via com-puter–an entirely new method of shopping different from in-homeshopping by catalog and/or by telephone (Kannan, Chang, & Whinston,1998; Palmer & Grifith, 1998; Wang, Lee, & Wang, 1998). It is no lon-ger appropriate to characterize all online shoppers as innovators, as on-line shopping is now so prevalent. However, online consumers morelikely to take risks on certain products or to face the uncertainty relatedto, for instance, purchasing an experience good online can still be la-beled innovators.

Citrin, Sprott, Silverman, and Stem (2000) found that consumeradoption of online shopping was influenced in part by their Internet us-age and their domain-specific innovativeness. Internet usage is relatedto the amount of time spent online and the activities performed online(e.g., looking for information, shopping), and domain-specific innova-tiveness relates to consumer willingness to purchase a specific type ofproduct online (e.g., music CDs but not apparel). Innovators are morelikely to have favorable attitudes toward online shopping than otherconsumers; innovators are also more likely to be innovative in regard toand knowledgeable about the Internet than consumers who do not pur-chase goods or services online (Goldsmith & Goldsmith, 2002). Poten-tial online shoppers exhibit higher levels of venturesomeness, morefavorable attitudes toward change, more frequent Internet use, and life-styles less oriented toward recreational shopping (Siu & Cheng, 2001).These findings led to the following hypothesis:

H2a: Consumer innovativeness will result in lower levels ofperceived risk.

Consumer Experience with Online Shopping

Consumer acceptance of online shopping is influenced by prior expe-rience (Foucault & Scheufele, 2002; Liang & Huang, 1998); individualswho have never bought anything online are more risk-averse than con-sumers who have made repeated purchases online (Tan, 1999). Com-pared to consumers who lack online buying experience, consumers withprevious experience buying online have more often purchased apparel(Goldsmith & Goldsmith, 2002). As consumers gain more experiencewith online buying, they develop confidence to progress from small tolarger purchases (Seckler, 2000), and from search to experience goods.Based on these findings, the following hypotheses are suggested:

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H2b: The frequency of online shopping will be inversely related toCPR.H2c: The amount of money spent online will be inversely relatedto CPR.H2d: More positive evaluations of past experiences with onlineshopping will result in lower levels of CPR.

Consumer-Perceived Risk and Risk-Reduction Strategies

According to Roselius (1971), if there is a strong motive to buy aproduct and a low tolerance for the associated risk, buyers are willing toseek ways of resolving the risk. On the other hand, buyers would neitherbe motivated to resolve risk, nor willing to pay for the methods of re-solving risk, in a situation where they do not perceive an actual risk orthe risk is not perceived as important. Risk-reduction strategies (RRS)improve consumer certainty that a purchase will not fail.

Consumers engage in risk-handling tactics to cope with uncertainconsequences resulting from the selection of a particular retail patronagemode–for instance, the decision to make a purchase online (Hawes &Lumpkin, 1986). Ingene and Hughes (1985) proposed a model of thethree-stage risk-management process in consumer decision-making: riskperception, risk reduction, and risk management. If inherent risk is per-ceived, risk reduction will be initiated–consumers will gather informationor rely on endorsements. Consumers consider RRS more important whenthey perceive a higher level of risk; thus:

H3: CPR will be positively related to importance of RRS.

Online Shopping Convenience and Risk-Reduction Strategies

Most researchers have acknowledged that convenience is the primaryfactor in the acceptance of in-home shopping, including online shop-ping (Cox & Rich, 1964; Darian, 1987; Korgaonkar, 1981). Jarvenpaaand Todd (1996/1997) indicated that convenience was the singlemostsalient factor for shoppers interested in reducing physical effort, savingtime, and enhancing shopping convenience. Several studies have con-firmed enhanced convenience, saved time, and financial savings as themajor advantages of online shopping (Burke, 1997; Dennis, Harris, &Sandhu, 2002; Morganosky & Cude, 2000).

According to Roselius (1971), RRS is more likely to be employed ina situation where a consumer has a strong motive to buy a product.

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Therefore, consumers with a greater perception of the convenience ofonline shopping should have more powerful incentives to shop online,making their intent to shop online high (Burke, 1997). Cox and Rich(1964) insisted that telephone shopping depended upon consumer adop-tion of an effective risk-reduction strategy. Thus, consumers who be-lieve that Internet shopping has a high level of convenience, yet alsoperceive a high level of risk, will be more motivated to seek RRS. It isalso expected that:

H4: Internet shopping convenience will be positively related toimportance of RRS.

E-Tailer, Product Type, Consumer-Perceived Risk,and Risk-Reduction Strategies

We investigated how each of four types of CPR and each of threetypes of RRS differ by e-tailer and product type because CPR appears tovary across product type and shopping format. Hawes and Lumpkin(1986) found that consumers perceived higher social and financial riskswhen purchasing apparel. Foucault and Scheufele (2002) found that therecommendation of friends was most positively correlated with inten-tion to purchase textbooks online, while Then and DeLong (1999) iden-tified name brand recognition as a key factor in purchasing apparel.Therefore, the following hypotheses are offered:

H5a: The level of each type of CPR is likely to differ by e-tailertype.H5b: The level of each type of CPR is likely to differ by producttype.H6a: The importance of each RRS is likely to differ by e-tailertype.H6b: The importance of each RRS is likely to differ by producttype.

METHODOLOGY

Research Design and Data Collection

We used a factorial design with two levels of product type (search andexperience goods) and two levels of e-tailer type (single- and multi-chan-nel e-tailers) to investigate whether or not there are differences in CPR

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and also to explore the importance of RRS. Four versions of the instru-ment incorporated a between-subjects design and subjects were assignedto one of four groups. A convenience sample of students studying at aMidwestern university was used in this study. A total of 289 question-naires was distributed to students; 261 usable questionnaires were re-turned, yielding a response rate of 82 percent.

Instrument

The most frequently purchased product categories on the Internetwere identified based on focus group interviews and two product typeswere selected for this study. After considering price and product attrib-utes, we selected a music CD for a search good and a sweater for anexperience good. Two e-tailer types (single-channel e-tailer and multi-channel e-tailer) were also selected. One of the following four hypothet-ical purchase situations was given to respondents: (1) buying a musicCD from a single-channel e-tailer, (2) buying a music CD from a multi-channel e-tailer, (3) buying a sweater from a single-channel e-tailer, (4)buying a sweater from a multi-channel e-tailer.

Consumer-Perceived Risk

The scale measuring CPR incorporated several scales from previousstudies (Jarvenpaa & Todd, 1996/1997; Kim & Lennon, 2000; Siu &Cheng, 2001; Tan, 1999). The scale was designed to measure five typesof perceived risk: privacy risk (three items), financial risk (three items),timing risk (two items), performance risk (three items), and social risk(two items). Respondents were asked to indicate their level of agree-ment with each item listed. A summary perceived risk variable wascreated by summing and averaging these items.

The Importance of Risk-Reduction Strategies

Money-back guarantees, retailer reputation, brand image, and refer-ence group appeal were the four types of RRS. Respondents were askedto indicate the level of importance of four types of RRS when buying amusic CD or sweater from a single-channel e-tailer or multi-channele-tailer.

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Internet Shopping Convenience

We modified the scale developed by Kaufman-Scarborough andLindquist (2002) to measure the perception of online shopping conve-nience. The original instrument measured perceptions of non-store con-venience, focusing on the Internet, television infomercials, advertisingaccompanying television programming, television shopping channels,and print catalogs. The revised scale consisted of eight items and thereliability coefficient for the scale was .7440. In our study, respondentswere asked to indicate the likelihood that online shopping would lead tovarious situations (e.g., comparison shopping).

Consumer Innovativeness

According to Citrin et al. (2000), domain-specific innovation may bea deeper construct of innovativeness more specific to an area of interestin that it reflects the tendency to learn about and adopt innovationswithin a specific domain. Thus, our study used two scales to measureconsumer innovativeness: domain-specific innovativeness and a gener-alized personality trait innovativeness (Donthu & Gilliland, 1996).Using a six-item and three-item approach, respondents were asked toindicate their level of agreement with each item listed.

Experience with Online Shopping

We asked respondents to indicate their frequency of online shopping,expenditures for online shopping, and satisfaction with past onlineshopping experiences overall, as well as satisfaction with the purchaseof music CDs or clothing specifically. Satisfaction with past onlineshopping was measured by the question: “How would you characterizeyour past shopping experience about overall products and product X onthe Internet?”

RESULTS

Demographic Characteristics

The total sample consisted of 261 respondents from all four sub-sample groups. The demographic profile of the sample is shown inTable 1. Eighty-two percent of the sample was female; 97 percent of the

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sample was single. The ages in the sample ranged from 18 to 43, with amean age of 21. About 43 percent of the sample had been using theInternet more than 7 years and 22 percent of the sample used theInternet more than 20 hours per week; 95 percent of the sample ownedcomputers. Twenty-eight percent of the sample had never made an on-line purchase, and 40 percent of the sample had purchased items onlineone to three times. Only 5 percent of the sample had purchased itemsonline more than 12 times. The vast majority had not purchased a CDthrough the Internet; more than half the sample had not purchasedclothing online.

Hyun-Joo Lee and Patricia Huddleston 17

TABLE 1. Demographic Characteristics of the Sample (N = 261)

Variable N %

GenderMale 47 18Female 214 82

Age18-19 35 13.420-23 209 80.124> 17 6.6

Class levelFreshman 52 19.9Sophomore 51 19.5Junior 82 31.4Senior 75 28.7Other 1 0.04

Computer ownershipYes 248 95No 13 5

Marital statusSingle 252 96.6Married 9 3.4

Internet experience1 to 3 years 13 54 to 6 years 137 52.57 years or more 111 42.5

Internet use (weekly)Less than an hour 2 0.081 to 5 hours 56 21.56 to 10 hours 85 32.611 to 20 hours 59 22.621 to 30 hours 34 13Over 30 hours 25 9.6

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Reliability Test

The reliabilities of the scales used to measure each variable wereexamined by computing Cronbach’s alpha coefficient and are presentedin Table 2. All but two scales reached the .70 level; financial risk and so-cial risk variables were .6863 and .5063, respectively. The reliability forfinancial risk was marginally acceptable because the survey method isexploratory in nature. Due to low reliability, five items (two items forsocial risk, one item for reference group appeal, one item for Internetshopping convenience, and one item for consumer innovativeness)were not included in subsequent analyses.

Hypotheses Testing

H1a and H1b were tested using two-way ANOVA (between-groups).Two main effects (product and e-tailer type) and an interaction effect onCPR were examined. Figure 2 shows a plot of the mean scores for CPRand Tables 3 and 4 present the results of two-way ANOVA. CPR waslower when consumers purchased a product from a multi-channele-tailer than from a single-channel e-tailer (F = 20.005, P < .001). Therewas no significant difference in CPR by search or experience good.Therefore, H1a was supported but H1b was not. In addition, the e-tailerby product interaction was not significant.

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TABLE 2. Reliability Coefficients of Variables

Variables Scale Number ofItems

CoefficientAlpha

Consumer-perceived risk 11 .8221

Privacy risk 7-point agree/disagree 3 .8367

Timing risk 7-point agree/disagree 2 .7019

Performance risk 7-point agree/disagree 3 .7672

Financial risk 7-point agree/disagree 3 .6863

Risk-reduction strategies 7-point not important/veryimportant

3 .7168

Internet shopping convenience 7-point very unlikely/very likely 7 .8016

Consumer innovativeness 8 .8094

Domain-specific 7-point agree/disagree 6 .8381

General 7-point agree/disagree 2 .7656

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Hypotheses 2a through 2d were tested using stepwise multipleregression analysis (Table 5). Based on this analysis, frequency ofInternet shopping was found to be the strongest predictor of CPR (F =24.856, p < .001); as the frequency of Internet shopping increased, CPRdeclined (� = �.246, t = �4.034, p < .000). Consumer innovativeness,as expected, exhibited an inverse relationship with CPR (� = �.242,

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FIGURE 2. Effects of E-tailer and Product Types on CPR

TABLE 3. Mean Scores of Consumer-Perceived Risk

Music CDs Sweaters

Single-Channel(n = 71)

Multi-Channel(n = 52)

Single-Channel(n = 72)

Multi-Channel(n = 66)

CPR* 3.87 3.29 3.72 3.32

*Note: Measured on a 7-point Likert scale, 1 = “strongly disagree” to 7 = “strongly agree.”

TABLE 4. Differences in Consumer-Perceived Risk by E-tailer and ProductType

Independent Variables df F Sig.

E-tailer 1 20.005 .000***

Product 1 .269 .604

E-tailer–Product 1 .753 .386

Note: Dependent variables: CPR, ***p < .001.

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t = �3.974, p < .000). Thus, H2a and H2b were supported. Our modelexplained approximately 16 percent of variance in CPR. Expendituresfor Internet shopping and satisfaction with past Internet shoppingexperiences were not predictors of CPR. Therefore, Hypotheses 2c and2d were not supported.

H3 and H4 were tested using stepwise multiple regression analysis.As shown in Table 6, results of the regression analysis (F = 8.234,p < .001) showed a significant positive relationship between Internetshopping convenience and the importance of RRS (� = .053, t = 3.553,p < .001). In addition, CPR was found to be positively related to theimportance of RRS, suggesting that as CPR increases, the importance ofRRS increases (� = .063, t = 2.747, p < .01). Therefore, Hypotheses 3 and4 were supported. However, only six percent of variance in the impor-tance of RRS was explained by this model.

To test hypotheses 5a, 5b, 6a, and 6b, we used two-way multivariateanalysis of variance (MANOVA). Tables 7 and 8 present mean scoresfor four types of CPR and the results of the MANOVA and Hotelling’sTrace analysis of the level of risk type by e-tailer and product type.There is no significant difference in each of the four types of CPR byproduct type (F = 1.560, p > .05); there are significant differences ineach of four types of CPR between single- and multi-channel e-tailers(F = 5.524, p < .001). Therefore, H5a was supported but H5b was not.In addition, the e-tailer by product interaction was not significant. To

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TABLE 5. Predictors of Consumer-Perceived Risk

IndependentVariables

UnstandardizedCoefficients (B)

StandardizedCoefficients (�)

T-Value Sig.

Frequency �.233 �.246 �4.034 .000***

Innovativeness �.215 �.242 �3.974 .000***

R2 = .162 F = 24.856***Note: Dependent variables–CPR, ***p < .001.

TABLE 6. Predictors of Importance of Risk-Reduction Strategies

Independent Variables UnstandardizedCoefficients (B)

StandardizedCoefficients (�)

T-Value Sig.

Internet shopping convenience .188 .053 3.553 .000***

CPR .172 .063 2.747 .006**

R2 = .060 F = 8.234***Note: Dependent variables–Importance of RRS, ***p < .001; **p < .01.

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examine the differences in perceived risk by type of e-tailer in moredetail, we conducted univariate analysis (see Table 9). The mean scoresfor timing (F = 13.589, p < .001), privacy (F = 14.696, p < .001), finan-cial (F = 5.566, p < .01), and performance risk (F = 7.208, p < .05) weresignificantly higher for single channel e-tailers.

Hyun-Joo Lee and Patricia Huddleston 21

TABLE 7. Mean Scores for Four Types of Consumer-Perceived Risk

CPR* Music CD Sweater

Single-Channel Multi-Channel Single-Channel Multi-Channel

Timing risk 5.23 4.33 5.02 4.62

Privacy risk 4.41 3.51 4.26 3.80

Financial risk 3.69 3.28 3.32 3.07

Performancerisk

2.61 2.37 2.72 2.23

*Note: Measured on a 7-point Likert scale, 1 = “stronly disagree” to 7 = “strongly agree.”

TABLE 8. Differences in Four Types of Perceived Risk by E-tailer and ProductType

Dependent Variables Hotelling's Trace df F Sig.

Four types of CPR E-tailer .087 3 5.524 .000***

Product .025 3 1.560 .185

E-tailer * product .023 3 1.461 .215

Note: Independent variables–E-tailer type and product type, *** p < .001.

TABLE 9. Univariate Analysis of Four Types of Perceived Risk Differences

DependentVariables

Mean Score df MeanSquare

F Sig.

Single-ChannelE-tailers(n = 143)

Multi-ChannelE-tailers(n = 118)

Timing risk 5.13 4.49 1 27.315 13.589 .000***

Privacy risk 4.34 3.68 1 29.293 14.696 .000***

Financial risk 3.51 3.16 1 7.018 5.566 .019*

Performancerisk

2.66 2.29 1 8.293 7.208 .008**

Note: ***p < .001, **p < .01, *p < .05.

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Hypotheses 6a and 6b were also tested using two-way MANOVA.Mean scores for the importance of three types of RRS are shown in Table10. The results of the MANOVA and Hotelling’s Trace analysis of theimportance of RRS by e-tailer and product type indicate no significantdifferences in the importance of each of three types of RRS by producttype (F = 1.203, p > .05), or by e-tailer type (F = 2.096, p > .05). Nor wasthe e-tailer by product interaction significant. Therefore, H6a and H6bwere not supported.

DISCUSSION

Our results indicate that e-tailer type have a significant influence onCPR in online shopping, but product type does not. Specifically, con-sumers perceived lower levels of risk when they purchased a productfrom multi-channel e-tailers than from single-channel e-tailers. Theadvantages of multi-channel e-tailers–such as well-established brandnames, easy returns and exchanges, greater reliability and trustworthi-ness–may decrease CPR. We had expected higher risk to be associatedwith experience goods (i.e., apparel); there was no difference, however,in CPR of search goods versus experience goods. Perhaps CPR in pur-chasing apparel online is declining due to offline apparel retailers withestablished brand names entering e-commerce and offering extendedsizes, colors, and selection as well as liberal return policies (Brown &Iorio, 2000).

Consumer innovativeness was inversely related to CPR, supportingthe idea that innovators have the ability to cope with uncertainty andrisk (Rogers, 1983). The relationship between experience with onlineshopping and CPR was limited to frequency of online shopping, whichexhibited an inverse relationship to CPR. Our findings suggest that

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TABLE 10. Mean Scores for Three Types of Risk-Reduction Strategies

RRS* Music CD Sweater

Single-ChannelE-tailer

Multi-ChannelE-tailer

Single-ChannelE-tailer

Multi-ChannelE-tailer

Money-back guarantee 6.51 6.25 6.40 6.21

Retailer reputation 5.93 5.83 5.93 5.76

Brand image 5.39 5.48 5.08 5.27

*Note: Measured on a 7-point Likert scale, 1 = “not important” to 7 = “very important.”

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more frequent online shoppers experience lower levels of CPR, whilethe effects of expenditures on online shopping and satisfaction with pastonline shopping on CPR were not significant. Caution should be takenin extrapolating our findings, because the majority of our sample hasnever purchased music or apparel online. Thus, respondents more expe-rienced with online purchases of CDs and clothing may providedifferent results. Both CPR and online shopping convenience were pos-itively related to the importance of RRS. It is clear that consumers placegreater importance on RRS when they perceive a higher level of risk.Also, the more convenient consumers perceive online shopping to be,the more importance they place on RRS. We hypothesized that each offour types of CPR and the importance of each of three types of RRSwould differ by e-tailer and product type. We found it interesting thatthere was no difference in the level of CPR and the importance of RRSby product, but there were variations by e-tailer format. The highestlevel of CPR for single-channel e-tailers was timing risk, followed byprivacy, financial, and performance risk. This is inconsistent with previ-ous research indicating that privacy risk is the highest perceived risk inonline shopping (Liebermann & Stashevsky, 2002; Weber & Roehl,1999). This may be an anomaly of our sample, but it may also be a posi-tive signal–if younger consumers perceive lower privacy risk than oldergroups of consumers, they may be more likely to shop online. The mostimportant RRS was a money-back guarantee, followed by retailer repu-tation and brand image, confirming previous research (Akaah &Korgaonkar, 1988; Van den Poel & Leunis, 1999). Although the impor-tance of three types of RRS did not differ by e-tailer or product type, allwere rated as very important by our sample (> 5 on a 7-point scale).

IMPLICATIONS

Although online shopping is rapidly growing, consumers still perceiverisk when they shop online, especially with single-channel e-tailers.The goal of our study was to increase knowledge of CPR and RRS inonline shopping. This study makes a unique contribution by success-fully differentiating the highest levels of CPR for college students, aconsumer segment that represents the future of online shopping. Gain-ing new insights into the importance of timing risk for this age group is acrucial research direction, one we have addressed with this study. Otherstudies have found privacy risk to be dominant in online shopping, thus

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our study provides another perspective by drawing attention to thesignificance of timing risk.

The finding that a higher level of CPR is associated with single-channel e-tailers is also important. In our study, four types of CPR weresignificantly higher for single-channel e-tailers than for multi-channele-tailers. Timing risk was the highest risk and exerted the strongest neg-ative effect. Because single-channel e-tailers cannot provide easy re-turns and exchanges if products fail, consumers perceive a higher levelof timing risk compared to multi-channel e-tailers. As a possible solu-tion for this disadvantage, single-channel e-tailers might form strategicpartnerships with offline retailers for more convenience in returns orexchanges, allowing consumers to buy products online from single-channel e-tailers, but to return or exchange items at a physical store.This strategy is likely to reduce perceived timing risk and improve con-sumer perceptions of the convenience of online shopping. Anothermajor disadvantage for single-channel e-tailers is lower brand namerecognition. Less-known brand names make consumers less confidentof their buying decisions, which then leads consumers to perceive ahigher level of risk in purchasing products online. Thus, single-channele-tailers must invest heavily in marketing and advertising to establish acredible online brand. For example, single-channel e-tailers can spreadbrand awareness by buying advertising space on the web sites ofcompanies with established brand names. Consumer innovativenessand frequency of online shopping were inversely related to CPR. Be-cause making frequent online purchasing reduces CPR, e-tailers mustwork to retain current customers and encourage them to buy frequentlyon the Internet, perhaps through the use of reward programs. Ama-zon.com, for instance, has anchored its e-tailer identity and attracted re-peat customers by its use of multiple types of rewards, including free“super saver shipping” for purchases over a set amount (usually $25),customer “Gold Boxes” (which feature enticing and sometimes dis-counted products specifically selected based on customers’ past pur-chases), and the opportunity for shoppers to make product referrals tofriends at check-out (customers receive future product discounts for re-ferrals). Online coupon codes is another rewards-based approach; con-sumers can either immediately apply an electronic coupon code at thetime of purchase, and/or they might receive a coupon code for their nextpurchase along with their electronic receipt. Similarly, when consumersmake an online purchase, they might be referred to and/or receive anelectronic coupon code for a partner e-tailer.

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We confirmed that money-back guarantees and retailer reputationwere the most critical RRS for online shopping. Our study comparedCPR and RRS by four types of purchase situations (single- and multi-channel e-tailers by two product types) and found that neither e-tailernor product type influenced importance of RRS. We found positiverelationships between CPR, online shopping convenience, and the im-portance of RRS. E-tailers can enhance consumer experiences andreduce their sense of risk with money-back guarantees, warranties, orsamples. Our results show that consumers who perceive online shop-ping as highly convenient place greater value on RRS to increase theircertainty and ultimately facilitate their purchase-making via theInternet. Because timing risk was the highest CPR for our sample,e-tailers must try to reduce this perceived risk by offering fast delivery,informing customers about probable wait time, offering purchase track-ing services, and allowing returns or exchanges at a nearby store. Tofacilitate ease of exchange or returns, single-channel e-tailers mightpromise free return shipping, and even include a prepared envelope orbox for returned items. To mitigate privacy risk–the second highestlevel of risk in our study–e-tailers can convince consumers that shop-ping and transmitting sensitive information online is secure by callingattention to their secure servers, and by offering trust marks, such asBBBBOnline and VeriSign (Wingfield, 2002).

LIMITATIONS AND RECOMMENDATIONSFOR FUTURE RESEARCH

Because we used a student sample, our findings cannot be generalizedto a broader population and future research should recruit a more diversesample. Many other studies confirm that CPR is product specific, thusa larger range of product categories should also be included in futureresearch. Our study did not provide respondents with the extensive prod-uct information normally presented online in a real purchase situation.Therefore, future research should be conducted in a realistic online shop-ping environment. Future research should also consider additional con-sumer characteristics and motivational variables (such as time poverty)that may influence CPR and RRS. Future studies should examine howCPR and RRS modify buying intentions and affect actual online buyingbehavior. If future research can detect the impact of RRS on buying inten-tion and buying behavior, we can more accurately predict online shop-ping behavior and more effectively implement retail strategies.

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