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International Journal of Information Management 33 (2013) 927– 939

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International Journal of Information Management

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The mediating role of consumer trust in an online merchant inpredicting purchase intention

Ilyoo B. Hong ∗, Hoon S. ChaCollege of Business and Economics, Chung-Ang University, Bubhakkwan Bldg., Rm. 1404, 221 Heuksuk-dong, Dongjak-ku, Seoul 156-756, Republic of Korea

a r t i c l e i n f o

Article history:Available online 26 September 2013

Keywords:Online shoppinge-CommercePerceived riskTrustPurchase intention

a b s t r a c t

It is widely known in related literature that trust in a merchant reduces the perceived risk of an onlinetransaction. However, there are theoretical reasons to postulate that the perceived risk acts as a barrierto consumer trust. Furthermore, existing studies suggest that trust is an important predictor of purchaseintention. Thus, this research aims at investigating the mediating role of consumer trust in an online mer-chant in the relationships between components of perceived risk and purchase intention: (1) examiningthe total effect without mediation, and (2) examining the mediation effect. When we probed the totaleffect, the findings revealed that performance, psychological, financial, and online payment risks have asignificant negative influence on purchase intention. On the other hand, an examination of the mediationeffect indicated that trust in an online merchant completely mediates the effect of performance risk, butpartially mediates that of the psychological risk. Given the mixture of unmediated as well as mediatedeffect of perceived risks on purchase intention, the paper concludes that efforts, made by online mer-chants, to lessen certain types of risk will first improve consumer trust, and then ultimately, increaseconsumer’s intention to buy online.

© 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Internet-based commerce has undergone explosive growth overthe past decade as consumers today find it more economical aswell as more convenient to shop online. Nevertheless, the shift inthe common mode of shopping from offline to online commercehas caused consumers to have worries over issues, such as privateinformation leakage, online fraud, discrepancy in product qual-ity and grade, unsuccessful delivery, and so forth. Unfortunately,there has been a steady increase in the number of incidents thatcause consumers to have such worries; for example, the Inter-net Crime Complaint Center reports that it has received 303,809Internet fraud complaints in 2010, up from 95,064 complaints in2003 (Center, 2011a). Meanwhile, the number of privacy breachesreported in the U.S. has increased from 157 in 2005 to 662 in 2010(Center, 2011b). Therefore, today’s consumers feel unsafe aboutmaking purchases online. The concerns that consumers have overonline buying are collectively termed as consumers’ perceived risk.

Numerous studies have been conducted to examine the role ofperceived risk as a chief barrier to online purchases and to under-stand the theoretical relationships among perceived risk, trust, andpurchase intentions. However, most studies (for example, Cheung

∗ Corresponding author. Tel.: +82 2 820 5549; fax: +82 2 813 8910.E-mail address: ihong@cau.ac.kr (I.B. Hong).

& Lee, 2001; Corbitt, Thanasankit, & Yi, 2003; Flavian, Guinaliu,& Gurrea, 2005; Gefen, 2002; Gefen, Karahanna, & Straub, 2003;Jarvenpaa, Tractinsky, & Vitale, 2000; Pavlou, 2003; Salam, Iyer,Palvia, & Singh, 2005) focus on empirically investigating the effectsof trust on perceived risk with little attention devoted to the effectsof perceived risk on trust. While the influence of trust on perceivedrisk is worth studying, the influence in the opposite direction isequally important, enabling insights into the potential of perceivedrisk as an inhibitor of trust. For example, a consumer who perceiveshuge risk concerning an online transaction is likely to foresee agreat potential of loss and thus, places little trust in the merchant.According to Pavlou (2003), the primary source of the perceived riskis either the technological uncertainty of the Internet environmentor the behavioral uncertainty of the transaction partner. Due to suchtypes of uncertainty, the increase in worries over the perceived riskmay negatively affect trust. For example, if a consumer who sendssensitive transaction data over the Internet is concerned that hisor her private information may leak out due to a lack of security,trust may decrease (Olivero & Lunt, 2004). By the same token, ifthe consumer feels that the online merchant has the potential toprofit by behaving in an opportunistic manner by taking advantageof the remote, impersonal nature of online commerce, then it isunlikely that the merchant will be trusted. That is, the more likelyit is for a danger to occur, the lesser is the trust and the greater isthe need to control the transaction (Olivero & Lunt, 2004). Thus,the related studies as a whole indicate that while some researchersnoted the influence of the overall perceived risk on the trust level,

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not much attention has been given to the effects of different typesof perceived risk. Meanwhile, the related literature suggests thatconsumer trust in an online merchant is a key predictor of pur-chase intention (Hong & Cho, 2011; Pavlou, 2003; Pavlou & Gefen,2004; Verhagen, Meents, & Tan, 2006). Then, we are led to believethat the relationship between perceived risk and purchase inten-tion can be indirect as well as direct; moreover, it can be mediatedby consumer trust. However, little attention has been given to thisresearch issue to date.

The present research is a step toward closing that gap in extantresearch. It aims at addressing the need to study the intriguingrelationships between perceived risk and purchase intention in ane-commerce setting. To accomplish the research purpose, we estab-lished two research questions. First, does perceived risk act as aninhibitor of purchase intention? Second, does trust in an onlinemerchant mediate the relationship between perceived risk andpurchase intention? We classified perceived risk into six differenttypes based on literature, and empirically analyzed both the directand the mediation effects of each dimension of perceived risk uponpurchase intention.

The contribution of our research has both theoretical and practi-cal dimensions. Theoretically, it will contribute to the existing bodyof knowledge by providing new insights into the mediating role ofconsumer trust in an online merchant in the relationships betweendimensions of perceived risk and purchase intention. Practically,the research will help e-businesses develop strategies to reducethe specific types of perceived risk found to negatively influencetrust, thereby engendering consumer trust in an online merchantand ultimately increasing online sales.

2. Literature review

2.1. Perceived risk

Bauer (1960) proposed that consumer behavior could be viewedas an instance of risk taking. He maintained that consumer behav-ior involves risk in the sense that any action of a consumer willproduce consequences that one cannot anticipate and of which atleast some are likely to be unpleasant. An individual perceives asituation as bearing risk if entering this situation might lead tonegative consequences, and also if the individual is not able tocontrol the occurrence of these consequences (Koller, 1988). Thus,the more negative are the consequences and the less the individ-ual can control the consequences, the higher is the level of theperceived risk. Bauer (1960) emphasized that it is not a “real world”(or objective) risk but a perceived (or subjective) risk that influencesconsumer behavior. In the context of electronic commerce, Cox andRich (1964) defined “perceived risk” as the nature and amount ofrisk perceived by a consumer in contemplating a particular pur-chase decision. A consumer perceives risk because prior to makinga purchase, she cannot always be certain that the planned purchasewill allow her to achieve her goals of purchasing. The uncertaintyperceived by the consumer with regard to the choice of a product,brand, retailer, or channel determines the nature of the risk. Mean-while, the amount of risk perceived by the consumer is a functionof two general factors: the amount at stake in the purchase deci-sion, and the individual’s feeling of a subjective certainty that shewill “win” or “lose” all or some of the amount at stake (Cox & Rich,1964).

The risks perceived by consumers in traditional commerce areclassified from various perspectives in the literature. While theyeach exhibit unique classification schemes, these studies (for exam-ple, Jacoby and Kaplan, 1972; Kurtz & Clow, 1997; Peter & Ryan,1976; Schiffman & Kanuk, 1994; Stone & Gronhaug, 1993; Taylor,1974; Zikmund & Scott, 1977) have focused on four essential

types of risk including financial, performance, psychological, andsocial risks. Meanwhile, risks faced by online consumers are thoseengendered by the Internet as a sales channel, in addition to thetraditional consumer risks. The use of the Internet, as a mode ofpurchase, creates risks for online transactions with the merchantsince transactions are remote, involving no face-to-face contactbetween the merchant and the consumer (Cases, 2002). For exam-ple, Internet-based shopping requires a delivery process, unless anorder is placed for a digital product that can be delivered online viathe Internet; therefore, there is a risk for inconsistency betweenthe ordered product and the delivered product (Ward & Lee, 2000).In addition, consumers may perceive a payment risk because theyare likely to pay by a credit card, and thus, important personalinformation needs to be transmitted when the payment transac-tion is executed. Although security measures, such as encryptionand authentication, are in place, consumers feel insecure about thepossibility of personal information leakage that may result fromhacking during the course of an online transaction. Jarvenpaa andTodd (1997) pointed out personal and privacy risks as well as eco-nomic, social, and performance risks in Internet-based transactions.Personal (or payment) risk refers to the fear of giving one’s credit-card number online, and privacy risk is associated with the buyer’sfear that personal information will be collected without authoriza-tion. Based on the above theoretical evidence, it is inferred thatInternet transactions can introduce delivery and payment risks inaddition to the common risks inherent in traditional commerce.Additionally, we propose an integrative model of risk dimensions:performance, psychological, social, financial, online payment, anddelivery risks.

2.2. Trust

Trust has been widely studied over the years, as it is recognizedas a key element in relationships between individuals, betweenorganizations, and between an individual and an organization.Nevertheless, trust is perhaps one of the most highly challengingnotions in which concepts are hardly agreed upon by researchers(Hong & Cho, 2011). As Lee and Turban (2001) noted, trust has beenexamined in various contexts including buyer–seller relationships,strategic alliances, and labor–management negotiations. In general,trust is defined as the willingness of a party to be vulnerable to theactions of another party based on the expectation that the otherwill perform a particular action important to the trustor, irrespec-tive of the ability to monitor or control that other party (Mayer,Davis, & Schoorman, 1995). Morgan and Hunt (1994) defined trustas the belief that the trustee will behave in a favorable manner. Fur-ther, they state that trust is critical in successful alliances betweenfirms. As such, trust refers to believing that the trustee will notdo harm to the trustor and that negative consequences will notoccur.

In the context of electronic commerce, trust becomes an evenmore important issue since exchange relationships are based onthe impersonal nature of the Internet infrastructure. In particular,consumers face the challenge of buying a product or service onlinefrom an unfamiliar merchant; moreover, they cannot actually seeor touch the product. Trust plays a central role in helping con-sumers overcome the perceptions of risk and insecurity (McKnight,Choudhury, & Kacmar, 2002). Since privacy and security concernsare major barriers to the Internet channel, without trust, customerswill not give vendors their personal information, including creditcard information (Hoffman, Novak, & Peralta, 1999). Therefore,online trust is formed slowly over time as a consumer gains experi-ence through repeated transactions (Cheskin-Research, 1999). Forthe purpose of the present research, trust is defined as the con-sumer’s belief that the online merchant will not behave in an

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opportunistic manner and that the e-commerce environment issecure enough to provide risk-free transactions.

2.3. Perceived risk and trust

Trust and perceived risk are in a very close and inseparable rela-tionship. Most existing studies predominantly focus on the effectsof trust on the perceived risk (Cheung & Lee, 2001; Corbitt et al.,2003; Flavian et al., 2005; Gefen, 2002; Gefen et al., 2003; Jarvenpaaet al., 2000; Pavlou, 2003; Salam et al., 2005). For example, Pavlou(2003) and Jarvenpaa et al. (2000) reported that an increase inconsumer trust in an online merchant lessens the perceived risk.However, Mayer et al. (1995, p. 711) noted that “it is unclearwhether risk is an antecedent to trust, is trust, or is an outcomeof trust.”

Johnson-George and Swap (1982) state that the “willingness totake risks may be one of the few characteristics common to all trustsituations.” The presence of trust implies the acceptance of a certaindegree of risk toward the loss when the expected outcome is posi-tive. While the trustor chooses to trust if the risk that she has to takeis within an acceptable range, the trustor has no choice but to giveup making the trusting choice in case the risk is likely to go beyondthe limit. Therefore, the perceived risk can be an important pre-dictor of the trusting decision. Further, Deutsch (1973) postulatedthat a trusting choice will be made if the subjective probability ofan event of positive valence is higher than the subjective probabil-ity of an event of negative valence. That is, a trustor will not chooseto trust in case risks are expected to be greater than benefits. Thistheory is applied to the electronic commerce setting. If a consumerassociates high risk with an online transaction, then the level oftrust in the online merchant decreases and the need to control thetransaction increases (Olivero & Lunt, 2004).

Studies of perceived risk suggest that a key source of perceivedrisk is uncertainty. Ring and Van de Ven (1994) found that therisks inherent in a transaction increase in proportion to reductionsin time, information, or controllability. Thus, under circumstanceswhere there are time pressures or a lack of information or diffi-culties in controlling the trustee’s behavior, uncertainty will bepresent. Pavlou (2003) suggested that risks in electronic commerceare introduced by both the impersonal nature of the online envi-ronment and the uncertainty of using the Internet for transactions.Then, such uncertainty has two components: behavioral uncer-tainty from the transaction partner and environmental uncertaintyfrom the technical environment of online transactions (Pavlou,2003; Ring & Van de Ven, 1994). Behavioral uncertainty existsbecause the Web vendor has the chance to behave in an opportunis-tic manner, whereas environmental uncertainty exists due to theunpredictable characteristics of the Internet infrastructure. Whenbehavioral uncertainty is high, a consumer is likely to feel that thetransaction partner may potentially bring about a loss upon him orher by taking advantage of the remote, impersonal nature of theonline transaction. On the other hand, in situations where envi-ronmental uncertainty is high, the consumer is most likely to fearthat an unauthorized person may take his or her personal infor-mation, even if the merchant’s server is equipped with protectivetechnologies such as encryption.

Some empirical studies (for example, Jarvenpaa & Leidner, 1999;Pavlou, 2003) found that perceived risk has a direct negativeinfluence on transaction intentions. They suggest that consumersperceiving a great risk are motivated to avoid engagement in thetransaction since they are not sure they can expect a positive pay-off. However, in the present research, we will examine the potentialrole of trust as a mediator between risk and transaction intentions.It is important to note that a consumer may not wish to partici-pate in an online transaction because s/he is not quite sure that theonline merchant will act favorably in the interest of the consumer,

not merely because a risk is present. That is, the consumer maychoose not to shop online because the transaction partner can-not be reasonably trusted. Since a consumer as a whole cannotaccurately predict the likelihood that the partner will behave inan opportunistic manner and thus, can only guess the degree ofrisk under uncertainty, the actual risk perception will be devel-oped based on the exposure to media concerning related incidentsor on past shopping experiences. Moreover, if the risk perceivedover time goes beyond the level that s/he can tolerate, then theconsumer may choose to abandon the trusting choice. Therefore,we will develop a research model centered on the role of trust as avariable that mediates the relationship between perceived risk andpurchase intention.

3. Conceptual model and hypotheses

3.1. Conceptual model development

The purpose of this paper is to examine the relationshipsbetween dimensions of perceived risk and purchase intention. Inparticular, we will explore the mediating role of consumer trust insuch relationships. Earlier in the literature review, we provided thetheoretical grounds for the impact that perceived risk has on trust.A close examination of the relationship between risk and trust indi-cates that the influence of risk is valid for only some specific typesof risk rather than the overall perceived risk. For example, if a con-sumer cannot trust the merchant because s/he may behave in anopportunistic manner, then what makes the consumer unable totrust the merchant is most likely to be either a financial or a per-formance risk. Moreover, it would be possible that certain typesof risk may have a direct influence on purchase intention withoutthe mediating role of consumer trust (Jarvenpaa & Leidner, 1999;Pavlou, 2003). For that reason, in order to correctly understand thecausal relationship between perceived risk and trust, we need tofocus on the types of perceived risk as independent variables andtheir differential impacts on trust through an empirical analysis.As we observed in the literature review, the related studies suggestthat the types of risk perceived by a consumer in an electronic com-merce setting include performance, psychological, social, financial,online payment, and delivery risks. Thus, we will use this taxonomyin order to classify the perceived risk in our research.

Our conceptual model is presented in Fig. 1. The first sixhypotheses, namely H1-1, H1-2, H1-3, H1-4, H1-5, and H1-6, focuson the “total” effects of the dimensions of perceived risk on pur-chase intention. The next six hypotheses, namely, H2-1, H2-2, H2-3,H2-4, H2-5, and H2-6, are designed to explore the mediation effectsof consumer trust in the relationships between the individual typesof perceived risk and purchase intention. We will look at the the-oretical background for each of the hypotheses in the followingsubsection.

3.2. Hypothesis development

Fig. 2 describes the unmediated and mediated models follow-ing Baron and Kenny’s notation (Baron & Kenny, 1986; Frazier, Tix,& Barron, 2004; Shrout & Bolger, 2002), where the types of riskare not specified for simplicity. In the unmediated model as shownin the figure, we posited that perceived risk negatively influencepurchase intention. Path c in this model is called the total effect.On the other hand, in the mediated model also shown in the fig-ure, we hypothesized that the effect of perceived risk on purchaseintention was mediated by trust. Path c′ is called the direct effect,while paths a and b are called the indirect effect. If perceived riskno longer directly affects purchase intention (i.e., path c′ = 0) aftertrust has been controlled, complete mediation exists. When path c′

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Social risk

Performance risk

Psychological risk

Financial risk

Online payment risk

Delivery risk

Trust

Purchase intention

H1-1

H1-2

H1-3

H1-4

H1-5

H1-6

Perceived risk

Control variables:

• Age

• Gender • Internet usage

• Internet shopping frequency

H2-1

H2-2

H2-3

H2-4

H2-5

H2-6 H2-1,2,3,4,5,6

Fig. 1. The conceptual model.

is reduced in absolute size but is greater than zero, partial media-tion exists. As we can see in Fig. 1, we established one hypothesis foreach type of risk in the unmediated model for a total of six hypothe-ses, whereas we formulated six additional hypotheses in order toexamine the mediating role of consumer trust in the relationshipsbetween each dimension of perceived risk and purchase intention.

3.2.1. Examining the total effect: Perceived risk and purchaseintention

Related studies have in general found a negative relationshipbetween the overall perceived risk and purchase intention. Forexample, according to Jarvenpaa et al. (2000), the theory of plannedbehavior predicts that a consumer is likely to buy from an onlinestore, which is perceived to be low in risk, although the consumer’sattitudes toward the merchant are not positive. In the contextof Internet shopping, perceived risk may reduce consumer’s per-ception of behavioral control that refers to the extent to which aconsumer feels that engaging in a behavior is completely up tohim or her (Jarvenpaa et al., 2000, p. 50). Pavlou (2003) also sug-gests that perceived risk is negatively related to purchase intention.He suggests that transaction intentions are influenced by beliefsabout online retailers that are partly determined by the behavioraland environmental factors that may lead to risk perceptions. Giventhat losses are likely, a consumer will have no reason to engage in atransaction. The negative relationship found by the existing relatedstudies between perceived risk and intention to buy is likely to holdtrue for the individual dimensions of perceived risk, although thedifferential impact of each dimension of risk may vary with productclasses or consumers.

First, compared to perceived risk in traditional shopping, therisk associated with the product performance in online shopping isespecially significant because of consumers’ limited ability to com-municate through the Internet and to accurately judge the quality ofthe product. For example, when consumers have difficulty grasp-ing the features of products such as clothes, shoes, and furnituresolely from Website pictures, they could be easily concerned thatthe product ordered might not be exactly as it appeared on theWebsite or might not perform up to their expectations (Hassan,Kunz, Pearson, & Mohamed, 2006). Indeed, many online stores havewitnessed the negative impact of performance risk perceptions onactual sales and thus have been trying to come up with variousmechanisms to lower consumers’ perceptions of performance risk.For instance, instead of simply displaying pictures for the prod-uct features, some online stores host a discussion forum to allowconsumers to freely exchange their comments, opinions, or recom-mendations about the products (Garbarino & Strahilevitz, 2004),which provide useful purchase guidelines for online consumers.Given the discussion above, we propose the following hypothesis:

H1-1. Performance risk is negatively related to purchase inten-tion.

Second, an online consumer could experience psychological dis-comfort due to personal ego in making purchase decision (Jacoby& Kaplan, 1972). This type of psychological loss may result fromconsumers’ lack of experience in buying products or services. Ingeneral, consumers with less online shopping experience may feelmore mental discomfort from potentially making the wrong prod-uct choice than those with more experience of shopping online.

Perceived

risk

Purchase

intention

Perceived

risk

Purchase

intention

Trust

c

c’

a b

Total effect

Indirect effect Indirect effect

Direct effect

Unmediated

Model

Mediated

Model

Fig. 2. The total effect vs. direct effect vs. indirect effect.

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For example, consumers with prior experience would feel less con-cerned as they know how to choose products best aligned withtheir expectations and how to return products that they do notlike. Therefore, as consumers perceive more psychological risk, theymay experience greater level of anxieties and be less willing to buyonline. Therefore, we propose the following hypothesis:

H1-2. Psychological risk is negatively related to purchase inten-tion.

Third, Hassan et al. (2006) states that online shoppers areconcerned about the reaction of others who think of the onlineprospective shopper as being foolish or showy. Cases (2002) alsodefines social risk as the fear of the reaction of friends and familyconcerning the Internet as a mode of purchase. The rise of Inter-net has increased the convenience of shopping in any place and atany time; however, at the same time, consumers can get caughtup by the sheer variety of items and an illusion that they have notspent too much money. As the online shopping addiction and simi-lar compulsive online behaviors on the Internet become importantsocial issues and problems, online consumers become more afraidof their acquaintances’ view about online shopping. When con-sumers’ perceived benefits of online shopping are outweighed byperceived social risk, the purchase will likely be avoided. Therefore,the following hypothesis can be formulated:

H1-3. Social risk is negatively related to purchase intention.

Fourth, financial risk is defined as the probability of mone-tary loss associated with purchasing a product. Thus, in the onlineenvironment, purchasing by consumers has been dominated byproducts that carry lower levels of financial risk such as books,clothes, and music files. Although the online purchase has beengradually expanding its area over more expensive products, like alaptop computer and even an automobile, many online consumersstill perceive relatively high financial risk with those expensiveproducts. Thus, consumers may be more hesitant when purchasinga product or a service likely to have potentially high economic loss.Based on the above grounds, we suggest the following hypothesis:

H1-4. Financial risk is negatively related to purchase intention.

Fifth, a risk dimension that can become a key considerationin online shopping is the perceived risk associated with onlinepayment. Various surveys have shown that Internet users areincreasingly concerned about the possibility that their private andcredit card information may be captured, collected, and misusedby a hacker or even by online marketers without permission. Theseconcerns will cause the consumer to look for an alternative modeof shopping (e.g., making purchases at a department store). As aresult, we propose the following hypotheses.

H1-5. Online payment risk is negatively related to purchase inten-tion.

Finally, when purchasing online, a consumer needs to wait foran order to arrive. The shipment containing the ordered productcould be lost or delivered to a wrong address if there is a lack of busi-ness experience on the part of the delivery company. In addition,it is possible that the order arrives later than expected, providedthat there is a backorder on the ordered product (Cases, 2002).A consumer who has strong perception of delivery risk will mostlikely lose interest in the online purchase. Therefore, we proposethe following hypothesis:

H1-6. Delivery risk is negatively related to purchase intention.

3.2.2. Examining the mediation effect: Perceived risk, trust, andpurchase intention

Prior studies on the relationship between risk and trust focuson examining the causal relationship between the two constructs

where trust is viewed as an antecedent of the perceived risk. How-ever, as we have seen in the literature review, the risks that resultfrom behavioral and environmental uncertainties are what makean online merchant untrustworthy to consumers.

Behavioral uncertainty is associated with consumers’ concernsthat the online vendor may not behave in a socially responsi-ble manner based on opportunistic calculation. Environmentaluncertainty has to do with the possibility that consumers’ privateinformation may leak out as transactional data are transmitted overthe Internet (Pavlou, 2003). Pavlou (2003) suggested that behav-ioral uncertainty may lead to economic risk (i.e., financial loss),personal risk (i.e., the likelihood that the consumer may be a vic-tim due to the use of unsafe products or services), seller risk (i.e.,negative consequences that may result because of the inability tomonitor the seller’s transactions), and online payment risk (i.e.,the danger that exists because the consumer’s private informa-tion is given to a third party). On the other hand, environmentaluncertainty that surrounds the online transactional infrastructuremay result in economic risk (i.e., concerns over financial loss) andprivacy risk (i.e., the likelihood that the consumer’s private infor-mation may be stolen or illegally disclosed). Likewise, when aconsumer perceives risks due to the uncertainty associated withthe transaction partner or with the online transactional infrastruc-ture, the consumer would find it difficult to trust the transactionmechanism and furthermore, to participate in the online transac-tion. This is particularly true when we consider Mayer et al.’s (1995)definition of trust as “the willingness of a party to be vulnerable tothe actions of another party based on the expectation that the otherwill perform a particular action important to the trustor, irrespec-tive of the ability to monitor or control that other party.” While aconsumer can trust the other party despite the presence of somedegree of risk, once the amount and probability of the risk goesbeyond an acceptable range, the consumer is most likely to give upthat trust (Gefen, 2000).

Meanwhile, the causal relationship between trust and pur-chase intention has been also noted by researchers. Jarvenpaaet al. (2000) applied the theory of reasoned action (TRA) to Web-based shopping, and concluded that a consumer’s online purchaseintentions are influenced by attitude, and attitude is affected byconsumer trust. Heijden, Verhagen, and Creemers (2003) con-ducted an empirical study based on TRA, and reported a similarfinding; trust has an indirect effect on transaction intentionsthrough the attitude as a mediator. In online commerce, trust in atransaction partner represents behavioral beliefs about the partner,and these beliefs can change the consumer’s behavioral intentionsfor online transactions. However, the majority of other related stud-ies (for example, Gefen et al., 2003; Salam et al., 2005) providecontrary research findings indicating that trust has a direct impacton purchase intentions. To cite one example, Shankar, Urban, andSultan (2002) found that online trust has a significant influence onpurchase intention and customer loyalty, and confirmed a directrelationship between trust and purchase intention. The above lineof reasoning leads us to believe that trust is most likely to play amediating role between perceived risk and purchase intention.

At the level of individual components of perceived risk, themediation effect is likely to hold true. That is, the relationshipbetween each type of perceived risk and purchase intention is likelyto be indirect and to be mediated by consumer trust in an onlinemerchant. Each of these risk dimensions will act as an antecedent totrust that in turn will become an antecedent to purchase intention.

First, strong perception of product performance risk will resultin minimal trust in the online merchant. For example, an onlineconsumer considering purchasing such products as fresh fruits, afabric detergent, or a computer often takes precautions to ensurethat the product under consideration for online purchase meets hisor her performance expectations. Are the fruits really fresh? Will

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the fabric detergent surely function to remove all the dirts? Will thecomputer run fast and store large amounts of data? If the consumerhas some doubts concerning these performance questions, he willput little trust in the merchant. And low consumer trust is likely tolead to little or no intention to make a purchase. Based on this lineof reasoning, we propose the following hypothesis:

H2-1. Trust in an online merchant mediates the relationshipbetween performance risk and purchase intention.

Second, a consumer who is much worried about the potentialpsychological discomfort that may result from the incorrect choiceof a product will first lower her trust in the online merchant, whichwill, in turn, act to negatively influence her intention to buy online.For example, an online consumer considering the purchase of afashion clothing product may be most likely serious about the visualmatch between her image and the product. Since the online storedoes not provide a way of fitting the clothing on, the consumer willhave intense psychological concerns. Under this circumstance, shewill begin to distrust the online transaction system as far as the fitof the clothing is concerned, and will probably choose not to buyonline. Thus, we propose the following hypothesis:

H2-2. Trust in an online merchant mediates the relationshipbetween psychological risk and purchase intention.

Third, if a consumer is much concerned that his acquaintancesmay feel that the purchased product does not appear suitable forher, then she will be reluctant to put trust in the online store.Typical examples of products of this category include laptops,smartphones, wrist watches, and a mink robe, which all tend toeasily grab the attention of fellow workers, friends, and familymembers. Although the consumer perceives no psychological risk(i.e., there is a good fit between her self-image and the product), ifshe feels that other people is likely to find it unsuitable for her, thenthe social risk may grow large. A substantial amount of social riskwill render the online merchant less trustworthy. As a result, theconsumer will no longer trust the online purchase system, therebyabandoning the purchase intention. Therefore, we propose the fol-lowing hypothesis:

H2-3. Trust in an online merchant mediates the relationshipbetween social risk and purchase intention.

Fourth, given that a consumer has unusual worries over bigfinancial loss associated with the opportunistic behavior of theseller, his trust in that seller will diminish. For example, a computeruser considering the online purchase of a $ 4000 Apple Mac Pro oneBay may be concerned about the possibility that the unknowneBay seller might take his payment without shipping the orderedproduct. If the records show that the seller has a minimal numberof positive ratings on eBay, then the risk perception for that sellerwill be quite strong. As a result, the consumer will have no inten-tion to buy online from that merchant. Therefore, we propose thefollowing hypothesis:

H2-4. Trust in an online merchant mediates the relationshipbetween financial risk and purchase intention.

Fifth, provided that a consumer is unusually concerned that hisprivate and financial information may leak out due to the possibilityof a hacking incident, he is likely to lose trust in the online envi-ronment, in which case there will be no further desire to buy. Forexample, when customers find that an online shopping site doesnot provide minimum security protection (e.g., security protocol,keyboard encrypting, electronic certificates, etc.), they are likely todoubt the reliability of the Website and even to choose not to buyonline. Thus, we propose the following hypothesis:

H2-5. Trust in an online merchant mediates the relationshipbetween online payment risk and purchase intention.

Finally, an online shopper who experienced several incidentsof wrong delivery and thus perceives strong delivery risk will nolonger trust the online merchant, and probably intend not to buy.It is also applicable when the online stores outsource their deliveryprocess to the third party service providers. We can take Ama-zon.com and eBay.com for example. Amazon uses UPS as theirmajor delivery service provider, but sellers in eBay often ship viasmaller, less reliable delivery companies. With the higher level ofperceived delivery risk, consumers may put less trust in the onlinestore, and thus, may look for other alternatives to buy online from.Based on the theoretical grounds, we propose the following hypoth-esis:

H2-6. Trust in an online merchant mediates the relationshipbetween delivery risk and purchase intention.

Overall, whether a consumer considering an online purchaseperceives risk with regards to product performance, psychologi-cal/social damage, monetary loss, online payment, or delivery, thatdimension of risk intensely perceived by the consumer will firstlower consumer trust in a merchant, thereby eventually makingthe consumer reluctant to buy online from that merchant.

4. Research methodology

To test the research model, we employed an empirical studyusing data from online survey responses. The survey participantswere undergraduate students at a large university who voluntarilyparticipated in the survey for extra credit. Although student par-ticipants may not fully represent the online shopper population,many previous studies (Bhatnagar, Misra, & Rao, 2000; Featherman& Pavlou, 2003; Gefen, 2000; Jarvenpaa et al., 2000; Jarvenpaa &Tractinsky, 1999; Lee & Turban, 2001; Pavlou, 2003) showed thatcollege students are a good surrogate for online consumers. Indeed,the data collected in the present study also indicated that the par-ticipants are active online consumers: over 90% of the respondentsreported that had shopped online least once in the last six months.

4.1. Measures

A survey questionnaire was designed to measure the researchconstructs under consideration in this study. Above all, theperceived risk construct was not measured as an overall perceivedrisk, but as individual dimensions or components of the perceivedrisk. We considered a total of six types of perceived risk: per-formance, psychological, social, financial, online payment, anddelivery risk (Cases, 2002; Jacoby & Kaplan, 1972).

Performance risk was defined as the likelihood of problems asso-ciated with purchasing unfamiliar brands or defective products.Psychological risk was defined as the likelihood of an insufficientfit between the purchased product and the consumer’s self-imageor self-concept. Social risk was defined as the likelihood of the pur-chased product influencing others’ view of the consumer. Financialrisk was defined as the likelihood of some financial loss result-ing from overpriced products, online fraud, or from unexpectedexpenses (e.g., a 15% restocking fee). Online payment risk refersto the likelihood that a consumer’s private information, includingpersonal and credit card information, may be exposed to poten-tial threats, and that such private information may be misused.Finally, delivery risk was defined as the likelihood of a delivery prob-lem (e.g., late delivery of products, delivery to a wrong address,and delivery of a wrong product). Each type of risk was measuredusing three item variables. Many of these measurement items wereadapted from existing consumer behavior and e-commerce litera-ture (Featherman & Pavlou, 2003; Jarvenpaa & Todd, 1997; Pavlou,2003; Schiffman & Kanuk, 1994; Stone & Gronhaug, 1993).

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Table 1Profile of the respondents (n = 206).

Attribute Value Frequency Percentage (%)

Gender Male 141 68.4Female 65 31.6

Age 20s 202 98.030s 4 2.0

Internet shopping frequency Never 4 9.2Less than once every six months 20 40.3At least once every six months 17 30.6At least once every three months 63 8.3At least once a month 83 9.7At least once a week 19 1.9

Weekly Internet usage Less than 10 h 73 35.4Between 10 h and 30 h 111 53.9More than 30 h 22 10.7

On the other hand, trust was defined as the extent to which aconsumer believes that the merchant will behave in the interestof the consumer in purchasing a product online. Three items wereused to measure this construct, drawn from Pavlou (2003) and Hongand Cho (2011).

Finally, purchase intention was defined as the consumer’s incli-nation to purchase online. This construct was measured by threeitems designed to rate the extent to which a respondent choosesto buy from an online merchant and recommend the merchant toacquaintances. The items used were drawn from Jarvenpaa et al.(2000). All the above five-point Likert scales ranged from 1 stronglydisagree to 5 strongly agree. The resulting twenty-four (24) items– and the list of the survey questions making up each measure –are summarized in Table A1.

4.2. Survey procedure and data analysis

Student participants were asked not only about their overallshopping experience but also about their risk perceptions, trust-ing beliefs, and purchase intention with regards to a popular onlineretailer that sells a wide variety of products to consumers. Whilewe provided the store name “Interpark.com” in the questionnaireas a representative online storefront, students were instructed toconsider other familiar storefronts (e.g., Lotte.com or Samsung-mall.com) as well in answering the questionnaire items in order toavoid store-dependent responses. Interpark.com, founded in 1997,is Korea’s first Internet-based shopping mall selling a broad rangeof goods and services including collectibles, appliances, computers,equipment, vehicles, food, tickets, clothes, jewelry, and tour pack-ages. In 2011, Interpark.com has recorded market shares of 27% inbooks, 70% in entertainment tickets, 50% in tour products, and 8% ingeneral merchandise in Korea. Although it recently introduced anonline marketplace within the same Website (just as Amazon.comstorefront and marketplace coexist on a single Website), the salesare predominantly generated by the digital storefront portion ofthe business.

Prior to the main survey, we conducted a pilot test using 25students in order to make sure that the questionnaire items wereproperly developed to meet the research objectives. We exam-ined the responses to the preliminary instrument for consistencyand revised the items in the questionnaire, such that there are noredundant items; all items are phrased clearly and concisely. Then,we surveyed a total of 214 students in order to access a suitablesample of consumers who experienced B2C online shopping. Aftereliminating observations with missing and unusable data, we used206 observations to test the model and hypotheses. The partici-pants were 68% male and 32% female, and most of the respondentswere aged between their 20s and 30s. The respondents’ profile issummarized in Table 1.

We used structural equation modeling (SEM) in order to analyzethe data collected and test the research model. SEM is a statistical

technique that incorporates factor analysis (using a measurementmodel) and path analysis (using a structural model) (Qureshi &Compeau, 2009; Wetzels, Odekerken-Schroder, & Oppean, 2009).The advantages of SEM compared to other statistical techniquesinclude more flexible assumptions (e.g., partial allowance of multi-collinearity) and less measurement error with confirmatory factoranalysis (CFA) enabled by multiple indicators per construct. In par-ticular, we tested the model through partial least squares (PLS)using SmartPLS 2.0 with bootstrapping (Wetzels et al., 2009).

5. Results

5.1. Measurement model assessment

The internal consistency (reliability) statistics were assessed byCronbach’s alpha and composite reliability (Dillon Goldstein’s Rho),and the results are summarized in Table 2. All Cronbach’s Alpha andcomposite reliability values exceeded the recommended reliabilitythreshold of 0.7 (Fornell & Larcker, 1981). Therefore, all of the ques-tionnaire items were deemed reliable. In addition, we tested theconvergent validity by examining the average variance extracted(AVE), which measures the percentage of the variance of the mea-surement items that can be accounted for by the constructs relativeto the measurement error. Table 2 illustrates that for each con-struct, the AVE value was greater than the cut-off value of 0.5 (Yoo& Alavi, 2001).

Further, we tested the discriminant validity by examiningwhether a latent variable better explains the variance of its ownindicators than the variance of other latent variables. To validatethis, we compared the square root of AVE for each construct withits cross-correlation with other constructs. The results supportedthe discriminant validity of our constructs in that in all cases, thediagonal elements in the matrix (i.e., the square root of AVE) werehigher than the off-diagonal elements in the corresponding rowsand columns, as shown in Table 2.

Lastly, we tested the convergent validity using the factor andcross loadings of all indicator items in relation to their respectivelatent constructs. The results are summarized in Table 3, whichindicate that all items loaded (i) on their respective constructswith a factor between 0.70 and 0.95 and (ii) more highly on theirrespective constructs than on any other construct. Further, theseentire factor loadings were highly significant (t-statistics > 11.377,p < 0.001) based on the SmartPLS output. Therefore, we can con-firm that these indicator items accurately represent distinct latentconstructs.

5.2. Structural model assessment

The assessment of the structural model includes estimation ofthe path coefficients and R2 values. In particular, to measure the

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934 I.B. Hong, H.S. Cha / International Journal of Information Management 33 (2013) 927– 939Ta

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effect of mediation in the research model, we sequentially assessedtwo separate structural models: the unmediated model and themediated model.

Fig. 3 and Table 4 show the unmediated structural modelresults with the ̌ values of all path coefficients. We found thatperformance risk ( ̌ = −0.160, t-statistic = 2.328, p < 0.05), psycho-logical risk ( ̌ = −0.177, t-statistic = 2.643, p < 0.01), financial risk( ̌ = −0.167, t-statistic = 2.270, p < 0.05), and online payment risk( ̌ = −0.201, t-statistic = 2.967, p < 0.01) negatively affect purchaseintention. However, we could not find a significant influence eitherfrom the social risk ( ̌ = −0.121, t-statistic = 1.638, n.s.) or delivery( ̌ = −0.063, t-statistic = 0.905, n.s.) risk. The R2 for purchase inten-tion was 0.29, reflecting that the variation in the given risk factorsexplains 29% of the total variance of consumer purchase intention.

Fig. 4 and Table 5 show the mediated structural model resultswith the ̌ values of all path coefficients. Consistent with theunmediated model, social risk and delivery risk did not show anysignificant influence either in the direct or indirect path. We foundthat performance risk ( ̌ = −0.273, t-statistic = 3.887, p < 0.01) andpsychological risk ( ̌ = −0.174, t-statistic = 2.114, p < 0.05) have anegative and significant impact on trust. Note that, after control-ling trust, psychological risk still kept its direct impact on purchaseintention ( ̌ = −0.101, t-statistic = 1.836, p < 0.1); however, perfor-mance risk no longer showed a direct influence on purchaseintention ( ̌ = −0.038, t-statistic = 0.584, n.s.). Financial risk andonline payment risk did not affect trust; yet, it only presented adirect impact on purchase intention ( ̌ = −0.161, t-statistic = 2.384,p < 0.05 and ̌ = −0.154, t-statistic = 2.394, p < 0.01, respectively).Lastly, we found a significant positive impact of trust on purchaseintention ( ̌ = 0.428, t-statistic = 7.700, p < 0.01), which is neces-sary to support the hypotheses regarding the indirect impact ofperceived risk on purchase intention by means of trust. R2 for pur-chase intention was 0.43, which is far greater than 0.291 found inthe unmediated model. In terms of R2, we found that R2 increasedgreatly from 0.291 in the unmediated model to 0.428 in the medi-ated model, which implies that the mediated model has a better fitthan the original model.

Given the results of the mediated model, we further examinedthe mediation effect of trust following the Baron and Kenny (1986)steps. Using the same notations shown in Fig. 2 (c, a, b, and c′) inthe previous section, Table 6 presents the outcomes of the analysisin order to examine the mediational hypotheses.

There are several ways to assess whether the mediated effectis significant or not. In particular, we tested the significance of theindirect effect (product of paths a and b) using the Sobel test (Sobel,1982). The test statistic1 for both performance risk (z = −3.47,p < 0.01) and psychological risk (z = −2.04, p < 0.05) showed thattrust was a significant mediator.

The amount of mediation is often defined as the reduction ofthe effect of the initial variable on the outcome or the differencebetween the total effect and direct effect (i.e., |c−c′|). Theoretically,this is same as the indirect effect or product of paths a and b (i.e.,|c − c′| ≈ |ab|). Baron and Kenny (1986) suggested that a small effectsize would be |ab| = 0.01, medium size would be |ab| = 0.09, and largesize would be |ab| = 0.25. In our results, performance risk showedthat |c − c′| = 0.160 (where c′ = 0 since the path coefficient is notstatistically significant), which is slightly greater than |ab| = 0.117.On the other hand, psychological risk showed that |c − c′| = 0.076,which is almost the same as the value of |ab| = 0.075. As a result,we concluded that medium to large size mediated effects for per-formance risk, and small to medium size mediated effects forpsychological risk.

1 z = ab(b2SE2

a )+(a2SE2b

).

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Table 3The cross-loading matrix.

PER PSR SOR FIR PRR DER TR PI GEN AGE IUH ISF

PER1 0.84 0.28 0.29 0.33 0.27 0.25 −0.38 −0.36 0.00 −0.16 0.02 0.13PER2 0.87 0.22 0.17 0.26 0.32 0.27 −0.27 −0.29 0.05 −0.11 −0.11 0.06PER3 0.82 0.19 0.20 0.32 0.25 0.14 −0.38 −0.25 0.05 −0.09 0.00 −0.01PSR1 0.20 0.91 0.58 0.32 0.07 0.26 −0.34 −0.34 0.02 −0.06 −0.10 0.05PSR2 0.19 0.92 0.55 0.36 0.03 0.20 −0.32 −0.31 −0.01 −0.07 −0.07 0.06PSR3 0.33 0.71 0.26 0.20 0.27 0.27 −0.23 −0.26 −0.01 −0.03 −0.16 −0.07SOR1 0.30 0.43 0.84 0.24 0.05 0.18 −0.19 −0.26 −0.06 −0.06 −0.13 0.09SOR2 0.28 0.48 0.91 0.36 0.02 0.19 −0.27 −0.29 −0.09 −0.01 −0.12 0.04SOR3 0.14 0.53 0.82 0.20 0.10 0.28 −0.35 −0.29 −0.05 −0.04 −0.06 0.03FIR1 0.22 0.29 0.17 0.77 0.10 0.21 −0.10 −0.21 0.13 −0.10 0.02 0.06FIR2 0.31 0.22 0.08 0.72 0.13 0.20 −0.12 −0.18 0.09 −0.07 −0.02 0.01FIR3 0.34 0.32 0.37 0.90 0.19 0.30 −0.30 −0.37 0.13 −0.14 −0.04 0.06PRR1 0.27 0.07 −0.02 0.06 0.87 0.26 −0.17 −0.22 0.08 −0.02 −0.03 −0.01PRR2 0.22 0.06 −0.02 0.06 0.85 0.22 −0.11 −0.23 0.09 −0.03 −0.02 0.00PRR3 0.33 0.16 0.16 0.28 0.87 0.30 −0.31 −0.31 0.07 −0.09 −0.07 0.12DER1 0.15 0.24 0.24 0.32 0.27 0.86 −0.17 −0.17 0.04 −0.09 −0.03 0.07DER2 0.18 0.29 0.24 0.27 0.27 0.90 −0.24 −0.13 −0.01 −0.06 −0.09 0.02DER3 0.34 0.19 0.17 0.20 0.25 0.81 −0.21 −0.15 0.07 −0.16 −0.14 0.01TR1 −0.35 −0.32 −0.30 −0.22 −0.16 −0.19 0.86 0.46 −0.03 0.04 0.02 −0.08TR2 −0.34 −0.31 −0.23 −0.25 −0.25 −0.16 0.88 0.53 −0.04 0.03 0.03 0.02TR3 −0.39 −0.30 −0.31 −0.19 −0.25 −0.28 0.88 0.50 0.00 −0.01 0.10 −0.10PI1 −0.29 −0.32 −0.27 −0.33 −0.24 −0.20 0.47 0.89 0.00 0.00 0.02 −0.13PI2 −0.37 −0.32 −0.23 −0.31 −0.31 −0.08 0.50 0.90 −0.06 0.01 0.07 −0.04PI3 −0.25 −0.28 −0.35 −0.25 −0.22 −0.17 0.49 0.77 0.01 0.10 0.06 −0.28GEN 0.04 0.00 −0.08 0.14 0.09 0.04 −0.03 −0.02 1.00 −0.36 −0.06 −0.02AGE −0.14 −0.07 −0.04 −0.14 −0.06 −0.12 0.03 0.04 −0.36 1.00 −0.09 −0.10IUH −0.03 −0.12 −0.12 −0.03 −0.05 −0.10 0.06 0.06 −0.06 −0.09 1.00 0.09ISF −0.07 −0.02 −0.06 −0.06 −0.06 −0.04 0.06 0.17 0.02 0.10 −0.09 1.00

Social risk

Performance risk

Psychological risk

Financial risk

Online payment risk

Delivery risk

Purchase intention

R2 = 0.291

Perceived risk

Control Variables:

• Age: -0.039 (0.625)

• Gender: 0.002 (0.027) • Internet usage: 0.016 (0.296)

• Internet shopping f requency:0.132 (1.887)*

- 0.160 (2.328)**

- 0.121(1.638)

- 0.177 (2.643)***

- 0.167 (2.270)**

0.063 (0.905)

- 0.201 (2.967)***

Fig. 3. The results of the unmediated research model.

Table 4Summary of the results of the unmediated model.

Hypothesis Effect Coefficient S.E. t-Statistics Conclusion

H1-1 Performance risk → purchase intention −0.160 0.069 2.328** SupportedH1-2 Psychological risk → purchase intention −0.177 0.067 2.643*** SupportedH1-3 Social risk → purchase intention −0.121 0.074 1.638 Not supportedH1-4 Financial risk → purchase intention −0.167 0.073 2.270** SupportedH1-5 Online payment risk → purchase intention −0.201 0.068 2.967*** SupportedH1-6 Delivery risk → purchase intention −0.063 0.069 0.905 Not supportedControl Age −0.039 0.060 0.625

Gender 0.002 0.063 0.027Internet usage 0.016 0.055 0.296Internet shopping frequency 0.132 0.070 1.887*

* p < 0.1.** p < 0.05.

*** p < 0.01.

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936 I.B. Hong, H.S. Cha / International Journal of Information Management 33 (2013) 927– 939

Social risk

Performance risk

Psychological risk

Financial risk

Online payment risk

Delivery risk

Trust

R2 = 0.258

Purchase intention

R2 = 0.428

-0.038 (0.584)

0.077(1.053)

-0.154 (2.394)***

0.088 (1.345)

Perceived risk

Control Variables:

• Age: -0.012 (0.217)

•Gender: 0.016 (0.291) •Internet Usage: 0.019 (0.361)

• Internet Shopping F requency:0.124 (1.875)**

-0.273 (3.887)***

-0.174 (2.114)**

-0.126 (1.580)

-0.015 (0.242)

-0.115 (1.632)

-0.046 (0.605)

-0.161 (2.384)**

-0.101 (1.836)*

0.428(7.700)***

Fig. 4. The results of the mediated research model.

A different way to describe the amount of mediation is in termsof the proportion of the total effect that is mediated, which isdefined by ab/c (Frazier et al., 2004; Shrout & Bolger, 2002). Giventhe path coefficients, we obtain 0.117/0.160 = 0.73 for performancerisk and 0.075/0.177 = 0.42 for psychological risk. Thus, about 73%of the total effect of performance risk on purchase intention ismediated by trust, and about 42% of the total effect of psychologi-cal risk on purchase intention is mediated by trust. Similar to thisapproach, we examined the types of mediation as well. The rela-tionship between performance risk and purchase intention was

completely mediated by trust (i.e., c′ = 0, n.s). In contrast, the rela-tionship between psychological risk and purchase intention waspartially mediated by trust, where the absolute size of the directpath coefficient was reduced by |c − c′| = 0.160, while c′ is still notzero.

6. Discussion

The findings of the present research point to a set of implicationsfor the academics. Most of all, our analysis of the cause-and-effect

Table 5Summary of the results of the mediated model.

Hypothesis Effect Coefficient S.E. t-Statistics Conclusion

H2-1 Performance risk → purchase intention −0.038 0.066 0.584 SupportedPerformance risk → trust −0.273 0.070 3.887***

H2-2 Psychological risk → purchase intention −0.101 0.055 1.836* SupportedPsychological risk → trust −0.174 0.082 2.114**

H2-3 Social risk → purchase intention −0.077 0.073 1.053 Not supportedSocial risk → trust −0.126 0.080 1.580

H2-4 Financial risk → purchase intention −0.161 0.068 2.384** Not supportedFinancial risk → trust −0.015 0.064 0.242

H2-5 Online payment risk → purchase intention −0.154 0.064 2.394** Not supportedOnline payment risk → trust −0.115 0.071 1.632

H2-6 Delivery risk → purchase intention 0.088 0.066 1.345 Not supportedDelivery risk → trust −0.046 0.077 0.605

H2-1–6 Trust → purchase intention 0.428 0.056 7.700*** –Control Gender 0.017 0.057 0.291 –

Age −0.013 0.057 0.217 –Internet usage 0.020 0.054 0.361 –Internet shopping frequency 0.124 0.066 1.875* –

* p < 0.1.** p < 0.05.

*** p < 0.01.

Table 6Summary of the results for mediation effect.

Risk type Path Path coefficient S.E. t-test Sobel test Mediation type

Performance risk c −0.160 0.069 2.328** z = −3.47 (p < 0.01) Complete mediationa −0.273 0.070 3.887***

b 0.428 0.056 7.700***

c′ −0.038 0.066 0.584Psychological risk c −0.177 0.067 2.643*** z = −2.04 (p < 0.05) Partial mediation

a −0.174 0.082 2.114**

b 0.428 0.056 7.700***

c′ −0.101 0.055 1.836*

* p < 0.1.** p < 0.05.

*** p < 0.01.

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relationship between the perceived risk and purchase intentionwas conducted by separating the risk into six dimensions. Exist-ing research often considers perceived risk as a unidimensionalconstruct, and thus, focuses on demonstrating that the perceivedrisk as a whole tends to inhibit consumer attitude and transactionintentions. On the contrary, our results revealed that the impactof perceived risk on its consequences were different dependingon the dimensions of the perceived risk. The results provide sub-stantial support for the research model as shown in Fig. 1. Four(H1-1, H1-2, H1-4, and H1-5) out of six hypotheses were sup-ported, regarding the unmediated influence of perceived risk. Wealso found two significant mediation effects supporting H2-1 andH2-2.

In the unmediated model, while we found that the consumer’sperceived risk mostly has a negative influence on purchase inten-tion (i.e., performance risk, psychological risk, financial risk, andonline payment risk), two types of perceived risk (i.e., social riskand delivery risk) turned out not to influence purchase intention;thus, H1-3 and H1-6 were rejected. The lack of support for thesetwo hypotheses may be the outcome of the changes in the recentonline shopping environment. A likely reason why there was nosignificant influence of social risk was that Internet shopping hasbecome so popular amongst users that nobody considers purchas-ing a product online as unusual. In particular, with the advance ofWeb 2.0 tools, such as social network systems, consumers who havepurchased products are increasingly sharing their buying experi-ence online. Furthermore, potential customers can also have accessto useful suggestions from others and anticipate what responsestheir friends and family will give concerning their online purchasein advance. As a result, other people’s views on online shopping areno longer a concern. With regards to the delivery risk, most onlinemerchants outsource their delivery function to well-known spe-cialized companies, such as UPS, FedEx, and DHL. With advancesin new technologies, such as RFID and wireless barcode readingdevices, these companies provide real-time tracking information.In particular, in a metropolitan area, same-day delivery service isvery common and reliable. In addition, the consumer knows thatin the event of incorrect delivery he can always call the customerservice to identify the potential problem and request that the ordershould be reshipped to the correct address. Hence, a consumer whois willing to buy a product online is not likely to abandon his inten-tion, even if he has some worries over correct delivery of the order,because he will assure himself that any potential issue with deliverycan be properly addressed by the vendor.

In the mediated model, it was confirmed that performance andpsychological dimensions of perceived risk and trust are in a veryclose, inseparable relationship, and this finding is consistent withother studies (Johnson-George & Swap, 1982; Olivero and Lunt,2004). From a managerial perspective, to reduce the perceived per-formance risk, a firm may consider ways to reduce discrepancies inproduct appearance, specification, and quality as advertized in theonline Website. For example, Matsuhita Electric Works has decidedto allow consumers to design their kitchen in virtual reality andchoose matching appliances (Haag & Cummings, 2009). Likewise,a CAVE (cave automatic virtual environment) provides a 3D virtualreality room where one can even talk with a remotely located salesperson, feeling that she is in the same room. Similar technologies,such as hepatic interfaces and custom-fit clothes through biomet-rics, are evolving to overcome the limitations of online shopping.As another way to reduce performance risk, firms may utilize activemarketing, online advertising, and promotional activities in orderto attract consumers by emphasizing that the quality and perfor-mance of the products purchased online is as good as that of thosepurchased offline. For example, firms can promote the active par-ticipation of existing shoppers through a discussion board wherethey can post reviews indicating that their purchased products met

their expectations. This may reduce the risk associated with prod-uct discrepancy.

To overcome the psychological risks for consumers, it is sug-gested that online merchants focus on identifying target customersand offering products that best meet the psychological needs ofthose customers. In addition, it may be necessary to improve theprocess related to returning and exchanging products purchasedonline. When consumers know that they can easily return orexchange any product with which they do not feel quite comfort-able, much of their psychological concerns will be relieved. Forexample, Amazon.com provides an automated process of enablinga customer to request a return and to print a return address label,and thus, customers feel that the cost of resolving the psychologicaldiscomfort resulting from the wrong choice of a product is minimal.Online merchants must keep in mind that online trust is formedslowly over time as consumers gain experience through repeatedtransactions (Cheskin-Research, 1999).

Meanwhile, it is interesting to note that the financial risk andonline payment risk had a direct negative influence on purchaseintention but not on the consumer’s trust in a merchant. In termsof financial risk, many price-comparison Websites (e.g., pricegrab-ber.com, nextag.com, and bizrate.com) are available to provideconsumers with easily accessible and reliable price information.Moreover, consumers do not consider a merchant trustworthymerely based on its relatively low prices. Instead, in addition to pri-cing information, these Websites also provide sellers’ ratings basedon existing shoppers’ reviews, which may be critical informationfor consumers in building their trust. Indeed, people often shop athighly reputable stores that they trust, such as Amazon.com, eventhough prices may be higher than those of competitors. Meanwhile,one possible reason why online payment risk was directly relatedto purchase intention may be that today more and more onlinestores tend to outsource the online payment function to a reli-able third party payment solution provider in order to avoid risksassociated with payment handling. For example, PayPal has beenembedded in many online stores and has processed over $ 71 billionthrough 87 million registered users in 2009 (www.wikipedia.com,retrieved on Sep. 10, 2013). Although the relevant risks associatedwith using this kind of specialized payment service still exist andhave an impact on purchase intention, they may not reduce theconsumers’ trust in the store itself.

7. Conclusions

Recently, the Internet is being widely used as an important vehi-cle to conduct business transactions online, as it removes time andspace barriers to enable convenient 24/7 shopping for customers.We have seen steady growth in electronic commerce sales as well asthe number of online consumers. Such changes have been drivenin part by improvements in the Web-based ordering system andreduction in transaction costs. Nevertheless, consumer perceptionsof risks associated with online purchases remain a great obstacleto the continued growth of electronic commerce. In this context,this paper focused on investigating the intriguing relationshipsamong dimensions of perceived risk, consumer trust, and purchaseintention. An empirical study was conducted in two phases: (1)examining the total effect without mediation, and (2) examiningthe mediation effect.

When we probed the total effect under the unmediated model,the findings revealed that performance, psychological, financial,and online payment risks have significant negative influence onpurchase intention. On the other hand, an examination of the medi-ation effect under the mediated model indicated that trust in anonline merchant completely mediates the effect of performance riskbut partially mediates that of psychological risk. Given the mixture

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of unmediated as well as mediated effects of perceived risks onpurchase intention, the findings have confirmed that while thereis a negative relationship between perceived risk and purchaseintention, this relationship is also mediated by consumer trust inan online merchant. Therefore, it is reasonably conceivable thatefforts made by online merchants to lessen certain types of riskwill first improve consumer trust, and then ultimately increase theconsumer’s intention to purchase online.

7.1. Implications

The present research offers academic as well as practicalimplications. First, it makes scholarly contributions by providingnew insights into the theoretical relationships among perceivedrisk, consumer trust, and purchase intention. Unlike the existingresearch, our research has found that some, if not all, compo-nents of perceived risk are an inhibitor of consumer trust. It wasrevealed that performance and psychological risks have a neg-ative effect on trust. Research findings further indicate that therelationships between these two risks and purchase intention aremediated by trust in an online merchant. In addition, some compo-nents of perceived risk (i.e., performance, psychological, financial,and online payment risks) were shown to have a negative influ-ence on consumer’s intention to buy online. This finding providesnew insights, since we addressed the effects of the individual com-ponents of perceived risk, rather than that of perceived risk as awhole.

On the other hand, the study provides practical implications formanagers of electronic commerce firms. First, the mediating roleof consumer trust in the relationships between perceived risks andpurchase intention suggests that an online merchant can increasesales by first lowering the perceived risks, thereby improving con-sumer trust that then will function to boost purchase intention.In order to make this vision a reality, electronic commerce firmswill need to ensure that consumer trust can be enhanced by effec-tively reducing performance and psychological risks associatedwith online purchases. Furthermore, practitioners must keep inmind that purchase intention is directly affected by performance,psychological, financial, and online payment risks. It implies thatthey should make efforts to mitigate these risk perceptions if theyare to increase revenues.

7.2. Limitations

Despite the potential contributions mentioned earlier, thisresearch is subject to a few limitations. The first shortcom-ing is that the use of students as respondents in the surveymakes the research results less realistic than when actual con-sumers were employed. Although an increasing number of collegestudents today are online consumers themselves, the range of prod-ucts they buy online is somewhat limited. Second, the researchmodel may have overlooked other antecedents to consumer trust.While the estimation shows that purchase intention is signifi-cantly influenced by consumer trust, purchase intension could beaffected by other factors, such as the reputation of the onlinemerchant and the advertisement, which may be also correlatedwith consumer trust. Should this be the case, trust and pur-chase intention may have no direct causal connection. Third, thepresent research does not take into account the reputation of anonline store. In general, consumer trust depends largely on thereputation of online stores. For example, many consumers haveconfidence in buying goods from Amazon, yet perceive a consider-able amount of risk when buying from an unknown e-commerceWebsite.

Appendix A. Table A1. List of item variables and surveyquestions.

Item code Questionnaire

PeR1 The product quality may be lower than that advertised inthe online store

PeR2 The product appearance may be different from the productpicture shown in the online store

PeR3 The product dimension may be different from thatadvertised in the online store

PsR1 If I bought a product from the online store, I would abasemyself

PsR2 If I bought a product from the online store, it would not fitwith my image

PsR3 The online store would not sell high-class productsSoR1 If I bought a product from the online store, I would be held

in lower esteem by my friends and familiesSoR2 If I bought a product from the online store, I would be

negatively thought of by my friends and familySoR3 If I bought a product from the online store, I would be

demeaned by my friends and familyFiR1 I would be concerned that the product in the online store

may be more expensive than products in a different placeFiR2 I would be concerned that I might be able to buy the same

product at a different place at a lower price than in theonline store

FiR3 If I bought a product from the online store, I may suffermonetary loss due to sales fraud

OpR1 I would be concerned as to whether the online store isequipped with a security monitoring tool

OpR2 I would be concerned as to whether the online store isequipped with a security-enabled log-in process

OpR3 I would be concerned as to whether the online storeappropriately manages customers’ private information

DeR1 If I bought a product from the online store, I would beconcerned as to whether the product would be deliveredto a wrong address

DeR2 If I bought a product from the online store, I would beconcerned as to whether the product would be lost duringdelivery

DeR3 If I bought a product from the online store, I would beconcerned as to whether a wrong product would bedelivered

TR1 I trust the online store and would purchase products fromthis Website

TR2 I believe that the online store is trustworthy.TR3 I believe the online store will keep its promises and

commitmentsPI1 I would like to purchase a product from this online storePI2 I would like to recommend my friends and family to

purchase a product from this online storePI3 If there is a product that I want to purchase, I would like to

use the online storeAge What is your age?Gender What is your gender?

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Ilyoo B. Hong is presently professor of management information systems at Chung-Ang University, Seoul, Korea. He earned his Ph.D. degree in MIS from the Universityof Arizona, his MS in Business from the University of Illinois at Urbana-Champaign,and his B.S. in Management from Indiana University. He was a visiting scholar at theUCLA Anderson School of Management. Ilyoo Hong has published in such journals asDecision Sciences, Information & Management, and International Journal of Informa-tion Management, among others. He also presented academic papers at numerousinternational conferences, including HICSS. His research interests include buildingonline consumer trust, measuring the quality and impact of Web-based informationsystems, and information disclosure in social networking sites.

Hoon S. Cha is associate professor of management information systems at Chung-Ang University. He holds an M.S. and Ph.D. in Management Information Systemsfrom the University of Arizona and a B.S. in Material Sciences and Engineering fromSeoul National University. He worked for Samsung for three years as an IT consult-ant. His research examines the impact of IT offshoring decisions on firm knowledgeand costs and the allocation of IT investment among business functions and itseffects on business value. His publications have appeared in MIS Quarterly, Jour-nal of Management Information Systems, and Communications of the ACM, amongothers.