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A STUDY OF PERCEIVED RISK AND RISK REDUCTIONOF PURCHASING AIR‐TICKETS ONLINELisa Hyunjung Kim a , Hailin Qu b & Dong Jin Kim ca School of Hotel and Restaurant Administration at Oklahoma State University ,Stillwater, OK 74078, USA E-mail:b School of Hotel and Restaurant Administration at Oklahoma State University ,Stillwater, OK 74078, USA E-mail:c Department of Food Technology & Foodservice Management at Yeungnam University ,Gyeongsan, South Korea E-mail:Published online: 02 Nov 2010.

To cite this article: Lisa Hyunjung Kim , Hailin Qu & Dong Jin Kim (2009) A STUDY OF PERCEIVED RISK AND RISKREDUCTION OF PURCHASING AIR‐TICKETS ONLINE, Journal of Travel & Tourism Marketing, 26:3, 203-224, DOI:10.1080/10548400902925031

To link to this article: http://dx.doi.org/10.1080/10548400902925031

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A STUDY OF PERCEIVED RISK AND RISKREDUCTION OF PURCHASING AIR-TICKETS

ONLINE

Lisa Hyunjung KimHailin Qu

Dong Jin Kim

ABSTRACT. The concept of perceived risk explains consumers’ purchasing behavior thatinvolves risk with unanticipated or uncertain consequences. Using perceived risk theory, thisstudy explored customers’ risk perceptions regarding online air-ticket purchases. This studydiscovered that security risk was the most important predictor to overall risk regarding onlineair-ticket purchases. In addition, nonpurchasers perceived a higher risk than online purchasers,in terms of performance, security, financial, psychological, and time risks. Regarding risk-reduction strategies, shopping around over the web was more important to online purchasersthan to non-purchasers. In addition, reputation of web vendor, well-known brand, symbol ofsecurity approval, and recommendation of family and friends were perceived as preferred risk-reduction strategies when making online air-ticket purchases. Further, this study’s resultsrevealed that respondents’ perceived risks of online air-ticket purchases differed according todemographic characteristics. The implications of the research findings for online marketingactivities are discussed.

KEYWORDS. Perceived risk, online purchasing, air ticket, risk-reduction strategies, onlineshopping, online security

INTRODUCTION

Travelers are not free from perceived risk.They are faced with an array of possiblenegative consequences of purchasing a par-ticular travel product/service. For example,travelers may worry about the stability of the

air-ticket’s price if the purchase decision isdelayed. If they feel that the purchase is toorisky to make, purchasing cannot be com-pleted. The influence of perceived risk,therefore, often dominates travelers’ deci-sion-making processes (Maser & Weiermair,1998; Mitchell, Davies, Moutinho, & Vassos,

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Lisa Hyunjung Kim is a PhD Candidate in the School of Hotel and Restaurant Administration atOklahoma State University, Stillwater, OK 74078, USA (E-mail: hj.kim@okstate.edu).

Hailin Qu, is Professor in the School of Hotel and Restaurant Administration at Oklahoma StateUniversity, Stillwater, OK 74078, USA (E-mail: qhailin@okstate.edu).

Dong Jin Kim, is Assistant Professor in the Department of Food Technology & FoodserviceManagement at Yeungnam University, Gyeongsan, South Korea (E-mail: dj-kim@ynu.ac.kr).

Journal of Travel & Tourism Marketing, 26:203–224, 2009Copyright # Taylor & Francis Group, LLCISSN: 1054-8408 print / 1540-7306 onlineDOI: 10.1080/10548400902925031

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1999; Moutinho, 1987; Roehl & Fesenmaier,1992; Sonmez & Graefe, 1998; Um &Crompton, 1992).

The risks involved in purchasing airlinetickets online are more intense compared totraditional air-ticket purchases (Cunningham,Gerlach, Harper, & Young, 2005). Online air-ticket purchases require consumers to com-plete several routines to make a reservationthemselves. Moreover, possible credit cardfraud makes consumers hesitant to reserve airtickets online (Law & Leung, 2000). Increasedperceived risk, therefore, significantly influ-ences consumers’ reluctance to shop online.As a result, online companies must under-stand the nature of perceived risk and subse-quently implement proper strategies to reducerisk perception in online air-ticket purchases.

Perceived risk theory is a promisingconcept for understanding tourists’ behaviorin decision making (Cunningham et al.,2005; Hsu & Lin, 2006; Moutinho, 1987;Roehl & Fesenmaier, 1992). Perceived risktheory suggests that risk is multidimensional;that is, it is related to financial, performance,psychological, social, physical, and timingfactors (Jacoby & Kaplan, 1972; Roselius,1971). Each risk dimension is not equallyinfluential to potential travelers, however.Because risk perception is subjective, somerisk dimensions may be more dominant forone person than for another. For example,one potential traveler may place moreimportance on physical risk, while anothermay emphasize the financial risk for thesame travel destination (Roehl &Fesenmaier). Further, perceived risk is situa-tion specific (Mitchell & Vassos, 1997; Roehl& Fesenmaier). For instance, financial risk ishighly associated with purchasing a hotelroom, while physical risk dominates pur-chasing a fast food meal (Mitchell &Greatorex, 1993). Although it is suggestedthat perceived risk be understood through asituation-specific perspective, perceived riskin online air-ticket purchases has beenlargely ignored.

Recently, however, researchers havestarted to give more attention to riskperception in online air-ticket purchases.

Cunningham et al. (2005) found that finan-cial and performance risks are recognized astwo major influences to the overall riskperception of online air-ticket purchases.Unfortunately, however, their study failedto include the most significant type of risk inonline shopping; that is, security risk. Lawand Leung (2000), however, showed thatsecurity risk is the most influential risk inonline air-ticket purchases. Without examin-ing the effect of security risk, we haveinsufficient information about overall riskperception. This study, therefore, aims tounderstand the broader risk perception inonline air-ticket purchases by includingsecurity risk.

Previous research suggests that experiencehas a moderating effect on risk perception(Kim & Lennon, 2000; Mitchell & Prince,1993; Moutinho, 1987; Sonmez & Graefe,1998). That is, the more experiences aconsumer has, the less perceived risk he hasas well. Experience increases consumer con-fidence in purchasing a particular product(Fenech & O’Cass, 2001; Sonmez & Graefe).For example, the consumer’s actual experi-ence at a tourism destination enables trave-lers to compare their perceptions and thereality, thus altering their risk perceptionaccordingly. Further, this results in moreconfident decision making for future beha-vioral intentions (Sonmez & Graefe).Researchers have argued that purchasersperceive less risk than nonpurchasersbecause of their experience (Fenech &O’Cass; Mitchell & Boustani, 1993;Mitchell & Prince; Spence, Engel, &Blackwell, 1970). If we understand thesimilarities and differences in risk perceptionof both online consumers with and withoutexperience, it would be possible to adoptdifferent marketing strategies for reachingeach group. Thus, this study focuses onwhether risk is perceived differently and howeach risk dimension is influenced by theonline air-ticket purchasing experience.Further, risk perception is influenced byconsumers’ demographic characteristics(Pope, Brown, & Forrest, 1999). Becausedemographics provide useful segmentation

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information, demographics’ influence on riskperception will be explored as well.

After being exposed to perceived risk,consumers attempt to reduce the uncertaintyor negative consequences of their decisions byusing risk-reduction strategies (Mitchell et al.,1999). Purchasing from a well-known brand oron the recommendation of a family member orfriend are examples of risk-reduction strate-gies. It is suggested that certain risk-reductionstrategies could be more influential to reducingrisk for a particular risk group. For example,providing a child-friendly travel product is auseful risk-reduction strategy for a pleasuretravel group with children (Roehl &Fesenmaier, 1992). Thus, if online air-ticketpurchasers and nonpurchasers seek differentrisk-reduction strategies, this informationwould be important and practical to onlinecompanies so that they can provide properrisk-reduction strategies to encourage onlineshopping.

The current study aims to investigate: (a)whether there are any differences in thedegree of perceived risk between online air-ticket purchasers and nonpurchasers; (b)whether there are any relationships betweenperceived risk and online shoppers’ demo-graphic characteristics; and (c) whether thereare any differences in the perceived impor-tance of risk-reduction strategies betweenonline purchasers and nonpurchasers.

LITERATURE REVIEW

Perceived Risk

The concept of perceived risk has receivedgreat attention to understand consumers’behavior in purchasing services (Mitchell &Greatorex, 1993). It explains that consumers’purchasing behavior involves risk in thesense that the consumer will be faced withunanticipated and uncertain consequences,some of which are prone to be unpleasant(Bauer, 1960). An important characteristic ofperceived risk is that it originates only frompotentially negative outcomes. Consumersdesire positive outcomes from purchasing

behavior, which meet or exceed their expec-tations. However, when negative outcomesare confronted, the level of expectation setby consumers cannot be fulfilled, andeventually satisfaction is not achieved(Stone & Grønhaug, 1993). Another impor-tant characteristic of perceived risk is uncer-tainty. Uncertainty can be defined as theconsumers’ subjective possibility of out-comes that derived from purchase decisions(Cox & Rich, 1964; Hoffman & Turley,2002). Since the consequences of purchasingbehavior are not identified, consumers havedifficulty in assessing the possibility of thenegative outcomes. Therefore, consumersonly react to the amount of risk that theyactually perceive in a subjective manner(Cunningham, 1967).

Traditionally, perceived risk has beenidentified as a dominant influence in theearly stages of the consumer buying process.Generally, as consumers recognize a need fora certain product or service, they simulta-neously perceive risk. Consumers continueshopping when the perceived risk of obtain-ing a product falls between their minimumand maximum threshold levels (Dowling,1986). If the perceived risk of a productextends beyond a consumer’s maximumacceptable level, the consumer avoids pur-chasing or increases his/her risk-handlingactivities. On the other hand, if perceivedrisk is below the consumer’s minimumacceptable level, purchase may be alsorejected because of boredom, the desire forvariety, or to pursue a product that includesmore risk (Dowling). Scholars have arguedthat increased perceived risk stimulates con-sumers to search for more information inorder to reduce related risk (Cox, 1967;Dowling & Staelin, 1994; Mitchell &Greatorex, 1993). This notion is supportedin the second and third stages of the buyingprocess—that searching for informationand evaluating alternatives are, in fact, risk-handling strategies consumers use(Cunningham et al., 2004; Murray, 1991).

Some researchers have studied differentperceived risk types at different stages of thebuying process, from need recognition to

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postpurchase behavior (Cunningham et al.,2005). According to its definition, however,perceived risk is the uncertainty of theoutcome of the decision and concern aboutthe consequences of the decision (Assael,1998). Once a purchase decision has beenmade and a product has been consumed orexperienced, consumers are faced with theconsequences of their purchase (Mitchell &Boustani, 1994). Precisely speaking, per-ceived risk in the postpurchase evaluationstage may not be meaningful because it is notperceived risk; instead, consumers evaluatethe consequences of purchasing decisions.This is in the same conceptual line withMurray’s (1991) definition of perceived riskas prepurchase uncertainty. This may be onereason why perceived risk is emphasized inthe early stages of the consumer buyingprocess. If negative consequences areencountered, consumers strive to reduce thecognitive dissonance that results (Mitchell &Boustani, 1994). This study, therefore, fol-lows the traditional view of perceived riskthat relates to the early stages of the buyingprocess.

Consumers perceive several types of riskin purchasing situations. Traditionally, sixtypes of risk have been identified in theoverall risk concept: financial, performance,psychological, social, physical, and time risks(Jacoby & Kaplan, 1972; Roselius, 1971).Several researchers have defined those sixtypes of risk (Garner, 1986; Mitchell &Greatorex, 1993; Pope et al., 1999; Roehl &Fesenmaier, 1992). The following is thesummarized definitions of the six types ofrisk. Financial risk refers to the possibility ofnot getting value for the money fromproduct/service failure or paying more thannecessary. Performance risk involves thepossibility of the failure of purchased pro-ducts/services in performance. Psychologicalrisk involves the possibility of negative effecton personality or self-image by purchasingproducts/services. Social risk is the prob-ability of negative effect on opinions ofreference groups by purchasing behavior.Physical risk involves the possibility ofhealth hazard resulting from the purchase.

Lastly, time risk involves the possibility ofthe waste of time or loss of convenience frompurchasing.

In addition to those traditionally acceptedsix types of risk, one of the fastest growingrisk issues in online shopping is security risk(Harrison-Walker, 2002). Security risk canbe defined as risk involving perceived inse-curity in transmitting sensitive informationthrough online transaction (Salisbury,Pearson, Pearson, & Miller, 2001). Securityrisk is derived from the uncertainty that thequality of products/services over the web canbe different from seller to seller and thedifficulty of measuring the quality in acomputer-mediated environment (Grabner-Kraeuter, 2002). Miyazaki and Fernandez(2000) claim that as consumers perceivemore security risks, total risk perceived byconsumers would increase in online shop-ping. Therefore, security risk is one type ofperceived risk in this study.

Perceived Risk in the Travel Industry

Scholars have identified that purchasingservices is generally perceived as riskier thanpurchasing products (Lewis, 1976; Mitchell &Greatorex, 1993; Yavas, 1987). The uniquecharacteristics of services are the majorreasons behind this phenomenon. Amongfour identified characteristics, intangibilityhas received the most attention in terms ofincreased uncertainty in purchasing (DeRuyter, Wetzels, & Kleijnen, 2001;McDougall & Snetsinger, 1990; Murray &Schlacter, 1990; Zeithaml & Bitner, 1996). Theintangible nature of travel products andservices prevents consumers from evaluatingthe product prior to the actual experience.Consequently, intangibility increases risk inmaking the decision to purchase travel ser-vices and products (Roehl & Fesenmaier,1992; Yavas). Moreover, intangibility makespostpurchase evaluation more important withservices than with products, because experi-ence is a critical standard to evaluate servicequality (Murray & Schlacter).

The inseparability of travel products andservices is another source of uncertainty.

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Travelers must be part of producing travelproducts/services, and interactions at thisstage are essential for a successful travelexperience. While producing and consumingtravel are done simultaneously, travelers arerequired to purchase first and then experi-ence what they have purchased, which resultsin increased perceived risk (Zeithaml,Parasuraman, & Berry, 1985).

Moreover, possible variations in perfor-mance undermine certainty (Mitchell &Greatorex, 1993). Without standardized per-formance, quality cannot be consistent witheach travel experience. Although travelersmay be satisfied one time, they cannot becompletely sure about the satisfactory qual-ity of the same travel product/service for thenext purchase.

Travel products/services cannot be inven-toried; the industry is largely affected byfluctuations in demand (Zeithaml et al.,1985). Price is subjective to demand attransaction time, and performance may fallbelow expectations if over-demand occurs(Mitchell & Greatorex, 1993). These perish-able characteristics of travel increase theperceived risk in travel decisions.

The overall perceived risk in travel deci-sions is the aggregate of several types of riskfactors (Murray, 1991). These types ofperceived risk can be understood throughperceived risk theory. For example, Roehland Fesenmaier’s (1992) definitions of vaca-tion risk components are the conceptualextension of perceived risk theory that havebeen modified to fit the travel context. It isevident that several types of risk are closelyassociated with travel decisions.

First, travel products/services are one ofthe most expensive items to purchase(Mitchell et al., 1999). They can accountfor a considerable amount of householdexpenditures. Consumers, however, cannottry travel products/services before purchas-ing; thus, it is difficult to evaluate the valuefor the money before the actual experienceoccurs. The absence of pretrial abilitycompared to financial significance eventuallyincreases financial risk (Holloway, 2004). Inaddition, it is common to find a range of

prices for the same travel product based ondifferent vendors, time of purchase, ormarket conditions. Consequently, consumersmay find the products/service difficult toevaluate, even if the price they pay is in theconsumer’s acceptable range. This, too,increases the uncertainty in the financialinvestment (Mitchell et al., 1999).

Second, when traveling, people deal withseveral travel products/services such asaccommodations, transportation, food,activities, and events. Potential travelers areuncertain whether each product will performas they expect, raising anxiety about productperformance. For example, hotel facilitiesmay be unacceptable or hotel employees maybe unfriendly and not helpful to theircustomers. Further, travel products/servicesinclude some elements that travel marketerscannot control (Hsu & Lin, 2006). Forexample, interactions between travelers andlocal residents and among other travelers areinevitable, yet are hard to control ingredientsof travel. Unpleasant incidents, as well asthings such as undesirable weather can causenegative outcomes (Mitchell et al., 1999).The possibility of a negative or disappointingtravel experience is more likely to providetravelers with psychological discomfort(Sonmez & Graefe, 1998). If dissatisfactionoccurs, it is impossible to be compensatedfor spent time; thus, this element increasestime risk (Roehl & Fesenmaier, 1992).

Third, in making travel decisions, a poten-tial traveler usually faces certain physicalhazards that could be caused by purchasingthe products/services. For example, consu-mers may have concerns related to a possiblecar accident during the trip or contracting anillness due to poor hygiene in the area they arevisiting. Moreover, terrorist attacks or naturaldisasters can cause consumers to avoid certainregions with higher physical risk (Sonmez &Graefe, 1998). Further, scholars have identi-fied that the possibility of mechanical orequipment problems on a trip is the highestrisk dimension in pleasure travel (Roehl &Fesenmaier, 1992).

Fourth, purchasing travel products andservices is closely related to social status and

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group identity (Mitchell et al., 1999). Apotential traveler may be concerned aboutpurchasing a certain travel product that isagreeable with his/her reference group suchas family members and peers. For example, apotential traveler might be concerned withhis/her friends’ opinion if he/she travels to aparticular destination. For example, Sonmezand Graefe (1998) find that social risk isnegatively related to the intention to travel toEurope. The possibility of being embar-rassed by purchasing a poor travel product,therefore, increases social risk (Moutinho,1987).

Perceived risk in online air-ticket pur-chases has been understood by six tradition-ally accepted types of risk: financial,performance, physical, psychological, social,and time (Cunningham et al., 2005).Performance, physical, social, and financialrisks have been found to be dominant riskfactors across the consumer buying processin online air-ticket purchases (Cunninghamet al., 2005). Further, the fear of possiblecredit card fraud and confidentiality ofpersonal information are important inhibi-tors of online air-ticket sales (Kolsaker, Lee-Kelley, & Choy, 2004; Ueltschy, Krampf, &Yannopoulos, 2004).

Shopping Experience

As consumers recognize a need for acertain product/service, they also perceive arelated risk. In making travel decisions, priorexperience is an excellent internal source ofrich information that is unavailable to first-time travelers (Crotts, 1999; Robinson &Kearney, 1994). This lack of experience isone of the major reasons for perceived risk inmaking travel decisions (Moutinho, 1987).On the other hand, a satisfactory experiencewith a destination reduces perceived riskrelated to the destination, and also increasesa consumer’s intention to visit the samedestination again (Sonmez & Graefe, 1998).

Related literature reports that as shoppingexperience increases, consumers perceive lessrisk in a certain product/service (Mitchell &Prince, 1993; Spence et al., 1970). The

influence of experience as a risk reducer ismore salient in services than in productsbecause of the unique characteristics ofservices (Mitchell & Prince). However, asconsumers have experienced a certain pro-duct/service over time, they become morecertain of the performance of the product/service and, consequently, perceived risk canbe reduced (Fenech & O’Cass, 2001; Mitchell& Prince). Based on the above rationale, it isexpected that online air-ticket purchaserswould perceive less risk than nonpurchasersbecause they have more shopping experi-ences. Therefore, hypothesis 1 is as follows:

Hypothesis 1—Online air-ticket pur-chasers differ from nonpurchasers onthe degree of perceived risk.

Demographic characteristics such as gen-der, age, and income have been adopted as areasonable segmentation tool and a predic-tor of useful explanations of consumerbehavior. Many researchers have showninterest in the demographics of the Internetusers to facilitate online marketing practice(Donthu & Garcia, 1999; Fram & Grady,1995; Katz, Rice, & Aspden, 2001; Kim &Kim, 2004; Pitkow & Kehoe, 1996; Weber &Roehl, 1999). For example, it was reportedin earlier studies that the majority of Internetusers were male (e.g., Pitkow & Kehoe).However, the proportion of female onlinepopulation has increased, and the overallInternet population became evenly com-prised of men and women (Howard, Raine,& Jones, 2001). In addition, Miller (1996)insists that age is a meaningful variable tosegment Internet users, reporting that youngadults in their 30s or 40s are usual Internetusers. Prior studies also suggest that Internetusers earn higher incomes which leads tomore discretional money than the traditionalretail shopper (Donthu & Garcia; Fram &Grady; Pitkow & Kehoe; Weber & Roehl).

The consumer’s type can influence his/herrisk perception (Pope et al., 1999). Forexample, previous research show that genderresponds differently as the risk conditions

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change (Darley & Smith, 1995; Pope et al.).Based on the above discussion, hypothesis 2is as follows:

Hypothesis 2—The degree of perceivedrisk in online purchasing is significantlydifferent according to different consu-mers’ demographic characteristics (gen-der, marital status, position, age,income, and Internet usage).

Risk-Reduction Strategies

Consumers use risk-reduction strategies todecrease the uncertainty or consequences ofan unsatisfactory decision (Mitchell et al.,1999; Tan, 1999). Generally, brand image,company reputation, recommendations offamily and friends, inexpensive price, andspecial offers have been suggested as usefulrisk-reduction strategies (Akaah &Korgaonkar, 1988; Mitchell & Boustani,1994; Tan).

In the context of travel, consumers try toreduce risk by increasing the certainty orreducing the negative consequences. Forexample, Mitchell et al. (1999) found that‘‘reading independent travel reviews on thedestination’’ (increasing the certainty) and‘‘purchasing some kind of travel insurance’’(reducing the negative consequences) are twoof the most effective risk-reduction strategiesin ‘‘package holiday’’ purchasing. In addi-tion, travelers can apply risk-reductionstrategies differently based on their riskperception. Roehl and Fesenmaier (1992)classified travelers into three groups basedon their risk perception for pleasure traveland recommended ways to address specificconcerns about risk reduction. For example,Roehl and Fesenmaier suggest, based on riskperception and demographic profiles, thatchild-friendly travel products can be a usefulstrategy for the demographic that haschildren, but perceives high risk regardingequipment failure and physical danger.

In this study, risk-reduction strategies areselected from the studies of Mitchell andGreatorex (1993) and Mitchell and Boustani

(1994). The eight items used as risk-reduction strategies in this study are: shop-ping around over the web, reputation of webvendor, recommendation of family andfriends, well-known brand, special offers,cheapest brand, reading product informa-tion, and symbol of security approval(Table 1). Symbol of security approval isincluded in this study because approval ofsecurity on a website by a trusted third party(i.e., VeriSign and TRUSTe) reduces securityrisk in online transactions, increasing sales(Miyazaki & Krishnamurthy, 2002). Thewarranty was not included because custo-mers purchase services with little expectationof material and economic return on theirpurchase of an intangible (Moutinho, 1987).Services are usually sold without warranties(Grace & O’Cass, 2002; Zeithmal & Bitner,1996).

Mitchell and Boustani (1993) explain thatthe importance of risk-reduction strategiesdoes not vary between purchasers andnonpurchasers. If the important risk-reduction strategies are not varied betweenonline purchasers and nonpurchasers, onlinemarketers can use certain risk-reductionstrategies in their advertising and promo-tions for both groups. However, if they arevaried, it would be useful and valuableinformation to help online marketers indeveloping risk-reduction strategies for eachgroup (Mitchell & Boustani, 1993). There-fore, hypothesis 3 is established as follows:

Hypothesis 3—Online air-ticket pur-chasers perceive the importance ofrisk-reduction strategies differentlyfrom nonpurchasers.

METHODS

Instrument

A questionnaire survey was designed andadministrated in this study. The items of thequestionnaire were developed based on themulti-item scales used by Stone andGrønhaug (1993), Pope et al. (1999), and

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Salisbury et al. (2001). In addition, the itemscomprising risk-reduction strategies wereestablished based on the studies by Mitchelland Greatorex (1993) and Mitchell andBoustani (1994). The 24 risk items were usedto measure perceived risk including financialrisk (4 items), performance risk (4 items),psychological risk (3 items), social risk (3items), physical risk (3 items), time risk (3items), and security risk (4 items). Inaddition, three items were added to measureoverall risk in online air-ticket purchases. Tomeasure perceived risk, a 6-point rating scalewas utilized ranging from 1 (strongly dis-agree) to 6 (strongly agree). The level ofimportance of eight risk-reduction strategieswas measured by using a 7-point rating scaleranging from 1 (not very important) to 7 (veryimportant).

The main reason for using different ratingscales is to facilitate comparisons betweenthis and other studies (Mitchell & Greatorex,1993). Mitchell and Greatorex used an even-number rating scale (4-point) for measuringperceived risk and an odd-number ratingscale (5-point) for measuring the useful risk-reduction strategies. To measure perceivedrisk, numerous studies used even-number 4-or 6-point rating scales (Cunningham, 1967;Dash, Schiffman, & Berenson, 1976;Hoover, Green, & Saegert, 1978; Lee &Tan, 2003; Mitchell & Greatorex, 1993;Roehl & Fesenmaier, 1992; Toh & Heeran,1982; Yavas & Tuncalp, 1985). On the otherhand, odd-number 5- or 7-point rating scaleswere used in major studies of risk-reductionstrategies (Dowling & Staelin, 1994; Mitchell& Boustani, 1993; Mitchell et al., 1999;

Mitchell & Vassos, 1997). Furthermore,Lehmann (1989, p. 213) explained that ‘‘thedifferences in results between well-donestudies using, for example, five and six scalepoints are essentially ‘unnoticeable.’’’

To assess and establish the reliability andvalidity of the survey instrument, a pilot testwas conducted. A total of 10 questionnaireswere distributed to the pilot group, who werein the target population including fourprofessors and six graduate students. Thesample size for the pilot study is relativelysmall. As an important element of a goodstudy design, a pilot study can increase theprobability of the study’s success (VanTeijlingen & Hundley, 2001). In futurestudies, the sample size used in the pilotstudy should be sufficiently large. Thequestionnaire was revised based on thecomments and suggestions provided fromthe pilot test.

Sampling and Data Collection

The target population of this study wasfaculty, administrators, staff members, andstudents in the seven universities in theUnited States. A two-stage sampling proce-dure was adopted to draw samples. First, aconvenience sampling was employed to selectthe seven universities. The seven universitieswere chosen from different regions of theUnited States (one from the East coast, onefrom the West coast, two from the North,one from the South, and two from theCentral states). This sampling was chosento avoid bias that could be derived fromgeographic concentration. Next, an e-mail

TABLE 1. Risk-Reduction Strategies

Risk-Reduction Strategies Mitchell & Greatorex (1993) Mitchell & Boustani(1994)

In This Study

Shopping around over the web ! ! !Reputation of vendor ! ! !Recommendation of family and friends ! ! !Well-known brand ! ! !Special offers ! ! !Cheapest brand ! ! !Reading product information ! !Symbol of security approval !

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search function was used on each of theselected universities’ websites to draw thesamples randomly. Based on past researchfindings indicating the response rate for anonline survey should be around 10% (Klose,List, & Silver, 2001; Smith & Whitlark,2001), the sample size of the study was setat 300, with an estimated response rate ofapproximately 5% to 8%. Thus, 4,326 nameswere randomly selected to participate in thisstudy from the target population.

It is important to note that the general-izability of a student sample to the generalpopulation has been challenged for itsrepresentativeness. In major studies on per-ceived risk, however, student samples werelargely used to understand consumers’ per-ceived risk in relation to various products/services, and the results were successfullygeneralized (Mitchell et al., 1999; Mitchell &Vassos, 1997; Mitchell & Greatorex, 1993;Mitra, Reiss, & Capella, 1999; Pope et al.,1999; Stone & Grønhaug, 1993; Tan, 1999).Furthermore, Wang (2001) insisted that astudent sample was not systematically differ-ent from that of other potential onlineconsumers in terms of the psychologicalprocesses. In fact, college students are amongthe most active online purchasers, and theirInternet experiences and actual online pur-chases, technological advances, and innova-tive nature qualify them as an appropriatesample for online shopping research (Yoo &Donthu, 2001). Specifically, there is evidencethat a student sample is appropriate forunderstanding general risk perception relatedto online air-ticket purchases. For example,Cunningham et al. (2005) successfully used astudent sample to study perceived risk inpurchasing air-tickets online. The authorsargue, therefore, that a student sample isappropriate because students are actualonline air-ticket purchasers, not ‘‘surrogate,’’and homogeneity of the sample is desirablefor applying theory. These qualities areclearly applicable for the current study; infact, 60% of our sample was actual customersand applying perceived risk theory isintended to understand this particular con-sumer behavior.

In addition, compared to the generalpopulation, a student sample is usuallyconsidered low in financial ability. It isnoteworthy, however, that college studentsin the 19- to 25-year-old age bracket havesignificant buying power. In fact, Gardyn(2002) insists that college students in this agerange should be regarded as serious con-sumers with $200 billion in buying powereach year. Approximately 80% of collegestudents in this age range are employed atleast part-time and have more than $400 fordiscretionary purchases each month (Martin& Turley, 2004). This indicates a high levelof spending power. Moreover, college stu-dents comprise a significant portion ofonline shoppers (Lee & Allaway, 2002).Therefore, the current sample is deemedappropriate for the purposes of this study.

The questionnaire was posted on a desig-nated website and an e-mail message includ-ing a hyperlink to the survey website wassent to selected subjects asking for theirparticipation in the survey. Data was col-lected during the first 2 weeks in April 2003.Monetary incentives of $50 for two ran-domly drawn respondents, and follow-up e-mails were used to increase the response rate.

Data Analysis

Descriptive statistics, principal componentanalysis, independent sample t test, and one-way analysis of variance (ANOVA) wereused to explore perceived risk and risk-reduction strategies in online air-ticket pur-chases. Descriptive statistics were utilized tosee the distribution of respondents’ demo-graphic profiles and online air-ticket pur-chasing experiences. Principal componentanalysis and reliability test were performedto assess applicability of perceived riskdimensions to online air-ticket purchases.The multiple regression analysis was used toidentify the impact of the seven perceivedrisk dimensions on perceived overall risk.Subsequently, an independent sample t testwas used to identify the differences in riskperception and risk-reduction strategiesaccording to purchasing experience. In

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addition, the ANOVA was utilized toexamine the differences in risk perceptionin terms of demographic characteristics.

RESULTS

A total of 4,326 e-mail messages weresent and 422 e-mail messages were undeli-verable. A total of 3,904 e-mail messageswere delivered and 334 responses werereceived after the follow-up e-mail.However, after screening, 24 responses weredeleted due to the excessive amount ofmissing data. Finally, a total of 310responses (7.9% response rate) were usedfor further analysis.

To investigate a nonresponse bias in thisstudy, differences between the early respon-dents (198 out of 310) and the late respon-dents’ (112 out of 310) demographiccharacteristics, online air-ticket purchasingexperience, perceived risk, and the impor-tance of risk-reduction strategies were exam-ined. The results of chi-square showed thatno significant differences existed between theearly and late respondents in demographiccharacteristics and online air-ticket purchas-ing experience (p ( .10). In addition, theresults of the independent sample t test

revealed that there were no significantdifferences between the early and laterespondents on perceived risk and risk-reduction strategies (p ( .10).

Respondents’ Profile

Table 2 reports the demographic profilesof the respondents. The respondents heldsimilar proportions of men and women, and71.5% (218) were single. Sixty percent (60%)of the respondents were between 18 and 24years of age and the majority of therespondents (81%) were students (60.2%were undergraduate and 20.9% were gradu-ate students), as expected. Since a majorityof the respondents were students, the sam-pling distribution was skewed to young andlow income groups. Most of the respondents(95.4%) had been using the Internet for 4years or more and only 4.6% of respondentshad used the Internet for less than 4 years. Interms of online purchases, 60.0% (186) ofrespondents had purchased air-tickets onlineduring the last 6 months, and 40.0% (124)had not. Among the respondents of purchas-ing group, 58.1% (108) had purchasingexperience of 1–2 time(s) and 23.1% (43)had 3 or more times during the last 6months.

TABLE 2. Demographic Profiles of the Respondents

Variable Frequency Percent Variable Frequency Percent

Gender Male 153 49.8 Marital Single 218 71.5

Female 154 50.2 Status Married 87 28.5

Age 18–20 48 15.5 Annual Less than $5,000 89 31.1

21–24 138 44.5 Income $5,000–$9,999 60 21.0

25–30 37 11.9 $10,000–$19,999 56 19.6

31–35 25 8.1 $20,000–$49,999 51 17.8

36–45 16 5.2 $50,000 or more 30 10.5

46–55 26 8.4

56 or more 10 3.2

Internet Less than 4 years 14 4.6

Usage 4–5 years 62 20.3

Grade Freshman 19 6.4 (years) 6–7 years 69 31.4

Position Sophomore 17 5.7 8 years or more 134 43.7

Junior 64 21.5

Senior 79 26.6

Experience Yes 186 60.0

Graduate 62 20.9

No 124 40.0

Staff 21 7.1

If yes,

during

the last 6

months

Never 35 18.8

Faculty 21 7.1

1–2 time(s) 108 58.1

Administrator 6 2.0

3–4 times 30 16.1

Other 8 2.7

5–7or more 9 4.9

8 or more 4 2.1

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Perceived Risk Dimensions

The principle component analysis withvarimax rotation was conducted to testapplicability of the perceived risk dimensionsto the online air-ticket purchases. The resultsrevealed a seven-factor solution with the clearloadings. As shown in Table 3, all factorloading scores were higher than .67 and theseven factors accounted for more than 84% ofthe variation in the original 24 items. Further,Cronbach’s alpha scores were computed foreach factor in order to test the reliability of themeasurement items. The results showed thatthe seven perceived risk dimensions werereliable (ranging from .873 to .945). Table 3summarizes the results of a principal compo-nent analysis and reliability tests.

Before the hypothesis 1 was tested, themultiple regression analysis was used toidentify the impact of the seven perceivedrisk dimensions on overall risk (Table 4). Theresults of the regression analysis showed thatsix coefficients had positive signs asexpected. This indicated that there was apositive relationship between the six inde-pendent variables and the dependent vari-able. It also suggested that online shoppers’perceived overall risk is largely dependentupon these six online shopping risks. Theywere, therefore, the determinant factors orthe best predictors of an online shopper’sperceived overall online shopping risk.

The coefficient of determination (R2) OF0.704 indicated that more than 70 percent ofthe variance in the overall risk was explainedby the six risk dimensions. The F ratio has avalue of 95.961 and significance at 0.000.Therefore, it could be concluded that theregression model adopted in this study couldhave not occurred by chance and is con-sidered significant.

The t-statistic test was used for testingwhether the six independent variables con-tributed information to the predicator of thedependent variable, overall risk. In thisstudy, if the t value of an independentvariable was found to be significant at 0.05level, that variable was considered in themodel. The result of t statistic showed that

six out of seven dimensions were significant(p ( 0.05) independent variables in theregression model. The model was written asfollows:

Y~2:189z0:427X1z0:686X2z0:454X3

z0:094X5z0:537X6z0:153X7,

where:

Y 5 perceived overall risk,X1 5 performance risk,X2 5 security risk,X3 5 financial risk,X5 5 physical risk,X6 5 psychological risk,X7 5 time risk.

The partial correlation coefficient, b, wasused to indicate the impact. The dimensionwith the greatest impact was ‘‘security risk’’(b2 5 .686). Followed by ‘‘psychological risk’’(b6 5 .537), ‘‘financial risk’’ (b3 5 .454),‘‘performance risk’’ (b1 5 .427), ‘‘time risk’’(b7 5 .153), and ‘‘physical risk’’ (b5 5 .094).

The standardized coefficients (Std. beta)can be used to identify the relative impor-tance of each independent variable. Theresults showed that ‘‘security risk’’ (Std.beta2 5 .0.540) was the most importantfactor in explaining ‘‘overall risk’’ in onlineair-ticket purchases. Followed by ‘‘psycho-logical risk’’ (Std. beta6 5 .414), ‘‘financialrisk’’ (Std. beta3 5 .358), ‘‘performance risk’’(Std. beta1 5 .336), ‘‘time risk’’ (Std. beta7 5

.120), and ‘‘physical risk’’ (Std. beta5 5

.073).

Perceived Risk Differences

The results of the independent sample ttest showed that, except for factor four,social risk and factor five, physical risk;nonpurchasers perceived significantly higherrisk on financial, performance, psychologi-cal, security, and time risks than onlinepurchasers (p ( .05). Overall, nonpurchasersperceived higher risk than online purchasersin online air-ticket purchases. Therefore,hypothesis 1 was failed to reject (SeeTable 5).

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TABLE 3. Principal Component Analysis of Perceived Risk Items

Factor a Factor Loading Eigen Value

Factor 1. Performance risk (.945) b 3.649 (15.202)c

I am not confident about the ability of an online airline vendor to perform as

expected.

.891

As I consider the purchase of airline tickets over the web, I worry about whether

they will perform as they are supposed to.

.876

If I were to purchase airline tickets over the web, I would be concerned that they

would not provide the level of benefits that I would be expecting.

.779

Considering the possible problems associated with an online airline tickets

vendor’s performance, a lot of risk would be involved with purchasing them over

the web.

.750

Factor 2. Security risk (.912) b 3.391 (14.131)c

I would feel insecure sending sensitive information over the web. .881

If you purchase airline tickets over the web, your credit card details are likely to be

stolen.

.869

Overall, the web is an unsafe place to transmit sensitive information. .852

The web is an insecure means through which to send sensitive information. .804

Factor 3. Financial risk (.898) b 2.995 (12.479)c

If I bought airline tickets over the web, I would be concerned that the financial

investment I would make would not be wise.

.813

Purchasing airline tickets over the web would not provide value for the money I

spent.

.748

Purchasing airline tickets over the web would be an inappropriate way to spend

my money.

.713

If I bought airline tickets over the web, I would be concerned that I really would not

get my money’s worth from the tickets.

.672

Factor 4. Social risk (.873) b 2.841 (11.838)c

Purchasing airline tickets over the Web would cause me to think of it as foolish by

some people whose opinion I value.

.833

Purchasing airline tickets over the Web will adversely affect others’ opinion of me. .818

The thought of buying airline tickets over the Web causes me concern because

some friends would think I was just being showy.

.800

Factor 5. Physical risk (.916) b 2.708 (11.285)c

I am concerned that using the web may lead to uncomfortable physical side

effects such as bad sleeping, backaches, and the like.

.897

I am concerned about the potential health-related risks associated with purchasing

airline tickets over the web.

.831

One concern I have about purchasing airline tickets over the web is that eyestrain

could result due from looking at the computer.

.802

Factor 6. Psychological risk (.935) b 2.623 (10.927)c

The thought of purchasing airline tickets over the web gives me a feeling of

unwanted anxiety.

.882

The thought of purchasing airline tickets over the web makes me feel

psychologically uncomfortable.

.862

The thought of purchasing airline tickets over the web causes me to experience

unnecessary tension.

.812

Factor 7. Time risk (.882) b 2.127 (8.864)c

Purchasing airline tickets over the web will take too much time or be a waste of

time.

.779

Purchasing airline tickets over the web could lead to an inefficient use of my time. .735

The demands on my schedule are such that purchasing airline tickets over the

web could create even more time pressures on me that I don’t need.

.684

Total variance explained % 84.727

a Principal component factors with iterations: Varimax rotation.b Reliability score (Cronbach’s alpha) for each factor grouping is shown in parentheses.c Variance explained percentage for each factor is shown in parentheses.

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Perceived Risk and Respondents’Demographics

An ANOVA was applied to examinewhether significant relationships and differ-ences existed between the respondents’ riskperceptions and their demographic charac-teristics (hypothesis 2). According to theresult on Table 6, overall risk perception washigher for females in comparison to males.Females perceived a higher risk on perfor-mance, security, and psychological risks thantheir counterparts. In terms of marital status,singles perceived higher risk on performancerisk than married. However, the married

people perceived a higher risk on physicalrisk than their counterparts. Students per-ceived a higher risk on performance, finan-cial, social, and physical risks than others. Inaddition, respondents in the 18- to 30-years-of-age group perceived higher risk on per-formance, financial, social, and physicalrisks than respondents who were 31-years-of-age and older. The results also revealedthat the respondents’ perceived risk wassignificantly different related to perfor-mance, financial, physical, and overall risksamong different income levels. A furtherpost hoc test (Tukey’s test) showed that therespondents with an income level of less than

TABLE 4. The Results of Multiple Regression Analysis

Model Sum of Square df Mean Square F Sig.

Regression 328.234 7 46.891 95.961 .000

Residual 137.797 282 .489

Total 466.031 289

Variables in the Model

b Std. b t Sig.

Constant 2.188 53.250 .000

X1. Performance risk .427 .336 10.386 .000

X2. Security risk .686 .540 16.700 .000

X3. Financial risk .454 .358 11.061 .000

X5. Physical risk .094 .073 2.316 .021

X6. Psychological risk .537 .414 12.827 .000

X7. Time risk .153 .120 3.742 .000

Excluded Variable in the Model

X4. Social risk .051 .039 1.194 .233

Note. Dependent variable: Perceived overall risk.

TABLE 5. Perceived Risk Between Online Purchasers and Nonpurchasers

Factor Online Purchasing Experience

Yes (n 5 186) No (n 5 124) t p

Factor 1. Performance risk 1.99a (1.04)b 3.14 (1.42) 27.75 .000*

Factor 2. Security risk 2.56 (1.14) 3.10 (1.41) 23.77 .000*

Factor 3. Financial risk 1.44 (.74) 2.17 (1.21) 25.97 .000*

Factor 4. Social risk 1.35 (.68) 1.48 (1.00) 21.37 .173

Factor 5. Physical risk 1.49 (.94) 1.41 (.98) .66 .507

Factor 6. Psychological risk 1.37 (.75) 1.77 (1.20) 23.27 .001*

Factor 7. Time risk 1.52 (.87) 1.75 (1.08) 22.12 .035*

Overall risk 1.72 (0.91) 2.43 (1.36) 25.11 .000*

a Mean; b Standard deviation.*p ( .05.

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$50,000 perceived a higher risk on perfor-mance, financial, and overall risks thanrespondents with an income level of$50,000 or above. Furthermore, respondentswith an income level of less than $10,000perceived a higher risk on physical thanrespondents with an income level of $50,000or above. In addition, significant differenceswere discovered between respondents interms of Internet usage. The respondentswho have accessed the Internet for 5 years orless perceived higher risk on performance,financial, psychological, and overall risksthan other groups.

Perceived Differences on Risk-ReductionStrategy

An independent sample t test was used toexamine the difference in the importance ofrisk-reduction strategies between online air-ticket purchasers and nonpurchasers(hypothesis 3). The results showed that therewas only a significant difference in theimportance of shopping around over theweb as a risk-reduction strategy betweenonline air-ticket purchasers and non-purcha-sers (p ( .05). Online purchasers ratedsignificantly higher on ‘‘shopping around

TABLE 6. Risk Perception According to Demographic Characteristics

DemographicCharacteristics

F1 (PERF) F2 (SEC) F3 (FIN) F4 (SOC) F5 (PHY) F6 (PSY) F7 (TIM) OVERALL

Gender

Male 2.22 2.59 1.70 1.37 1.36 1.39 1.57 1.81

Female 2.64 2.99 1.72 1.37 1.50 1.68 1.60 2.21

t value 22.842** 22.767** 2.178 2.074 21.482 22.653** 2.269 23.059**

Marital Status

Single 2.51 2.74 1.76 1.36 1.35 1.49 1.55 1.99

Married 2.17 2.89 1.55 1.37 1.61 1.62 1.67 2.01

t value 2.109* 2.903 1.894 2.110 22.058* 21.059 21.063 2.109

Position

Students 2.54 2.82 1.79 1.44 1.49 1.55 1.62 2.03

Others a 2.09 2.80 1.46 1.11 1.21 1.54 1.51 2.01

t value 2.337* .094 2.623* 4.809** 2.933** 0.032 0.867 0.125

Age

18–30 2.56 2.78 1.80 1.43 1.50 1.55 1.60 2.02

31 years or

above

2.14 2.83 1.46 1.16 1.22 1.49 1.52 1.99

t value 2.436* 2.320 2.934** 3.532** 3.293** .503 .684 .156

Income

$9,999 or

under (A)

2.56 2.82 1.81 1.42 1.55 1.59 1.65 2.10

$10,000–

$49,999 (B)

2.42 2.79 1.70 1.28 1.34 1.52 1.50 2.00

$50,000 or

above (C)

1.60 2.58 1.22 1.31 1.17 1.21 1.58 1.44

F value 7.531** .461 4.785** 1.246 3.254* 1.917 .961 3.985*

Tukey’s test A, B . C A, B . C A . C A,B . C

Internet Usage

5 years or

under (A)

2.74 2.98 2.03 1.50 1.54 1.77 1.67 2.21

6–7 years (B) 2.51 2.75 1.74 1.49 1.42 1.45 1.62 2.03

8 years or

above (C)

2.24 2.63 1.50 1.28 1.44 1.43 1.56 1.80

F value 3.711* 1.814 7.482** 2.445 .356 3.410* .312 3.552*

Tukey’s test A . C A . C A . C A . C

a Others: faculty, staff, administrators, and other.*p ( .05; **p ( .01.

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over the web’’ as an important risk-reductionstrategy than nonpurchasers. However, nosignificant differences were found betweenthe two groups on the other risk-reductionstrategies. It could be concluded thathypothesis 3 is rejected except for the risk-reduction strategy of shopping around overthe web. Table 7 shows that the ‘‘reputationof web vendor’’ was perceived to be the mostimportant risk-reduction strategy to onlineair-ticket purchasers. On the other hand,‘‘well-known brand’’ was perceived to be themost important risk-reduction strategy fornonpurchasers. In addition, the importantrisk-reduction strategies ranking on ‘‘reputa-tion of web vendor,’’ ‘‘well-known brand,’’‘‘symbol of security approval,’’ and ‘‘recom-mendation of family and friends’’ weresimilar for both online air-ticket purchasersand nonpurchasers. Table 8 shows thehypotheses and the results of the study.

DISCUSSIONS

Whenever people try to purchase an air-ticket online, they confront a certain degreeof risk. Because they desire satisfactoryoutcomes from that purchase, their primaryconcern is whether the decision they makemay result in an undesirable purchasingexperience. Perceived risk from uncertaintyand possible negative outcomes are signifi-cant barriers that hamper purchasing deci-sions. We, therefore, need to understand the

nature of perceived risk and provide effectiveways to reduce consumers’ perceived risk toensure the steady growth of the online air-ticket market. This study provides severalimportant answers related to these questions.

Perceived Risk

This study found that nonpurchasersperceived an overall higher risk thanonline-purchasers in terms of buying an air-ticket online. These results confirm thatexperience is a source of acquiring internalinformation that is critical to increasingcertainty and reducing risk in the onlinecontext of air-ticket purchases.

Among the seven types of perceived risk,five types of risk—performance, security,financial, psychological, and time—were per-ceived much riskier by nonpurchasers com-pared to experienced consumers. It is clear thatthese types of risks should be reduced carefullyto convert nonpurchasers into purchasers andthus increase online air-ticket sales. In parti-cular, performance and security risks needcareful attention due to their significance.

Previous research suggests that securityrisk is the most significant barrier to non-purchasers of online air tickets (Law &Leung, 2000). The current study’s results,however, revealed that performance risk isthe most influential in potential consumersavoiding online purchases. Nonpurchasers’concern about the performance of webvendors and the air ticket itself exceeds the

TABLE 7. The Importance of Risk-Reduction Strategies

Risk-Reduction Strategies Online Purchasers Nonpurchasers t

Mean Rank Mean Rank

Reputation of web vendor 5.92 (1.24)a 1 5.83 (1.54) 2 .61

Well-known brand 5.69 (1.34) 2 5.90 (1.40) 1 21.28

Symbol of security approval 5.45 (1.49) 3 5.70 (1.47) 3 21.40

Recommendation of family and friends 5.28 (1.40) 4 5.48 (1.50) 4 21.21

Shopping around over the web 5.23 (1.68) 5 4.76 (1.77) 7 2.34*

Special offers 5.18 (1.54) 6 4.88 (1.74) 6 1.52

Reading product information 4.93 (1.70) 7 5.07 (1.66) 5 2.70

Cheapest brand 4.72 (1.67) 8 4.34 (1.78) 8 1.89

a Standard deviation.*p ( .05.

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concerns about transaction-specific risk.Online air-ticket reservations require con-sumers to take full responsibility for findingand comparing prices offered by multipleproviders in order to book and confirmtickets (Cunningham et al., 2005; Law &Leung). Because air tickets are expensive topurchase, but hard to appreciate theirmonetary value in advance, a mistake inthe process can result in a significant loss tothe purchaser. This study clearly shows thatnonpurchasers are more influenced by thiscompelling factor. Thus, it is necessary toassure that web vendors’ performance isreliable in order to convert nonpurchasersto purchasers. Furthermore, nonpurchasersmust be persuaded that purchasing air-tickets online is simply a different way topurchase exactly the same product that hasbeen sold in a traditional way.

The current study found that security riskis another major concern for both onlinepurchasers and nonpurchasers. Becausecredit cards are the only payment methodfor online air-ticket purchases (Law &Leung, 2000), it is clear that the fear of poorsecurity hampers the growth of online air-ticket sales. Security risk is directly asso-ciated with the trustworthiness of online webvendors (Aiken & Boush, 2006). Reducingsecurity risk is essential to building trustbetween consumers and web vendors.Fortunately, online web vendors havebecome more aware of the importance ofconsumers’ security-related concerns. As aresult, they have adopted strategies to reduceuncertainty by increasing statements and

practices related to security, privacy, andconfidentiality associated with their website.Increased levels of security-related disclo-sures reduce consumer’s risk perceptions andhave a positive influence on online purchaseintention (Miyazaki & Fernandez, 2000).Recent research suggests that a third-partycertification is more effective than advertis-ing or objective-source ratings (i.e.,Consumer Reports; Aiken & Boush).

As a whole, respondents evaluated secur-ity risk as the most significant risk factor totheir overall risk perception. This findingsupports the results of Kolsaker et al.’s study(2004) that examined financial transactionsand personal information as being signifi-cant to understanding consumers’ risk per-ception in reserving air-tickets online.Compared to Kolsaker et al.’s study, whichwas conducted in Hong Kong, our similarresults indicate that the importance ofreducing security risks is considerable,regardless of cultural differences.

In addition to security risks, psychologicalrisks were found to be the second mostimportant risk factor for overall risk percep-tion. Psychological risk is significant inrelation to a rather expensive product witha complex and difficult decision making(Stone & Grønhaug., 1993). Psychologicalrisk can be caused by unfamiliarity with theonline suppliers (Laroche, McDougall,Bergeron, & Yang, 2004). Although existingand new vendors have rushed into theprofitable business to sell online travelproducts/services, familiarity should be akey issue that vendors address. This finding

TABLE 8. Hypotheses and Results of the Study

Hypotheses Results Explanation

H1 Partial support Online air-ticket purchasers perceived less risk than non-purchasers in terms

of performance, security, financial, psychological, and time risks. There was

no significant difference in the degree of perceived social and physical risk

between two groups.

H2 Strong support The degree of perceived risk in online purchasing was significantly different

according to different consumers’ demographic characteristics

H3 Little support Online air-ticket purchasers perceived ‘‘shopping around over the web’’ as a

more important risk-reduction strategy than nonpurchasers did. There was no

significant difference in the importance of the other risk-reduction strategies

between the two groups.

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supports the notion that the reputation ofweb vendors and well-known brands areimportant risk-reduction strategies. Thesestrategies are effective in reducing unwantedworry/anxiety related to online purchases, aswell as concerns about a web vendor’sperformance.

On the other hand, the impact of ‘‘socialrisk’’ was negligible to ‘‘overall risk’’ in thepresent study. This result is consistent withprevious studies that social risk is not asignificant risk dimension in purchasing air-tickets online (Cunningham et al., 2005;Ueltschy et al., 2004). This may be becauseair tickets are not socially desired goods thatreflect self-image (as are jewelry, cars, andhomes), which are subjected to the opinionsof reference groups.

The results of the study provided someuseful information in understanding therelationship between perceived risk andonline shoppers’ demographic characteris-tics. For example, females perceived a higherrisk on performance, security, psychological,and overall risks than males. Overall,females were more concerned with theunanticipated or uncertain consequencesthan males. It is consistent with the notionthat females are more risk averse than males(Maltby, Chudry, & Wedande, 2003). Giventhat the proportion of female online popula-tion has increased (Howard, Raine, & Jones,2001) and even new Internet users are morelikely to be females (Katz et al., 2001), onlinemarketers should be aware that females havehigher perceived risk than males. Moreover,the majority of respondents of this studywere under 30 and most of them were collegestudents. This group of young online pur-chasers perceived higher risk on perfor-mance, financial, social, and physical risks.

Risk-Reduction Strategies

Implementing needed risk-reduction stra-tegies is vital to reduce consumers’ perceivedrisk and eventually enhance the consumers’willingness to purchase online. This studyfound that shopping around over the webwas a more important risk-reduction strategy

to online purchasers than nonpurchasers.Shopping around as a risk-reduction strat-egy involves finding the best value—that is,high quality and low price—in the market(Sirgy & Su, 2000). Online marketersshould provide the best value to reduceuncertainty perceived by online purchasers.This facilitates more knowledge and con-fidence in selecting a product (Bao, Zhou,& Su, 2003). However, the practical impli-cation of this finding might not be sig-nificant because shopping around over theweb was perceived as a moderate risk-reduction strategy for online purchasers(ranked fifth), and as one of the leastimportant risk-reduction strategies for non-purchasers (ranked seventh). On the otherhand, reputation of web vendor, well-known brand, symbol of security approval,and recommendation of family and friendswere considered as important risk-reductionstrategies for both online purchasers andnonpurchasers. Those risk-reduction strate-gies would be useful to motivate existingonline purchasers to purchase more and toprovide nonpurchasers certainty aboutwhat they purchase.

Reputation of web vendors was rated asthe most important risk-reduction strategyby the respondents. The good reputation of aweb vendor reduced perceived risk in onlineair-ticket purchases. This finding supportsthe claim that good reputation helps reduceuncertainty in purchase decisions (Grabner-Kraeuter, 2002). Reputation is derived fromtrustworthy performance of companies, andit is a significant factor in building trustbetween companies and customers (Grabner-Kraeuter; Schoenbachler & Gordon, 2002).Trust leads customers to provide theirpersonal information to their web vendors,and, consequently, enhances the relationshipbetween customers and companies (Grabner-Kraeuter). To build a good reputation, webvendors who already have a well-establishedreputation in the traditional market canbenefit from their current reputation.Providing company information such asbackground, mission statement, and com-pany’s news to customers on their websites

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would help to build reputation(Katerattanakul, 2002). However, web ven-dors who have not previously established areputation should develop their reputationto reduce uncertainty and provide confi-dence to customers in their purchase deci-sions. Grabner-Kraeuter suggests twoimportant strategies in building a goodreputation for new web vendors; namely,(a) collaborating with other companies whohave already built a good reputation, or (b)running a virtual community, which con-tributes to closer relationship with theircustomers.

Recommendation of family and friendswas an important risk-reduction strategy. Ingeneral, consumers are sensitive to the opi-nion of family and friends when purchasingservices because of the difficulty involved inassessing service qualities. Perry and Hamm(1969) indicate that as perceived riskincreases, the importance of word-of-mouthas a source for reducing related risk alsoincreases. Murray (1991) emphasizes thepower of word-of-mouth endorsements as itprovides greater importance for consumers toreduce perceived risk because of its clarifica-tion and feedback opportunities, verses whatthe mass media communications do. Theauthor also suggests that word-of-mouth isconsidered more trustworthy in reducingperceived risk for services than for products.Hence, the power of word-of-mouth endorse-ments should not be overlooked by the onlinemarketers. Furthermore, online marketersshould ask for positive word-of-mouth fromcustomers and offer incentives for it. At thesame time, they need to be aware of negativeword-of-mouth. Consumers usually hesitateto complain about the poor service or productperformance. Rather, they just stop using theproduct/service and tell their acquaintancesabout their bad experiences. Thus, onlinemarketers should actively listen to customers’complaints and resolve the problem beforenegative word-of-mouth information spreads.

Although Connolly and Olsen (2000)insist that the value of brand would becomedesensitized by general travelers due to thegrowing use of the Internet, the results of

this study supported the notion that a well-known brand was a preferred risk-reductionstrategy. Brand name plays an importantrole for customers to evaluate the product/service quality (Rao & Ruekert, 1994;Richardson, Dick, & Jain, 1994; Srinivasan& Till, 2002). When customers evaluate aproduct/service that is high in credencequality prior to trial, they are more likelyto rely on the brand name to infer the qualityof the product/service. In this case, well-known brand name provides credence to theproduct/service and this credence eventuallyhelps reduce perceived risk in purchasedecisions (Srinivasan & Till).

The difficulty in assessing the quality ofvendors in terms of security makes securityrisk one of the most critical risks in purchas-ing products/services online. This study foundthat a symbol of security was an importantrisk-reduction strategy in online air-ticketpurchases. Fenech and O’Cass (2001) suggestthat online marketers should adopt the use ofcustomer protection to help consumers over-come their security concerns in online trans-actions. In this perspective, using the symbolof security approval is an effective way toensure the security of their websites. Forexample, the third-party seals of approvalsuch as VeriSign and TRUSTe can beeffective in reducing consumers’ perceivedrisk in online shopping (Miyazaki &Krishnamurthy, 2002).

On the other hand, cheapest brand wasranked as the least important risk-reductionstrategy by both online purchasers andnonpurchasers. This result may disappointmarketers who concentrate on cheapest brandimage targeting cost conscious consumers.Frequently, online marketers believe thatonline consumers primarily focus on priceand this forces a hard sell based on price.However, in related studies (e.g., Kim & Kim,2004; McCole, 2002) as well as the currentstudy, this assertion is not always supported.Especially, Kim and Kim point out that asonline customers gain more online experiencethey are less likely to rely on price in theirpurchase decisions. With the growth of onlinesales and the increasing number of online

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customers, it would be beneficial to use risk-reduction strategies along with other dimen-sions rather than solely based on price. Thismight be a good way to reevaluate the costeffectiveness as a risk-reduction strategy.

CONCLUSIONS

This study draws attention to the directapplication of perceived risk theory and tounderstand consumers’ purchasing behaviorin the online market. The results of the studyprovide useful information on the identifica-tion of perceived risk in online air-ticketpurchases. As Pope et al. (1999) insist,perceived risk varies from product to pro-duct. The product-specific identification ofperceived risk is one of the contributions ofthe current study. Furthermore, this studyidentified that security risk, which is not atype of perceived risk in the traditionalmarket, is most important to understandingoverall risk. In addition, the study revealedthat non-purchasers perceived higher riskthan online purchasers. It supports theresults of several scholars that experience isa major factor in reducing risk in onlinepurchases. On the other hand, it alsoprovides some different findings from Pires,Stanton, and Eckford (2004) and Ueltschy etal. (2004) that experience does not influencethe level of consumer risk in online air-ticketshopping. More specifically, performance,security, financial, psychological, and timerisks were perceived as more important bynonpurchasers than online purchasers.Lastly, this study identified practical impor-tant risk-reduction strategies, which areapplicable to both purchasers and non-purchasers. These results would be beneficialto online air-ticket marketers who want toretain current customers and draw potentialcustomers.

Limitations

This study, although providing interestinginformation related to online air-ticket pur-chases, has its limitations. Because this study

was conducted in the United States, the resultsthat would be applied would be limited to theUnited States. Weber & Hsee (1998) suggestthat cross-cultural differences would influenceconsumers’ risk perception. We can assumethat cultural differences might play an impor-tant role in consumers’ perceived risk whilepurchasing air-tickets online. Therefore, theinfluence of cultural differences in online air-ticket purchases should be addressed in thefuture research initiations.

Although we argue that our sample is notsignificantly different from the general popula-tion in terms of purchasing air-tickets online,we believe that our results may be more usefulfor companies, particularly those that targetstudent population. For example, severalwebsites such as STATRAVEL andSTUDENTUNIVERSE, which target stu-dents for better deals on travel products,including air-ticket purchases, may be morebeneficial. Thus, results should be consideredwith caution and sampling limitations. Futureresearch should use a broader samplingpopulation.

In addition, although monetary incentivesand follow-up e-mails were utilized to increasethe response rate, the response rate wasrelatively low. The short attention span ofapproximately 15 seconds and the huge amountof junk mail that online users receive might be areason for the low response rate (Kim, Nam, &Stimpert, 2004). Therefore, an effective way toobtain a high response rate in online surveysshould be used in future research.

While investigating perceived risk on theproduct category in online shopping isimportant (Miyazaki & Fernandez, 2000);the usefulness of this study would be limitedto online marketers dealing with air-ticketsales. Therefore, future research shouldexamine perceived risk in other travel-relatedproducts/services such as hotel reservations,car rentals, and cruises.

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