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The Effect of Perceived Risk on Purchase Intention inPurchasing Airline Tickets OnlineLisa Hyunjung Kim a , Dong Jin Kim b & Jerrold K. Leong PhD ca School of Hotel and Restaurant Administration , Oklahoma State University , E-mail:b School of Hotel and Restaurant Administration , Oklahoma State University , E-mail:c School of Hotel and Restaurant Administration , Oklahoma State University , E-mail:Published online: 11 Oct 2008.

To cite this article: Lisa Hyunjung Kim , Dong Jin Kim & Jerrold K. Leong PhD (2005) The Effect of Perceived Risk on PurchaseIntention in Purchasing Airline Tickets Online, Journal of Hospitality & Leisure Marketing, 13:2, 33-53, DOI: 10.1300/J150v13n02_04

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The Effect of Perceived Riskon Purchase Intention

in Purchasing Airline Tickets Online

Lisa Hyunjung KimDong Jin Kim

Jerrold K. Leong

ABSTRACT. This study examined the effect of perceived risk on pur-chase intention in online airline ticket purchases. Seven types of per-ceived risk were used to measure customer’s perceived risk in onlineairline ticket purchases. The results revealed that the seven risk dimen-sions were positively correlated with one another, whereas they werenegatively correlated with customer’s purchase intention. In addition,the results of multiple regression analysis showed that the six perceivedrisk dimensions significantly affected customer’s willingness to pur-chase airline tickets online. [Article copies available for a fee from TheHaworth Document Delivery Service: 1-800-HAWORTH. E-mail address:<docdelivery@haworthpress.com> Website: <http://www.HaworthPress.com>© 2005 by The Haworth Press, Inc. All rights reserved.]

Lisa Hyunjung Kim is a Doctoral Student, School of Hotel and Restaurant Adminis-tration, Oklahoma State University (E-mail: lisahrad@hotmail.com).

Dong Jin Kim is a Doctoral Student, School of Hotel and Restaurant Administra-tion, Oklahoma State University (E-mail: dj.kim@okstate.edu).

Jerrold K. Leong, PhD, is Associate Professor, School of Hotel and Restaurant Ad-ministration, Oklahoma State University (E-mail: leong@okstate.edu).

Address correspondence to: Lisa Hyunjung Kim, Oklahoma State University,School of Hotel and Restaurant Administration, 210 HESW, Stillwater, OK 74078-6173 (E-mail: lisahrad@hotmail.com).

Journal of Hospitality & Leisure Marketing, Vol. 13(2) 2005Available online at http://www.haworthpress.com/web/JHLM

© 2005 by The Haworth Press, Inc. All rights reserved.doi:10.1300/J150v13n02_04 33

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KEYWORDS. Perceived risk, purchase intention, online travel, airlineticket

INTRODUCTION

The Internet has changed the business environment in many markets.Numerous companies went online to take advantage of the Internet,which enables consumers to search for information and purchase prod-ucts/services with relatively low costs by direct interaction with onlineretailers. As the Internet user population increases, the Internet has beena major marketing tool for many products/services (McGaughey & Ma-son, 1998). The increased popularity of this new technology in businesshad significant effects on both online companies and consumers. Mar-keters use the Internet as a substitute for traditional channel intermedi-aries since it facilitates marketing communications, sales transactionsand logistics (Burke, 1997). Moreover, consumers have opportunitiesto find out product information with rapid process and save time andmoney through the Internet (McGaughey & Mason, 1998).

The travel industry application is most conductive to electronic com-merce adoptions. According to ComScore (2002a), online travel sectoris the largest consumer e-commerce sector with outpacing growth ratescompared to the non-travel sector. Among total online sales of $17.5billion for the second quarter of 2002, online travel sales comprised46% of the total online sales with $7.8 billion (ComScore, 2002b).eTourism newsletter (2001) also reported that airline tickets were themost popular travel-related items purchased through the Internet, fol-lowed by a hotel reservation and car rental.

Consumers perceive risk in most purchasing situations (Tan, 1999).Introduced by Bauer (1960), the concept of perceived risk has been gen-erally identified as the unexpected and uncertain consequences that arelikely to be unpleasant. Perceived risk theory was adopted to explainconsumer behavior, since consumers were expected to evade negativeoutcomes instead of maximizing utility (Mitchell, Davies, Moutinho, &Vassos, 1999). Researchers have identified perceived risk as a multidi-mensional construct including financial risk, performance risk, psycho-logical risk, social risk, physical risk, and time risk (Jacoby & Kaplan,1972; Roselius, 1971). In addition, considering the increasing use of theInternet as a new market environment, one outstanding concern of con-sumers when purchasing products/services online is security risk (Har-rison-Walker, 2002).

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According to the perceived risk theory, consumers perceive risk in asubjective manner. That is, in the same purchase situation, two differentindividuals may perceive risk at two different levels (Murphy & Enis,1986). Jarvenpaa and Todd (1997) explained online shopping as a newmode of shopping that is affected by perceived risk. Furthermore, Tan(1999) insisted that consumers may perceive a higher risk with onlineshopping than in-store shopping.

Purchase intention has been studied by researchers as an importantindicator for the actual purchasing decision (Tan, 1999). The impor-tance of perceived risk theory is its ability to explain its direct influenceon purchase intention (Mitchell et al., 1999). It has been successfullyproven that purchase intention is negatively affected by perceived risk(Gefen, 2002; Mitchell et al., 1999; Sweeney, Soutar, & Johnson, 1999;Thorelli, Lim, & Ye, 1988; Wood & Scheer, 1996). Besides the afore-mentioned six types of risk, security risk in online shopping is consid-ered as one of the most important factors to influence online purchaseintention (Yoon, 2002; Salisbury, Pearson, Pearson, & Miller, 2001;Miyazaki & Fernandez, 2000). As the perceived security risk in a cer-tain product becomes less, the probability to purchase the product byconsumers would be increased.

Perceived risk theory has been widely used in travel research sincetravel related products/services are typically expensive, infrequentlypurchased, highly involved, and a complex choice (Assael, 1998;Mitchell et al., 1999). However, there has been little research to identifythe perceived risk and its effect on purchase intention for the onlinetravel market, which would be valuable to understand consumer behav-ior in online shopping and provide logical reasons why consumersavoid purchasing products/services online (Pope, Brown, & Forrest,1999). In consideration of airline tickets as the most dominant travel-re-lated item, this study focuses on the effect of perceived risk on purchaseintention in purchasing airline tickets online.

LITERATURE REVIEW

Perceived Risk Theory

Since Bauer (1960) first introduced the concept of perceived risk,perceived risk has received a great deal of attention by researchers tounderstand the consumer behavior relative to tangible product choice(Cunningham, 1967), mode of purchasing (Cox & Rich, 1964; Spence,

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Engel, & Blackwell, 1970), adoption of new product (Bearden &Shimp, 1982), and risk relievers (Roselius, 1971; Akaah & Korgaonkar,1988). Perceived risk can be viewed as the subjective probability of lossderived from unanticipated and uncertain consequences, which areprone to being unpleasant (Bauer, 1960). According to Cox (1967), theconsumer’s perception of the kind and degree of risk affects consumerbehavior in the decision-making process; whether it is actually a risk ornot. The author also suggested that it is possible that consumers mightnot be conscious of perceived risk, even though he/she responded to it ina positive manner.

An important concept of perceived risk is that this risk is subjective innature. In other words, consumers could identify risk in the subjectivemanner. According to Cox and Rich (1964), consumers only perceive acertain amount of risk in relation to their subjective interpretation of riskand amount at stake determined by the importance of buying goals inthe purchase decision. In addition, assuming all other factors are equal,consumers desire less risks rather than larger ones since consumers arelikely to maximize the desirable consequences and to minimize the risksrelated to (Mitchell et al., 1999)

The significant characteristic of perceived risk which differentiates itfrom other disciplines, such as behavioral decision theory, is that it origi-nates only from potentially negative outcomes (Stone & Gronhaug, 1993;Dholakia, 2001). Generally, consumers expect a positive outcome whichmay meet or exceed their expectations from their purchase experience.However, if the purchasing transaction fails and negative consequencesare usually experienced, the purchase expectation can not be accom-plished (Stone & Gronhaug, 1993). Since consumers are more likely toevade negative outcomes instead of maximizing utility (Mitchell et al.,1999), the perceived risk theory provides a powerful explanation relativeto consumer behavior in order to understand how consumers perceiverisks and avoid negative outcomes when purchasing products/services.

Perceived risk theory can also provide a more powerful explanationas to the rationale of consumer behavior when purchasing servicesrather than purchasing products (Mitchell & Greatorex, 1993). The de-gree of perceived risk in purchasing services is much greater than pur-chasing products, since customers usually purchase services first andthen evaluate them, which results in increased uncertainty (Mitra, Reiss, &Capella, 1999). When studying perceived risk relative to tangible prod-ucts and intangible products, Lewis (1976) insisted that perceived riskin pure services is usually higher than that in pure products. Specifi-cally, Mitchell and Greatorex (1993) identified and explained the four

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key characteristics that increase the degree of perceived risk: intangibil-ity, heterogeneity, perishability, and inseperability. The lack of physicalsubstance in services, referred to as intangibility, leads consumers tomake purchasing decisions without a trial period prior to purchase,which will eventually increase perceived risk in purchasing services byconsumers. The variation in performance of services within the transac-tion process is referred to as heterogeneity, results in decreasing cer-tainty, and eventually increasing perceived risk by consumers. Thediversion of performance of services along with periodic demand varia-tion, referred as to perishability, increases perceived risk in terms of un-fulfilled expectation by consumers. Finally, the simultaneity of productionand consumption of services, referred as to inseperability, demandsmore consumer involvement, which eventually increases perceived riskin services than in products (Berry, Seiders, & Grewal, 2002; Mitchell &Greatorex, 1993).

Types of Perceived Risk

Researchers have identified that perceived risk has a multidimen-sional construct which consists of several subdimensions. The five ma-jor components of perceived risk can be defined as: financial risk,performance risk, psychological risk, social risk, and physical risk(Jacoby & Kaplan, 1972). In addition, Roselius (1971) identified timerisk as a significant component of perceived risk. In general, the impor-tance of these types of risk can vary based on a particular situation thatan individual encounters.

Financial risk can be identified as the possibility of monetary lossthat results from inappropriate purchasing decisions or the possibility ofnot getting value for the money spent. Performance risk, also calledfunctional risk, involves the consumer’s belief that a purchased prod-uct/service will not perform as expected or will not offer preferred bene-fits to a consumer. This risk is perceived more prominently when theconsumer can not try the product or service before purchasing. Psycho-logical risk refers to the possibility of failure in reflecting one’s person-ality or self image by purchasing. Social risk involves the individual’sself-image resulted from purchasing a product/service. This type of riskis mostly concerned with the opinions of reference groups. Socially de-sired goods such as jewelry, cars, and homes are most subject to socialrisk. Physical risk stems from the possibility of physical threat causedby purchasing a product. It concerns physical health such as injury,sickness, and well-being from purchasing products/services. Time risk

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arises from the possibility of time loss for product failure (Roehl &Fesenmaier, 1992; Mitchell & Greatorex, 1993; Pope et al., 1999;Jarvenpaa & Todd, 1997). Furthermore, Garner (1986) defined thosesix types of perceived risk for services. Table 1 shows the six types ofperceived risk for services (Garner, 1986).

Perceived risk can be affected by not only the individual’s subjectivelevel of risk perception, but also by the mode of shopping. When consid-ering perceived risk associated with online shopping, Jarvenpaa andTodd (1997) claimed that the consumers’ level of adoption of this newmarket environment was affected by the associated perceived risk. If thepurchasing situation is associated with a high technological form such asthe Internet, consumers become more skeptical when purchasing prod-ucts/services online because of the potential risk (Pope et al., 1999; Tan,1999). In other words, the consumer’s perception of risk would be higherfor online shopping as compared to alternative modes of shopping.

A growing concern for online shopping is the security issue. The secu-rity issue is the main reason for consumers to hesitate when purchasingproducts/services over the Internet (Harrison-Walker, 2002). Salisbury etal. (2001) identified that security risk involves the consumer’s perceptionof risk while transmitting sensitive information such as credit card infor-mation over the web. This finding is supported by the work of IpsosReid(2001) who reported that 46% of the respondents among the 8,500 peoplein 16 countries claimed that the credit card security was the major barrierin online shopping. Considering the measure of perceived risk from theconsumer survey, Miyazaki and Fernandez (2000) emphasized the im-portance of studying consumer’s perceived risk and security risk in on-line shopping:

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TABLE 1. The Six Types of Perceived Risk for Services

Type Definition

Financial risk The risk that the service purchased will not attain the best possible monetary gain for theconsumer.

Performance risk The risk that the service purchased will not be completed in the manner which will result incustomer satisfaction.

Psychological risk The risk that the selection of performance of the producer will have a negative effect on theconsumer’s peace of mind or self-perception.

Social risk The risk that the selection of the service provider will affect in a negative way the perceptionof other individuals about the purchaser.

Physical risk The risk that the performance of the service will result in a health hazard to the consumer.

Time risk The risk that the consumer will waste time, lose convenience or waste effort in getting a serviceredone.

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Various risk dimensions may be more salient depending on theproduct category that is being considered for online purchase. Fi-nally, instead of examining only perceived risk toward general on-line shopping, specific assessments of risk regarding privacy,online retailer fraud, and the security of online transaction systemswould be helpful for understanding those aspects that may be in-fluenced by online disclosures. (p. 59)

Since this study investigates perceived risk in purchasing airline tick-ets online, security risk should also be considered in order to understandthe consumers’ perceived risk in purchasing airline tickets online.Therefore, security risk is included as one of the risk dimensions to in-vestigate when identifying the effects of perceived risk on purchase in-tention of online airline ticket purchases.

Perceived Risk and Purchase Intention

Perceived risk theory has great potential when explaining how a per-ceived risk directly influences purchase intention, which is usually referredto as a successful indicator for forecasting the actual purchasing decision(Mitchell et al., 1999). Several researchers have discovered that there is anegative relationship between perceived risk and purchase intention(Gefen, 2002; Mitchell et al., 1999; Sweeney et al., 1999; Thorelli et al.,1988; Wood & Scheer, 1996). While studying perceived risk in holidayproducts, Mitchell et al. (1999) found out that purchase intention decreasesas the risk related to the purchase increases. In addition, the authors indi-cated that people with low perceived risk are more inclined to purchaseholiday products than people with high perceived risk. Thorelli et al. (1988)suggested that perceived risk is a significant factor which influences con-sumer’s purchase intention. Furthermore, the authors insisted that the pur-chase intention is negatively driven by the amount of perceived risk in thestudy of trust and trustworthiness among online consumers. Gefen (2002)also claimed that perceived risk has an influence on purchase intention aswell as window-shop on the web. Assessing the value of the deal in a printadvertisement, Wood and Scheer (1996) proved that the perceived risk hasa significant effect on purchase intention. Moreover, the negative influenceof perceived risk on purchase intention is identified with perceived value asa mediator (Sweeney et al., 1999).

Security risk, which is included in this study as a dimension of per-ceived risk, also has a notable influence on purchase intention. Yoon(2002) addressed the security issue in online shopping as one of the most

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important antecedents of purchase intention. Researchers have found thatperceived web security by consumers can be positively related to pur-chase intention. Miyazaki and Fernandez (2000) insisted the amount ofstatement related to security issues on a web site would positively effecton purchase intention. Salisbury et al. (2001) also demonstrated similarresults that security has a greater effect on purchase intention than that ofease and utility when purchasing products/services. Given the likely ef-fect of security on purchase intention, we can infer that if consumers per-ceive lesser security risk in a certain product/service, the probability topurchase that product/service would be increased. Inversely, the moreconsumers perceive a security risk, the lesser the probability to purchasethe product/service by the consumers.

Although previous research successfully proved that perceived risknegatively affects purchasing intention, it has failed to explain how eachrisk dimension affects purchase intention in online shopping. In particu-lar, product-category risk in terms of airline tickets in online shopping isinvestigated in this study. That is, perceived risk in purchasing airlinetickets online means the risk an individual perceives when purchasingany airline tickets online. Therefore, the purpose of this study is to in-vestigate the effect of perceived risks on purchase intention inpurchasing airline tickets online.

METHOD

Measures

In this study, a questionnaire comprised of 24 perceived risk items wasdesigned to measure the online shopper’s perceived risk toward each state-ment. The questionnaire items for perceived risk pertaining to online airlineticket purchase were developed from the multi-item scales by Stone andGronhaug (1993), Pope et al. (1999) and Salisbury et al. (2001). In order toassess and establish the validity of the survey instrument, a pretest was con-ducted. A total of ten questionnaires were distributed to four professors andsix graduate students in February, 2003. A complete questionnaire was de-signed based on the comments collected during the pre-testing period. The24 items used to measure perceived risk were, as shown in Table 2, com-posed of social risk (3 items), time risk (3 items), financial risk (4 items),performance risk (4 items), physical risk (3 items), psychological risk (3items), and security risk (4 items). For a better illustration of the measuresof perceived risk, Table 2 contains a summary of scale items used in the

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study with mean scores and standard deviations. All 24 perceived riskitems were associated with a six-point Likert scale anchored by 1 (stronglydisagree) and 6 (strongly agree). In addition, purchase intention was mea-sured using a seven-point Likert scale ranging from 1 (definitely will notbuy) to 7 (definitely will buy) in the study.

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TABLE 2. Perceived Risk Scale Used in the Study

Perceived Risks Mean SD

Social risk (SOC)

The thought of buying airline tickets over the Web causes me concern because some friendswould think I was just being showy (SOC1)Purchasing airline tickets over the Web would cause me to think of it as foolish by somepeople whose opinion I value (SOC2)Purchasing airline tickets over the Web will adversely affect others’ opinion of me (SOC3)

1.35

1.49

1.34

.933

.988

.876

Time risk (TIM)

The demands on my schedule are such that purchasing airline tickets over the Web couldcreate even more time pressures on me that I don’t need (TIM1)Purchasing airline tickets over the Web could lead to an inefficient use of my time (TIM2)Purchasing airline tickets over the Web will take too much time or be a waste of time (TIM3)

1.60

1.601.61

1.038

1.0881.073

Financial risk (FIN)

Purchasing airline tickets over the Web would be an inappropriate way to spend my money(FIN1)If I bought airline tickets over the Web, I would be concerned that the financial investment Iwould make would not be wise (FIN2)If I bought airline tickets over the Web, I would be concerned that I really would not get mymoney’s worth from the tickets (FIN3)Purchasing airline tickets over the Web would not provide value for the money I spent (FIN4)

1.55

1.84

1.84

1.70

1.059

1.208

1.277

1.094

Performance risk (PER)

As I consider the purchase of airline tickets over the Web, I worry about whether they willperform as they are supposed to (PER1)If I were to purchase airline tickets over the Web, I would be concerned that they would notprovide the level of benefits that I would be expecting (PER2)Considering the possible problems associated with an online airline tickets vendor’sperformance, a lot of risk would be involved with purchasing them over the Web (PER3)I am not confident about the ability of an online airline vendor to perform as expected (PER4)

2.57

2.25

2.51

2.45

1.535

1.393

1.436

1.362

Physical risk (PHY)

One concern I have about purchasing airline tickets over the Web is that eyestrain couldresult due from looking at the computer (PHY1)I am concerned that using the Web may lead to uncomfortable physical side effects suchas bad sleeping, backaches, and the like (PHY2)I am concerned about the potential health-related risks associated with purchasing airlinetickets over the Web (PHY3)

1.54

1.49

1.35

1.108

1.080

.898

Psychological risk (PSY)

The thought of purchasing airline tickets over the Web makes me feel psychologicallyuncomfortable (PSY1)The thought of purchasing airline tickets over the Web gives me a feeling of unwanted anxiety(PSY2)The thought of purchasing airline tickets over the Web causes me to experience unnecessarytension (PSY3)

1.48

1.58

1.53

1.051

1.077

.967

Security risk (SEC)

The web is an insecure means through which to send sensitive information (SEC1)If you purchase airline tickets over the Web, your credit card details are likely to be stolen(SEC2)I would feel insecure sending sensitive information over the Web (SEC3)Overall, the Web is an unsafe place to transmit sensitive information (SEC4)

2.802.61

2.942.76

1.4831.347

1.4751.433

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Sampling and Data Collection

A two stage sampling procedure was administrated to draw the sam-ples. First, the seven universities, which offer e-mail search functionson their web sites, were conveniently selected. Those seven universitieswere cautiously chosen to decrease the bias that may result in a particu-lar geographic concentration. The seven universities included one eastcoast university, one west coast university, two northern universities,one southern university, and two central universities. Then, an individ-ual sample was randomly selected from e-mail lists of the seven univer-sity web sites located in seven different regions. Since the sevenuniversities did not provide one entire e-mail addresses list, an e-mailaddress search function on their web sites was used. To draw the sam-ples, 26 coins labeled with the letter A through Z were arranged in a boxand sets of four coins were randomly selected. Each letter was randomlyselected with replacement. That is, each letter was selected in such away that after one letter was selected, that selected letter was replacedinto the box again in order to select the next letter. Once four letterswere selected the first and second selected letters were utilized tochoose the first name, and the third and fourth letters were used tochoose the last name. After samples were selected, careful investigationwas conducted in order to avoid duplicate names in the list. A total of4,326 samples were drawn from the sampling procedure. The samplewas drawn based on sample size approach. Assuming nearly a 10% re-sponse rate by an online survey (Smith & Whitlark, 2001; Klose, List, &Silver, 2001), and expecting about 400 responses, approximately 600samples were drawn from each university. When approximately 600samples were selected in each university, the sampling procedure wasfinished for each university.

The data for this study was obtained through an online survey. E-mailmessages were sent to invite the participants to a designated web site tocomplete the online survey. As the participant clicked on the hyperlink tothe study on his/her e-mail message, he/she went to an instruction webpage addressing him/her about the survey. The data was collected fromMarch 3 through March 16, 2003. In order to increase the response rate,several methods were applied. First, an animated picture was used todraw the respondent’s attention to the recruitment e-mail and introduc-tion web page. Second, a monetary incentive of $50 for two randomlychosen respondents was adopted. Third, confidentiality of responses wasguaranteed. Finally, after five days from the initial e-mail, a follow-upe-mail was sent to the sample that did not respond or did not leave their

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e-mail addresses for monetary incentive introduced during the first re-cruitment.

A total of 4,326 recruitment e-mail messages were sent and 422 wereundeliverable. Out of 3,904 delivered e-mails, 213 responses were ob-tained before the follow-up e-mail was sent. Since 207 respondents lefttheir e-mail addresses for monetary incentive, 3,697 follow-up e-mailswere sent and 121 responses were obtained. Out of 334 responses ob-tained, 24 responses were eliminated (15 from before and 9 from afterthe follow-up procedure) because of an excessive amount of missingdata. Finally, 310 responses (7.9% response rate based on the initial de-liverable e-mails of 3,904) were analyzed in this study.

The follow-up procedure enabled an investigation of any non-re-sponse bias in the study. The early respondents (198 out of 310) werecompared to the late respondents (112 out of 310) on the demographicprofiles, online airline ticket purchase experience, and the 24 perceivedrisk items. The chi-square test revealed no significant differences be-tween the early and late respondents on demographic profiles and on-line airline ticket purchase experience (p < .10). In addition, the t-testresults indicated that there is no significant difference between the twogroups on the 24 perceived risk items (p < .10).

Since the samples were drawn from several University web sites, therespondents consisted primarily of undergraduate and graduate studentsas well as faculty and staff. The validity of the students samples havebeen doubted because the student population does not adequately repre-sent the general population (Yoo, Donthu, & Lee, 2000). However,Ahmad (2002) insisted that college students represent one of the mostdynamic demographic segments in terms of online shopping. Investi-gating students’ attitudes in online shopping, the College Stores Re-search and Educational Foundation (2001) emphasized that the collegestudent’s purchasing behavior was an important indicator of consumervalues and explained that college students shop online regularly andtheir spending in online shopping will be growing significantly. HarrisIn-teractive (2002) addressed that college students were the most con-nected segment in the U.S., showing that 93% of American collegestudents access the Internet regularly. In addition, online travel ticketswere the top item purchased by college students, accounting for 33% oftheir total online purchases (National Association of College Stores,2001). Therefore, college students were deemed an appropriate samplefor this study.

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Data Analysis

The collected data was analyzed using Statistical Package for Social Sci-ence (SPSS) version 11.0 and LISREL version 8.3. Statistical techniquessuch as descriptive statistics, factor analysis, and multiple regression analy-sis were used to achieve the objective of this study. First, simple frequencieswere computed to display the distribution of respondents’ demographicprofiles, and their online airline ticket purchase experiences. Then, both re-liability test and common factor analysis were employed to assess the uni-dimensionality of the measurement scale. Given that the factor structureused in this study was expected to conform to the well-defined theoreticalstructure of perceived risk, confirmatory factor analysis was utilized to as-sess the measurement model. After that, correlation coefficients among theperceived risk dimensions were calculated and purchase intention was re-gressed on the perceived risk dimensions.

RESULTS

The respondents for the empirical investigation included an equal dis-tribution of males (49.8%) and females (50.2%) and a broad cross-sectionof age and income groups. However, the sample distribution was skewedtoward college and graduate students and, consequently, to younger andlower income groups since the sample was drawn from university websites. In terms of online airline purchase experiences, 60.0% (186) of therespondents had purchased an airline ticket online at the time of the sur-vey. In the last six months, 17.7% (33) of them had not purchased airlineticket online and 82.3% (153) of them had purchased an airline ticket on-line more than once. Table 3 summarizes demographic profiles and on-line airline purchase experiences of the respondents.

Following the procedures used by Sin et al. (2002), the unidimensionalityof the measurement scales of the seven perceived risk dimensions were as-sessed using principal axis factoring with varimax rotation. Unidimensionalityrefers to question clarity, in that unidimensional survey questions ask onetopic at a time. Factor analysis is the typical approach to assessingunidimensionality of measurement scales analysis (Schall, 2003). For eachof the seven perceived risk dimensions, a single factor was extracted. Theresults of the factor analyses indicated that the seven factors were consis-tent with the intended measures and accounted for more than 77% of thevariance in the data. In addition, to test reliability of the seven subscales,Cronbach’s alpha coefficients for each factor were calculated and the mea-

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surements were found to be reliable (ranging from .87 to .95). However, itis suggested that confirmatory factor analysis provides a stricter interpreta-tion of unidimensionality (Gerbing & Anderson, 1988). Thus, the subscaleswere evaluated for unidimensionality, convergent validity, and discriminantvalidity with a confirmatory factor analysis using LISREL. The measure-ment items that were purported to load on a particular factor must load on itand the factors themselves were allowed to correlate. The measurementmodels were evaluated using maximum likelihood estimation. A numberof fit indices such as chi-square, confirmatory fit index (CFI), incrementalfit index (IFI), relative fit index (RFI), normed fit index (NFI), and good-ness of fit index (GFI) were used to assess the overall fit of the measure-ment models in the current study. Despite the large chi-square statistic (c2 =877.10, df = 231, p < .01), other fit indices (CFI = .90, IFI = .90, RFI = .85,NFI = .88, and GFI = .80) indicated an acceptable fit of the model. Table 4summarizes the results of a confirmatory factor analysis and reliabilitytests.

One approach to validate the multidimensional perceived risk frame-work is to assess the intercorrelations between the perceived risk subscales(Baldwin & Caldwell, 2003). The consistency of the subscales is examinedin that the subscales were theoretically similar or close together. Thus, the

Kim, Kim, and Leong 45

TABLE 3. Demographic Profiles and Online Airline Purchase Experiences ofthe Respondents

Variable Frequency % Variable Frequency %

Gender Male 153 49.8 Education level/ Freshman 19 6.4

Female 154 50.2 Job status Sophomore 17 5.7

Age 18-20 48 16.0 Junior 64 21.5

21-24 138 46.0 Senior 79 26.6

25-30 37 12.3 Graduate 62 20.9

31-35 25 8.3 Staff 21 7.1

36-45 16 5.3 Faculty 21 7.1

46-55 26 8.7 Administrator 6 2.0

56 or more 10 3.3 Other 8 2.7

Annual Less than $5,000 89 31.1

Income $5,000-$9,999 61 21.3 Purchase Yes 186 60.0

$10,000-$19,999 56 19.6 Experience No 124 40.0

$20,000-$49,999 51 17.8 Purchase during Never 33 17.7

$50,000 or more 30 10.5 the last 6 months 1-3 132 71.0

Marital Single 218 71.5 (n = 186) 4-6 15 8.1

Status Married 87 28.5 7 or more 6 3.2

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forms of the seven perceived risk dimensions were hypothesized to becorrelated and were consistent with the theoretical continuum. As ex-pected, all seven perceived risk subscales were found to be positively cor-related with one another. As seen from Table 5, the correlation coefficientbetween social risk and security risk was significant at the .05 level, whileothers were significant at the .01 level. The results compare favorably tothose of Stone and Gronhaug (1993) and support the construct validity ofthe measurement items. In addition, the seven perceived risk dimensionswere negatively correlated with purchase intention, as expected. The corre-lation coefficient between purchase intention and physical risk was signifi-

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TABLE 4. Confirmatory Factor Analysis of Perceived Risk Items

Standardized Solution

Item SOC TIM FIN PER PHY PSY SEC t-value

SOC1 .79 –a

SOC2 .85 17.74

SOC3 .95 21.19

TIM1 .83 –a

TIM2 .97 22.00

TIM3 .83 17.05

FIN1 .76 –a

FIN2 .86 18.13

FIN3 .82 16.78

FIN4 .90 19.23

PER1 .89 –a

PER2 .88 19.21

PER3 .86 18.58

PER4 1.03 25.36

PHY1 .85 –a

PHY2 .90 19.47

PHY3 .91 19.57

PSY1 .89 –a

PSY2 .96 21.93

PSY3 .88 18.86

SEC1 .74 –a

SEC2 .87 18.56

SEC3 .93 20.43

SEC4 .86 18.20

Cronbach’s a .87 .88 .90 .95 .92 .94 .91

Chi-square = 877.10 (df = 231, p < .01)

CFI = .90; IFI = .90; RFI = .85; NFI = .88; GFI = .80

a. t-values for these parameters were not available because they were fixed for scaling purposes.

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cant at the .05 level, while the correlation coefficients between purchaseintention and other risks were significant at the .01 level.

Furthermore, purchase intention was regressed on the perceived riskdimensions in order to investigate the effect of the perceived risk on pur-chase intention. In order to assess the goodness-of-fit of the regressionmodel, coefficient of determination (R2) and F ratio were examined. Theregression equation showed an adjusted R2 of .383, suggesting that about38.3% of the variation in purchase intention was accounted for by thisequation. The F-ratio, which explains whether the results of the regres-sion model could have occurred by chance, has a value of 26.300 and sig-nificant at the .01 level. In sum, the regression model used in the currentstudy could have not occurred by chance and is considered significant.The result of multiple regression analysis revealed that all the perceivedrisk dimensions with the exception of physical risk significantly affect therespondent’s purchase intention at the .01 level. In addition, it is foundthat performance risk had the strongest effect on purchase intention fol-lowed by financial risk and time risk. Table 5 summarizes the results ofcorrelation and multiple regression analyses.

DISCUSSIONS

Perceived risk has been accepted as a valuable concept to gain insightinto how consumers perceive various risk dimensions and their inclina-tion to avoid negative outcomes when purchasing products/services. Asmentioned earlier, perceived risk has not received great attention related

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TABLE 5. The Results of Correlation and Multiple Regression Analyses

DVa SOC TIM FIN PER PHY PSY SEC Std. b t-value

SOC –.270** –.139 –2.984**

TIM –.397** .644** –.251 –5.384**

FIN –.502** .527** .641** –.315 –6.773**

PER –.557** .303** .392** .638** –.397 –8.532**

PHY –.121* .629** .588** .416** .200** .054 1.168

PSY –.346** .330** .348** .451** .558** .319** –.175 –3.757**

SEC –.335** .137* .204** .334** .515** .167** .470** –.173 –3.715**

Mean 4.85 1.40 1.61 1.73 2.45 1.46 1.53 2.77 F = 26.300**

S.D. 1.83 .83 .96 1.02 1.33 .95 .97 1.28 Adjusted R2 = .383

*p < .05, ** p < .01.a. Dependent Variable: Purchase intention next 12 months.b. Independent Variable: Seven perceived risk dimensions.

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to online shopping. In this study, perceived risk associated with onlineshopping, especially in purchasing airline tickets, was investigated and,overall, the results of this study indicated that perceived risk is applica-ble in understanding consumer behavior of the online airline ticketpurchase decision.

Descriptive statistics of the measurement items indicated that the re-spondents were concerned primarily with security risk when purchasingairline tickets online, followed by performance and financial risks (seeTable 2). This finding is consistent with other research related to e-com-merce, whereas security issues were the major barrier making custom-ers hesitant to buy products or services online (i.e., Harrison-Walker,2002; Haussman, 2002). Overall, security seems to be a hygiene factorrather than a satisfier in online business settings, because its impact onpurchase intention was much less than those of performance and finan-cial risks (see Table 5). Therefore, it is imperative for e-marketing man-agers to recognize the importance of security in online business and todevelop strategies for dealing with current and potential customer’sconcerns regarding security issues. Marketing managers must developstrategies to assure customers of the security associated with their websites purchases. One way to reduce security risk perceived by consum-ers is using symbols of security approval such as Verisign®.

The results of the correlation analyses revealed that all the seven riskdimensions were positively correlated with one another, which indicatedthe construct validity of the measurement items (see Table 4). In addition,the results of factor analyses and reliability tests indicated that the per-ceived risk measurement items with seven subscales were acceptable inonline airline ticket purchase. However, as Pope et al. (1999) pointed out,perceived risk may vary from product group to product group. Thus, rep-lication of this finding along with further research section of the reliabil-ity and discriminant validity is needed.

The major objective of this study was to investigate the effect of per-ceived risks on purchase intention when purchasing airline tickets on-line. Out of the seven perceived risk dimensions, six risk dimensions(i.e., performance, financial, time, psychological, security, and socialrisks) were found to have significant impacts on purchase intention. Theresults of the current study indicated that performance risk and financialrisk were the predominant risk dimensions in explaining consumer’spurchase intention of airline tickets online (see Table 5). This is reason-able because purchase decision related to airline tickets is a relativelyexpensive one. This implies that consumers are not sure what theywould get as compensation for their money in purchasing airline tickets

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online. As Parasuraman, Zeithaml, and Berry (1985) claimed, servicesare likely to be performance rather than products. Eventually, custom-ers can not test or verify the services in advance. This would causehigher performance risk in purchasing services; in this case, purchasingairline tickets online. On the other hand, the results of the study byStone and Gronhaug (1993) which was conducted when respondentspurchased a personal computer showed that financial risk and psycho-logical risk were the predominant risk dimensions in explaining con-sumer’s overall perceived risk. In their study, financial risk may capturethe essence of performance risk as they indicated.

The physical distance between the customer and company in onlinebusiness as well as no existence of human interaction in its nature makescustomers concerned more about performance and financial risks. Thus,in order to alleviate customer’s performance and financial risks, a focusshould be placed on how to reduce performance and financial risks per-ceived by customers, and how to assure customers what they would get asa compensation for their sacrifice for prepayment of their online airlinetickets. In addition, considering today’s time-conscious customers, thesuccess of e-commerce lies in the incorporation of consumer’s prefer-ences and their information needs into web site design and the marketer’se-commerce business concept. E-commerce is not different from anyother business model in that if a business firm fails to develop a web sitethat delivers satisfactory information and online experience, it will notsucceed. Web sites which are designed logically and clearly would savecustomer’s time in searching for the information they want. Furthermore,a responsive customer service function that allows customers the abilityto communicate with a company should be integrated into the web site.On the other hand, the role of physical risk was negligible in this study, asit was in the study by Stone and Gronhaug (1993) and Pope at al. (1999).Our explanation for this is that the measurement items used in this studyfocused on the hypothesized situation that customers purchase airlinetickets “online” instead of the fact that getting “onboard.”

LIMITATIONS AND SUGGESTIONSFOR FUTURE RESEARCH

This study, although raising interesting matters related to risk in onlinepurchase of airline tickets, was subject to some limitations. A major limita-tion of this study was related to the sampling procedure and low responserate. For the study, the sample was drawn from university web sites and

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was skewed to college/graduate students who are younger and often in alower income group. Thus, the study was restricted to generalization ratherthan specific application of the findings. In addition, the response rate waslow. Even though a follow-up procedure was adopted, a major problemwith low response rate (i.e., the possibility that those who respond arelikely different from those who do not respond), still remains. It seems thatthe monetary incentive used for the current study made little difference inthe response rate, which was indicated by Comley (2000), who docu-mented that incentives have little impact on response rates in online sur-veys. However, it is worthy to note that the low response rate may indicatea lack of interest or knowledge of samples. Future research should becrafted to obtain high response rate with a wider range of consumer groups.

This study investigated which risk dimensions consumers perceive tohave an impact on their purchase intention in online airline ticket pur-chases. However, the study did not investigate how to resolve the cus-tomer’s perceived risk. The results of this study revealed that theconsumer’s perceived risk was negatively correlated with purchase inten-tion. Thus, it is necessary to understand how to decrease customer’s per-ceived risk. Adopting an appropriate risk reduction strategy is importantbecause it is a significant interaction tool between the company and itsconsumers. Therefore, it is recommended that one can extend this studyand explore the kinds of risk reduction strategies that would be suitablefor online airline ticket sales.

There are other important variables that might have an effect on cus-tomer’s purchase intention except perceived risk. In other words, perceivedrisk alone may not be sufficient as a predictor of purchase intention. For ex-ample, customer’s perceived quality and perceived value toward airlinecompanies’ web sites could be a key consideration as well as perceived risk.Demographic and behavioral characteristics such as gender, age group, pastonline purchase experience, and technology inclination could have an effecton customer’s purchase intention as well. Therefore, more sophisticatedmodels with more variables need to be investigated in order to enhance ourunderstanding of consumer behavior in online market.

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