Beldona morrisontm2005onlineshoppingmotivations


Published on

Published in: Education, Travel, Business
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Beldona morrisontm2005onlineshoppingmotivations

  1. 1. ARTICLE IN PRESS Tourism Management 26 (2005) 561–570 Online shopping motivations and pleasure travel products: a correspondence analysis Srikanth Beldonaa,*, Alastair. M. Morrisonb, Joseph O’Learyc a Department of Nutrition and Hospitality Management, East Carolina University, 322A Austin, Greenville, NC 27858, USA b School of Consumer & Family Sciences, Purdue University, West Lafayette, IN, USA c Department 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 to inherent individual consumer characteristics. For example, convenience, price comparison, and lower Apart from accommodations, flights and car rentals, prices were identified as the three main reasons why the growth of travel offerings on the Internet now Internet users buy travel products online (Starkov & include vacation packages, cruises, events, tours and Price, 2003). A key question to ask here is ‘‘What are the attractions (NYU/Phocus Wright Report, 2003). In fact, customer motivations that differentiate the purchase of there is a gradual shift amongst travel technology low and high complex travel products? Based on these vendors to move beyond accommodations, flights and motivations, one can identify the relevant features and car rentals to encompass cruises, destinations and others capabilities required of online travel websites. (NYU/Phocus Wright Report, 2003). The purpose of this study is to evaluate the relation- While the industry faces this transition, the authors of ship between consumer purchase motivations across low this paper find no evidence of research, empirical or and high complex travel products. Consumer purchase otherwise that addresses online buying behavior of motivations are grounded in consumer behavior theories complex travel products. Many studies have evaluated across both offline and online contexts, and more demographic, Internet usage and behavioral predictors of specifically travel marketing. The paper then discusses online travel purchase behavior (Bonn, Furr, & Susskind, the theoretical and practical implications of the findings, 1999; Weber & Roehl, 1999; Morrison, Su, O’Leary, & and suggests directions for future research. The study is Cai, 2001; Beldona, Morrison, & Ismail, 2003). However, largely exploratory in nature. facets of travel products such as events, attractions, tours and packages have their own unique product character- istics. Arguably, perceived risk behind the quality of each 2. Conceptual background of these services can significantly vary. Therefore, the propensity to buy the range of low to 2.1. Overview of travel products high complex travel products will also largely vary due *Corresponding author. Tel.: +1-252-328-2190; fax: 1-252-328- The tourism market place is not well defined as it 4276. involves an amalgam of heterogeneous businesses E-mail address: (S. Beldona). services such as transport, accommodation, restaurant 0261-5177/$ - see front matter r 2004 Published by Elsevier Ltd. doi:10.1016/j.tourman.2004.03.008
  2. 2. ARTICLE IN PRESS 562 S. Beldona et al. / Tourism Management 26 (2005) 561–570 and retailing (Carlsen, 1996). This is compounded by the In the online context, the most compelling motivation absence of a meaningful taxonomy in literature that can became the convenience to shop 24/7 from the luxury of delineate key characteristics between travel products. At one’s home (Swaminthan, Lepkowska-White, & Rao, a broad level, travel products can be classified based on 1999). In the travel context, where many components complexity. Flights, accommodation, and car rentals may make up for the travel experience, this combination can be categorized as products of low complexity, of convenience, immediacy and rich information is whereas land-based vacations, cruises and tours can be highly effective. For example, websites like Travelocity considered products of high complexity. and Expedia provide aggregated services such as flights, Economics of information theory (Nelson, 1970; accommodations, and car rentals that are aimed at Darby & Karni, 1973) categorized products into search, being a one-stop-shop built around convenience. experience and credence types based on how consumers The travel decision-making process is a complex evaluate them. Products with search qualities can be multi-stage process layered along a hierarchical set of fully evaluated prior to purchase, whereas experience- activities (Fesenmaier & Jeng, 2000). Here too, conve- based products must be first purchased and consumed nience can serve as a key driver of the travel planning before the consumer is able to evaluate. Darby and process. On the Internet, consumers can self-build a Karni (1973) extended this to include credence goods combination of various complementary travel products which consumers can never fully evaluate even after with relatively less difficulty when compared to the purchase and consumption. Zeithaml (1981) integrated traditional context. However, the Internet can add to this categorization to marketing, and posited that the complexity of the process too because of the services exhibit more experience and credence qualities plethora of sources needed to coordinate and piece due to their unique characteristics namely intangibility, together this process. For example accommodations can non-standardization and inseparability. be bought from accommodation sites, intermediaries, Bringing the realm of travel products within this airlines, discounts, and even destination sites. Of course, categorization provides cues on the nature of search and the level of detail provided by each of these websites purchase in the online medium. On the Internet, travel varies based on what the core and secondary offerings suppliers can provide greater detail on features of are. products using a wide array of tools. Depending on Website characteristics and purchase intentions are the type of product, these may be comparison charts, better explained under the framework of the Technology virtual tours, video and graphics in video as well as still Adoption Model (Davis, 1989; Davis, Bagozzi, & image formats. Flights, accommodations and car rentals Warshaw, 1989). Lee, Park, and Ahn (2001) expanded are standardized services that can be placed within the on the original TAM model and introduced an e-Com easier to evaluate context as there are more known adoption model that included perceived ease of use, parameters of tangibility (Zeithaml, 1981; Mittal, 1999). perceived usefulness, perceived risk with products/ In contrast, complex travel products such as cruises, services, and perceived risk in the context of online land-based vacations, tours, activities and attractions transaction. An easy to use travel website would imply can be arguably placed in the difficult to evaluate aspects such as navigability, efficiency, consistency and context. Prior research also indicates a linear relation- compatibility (Morrison, Taylor, Morrison, & Morri- ship between perceived risk in a service and the son, 1999). Another aspect of the website that relates to extent of detail of search in services (Murray & perceived ease of use is the information, features and Schlacter, 1990). functionality available on the site. This is especially the case with complex products such as tours, packages and 2.2. Consumer motivations to shop cruises, where consumers seek exhaustive information before making the purchase decision. Online service At a fundamental level, consumer motivation to shop encounter satisfaction was higher when information is best explained by motivation theory, which contends content at the web site was deeper (Shankar, Smith, & that cognitive or affective motives seek individual Rangaswamy, 2000). gratification and satisfaction (McGuire, 1974). Several Research on efficacy of websites is extensive, with studies have evaluated consumer motivations to shop many works aimed at evaluating a diverse range of across a range of contexts such as malls, mail order providers in the hospitality industry (Kasavana, 1997, catalogs, and supermarkets (Bellenger & Kargaonkar, 2001; Morrison et al., 1999). However, specifics of 1980; Gehrt & Shim, 1998; Darden & Ashton, 1974/ website effectiveness such as technical performance are 1975). Shopping motivations in the generic grocery outside the context of this study. This study is structured context can be distilled into shopping contexts namely around an intermediate meeting ground that has overall savings, convenience, information seeking, social consumer motivations and website characteristics at interaction, and shopping experience (Rohm & Swami- two ends of the continuum. While on one hand, it deals nathan, 2004). with underlying motivations, it also matches these with
  3. 3. ARTICLE IN PRESS S. Beldona et al. / Tourism Management 26 (2005) 561–570 563 salient features of travel websites that guided consumers man & Colby, 2001). In this context, broadband users to buy the travel product in question. Put differently, (early adopters) were found to show greater likelihood this study is about websites providing specific web of buying travel products online compared to narrow- shopping capabilities (push) to satisfy (pull) relevant band users who are typically late adopters (Beldona, consumer motivations. Kline, & Morrison, 2004). Perceived usefulness of a website can also be gauged Attitude towards a medium can also serve as a strong by the website’s ability to attract existing customers and predictor of marketing exchange, and is also integral to provide services such as redemption of rewards or miles technology adoption theory (Parasuraman & Colby, points (Shankar et al., 2000). Results show that loyalty 2001). For example, broadband users are considered to to the service provider is higher when the service is be progressive, success oriented, and ahead in the chosen online than offline (Shankar et al., 2000). Many technology adoption curve (Jackson, Montigni, & hospitality and travel organizations have leveraged the Pearce, 2001). Grounded in the theory of reasoned web’s capabilities to provide interfaces for customers to action (Fishbein & Ajzen, 1975), it suggests that manage their rewards. For example, Marriott has attitudes can be used to predict behavioral intentions and provides unique services to and behaviors. its members on its website. Customers can access and There have been other studies that have supported book online, while simultaneously keeping score of their online experience or tenure as a key determinant of points or miles earned. online buying behavior (Bellman, Lohse, & Johnson, Loyalty practices and policies enhance responsive- 1999; Ratchford & Talukdar, 2001; Beldona et al., ness, availability and convenience to the customer, who 2003a, b). Findings indicate that greater the number of in turn may choose these specific websites for repeat months/years the user spent online combined with purchases. Degeratu, Rangaswamy, and Wu (2000) higher frequency of Internet usage; greater was found that brand names become more important online the likelihood of buying (Bellman et al., 1999; in the case of low-involvement products as opposed to Weber & Roehl, 1999; Beldona et al., 2003, 2004). high-involvement products. In a study of retail grocery Additionally, domain-specific consumer innovativeness products, findings indicated that consumers did more was found to moderate frequency of usage as an screening of information on the basis of brand names antecedent of online purchase behavior (Citrin, Sprott, online as opposed offline (Andrews & Currim, 2000). In Silverman, & Stem, 2000). the case of travel products, one may argue that However, delving a little further into the Internet standardized products such as flights, accommodation, experience reveals a more evolving construct. Under- and car rentals can be considered to be within this standing Internet experiences is grounded in the ability context, where the brand name could be a major draw to process information effectively. This in turn is for purchase. influenced by education, intelligence, product experi- Low prices can also be one of the reasons as to why ence, relevant knowledge, and message difficulty (Ma- people buy online. It has been found to be a major cInnis & Jaworski, 1989). Hoffman and Novak (1996) driver of online travel purchasing (Starkov & Price, found that experienced users were attracted to techni- 2003; PhocusWright Report, 2000). Price sensitivity is cally advanced sites with novel features that present higher online, but this is due to online promotions more challenges. Alwitt and Hamer (2000) posit that being stronger signals of price discounts (Degeratu consumers increase their control with more time spent et al., 2000). on the Internet, and in turn develop finer expectations of their interactions with businesses in general. Findings 2.3. Internet experience indicated an inverted ‘‘U’’ relationship between web usage expertise and consumers’ expectations of service Technology adoption theory also has been used to providers, where consumers with moderate levels of web explain purchasing propensities on the Internet. The usage expertise have higher expectations than do technology adoption cycle states that when a technology consumers with low or high levels of web usage is introduced in the market, its adoption stages are expertise. Hammond, McWilliam, and Diaz (1998) characterized by five segments, namely explorers, showed prior experience is an important moderator of pioneers, skeptics, paranoids, and laggards (Parasura- users’ attitudes towards the Web, although its influence man & Colby, 2001). There are individual characteristics is not linear. The heaviest users are enthusiasts for the that go to distinguish each of the above segments. medium, while moderate and light users perceive it as a Segments vary based on a combination of optimism, source of information, but not for entertainment or fun. innovativeness, discomfort, and insecurity in attitudes Novelty as a construct has also been categorized as a towards the technology. The important aspect about key value driver in online consumer behavior (Amit & technology adoption is that each segment develops over Zott, 2000). How a person buys is largely driven by the time to become a viable customer segment (Parasura- novelty of the purchase occasion, an aspect relevant to
  4. 4. ARTICLE IN PRESS 564 S. Beldona et al. / Tourism Management 26 (2005) 561–570 the Internet when viewed as a technological innovation. Table 1 This means that novelty of purchasing may itself be a Table of online shopping motivations driver, but may wear off as repeated purchases occur Reasons for purchasing at the website over a period of time. Subsequent visits to a website may 1. Ability to use rewards/travel points have marginal effects on purchasing as the shopper may 2. Availability not register the stimuli that played a persuading role 3. Detailed information during earlier visits (Park, Iyer, & Smith, 1989). On the 4. Ease of booking contrary, the likelihood of purchasing may increase 5. Familiar with company through increased familiarity with purchase system in 6. Offered independent ratings of product (e.g. hotel star rating) 7. Low price place (Beatty & Ferrell, 1998). This paradox is best 8. Recommendations by friends/family explained by the theory of consumer innovation, which 9. Testimonials on site/chat line/online bulletin board in itself can play a significant role in how novelty is 10. Other reason (specify)— dealt with in the purchase process (Hirschman, 1980). Consumer innovation is largely driven by innate personality characteristics in every individual (Hirsch- man, 1980). and recoded back to any of the six chosen categories based on the response given. A total of 44 observations were added into the contingency table based on this 3. Methodology and analysis recoding from the open category. User skill level was used as a control or moderating Data for this study came from a November 2001 variable. This was constructed using a combination of survey conducted by the Canadian Tourism Commis- three variables namely online tenure, type of Internet sion (CTC). The survey was conducted using telephone connection and the extent to which multimedia applica- and respondents were randomly selected from telephone tions are used such as Acrobat, Flash, Quicklime, directories in the United States and Canada. A Windows Player. Users who have spent more than 3 computer-aided telephone interface (CATI) system was years on the Internet, have broadband connections and used to record responses. Initially, the number of cases use more than two multimedia applications were representing the US was 1364 and that of Canada was categorized as high-skilled users. Low-skilled users were 1161. To ensure parity in the sample size for comparable categorized if they had spent less than 3 years online, representation within the analysis, the number of US have narrowband connections, and use less than two cases was randomly scaled down to 1145. This led the multimedia applications. total number of cases to be used for analysis to be 2306. Correspondence analysis using multi-way tables was Questions for the specific analysis to be done were chosen as the statistical technique to analyze the data. It drawn from a pool of questions that sought dichot- is a statistical method to depict associations between two omous (1=yes, 0=no) responses on the reason for or more categorical variables. It provides a visualization purchasing at a particular site as opposed to another of the association along with some referential statistics to site. The question posed was ‘‘For your trip to determine the number of dimensions prevalent between DESTINATION (subject specific and mentioned earlier the associations. In effect, it is a geometric technique that in questionnaire), what is the main reason that you draws from the row and column points in the purchased your airfare at SITE (selected in an earlier contingency table, and place categories (levels) of the question) versus another site? The list reasons are variables as points in low-dimensional visual space, so as provided in Table 1. A ‘‘select all that apply’’ option to best fit their associations in the table. Put differently, was given for respondents to answer. A more detailed correspondence analysis is a sophisticated technique that list of frequencies is presented in the contingency table gives a powerful representation of association between created in Table 2. The 10th category called ‘‘other’’ categorical variables by giving a comprehensive view of recorded open responses. Only six categories were the data (in the contingency table) for effective chosen for final analysis namely ability to use rewards/ interpretation. points, availability, detailed information, ease of book- Correspondence analysis is a widely used technique in ing, familiar with company and low price. Independent marketing research. It is used to examine similarities and ratings, recommendations, and testimonials were three associations between attributes and brands. In tourism other categories discarded from the analysis because of marketing literature too, correspondence analysis is the low frequency counts that they reported. Only 4.3% becoming a much used technique (Gursoy & Chen, cases reported independent ratings as a reason to buy, 2000; Chen, 2000). As a statistical technique of choice, 5.1% reported recommendations, and 1.18% of the correspondence analysis is very useful when associations cases reported testimonials as a reason for purchase. between two or more multi-level categorical variables Open responses in the ‘‘other’’ category were analyzed have to be examined. In tourism marketing specifically,
  5. 5. ARTICLE IN PRESS S. Beldona et al. / Tourism Management 26 (2005) 561–570 565 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 it is an extremely useful application because of the large explained by each dimension. These are called singular number of categorical variables used for analysis. values, and they should be greater than 0.20 to be Correspondence analysis derives commonalities accepted as a viable dimension (Hair et al., 1998). amongst categorical variables akin to principal compo- Singular values for dimensions extracted indicate nents analysis for continuous data (Garson, 2001). a uni-dimensional solution with a 0.23 value for However, it must be mentioned that correspondence dimension I. analysis is purely an exploratory technique, and that A total chi-square statistic is also provided (236.074) statistical significance of relationships should not be as a measure of association between the rows and assumed (Hair, Anderson, Tatham, & Black, 1998). columns, and the number of dimensions extracted. Table 2 illustrates the multi-way table to be analyzed. Dimension I explains for 83.32% of the variance of This multi-way table shows frequencies of a three-way the cumulative solution. Tables 4 and 5 provide more cross tabulation matrix comprising user skills (two detail to understand the actual decomposition of the levels), travel product component (eight levels), and variance based on individual contributions of column reason to purchase (six levels). A multi-way table can be and row points in the contingency table. Table 4 outlines analyzed in correspondence analysis using an approach the travel services, and how they correspond to the called ‘‘stacking’’ (Friendly, 1995). A three-way table, of dimensions. Coordinate values of each travel compo- size I Â J Â K can be sliced into I two-way tables, each nent are shown in column 2 across both dimensions. J Â K. In our case, the frequencies were sliced into Inertia is a term used in correspondence analysis to two (high and low user level) tables cross-tabulated describe the variance of that point along the dimension across travel product component (J) and reason to in contention (Hair et al., 1998). Each point is explained purchase (K). along the dimensions in quantitative terms and these values are illustrated in the column titled ‘‘explained by dimension’’ in Table 4. The cumulative total of 4. Results variances explained by a point along the two dimensions is then illustrated in the final column titled ‘‘total’’. One Proc Corresp from SAS Version 8.2 was used to will note that with the exception of packages amongst analyze the data. In correspondence analysis, k À 1 low skill users (28.8%), all other travel components dimensions are drawn based on the number of explained dimensions above the range of 50% of the categories in the column of the contingency table variance. Hair et al. (1998) suggest that points that do (Garson, 2001). With six reasons for purchase spread not contribute to the dimension over and above 50% across the rows, five dimensions were drawn from the should be removed in the joint plot. In this case, it was analysis as is evident in Table 3. Correspondence decided to drop packages completely from the analysis analysis provides statistical measures of describing the even though one of its points (low skills) had explained a number of dimensions, and the proportion of variance little more than 50%. This is because it complements
  6. 6. ARTICLE IN PRESS 566 S. Beldona et al. / Tourism Management 26 (2005) 561–570 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 À0.0221 0.2177 0.0156 0.9561 0.0072 0.9633 Activities À0.3860 0.0381 0.0789 0.0073 0.9370 0.0091 0.9461 Attractions À0.3200 À0.0698 0.0495 0.0223 0.8044 0.0383 0.8427 Car rentals 0.1704 À0.1365 0.0549 0.3330 0.5812 0.3727 0.9539 Events À0.4018 À0.0876 0.0841 0.0378 0.8947 0.0425 0.9372 Accommodations À0.1698 À0.0249 0.0974 0.0197 0.8393 0.0180 0.8573 Tours À0.2609 À0.0298 0.0205 0.0025 0.7903 0.0103 0.8006 Packages À0.1283 À0.0781 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 À0.1856 À0.0337 0.0104 0.0032 0.7888 0.0260 0.8148 Attractions À0.0294 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 À0.3339 0.0247 0.0510 0.0026 0.8785 0.0048 0.8833 Accommodations À0.2368 0.0878 0.1155 0.1501 0.8490 0.1167 0.9657 Tours À0.3965 0.0370 0.0206 0.0017 0.6803 0.0059 0.6862 Packages 0.0532 À0.0229 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 À0.1667 À0.0515 0.0909 0.0821 0.7465 0.0713 0.8178 Information detail À0.3019 0.0537 0.2907 0.0870 0.9188 0.0291 0.9479 Ease of booking À0.0860 0.0176 0.0360 0.0142 0.7013 0.0294 0.7307 Familiarity 0.3239 À0.0766 0.2035 0.1078 0.8474 0.0475 0.8949 Low price 0.1891 À0.0580 0.1390 0.1238 0.8331 0.0784 0.9115 with the other corresponding low value in the ‘‘high 50% of the variance, with most in 80–95% range. skill’’ category. Dimension I is explained by all six perceived catalysts of Table 5 illustrates all six catalysts or drivers of purchase. This is the column that explains the contribu- purchase. Just as factor loadings are used in conven- tion of points to inertia (variance) in percentage terms of tional factor analysis to ascribe meaning to dimensions, a particular dimension. To visualize association of so are ‘‘contribution of points to dimensions’’ used to points in low two-dimensional space, a correspondence intuit the meaning of correspondence dimensions map displays dimensions that emerge from principal (Garson, 2001). Clearly, all points explain more than components analysis of point distances (Garson, 2001).
  7. 7. ARTICLE IN PRESS S. Beldona et al. / Tourism Management 26 (2005) 561–570 567 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. Also called the joint plot (Fig. 1), the plot reveals the may recall that the 2001 was a very defining period for relationships between online shopping motivations and the accommodation sector in the online segment travel products of low and high complexity. First look (Starkov, 2001). During this period, hoteliers were shows two distinct sides on the X-axis. While rewards/ struggling to provide price transparency on the Internet. points, familiarity and price are positioned along one Many hotels had not yet effectively placed inventory side, the other side of the same axis show the remaining online, and this reflects strongly on the visual map. three namely ease of use, information detail and Another plausible reason for accommodations to fall availability. Therefore, this dimension is suggestively within the informational is that the purpose of trip is named transactional/informational. largely pleasure driven. One may contend that the Clearly, flights and car rentals purchases by both low nuances of accommodations for pleasure trips face and high experienced users are strongly associated with relatively greater scrutiny as opposed to the same for low prices and familiarity. On the other hand, events, business trips. accommodation and tour purchases amongst experi- Highly skilled users attach more importance to enced users followed by activities from less experienced detailed information when purchasing tours, accommo- users appear strongly associated with detailed informa- dation and events online. In contrast, less skilled users tion. Another cluster groups activity purchases among attach more importance to availability, when it comes to less experienced users followed by accommodation buying accommodations online. Interestingly, a reverse purchases from experienced users with the availability like situation exists when it comes to ‘‘activities’’, where factor. high-skilled users consider availability as very important When viewed holistically, the joint plot provides more compared to less skilled users who put information than just an association of relationships in clusters. It detail ahead of everything else. While low-skilled users delineates travel components based on consumer per- perceive detailed information as key to purchasing ceptions of situational criteria attached to travel ‘‘tours’’ online, high-skilled users see availability as services. For example, flights and car rentals are more important in the same sector. relatively more established sectors in the online travel segment. These sectors have greater price transparency, which drives consumers to seek more evaluative information on that front. Familiarity is imperative to 5. Discussion and implications these purchases as are reward points to be gained from purchasing them online. In contrast, consumers attach Two key findings emerge from the study. Firstly, more importance to availability, detailed information online shopping motivations of travel products of low and ease of use to services that are not as yet established. and high complexity are distinctively different. Sec- Interestingly, accommodations fall within this group, ondly, online shopping motivations vary depending on along with activities, tours, attractions and events. One user skill levels. Importantly, user skills are a function of
  8. 8. ARTICLE IN PRESS 568 S. Beldona et al. / Tourism Management 26 (2005) 561–570 online tenure, type of Internet connection and the types events can improve their online availability. In fact, of applications used to navigate the Internet. findings provide pointers on the behavioral antecedents While purchase of less complex products such as of buying pleasure travel products on the Internet. This flights and car rentals are driven by motivations with is especially relevant given the projected growth in transactional objectives, shopping motivations behind online sales of complex leisure products over the next complex such as tours, activities and attractions are few years. driven by informational parameters. Specifically, re- The findings clearly indicate that lodging managers wards/points and price grouped with flights and car should strive to improve availability of rooms as well as rentals, and motivations such as detailed information more information about the property and its surround- and availability were largely associated with accommo- ings. Informational detail can be in the form of dations, tours, attractions, activities and events. Greater proximity to shopping centers, surrounding attractions information detail with products of high complexity can and related detail. Findings are more pertinent to resort be associated with greater perceived risk as well as the managers because the context of this study is largely need for more control too. Familiarity was more pleasure oriented. For managers of airlines and car associated with less complex products, which can be rentals, findings emphasize the need for websites to have attributed to the structural maturity of the industry in greater customer relationship management tools. Online terms of the presence of established brands. customers of these products are looking for ways to Low- and high-skilled Internet users are distinctively derive greater value from web based interactions. In different. At the outset, one can generalize that high- many ways, this can be interpreted as enhanced control skilled users place greater emphasis on information for customers so that they can effectively manage their detail when it comes to travel products of high rewards programs. complexity. This is illustrated by key associations of Findings of the study can also help in the develop- this group with information detail in the case of ment of customer centric travel reservation systems. As activities, events, tours and accommodations. However, the travel distribution system moves towards integrating in the case of accommodations and activities, low-skilled various components of travel using common standards, users were driven more by availability than other the results of this study can help in the design of systems motivations. based on customer requirements of components. Future One can explain the above phenomenon of avail- research can develop a comprehensive evaluation of ability over detailed information using novelty theory travel components within one single system, and (Amit & Zott, 2000). How a person buys is largely evaluate customer perceptions towards it. driven by the novelty of the purchase occasion, an A big limitation of the study is the absence of aspect relevant to the Internet when viewed as a ‘‘complementarities’’ as a driver of purchase. A few technological innovation. This means that novelty of respondents in the open category did indicate that they purchasing may itself be a driver, but may wear off or bought specific components simply because they were improve with repeated purchases over a period of time. part of a larger package. Future research should Repeat buyers will need stronger stimuli such as detailed investigate complementarities and the specific relation- information compared to earlier stimuli (availability) in ships between the various components in it. For this case that played a persuading role (Park et al., example, flights and accommodations can be considered 1989). Again, a relatively less established segment such more complementary compared to flights and other as activities can attract more experienced users wherein services. Reason enough that Orbitz sells rooms as a availability becomes very important. Less experienced value addition to its core service of flights. Another users seeking the same product may perceive greater risk limitation of the study is the inability to identify and seek more information, a fact illustrated in the joint motivations specific to ‘‘packages’’. One may recall plot. Future research can investigate this gap and from the ‘‘Findings’’ section that this component had to specifically examine evolving purchasing behaviors be removed as it did not add substantively to the based on experience in greater depth. dimensionality of the plot. Future research should There are several implications for online and offline separately identify the key online shopping motivations travel marketers in general. Travel marketers can use the of packages along with other complex products such as findings in the areas of website design and promotional cruises, etc. activities. Websites can be tailored more effectively to Although exploratory, this study paves the way for a meet needs of users based on skill levels. For example, more detailed study of the drivers purchase in travel websites can have alternative gateways based connection websites across all travel components. Several aspects speed, as well as customized features based on identified can be studied such as the breadth of choices, skill levels. Skilled users can be provided with detailed personalization, information representation, bundling, informational content using multiple media. Destination testimonials and recommendations. A comprehensive marketers who typically provide tours, activities and study that captures a wider range of constructs can
  9. 9. ARTICLE IN PRESS S. Beldona et al. / Tourism Management 26 (2005) 561–570 569 improve upon the relevance of the prevailing findings of Carlsen, J. (1996). Tourism destination image. Asia Pacific Advances in this study. 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. 6. Conclusion Citrin, A. V., Sprott, D. E., Silverman, S. N., & Stem Jr., D. E. (2000). Adoption of Internet shopping: The role of consumer innovative- The study is the first of its kind in online travel ness. Industrial Management and Data Systems, 100(7), 294–300. Darby, M. R., & Karni, E. (1973). Free competition and the optimal behavior literature that captures all travel components amount of fraud. Journal of Law and Economics, 16, 67–88. within a combined framework, and evaluates the drivers Darden, W. R., & Ashton, D. (1974/1975). Psychographic profiles of of purchase behavior. Correspondence analysis identi- patronage preference groups. Journal of Retailing, 50, 99–112. fied a one-dimensional solution with transactional and Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and informational features on either side of the dimension. user acceptance of information technology. Management Informa- The study identified the heterogeneity of travel products tion Systems Quarterly, September, 319–339. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User within the ambit of Internet commerce. Flights and car acceptance of computer technology: A comparison of two rentals had greater transactional qualities, with low theoretical models. Management Science, 35(8), 982–1003. prices and familiarity being the key drivers of purchase. Degeratu, A. M., Rangaswamy, A., & Wu, J. (2000). Consumer choice On the other hand, consumers attached more impor- behavior in online and traditional supermarkets: The effects of tance on informational aspects in the case of tours, brand name, price and other search attributes. International Journal of Research in Marketing, 17(1), 55–78. activities, accommodation and events. Consumers iden- Fesenmaier, D. R., & Jeng, J. (2000). Assessing structure in the tified information detail and ease of use as key drivers of pleasure trip planning process. Tourism Analysis, 5(3), 13–28. purchase of these products. Points and familiarity Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and loaded largely as transactional, whereas ease of use behavior: An introduction to theory and research. Reading, MA: reflected as being more informational. Managerial and Addison-Wesley. theoretical implications are discussed along with direc- Friendly, M. (1995). Categorical data analysis with graphics (online). Available tions of future research. (December 10, 2002). Garson, D. G. (2001). PA 765 statnotes (online). An online textbook. Available References (May 25, 2002). Gehrt, K., & Shim, S. (1998). A shopping orientation segmentation of French consumers: Implications for catalog marketing. Journal of Alwitt, L. F., & Hamer, L. O. (2000). The effect of Internet experience Interactive Marketing, 12, 34–46. on consumer expectations of responsiveness and control in offline Gursoy, D., & Chen, J. S. (2000). Competitive analysis of cross services marketing interactions. Proceedings of the 33rd Hawaii cultural information search behavior. Tourism Management, 21(6), international conference on system sciences, January 4–7, Maui, Hawaii. 583–590. Amit, R., & Zott, C. (2000). Value drivers of e-Commerce business Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). models (online). Working Paper, Wharton Business School, Uni- Multivariate data analysis (5th ed.). Upper Saddle River, NJ: versity of Pennsylvania (February). Available http://knowledge. Prentice-Hall. (January 10, 2003). Hammond, K., McWilliam, G., & Diaz, A. N. (1998). Fun and work Andrews, R. L., & Currim, I. S. (2000). Behavioral differences between on the Web: Differences in attitudes between novices and consumers attracted to shopping online vs traditional super- experienced users. Advances in Consumer Research, 25, 372–378. markets: Implications for enterprise design and marketing strategy. Hirschman, E. C. (1980). Innovativeness, novelty seeking, and Paper presented at the second marketing science and the internet consumer creativity. Journal of Consumer Research, 7, 283–295. conference, University of Southern California (April). Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia Beatty, S. E., & Ferrell, M. E. (1998). Impulse buying: Modeling its computer-mediated environments: Conceptual foundations. Jour- precursors. Journal of Retailing, 2(74), 169–191. nal of Marketing, 60, 50–68. Beldona, S., Kline, S., & Morrison, A. M. (2004). Utilitarian value in Jackson, P., Montigni, E., & Pearce, F. (2001). Turning on broadband the Internet: Differences between broadband & narrowband users. users. Forrester research (online). Forrester Research. Available Journal of Travel & Tourism Marketing, 17(2–3). Beldona, S., Morrison, A. M., & Ismail, J. (2003). The impact of 0,1338,12349,00.html [November 12, 2001]. Internet usage charcteristics on online travel behavior. Advances in Kasavana, M. L. (1997). Critiquing club Websites on the Internet. Hospitality and Tourism Research, 8, 22–28. Paper presented at the Club Management, November–December, 63–70. eighth annual graduate conference in hospitality and tourism, Las Kasavana, M. L. (2001). eMarketing: Restaurant Websites that Vegas, Nevada, January 5–7, 2003. click. Journal of Hospitality and Leisure Marketing, 9(3–4), Bellenger, D. N., & Kargaonkar, P. K. (1980). Profiling the 161–178. recreational shopper. Journal of Retailing, 56(3), 77–82. Lee, D., Park, J., & Ahn, J. (2001). On the explanation of factors Bellman, S., Lohse, G., & Johnson, E. J. (1999). Predictors of affecting e-commerce adoption. Proceedings of the 22nd interna- online buying behavior. Communications of the ACM, 42(12), tional conference on information systems (ICIS 2001), New Orleans, 32–38. LA, December 16–19, 2001 (pp. 109–120). Bonn, M. A., Furr, H. L., & Susskind, A. M. (1999). Predicting a MacInnis, D. J., & Jaworski, B. J. (1989). Information processing from behavioral profile for pleasure travelers on the basis of Internet use advertisements: Toward an integrative framework. Journal of segmentation. Journal of Travel Research, 37, 333–340. Marketing, 53(4), 1–24.
  10. 10. ARTICLE IN PRESS 570 S. Beldona et al. / Tourism Management 26 (2005) 561–570 Mcguire, W. (1974). Psychological motives and communication Survey, Vol. No. 2–6, November. Abstract available http:// gratification. In J. F. Blumler, & J. Katz (Eds.), The uses of mass (April 19, 2002). communications: Current perspectives on gratification research (pp. Ratchford, B. T., & Talukdar, D. (2001). A model of consumer choice 106–167). Beverly Hills: Sage. of the Internet as an information source. International Journal of Mittal, B. (1999). The advertising of services: Meeting the challenge of Electronic Commerce, 5(3), 7–21. intangibility. Journal of Services Research, 2(1), 98–116. Rohm, A. J., & Swaminathan, V. (2004). A typology of online Morrison, A., Su, J., O’Leary, J., & Cai, L. (2001). Predicting usage of shoppers based on shopping motivations. Journal of Business the Internet for travel bookings: An exploratory study. Information Research, 57(7), 748–757. Technology and Tourism, 4(1), 15–30. Shankar, V., Smith, R., & Rangaswamy, A. (2000). Customer Morrison, A. M., Taylor, J. S., Morrison, A. J., & Morrison, A. D. satisfaction and loyalty in online and offline environments. Working (1999). Marketing small hotels on the World Wide Web. Paper, Center for Electronic Commerce Research, Smeal College of Information Technology and Tourism, 2(2), 97–113. Business, Pennsylvania State University (online). Available http:// Murray, K., & Schlacter, J. (1990). The impact of services versus goods (June 5, 2003). on consumers’ assessment of perceived risk and variability. Journal Starkov, M. (2001). How to turn lookers into bookers: Recommendation of the Academy of Marketing Science, 18(1), 52–65. engines in travel and hospitality (online), Hotel Online. Special Nelson, P. (1970). Information and consumer behavior. Journal of Report (August). Available Political Economy, 78(2), 311–329. News/PR2001 3rd/Aug01 EnginesinTravel.html [July 12, 2002]. NYU/PhocusWright Report. (2003). Building the online vacation Starkov, M., & Price, J. (2003). Online travelers prefer booking directly package marketplace: Survey results. Working Paper, Preston on the hotel website (online). WiredHotelier.Com. Available http:// Robert Tisch Center for Sports & Recreation Management, School (April 15, 2003). of Continuing and Professional Studies, New York University, NY. Swaminathan, V., Lepkowska-White, E., & Rao, B. P. (1999). Parasuraman, A., & Colby, C. (2001). Techno-ready marketing: Browsers or buyers in cyberspace? An investigation of factors How and why your customers adopt technology. New York: influencing electronic exchange. Journal of Computer Mediated The Free Press. Communication, 5(2), 1–23. Park, C. W., Iyer, E. S., & Smith, D. C. (1989). The effects of Weber, K., & Roehl, W. S. (1999). Profiling people searching for and situational factors on in-store grocery shopping behavior: The role purchasing travel products on the World Wide Web. Journal of of store environment and time available for shopping. Journal of Travel Research, 37, 291–298. Consumer Research, 15, 422–433. Zeithaml, V. A. (1981). How consumers’ evaluation processes differ PhocusWright Report. (2000). Consumer behavior, patterns, attitude between goods and services. In J. H. Donnelly, & W. R. George and outlook (online), Phocuswright 2000 Travel E-commerce (Eds.), Marketing of services (pp. 186–190). Chicago: AMA.