Tourism Management 26 (2005) 561–570

ARTICLE IN PRESS

*Correspondi

4276.

E-mail addre

0261-5177/$ - see

doi:10.1016/j.tou

Online shopping motivations and pleasure travel products: acorrespondence analysis

Srikanth Beldonaa,*, Alastair. M. Morrisonb, Joseph O’Learyc

aDepartment of Nutrition and Hospitality Management, East Carolina University, 322A Austin, Greenville, NC 27858, USAbSchool of Consumer & Family Sciences, Purdue University, West Lafayette, IN, USA

cDepartment of Recreation, Leisure and Tourism Services, Texas A&M University, TX, USA

Received 31 October 2003; accepted 1 March 2004

Abstract

The purpose of this study was to examine purchase motives of pleasure travel components of low and high complexity in a Web

environment. Motives to buy travel components of a typical pleasure vacation were differentiated using the economics of search framework

and mapped using correspondence analysis. Findings showed a uni-dimensional solution that was named informational/transactional. While

purchase of activities, accommodation, events and attractions demanded more informational contexts behind purchase; purchase of car

rentals and airline tickets were driven by transactional contexts. Theoretical and marketing implications are discussed.

r 2004 Published by Elsevier Ltd.

Keywords: Shopping motivations; Product complexity; Search characteristics; Online travel purchase behavior

1. Introduction

Apart from accommodations, flights and car rentals,the growth of travel offerings on the Internet nowinclude vacation packages, cruises, events, tours andattractions (NYU/Phocus Wright Report, 2003). In fact,there is a gradual shift amongst travel technologyvendors to move beyond accommodations, flights andcar rentals to encompass cruises, destinations and others(NYU/Phocus Wright Report, 2003).While the industry faces this transition, the authors of

this paper find no evidence of research, empirical orotherwise that addresses online buying behavior ofcomplex travel products. Many studies have evaluateddemographic, Internet usage and behavioral predictors ofonline travel purchase behavior (Bonn, Furr, & Susskind,1999; Weber & Roehl, 1999; Morrison, Su, O’Leary, &Cai, 2001; Beldona, Morrison, & Ismail, 2003). However,facets of travel products such as events, attractions, toursand packages have their own unique product character-istics. Arguably, perceived risk behind the quality of eachof these services can significantly vary.Therefore, the propensity to buy the range of low to

high complex travel products will also largely vary due

ng author. Tel.: +1-252-328-2190; fax: 1-252-328-

ss: beldonas@mail.ecu.edu (S. Beldona).

front matter r 2004 Published by Elsevier Ltd.

rman.2004.03.008

to inherent individual consumer characteristics. Forexample, convenience, price comparison, and lowerprices were identified as the three main reasons whyInternet users buy travel products online (Starkov &Price, 2003). A key question to ask here is ‘‘What are thecustomer motivations that differentiate the purchase oflow and high complex travel products? Based on thesemotivations, one can identify the relevant features andcapabilities required of online travel websites.The purpose of this study is to evaluate the relation-

ship between consumer purchase motivations across lowand high complex travel products. Consumer purchasemotivations are grounded in consumer behavior theoriesacross both offline and online contexts, and morespecifically travel marketing. The paper then discussesthe theoretical and practical implications of the findings,and suggests directions for future research. The study islargely exploratory in nature.

2. Conceptual background

2.1. Overview of travel products

The tourism market place is not well defined as itinvolves an amalgam of heterogeneous businessesservices such as transport, accommodation, restaurant

ARTICLE IN PRESSS. Beldona et al. / Tourism Management 26 (2005) 561–570562

and retailing (Carlsen, 1996). This is compounded by theabsence of a meaningful taxonomy in literature that candelineate key characteristics between travel products. Ata broad level, travel products can be classified based oncomplexity. Flights, accommodation, and car rentalscan be categorized as products of low complexity,whereas land-based vacations, cruises and tours can beconsidered products of high complexity.Economics of information theory (Nelson, 1970;

Darby & Karni, 1973) categorized products into search,experience and credence types based on how consumersevaluate them. Products with search qualities can befully evaluated prior to purchase, whereas experience-based products must be first purchased and consumedbefore the consumer is able to evaluate. Darby andKarni (1973) extended this to include credence goodswhich consumers can never fully evaluate even afterpurchase and consumption. Zeithaml (1981) integratedthis categorization to marketing, and posited thatservices exhibit more experience and credence qualitiesdue to their unique characteristics namely intangibility,non-standardization and inseparability.Bringing the realm of travel products within this

categorization provides cues on the nature of search andpurchase in the online medium. On the Internet, travelsuppliers can provide greater detail on features ofproducts using a wide array of tools. Depending onthe type of product, these may be comparison charts,virtual tours, video and graphics in video as well as stillimage formats. Flights, accommodations and car rentalsare standardized services that can be placed within theeasier to evaluate context as there are more knownparameters of tangibility (Zeithaml, 1981; Mittal, 1999).In contrast, complex travel products such as cruises,land-based vacations, tours, activities and attractionscan be arguably placed in the difficult to evaluatecontext. Prior research also indicates a linear relation-ship between perceived risk in a service and theextent of detail of search in services (Murray &Schlacter, 1990).

2.2. Consumer motivations to shop

At a fundamental level, consumer motivation to shopis best explained by motivation theory, which contendsthat cognitive or affective motives seek individualgratification and satisfaction (McGuire, 1974). Severalstudies have evaluated consumer motivations to shopacross a range of contexts such as malls, mail ordercatalogs, and supermarkets (Bellenger & Kargaonkar,1980; Gehrt & Shim, 1998; Darden & Ashton, 1974/1975). Shopping motivations in the generic grocerycontext can be distilled into shopping contexts namelyoverall savings, convenience, information seeking, socialinteraction, and shopping experience (Rohm & Swami-nathan, 2004).

In the online context, the most compelling motivationbecame the convenience to shop 24/7 from the luxury ofone’s home (Swaminthan, Lepkowska-White, & Rao,1999). In the travel context, where many componentsmay make up for the travel experience, this combinationof convenience, immediacy and rich information ishighly effective. For example, websites like Travelocityand Expedia provide aggregated services such as flights,accommodations, and car rentals that are aimed atbeing a one-stop-shop built around convenience.The travel decision-making process is a complex

multi-stage process layered along a hierarchical set ofactivities (Fesenmaier & Jeng, 2000). Here too, conve-nience can serve as a key driver of the travel planningprocess. On the Internet, consumers can self-build acombination of various complementary travel productswith relatively less difficulty when compared to thetraditional context. However, the Internet can add tothe complexity of the process too because of theplethora of sources needed to coordinate and piecetogether this process. For example accommodations canbe bought from accommodation sites, intermediaries,airlines, discounts, and even destination sites. Of course,the level of detail provided by each of these websitesvaries based on what the core and secondary offeringsare.Website characteristics and purchase intentions are

better explained under the framework of the TechnologyAdoption Model (Davis, 1989; Davis, Bagozzi, &Warshaw, 1989). Lee, Park, and Ahn (2001) expandedon the original TAM model and introduced an e-Comadoption model that included perceived ease of use,perceived usefulness, perceived risk with products/services, and perceived risk in the context of onlinetransaction. An easy to use travel website would implyaspects such as navigability, efficiency, consistency andcompatibility (Morrison, Taylor, Morrison, & Morri-son, 1999). Another aspect of the website that relates toperceived ease of use is the information, features andfunctionality available on the site. This is especially thecase with complex products such as tours, packages andcruises, where consumers seek exhaustive informationbefore making the purchase decision. Online serviceencounter satisfaction was higher when informationcontent at the web site was deeper (Shankar, Smith, &Rangaswamy, 2000).Research on efficacy of websites is extensive, with

many works aimed at evaluating a diverse range ofproviders in the hospitality industry (Kasavana, 1997,2001; Morrison et al., 1999). However, specifics ofwebsite effectiveness such as technical performance areoutside the context of this study. This study is structuredaround an intermediate meeting ground that hasconsumer motivations and website characteristics attwo ends of the continuum. While on one hand, it dealswith underlying motivations, it also matches these with

ARTICLE IN PRESSS. Beldona et al. / Tourism Management 26 (2005) 561–570 563

salient features of travel websites that guided consumersto buy the travel product in question. Put differently,this study is about websites providing specific webshopping capabilities (push) to satisfy (pull) relevantconsumer motivations.Perceived usefulness of a website can also be gauged

by the website’s ability to attract existing customers andprovide services such as redemption of rewards or milespoints (Shankar et al., 2000). Results show that loyaltyto the service provider is higher when the service ischosen online than offline (Shankar et al., 2000). Manyhospitality and travel organizations have leveraged theweb’s capabilities to provide interfaces for customers tomanage their rewards. For example, Marriott hasmarriottrewards.com and provides unique services toits members on its website. Customers can access andbook online, while simultaneously keeping score of theirpoints or miles earned.Loyalty practices and policies enhance responsive-

ness, availability and convenience to the customer, whoin turn may choose these specific websites for repeatpurchases. Degeratu, Rangaswamy, and Wu (2000)found that brand names become more important onlinein the case of low-involvement products as opposed tohigh-involvement products. In a study of retail groceryproducts, findings indicated that consumers did morescreening of information on the basis of brand namesonline as opposed offline (Andrews & Currim, 2000). Inthe case of travel products, one may argue thatstandardized products such as flights, accommodation,and car rentals can be considered to be within thiscontext, where the brand name could be a major drawfor purchase.Low prices can also be one of the reasons as to why

people buy online. It has been found to be a majordriver of online travel purchasing (Starkov & Price,2003; PhocusWright Report, 2000). Price sensitivity ishigher online, but this is due to online promotionsbeing stronger signals of price discounts (Degeratuet al., 2000).

2.3. Internet experience

Technology adoption theory also has been used toexplain purchasing propensities on the Internet. Thetechnology adoption cycle states that when a technologyis introduced in the market, its adoption stages arecharacterized by five segments, namely explorers,pioneers, skeptics, paranoids, and laggards (Parasura-man & Colby, 2001). There are individual characteristicsthat go to distinguish each of the above segments.Segments vary based on a combination of optimism,innovativeness, discomfort, and insecurity in attitudestowards the technology. The important aspect abouttechnology adoption is that each segment develops overtime to become a viable customer segment (Parasura-

man & Colby, 2001). In this context, broadband users(early adopters) were found to show greater likelihoodof buying travel products online compared to narrow-band users who are typically late adopters (Beldona,Kline, & Morrison, 2004).Attitude towards a medium can also serve as a strong

predictor of marketing exchange, and is also integral totechnology adoption theory (Parasuraman & Colby,2001). For example, broadband users are considered tobe progressive, success oriented, and ahead in thetechnology adoption curve (Jackson, Montigni, &Pearce, 2001). Grounded in the theory of reasonedaction (Fishbein & Ajzen, 1975), it suggests thatattitudes can be used to predict behavioral intentionsand behaviors.There have been other studies that have supported

online experience or tenure as a key determinant ofonline buying behavior (Bellman, Lohse, & Johnson,1999; Ratchford & Talukdar, 2001; Beldona et al.,2003a, b). Findings indicate that greater the number ofmonths/years the user spent online combined withhigher frequency of Internet usage; greater wasthe likelihood of buying (Bellman et al., 1999;Weber & Roehl, 1999; Beldona et al., 2003, 2004).Additionally, domain-specific consumer innovativenesswas found to moderate frequency of usage as anantecedent of online purchase behavior (Citrin, Sprott,Silverman, & Stem, 2000).However, delving a little further into the Internet

experience reveals a more evolving construct. Under-standing Internet experiences is grounded in the abilityto process information effectively. This in turn isinfluenced by education, intelligence, product experi-ence, relevant knowledge, and message difficulty (Ma-cInnis & Jaworski, 1989). Hoffman and Novak (1996)found that experienced users were attracted to techni-cally advanced sites with novel features that presentmore challenges. Alwitt and Hamer (2000) posit thatconsumers increase their control with more time spenton the Internet, and in turn develop finer expectations oftheir interactions with businesses in general. Findingsindicated an inverted ‘‘U’’ relationship between webusage expertise and consumers’ expectations of serviceproviders, where consumers with moderate levels of webusage expertise have higher expectations than doconsumers with low or high levels of web usageexpertise. Hammond, McWilliam, and Diaz (1998)showed prior experience is an important moderator ofusers’ attitudes towards the Web, although its influenceis not linear. The heaviest users are enthusiasts for themedium, while moderate and light users perceive it as asource of information, but not for entertainment or fun.Novelty as a construct has also been categorized as a

key value driver in online consumer behavior (Amit &Zott, 2000). How a person buys is largely driven by thenovelty of the purchase occasion, an aspect relevant to

ARTICLE IN PRESS

Table 1

Table of online shopping motivations

Reasons for purchasing at the website

1. Ability to use rewards/travel points

2. Availability

3. Detailed information

4. Ease of booking

5. Familiar with company

6. Offered independent ratings of product (e.g. hotel star rating)

7. Low price

8. Recommendations by friends/family

9. Testimonials on site/chat line/online bulletin board

10. Other reason (specify)—

S. Beldona et al. / Tourism Management 26 (2005) 561–570564

the Internet when viewed as a technological innovation.This means that novelty of purchasing may itself be adriver, but may wear off as repeated purchases occurover a period of time. Subsequent visits to a website mayhave marginal effects on purchasing as the shopper maynot register the stimuli that played a persuading roleduring earlier visits (Park, Iyer, & Smith, 1989). On thecontrary, the likelihood of purchasing may increasethrough increased familiarity with purchase system inplace (Beatty & Ferrell, 1998). This paradox is bestexplained by the theory of consumer innovation, whichin itself can play a significant role in how novelty isdealt with in the purchase process (Hirschman, 1980).Consumer innovation is largely driven by innatepersonality characteristics in every individual (Hirsch-man, 1980).

3. Methodology and analysis

Data for this study came from a November 2001survey conducted by the Canadian Tourism Commis-sion (CTC). The survey was conducted using telephoneand respondents were randomly selected from telephonedirectories in the United States and Canada. Acomputer-aided telephone interface (CATI) system wasused to record responses. Initially, the number of casesrepresenting the US was 1364 and that of Canada was1161. To ensure parity in the sample size for comparablerepresentation within the analysis, the number of UScases was randomly scaled down to 1145. This led thetotal number of cases to be used for analysis to be 2306.Questions for the specific analysis to be done were

drawn from a pool of questions that sought dichot-omous (1=yes, 0=no) responses on the reason forpurchasing at a particular site as opposed to anothersite. The question posed was ‘‘For your trip toDESTINATION (subject specific and mentioned earlierin questionnaire), what is the main reason that youpurchased your airfare at SITE (selected in an earlierquestion) versus another site? The list reasons areprovided in Table 1. A ‘‘select all that apply’’ optionwas given for respondents to answer. A more detailedlist of frequencies is presented in the contingency tablecreated in Table 2. The 10th category called ‘‘other’’recorded open responses. Only six categories werechosen for final analysis namely ability to use rewards/points, availability, detailed information, ease of book-ing, familiar with company and low price. Independentratings, recommendations, and testimonials were threeother categories discarded from the analysis because ofthe low frequency counts that they reported. Only 4.3%cases reported independent ratings as a reason to buy,5.1% reported recommendations, and 1.18% of thecases reported testimonials as a reason for purchase.Open responses in the ‘‘other’’ category were analyzed

and recoded back to any of the six chosen categoriesbased on the response given. A total of 44 observationswere added into the contingency table based on thisrecoding from the open category.User skill level was used as a control or moderating

variable. This was constructed using a combination ofthree variables namely online tenure, type of Internetconnection and the extent to which multimedia applica-tions are used such as Acrobat, Flash, Quicklime,Windows Player. Users who have spent more than 3years on the Internet, have broadband connections anduse more than two multimedia applications werecategorized as high-skilled users. Low-skilled users werecategorized if they had spent less than 3 years online,have narrowband connections, and use less than twomultimedia applications.Correspondence analysis using multi-way tables was

chosen as the statistical technique to analyze the data. Itis a statistical method to depict associations between twoor more categorical variables. It provides a visualizationof the association along with some referential statistics todetermine the number of dimensions prevalent betweenthe associations. In effect, it is a geometric technique thatdraws from the row and column points in thecontingency table, and place categories (levels) of thevariables as points in low-dimensional visual space, so asto best fit their associations in the table. Put differently,correspondence analysis is a sophisticated technique thatgives a powerful representation of association betweencategorical variables by giving a comprehensive view ofthe data (in the contingency table) for effectiveinterpretation.Correspondence analysis is a widely used technique in

marketing research. It is used to examine similarities andassociations between attributes and brands. In tourismmarketing literature too, correspondence analysis isbecoming a much used technique (Gursoy & Chen,2000; Chen, 2000). As a statistical technique of choice,correspondence analysis is very useful when associationsbetween two or more multi-level categorical variableshave to be examined. In tourism marketing specifically,

ARTICLE IN PRESS

Table 2

Multi-way table of user skill level, travel product and online shopping motivation

Travel product component Rewards Availability Information detail Ease of booking Familiarity Low price

Low user skill level

Flights 83 129 85 183 109 208

Activities 3 26 32 42 7 15

Attractions 3 31 25 31 8 16

Car rentals 28 72 53 112 68 113

Events 1 33 28 37 5 19

Accommodations 32 147 179 217 75 147

Tours 2 14 16 21 3 15

Packages 5 26 22 38 8 30

High user skill level

Flights 70 74 76 142 78 129

Activities 3 15 16 18 7 12

Attractions 7 10 12 19 5 10

Car rentals 34 49 46 84 43 81

Events 4 25 29 28 8 14

Accommodations 29 97 113 141 24 82

Tours 1 8 6 12 1 3

Packages 7 16 15 24 9 24

S. Beldona et al. / Tourism Management 26 (2005) 561–570 565

it is an extremely useful application because of the largenumber of categorical variables used for analysis.Correspondence analysis derives commonalities

amongst categorical variables akin to principal compo-nents analysis for continuous data (Garson, 2001).However, it must be mentioned that correspondenceanalysis is purely an exploratory technique, and thatstatistical significance of relationships should not beassumed (Hair, Anderson, Tatham, & Black, 1998).Table 2 illustrates the multi-way table to be analyzed.This multi-way table shows frequencies of a three-waycross tabulation matrix comprising user skills (twolevels), travel product component (eight levels), andreason to purchase (six levels). A multi-way table can beanalyzed in correspondence analysis using an approachcalled ‘‘stacking’’ (Friendly, 1995). A three-way table, ofsize I � J � K can be sliced into I two-way tables, eachJ � K. In our case, the frequencies were sliced intotwo (high and low user level) tables cross-tabulatedacross travel product component (J) and reason topurchase (K).

4. Results

Proc Corresp from SAS Version 8.2 was used toanalyze the data. In correspondence analysis, k � 1dimensions are drawn based on the number ofcategories in the column of the contingency table(Garson, 2001). With six reasons for purchase spreadacross the rows, five dimensions were drawn from theanalysis as is evident in Table 3. Correspondenceanalysis provides statistical measures of describing thenumber of dimensions, and the proportion of variance

explained by each dimension. These are called singularvalues, and they should be greater than 0.20 to beaccepted as a viable dimension (Hair et al., 1998).Singular values for dimensions extracted indicatea uni-dimensional solution with a 0.23 value fordimension I.A total chi-square statistic is also provided (236.074)

as a measure of association between the rows andcolumns, and the number of dimensions extracted.Dimension I explains for 83.32% of the variance ofthe cumulative solution. Tables 4 and 5 provide moredetail to understand the actual decomposition of thevariance based on individual contributions of columnand row points in the contingency table. Table 4 outlinesthe travel services, and how they correspond to thedimensions. Coordinate values of each travel compo-nent are shown in column 2 across both dimensions.Inertia is a term used in correspondence analysis todescribe the variance of that point along the dimensionin contention (Hair et al., 1998). Each point is explainedalong the dimensions in quantitative terms and thesevalues are illustrated in the column titled ‘‘explained bydimension’’ in Table 4. The cumulative total ofvariances explained by a point along the two dimensionsis then illustrated in the final column titled ‘‘total’’. Onewill note that with the exception of packages amongstlow skill users (28.8%), all other travel componentsexplained dimensions above the range of 50% of thevariance. Hair et al. (1998) suggest that points that donot contribute to the dimension over and above 50%should be removed in the joint plot. In this case, it wasdecided to drop packages completely from the analysiseven though one of its points (low skills) had explained alittle more than 50%. This is because it complements

ARTICLE IN PRESS

Table 3

Determination of dimensionality

Dimension Singular value Inertia Chi square Proportion explained Cumulative proportion

1 0.23264 0.05412 236.074 83.22 83.22

2 0.07562 0.00572 24.947 8.79 92.02

3 0.05181 0.00268 11.709 4.13 96.15

4 0.03862 0.00149 6.507 2.29 98.44

5 0.03186 0.00101 4.427 1.56 100.00

0.06503 283.664 100.00

Table 4

Dimensions and their correspondence to travel products

Travel services Coordinates Contribution to inertia Explained by dimension Total

I II I II I II

Low user skill level

Flights 0.2540 �0.0221 0.2177 0.0156 0.9561 0.0072 0.9633

Activities �0.3860 0.0381 0.0789 0.0073 0.9370 0.0091 0.9461

Attractions �0.3200 �0.0698 0.0495 0.0223 0.8044 0.0383 0.8427

Car rentals 0.1704 �0.1365 0.0549 0.3330 0.5812 0.3727 0.9539

Events �0.4018 �0.0876 0.0841 0.0378 0.8947 0.0425 0.9372

Accommodations �0.1698 �0.0249 0.0974 0.0197 0.8393 0.0180 0.8573

Tours �0.2609 �0.0298 0.0205 0.0025 0.7903 0.0103 0.8006

Packages �0.1283 �0.0781 0.0090 0.0315 0.3914 0.1449 0.5363

High user skill level

Flights 0.2416 0.1033 0.1407 0.2434 0.8323 0.1521 0.9844

Activities �0.1856 �0.0337 0.0104 0.0032 0.7888 0.0260 0.8148

Attractions �0.0294 0.2128 0.0002 0.1144 0.0173 0.9035 0.9208

Car rentals 0.1843 0.0307 0.0485 0.0127 0.9669 0.0268 0.9937

Events �0.3339 0.0247 0.0510 0.0026 0.8785 0.0048 0.8833

Accommodations �0.2368 0.0878 0.1155 0.1501 0.8490 0.1167 0.9657

Tours �0.3965 0.0370 0.0206 0.0017 0.6803 0.0059 0.6862

Packages 0.0532 �0.0229 0.0011 0.0020 0.2432 0.0451 0.2883

Table 5

Dimensions and their correspondence to online shopping motivations

Online travel search modes Coordinates Contribution to inertia Explained by dimension Total

I II I II I II

Rewards/points 0.4260 0.2163 0.2399 0.5850 0.7862 0.2026 0.9888

Availability �0.1667 �0.0515 0.0909 0.0821 0.7465 0.0713 0.8178

Information detail �0.3019 0.0537 0.2907 0.0870 0.9188 0.0291 0.9479

Ease of booking �0.0860 0.0176 0.0360 0.0142 0.7013 0.0294 0.7307

Familiarity 0.3239 �0.0766 0.2035 0.1078 0.8474 0.0475 0.8949

Low price 0.1891 �0.0580 0.1390 0.1238 0.8331 0.0784 0.9115

S. Beldona et al. / Tourism Management 26 (2005) 561–570566

with the other corresponding low value in the ‘‘highskill’’ category.Table 5 illustrates all six catalysts or drivers of

purchase. Just as factor loadings are used in conven-tional factor analysis to ascribe meaning to dimensions,so are ‘‘contribution of points to dimensions’’ used tointuit the meaning of correspondence dimensions(Garson, 2001). Clearly, all points explain more than

50% of the variance, with most in 80–95% range.Dimension I is explained by all six perceived catalysts ofpurchase. This is the column that explains the contribu-tion of points to inertia (variance) in percentage terms ofa particular dimension. To visualize association ofpoints in low two-dimensional space, a correspondencemap displays dimensions that emerge from principalcomponents analysis of point distances (Garson, 2001).

ARTICLE IN PRESS

Fig. 1. Joint plot of online shopping motivations, travel products & internet skill use level. FTL=flights (low skill), ACL=activities (low skill),

ATL=attractions (low skill), CRL=car rental (low skill), EVL=events (low skill), AML=accommodation (low skill), TOL=tours (low skill),

FTH=flights (high skill), ACH=activities (high skill), ATH=attractions (high skill), CRH=car rental (high skill), EVH=events (high skill),

AMH=accommodation (high skill), TOH=tours (high skill), PT=rewards/points, AV=availability, IN=information in detail, ES=easy to use,

FM=familiarity, PR=price.

S. Beldona et al. / Tourism Management 26 (2005) 561–570 567

Also called the joint plot (Fig. 1), the plot reveals therelationships between online shopping motivations andtravel products of low and high complexity. First lookshows two distinct sides on the X-axis. While rewards/points, familiarity and price are positioned along oneside, the other side of the same axis show the remainingthree namely ease of use, information detail andavailability. Therefore, this dimension is suggestivelynamed transactional/informational.Clearly, flights and car rentals purchases by both low

and high experienced users are strongly associated withlow prices and familiarity. On the other hand, events,accommodation and tour purchases amongst experi-enced users followed by activities from less experiencedusers appear strongly associated with detailed informa-tion. Another cluster groups activity purchases amongless experienced users followed by accommodationpurchases from experienced users with the availabilityfactor.When viewed holistically, the joint plot provides more

than just an association of relationships in clusters. Itdelineates travel components based on consumer per-ceptions of situational criteria attached to travelservices. For example, flights and car rentals arerelatively more established sectors in the online travelsegment. These sectors have greater price transparency,which drives consumers to seek more evaluativeinformation on that front. Familiarity is imperative tothese purchases as are reward points to be gained frompurchasing them online. In contrast, consumers attachmore importance to availability, detailed informationand ease of use to services that are not as yet established.Interestingly, accommodations fall within this group,along with activities, tours, attractions and events. One

may recall that the 2001 was a very defining period forthe accommodation sector in the online segment(Starkov, 2001). During this period, hoteliers werestruggling to provide price transparency on the Internet.Many hotels had not yet effectively placed inventoryonline, and this reflects strongly on the visual map.Another plausible reason for accommodations to fallwithin the informational is that the purpose of trip islargely pleasure driven. One may contend that thenuances of accommodations for pleasure trips facerelatively greater scrutiny as opposed to the same forbusiness trips.Highly skilled users attach more importance to

detailed information when purchasing tours, accommo-dation and events online. In contrast, less skilled usersattach more importance to availability, when it comes tobuying accommodations online. Interestingly, a reverselike situation exists when it comes to ‘‘activities’’, wherehigh-skilled users consider availability as very importantcompared to less skilled users who put informationdetail ahead of everything else. While low-skilled usersperceive detailed information as key to purchasing‘‘tours’’ online, high-skilled users see availability asmore important in the same sector.

5. Discussion and implications

Two key findings emerge from the study. Firstly,online shopping motivations of travel products of lowand high complexity are distinctively different. Sec-ondly, online shopping motivations vary depending onuser skill levels. Importantly, user skills are a function of

ARTICLE IN PRESSS. Beldona et al. / Tourism Management 26 (2005) 561–570568

online tenure, type of Internet connection and the typesof applications used to navigate the Internet.While purchase of less complex products such as

flights and car rentals are driven by motivations withtransactional objectives, shopping motivations behindcomplex such as tours, activities and attractions aredriven by informational parameters. Specifically, re-wards/points and price grouped with flights and carrentals, and motivations such as detailed informationand availability were largely associated with accommo-dations, tours, attractions, activities and events. Greaterinformation detail with products of high complexity canbe associated with greater perceived risk as well as theneed for more control too. Familiarity was moreassociated with less complex products, which can beattributed to the structural maturity of the industry interms of the presence of established brands.Low- and high-skilled Internet users are distinctively

different. At the outset, one can generalize that high-skilled users place greater emphasis on informationdetail when it comes to travel products of highcomplexity. This is illustrated by key associations ofthis group with information detail in the case ofactivities, events, tours and accommodations. However,in the case of accommodations and activities, low-skilledusers were driven more by availability than othermotivations.One can explain the above phenomenon of avail-

ability over detailed information using novelty theory(Amit & Zott, 2000). How a person buys is largelydriven by the novelty of the purchase occasion, anaspect relevant to the Internet when viewed as atechnological innovation. This means that novelty ofpurchasing may itself be a driver, but may wear off orimprove with repeated purchases over a period of time.Repeat buyers will need stronger stimuli such as detailedinformation compared to earlier stimuli (availability) inthis case that played a persuading role (Park et al.,1989). Again, a relatively less established segment suchas activities can attract more experienced users whereinavailability becomes very important. Less experiencedusers seeking the same product may perceive greater riskand seek more information, a fact illustrated in the jointplot. Future research can investigate this gap andspecifically examine evolving purchasing behaviorsbased on experience in greater depth.There are several implications for online and offline

travel marketers in general. Travel marketers can use thefindings in the areas of website design and promotionalactivities. Websites can be tailored more effectively tomeet needs of users based on skill levels. For example,websites can have alternative gateways based connectionspeed, as well as customized features based on identifiedskill levels. Skilled users can be provided with detailedinformational content using multiple media. Destinationmarketers who typically provide tours, activities and

events can improve their online availability. In fact,findings provide pointers on the behavioral antecedentsof buying pleasure travel products on the Internet. Thisis especially relevant given the projected growth inonline sales of complex leisure products over the nextfew years.The findings clearly indicate that lodging managers

should strive to improve availability of rooms as well asmore information about the property and its surround-ings. Informational detail can be in the form ofproximity to shopping centers, surrounding attractionsand related detail. Findings are more pertinent to resortmanagers because the context of this study is largelypleasure oriented. For managers of airlines and carrentals, findings emphasize the need for websites to havegreater customer relationship management tools. Onlinecustomers of these products are looking for ways toderive greater value from web based interactions. Inmany ways, this can be interpreted as enhanced controlfor customers so that they can effectively manage theirrewards programs.Findings of the study can also help in the develop-

ment of customer centric travel reservation systems. Asthe travel distribution system moves towards integratingvarious components of travel using common standards,the results of this study can help in the design of systemsbased on customer requirements of components. Futureresearch can develop a comprehensive evaluation oftravel components within one single system, andevaluate customer perceptions towards it.A big limitation of the study is the absence of

‘‘complementarities’’ as a driver of purchase. A fewrespondents in the open category did indicate that theybought specific components simply because they werepart of a larger package. Future research shouldinvestigate complementarities and the specific relation-ships between the various components in it. Forexample, flights and accommodations can be consideredmore complementary compared to flights and otherservices. Reason enough that Orbitz sells rooms as avalue addition to its core service of flights. Anotherlimitation of the study is the inability to identifymotivations specific to ‘‘packages’’. One may recallfrom the ‘‘Findings’’ section that this component had tobe removed as it did not add substantively to thedimensionality of the plot. Future research shouldseparately identify the key online shopping motivationsof packages along with other complex products such ascruises, etc.Although exploratory, this study paves the way for a

more detailed study of the drivers purchase in travelwebsites across all travel components. Several aspectscan be studied such as the breadth of choices,personalization, information representation, bundling,testimonials and recommendations. A comprehensivestudy that captures a wider range of constructs can

ARTICLE IN PRESSS. Beldona et al. / Tourism Management 26 (2005) 561–570 569

improve upon the relevance of the prevailing findings ofthis study.

6. Conclusion

The study is the first of its kind in online travelbehavior literature that captures all travel componentswithin a combined framework, and evaluates the driversof purchase behavior. Correspondence analysis identi-fied a one-dimensional solution with transactional andinformational features on either side of the dimension.The study identified the heterogeneity of travel productswithin the ambit of Internet commerce. Flights and carrentals had greater transactional qualities, with lowprices and familiarity being the key drivers of purchase.On the other hand, consumers attached more impor-tance on informational aspects in the case of tours,activities, accommodation and events. Consumers iden-tified information detail and ease of use as key drivers ofpurchase of these products. Points and familiarityloaded largely as transactional, whereas ease of usereflected as being more informational. Managerial andtheoretical implications are discussed along with direc-tions of future research.

References

Alwitt, L. F., & Hamer, L. O. (2000). The effect of Internet experience

on consumer expectations of responsiveness and control in offline

services marketing interactions. Proceedings of the 33rd Hawaii

international conference on system sciences, January 4–7, Maui,

Hawaii.

Amit, R., & Zott, C. (2000). Value drivers of e-Commerce business

models (online). Working Paper, Wharton Business School, Uni-

versity of Pennsylvania (February). Available http://knowledge.

wharton.upenn.edu/PDFs/978.pdf (January 10, 2003).

Andrews, R. L., & Currim, I. S. (2000). Behavioral differences between

consumers attracted to shopping online vs traditional super-

markets: Implications for enterprise design and marketing strategy.

Paper presented at the second marketing science and the internet

conference, University of Southern California (April).

Beatty, S. E., & Ferrell, M. E. (1998). Impulse buying: Modeling its

precursors. Journal of Retailing, 2(74), 169–191.

Beldona, S., Kline, S., & Morrison, A. M. (2004). Utilitarian value in

the Internet: Differences between broadband & narrowband users.

Journal of Travel & Tourism Marketing, 17(2–3).

Beldona, S., Morrison, A. M., & Ismail, J. (2003). The impact of

Internet usage charcteristics on online travel behavior. Advances in

Hospitality and Tourism Research, 8, 22–28. Paper presented at the

eighth annual graduate conference in hospitality and tourism, Las

Vegas, Nevada, January 5–7, 2003.

Bellenger, D. N., & Kargaonkar, P. K. (1980). Profiling the

recreational shopper. Journal of Retailing, 56(3), 77–82.

Bellman, S., Lohse, G., & Johnson, E. J. (1999). Predictors of

online buying behavior. Communications of the ACM, 42(12),

32–38.

Bonn, M. A., Furr, H. L., & Susskind, A. M. (1999). Predicting a

behavioral profile for pleasure travelers on the basis of Internet use

segmentation. Journal of Travel Research, 37, 333–340.

Carlsen, J. (1996). Tourism destination image. Asia Pacific Advances in

Consumer Research, 2, 90–91.

Chen, J. S. (2000). Cross-cultural differences in travel information

acquisition among tourists from three pacific-rim countries. Journal

of Hospitality and Tourism Research, 24(2), 239–251.

Citrin, A. V., Sprott, D. E., Silverman, S. N., & Stem Jr., D. E. (2000).

Adoption of Internet shopping: The role of consumer innovative-

ness. Industrial Management and Data Systems, 100(7), 294–300.

Darby, M. R., & Karni, E. (1973). Free competition and the optimal

amount of fraud. Journal of Law and Economics, 16, 67–88.

Darden, W. R., & Ashton, D. (1974/1975). Psychographic profiles of

patronage preference groups. Journal of Retailing, 50, 99–112.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and

user acceptance of information technology. Management Informa-

tion Systems Quarterly, September, 319–339.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User

acceptance of computer technology: A comparison of two

theoretical models. Management Science, 35(8), 982–1003.

Degeratu, A. M., Rangaswamy, A., & Wu, J. (2000). Consumer choice

behavior in online and traditional supermarkets: The effects of

brand name, price and other search attributes. International Journal

of Research in Marketing, 17(1), 55–78.

Fesenmaier, D. R., & Jeng, J. (2000). Assessing structure in the

pleasure trip planning process. Tourism Analysis, 5(3), 13–28.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and

behavior: An introduction to theory and research. Reading, MA:

Addison-Wesley.

Friendly, M. (1995). Categorical data analysis with graphics (online).

Available http://www.math.yorku.ca/SCS/Courses/grcat/grc5.html

(December 10, 2002).

Garson, D. G. (2001). PA 765 statnotes (online). An online textbook.

Available http://www2.chass.ncsu.edu/garson/pa765/statnote.htm

(May 25, 2002).

Gehrt, K., & Shim, S. (1998). A shopping orientation segmentation of

French consumers: Implications for catalog marketing. Journal of

Interactive Marketing, 12, 34–46.

Gursoy, D., & Chen, J. S. (2000). Competitive analysis of cross

cultural information search behavior. Tourism Management, 21(6),

583–590.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998).

Multivariate data analysis (5th ed.). Upper Saddle River, NJ:

Prentice-Hall.

Hammond, K., McWilliam, G., & Diaz, A. N. (1998). Fun and work

on the Web: Differences in attitudes between novices and

experienced users. Advances in Consumer Research, 25, 372–378.

Hirschman, E. C. (1980). Innovativeness, novelty seeking, and

consumer creativity. Journal of Consumer Research, 7, 283–295.

Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia

computer-mediated environments: Conceptual foundations. Jour-

nal of Marketing, 60, 50–68.

Jackson, P., Montigni, E., & Pearce, F. (2001). Turning on broadband

users. Forrester research (online). Forrester Research. Available

http://www.forrester.com/ER/Research/Report/Summary/

0,1338,12349,00.html [November 12, 2001].

Kasavana, M. L. (1997). Critiquing club Websites on the Internet.

Club Management, November–December, 63–70.

Kasavana, M. L. (2001). eMarketing: Restaurant Websites that

click. Journal of Hospitality and Leisure Marketing, 9(3–4),

161–178.

Lee, D., Park, J., & Ahn, J. (2001). On the explanation of factors

affecting e-commerce adoption. Proceedings of the 22nd interna-

tional conference on information systems (ICIS 2001), New Orleans,

LA, December 16–19, 2001 (pp. 109–120).

MacInnis, D. J., & Jaworski, B. J. (1989). Information processing from

advertisements: Toward an integrative framework. Journal of

Marketing, 53(4), 1–24.

ARTICLE IN PRESSS. Beldona et al. / Tourism Management 26 (2005) 561–570570

Mcguire, W. (1974). Psychological motives and communication

gratification. In J. F. Blumler, & J. Katz (Eds.), The uses of mass

communications: Current perspectives on gratification research (pp.

106–167). Beverly Hills: Sage.

Mittal, B. (1999). The advertising of services: Meeting the challenge of

intangibility. Journal of Services Research, 2(1), 98–116.

Morrison, A., Su, J., O’Leary, J., & Cai, L. (2001). Predicting usage of

the Internet for travel bookings: An exploratory study. Information

Technology and Tourism, 4(1), 15–30.

Morrison, A. M., Taylor, J. S., Morrison, A. J., & Morrison, A. D.

(1999). Marketing small hotels on the World Wide Web.

Information Technology and Tourism, 2(2), 97–113.

Murray, K., & Schlacter, J. (1990). The impact of services versus goods

on consumers’ assessment of perceived risk and variability. Journal

of the Academy of Marketing Science, 18(1), 52–65.

Nelson, P. (1970). Information and consumer behavior. Journal of

Political Economy, 78(2), 311–329.

NYU/PhocusWright Report. (2003). Building the online vacation

package marketplace: Survey results. Working Paper, Preston

Robert Tisch Center for Sports & Recreation Management, School

of Continuing and Professional Studies, New York University, NY.

Parasuraman, A., & Colby, C. (2001). Techno-ready marketing:

How and why your customers adopt technology. New York:

The Free Press.

Park, C. W., Iyer, E. S., & Smith, D. C. (1989). The effects of

situational factors on in-store grocery shopping behavior: The role

of store environment and time available for shopping. Journal of

Consumer Research, 15, 422–433.

PhocusWright Report. (2000). Consumer behavior, patterns, attitude

and outlook (online), Phocuswright 2000 Travel E-commerce

Survey, Vol. No. 2–6, November. Abstract available http://

www.phocuswright.com (April 19, 2002).

Ratchford, B. T., & Talukdar, D. (2001). A model of consumer choice

of the Internet as an information source. International Journal of

Electronic Commerce, 5(3), 7–21.

Rohm, A. J., & Swaminathan, V. (2004). A typology of online

shoppers based on shopping motivations. Journal of Business

Research, 57(7), 748–757.

Shankar, V., Smith, R., & Rangaswamy, A. (2000). Customer

satisfaction and loyalty in online and offline environments. Working

Paper, Center for Electronic Commerce Research, Smeal College of

Business, Pennsylvania State University (online). Available http://

www.ebrc.psu.edu/whatsNew/ (June 5, 2003).

Starkov, M. (2001). How to turn lookers into bookers: Recommendation

engines in travel and hospitality (online), Hotel Online. Special

Report (August). Available http://www.hotelonline.com/Neo/

News/PR2001 3rd/Aug01 EnginesinTravel.html [July 12, 2002].

Starkov, M., & Price, J. (2003). Online travelers prefer booking directly

on the hotel website (online). WiredHotelier.Com. Available http://

www.wiredhotelier.com/news/4015607.html (April 15, 2003).

Swaminathan, V., Lepkowska-White, E., & Rao, B. P. (1999).

Browsers or buyers in cyberspace? An investigation of factors

influencing electronic exchange. Journal of Computer Mediated

Communication, 5(2), 1–23.

Weber, K., & Roehl, W. S. (1999). Profiling people searching for and

purchasing travel products on the World Wide Web. Journal of

Travel Research, 37, 291–298.

Zeithaml, V. A. (1981). How consumers’ evaluation processes differ

between goods and services. In J. H. Donnelly, & W. R. George

(Eds.), Marketing of services (pp. 186–190). Chicago: AMA.